Download - SE Dev as an Intervention Agst Malaria
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Socioeconomic development as an intervention against
malaria a systematic review and meta-analysisLucy S Tusting Barbara Willey Henry Lucas John Thompson Hmooda T Kafy Richard Smith Steve W Lindsay
SummaryBackground Future progress in tackling malaria mortality will probably be hampered by the development of resistanceto drugs and insecticides and by the contraction of aid budgets Historically control was often achieved without malaria-specific interventions Our aim was to assess whether socioeconomic development can contribute to malaria control
Methods We did a systematic review and meta-analysis to assess whether the risk of malaria in children aged0ndash15 years is associated with socioeconomic status We searched Medline Web of Science Embase the CochraneDatabase of Systematic Reviews the Campbell Library the Centre for Reviews and Dissemination Health SystemsEvidence and the Evidence for Policy and Practice Information and Co-ordinating Centre evidence library for studies
published in English between Jan 1 1980 and July 12 2011 that measured socioeconomic status and parasitologicallyconfirmed malaria or clinical malaria in children Unadjusted and adjusted effect estimates were combined in fixed-effects and random-effects meta-analyses with a subgroup analysis for different measures of socioeconomic status We used funnel plots and Eggerrsquos linear regression to test for publication bias
Findings Of 4696 studies reviewed 20 met the criteria for inclusion in the qualitative analysis and 15 of these reportedthe necessary data for inclusion in the meta-analysis The odds of malaria infection were higher in the poorestchildren than in the least poor children (unadjusted odds ratio [OR] 1∙66 95 CI 1∙35ndash2∙05 plt0∙001 I sup2=68adjusted OR 2∙06 1∙42ndash2∙97 plt0∙001 I sup2=63) an effect that was consistent across subgroups
Interpretation Although we would not recommend discontinuation of existing malaria control efforts we believe thatincreased investment in interventions to support socioeconomic development is warranted since such interventionscould prove highly effective and sustainable against malaria in the long term
Funding UK Department for International Development
IntroductionMalaria remains one of the most serious public healthproblems worldwide with 2middot57 billion people at risk offalciparum malaria in 20101 Although the burden ofmalaria is falling globally morbidity and mortalityremain high with estimates of total reported deaths in2010 between 655 0002 and 1∙24 million3 with anestimated 82∙69 million disability-adjusted life years lostin 20104 In addition to direct health effects malaria alsohas a serious negative effect on socioeconomic develop-ment and indeed ldquowhere malaria prospers most human
societies have prospered leastrdquo5 This effect is shown bythe relation between an index of income and education6 and the cumulative probability of malaria deaths in43 African countries3 in children aged 0ndash5 years (figure 1)and in all age groups (adults and children Rsup2=0∙256p=0∙001) in 2010 (appendix p 1ndash2)
Costs associated with the burden of malaria constitute5∙8 of the total gross domestic product of sub-SaharanAfrica (roughly US$12 billion annually)7 Both nationalincome8 and rates of economic growth5 are lower inmalaria-endemic countries than in countries where thedisease is not endemic One estimate8 suggests that a 10reduction in malaria is associated with 0∙3 increasedgrowth and other research has shown similar effectsizes9 Indeed these findings together with others for
HIVAIDS provided the impetus for the establishment ofthe Global Fund to Fight AIDS Tuberculosis and Malaria7 Malaria control and elimination is therefore seen asintegral to the economic prosperity of malaria-endemiccountries10 This worldwide recognition also ensured thatmalaria was the focus of one of the MillenniumDevelopment Goals11
However efforts to control malaria are almost alwaysfocused on reduction of the disease through interventionsthat are derived solely from the health sector and aresuitable for rapid and massive scale-up Long-lasting
insecticidal nets (LLINs) and indoor residual sprayingare both highly effi cient methods of reducing trans-mission quickly and combined with artemisinin-basedcombination therapy are undoubtedly a major reason forthe reduction in the malaria burden in sub-SaharanAfrica12 However such strong pressure on vector andparasite populations will inevitably lead to the selectionand spread of resistant strains of mosquitoes and malariaparasites respectively Resistance to artemisinins whichhas emerged in malaria parasites in southeast Asia13 willprobably spread globally Resistance to all four classes ofinsecticide available for indoor residual spraying (in-cluding the pyrethroids the only insecticides currentlyavailable for impregnation of bednets) has now beendocumented in sub-Saharan Africa14
Published Online
June 19 2013
httpdxdoiorg101016
S0140-6736(13)60851-X
See OnlineComment
httpdxdoiorg101016
S0140-6736(13)61211-8
London School of Hygiene amp
Tropical Medicine London UK
(L S Tusting MSc B Willey PhDProf R Smith PhD) Institute of
Development Studies
Brighton UK
(H Lucas MSc J Thompson PhD)
National Malaria Control
Programme Federal Ministry
of Health Khartoum Sudan
(H T Kafy MSc) and School of
Biological and Biomedical
Sciences Durham University
Durham UK
(Prof S W Lindsay PhD)
Correspondence to
Prof Steve W Lindsay School of
Biological and Biomedical
Sciences Durham University
South Road Durham DH1 3LE UKswlindsaydurhamacuk
See Online for appendix
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The honeymoon period for malaria control is
threatened both by resistance and in the wake of therecent economic crisis by so-called donor fatiguecreating a serious risk of a resurgence of malaria as hasoccurred repeatedly in the past15 Other interventionsmust be considered as is recognised in the integratedvector management strategies supported by WHO16 which through combining efforts to control severalvector-borne diseases can yield sustainable and cost-effective reductions in the transmission of malarialymphatic filariasis dengue and other diseases17
However since malaria control in many countries hashistorically been achieved without such malaria-specificinterventions socioeconomic development could poten- tially provide an effective and sustainable means of
control in malaria-endemic countries Based on thishypothesis we did a systematic review and meta-analysis of the evidence for the relation between risk ofmalaria infection and socioeconomic status in childrenaged 0ndash15 years
MethodsSearch strategy and eligibility criteriaWe followed recommendations made by the Meta-analysis of Observational Studies in Epidemiology18 andthe Preferred Reporting Items for Systematic Reviewsand Meta-Analyses groups19 We searched Medline Webof Science Embase the Cochrane Database of SystematicReviews the Campbell Library the Centre for Reviewsand Dissemination Health Systems Evidence and theEvidence for Policy and Practice Information and Co-ordinating Centre evidence library to identify studiespublished in English between Jan 1 1980 and July 122011 We selected synonymous terms and used theseto develop the search strategy (appendix pp 3ndash4)
Bibliographies of relevant studies retrieved from the
searches were checked for additional publications Thesearch strategy was not limited by study design Weexcluded reports published before 1980 since we soughtto examine evidence from the period most applicable tothe present status of malaria control
Studies retrieved were eligible for inclusion if theysatisfied all our criteria the study population consistedof children aged 0ndash15 years the association betweensocioeconomic status and malaria was assessed andthe outcome of interest was prevalence of micro-scopically confirmed or rapid diagnostic test-confirmedPlasmodium falciparum infection or clinical malaria(fever and P falciparum infection) Low socioeconomicstatus was defined as not owning defined household
assets a low household income a low score in an asset-based index of socioeconomic status constructed withprincipal components or factor analysis or parentshaving an unskilled rather than a skilled occupationCross-sectional case-control and cohort studies wereall included in the analysis Studies with low responserates were included Only studies done in local popu-lations of countries classified as malaria-endemic20 wereincluded and studies with populations of migrantsdisplaced people or military personnel were excludedStudies in which the outcome was severe malaria con-genital malaria or in which most infections were notP falciparum were also excluded
0 0middot1 0middot2 0middot3 0middot4 0middot5 0middot6
R2=0middot3311
p=0middot000
0middot70
10
20
30
40
50
60
C u m u l a t i v e p r o b a b i l i t y o f m a l a r i a d e a t h
p e r 1 0 0 0 c h i l d r e n
a g e d 0 ndash 5 y e a r s
Human development index for income and education
Figure 983089 Malaria burden and human development index for income and education in 43 countries in
sub-Saharan Africa
Data for cumulative probability of malaria death per 1000 children aged 0ndash5 years are for 2010 and were taken from
Murray and colleagues3 Our human development index for income and education is for 2011 and was calculated
from the UN Development Programme website6 and was derived from three variables gross national income per
head in purchasing power parity terms for 2011 (constant international 2005 US$) expected years of schooling for
children as of 2011 and mean years of schooling for adults as of 2011 Methods for the calculation are shown in theappendix (p 1)6 All 43 countries in sub-Saharan Africa for which data for both variables were available were included Figure 983090 Study selection
6106 records identified throughsearching databases
4696 records after duplicatesremoved
4696 records screened
218 full-text articles assessedfor eligibility
20 studies included inqualitative analysis
15 studies included inquantitative analysis
(meta-analysis)
4 records identified from bibliographies of screened
studies
4478 records excluded by
screening title and abstract
198 full-text articles excluded 36 did not have required
study population 79 did not include
required exposure 69 did not include
required outcome
10 records were reviewscommentaries ormodelling papers
4 records for which fulltext could not belocated
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Data extraction
We first screened titles and abstracts and then onereviewer (LST) screened the relevant full-text articles SWLalso reviewed 22 (10) of the full-text articles screenedwhich were selected at random with any discrepanciesresolved by RS One reviewer (LST) extracted studycharacteristics and unadjusted and adjusted effect sizeswith 95 CIs and recorded the data in a standard form
We did quality and risk-of-bias assessments as recom-
mended by Wells and colleagues
21
Statistical analysisStudies that met the eligibility criteria and that reportedunadjusted or adjusted odds ratios (ORs) with 95 CIs orpresented suffi cient data for the calculation of unadjustedORs and 95 CIs were included in the meta-analysis We
Study
site
Study
design
n Participants Re cru it -
ment of
participants
Exp os ure Outcome Control
group
Measure
of effect
Un-
adjusted
effect
(95 CI)
Ad justed
effect
(95 CI)
Factors adjusted
for
Reason for
exclusion
from
quan-
titative
analysis
Studies included in the meta-analysis
Al-Taiar
et al23 2009
Yemen Case-
control
628 Aged
6 months to
10 years
Recruited
from health
centres
Low vs high
socioeconomic
status
Incidence of
clinical
malaria
(parasitaemia
plus fever)
Age-
matched
healthy
community
controls
OR 1middot76
(1middot21ndash
2middot57)
NA NA NA
Baragatti
et al24 2009
Burkina
Faso
Cross-
sectional
3354 Ag ed
6 months to
12 years
Randomly
sampled
from
community
Family has
irregular land
tenure vs
regular land
tenure
Pf PR None OR 2middot07
(1middot10ndash
3middot88)
1middot85
(1middot17ndash
2middot92)
Age land tenure
building density
equipment
education bednet
use and season
NA
Clarke et al25
2001
The
Gambia
Cross-
sectional
1196 Ag ed
6 months to
5 years
Cluster-
sampled
from 48
villages
Low vs higher
socioeconomic
status
Pf PR None OR 2middot34
(1middot35ndash
4middot05)
NA NA NA
Custodio
et al26 2009
Equatorial
Guinea
Cross-
sectional
552 Aged
0ndash5 years
Randomly
sampledfrom
community
Low vs high
socioeconomicstatus
Pf PR None OR 1middot49
(0middot98ndash2middot25)
NA NA NA
Gahutu
et al27 2011
Rwanda Cross-
sectional
749 Ag ed
0ndash5 years
Randomly
selected
from villages
health
centre and
district
hospital
Low
household
income
(lt5000
Rwandan
francs) vs
high income
(ge5000
Rwandan
francs)
Pf PR None OR 1middot59
(1middot05ndash
2middot40)
NA NA NA
Ghebreyseus
et al28 2000
Ethiopia Cross-
sectional
2114 Ag ed
0ndash10 years
Randomly
sampled
from
community
House does
not own a
radio vs
household
owns a radio
Incidence of
clinical
malaria
(parasitaemia
plus fever)
None OR 0middot97
(0middot60ndash
1middot59)
NA NA NA
Koram
et al29 1995
The
Gambia
Case-
control
768 Aged
3 months to
10 years
Recruited
from three
health
centres
Family does
not own a
refrigerator vs
family owns a
refrigerator
Incidence of
clinical
malaria
(parasitaemia
plus fever)
Healthy
controls
matched by
age date of
enrolment
and
neighbour-
hood
OR 2middot30
(1middot44ndash
3middot75)
2middot58
(1middot46ndash
4middot45)
Place of residence
travel history
ownership of
housing plot house
type crowding
motherrsquos knowledge
of malaria
insecticide use and
medicine use
NA
Krefis et al30
2010
Ghana Cross-
sectional
1496 Aged less
than 15 years
Recruited
when
visiting
major
hospital for
medical care
Low vs high
socioeconomic
status
Incidence of
clinical
malaria
(parasitaemia
plus fever)
None OR NA 1middot79
(1middot32ndash
2middot44)
Age sex ethnicity
number of children
in family motherrsquos
age and place of
residence
NA
(Continues on next page)
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Study
site
Study
design
n Participan ts Re cr uit-
ment of
participants
Exposure Outcome Control
group
Measure
of effect
Un-
adjusted
effect
(95 CI)
Ad justed
effect
(95 CI)
Factors adjusted
for
Reason for
exclusion
from
quan-
titative
analysis
(Continued from previous page)
OngrsquoEcha
et al31 2006
Kenya Case-
control
374 Aged
0ndash3 years
(children with
cerebral
malaria and
those with
previous
hospital visits
were
excluded)
Recruited
when
visiting
district
hospital with
symptoms
of malaria
Parents are
farmers vs
parents are
not farmers
Incidence of
clinical
malaria
(parasitaemia
plus fever)
Healthy
controls
recruited
from
maternal
and child
health clinic
OR 3middot85
(1middot64ndash
9middot09)
0middot92
(0middot41ndash
2middot04)
Child risk factors
(axillary
temperature
ge37middot5degC)
nutritional factors
house type and
mosquito control
measures
NA
Pullan
et al32 2010
Uganda Cross-
sectional
1770 Aged
5ndash15 years
Selected
from all
households
in district
Lowest vs
highest socio-
economic
status quintile
Pf PR None OR 1middot25
(0middot74ndash
2middot13)
NA NA NA
Ronald
et al33
2006
Ghana Cross-
sectional
296 Aged
1ndash9 years
Randomly
sampled
from
community
Decreasing
household
socio-
economic
status
Pf PR None OR 3middot22
(1middot95ndash
5middot32)
3middot95
(2middot26ndash
6middot90)
Age and travel to
rural areas
NA
Slutsker
et al34 1996
Malawi Cross-
sectional
3915 Aged
0ndash3 months
Infantsrsquo
mothers
were
enrolled into
a chemopro-
phylaxis
study at four
antenatalclinics
Low vs high or
medium socio-
economic
status
Pf PR None OR 1middot80
(1middot30ndash
2middot10)
NA NA NA
Villamor
et al35 2003
Tanzania Cross-
sectional
687 Aged 6ndash60
months
Children
were enrolled
in a vitamin
A supple-
mentation
trial when
admitted to
hospital with
pneumonia
No electricity
at home vs
electricity at
home
Pf PR None OR 1middot84
(1middot23ndash
2middot76)
NA NA NA
Winskill
et al36
2011
Tanzania Cross-
sectional
1438 Aged
6 months to
13 years
Randomly
selected
from 21
hamlets
Decreasing
household
socioeconomic
status
Pf PR None OR 1middot15
(0middot94ndash
1middot39)
NA NA NA
Yamamoto
et al37 2010
Burkina
Faso
Case-
control
283 Aged
0ndash9 years
Recruited by
passive case
detectionat central
laboratory
Low vs high
socioeconomic
status
Incidence of
clinical
malaria(parasitaemia
plus fever)
Controls
from
demographicsurveillance
system
database
matched for
age sex
ethnicity and
residence
OR 0middot47
(0middot20ndash
1middot08)
NA NA NA
Studies included in the qualitative analysis but excluded from the meta-analysis
Clark et al38
2008
Uganda Cohort 558 Aged
1ndash10 years
Recruited
from a
census
population
in one parish
1st and 2nd
(lowest) vs
4th wealth
quartile
(highest)
Incidence of
clinical
episodes of
malaria per
person-year
at risk
None RR 2middot04
(1middot54ndash
2middot70)
1middot30
(0middot96ndash
1middot79)
Age sickle cell trait
G6PD deficiency in
girls bednet use
household
crowding and
distance from
swamp
Not
possible to
calculate
OR
(Continues on next page)
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used the generic inverse-variance method for the meta-
analysis in which weight is given to each study accordingto the inverse of the variance of the effect to minimiseuncertainty about the pooled effect estimates Bothoutcomes (P falciparum infection and clinical malaria)were combined in the analysis We allocated the includedstudies into four subgroups according to the measure ofsocioeconomic status used asset ownership householdwealth socioeconomic index or parentsrsquo occupations Wedid separate analyses for unadjusted and adjusted ORsMissing data were not problematic since meta-regressionof individual data was not done
Initially we did a fixed-effects meta-analysis but ifI sup2 was large (gt50) which suggests substantial hetero-geneity between studies we used random-effectsanalysis Random-effects analysis adjusts the standard
errors of each study estimate of effect to include a
measure of variation in the effects reported betweenstudies We produced forest plots to visually assess theORs and 95 CIs of each study and used funnel plots toassess publication bias (with study size as a function ofeffect size) We used Eggerrsquos linear regression method totest for funnel plot asymmetry (ie to quantify the biascaptured by the funnel plot)22 Analyses were done withStata 11 and RevMan 5
ResultsOur initial search yielded 6106 records of which4696 remained after removal of duplicates (figure 2)20 records met our inclusion criteria (table)23ndash42 and ofthese 15 contained the necessary data for inclusion in thequantitative analysis (meta-analysis) Five records were
Study
site
Study
design
n Participant s R ecru it -
ment of
participants
Exp os ure Outcome Control
group
Measure
of effect
Un-
adjusted
effect
(95 CI)
Ad justed
effect
(95 CI)
Factors adjusted
for
Reason for
exclusion
from
quan-
titative
analysis
(Continued from previous page)
Klinkenberg
et al39 2006
Ghana Cross-
sectional
1744 Aged 6ndash60
months
Randomly
sampled
from
communities
near (lt1000
m) and less
near
(gt1000 m)
agricultural
sites in Accra
Socio-
economic
status below
vs above mean
for the city
Pf PR None In-
suffi cient
infor-
mation
provided
NA NA NA Not
possible to
calculate
OR
Kreuels
et al40 2008
Ghana Cohort 535 Aged
2ndash4 months
Recruited
from nine
villages after
visiting
health centre
(children
with chronic
diseases
excluded)
Family does
not have good
financial
situation vs
family has
good financial
situation
Incidence of
clinical
malaria
(parasitaemia
plus fever)
None Incidence
rate ratio
1middot59
(1middot33ndash
1middot89)
1middot52
(1middot27ndash
1middot82)
Sex ethnicity
season of birth (dry
or rainy season)
sickle cell trait
motherrsquos education
motherrsquos
occupation
knowledge of
malaria and
protective measures
Not
possible to
calculate
OR
Matthys
et al41 2006
Cocircte
drsquoIvoire
Cross-
sectional
672 Aged
0ndash15 years
Selected
from
farming and
non-farming
households
Low vs high
socioeconomic
status
Pf PR None OR NA 2middot44
(0middot88ndash
10middot00)
Age agricultural
zone crops grown
irrigation overnight
stays in temporary
farm huts and
distance to
permanent ponds
and fish ponds
Bayesian
credible
intervals
reported
only
Pullan
et al42
2010
Uganda Cross-
sectional
1844 A ged
5ndash15 years
All residents
of four
villages asked
to
participate
with 78
successfully
enrolled
Decreasing
household
socioeconomic
status
Pf PR None OR NA 2middot27
(0middot88ndash
25middot00)
Ag e bednet use Bayesian
credible
intervals
reported
only
OR=odds ratio Pf PR=Plasmodium falciparum parasite rate RR=risk ratio Socioeconomic status analysed as a continuous variable
Table Studies included in the systematic review and meta-analysis
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excluded from the quantitative analysis either because
Bayesian credible intervals were reported (n=2) or becauseORs could not be calculated from the available data (n=3)
Despite substantial overlap between CIs for both unadjust-
ed and adjusted results high I sup2 values from fixed-effectsanalysis suggested substantial heterogeneity between
Figure 983091 Association between low s ocioeconomic status and clinical malaria or parasitaemia in chi ldren aged 0ndash15 years
Pooled effects from random-effects meta-analyses for unadjusted (A) and adjusted (B) results are shown Studies are divided into subgroups by measure ofsocioeconomic status used Error bars show 95 CIs df=degrees of freedom
Odds ratio(95 CI)
WeightLog(odds ratio)
SE
Socioeconomic indexAl-Taiar et al23 (2009)Custodio et al26 (2009)Pullan et al32 (2010)Ronald et al33 (2006)Slutsker et al34 (1996)Winskill et al36 (2011)Yanamoto et al37 (2010)
SubtotalHeterogeneity τsup2=0middot12 χsup2=24middot51 df=6 (p=0middot0004) I2=76Test for overall effect Z=2middot56 (p=0middot01)
0middot565314 0middot397341 0middot223144 1middot169381 0middot587787 0middot139262 ndash0middot756122
0middot191170 0middot210810 0middot271367 0middot255894 0middot166032 0middot102621 0middot435365
1middot76 (1middot21ndash2middot56) 1middot49 (0middot98ndash2middot25) 1middot25 (0middot73ndash2middot13) 3middot22 (1middot95ndash5middot32) 1middot80 (1middot30ndash2middot49) 1middot15 (0middot94ndash1middot41)
0middot47 (0middot20ndash1middot10) 1middot49 (1middot10ndash2middot01)
8middot4 8middot0 6middot6 7middot0 9middot1 10middot5
4middot0 53middot5
Asset ownership
Baragatti et al24 (2009)Ghebreyseus et al28 (2000)Koram et al29 (1995)Villamor et al35 (2003)
SubtotalHeterogeneity τ2=0middot09 χ2=7middot14 df=3 (p=0middot07) I2=58Test for overall effect Z=2middot76 (p=0middot006)
0middot727549 ndash0middot028422 0middot832909 0middot611533
0middot322571 0middot249721 0middot238911 0middot205363
2middot07 (1middot10ndash3middot90) 0middot97 (0middot60ndash1middot59) 2middot30 (1middot44ndash3middot67) 1middot84 (1middot23ndash2middot76) 1middot70 (1middot17ndash2middot48)
5middot6 7middot1 7middot3 8middot1 28middot2
Reduced odds of malaria Increased odds of malaria
10middot50middot20middot1 2 5 10
Household wealthClarke et al25 (2001)Gahutu et al27 (2011)
SubtotalHeterogeneity τ2=0middot01 χ2=1middot22 df=1 (p=0middot27) I2=18
Test for overall effect Z=3middot25 (p=0middot001)
0middot851030 0middot463734
0middot279963 0middot211706
2middot34 (1middot35ndash4middot05) 1middot59 (1middot05ndash2middot41) 1middot85 (1middot28ndash2middot68)
6middot4 8middot0 14middot4
Parentsrsquo occupationOngrsquoecha et al31 (2006)SubtotalHeterogeneity NA
Test for overall effect Z=3middot10 (p=0middot002)
Total (all measures of socioeconomic status)Heterogeneity τ2=0middot10 χ2=40middot38 df=13 (p=0middot0001) I2=68Test for overall effect Z=4middot76 (plt0middot00001)Test for subgroup differences χ2=4middot46 df=3 (p=0middot22) I2=32middot83
1middot347074 0middot434886 3middot85 (1middot64ndash9middot02) 3middot85 (1middot64ndash9middot02)
4middot0
4middot0
1middot66 (1middot35ndash2middot05) 100middot0
A
Odds ratio(95 CI)
WeightLog(odds ratio)
SE
Socioeconomic indexKrefis et al30 (2010)Ronald et al33 (2006)
SubtotalHeterogeneity τsup2=0middot26 χsup2=6middot01 df=1 (p=0middot01) I2=83Test for overall effect Z=2middot38 (p=0middot02)
0middot579818 1middot373716
0middot154177 0middot284873
1middot79 (1middot32ndash2middot42)
3middot95 (2middot26ndash6middot90) 2middot56 (1middot18ndash5middot56)
27middot4
19middot0 46middot3
Asset ownershipBaragatti et al24 (2009)Koram et al29 (1995)SubtotalHeterogeneity τ2=0middot00 χ2=0middot80 df=1 (p=0middot37) I2=0Test for overall effect Z=4middot10 (plt0middot0001)
0middot615186 0middot947789
0middot233766 0middot290486
1middot85 (1middot17ndash2middot93) 2middot58 (1middot46ndash4middot56) 2middot11 (1middot48ndash3middot01)
22middot1 18middot6 40middot7
Reduced odds of malaria Increased odds of malaria
10middot50middot2 2 5
Parentsrsquo occupationsOngrsquoecha et al31 (2006)SubtotalHeterogeneity NATest for overall effect Z=0middot21 (p=0middot83)
ndash0middot086178 0middot410929 0middot92 (0middot41ndash2middot05) 0middot92 (0middot41ndash2middot05)
12middot9 12middot9
Total (all measures of socioeconomic status)Heterogeneity τ2=0middot11 χ2=10middot72 df=4 (p=0middot03) I2=63Test for overall effect Z=3middot83 (plt0middot0001)Test for subgroup differences χ2=4middot02 df=2 (p=0middot13) I2=50middot3
2middot06 (1middot42ndash2middot97) 100middot0
B
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studies (unadjusted effect size I sup2=68 adjusted effect size
I sup2=63) Therefore random-effects analysis was usedThe meta-analysis was restricted to comparisonsbetween the highest (least poor) and lowest (poorest)socioeconomic groups Subgroup analysis suggested thatlow socioeconomic status was associated with increasedodds of malaria irrespective of the measure used forsocioeconomic status with the exception of one study inwhich parentsrsquo occupations were used31 we thereforejudged that to pool all results would be appropriate Inthe meta-analyses for both unadjusted and adjustedresults the odds of malaria infection were higher in thepoorest children than in the least poor children (figure 3)
Visual assessment of funnel plots (appendix p 8)showed that the studies were distributed fairly
symmetrically about the combined effect size whichsuggests little publication bias However Eggerrsquos test forbias suggested funnel plot asymmetry for the unadjustedresults (bias coeffi cient 1middot70 95 CI ndash0middot97 to 4middot37p=0∙191) which suggests that publication bias (delayedpublication or location bias) small-study effects selectiveoutcome reporting or selective analysis reporting mighthave been present A test for funnel plot asymmetry wasnot possible for the adjusted effects since only fivestudies were included in the meta-analysis Overallquality assessment scores for risk of bias in studiesincluded in the quantitative analysis ranged from two toseven out of a maximum of eight (appendix p 6ndash7)
DiscussionOur findings suggest that low socioeconomic status isassociated with roughly doubled odds of clinical malariaor parasitaemia in children compared with higher socio-economic status within a locality This conclusion is sup-ported by a similar size and direction of effect noted inthe five studies excluded from the meta-analysis Sinceour analysis represents a comparison of the very poorestchildren with the least poor children within highly im-poverished communities the difference in the odds ofmalaria in the poorest children would probably be evengreater if the studies were expanded to include childrenfrom wealthier backgrounds The association between
socioeconomic status and malaria is not definitiveevidence for the direction of causality since the pooresthouseholds are not only more susceptible to the diseasebut are also more vulnerable to its costs such that thedisease itself can induce poverty For example asignificant positive association between low socio-economic status and malaria parasitaemia has beenreported in Tanzania43 with causality in both directionsFindings from Kenya44 and Nigeria45 suggest that the costsof malaria treatment (as a proportion of non-food monthlyincome) and subsequent financial setbacks are greater forpoorer than for more wealthy households Costs also varygeographically in Kenya44 and Papua New Guinea46 therisk of clinical disease is greater in low-transmissiondistricts with subsequently greater loss of income
Wealth is probably protective against malaria since it
renders prophylaxis and treatment more affordable
47ndash49
andis positively associated with other beneficial factorsincluding better-educated parents (which improvesprophylaxis and treatment for children) increasedhousing quality (which reduces house entry by malaria-transmitting mosquitoes) and improved nutritionalstatus of children (which could increase their subsequentability to cope with malaria infection)50ndash52 Malaria andpoverty therefore constitute a vicious cycle for the pooresthouseholds exacerbating differences in health and wealth
A major limitation of our meta-analysis is that themeasurement of risk factors was done with varyingprecision in the included studies and although we didsubgroup and random-effects analyses these are unlikely
to have fully accounted for heterogeneity in study designAnother important limitation is the poor quality of thestudies included in the meta-analysis which results fromthe nature of the study question (since randomisation forsocioeconomic status would not be practically or ethicallypossible) However the consistency of results acrossstudies and settings suggests that the finding of increasedodds of malaria in children of low socioeconomic status isrobust For our systematic review the main limitationwas the language of the search In particular notincluding publications in Spanish probably excludedmuch data from South America such that our findingscannot be generalised to that region Eggerrsquos testsuggested the presence of some forest plot asymmetryhowever statistical tests for forest plot asymmetry tend tohave low power53 and asymmetry might not be attributableto publication biasmdashit might also have arisen from poorstudy quality leading to artificially inflated effects in thesmaller studies selective outcome or analysis reportingor chance Incomplete retrieval (four full-text studiescould not be retrieved) might also have introduced bias
On the basis of our findings we advocate thatdevelopment programmes should be an essentialcomponent of malaria control Malaria elimination inmany high-income countries was achieved withoutmalaria-specific interventions prevalence started to fall inEurope and North America as a by-product of improved
living conditions and increased wealth5455 and afterRonald Ross deduced the mode of malaria transmission56 in 1897 more specific interventions became possibleincluding habitat modification (permanent elimination ofbreeding sitesmdasheg by installing and maintaining drains)habitat manipulation (temporary creation of unfavourableconditions for the vectormdasheg by fluctuating the amountof water in reservoirs) and modifications to humanhabitation or behaviour to reduce human-vector contactsuch as mosquito-proofing of houses57 As a result mostof Europe and North America is now characterised byanophelism without malaria which is testament to theeffectiveness of these control efforts together with areduced innate receptivity to malaria transmission thatstems from advances in nutrition health care and
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development58 Similar environmental management
strategies together with larval control also helped toreduce malaria transmission in many developingcountries during the 20th century including ZanzibarIndonesia Malaysia the Panama Canal and the CopperBelt of Zambia5960 Thus as transmission today falls inmuch of sub-Saharan Africa and elsewhere developmentwill contribute to the reduction and elimination of thedisease Several specific development interventions couldcontribute to malaria control (appendix p 5) which mightbe similar to malaria-specific interventions in terms ofcosts (appendix pp 9ndash10) An excellent example of howsuch interventions can work in practice can be seen inKhartoum Sudan (appendix p 11)
This approach has three major constraints First
accurate costing of the extent to which specificdevelopment interventions contribute to malaria controlis diffi cult Whereas measuring the effect of housescreening is straightforward measuring that ofimproved education or raised incomes is not Secondthe effectiveness of a development intervention dependson both the nature and intensity of malaria transmissionFor example house screening is probably most effectivein areas of low to moderate transmission where vectorsfeed indoors Countries that have eliminated malariasince 1900 have largely been temperate subtropical orislands8 and the high malaria burden in manydeveloping countries is not merely a product of povertyRather the specific ecological requirements of both themalaria parasite and its mosquito vector help todetermine the range of the disease61 Interventions haveto be highly effective and development should not bethought of as a standalone strategy but as a complementto malaria-specific interventions such as LLINs indoorresidual spraying and larval source management Thirdeconomic development gives rise to broader socialenvironmental and ecological changes that might insome circumstances lead to an increase in the burden ofmalaria (appendix p 5) as has been seen in Sri Lanka(appendix p 12) Nonetheless these constraints shouldnot be treated as barriers to the use of socioeconomicdevelopment as an intervention against malaria
(appendix p 5)In addition to initiatives such as the Millennium Villages
project which is operating in 14 villages in ten Africancountries to examine the effects of socioeconomicdevelopment62 further research is needed to address someimportant questions and to galvanise specialists in bothhealth and development to work more closely together onmalaria control For example randomised controlled trialsshould be considered to assess the effectiveness ofsocioeconomic interventions (eg improved education andnutrition) against malaria in different settings We mustalso investigate the causal pathways that lead fromdevelopment to successful malaria control and vice versaand develop an understanding of the relation betweenmalaria control birth rates and population growth
That malaria control remains largely the preoccupation
of the health sector alone is a failing of both those whowork in health and those who work in internationaldevelopment The disease severely compromises socio-economic development and its control and eliminationwould improve economic prosperity worldwide Theeffectiveness of available drugs and insecticides formalaria control will ultimately deteriorate with theemergence of parasites resistant to antimalarials and ofvectors resistant to insecticides and the development andprocurement costs of replacements will be high Donorfatigue is also an ever-present threat to interventions suchas LLINs indoor residual spraying and intermittentpreventive treatment especially in view of the economicsituation since the 2007ndash08 financial crisis63 However
several specific development interventions could beintroduced to aid both economic development andmalaria control Increased wealth and improved standardsof living that stem directly from socioeconomicdevelopment could prove fundamental in ensuring thatmalaria transmission continues to fall in much of AsiaSouth America and Africa as it happened historically inEurope and North America Socioeconomic developmentcould prove to be a very effective and sustainableintervention against malaria in the long term
Contributors
SWL and RS conceived of the study SWL RS LST HL and JTdeveloped the study design and the outline of the report LST searchedthe scientific literature did the meta-analysis and prepared the first
draft of the report BW provided advice on the systematic review andmeta-analysis HTK contributed the case study from Sudan (appendix)All authors reviewed the final version of the report
Conflicts of interest
We declare that we have no conflicts of interest
Acknowledgments
This work was supported by the UK Department for InternationalDevelopment and the Malaria Centre at the London School of Hygiene ampTropical Medicine (LSHTM) SWL was supported by the Research andPolicy for Infectious Disease Dynamics (RAPIDD) programme of theScience and Technology Directorate US Department of Homeland Securitythe Fogarty International Center (US National Institutes of Health) and theBill amp Melinda Gates Foundation JT was supported in part by a grant fromthe UK Economic and Social Research Council to the STEPS Centre and theInstitute of Development Studies (University of Sussex Brighton UK)We are grateful to Frida Kasteng (LSHTM) for providing information about
the costs of malaria and development interventionsReferences 1 Gething PW Patil AP Smith DL et al A new world malaria map
Plasmodium falciparum endemicity in 2010 Malar J 2011 10 378 2 WHO World Malaria Report 2011 Geneva World Health
Organization 2011 3 Murray CJL Rosenfeld LC Lim SS et al Global malaria mortality
between 1980 and 2010 a systematic analysis Lancet 2012 379 413ndash31 4 Murray CJL Vos T Lozano R et al Disability-adjusted life years
(DALYs) for 291 diseases and injuries in 21 regions 1990mdash2010a systematic analysis for the Global Burden of Disease Study 2010Lancet 2012 380 2197ndash223
5 Sachs J Malaney P The economic and social burden of malariaNature 2002 415 680ndash85
6 UNDP International human development indicators httphdrundporgendatabuild (accessed April 24 2012)
7 Sachs JD Macroeconomics and health investing in health for
economic development Geneva World Health Organization 2001
8132019 SE Dev as an Intervention Agst Malaria
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Articles
wwwthelancetcom Published online June 19 2013 httpdxdoiorg101016S0140-6736(13)60851-X 9
8 Gallup J Sachs J The economic burden of malariaAm J Trop Med Hyg 2001 64 85ndash96
9 Mandelbaum-Schmid J HIVAIDS hunger and malaria are theworldrsquos most urgent problems say economistsBull World Health Organ 2004 82 554ndash55
10 Kidson C Indaratna K Ecology economics and political will thevicissitudes of malaria strategies in Asia Parassitologia 1998 40 39ndash46
11 UNDP Millennium Development Goal 6 focuses on combatingHIVAIDS malaria and other diseases httpwebundporgmdggoal6shtml (accessed April 21 2012)
12 OrsquoMeara WP Mangeni JN Steketee R Greenwood B Changes inthe burden of malaria in sub-Saharan Africa Lancet Infect Dis 201010 545ndash55
13 Dondorp AM Fairhurst RM Slutsker L et al The threat ofartemisinin-resistant malaria N Engl J Med 2011 365 1073ndash75
14 Ranson H NrsquoGuessan R Lines J et al Pyrethroid resistance inAfrican anopheline mosquitoes what are the implications formalaria control Trends Parasitol 2011 27 91ndash98
15 Cohen J Smith D Cotter C et al Malaria resurgence a systematic
review and assessment of its causes Malar J 2012 11 122 16 WHO WHO position statement on integrated vector management
Wkly Epidemiol Rec 2008 83 177ndash84 17 van den Berg H Mutero CM Ichimori K WHO guidance on
policy-making for integrated vector management Geneva WorldHealth Organization 2012
18 Stroup DF Berlin JA Morton SC et al Meta-analysis ofobservational studies in epidemiology JAMA 2000 283 2008ndash12
19 Modher D Liberati A Tetzlaff J Altman DG Group TP Preferredreporting items for systematic reviews and meta-analyses thePRISMA statement PLoS Med 2009 6 e1000097
20 Roll Back Malaria Malaria endemic countries Geneva The RBMPartnership 2010 httpwwwrbmwhointendemiccountrieshtml(accessed June 8 2011)
21 Wells G Shea B OrsquoConnell D et al The Newcastle-Ottawa Scale(NOS) for assessing the quality of nonrandomised studies inmeta-analyses Ottawa University of Ottawa 2011
22 Egger M Smith GD Schneider M Minder C Bias in meta-analysisdetected by a simple graphical test BMJ 1997 315 629ndash34
23 Al-Taiar A Assabri A Al-Habori M et al Socioeconomic andenvironmental factors important for acquiring non-severe malariain children in Yemen a case-control studyTrans R Soc Trop Med Hyg 2009 103 72ndash78
24 Baragatti M Fournet F Henry M-C et al Social and environmentalmalaria risk factors in urban areas of Ouagadougou Burkina FasoMalar J 2009 8 13
25 Clarke SE Boslashgh C Brown RC Pinder M Walraven GELLindsay SW Do untreated bednets protect against malariaTrans R Soc Trop Med Hyg 2001 95 457ndash62
26 Custodio E Descalzo MAacute Villamor E et al Nutritional andsocio-economic factors associated with Plasmodium falciparum infection in children from Equatorial Guinea results from anationally representative survey Malar J 2009 8 225
27 Gahutu J-B Steininger C Shyirambere C et al Prevalence and riskfactors of malaria among children in southern highland Rwanda
Malar J 2011 10 134 28 Ghebreyesus TA Haile M Witten KH et al Household risk factorsfor malaria among children in the Ethiopian HighlandsTrans R Soc Trop Med Hyg 2000 94 17ndash21
29 Koram KA Bennett S Adiamah JH Greenwood BM Socio-economicrisk factors for malaria in a peri-urban area of The GambiaTrans R Soc Trop Med Hyg 1995 89 146ndash50
30 Krefis AC Schwarz NG Nkrumah B et al Principal componentanalysis of socioeconomic factors and their association withmalaria in children from the Ashanti Region Ghana Malar J 20109 201
31 Ongrsquoecha JM Keller CC Were T et al Parasitemia anemia andmalarial anemia in infants and young children in a ruralholoendemic Plasmodium falciparum transmission area Am J Trop Med Hyg 2006 74 376ndash85
32 Pullan RL Kabatereine NB Bukirwa H Staedke SG Brooker SHeterogeneities and consequences of Plasmodium species andhookworm coinfection a population based study in Uganda
J Infect Dis 2011 203 406ndash17
33 Ronald LA Kenny SL Klinkenberg E et al Malaria and anaemiaamong children in two communities of Kumasi Ghana
a cross-sectional survey Malar J 2006 1 105 34 Slutsker L Khoromana CO Hightower AW et al Malaria infection
in infancy in rural Malawi Am J Trop Med Hyg 1996 55 71ndash76 35 Villamor E Fataki MR Mbise RL Fawzi WW Malaria parasitaemia
in relation to HIV status and vitamin A supplementation amongpre-school children Trop Med Int Health 2003 8 1051ndash61
36 Winskill P Rowland M Mtove G Malima R Kirby M Malaria riskfactors in north-east Tanzania Malar J 2011 10 98
37 Yamamoto S Louis VR Sie A Sauerborn R Household risk factorsfor clinical malaria in a semi-urban area of Burkina Fasoa case-control study Trans R Soc Trop Med Hyg 2010 104 61ndash65
38 Clark TD Greenhouse B Njama-Meya D et al Factors determiningthe heterogeneity of malaria incidence in children in KampalaUganda J Infect Dis 2008 198 393ndash400
39 Klinkenberg E McCall P Wilson M et al Urban malaria andanaemia in children a cross-sectional survey in two cities of GhanaTrop Med Int Health 2006 11 578ndash88
40 Kreuels B Kobbe R Adjei S et al Spatial variation of malariaincidence in young children from a geographically homogeneousarea with high endemicity J Infect Dis 2008 197 85ndash93
41 Matthys B Vounatsou P Raso G et al Urban farming and malariarisk factors in a medium-sized town in Cocircte drsquoIvoireAm J Trop Med Hyg 2006 75 1223ndash31
42 Pullan RL Bukirwa H Staedke SG Snow RW Brooker S Plasmodium infection and its risk factors in eastern Uganda Malar J 2010 4 9
43 Somi MF Butler JRG Is there evidence for dual causation betweenmalaria and socioeconomic status Findings from rural TanzaniaAm J Trop Med Hyg 2007 77 1020ndash27
44 Chuma JM Thiede M Rethinking the economic costs of malaria atthe household level evidence from applying a new analyticalframework in rural Kenya Malar J 2006 5 76ndash89
45 Onwujekwe O Hanson K Are malaria treatment expenditurescatastrophic to different socio-economic and geographic groups andhow do they cope with payment A study in southeast NigeriaTrop Med Int Health 2010 15 18ndash25
46 Sicuri E Davy C Marinelli M et al The economic cost tohouseholds of childhood malaria in Papua New Guinea a focus onintra-country variation Health Policy Plann 2012 27 339ndash47
47 Matovu F Goodman C How equitable is bed net ownership andutilisation in Tanzania A practical application of the principles ofhorizontal and vertical equity Malar J 2009 8 109ndash21
48 Gingrich CD Hanson K Marchant T Mulligan J-A Mponda HPrice subsidies and the market for mosquito nets in developingcountries a study of Tanzaniarsquos discount voucher schemeSoc Sci Med 2011 73 160ndash68
49 Ahmed SM Haque R Haque U Hossain A Knowledge on thetransmission prevention and treatment of malaria among twoendemic populations of Bangladesh and their health-seekingbehaviour Malar J 2009 8 173
50 Saaka M Oosthuizen J Beatty S Effect of joint iron and zincsupplementation on malarial infection and anaemiaEast Afr J Public Health 2009 6 55ndash62
51 Worrall E Basu S Hanson K Is malaria a disease of povertyA review of the literature Trop Med Int Health 2005 10 1047ndash59 52 Barat LM Palmer N Basu S Worrall E Hanson K Mills A Do
malaria control interventions reach the poor Am J Trop Med Hyg 2004 71 174ndash78
53 Sterne JAC Sutton AJ Ioannidis JPA et al Recommendationsfor examining and interpreting funnel plot asymmetry inmeta-analyses of randomised controlled trials BMJ 2011 343 4002
54 Garcia-Martin G Status of malaria eradication in the AmericasAm J Trop Med Hyg 1972 21 617ndash33
55 Bruce-Chwatt L de Zulueta J The rise and fall of malaria inEurope London Oxford University Press 1980
56 Sinden RE Malaria mosquitoes and the legacy of Ronald RossBull World Health Organ 2007 85 894ndash96
57 Keiser J Singer BH Utzinger J Reducing the burden of malariain different eco-epidemiological settings with environmentalmanagement a systematic review Lancet Infect Dis 20055 695ndash708
8132019 SE Dev as an Intervention Agst Malaria
httpslidepdfcomreaderfullse-dev-as-an-intervention-agst-malaria 1010
Articles
10 www thelancet com Published online June 19 2013 httpdx doi org10 1016S0140-6736(13)60851-X
58 Randolph SE Rogers DJ The arrival establishment and spread ofexotic diseases patterns and predictions Nat Rev Microbiol 2010
8 361ndash71 59 Hay S Guerra C Tatem A Noor A Snow R The global distribution
and population at risk of malaria past present and futureLancet Infect Dis 2004 4 327ndash36
60 Killeen GF Fillinger U Kiche I Gouagna LC Knols BGEradication of Anopheles gambiae from Brazil lessons for malariacontrol in Africa Lancet Infect Dis 2002 2 618ndash27
61 Jetten T Takken W Anophelism without malaria in Europea review of the ecology and distribution of the genus Anopheles in
Europe Wageningen Wageningen Agricultural University 1994 62 Millennium Villages Project httpwwwmillenniumvillagesorg
the-villages (accessed May 16 2013) 63 Garrett L Global health hits crisis point Nature 2012 482 7
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2 wwwthelancetcom Published online June 19 2013 httpdxdoiorg101016S0140-6736(13)60851-X
The honeymoon period for malaria control is
threatened both by resistance and in the wake of therecent economic crisis by so-called donor fatiguecreating a serious risk of a resurgence of malaria as hasoccurred repeatedly in the past15 Other interventionsmust be considered as is recognised in the integratedvector management strategies supported by WHO16 which through combining efforts to control severalvector-borne diseases can yield sustainable and cost-effective reductions in the transmission of malarialymphatic filariasis dengue and other diseases17
However since malaria control in many countries hashistorically been achieved without such malaria-specificinterventions socioeconomic development could poten- tially provide an effective and sustainable means of
control in malaria-endemic countries Based on thishypothesis we did a systematic review and meta-analysis of the evidence for the relation between risk ofmalaria infection and socioeconomic status in childrenaged 0ndash15 years
MethodsSearch strategy and eligibility criteriaWe followed recommendations made by the Meta-analysis of Observational Studies in Epidemiology18 andthe Preferred Reporting Items for Systematic Reviewsand Meta-Analyses groups19 We searched Medline Webof Science Embase the Cochrane Database of SystematicReviews the Campbell Library the Centre for Reviewsand Dissemination Health Systems Evidence and theEvidence for Policy and Practice Information and Co-ordinating Centre evidence library to identify studiespublished in English between Jan 1 1980 and July 122011 We selected synonymous terms and used theseto develop the search strategy (appendix pp 3ndash4)
Bibliographies of relevant studies retrieved from the
searches were checked for additional publications Thesearch strategy was not limited by study design Weexcluded reports published before 1980 since we soughtto examine evidence from the period most applicable tothe present status of malaria control
Studies retrieved were eligible for inclusion if theysatisfied all our criteria the study population consistedof children aged 0ndash15 years the association betweensocioeconomic status and malaria was assessed andthe outcome of interest was prevalence of micro-scopically confirmed or rapid diagnostic test-confirmedPlasmodium falciparum infection or clinical malaria(fever and P falciparum infection) Low socioeconomicstatus was defined as not owning defined household
assets a low household income a low score in an asset-based index of socioeconomic status constructed withprincipal components or factor analysis or parentshaving an unskilled rather than a skilled occupationCross-sectional case-control and cohort studies wereall included in the analysis Studies with low responserates were included Only studies done in local popu-lations of countries classified as malaria-endemic20 wereincluded and studies with populations of migrantsdisplaced people or military personnel were excludedStudies in which the outcome was severe malaria con-genital malaria or in which most infections were notP falciparum were also excluded
0 0middot1 0middot2 0middot3 0middot4 0middot5 0middot6
R2=0middot3311
p=0middot000
0middot70
10
20
30
40
50
60
C u m u l a t i v e p r o b a b i l i t y o f m a l a r i a d e a t h
p e r 1 0 0 0 c h i l d r e n
a g e d 0 ndash 5 y e a r s
Human development index for income and education
Figure 983089 Malaria burden and human development index for income and education in 43 countries in
sub-Saharan Africa
Data for cumulative probability of malaria death per 1000 children aged 0ndash5 years are for 2010 and were taken from
Murray and colleagues3 Our human development index for income and education is for 2011 and was calculated
from the UN Development Programme website6 and was derived from three variables gross national income per
head in purchasing power parity terms for 2011 (constant international 2005 US$) expected years of schooling for
children as of 2011 and mean years of schooling for adults as of 2011 Methods for the calculation are shown in theappendix (p 1)6 All 43 countries in sub-Saharan Africa for which data for both variables were available were included Figure 983090 Study selection
6106 records identified throughsearching databases
4696 records after duplicatesremoved
4696 records screened
218 full-text articles assessedfor eligibility
20 studies included inqualitative analysis
15 studies included inquantitative analysis
(meta-analysis)
4 records identified from bibliographies of screened
studies
4478 records excluded by
screening title and abstract
198 full-text articles excluded 36 did not have required
study population 79 did not include
required exposure 69 did not include
required outcome
10 records were reviewscommentaries ormodelling papers
4 records for which fulltext could not belocated
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Data extraction
We first screened titles and abstracts and then onereviewer (LST) screened the relevant full-text articles SWLalso reviewed 22 (10) of the full-text articles screenedwhich were selected at random with any discrepanciesresolved by RS One reviewer (LST) extracted studycharacteristics and unadjusted and adjusted effect sizeswith 95 CIs and recorded the data in a standard form
We did quality and risk-of-bias assessments as recom-
mended by Wells and colleagues
21
Statistical analysisStudies that met the eligibility criteria and that reportedunadjusted or adjusted odds ratios (ORs) with 95 CIs orpresented suffi cient data for the calculation of unadjustedORs and 95 CIs were included in the meta-analysis We
Study
site
Study
design
n Participants Re cru it -
ment of
participants
Exp os ure Outcome Control
group
Measure
of effect
Un-
adjusted
effect
(95 CI)
Ad justed
effect
(95 CI)
Factors adjusted
for
Reason for
exclusion
from
quan-
titative
analysis
Studies included in the meta-analysis
Al-Taiar
et al23 2009
Yemen Case-
control
628 Aged
6 months to
10 years
Recruited
from health
centres
Low vs high
socioeconomic
status
Incidence of
clinical
malaria
(parasitaemia
plus fever)
Age-
matched
healthy
community
controls
OR 1middot76
(1middot21ndash
2middot57)
NA NA NA
Baragatti
et al24 2009
Burkina
Faso
Cross-
sectional
3354 Ag ed
6 months to
12 years
Randomly
sampled
from
community
Family has
irregular land
tenure vs
regular land
tenure
Pf PR None OR 2middot07
(1middot10ndash
3middot88)
1middot85
(1middot17ndash
2middot92)
Age land tenure
building density
equipment
education bednet
use and season
NA
Clarke et al25
2001
The
Gambia
Cross-
sectional
1196 Ag ed
6 months to
5 years
Cluster-
sampled
from 48
villages
Low vs higher
socioeconomic
status
Pf PR None OR 2middot34
(1middot35ndash
4middot05)
NA NA NA
Custodio
et al26 2009
Equatorial
Guinea
Cross-
sectional
552 Aged
0ndash5 years
Randomly
sampledfrom
community
Low vs high
socioeconomicstatus
Pf PR None OR 1middot49
(0middot98ndash2middot25)
NA NA NA
Gahutu
et al27 2011
Rwanda Cross-
sectional
749 Ag ed
0ndash5 years
Randomly
selected
from villages
health
centre and
district
hospital
Low
household
income
(lt5000
Rwandan
francs) vs
high income
(ge5000
Rwandan
francs)
Pf PR None OR 1middot59
(1middot05ndash
2middot40)
NA NA NA
Ghebreyseus
et al28 2000
Ethiopia Cross-
sectional
2114 Ag ed
0ndash10 years
Randomly
sampled
from
community
House does
not own a
radio vs
household
owns a radio
Incidence of
clinical
malaria
(parasitaemia
plus fever)
None OR 0middot97
(0middot60ndash
1middot59)
NA NA NA
Koram
et al29 1995
The
Gambia
Case-
control
768 Aged
3 months to
10 years
Recruited
from three
health
centres
Family does
not own a
refrigerator vs
family owns a
refrigerator
Incidence of
clinical
malaria
(parasitaemia
plus fever)
Healthy
controls
matched by
age date of
enrolment
and
neighbour-
hood
OR 2middot30
(1middot44ndash
3middot75)
2middot58
(1middot46ndash
4middot45)
Place of residence
travel history
ownership of
housing plot house
type crowding
motherrsquos knowledge
of malaria
insecticide use and
medicine use
NA
Krefis et al30
2010
Ghana Cross-
sectional
1496 Aged less
than 15 years
Recruited
when
visiting
major
hospital for
medical care
Low vs high
socioeconomic
status
Incidence of
clinical
malaria
(parasitaemia
plus fever)
None OR NA 1middot79
(1middot32ndash
2middot44)
Age sex ethnicity
number of children
in family motherrsquos
age and place of
residence
NA
(Continues on next page)
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Study
site
Study
design
n Participan ts Re cr uit-
ment of
participants
Exposure Outcome Control
group
Measure
of effect
Un-
adjusted
effect
(95 CI)
Ad justed
effect
(95 CI)
Factors adjusted
for
Reason for
exclusion
from
quan-
titative
analysis
(Continued from previous page)
OngrsquoEcha
et al31 2006
Kenya Case-
control
374 Aged
0ndash3 years
(children with
cerebral
malaria and
those with
previous
hospital visits
were
excluded)
Recruited
when
visiting
district
hospital with
symptoms
of malaria
Parents are
farmers vs
parents are
not farmers
Incidence of
clinical
malaria
(parasitaemia
plus fever)
Healthy
controls
recruited
from
maternal
and child
health clinic
OR 3middot85
(1middot64ndash
9middot09)
0middot92
(0middot41ndash
2middot04)
Child risk factors
(axillary
temperature
ge37middot5degC)
nutritional factors
house type and
mosquito control
measures
NA
Pullan
et al32 2010
Uganda Cross-
sectional
1770 Aged
5ndash15 years
Selected
from all
households
in district
Lowest vs
highest socio-
economic
status quintile
Pf PR None OR 1middot25
(0middot74ndash
2middot13)
NA NA NA
Ronald
et al33
2006
Ghana Cross-
sectional
296 Aged
1ndash9 years
Randomly
sampled
from
community
Decreasing
household
socio-
economic
status
Pf PR None OR 3middot22
(1middot95ndash
5middot32)
3middot95
(2middot26ndash
6middot90)
Age and travel to
rural areas
NA
Slutsker
et al34 1996
Malawi Cross-
sectional
3915 Aged
0ndash3 months
Infantsrsquo
mothers
were
enrolled into
a chemopro-
phylaxis
study at four
antenatalclinics
Low vs high or
medium socio-
economic
status
Pf PR None OR 1middot80
(1middot30ndash
2middot10)
NA NA NA
Villamor
et al35 2003
Tanzania Cross-
sectional
687 Aged 6ndash60
months
Children
were enrolled
in a vitamin
A supple-
mentation
trial when
admitted to
hospital with
pneumonia
No electricity
at home vs
electricity at
home
Pf PR None OR 1middot84
(1middot23ndash
2middot76)
NA NA NA
Winskill
et al36
2011
Tanzania Cross-
sectional
1438 Aged
6 months to
13 years
Randomly
selected
from 21
hamlets
Decreasing
household
socioeconomic
status
Pf PR None OR 1middot15
(0middot94ndash
1middot39)
NA NA NA
Yamamoto
et al37 2010
Burkina
Faso
Case-
control
283 Aged
0ndash9 years
Recruited by
passive case
detectionat central
laboratory
Low vs high
socioeconomic
status
Incidence of
clinical
malaria(parasitaemia
plus fever)
Controls
from
demographicsurveillance
system
database
matched for
age sex
ethnicity and
residence
OR 0middot47
(0middot20ndash
1middot08)
NA NA NA
Studies included in the qualitative analysis but excluded from the meta-analysis
Clark et al38
2008
Uganda Cohort 558 Aged
1ndash10 years
Recruited
from a
census
population
in one parish
1st and 2nd
(lowest) vs
4th wealth
quartile
(highest)
Incidence of
clinical
episodes of
malaria per
person-year
at risk
None RR 2middot04
(1middot54ndash
2middot70)
1middot30
(0middot96ndash
1middot79)
Age sickle cell trait
G6PD deficiency in
girls bednet use
household
crowding and
distance from
swamp
Not
possible to
calculate
OR
(Continues on next page)
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used the generic inverse-variance method for the meta-
analysis in which weight is given to each study accordingto the inverse of the variance of the effect to minimiseuncertainty about the pooled effect estimates Bothoutcomes (P falciparum infection and clinical malaria)were combined in the analysis We allocated the includedstudies into four subgroups according to the measure ofsocioeconomic status used asset ownership householdwealth socioeconomic index or parentsrsquo occupations Wedid separate analyses for unadjusted and adjusted ORsMissing data were not problematic since meta-regressionof individual data was not done
Initially we did a fixed-effects meta-analysis but ifI sup2 was large (gt50) which suggests substantial hetero-geneity between studies we used random-effectsanalysis Random-effects analysis adjusts the standard
errors of each study estimate of effect to include a
measure of variation in the effects reported betweenstudies We produced forest plots to visually assess theORs and 95 CIs of each study and used funnel plots toassess publication bias (with study size as a function ofeffect size) We used Eggerrsquos linear regression method totest for funnel plot asymmetry (ie to quantify the biascaptured by the funnel plot)22 Analyses were done withStata 11 and RevMan 5
ResultsOur initial search yielded 6106 records of which4696 remained after removal of duplicates (figure 2)20 records met our inclusion criteria (table)23ndash42 and ofthese 15 contained the necessary data for inclusion in thequantitative analysis (meta-analysis) Five records were
Study
site
Study
design
n Participant s R ecru it -
ment of
participants
Exp os ure Outcome Control
group
Measure
of effect
Un-
adjusted
effect
(95 CI)
Ad justed
effect
(95 CI)
Factors adjusted
for
Reason for
exclusion
from
quan-
titative
analysis
(Continued from previous page)
Klinkenberg
et al39 2006
Ghana Cross-
sectional
1744 Aged 6ndash60
months
Randomly
sampled
from
communities
near (lt1000
m) and less
near
(gt1000 m)
agricultural
sites in Accra
Socio-
economic
status below
vs above mean
for the city
Pf PR None In-
suffi cient
infor-
mation
provided
NA NA NA Not
possible to
calculate
OR
Kreuels
et al40 2008
Ghana Cohort 535 Aged
2ndash4 months
Recruited
from nine
villages after
visiting
health centre
(children
with chronic
diseases
excluded)
Family does
not have good
financial
situation vs
family has
good financial
situation
Incidence of
clinical
malaria
(parasitaemia
plus fever)
None Incidence
rate ratio
1middot59
(1middot33ndash
1middot89)
1middot52
(1middot27ndash
1middot82)
Sex ethnicity
season of birth (dry
or rainy season)
sickle cell trait
motherrsquos education
motherrsquos
occupation
knowledge of
malaria and
protective measures
Not
possible to
calculate
OR
Matthys
et al41 2006
Cocircte
drsquoIvoire
Cross-
sectional
672 Aged
0ndash15 years
Selected
from
farming and
non-farming
households
Low vs high
socioeconomic
status
Pf PR None OR NA 2middot44
(0middot88ndash
10middot00)
Age agricultural
zone crops grown
irrigation overnight
stays in temporary
farm huts and
distance to
permanent ponds
and fish ponds
Bayesian
credible
intervals
reported
only
Pullan
et al42
2010
Uganda Cross-
sectional
1844 A ged
5ndash15 years
All residents
of four
villages asked
to
participate
with 78
successfully
enrolled
Decreasing
household
socioeconomic
status
Pf PR None OR NA 2middot27
(0middot88ndash
25middot00)
Ag e bednet use Bayesian
credible
intervals
reported
only
OR=odds ratio Pf PR=Plasmodium falciparum parasite rate RR=risk ratio Socioeconomic status analysed as a continuous variable
Table Studies included in the systematic review and meta-analysis
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excluded from the quantitative analysis either because
Bayesian credible intervals were reported (n=2) or becauseORs could not be calculated from the available data (n=3)
Despite substantial overlap between CIs for both unadjust-
ed and adjusted results high I sup2 values from fixed-effectsanalysis suggested substantial heterogeneity between
Figure 983091 Association between low s ocioeconomic status and clinical malaria or parasitaemia in chi ldren aged 0ndash15 years
Pooled effects from random-effects meta-analyses for unadjusted (A) and adjusted (B) results are shown Studies are divided into subgroups by measure ofsocioeconomic status used Error bars show 95 CIs df=degrees of freedom
Odds ratio(95 CI)
WeightLog(odds ratio)
SE
Socioeconomic indexAl-Taiar et al23 (2009)Custodio et al26 (2009)Pullan et al32 (2010)Ronald et al33 (2006)Slutsker et al34 (1996)Winskill et al36 (2011)Yanamoto et al37 (2010)
SubtotalHeterogeneity τsup2=0middot12 χsup2=24middot51 df=6 (p=0middot0004) I2=76Test for overall effect Z=2middot56 (p=0middot01)
0middot565314 0middot397341 0middot223144 1middot169381 0middot587787 0middot139262 ndash0middot756122
0middot191170 0middot210810 0middot271367 0middot255894 0middot166032 0middot102621 0middot435365
1middot76 (1middot21ndash2middot56) 1middot49 (0middot98ndash2middot25) 1middot25 (0middot73ndash2middot13) 3middot22 (1middot95ndash5middot32) 1middot80 (1middot30ndash2middot49) 1middot15 (0middot94ndash1middot41)
0middot47 (0middot20ndash1middot10) 1middot49 (1middot10ndash2middot01)
8middot4 8middot0 6middot6 7middot0 9middot1 10middot5
4middot0 53middot5
Asset ownership
Baragatti et al24 (2009)Ghebreyseus et al28 (2000)Koram et al29 (1995)Villamor et al35 (2003)
SubtotalHeterogeneity τ2=0middot09 χ2=7middot14 df=3 (p=0middot07) I2=58Test for overall effect Z=2middot76 (p=0middot006)
0middot727549 ndash0middot028422 0middot832909 0middot611533
0middot322571 0middot249721 0middot238911 0middot205363
2middot07 (1middot10ndash3middot90) 0middot97 (0middot60ndash1middot59) 2middot30 (1middot44ndash3middot67) 1middot84 (1middot23ndash2middot76) 1middot70 (1middot17ndash2middot48)
5middot6 7middot1 7middot3 8middot1 28middot2
Reduced odds of malaria Increased odds of malaria
10middot50middot20middot1 2 5 10
Household wealthClarke et al25 (2001)Gahutu et al27 (2011)
SubtotalHeterogeneity τ2=0middot01 χ2=1middot22 df=1 (p=0middot27) I2=18
Test for overall effect Z=3middot25 (p=0middot001)
0middot851030 0middot463734
0middot279963 0middot211706
2middot34 (1middot35ndash4middot05) 1middot59 (1middot05ndash2middot41) 1middot85 (1middot28ndash2middot68)
6middot4 8middot0 14middot4
Parentsrsquo occupationOngrsquoecha et al31 (2006)SubtotalHeterogeneity NA
Test for overall effect Z=3middot10 (p=0middot002)
Total (all measures of socioeconomic status)Heterogeneity τ2=0middot10 χ2=40middot38 df=13 (p=0middot0001) I2=68Test for overall effect Z=4middot76 (plt0middot00001)Test for subgroup differences χ2=4middot46 df=3 (p=0middot22) I2=32middot83
1middot347074 0middot434886 3middot85 (1middot64ndash9middot02) 3middot85 (1middot64ndash9middot02)
4middot0
4middot0
1middot66 (1middot35ndash2middot05) 100middot0
A
Odds ratio(95 CI)
WeightLog(odds ratio)
SE
Socioeconomic indexKrefis et al30 (2010)Ronald et al33 (2006)
SubtotalHeterogeneity τsup2=0middot26 χsup2=6middot01 df=1 (p=0middot01) I2=83Test for overall effect Z=2middot38 (p=0middot02)
0middot579818 1middot373716
0middot154177 0middot284873
1middot79 (1middot32ndash2middot42)
3middot95 (2middot26ndash6middot90) 2middot56 (1middot18ndash5middot56)
27middot4
19middot0 46middot3
Asset ownershipBaragatti et al24 (2009)Koram et al29 (1995)SubtotalHeterogeneity τ2=0middot00 χ2=0middot80 df=1 (p=0middot37) I2=0Test for overall effect Z=4middot10 (plt0middot0001)
0middot615186 0middot947789
0middot233766 0middot290486
1middot85 (1middot17ndash2middot93) 2middot58 (1middot46ndash4middot56) 2middot11 (1middot48ndash3middot01)
22middot1 18middot6 40middot7
Reduced odds of malaria Increased odds of malaria
10middot50middot2 2 5
Parentsrsquo occupationsOngrsquoecha et al31 (2006)SubtotalHeterogeneity NATest for overall effect Z=0middot21 (p=0middot83)
ndash0middot086178 0middot410929 0middot92 (0middot41ndash2middot05) 0middot92 (0middot41ndash2middot05)
12middot9 12middot9
Total (all measures of socioeconomic status)Heterogeneity τ2=0middot11 χ2=10middot72 df=4 (p=0middot03) I2=63Test for overall effect Z=3middot83 (plt0middot0001)Test for subgroup differences χ2=4middot02 df=2 (p=0middot13) I2=50middot3
2middot06 (1middot42ndash2middot97) 100middot0
B
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studies (unadjusted effect size I sup2=68 adjusted effect size
I sup2=63) Therefore random-effects analysis was usedThe meta-analysis was restricted to comparisonsbetween the highest (least poor) and lowest (poorest)socioeconomic groups Subgroup analysis suggested thatlow socioeconomic status was associated with increasedodds of malaria irrespective of the measure used forsocioeconomic status with the exception of one study inwhich parentsrsquo occupations were used31 we thereforejudged that to pool all results would be appropriate Inthe meta-analyses for both unadjusted and adjustedresults the odds of malaria infection were higher in thepoorest children than in the least poor children (figure 3)
Visual assessment of funnel plots (appendix p 8)showed that the studies were distributed fairly
symmetrically about the combined effect size whichsuggests little publication bias However Eggerrsquos test forbias suggested funnel plot asymmetry for the unadjustedresults (bias coeffi cient 1middot70 95 CI ndash0middot97 to 4middot37p=0∙191) which suggests that publication bias (delayedpublication or location bias) small-study effects selectiveoutcome reporting or selective analysis reporting mighthave been present A test for funnel plot asymmetry wasnot possible for the adjusted effects since only fivestudies were included in the meta-analysis Overallquality assessment scores for risk of bias in studiesincluded in the quantitative analysis ranged from two toseven out of a maximum of eight (appendix p 6ndash7)
DiscussionOur findings suggest that low socioeconomic status isassociated with roughly doubled odds of clinical malariaor parasitaemia in children compared with higher socio-economic status within a locality This conclusion is sup-ported by a similar size and direction of effect noted inthe five studies excluded from the meta-analysis Sinceour analysis represents a comparison of the very poorestchildren with the least poor children within highly im-poverished communities the difference in the odds ofmalaria in the poorest children would probably be evengreater if the studies were expanded to include childrenfrom wealthier backgrounds The association between
socioeconomic status and malaria is not definitiveevidence for the direction of causality since the pooresthouseholds are not only more susceptible to the diseasebut are also more vulnerable to its costs such that thedisease itself can induce poverty For example asignificant positive association between low socio-economic status and malaria parasitaemia has beenreported in Tanzania43 with causality in both directionsFindings from Kenya44 and Nigeria45 suggest that the costsof malaria treatment (as a proportion of non-food monthlyincome) and subsequent financial setbacks are greater forpoorer than for more wealthy households Costs also varygeographically in Kenya44 and Papua New Guinea46 therisk of clinical disease is greater in low-transmissiondistricts with subsequently greater loss of income
Wealth is probably protective against malaria since it
renders prophylaxis and treatment more affordable
47ndash49
andis positively associated with other beneficial factorsincluding better-educated parents (which improvesprophylaxis and treatment for children) increasedhousing quality (which reduces house entry by malaria-transmitting mosquitoes) and improved nutritionalstatus of children (which could increase their subsequentability to cope with malaria infection)50ndash52 Malaria andpoverty therefore constitute a vicious cycle for the pooresthouseholds exacerbating differences in health and wealth
A major limitation of our meta-analysis is that themeasurement of risk factors was done with varyingprecision in the included studies and although we didsubgroup and random-effects analyses these are unlikely
to have fully accounted for heterogeneity in study designAnother important limitation is the poor quality of thestudies included in the meta-analysis which results fromthe nature of the study question (since randomisation forsocioeconomic status would not be practically or ethicallypossible) However the consistency of results acrossstudies and settings suggests that the finding of increasedodds of malaria in children of low socioeconomic status isrobust For our systematic review the main limitationwas the language of the search In particular notincluding publications in Spanish probably excludedmuch data from South America such that our findingscannot be generalised to that region Eggerrsquos testsuggested the presence of some forest plot asymmetryhowever statistical tests for forest plot asymmetry tend tohave low power53 and asymmetry might not be attributableto publication biasmdashit might also have arisen from poorstudy quality leading to artificially inflated effects in thesmaller studies selective outcome or analysis reportingor chance Incomplete retrieval (four full-text studiescould not be retrieved) might also have introduced bias
On the basis of our findings we advocate thatdevelopment programmes should be an essentialcomponent of malaria control Malaria elimination inmany high-income countries was achieved withoutmalaria-specific interventions prevalence started to fall inEurope and North America as a by-product of improved
living conditions and increased wealth5455 and afterRonald Ross deduced the mode of malaria transmission56 in 1897 more specific interventions became possibleincluding habitat modification (permanent elimination ofbreeding sitesmdasheg by installing and maintaining drains)habitat manipulation (temporary creation of unfavourableconditions for the vectormdasheg by fluctuating the amountof water in reservoirs) and modifications to humanhabitation or behaviour to reduce human-vector contactsuch as mosquito-proofing of houses57 As a result mostof Europe and North America is now characterised byanophelism without malaria which is testament to theeffectiveness of these control efforts together with areduced innate receptivity to malaria transmission thatstems from advances in nutrition health care and
8132019 SE Dev as an Intervention Agst Malaria
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8 wwwthelancetcom Published online June 19 2013 httpdxdoiorg101016S0140-6736(13)60851-X
development58 Similar environmental management
strategies together with larval control also helped toreduce malaria transmission in many developingcountries during the 20th century including ZanzibarIndonesia Malaysia the Panama Canal and the CopperBelt of Zambia5960 Thus as transmission today falls inmuch of sub-Saharan Africa and elsewhere developmentwill contribute to the reduction and elimination of thedisease Several specific development interventions couldcontribute to malaria control (appendix p 5) which mightbe similar to malaria-specific interventions in terms ofcosts (appendix pp 9ndash10) An excellent example of howsuch interventions can work in practice can be seen inKhartoum Sudan (appendix p 11)
This approach has three major constraints First
accurate costing of the extent to which specificdevelopment interventions contribute to malaria controlis diffi cult Whereas measuring the effect of housescreening is straightforward measuring that ofimproved education or raised incomes is not Secondthe effectiveness of a development intervention dependson both the nature and intensity of malaria transmissionFor example house screening is probably most effectivein areas of low to moderate transmission where vectorsfeed indoors Countries that have eliminated malariasince 1900 have largely been temperate subtropical orislands8 and the high malaria burden in manydeveloping countries is not merely a product of povertyRather the specific ecological requirements of both themalaria parasite and its mosquito vector help todetermine the range of the disease61 Interventions haveto be highly effective and development should not bethought of as a standalone strategy but as a complementto malaria-specific interventions such as LLINs indoorresidual spraying and larval source management Thirdeconomic development gives rise to broader socialenvironmental and ecological changes that might insome circumstances lead to an increase in the burden ofmalaria (appendix p 5) as has been seen in Sri Lanka(appendix p 12) Nonetheless these constraints shouldnot be treated as barriers to the use of socioeconomicdevelopment as an intervention against malaria
(appendix p 5)In addition to initiatives such as the Millennium Villages
project which is operating in 14 villages in ten Africancountries to examine the effects of socioeconomicdevelopment62 further research is needed to address someimportant questions and to galvanise specialists in bothhealth and development to work more closely together onmalaria control For example randomised controlled trialsshould be considered to assess the effectiveness ofsocioeconomic interventions (eg improved education andnutrition) against malaria in different settings We mustalso investigate the causal pathways that lead fromdevelopment to successful malaria control and vice versaand develop an understanding of the relation betweenmalaria control birth rates and population growth
That malaria control remains largely the preoccupation
of the health sector alone is a failing of both those whowork in health and those who work in internationaldevelopment The disease severely compromises socio-economic development and its control and eliminationwould improve economic prosperity worldwide Theeffectiveness of available drugs and insecticides formalaria control will ultimately deteriorate with theemergence of parasites resistant to antimalarials and ofvectors resistant to insecticides and the development andprocurement costs of replacements will be high Donorfatigue is also an ever-present threat to interventions suchas LLINs indoor residual spraying and intermittentpreventive treatment especially in view of the economicsituation since the 2007ndash08 financial crisis63 However
several specific development interventions could beintroduced to aid both economic development andmalaria control Increased wealth and improved standardsof living that stem directly from socioeconomicdevelopment could prove fundamental in ensuring thatmalaria transmission continues to fall in much of AsiaSouth America and Africa as it happened historically inEurope and North America Socioeconomic developmentcould prove to be a very effective and sustainableintervention against malaria in the long term
Contributors
SWL and RS conceived of the study SWL RS LST HL and JTdeveloped the study design and the outline of the report LST searchedthe scientific literature did the meta-analysis and prepared the first
draft of the report BW provided advice on the systematic review andmeta-analysis HTK contributed the case study from Sudan (appendix)All authors reviewed the final version of the report
Conflicts of interest
We declare that we have no conflicts of interest
Acknowledgments
This work was supported by the UK Department for InternationalDevelopment and the Malaria Centre at the London School of Hygiene ampTropical Medicine (LSHTM) SWL was supported by the Research andPolicy for Infectious Disease Dynamics (RAPIDD) programme of theScience and Technology Directorate US Department of Homeland Securitythe Fogarty International Center (US National Institutes of Health) and theBill amp Melinda Gates Foundation JT was supported in part by a grant fromthe UK Economic and Social Research Council to the STEPS Centre and theInstitute of Development Studies (University of Sussex Brighton UK)We are grateful to Frida Kasteng (LSHTM) for providing information about
the costs of malaria and development interventionsReferences 1 Gething PW Patil AP Smith DL et al A new world malaria map
Plasmodium falciparum endemicity in 2010 Malar J 2011 10 378 2 WHO World Malaria Report 2011 Geneva World Health
Organization 2011 3 Murray CJL Rosenfeld LC Lim SS et al Global malaria mortality
between 1980 and 2010 a systematic analysis Lancet 2012 379 413ndash31 4 Murray CJL Vos T Lozano R et al Disability-adjusted life years
(DALYs) for 291 diseases and injuries in 21 regions 1990mdash2010a systematic analysis for the Global Burden of Disease Study 2010Lancet 2012 380 2197ndash223
5 Sachs J Malaney P The economic and social burden of malariaNature 2002 415 680ndash85
6 UNDP International human development indicators httphdrundporgendatabuild (accessed April 24 2012)
7 Sachs JD Macroeconomics and health investing in health for
economic development Geneva World Health Organization 2001
8132019 SE Dev as an Intervention Agst Malaria
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wwwthelancetcom Published online June 19 2013 httpdxdoiorg101016S0140-6736(13)60851-X 9
8 Gallup J Sachs J The economic burden of malariaAm J Trop Med Hyg 2001 64 85ndash96
9 Mandelbaum-Schmid J HIVAIDS hunger and malaria are theworldrsquos most urgent problems say economistsBull World Health Organ 2004 82 554ndash55
10 Kidson C Indaratna K Ecology economics and political will thevicissitudes of malaria strategies in Asia Parassitologia 1998 40 39ndash46
11 UNDP Millennium Development Goal 6 focuses on combatingHIVAIDS malaria and other diseases httpwebundporgmdggoal6shtml (accessed April 21 2012)
12 OrsquoMeara WP Mangeni JN Steketee R Greenwood B Changes inthe burden of malaria in sub-Saharan Africa Lancet Infect Dis 201010 545ndash55
13 Dondorp AM Fairhurst RM Slutsker L et al The threat ofartemisinin-resistant malaria N Engl J Med 2011 365 1073ndash75
14 Ranson H NrsquoGuessan R Lines J et al Pyrethroid resistance inAfrican anopheline mosquitoes what are the implications formalaria control Trends Parasitol 2011 27 91ndash98
15 Cohen J Smith D Cotter C et al Malaria resurgence a systematic
review and assessment of its causes Malar J 2012 11 122 16 WHO WHO position statement on integrated vector management
Wkly Epidemiol Rec 2008 83 177ndash84 17 van den Berg H Mutero CM Ichimori K WHO guidance on
policy-making for integrated vector management Geneva WorldHealth Organization 2012
18 Stroup DF Berlin JA Morton SC et al Meta-analysis ofobservational studies in epidemiology JAMA 2000 283 2008ndash12
19 Modher D Liberati A Tetzlaff J Altman DG Group TP Preferredreporting items for systematic reviews and meta-analyses thePRISMA statement PLoS Med 2009 6 e1000097
20 Roll Back Malaria Malaria endemic countries Geneva The RBMPartnership 2010 httpwwwrbmwhointendemiccountrieshtml(accessed June 8 2011)
21 Wells G Shea B OrsquoConnell D et al The Newcastle-Ottawa Scale(NOS) for assessing the quality of nonrandomised studies inmeta-analyses Ottawa University of Ottawa 2011
22 Egger M Smith GD Schneider M Minder C Bias in meta-analysisdetected by a simple graphical test BMJ 1997 315 629ndash34
23 Al-Taiar A Assabri A Al-Habori M et al Socioeconomic andenvironmental factors important for acquiring non-severe malariain children in Yemen a case-control studyTrans R Soc Trop Med Hyg 2009 103 72ndash78
24 Baragatti M Fournet F Henry M-C et al Social and environmentalmalaria risk factors in urban areas of Ouagadougou Burkina FasoMalar J 2009 8 13
25 Clarke SE Boslashgh C Brown RC Pinder M Walraven GELLindsay SW Do untreated bednets protect against malariaTrans R Soc Trop Med Hyg 2001 95 457ndash62
26 Custodio E Descalzo MAacute Villamor E et al Nutritional andsocio-economic factors associated with Plasmodium falciparum infection in children from Equatorial Guinea results from anationally representative survey Malar J 2009 8 225
27 Gahutu J-B Steininger C Shyirambere C et al Prevalence and riskfactors of malaria among children in southern highland Rwanda
Malar J 2011 10 134 28 Ghebreyesus TA Haile M Witten KH et al Household risk factorsfor malaria among children in the Ethiopian HighlandsTrans R Soc Trop Med Hyg 2000 94 17ndash21
29 Koram KA Bennett S Adiamah JH Greenwood BM Socio-economicrisk factors for malaria in a peri-urban area of The GambiaTrans R Soc Trop Med Hyg 1995 89 146ndash50
30 Krefis AC Schwarz NG Nkrumah B et al Principal componentanalysis of socioeconomic factors and their association withmalaria in children from the Ashanti Region Ghana Malar J 20109 201
31 Ongrsquoecha JM Keller CC Were T et al Parasitemia anemia andmalarial anemia in infants and young children in a ruralholoendemic Plasmodium falciparum transmission area Am J Trop Med Hyg 2006 74 376ndash85
32 Pullan RL Kabatereine NB Bukirwa H Staedke SG Brooker SHeterogeneities and consequences of Plasmodium species andhookworm coinfection a population based study in Uganda
J Infect Dis 2011 203 406ndash17
33 Ronald LA Kenny SL Klinkenberg E et al Malaria and anaemiaamong children in two communities of Kumasi Ghana
a cross-sectional survey Malar J 2006 1 105 34 Slutsker L Khoromana CO Hightower AW et al Malaria infection
in infancy in rural Malawi Am J Trop Med Hyg 1996 55 71ndash76 35 Villamor E Fataki MR Mbise RL Fawzi WW Malaria parasitaemia
in relation to HIV status and vitamin A supplementation amongpre-school children Trop Med Int Health 2003 8 1051ndash61
36 Winskill P Rowland M Mtove G Malima R Kirby M Malaria riskfactors in north-east Tanzania Malar J 2011 10 98
37 Yamamoto S Louis VR Sie A Sauerborn R Household risk factorsfor clinical malaria in a semi-urban area of Burkina Fasoa case-control study Trans R Soc Trop Med Hyg 2010 104 61ndash65
38 Clark TD Greenhouse B Njama-Meya D et al Factors determiningthe heterogeneity of malaria incidence in children in KampalaUganda J Infect Dis 2008 198 393ndash400
39 Klinkenberg E McCall P Wilson M et al Urban malaria andanaemia in children a cross-sectional survey in two cities of GhanaTrop Med Int Health 2006 11 578ndash88
40 Kreuels B Kobbe R Adjei S et al Spatial variation of malariaincidence in young children from a geographically homogeneousarea with high endemicity J Infect Dis 2008 197 85ndash93
41 Matthys B Vounatsou P Raso G et al Urban farming and malariarisk factors in a medium-sized town in Cocircte drsquoIvoireAm J Trop Med Hyg 2006 75 1223ndash31
42 Pullan RL Bukirwa H Staedke SG Snow RW Brooker S Plasmodium infection and its risk factors in eastern Uganda Malar J 2010 4 9
43 Somi MF Butler JRG Is there evidence for dual causation betweenmalaria and socioeconomic status Findings from rural TanzaniaAm J Trop Med Hyg 2007 77 1020ndash27
44 Chuma JM Thiede M Rethinking the economic costs of malaria atthe household level evidence from applying a new analyticalframework in rural Kenya Malar J 2006 5 76ndash89
45 Onwujekwe O Hanson K Are malaria treatment expenditurescatastrophic to different socio-economic and geographic groups andhow do they cope with payment A study in southeast NigeriaTrop Med Int Health 2010 15 18ndash25
46 Sicuri E Davy C Marinelli M et al The economic cost tohouseholds of childhood malaria in Papua New Guinea a focus onintra-country variation Health Policy Plann 2012 27 339ndash47
47 Matovu F Goodman C How equitable is bed net ownership andutilisation in Tanzania A practical application of the principles ofhorizontal and vertical equity Malar J 2009 8 109ndash21
48 Gingrich CD Hanson K Marchant T Mulligan J-A Mponda HPrice subsidies and the market for mosquito nets in developingcountries a study of Tanzaniarsquos discount voucher schemeSoc Sci Med 2011 73 160ndash68
49 Ahmed SM Haque R Haque U Hossain A Knowledge on thetransmission prevention and treatment of malaria among twoendemic populations of Bangladesh and their health-seekingbehaviour Malar J 2009 8 173
50 Saaka M Oosthuizen J Beatty S Effect of joint iron and zincsupplementation on malarial infection and anaemiaEast Afr J Public Health 2009 6 55ndash62
51 Worrall E Basu S Hanson K Is malaria a disease of povertyA review of the literature Trop Med Int Health 2005 10 1047ndash59 52 Barat LM Palmer N Basu S Worrall E Hanson K Mills A Do
malaria control interventions reach the poor Am J Trop Med Hyg 2004 71 174ndash78
53 Sterne JAC Sutton AJ Ioannidis JPA et al Recommendationsfor examining and interpreting funnel plot asymmetry inmeta-analyses of randomised controlled trials BMJ 2011 343 4002
54 Garcia-Martin G Status of malaria eradication in the AmericasAm J Trop Med Hyg 1972 21 617ndash33
55 Bruce-Chwatt L de Zulueta J The rise and fall of malaria inEurope London Oxford University Press 1980
56 Sinden RE Malaria mosquitoes and the legacy of Ronald RossBull World Health Organ 2007 85 894ndash96
57 Keiser J Singer BH Utzinger J Reducing the burden of malariain different eco-epidemiological settings with environmentalmanagement a systematic review Lancet Infect Dis 20055 695ndash708
8132019 SE Dev as an Intervention Agst Malaria
httpslidepdfcomreaderfullse-dev-as-an-intervention-agst-malaria 1010
Articles
10 www thelancet com Published online June 19 2013 httpdx doi org10 1016S0140-6736(13)60851-X
58 Randolph SE Rogers DJ The arrival establishment and spread ofexotic diseases patterns and predictions Nat Rev Microbiol 2010
8 361ndash71 59 Hay S Guerra C Tatem A Noor A Snow R The global distribution
and population at risk of malaria past present and futureLancet Infect Dis 2004 4 327ndash36
60 Killeen GF Fillinger U Kiche I Gouagna LC Knols BGEradication of Anopheles gambiae from Brazil lessons for malariacontrol in Africa Lancet Infect Dis 2002 2 618ndash27
61 Jetten T Takken W Anophelism without malaria in Europea review of the ecology and distribution of the genus Anopheles in
Europe Wageningen Wageningen Agricultural University 1994 62 Millennium Villages Project httpwwwmillenniumvillagesorg
the-villages (accessed May 16 2013) 63 Garrett L Global health hits crisis point Nature 2012 482 7
8132019 SE Dev as an Intervention Agst Malaria
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Data extraction
We first screened titles and abstracts and then onereviewer (LST) screened the relevant full-text articles SWLalso reviewed 22 (10) of the full-text articles screenedwhich were selected at random with any discrepanciesresolved by RS One reviewer (LST) extracted studycharacteristics and unadjusted and adjusted effect sizeswith 95 CIs and recorded the data in a standard form
We did quality and risk-of-bias assessments as recom-
mended by Wells and colleagues
21
Statistical analysisStudies that met the eligibility criteria and that reportedunadjusted or adjusted odds ratios (ORs) with 95 CIs orpresented suffi cient data for the calculation of unadjustedORs and 95 CIs were included in the meta-analysis We
Study
site
Study
design
n Participants Re cru it -
ment of
participants
Exp os ure Outcome Control
group
Measure
of effect
Un-
adjusted
effect
(95 CI)
Ad justed
effect
(95 CI)
Factors adjusted
for
Reason for
exclusion
from
quan-
titative
analysis
Studies included in the meta-analysis
Al-Taiar
et al23 2009
Yemen Case-
control
628 Aged
6 months to
10 years
Recruited
from health
centres
Low vs high
socioeconomic
status
Incidence of
clinical
malaria
(parasitaemia
plus fever)
Age-
matched
healthy
community
controls
OR 1middot76
(1middot21ndash
2middot57)
NA NA NA
Baragatti
et al24 2009
Burkina
Faso
Cross-
sectional
3354 Ag ed
6 months to
12 years
Randomly
sampled
from
community
Family has
irregular land
tenure vs
regular land
tenure
Pf PR None OR 2middot07
(1middot10ndash
3middot88)
1middot85
(1middot17ndash
2middot92)
Age land tenure
building density
equipment
education bednet
use and season
NA
Clarke et al25
2001
The
Gambia
Cross-
sectional
1196 Ag ed
6 months to
5 years
Cluster-
sampled
from 48
villages
Low vs higher
socioeconomic
status
Pf PR None OR 2middot34
(1middot35ndash
4middot05)
NA NA NA
Custodio
et al26 2009
Equatorial
Guinea
Cross-
sectional
552 Aged
0ndash5 years
Randomly
sampledfrom
community
Low vs high
socioeconomicstatus
Pf PR None OR 1middot49
(0middot98ndash2middot25)
NA NA NA
Gahutu
et al27 2011
Rwanda Cross-
sectional
749 Ag ed
0ndash5 years
Randomly
selected
from villages
health
centre and
district
hospital
Low
household
income
(lt5000
Rwandan
francs) vs
high income
(ge5000
Rwandan
francs)
Pf PR None OR 1middot59
(1middot05ndash
2middot40)
NA NA NA
Ghebreyseus
et al28 2000
Ethiopia Cross-
sectional
2114 Ag ed
0ndash10 years
Randomly
sampled
from
community
House does
not own a
radio vs
household
owns a radio
Incidence of
clinical
malaria
(parasitaemia
plus fever)
None OR 0middot97
(0middot60ndash
1middot59)
NA NA NA
Koram
et al29 1995
The
Gambia
Case-
control
768 Aged
3 months to
10 years
Recruited
from three
health
centres
Family does
not own a
refrigerator vs
family owns a
refrigerator
Incidence of
clinical
malaria
(parasitaemia
plus fever)
Healthy
controls
matched by
age date of
enrolment
and
neighbour-
hood
OR 2middot30
(1middot44ndash
3middot75)
2middot58
(1middot46ndash
4middot45)
Place of residence
travel history
ownership of
housing plot house
type crowding
motherrsquos knowledge
of malaria
insecticide use and
medicine use
NA
Krefis et al30
2010
Ghana Cross-
sectional
1496 Aged less
than 15 years
Recruited
when
visiting
major
hospital for
medical care
Low vs high
socioeconomic
status
Incidence of
clinical
malaria
(parasitaemia
plus fever)
None OR NA 1middot79
(1middot32ndash
2middot44)
Age sex ethnicity
number of children
in family motherrsquos
age and place of
residence
NA
(Continues on next page)
8132019 SE Dev as an Intervention Agst Malaria
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Articles
4 wwwthelancetcom Published online June 19 2013 httpdxdoiorg101016S0140-6736(13)60851-X
Study
site
Study
design
n Participan ts Re cr uit-
ment of
participants
Exposure Outcome Control
group
Measure
of effect
Un-
adjusted
effect
(95 CI)
Ad justed
effect
(95 CI)
Factors adjusted
for
Reason for
exclusion
from
quan-
titative
analysis
(Continued from previous page)
OngrsquoEcha
et al31 2006
Kenya Case-
control
374 Aged
0ndash3 years
(children with
cerebral
malaria and
those with
previous
hospital visits
were
excluded)
Recruited
when
visiting
district
hospital with
symptoms
of malaria
Parents are
farmers vs
parents are
not farmers
Incidence of
clinical
malaria
(parasitaemia
plus fever)
Healthy
controls
recruited
from
maternal
and child
health clinic
OR 3middot85
(1middot64ndash
9middot09)
0middot92
(0middot41ndash
2middot04)
Child risk factors
(axillary
temperature
ge37middot5degC)
nutritional factors
house type and
mosquito control
measures
NA
Pullan
et al32 2010
Uganda Cross-
sectional
1770 Aged
5ndash15 years
Selected
from all
households
in district
Lowest vs
highest socio-
economic
status quintile
Pf PR None OR 1middot25
(0middot74ndash
2middot13)
NA NA NA
Ronald
et al33
2006
Ghana Cross-
sectional
296 Aged
1ndash9 years
Randomly
sampled
from
community
Decreasing
household
socio-
economic
status
Pf PR None OR 3middot22
(1middot95ndash
5middot32)
3middot95
(2middot26ndash
6middot90)
Age and travel to
rural areas
NA
Slutsker
et al34 1996
Malawi Cross-
sectional
3915 Aged
0ndash3 months
Infantsrsquo
mothers
were
enrolled into
a chemopro-
phylaxis
study at four
antenatalclinics
Low vs high or
medium socio-
economic
status
Pf PR None OR 1middot80
(1middot30ndash
2middot10)
NA NA NA
Villamor
et al35 2003
Tanzania Cross-
sectional
687 Aged 6ndash60
months
Children
were enrolled
in a vitamin
A supple-
mentation
trial when
admitted to
hospital with
pneumonia
No electricity
at home vs
electricity at
home
Pf PR None OR 1middot84
(1middot23ndash
2middot76)
NA NA NA
Winskill
et al36
2011
Tanzania Cross-
sectional
1438 Aged
6 months to
13 years
Randomly
selected
from 21
hamlets
Decreasing
household
socioeconomic
status
Pf PR None OR 1middot15
(0middot94ndash
1middot39)
NA NA NA
Yamamoto
et al37 2010
Burkina
Faso
Case-
control
283 Aged
0ndash9 years
Recruited by
passive case
detectionat central
laboratory
Low vs high
socioeconomic
status
Incidence of
clinical
malaria(parasitaemia
plus fever)
Controls
from
demographicsurveillance
system
database
matched for
age sex
ethnicity and
residence
OR 0middot47
(0middot20ndash
1middot08)
NA NA NA
Studies included in the qualitative analysis but excluded from the meta-analysis
Clark et al38
2008
Uganda Cohort 558 Aged
1ndash10 years
Recruited
from a
census
population
in one parish
1st and 2nd
(lowest) vs
4th wealth
quartile
(highest)
Incidence of
clinical
episodes of
malaria per
person-year
at risk
None RR 2middot04
(1middot54ndash
2middot70)
1middot30
(0middot96ndash
1middot79)
Age sickle cell trait
G6PD deficiency in
girls bednet use
household
crowding and
distance from
swamp
Not
possible to
calculate
OR
(Continues on next page)
8132019 SE Dev as an Intervention Agst Malaria
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Articles
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used the generic inverse-variance method for the meta-
analysis in which weight is given to each study accordingto the inverse of the variance of the effect to minimiseuncertainty about the pooled effect estimates Bothoutcomes (P falciparum infection and clinical malaria)were combined in the analysis We allocated the includedstudies into four subgroups according to the measure ofsocioeconomic status used asset ownership householdwealth socioeconomic index or parentsrsquo occupations Wedid separate analyses for unadjusted and adjusted ORsMissing data were not problematic since meta-regressionof individual data was not done
Initially we did a fixed-effects meta-analysis but ifI sup2 was large (gt50) which suggests substantial hetero-geneity between studies we used random-effectsanalysis Random-effects analysis adjusts the standard
errors of each study estimate of effect to include a
measure of variation in the effects reported betweenstudies We produced forest plots to visually assess theORs and 95 CIs of each study and used funnel plots toassess publication bias (with study size as a function ofeffect size) We used Eggerrsquos linear regression method totest for funnel plot asymmetry (ie to quantify the biascaptured by the funnel plot)22 Analyses were done withStata 11 and RevMan 5
ResultsOur initial search yielded 6106 records of which4696 remained after removal of duplicates (figure 2)20 records met our inclusion criteria (table)23ndash42 and ofthese 15 contained the necessary data for inclusion in thequantitative analysis (meta-analysis) Five records were
Study
site
Study
design
n Participant s R ecru it -
ment of
participants
Exp os ure Outcome Control
group
Measure
of effect
Un-
adjusted
effect
(95 CI)
Ad justed
effect
(95 CI)
Factors adjusted
for
Reason for
exclusion
from
quan-
titative
analysis
(Continued from previous page)
Klinkenberg
et al39 2006
Ghana Cross-
sectional
1744 Aged 6ndash60
months
Randomly
sampled
from
communities
near (lt1000
m) and less
near
(gt1000 m)
agricultural
sites in Accra
Socio-
economic
status below
vs above mean
for the city
Pf PR None In-
suffi cient
infor-
mation
provided
NA NA NA Not
possible to
calculate
OR
Kreuels
et al40 2008
Ghana Cohort 535 Aged
2ndash4 months
Recruited
from nine
villages after
visiting
health centre
(children
with chronic
diseases
excluded)
Family does
not have good
financial
situation vs
family has
good financial
situation
Incidence of
clinical
malaria
(parasitaemia
plus fever)
None Incidence
rate ratio
1middot59
(1middot33ndash
1middot89)
1middot52
(1middot27ndash
1middot82)
Sex ethnicity
season of birth (dry
or rainy season)
sickle cell trait
motherrsquos education
motherrsquos
occupation
knowledge of
malaria and
protective measures
Not
possible to
calculate
OR
Matthys
et al41 2006
Cocircte
drsquoIvoire
Cross-
sectional
672 Aged
0ndash15 years
Selected
from
farming and
non-farming
households
Low vs high
socioeconomic
status
Pf PR None OR NA 2middot44
(0middot88ndash
10middot00)
Age agricultural
zone crops grown
irrigation overnight
stays in temporary
farm huts and
distance to
permanent ponds
and fish ponds
Bayesian
credible
intervals
reported
only
Pullan
et al42
2010
Uganda Cross-
sectional
1844 A ged
5ndash15 years
All residents
of four
villages asked
to
participate
with 78
successfully
enrolled
Decreasing
household
socioeconomic
status
Pf PR None OR NA 2middot27
(0middot88ndash
25middot00)
Ag e bednet use Bayesian
credible
intervals
reported
only
OR=odds ratio Pf PR=Plasmodium falciparum parasite rate RR=risk ratio Socioeconomic status analysed as a continuous variable
Table Studies included in the systematic review and meta-analysis
8132019 SE Dev as an Intervention Agst Malaria
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Articles
6 wwwthelancetcom Published online June 19 2013 httpdxdoiorg101016S0140-6736(13)60851-X
excluded from the quantitative analysis either because
Bayesian credible intervals were reported (n=2) or becauseORs could not be calculated from the available data (n=3)
Despite substantial overlap between CIs for both unadjust-
ed and adjusted results high I sup2 values from fixed-effectsanalysis suggested substantial heterogeneity between
Figure 983091 Association between low s ocioeconomic status and clinical malaria or parasitaemia in chi ldren aged 0ndash15 years
Pooled effects from random-effects meta-analyses for unadjusted (A) and adjusted (B) results are shown Studies are divided into subgroups by measure ofsocioeconomic status used Error bars show 95 CIs df=degrees of freedom
Odds ratio(95 CI)
WeightLog(odds ratio)
SE
Socioeconomic indexAl-Taiar et al23 (2009)Custodio et al26 (2009)Pullan et al32 (2010)Ronald et al33 (2006)Slutsker et al34 (1996)Winskill et al36 (2011)Yanamoto et al37 (2010)
SubtotalHeterogeneity τsup2=0middot12 χsup2=24middot51 df=6 (p=0middot0004) I2=76Test for overall effect Z=2middot56 (p=0middot01)
0middot565314 0middot397341 0middot223144 1middot169381 0middot587787 0middot139262 ndash0middot756122
0middot191170 0middot210810 0middot271367 0middot255894 0middot166032 0middot102621 0middot435365
1middot76 (1middot21ndash2middot56) 1middot49 (0middot98ndash2middot25) 1middot25 (0middot73ndash2middot13) 3middot22 (1middot95ndash5middot32) 1middot80 (1middot30ndash2middot49) 1middot15 (0middot94ndash1middot41)
0middot47 (0middot20ndash1middot10) 1middot49 (1middot10ndash2middot01)
8middot4 8middot0 6middot6 7middot0 9middot1 10middot5
4middot0 53middot5
Asset ownership
Baragatti et al24 (2009)Ghebreyseus et al28 (2000)Koram et al29 (1995)Villamor et al35 (2003)
SubtotalHeterogeneity τ2=0middot09 χ2=7middot14 df=3 (p=0middot07) I2=58Test for overall effect Z=2middot76 (p=0middot006)
0middot727549 ndash0middot028422 0middot832909 0middot611533
0middot322571 0middot249721 0middot238911 0middot205363
2middot07 (1middot10ndash3middot90) 0middot97 (0middot60ndash1middot59) 2middot30 (1middot44ndash3middot67) 1middot84 (1middot23ndash2middot76) 1middot70 (1middot17ndash2middot48)
5middot6 7middot1 7middot3 8middot1 28middot2
Reduced odds of malaria Increased odds of malaria
10middot50middot20middot1 2 5 10
Household wealthClarke et al25 (2001)Gahutu et al27 (2011)
SubtotalHeterogeneity τ2=0middot01 χ2=1middot22 df=1 (p=0middot27) I2=18
Test for overall effect Z=3middot25 (p=0middot001)
0middot851030 0middot463734
0middot279963 0middot211706
2middot34 (1middot35ndash4middot05) 1middot59 (1middot05ndash2middot41) 1middot85 (1middot28ndash2middot68)
6middot4 8middot0 14middot4
Parentsrsquo occupationOngrsquoecha et al31 (2006)SubtotalHeterogeneity NA
Test for overall effect Z=3middot10 (p=0middot002)
Total (all measures of socioeconomic status)Heterogeneity τ2=0middot10 χ2=40middot38 df=13 (p=0middot0001) I2=68Test for overall effect Z=4middot76 (plt0middot00001)Test for subgroup differences χ2=4middot46 df=3 (p=0middot22) I2=32middot83
1middot347074 0middot434886 3middot85 (1middot64ndash9middot02) 3middot85 (1middot64ndash9middot02)
4middot0
4middot0
1middot66 (1middot35ndash2middot05) 100middot0
A
Odds ratio(95 CI)
WeightLog(odds ratio)
SE
Socioeconomic indexKrefis et al30 (2010)Ronald et al33 (2006)
SubtotalHeterogeneity τsup2=0middot26 χsup2=6middot01 df=1 (p=0middot01) I2=83Test for overall effect Z=2middot38 (p=0middot02)
0middot579818 1middot373716
0middot154177 0middot284873
1middot79 (1middot32ndash2middot42)
3middot95 (2middot26ndash6middot90) 2middot56 (1middot18ndash5middot56)
27middot4
19middot0 46middot3
Asset ownershipBaragatti et al24 (2009)Koram et al29 (1995)SubtotalHeterogeneity τ2=0middot00 χ2=0middot80 df=1 (p=0middot37) I2=0Test for overall effect Z=4middot10 (plt0middot0001)
0middot615186 0middot947789
0middot233766 0middot290486
1middot85 (1middot17ndash2middot93) 2middot58 (1middot46ndash4middot56) 2middot11 (1middot48ndash3middot01)
22middot1 18middot6 40middot7
Reduced odds of malaria Increased odds of malaria
10middot50middot2 2 5
Parentsrsquo occupationsOngrsquoecha et al31 (2006)SubtotalHeterogeneity NATest for overall effect Z=0middot21 (p=0middot83)
ndash0middot086178 0middot410929 0middot92 (0middot41ndash2middot05) 0middot92 (0middot41ndash2middot05)
12middot9 12middot9
Total (all measures of socioeconomic status)Heterogeneity τ2=0middot11 χ2=10middot72 df=4 (p=0middot03) I2=63Test for overall effect Z=3middot83 (plt0middot0001)Test for subgroup differences χ2=4middot02 df=2 (p=0middot13) I2=50middot3
2middot06 (1middot42ndash2middot97) 100middot0
B
8132019 SE Dev as an Intervention Agst Malaria
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Articles
wwwthelancetcom Published online June 19 2013 httpdxdoiorg101016S0140-6736(13)60851-X 7
studies (unadjusted effect size I sup2=68 adjusted effect size
I sup2=63) Therefore random-effects analysis was usedThe meta-analysis was restricted to comparisonsbetween the highest (least poor) and lowest (poorest)socioeconomic groups Subgroup analysis suggested thatlow socioeconomic status was associated with increasedodds of malaria irrespective of the measure used forsocioeconomic status with the exception of one study inwhich parentsrsquo occupations were used31 we thereforejudged that to pool all results would be appropriate Inthe meta-analyses for both unadjusted and adjustedresults the odds of malaria infection were higher in thepoorest children than in the least poor children (figure 3)
Visual assessment of funnel plots (appendix p 8)showed that the studies were distributed fairly
symmetrically about the combined effect size whichsuggests little publication bias However Eggerrsquos test forbias suggested funnel plot asymmetry for the unadjustedresults (bias coeffi cient 1middot70 95 CI ndash0middot97 to 4middot37p=0∙191) which suggests that publication bias (delayedpublication or location bias) small-study effects selectiveoutcome reporting or selective analysis reporting mighthave been present A test for funnel plot asymmetry wasnot possible for the adjusted effects since only fivestudies were included in the meta-analysis Overallquality assessment scores for risk of bias in studiesincluded in the quantitative analysis ranged from two toseven out of a maximum of eight (appendix p 6ndash7)
DiscussionOur findings suggest that low socioeconomic status isassociated with roughly doubled odds of clinical malariaor parasitaemia in children compared with higher socio-economic status within a locality This conclusion is sup-ported by a similar size and direction of effect noted inthe five studies excluded from the meta-analysis Sinceour analysis represents a comparison of the very poorestchildren with the least poor children within highly im-poverished communities the difference in the odds ofmalaria in the poorest children would probably be evengreater if the studies were expanded to include childrenfrom wealthier backgrounds The association between
socioeconomic status and malaria is not definitiveevidence for the direction of causality since the pooresthouseholds are not only more susceptible to the diseasebut are also more vulnerable to its costs such that thedisease itself can induce poverty For example asignificant positive association between low socio-economic status and malaria parasitaemia has beenreported in Tanzania43 with causality in both directionsFindings from Kenya44 and Nigeria45 suggest that the costsof malaria treatment (as a proportion of non-food monthlyincome) and subsequent financial setbacks are greater forpoorer than for more wealthy households Costs also varygeographically in Kenya44 and Papua New Guinea46 therisk of clinical disease is greater in low-transmissiondistricts with subsequently greater loss of income
Wealth is probably protective against malaria since it
renders prophylaxis and treatment more affordable
47ndash49
andis positively associated with other beneficial factorsincluding better-educated parents (which improvesprophylaxis and treatment for children) increasedhousing quality (which reduces house entry by malaria-transmitting mosquitoes) and improved nutritionalstatus of children (which could increase their subsequentability to cope with malaria infection)50ndash52 Malaria andpoverty therefore constitute a vicious cycle for the pooresthouseholds exacerbating differences in health and wealth
A major limitation of our meta-analysis is that themeasurement of risk factors was done with varyingprecision in the included studies and although we didsubgroup and random-effects analyses these are unlikely
to have fully accounted for heterogeneity in study designAnother important limitation is the poor quality of thestudies included in the meta-analysis which results fromthe nature of the study question (since randomisation forsocioeconomic status would not be practically or ethicallypossible) However the consistency of results acrossstudies and settings suggests that the finding of increasedodds of malaria in children of low socioeconomic status isrobust For our systematic review the main limitationwas the language of the search In particular notincluding publications in Spanish probably excludedmuch data from South America such that our findingscannot be generalised to that region Eggerrsquos testsuggested the presence of some forest plot asymmetryhowever statistical tests for forest plot asymmetry tend tohave low power53 and asymmetry might not be attributableto publication biasmdashit might also have arisen from poorstudy quality leading to artificially inflated effects in thesmaller studies selective outcome or analysis reportingor chance Incomplete retrieval (four full-text studiescould not be retrieved) might also have introduced bias
On the basis of our findings we advocate thatdevelopment programmes should be an essentialcomponent of malaria control Malaria elimination inmany high-income countries was achieved withoutmalaria-specific interventions prevalence started to fall inEurope and North America as a by-product of improved
living conditions and increased wealth5455 and afterRonald Ross deduced the mode of malaria transmission56 in 1897 more specific interventions became possibleincluding habitat modification (permanent elimination ofbreeding sitesmdasheg by installing and maintaining drains)habitat manipulation (temporary creation of unfavourableconditions for the vectormdasheg by fluctuating the amountof water in reservoirs) and modifications to humanhabitation or behaviour to reduce human-vector contactsuch as mosquito-proofing of houses57 As a result mostof Europe and North America is now characterised byanophelism without malaria which is testament to theeffectiveness of these control efforts together with areduced innate receptivity to malaria transmission thatstems from advances in nutrition health care and
8132019 SE Dev as an Intervention Agst Malaria
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Articles
8 wwwthelancetcom Published online June 19 2013 httpdxdoiorg101016S0140-6736(13)60851-X
development58 Similar environmental management
strategies together with larval control also helped toreduce malaria transmission in many developingcountries during the 20th century including ZanzibarIndonesia Malaysia the Panama Canal and the CopperBelt of Zambia5960 Thus as transmission today falls inmuch of sub-Saharan Africa and elsewhere developmentwill contribute to the reduction and elimination of thedisease Several specific development interventions couldcontribute to malaria control (appendix p 5) which mightbe similar to malaria-specific interventions in terms ofcosts (appendix pp 9ndash10) An excellent example of howsuch interventions can work in practice can be seen inKhartoum Sudan (appendix p 11)
This approach has three major constraints First
accurate costing of the extent to which specificdevelopment interventions contribute to malaria controlis diffi cult Whereas measuring the effect of housescreening is straightforward measuring that ofimproved education or raised incomes is not Secondthe effectiveness of a development intervention dependson both the nature and intensity of malaria transmissionFor example house screening is probably most effectivein areas of low to moderate transmission where vectorsfeed indoors Countries that have eliminated malariasince 1900 have largely been temperate subtropical orislands8 and the high malaria burden in manydeveloping countries is not merely a product of povertyRather the specific ecological requirements of both themalaria parasite and its mosquito vector help todetermine the range of the disease61 Interventions haveto be highly effective and development should not bethought of as a standalone strategy but as a complementto malaria-specific interventions such as LLINs indoorresidual spraying and larval source management Thirdeconomic development gives rise to broader socialenvironmental and ecological changes that might insome circumstances lead to an increase in the burden ofmalaria (appendix p 5) as has been seen in Sri Lanka(appendix p 12) Nonetheless these constraints shouldnot be treated as barriers to the use of socioeconomicdevelopment as an intervention against malaria
(appendix p 5)In addition to initiatives such as the Millennium Villages
project which is operating in 14 villages in ten Africancountries to examine the effects of socioeconomicdevelopment62 further research is needed to address someimportant questions and to galvanise specialists in bothhealth and development to work more closely together onmalaria control For example randomised controlled trialsshould be considered to assess the effectiveness ofsocioeconomic interventions (eg improved education andnutrition) against malaria in different settings We mustalso investigate the causal pathways that lead fromdevelopment to successful malaria control and vice versaand develop an understanding of the relation betweenmalaria control birth rates and population growth
That malaria control remains largely the preoccupation
of the health sector alone is a failing of both those whowork in health and those who work in internationaldevelopment The disease severely compromises socio-economic development and its control and eliminationwould improve economic prosperity worldwide Theeffectiveness of available drugs and insecticides formalaria control will ultimately deteriorate with theemergence of parasites resistant to antimalarials and ofvectors resistant to insecticides and the development andprocurement costs of replacements will be high Donorfatigue is also an ever-present threat to interventions suchas LLINs indoor residual spraying and intermittentpreventive treatment especially in view of the economicsituation since the 2007ndash08 financial crisis63 However
several specific development interventions could beintroduced to aid both economic development andmalaria control Increased wealth and improved standardsof living that stem directly from socioeconomicdevelopment could prove fundamental in ensuring thatmalaria transmission continues to fall in much of AsiaSouth America and Africa as it happened historically inEurope and North America Socioeconomic developmentcould prove to be a very effective and sustainableintervention against malaria in the long term
Contributors
SWL and RS conceived of the study SWL RS LST HL and JTdeveloped the study design and the outline of the report LST searchedthe scientific literature did the meta-analysis and prepared the first
draft of the report BW provided advice on the systematic review andmeta-analysis HTK contributed the case study from Sudan (appendix)All authors reviewed the final version of the report
Conflicts of interest
We declare that we have no conflicts of interest
Acknowledgments
This work was supported by the UK Department for InternationalDevelopment and the Malaria Centre at the London School of Hygiene ampTropical Medicine (LSHTM) SWL was supported by the Research andPolicy for Infectious Disease Dynamics (RAPIDD) programme of theScience and Technology Directorate US Department of Homeland Securitythe Fogarty International Center (US National Institutes of Health) and theBill amp Melinda Gates Foundation JT was supported in part by a grant fromthe UK Economic and Social Research Council to the STEPS Centre and theInstitute of Development Studies (University of Sussex Brighton UK)We are grateful to Frida Kasteng (LSHTM) for providing information about
the costs of malaria and development interventionsReferences 1 Gething PW Patil AP Smith DL et al A new world malaria map
Plasmodium falciparum endemicity in 2010 Malar J 2011 10 378 2 WHO World Malaria Report 2011 Geneva World Health
Organization 2011 3 Murray CJL Rosenfeld LC Lim SS et al Global malaria mortality
between 1980 and 2010 a systematic analysis Lancet 2012 379 413ndash31 4 Murray CJL Vos T Lozano R et al Disability-adjusted life years
(DALYs) for 291 diseases and injuries in 21 regions 1990mdash2010a systematic analysis for the Global Burden of Disease Study 2010Lancet 2012 380 2197ndash223
5 Sachs J Malaney P The economic and social burden of malariaNature 2002 415 680ndash85
6 UNDP International human development indicators httphdrundporgendatabuild (accessed April 24 2012)
7 Sachs JD Macroeconomics and health investing in health for
economic development Geneva World Health Organization 2001
8132019 SE Dev as an Intervention Agst Malaria
httpslidepdfcomreaderfullse-dev-as-an-intervention-agst-malaria 910
Articles
wwwthelancetcom Published online June 19 2013 httpdxdoiorg101016S0140-6736(13)60851-X 9
8 Gallup J Sachs J The economic burden of malariaAm J Trop Med Hyg 2001 64 85ndash96
9 Mandelbaum-Schmid J HIVAIDS hunger and malaria are theworldrsquos most urgent problems say economistsBull World Health Organ 2004 82 554ndash55
10 Kidson C Indaratna K Ecology economics and political will thevicissitudes of malaria strategies in Asia Parassitologia 1998 40 39ndash46
11 UNDP Millennium Development Goal 6 focuses on combatingHIVAIDS malaria and other diseases httpwebundporgmdggoal6shtml (accessed April 21 2012)
12 OrsquoMeara WP Mangeni JN Steketee R Greenwood B Changes inthe burden of malaria in sub-Saharan Africa Lancet Infect Dis 201010 545ndash55
13 Dondorp AM Fairhurst RM Slutsker L et al The threat ofartemisinin-resistant malaria N Engl J Med 2011 365 1073ndash75
14 Ranson H NrsquoGuessan R Lines J et al Pyrethroid resistance inAfrican anopheline mosquitoes what are the implications formalaria control Trends Parasitol 2011 27 91ndash98
15 Cohen J Smith D Cotter C et al Malaria resurgence a systematic
review and assessment of its causes Malar J 2012 11 122 16 WHO WHO position statement on integrated vector management
Wkly Epidemiol Rec 2008 83 177ndash84 17 van den Berg H Mutero CM Ichimori K WHO guidance on
policy-making for integrated vector management Geneva WorldHealth Organization 2012
18 Stroup DF Berlin JA Morton SC et al Meta-analysis ofobservational studies in epidemiology JAMA 2000 283 2008ndash12
19 Modher D Liberati A Tetzlaff J Altman DG Group TP Preferredreporting items for systematic reviews and meta-analyses thePRISMA statement PLoS Med 2009 6 e1000097
20 Roll Back Malaria Malaria endemic countries Geneva The RBMPartnership 2010 httpwwwrbmwhointendemiccountrieshtml(accessed June 8 2011)
21 Wells G Shea B OrsquoConnell D et al The Newcastle-Ottawa Scale(NOS) for assessing the quality of nonrandomised studies inmeta-analyses Ottawa University of Ottawa 2011
22 Egger M Smith GD Schneider M Minder C Bias in meta-analysisdetected by a simple graphical test BMJ 1997 315 629ndash34
23 Al-Taiar A Assabri A Al-Habori M et al Socioeconomic andenvironmental factors important for acquiring non-severe malariain children in Yemen a case-control studyTrans R Soc Trop Med Hyg 2009 103 72ndash78
24 Baragatti M Fournet F Henry M-C et al Social and environmentalmalaria risk factors in urban areas of Ouagadougou Burkina FasoMalar J 2009 8 13
25 Clarke SE Boslashgh C Brown RC Pinder M Walraven GELLindsay SW Do untreated bednets protect against malariaTrans R Soc Trop Med Hyg 2001 95 457ndash62
26 Custodio E Descalzo MAacute Villamor E et al Nutritional andsocio-economic factors associated with Plasmodium falciparum infection in children from Equatorial Guinea results from anationally representative survey Malar J 2009 8 225
27 Gahutu J-B Steininger C Shyirambere C et al Prevalence and riskfactors of malaria among children in southern highland Rwanda
Malar J 2011 10 134 28 Ghebreyesus TA Haile M Witten KH et al Household risk factorsfor malaria among children in the Ethiopian HighlandsTrans R Soc Trop Med Hyg 2000 94 17ndash21
29 Koram KA Bennett S Adiamah JH Greenwood BM Socio-economicrisk factors for malaria in a peri-urban area of The GambiaTrans R Soc Trop Med Hyg 1995 89 146ndash50
30 Krefis AC Schwarz NG Nkrumah B et al Principal componentanalysis of socioeconomic factors and their association withmalaria in children from the Ashanti Region Ghana Malar J 20109 201
31 Ongrsquoecha JM Keller CC Were T et al Parasitemia anemia andmalarial anemia in infants and young children in a ruralholoendemic Plasmodium falciparum transmission area Am J Trop Med Hyg 2006 74 376ndash85
32 Pullan RL Kabatereine NB Bukirwa H Staedke SG Brooker SHeterogeneities and consequences of Plasmodium species andhookworm coinfection a population based study in Uganda
J Infect Dis 2011 203 406ndash17
33 Ronald LA Kenny SL Klinkenberg E et al Malaria and anaemiaamong children in two communities of Kumasi Ghana
a cross-sectional survey Malar J 2006 1 105 34 Slutsker L Khoromana CO Hightower AW et al Malaria infection
in infancy in rural Malawi Am J Trop Med Hyg 1996 55 71ndash76 35 Villamor E Fataki MR Mbise RL Fawzi WW Malaria parasitaemia
in relation to HIV status and vitamin A supplementation amongpre-school children Trop Med Int Health 2003 8 1051ndash61
36 Winskill P Rowland M Mtove G Malima R Kirby M Malaria riskfactors in north-east Tanzania Malar J 2011 10 98
37 Yamamoto S Louis VR Sie A Sauerborn R Household risk factorsfor clinical malaria in a semi-urban area of Burkina Fasoa case-control study Trans R Soc Trop Med Hyg 2010 104 61ndash65
38 Clark TD Greenhouse B Njama-Meya D et al Factors determiningthe heterogeneity of malaria incidence in children in KampalaUganda J Infect Dis 2008 198 393ndash400
39 Klinkenberg E McCall P Wilson M et al Urban malaria andanaemia in children a cross-sectional survey in two cities of GhanaTrop Med Int Health 2006 11 578ndash88
40 Kreuels B Kobbe R Adjei S et al Spatial variation of malariaincidence in young children from a geographically homogeneousarea with high endemicity J Infect Dis 2008 197 85ndash93
41 Matthys B Vounatsou P Raso G et al Urban farming and malariarisk factors in a medium-sized town in Cocircte drsquoIvoireAm J Trop Med Hyg 2006 75 1223ndash31
42 Pullan RL Bukirwa H Staedke SG Snow RW Brooker S Plasmodium infection and its risk factors in eastern Uganda Malar J 2010 4 9
43 Somi MF Butler JRG Is there evidence for dual causation betweenmalaria and socioeconomic status Findings from rural TanzaniaAm J Trop Med Hyg 2007 77 1020ndash27
44 Chuma JM Thiede M Rethinking the economic costs of malaria atthe household level evidence from applying a new analyticalframework in rural Kenya Malar J 2006 5 76ndash89
45 Onwujekwe O Hanson K Are malaria treatment expenditurescatastrophic to different socio-economic and geographic groups andhow do they cope with payment A study in southeast NigeriaTrop Med Int Health 2010 15 18ndash25
46 Sicuri E Davy C Marinelli M et al The economic cost tohouseholds of childhood malaria in Papua New Guinea a focus onintra-country variation Health Policy Plann 2012 27 339ndash47
47 Matovu F Goodman C How equitable is bed net ownership andutilisation in Tanzania A practical application of the principles ofhorizontal and vertical equity Malar J 2009 8 109ndash21
48 Gingrich CD Hanson K Marchant T Mulligan J-A Mponda HPrice subsidies and the market for mosquito nets in developingcountries a study of Tanzaniarsquos discount voucher schemeSoc Sci Med 2011 73 160ndash68
49 Ahmed SM Haque R Haque U Hossain A Knowledge on thetransmission prevention and treatment of malaria among twoendemic populations of Bangladesh and their health-seekingbehaviour Malar J 2009 8 173
50 Saaka M Oosthuizen J Beatty S Effect of joint iron and zincsupplementation on malarial infection and anaemiaEast Afr J Public Health 2009 6 55ndash62
51 Worrall E Basu S Hanson K Is malaria a disease of povertyA review of the literature Trop Med Int Health 2005 10 1047ndash59 52 Barat LM Palmer N Basu S Worrall E Hanson K Mills A Do
malaria control interventions reach the poor Am J Trop Med Hyg 2004 71 174ndash78
53 Sterne JAC Sutton AJ Ioannidis JPA et al Recommendationsfor examining and interpreting funnel plot asymmetry inmeta-analyses of randomised controlled trials BMJ 2011 343 4002
54 Garcia-Martin G Status of malaria eradication in the AmericasAm J Trop Med Hyg 1972 21 617ndash33
55 Bruce-Chwatt L de Zulueta J The rise and fall of malaria inEurope London Oxford University Press 1980
56 Sinden RE Malaria mosquitoes and the legacy of Ronald RossBull World Health Organ 2007 85 894ndash96
57 Keiser J Singer BH Utzinger J Reducing the burden of malariain different eco-epidemiological settings with environmentalmanagement a systematic review Lancet Infect Dis 20055 695ndash708
8132019 SE Dev as an Intervention Agst Malaria
httpslidepdfcomreaderfullse-dev-as-an-intervention-agst-malaria 1010
Articles
10 www thelancet com Published online June 19 2013 httpdx doi org10 1016S0140-6736(13)60851-X
58 Randolph SE Rogers DJ The arrival establishment and spread ofexotic diseases patterns and predictions Nat Rev Microbiol 2010
8 361ndash71 59 Hay S Guerra C Tatem A Noor A Snow R The global distribution
and population at risk of malaria past present and futureLancet Infect Dis 2004 4 327ndash36
60 Killeen GF Fillinger U Kiche I Gouagna LC Knols BGEradication of Anopheles gambiae from Brazil lessons for malariacontrol in Africa Lancet Infect Dis 2002 2 618ndash27
61 Jetten T Takken W Anophelism without malaria in Europea review of the ecology and distribution of the genus Anopheles in
Europe Wageningen Wageningen Agricultural University 1994 62 Millennium Villages Project httpwwwmillenniumvillagesorg
the-villages (accessed May 16 2013) 63 Garrett L Global health hits crisis point Nature 2012 482 7
8132019 SE Dev as an Intervention Agst Malaria
httpslidepdfcomreaderfullse-dev-as-an-intervention-agst-malaria 410
Articles
4 wwwthelancetcom Published online June 19 2013 httpdxdoiorg101016S0140-6736(13)60851-X
Study
site
Study
design
n Participan ts Re cr uit-
ment of
participants
Exposure Outcome Control
group
Measure
of effect
Un-
adjusted
effect
(95 CI)
Ad justed
effect
(95 CI)
Factors adjusted
for
Reason for
exclusion
from
quan-
titative
analysis
(Continued from previous page)
OngrsquoEcha
et al31 2006
Kenya Case-
control
374 Aged
0ndash3 years
(children with
cerebral
malaria and
those with
previous
hospital visits
were
excluded)
Recruited
when
visiting
district
hospital with
symptoms
of malaria
Parents are
farmers vs
parents are
not farmers
Incidence of
clinical
malaria
(parasitaemia
plus fever)
Healthy
controls
recruited
from
maternal
and child
health clinic
OR 3middot85
(1middot64ndash
9middot09)
0middot92
(0middot41ndash
2middot04)
Child risk factors
(axillary
temperature
ge37middot5degC)
nutritional factors
house type and
mosquito control
measures
NA
Pullan
et al32 2010
Uganda Cross-
sectional
1770 Aged
5ndash15 years
Selected
from all
households
in district
Lowest vs
highest socio-
economic
status quintile
Pf PR None OR 1middot25
(0middot74ndash
2middot13)
NA NA NA
Ronald
et al33
2006
Ghana Cross-
sectional
296 Aged
1ndash9 years
Randomly
sampled
from
community
Decreasing
household
socio-
economic
status
Pf PR None OR 3middot22
(1middot95ndash
5middot32)
3middot95
(2middot26ndash
6middot90)
Age and travel to
rural areas
NA
Slutsker
et al34 1996
Malawi Cross-
sectional
3915 Aged
0ndash3 months
Infantsrsquo
mothers
were
enrolled into
a chemopro-
phylaxis
study at four
antenatalclinics
Low vs high or
medium socio-
economic
status
Pf PR None OR 1middot80
(1middot30ndash
2middot10)
NA NA NA
Villamor
et al35 2003
Tanzania Cross-
sectional
687 Aged 6ndash60
months
Children
were enrolled
in a vitamin
A supple-
mentation
trial when
admitted to
hospital with
pneumonia
No electricity
at home vs
electricity at
home
Pf PR None OR 1middot84
(1middot23ndash
2middot76)
NA NA NA
Winskill
et al36
2011
Tanzania Cross-
sectional
1438 Aged
6 months to
13 years
Randomly
selected
from 21
hamlets
Decreasing
household
socioeconomic
status
Pf PR None OR 1middot15
(0middot94ndash
1middot39)
NA NA NA
Yamamoto
et al37 2010
Burkina
Faso
Case-
control
283 Aged
0ndash9 years
Recruited by
passive case
detectionat central
laboratory
Low vs high
socioeconomic
status
Incidence of
clinical
malaria(parasitaemia
plus fever)
Controls
from
demographicsurveillance
system
database
matched for
age sex
ethnicity and
residence
OR 0middot47
(0middot20ndash
1middot08)
NA NA NA
Studies included in the qualitative analysis but excluded from the meta-analysis
Clark et al38
2008
Uganda Cohort 558 Aged
1ndash10 years
Recruited
from a
census
population
in one parish
1st and 2nd
(lowest) vs
4th wealth
quartile
(highest)
Incidence of
clinical
episodes of
malaria per
person-year
at risk
None RR 2middot04
(1middot54ndash
2middot70)
1middot30
(0middot96ndash
1middot79)
Age sickle cell trait
G6PD deficiency in
girls bednet use
household
crowding and
distance from
swamp
Not
possible to
calculate
OR
(Continues on next page)
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Articles
wwwthelancetcom Published online June 19 2013 httpdxdoiorg101016S0140-6736(13)60851-X 5
used the generic inverse-variance method for the meta-
analysis in which weight is given to each study accordingto the inverse of the variance of the effect to minimiseuncertainty about the pooled effect estimates Bothoutcomes (P falciparum infection and clinical malaria)were combined in the analysis We allocated the includedstudies into four subgroups according to the measure ofsocioeconomic status used asset ownership householdwealth socioeconomic index or parentsrsquo occupations Wedid separate analyses for unadjusted and adjusted ORsMissing data were not problematic since meta-regressionof individual data was not done
Initially we did a fixed-effects meta-analysis but ifI sup2 was large (gt50) which suggests substantial hetero-geneity between studies we used random-effectsanalysis Random-effects analysis adjusts the standard
errors of each study estimate of effect to include a
measure of variation in the effects reported betweenstudies We produced forest plots to visually assess theORs and 95 CIs of each study and used funnel plots toassess publication bias (with study size as a function ofeffect size) We used Eggerrsquos linear regression method totest for funnel plot asymmetry (ie to quantify the biascaptured by the funnel plot)22 Analyses were done withStata 11 and RevMan 5
ResultsOur initial search yielded 6106 records of which4696 remained after removal of duplicates (figure 2)20 records met our inclusion criteria (table)23ndash42 and ofthese 15 contained the necessary data for inclusion in thequantitative analysis (meta-analysis) Five records were
Study
site
Study
design
n Participant s R ecru it -
ment of
participants
Exp os ure Outcome Control
group
Measure
of effect
Un-
adjusted
effect
(95 CI)
Ad justed
effect
(95 CI)
Factors adjusted
for
Reason for
exclusion
from
quan-
titative
analysis
(Continued from previous page)
Klinkenberg
et al39 2006
Ghana Cross-
sectional
1744 Aged 6ndash60
months
Randomly
sampled
from
communities
near (lt1000
m) and less
near
(gt1000 m)
agricultural
sites in Accra
Socio-
economic
status below
vs above mean
for the city
Pf PR None In-
suffi cient
infor-
mation
provided
NA NA NA Not
possible to
calculate
OR
Kreuels
et al40 2008
Ghana Cohort 535 Aged
2ndash4 months
Recruited
from nine
villages after
visiting
health centre
(children
with chronic
diseases
excluded)
Family does
not have good
financial
situation vs
family has
good financial
situation
Incidence of
clinical
malaria
(parasitaemia
plus fever)
None Incidence
rate ratio
1middot59
(1middot33ndash
1middot89)
1middot52
(1middot27ndash
1middot82)
Sex ethnicity
season of birth (dry
or rainy season)
sickle cell trait
motherrsquos education
motherrsquos
occupation
knowledge of
malaria and
protective measures
Not
possible to
calculate
OR
Matthys
et al41 2006
Cocircte
drsquoIvoire
Cross-
sectional
672 Aged
0ndash15 years
Selected
from
farming and
non-farming
households
Low vs high
socioeconomic
status
Pf PR None OR NA 2middot44
(0middot88ndash
10middot00)
Age agricultural
zone crops grown
irrigation overnight
stays in temporary
farm huts and
distance to
permanent ponds
and fish ponds
Bayesian
credible
intervals
reported
only
Pullan
et al42
2010
Uganda Cross-
sectional
1844 A ged
5ndash15 years
All residents
of four
villages asked
to
participate
with 78
successfully
enrolled
Decreasing
household
socioeconomic
status
Pf PR None OR NA 2middot27
(0middot88ndash
25middot00)
Ag e bednet use Bayesian
credible
intervals
reported
only
OR=odds ratio Pf PR=Plasmodium falciparum parasite rate RR=risk ratio Socioeconomic status analysed as a continuous variable
Table Studies included in the systematic review and meta-analysis
8132019 SE Dev as an Intervention Agst Malaria
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Articles
6 wwwthelancetcom Published online June 19 2013 httpdxdoiorg101016S0140-6736(13)60851-X
excluded from the quantitative analysis either because
Bayesian credible intervals were reported (n=2) or becauseORs could not be calculated from the available data (n=3)
Despite substantial overlap between CIs for both unadjust-
ed and adjusted results high I sup2 values from fixed-effectsanalysis suggested substantial heterogeneity between
Figure 983091 Association between low s ocioeconomic status and clinical malaria or parasitaemia in chi ldren aged 0ndash15 years
Pooled effects from random-effects meta-analyses for unadjusted (A) and adjusted (B) results are shown Studies are divided into subgroups by measure ofsocioeconomic status used Error bars show 95 CIs df=degrees of freedom
Odds ratio(95 CI)
WeightLog(odds ratio)
SE
Socioeconomic indexAl-Taiar et al23 (2009)Custodio et al26 (2009)Pullan et al32 (2010)Ronald et al33 (2006)Slutsker et al34 (1996)Winskill et al36 (2011)Yanamoto et al37 (2010)
SubtotalHeterogeneity τsup2=0middot12 χsup2=24middot51 df=6 (p=0middot0004) I2=76Test for overall effect Z=2middot56 (p=0middot01)
0middot565314 0middot397341 0middot223144 1middot169381 0middot587787 0middot139262 ndash0middot756122
0middot191170 0middot210810 0middot271367 0middot255894 0middot166032 0middot102621 0middot435365
1middot76 (1middot21ndash2middot56) 1middot49 (0middot98ndash2middot25) 1middot25 (0middot73ndash2middot13) 3middot22 (1middot95ndash5middot32) 1middot80 (1middot30ndash2middot49) 1middot15 (0middot94ndash1middot41)
0middot47 (0middot20ndash1middot10) 1middot49 (1middot10ndash2middot01)
8middot4 8middot0 6middot6 7middot0 9middot1 10middot5
4middot0 53middot5
Asset ownership
Baragatti et al24 (2009)Ghebreyseus et al28 (2000)Koram et al29 (1995)Villamor et al35 (2003)
SubtotalHeterogeneity τ2=0middot09 χ2=7middot14 df=3 (p=0middot07) I2=58Test for overall effect Z=2middot76 (p=0middot006)
0middot727549 ndash0middot028422 0middot832909 0middot611533
0middot322571 0middot249721 0middot238911 0middot205363
2middot07 (1middot10ndash3middot90) 0middot97 (0middot60ndash1middot59) 2middot30 (1middot44ndash3middot67) 1middot84 (1middot23ndash2middot76) 1middot70 (1middot17ndash2middot48)
5middot6 7middot1 7middot3 8middot1 28middot2
Reduced odds of malaria Increased odds of malaria
10middot50middot20middot1 2 5 10
Household wealthClarke et al25 (2001)Gahutu et al27 (2011)
SubtotalHeterogeneity τ2=0middot01 χ2=1middot22 df=1 (p=0middot27) I2=18
Test for overall effect Z=3middot25 (p=0middot001)
0middot851030 0middot463734
0middot279963 0middot211706
2middot34 (1middot35ndash4middot05) 1middot59 (1middot05ndash2middot41) 1middot85 (1middot28ndash2middot68)
6middot4 8middot0 14middot4
Parentsrsquo occupationOngrsquoecha et al31 (2006)SubtotalHeterogeneity NA
Test for overall effect Z=3middot10 (p=0middot002)
Total (all measures of socioeconomic status)Heterogeneity τ2=0middot10 χ2=40middot38 df=13 (p=0middot0001) I2=68Test for overall effect Z=4middot76 (plt0middot00001)Test for subgroup differences χ2=4middot46 df=3 (p=0middot22) I2=32middot83
1middot347074 0middot434886 3middot85 (1middot64ndash9middot02) 3middot85 (1middot64ndash9middot02)
4middot0
4middot0
1middot66 (1middot35ndash2middot05) 100middot0
A
Odds ratio(95 CI)
WeightLog(odds ratio)
SE
Socioeconomic indexKrefis et al30 (2010)Ronald et al33 (2006)
SubtotalHeterogeneity τsup2=0middot26 χsup2=6middot01 df=1 (p=0middot01) I2=83Test for overall effect Z=2middot38 (p=0middot02)
0middot579818 1middot373716
0middot154177 0middot284873
1middot79 (1middot32ndash2middot42)
3middot95 (2middot26ndash6middot90) 2middot56 (1middot18ndash5middot56)
27middot4
19middot0 46middot3
Asset ownershipBaragatti et al24 (2009)Koram et al29 (1995)SubtotalHeterogeneity τ2=0middot00 χ2=0middot80 df=1 (p=0middot37) I2=0Test for overall effect Z=4middot10 (plt0middot0001)
0middot615186 0middot947789
0middot233766 0middot290486
1middot85 (1middot17ndash2middot93) 2middot58 (1middot46ndash4middot56) 2middot11 (1middot48ndash3middot01)
22middot1 18middot6 40middot7
Reduced odds of malaria Increased odds of malaria
10middot50middot2 2 5
Parentsrsquo occupationsOngrsquoecha et al31 (2006)SubtotalHeterogeneity NATest for overall effect Z=0middot21 (p=0middot83)
ndash0middot086178 0middot410929 0middot92 (0middot41ndash2middot05) 0middot92 (0middot41ndash2middot05)
12middot9 12middot9
Total (all measures of socioeconomic status)Heterogeneity τ2=0middot11 χ2=10middot72 df=4 (p=0middot03) I2=63Test for overall effect Z=3middot83 (plt0middot0001)Test for subgroup differences χ2=4middot02 df=2 (p=0middot13) I2=50middot3
2middot06 (1middot42ndash2middot97) 100middot0
B
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studies (unadjusted effect size I sup2=68 adjusted effect size
I sup2=63) Therefore random-effects analysis was usedThe meta-analysis was restricted to comparisonsbetween the highest (least poor) and lowest (poorest)socioeconomic groups Subgroup analysis suggested thatlow socioeconomic status was associated with increasedodds of malaria irrespective of the measure used forsocioeconomic status with the exception of one study inwhich parentsrsquo occupations were used31 we thereforejudged that to pool all results would be appropriate Inthe meta-analyses for both unadjusted and adjustedresults the odds of malaria infection were higher in thepoorest children than in the least poor children (figure 3)
Visual assessment of funnel plots (appendix p 8)showed that the studies were distributed fairly
symmetrically about the combined effect size whichsuggests little publication bias However Eggerrsquos test forbias suggested funnel plot asymmetry for the unadjustedresults (bias coeffi cient 1middot70 95 CI ndash0middot97 to 4middot37p=0∙191) which suggests that publication bias (delayedpublication or location bias) small-study effects selectiveoutcome reporting or selective analysis reporting mighthave been present A test for funnel plot asymmetry wasnot possible for the adjusted effects since only fivestudies were included in the meta-analysis Overallquality assessment scores for risk of bias in studiesincluded in the quantitative analysis ranged from two toseven out of a maximum of eight (appendix p 6ndash7)
DiscussionOur findings suggest that low socioeconomic status isassociated with roughly doubled odds of clinical malariaor parasitaemia in children compared with higher socio-economic status within a locality This conclusion is sup-ported by a similar size and direction of effect noted inthe five studies excluded from the meta-analysis Sinceour analysis represents a comparison of the very poorestchildren with the least poor children within highly im-poverished communities the difference in the odds ofmalaria in the poorest children would probably be evengreater if the studies were expanded to include childrenfrom wealthier backgrounds The association between
socioeconomic status and malaria is not definitiveevidence for the direction of causality since the pooresthouseholds are not only more susceptible to the diseasebut are also more vulnerable to its costs such that thedisease itself can induce poverty For example asignificant positive association between low socio-economic status and malaria parasitaemia has beenreported in Tanzania43 with causality in both directionsFindings from Kenya44 and Nigeria45 suggest that the costsof malaria treatment (as a proportion of non-food monthlyincome) and subsequent financial setbacks are greater forpoorer than for more wealthy households Costs also varygeographically in Kenya44 and Papua New Guinea46 therisk of clinical disease is greater in low-transmissiondistricts with subsequently greater loss of income
Wealth is probably protective against malaria since it
renders prophylaxis and treatment more affordable
47ndash49
andis positively associated with other beneficial factorsincluding better-educated parents (which improvesprophylaxis and treatment for children) increasedhousing quality (which reduces house entry by malaria-transmitting mosquitoes) and improved nutritionalstatus of children (which could increase their subsequentability to cope with malaria infection)50ndash52 Malaria andpoverty therefore constitute a vicious cycle for the pooresthouseholds exacerbating differences in health and wealth
A major limitation of our meta-analysis is that themeasurement of risk factors was done with varyingprecision in the included studies and although we didsubgroup and random-effects analyses these are unlikely
to have fully accounted for heterogeneity in study designAnother important limitation is the poor quality of thestudies included in the meta-analysis which results fromthe nature of the study question (since randomisation forsocioeconomic status would not be practically or ethicallypossible) However the consistency of results acrossstudies and settings suggests that the finding of increasedodds of malaria in children of low socioeconomic status isrobust For our systematic review the main limitationwas the language of the search In particular notincluding publications in Spanish probably excludedmuch data from South America such that our findingscannot be generalised to that region Eggerrsquos testsuggested the presence of some forest plot asymmetryhowever statistical tests for forest plot asymmetry tend tohave low power53 and asymmetry might not be attributableto publication biasmdashit might also have arisen from poorstudy quality leading to artificially inflated effects in thesmaller studies selective outcome or analysis reportingor chance Incomplete retrieval (four full-text studiescould not be retrieved) might also have introduced bias
On the basis of our findings we advocate thatdevelopment programmes should be an essentialcomponent of malaria control Malaria elimination inmany high-income countries was achieved withoutmalaria-specific interventions prevalence started to fall inEurope and North America as a by-product of improved
living conditions and increased wealth5455 and afterRonald Ross deduced the mode of malaria transmission56 in 1897 more specific interventions became possibleincluding habitat modification (permanent elimination ofbreeding sitesmdasheg by installing and maintaining drains)habitat manipulation (temporary creation of unfavourableconditions for the vectormdasheg by fluctuating the amountof water in reservoirs) and modifications to humanhabitation or behaviour to reduce human-vector contactsuch as mosquito-proofing of houses57 As a result mostof Europe and North America is now characterised byanophelism without malaria which is testament to theeffectiveness of these control efforts together with areduced innate receptivity to malaria transmission thatstems from advances in nutrition health care and
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8 wwwthelancetcom Published online June 19 2013 httpdxdoiorg101016S0140-6736(13)60851-X
development58 Similar environmental management
strategies together with larval control also helped toreduce malaria transmission in many developingcountries during the 20th century including ZanzibarIndonesia Malaysia the Panama Canal and the CopperBelt of Zambia5960 Thus as transmission today falls inmuch of sub-Saharan Africa and elsewhere developmentwill contribute to the reduction and elimination of thedisease Several specific development interventions couldcontribute to malaria control (appendix p 5) which mightbe similar to malaria-specific interventions in terms ofcosts (appendix pp 9ndash10) An excellent example of howsuch interventions can work in practice can be seen inKhartoum Sudan (appendix p 11)
This approach has three major constraints First
accurate costing of the extent to which specificdevelopment interventions contribute to malaria controlis diffi cult Whereas measuring the effect of housescreening is straightforward measuring that ofimproved education or raised incomes is not Secondthe effectiveness of a development intervention dependson both the nature and intensity of malaria transmissionFor example house screening is probably most effectivein areas of low to moderate transmission where vectorsfeed indoors Countries that have eliminated malariasince 1900 have largely been temperate subtropical orislands8 and the high malaria burden in manydeveloping countries is not merely a product of povertyRather the specific ecological requirements of both themalaria parasite and its mosquito vector help todetermine the range of the disease61 Interventions haveto be highly effective and development should not bethought of as a standalone strategy but as a complementto malaria-specific interventions such as LLINs indoorresidual spraying and larval source management Thirdeconomic development gives rise to broader socialenvironmental and ecological changes that might insome circumstances lead to an increase in the burden ofmalaria (appendix p 5) as has been seen in Sri Lanka(appendix p 12) Nonetheless these constraints shouldnot be treated as barriers to the use of socioeconomicdevelopment as an intervention against malaria
(appendix p 5)In addition to initiatives such as the Millennium Villages
project which is operating in 14 villages in ten Africancountries to examine the effects of socioeconomicdevelopment62 further research is needed to address someimportant questions and to galvanise specialists in bothhealth and development to work more closely together onmalaria control For example randomised controlled trialsshould be considered to assess the effectiveness ofsocioeconomic interventions (eg improved education andnutrition) against malaria in different settings We mustalso investigate the causal pathways that lead fromdevelopment to successful malaria control and vice versaand develop an understanding of the relation betweenmalaria control birth rates and population growth
That malaria control remains largely the preoccupation
of the health sector alone is a failing of both those whowork in health and those who work in internationaldevelopment The disease severely compromises socio-economic development and its control and eliminationwould improve economic prosperity worldwide Theeffectiveness of available drugs and insecticides formalaria control will ultimately deteriorate with theemergence of parasites resistant to antimalarials and ofvectors resistant to insecticides and the development andprocurement costs of replacements will be high Donorfatigue is also an ever-present threat to interventions suchas LLINs indoor residual spraying and intermittentpreventive treatment especially in view of the economicsituation since the 2007ndash08 financial crisis63 However
several specific development interventions could beintroduced to aid both economic development andmalaria control Increased wealth and improved standardsof living that stem directly from socioeconomicdevelopment could prove fundamental in ensuring thatmalaria transmission continues to fall in much of AsiaSouth America and Africa as it happened historically inEurope and North America Socioeconomic developmentcould prove to be a very effective and sustainableintervention against malaria in the long term
Contributors
SWL and RS conceived of the study SWL RS LST HL and JTdeveloped the study design and the outline of the report LST searchedthe scientific literature did the meta-analysis and prepared the first
draft of the report BW provided advice on the systematic review andmeta-analysis HTK contributed the case study from Sudan (appendix)All authors reviewed the final version of the report
Conflicts of interest
We declare that we have no conflicts of interest
Acknowledgments
This work was supported by the UK Department for InternationalDevelopment and the Malaria Centre at the London School of Hygiene ampTropical Medicine (LSHTM) SWL was supported by the Research andPolicy for Infectious Disease Dynamics (RAPIDD) programme of theScience and Technology Directorate US Department of Homeland Securitythe Fogarty International Center (US National Institutes of Health) and theBill amp Melinda Gates Foundation JT was supported in part by a grant fromthe UK Economic and Social Research Council to the STEPS Centre and theInstitute of Development Studies (University of Sussex Brighton UK)We are grateful to Frida Kasteng (LSHTM) for providing information about
the costs of malaria and development interventionsReferences 1 Gething PW Patil AP Smith DL et al A new world malaria map
Plasmodium falciparum endemicity in 2010 Malar J 2011 10 378 2 WHO World Malaria Report 2011 Geneva World Health
Organization 2011 3 Murray CJL Rosenfeld LC Lim SS et al Global malaria mortality
between 1980 and 2010 a systematic analysis Lancet 2012 379 413ndash31 4 Murray CJL Vos T Lozano R et al Disability-adjusted life years
(DALYs) for 291 diseases and injuries in 21 regions 1990mdash2010a systematic analysis for the Global Burden of Disease Study 2010Lancet 2012 380 2197ndash223
5 Sachs J Malaney P The economic and social burden of malariaNature 2002 415 680ndash85
6 UNDP International human development indicators httphdrundporgendatabuild (accessed April 24 2012)
7 Sachs JD Macroeconomics and health investing in health for
economic development Geneva World Health Organization 2001
8132019 SE Dev as an Intervention Agst Malaria
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wwwthelancetcom Published online June 19 2013 httpdxdoiorg101016S0140-6736(13)60851-X 9
8 Gallup J Sachs J The economic burden of malariaAm J Trop Med Hyg 2001 64 85ndash96
9 Mandelbaum-Schmid J HIVAIDS hunger and malaria are theworldrsquos most urgent problems say economistsBull World Health Organ 2004 82 554ndash55
10 Kidson C Indaratna K Ecology economics and political will thevicissitudes of malaria strategies in Asia Parassitologia 1998 40 39ndash46
11 UNDP Millennium Development Goal 6 focuses on combatingHIVAIDS malaria and other diseases httpwebundporgmdggoal6shtml (accessed April 21 2012)
12 OrsquoMeara WP Mangeni JN Steketee R Greenwood B Changes inthe burden of malaria in sub-Saharan Africa Lancet Infect Dis 201010 545ndash55
13 Dondorp AM Fairhurst RM Slutsker L et al The threat ofartemisinin-resistant malaria N Engl J Med 2011 365 1073ndash75
14 Ranson H NrsquoGuessan R Lines J et al Pyrethroid resistance inAfrican anopheline mosquitoes what are the implications formalaria control Trends Parasitol 2011 27 91ndash98
15 Cohen J Smith D Cotter C et al Malaria resurgence a systematic
review and assessment of its causes Malar J 2012 11 122 16 WHO WHO position statement on integrated vector management
Wkly Epidemiol Rec 2008 83 177ndash84 17 van den Berg H Mutero CM Ichimori K WHO guidance on
policy-making for integrated vector management Geneva WorldHealth Organization 2012
18 Stroup DF Berlin JA Morton SC et al Meta-analysis ofobservational studies in epidemiology JAMA 2000 283 2008ndash12
19 Modher D Liberati A Tetzlaff J Altman DG Group TP Preferredreporting items for systematic reviews and meta-analyses thePRISMA statement PLoS Med 2009 6 e1000097
20 Roll Back Malaria Malaria endemic countries Geneva The RBMPartnership 2010 httpwwwrbmwhointendemiccountrieshtml(accessed June 8 2011)
21 Wells G Shea B OrsquoConnell D et al The Newcastle-Ottawa Scale(NOS) for assessing the quality of nonrandomised studies inmeta-analyses Ottawa University of Ottawa 2011
22 Egger M Smith GD Schneider M Minder C Bias in meta-analysisdetected by a simple graphical test BMJ 1997 315 629ndash34
23 Al-Taiar A Assabri A Al-Habori M et al Socioeconomic andenvironmental factors important for acquiring non-severe malariain children in Yemen a case-control studyTrans R Soc Trop Med Hyg 2009 103 72ndash78
24 Baragatti M Fournet F Henry M-C et al Social and environmentalmalaria risk factors in urban areas of Ouagadougou Burkina FasoMalar J 2009 8 13
25 Clarke SE Boslashgh C Brown RC Pinder M Walraven GELLindsay SW Do untreated bednets protect against malariaTrans R Soc Trop Med Hyg 2001 95 457ndash62
26 Custodio E Descalzo MAacute Villamor E et al Nutritional andsocio-economic factors associated with Plasmodium falciparum infection in children from Equatorial Guinea results from anationally representative survey Malar J 2009 8 225
27 Gahutu J-B Steininger C Shyirambere C et al Prevalence and riskfactors of malaria among children in southern highland Rwanda
Malar J 2011 10 134 28 Ghebreyesus TA Haile M Witten KH et al Household risk factorsfor malaria among children in the Ethiopian HighlandsTrans R Soc Trop Med Hyg 2000 94 17ndash21
29 Koram KA Bennett S Adiamah JH Greenwood BM Socio-economicrisk factors for malaria in a peri-urban area of The GambiaTrans R Soc Trop Med Hyg 1995 89 146ndash50
30 Krefis AC Schwarz NG Nkrumah B et al Principal componentanalysis of socioeconomic factors and their association withmalaria in children from the Ashanti Region Ghana Malar J 20109 201
31 Ongrsquoecha JM Keller CC Were T et al Parasitemia anemia andmalarial anemia in infants and young children in a ruralholoendemic Plasmodium falciparum transmission area Am J Trop Med Hyg 2006 74 376ndash85
32 Pullan RL Kabatereine NB Bukirwa H Staedke SG Brooker SHeterogeneities and consequences of Plasmodium species andhookworm coinfection a population based study in Uganda
J Infect Dis 2011 203 406ndash17
33 Ronald LA Kenny SL Klinkenberg E et al Malaria and anaemiaamong children in two communities of Kumasi Ghana
a cross-sectional survey Malar J 2006 1 105 34 Slutsker L Khoromana CO Hightower AW et al Malaria infection
in infancy in rural Malawi Am J Trop Med Hyg 1996 55 71ndash76 35 Villamor E Fataki MR Mbise RL Fawzi WW Malaria parasitaemia
in relation to HIV status and vitamin A supplementation amongpre-school children Trop Med Int Health 2003 8 1051ndash61
36 Winskill P Rowland M Mtove G Malima R Kirby M Malaria riskfactors in north-east Tanzania Malar J 2011 10 98
37 Yamamoto S Louis VR Sie A Sauerborn R Household risk factorsfor clinical malaria in a semi-urban area of Burkina Fasoa case-control study Trans R Soc Trop Med Hyg 2010 104 61ndash65
38 Clark TD Greenhouse B Njama-Meya D et al Factors determiningthe heterogeneity of malaria incidence in children in KampalaUganda J Infect Dis 2008 198 393ndash400
39 Klinkenberg E McCall P Wilson M et al Urban malaria andanaemia in children a cross-sectional survey in two cities of GhanaTrop Med Int Health 2006 11 578ndash88
40 Kreuels B Kobbe R Adjei S et al Spatial variation of malariaincidence in young children from a geographically homogeneousarea with high endemicity J Infect Dis 2008 197 85ndash93
41 Matthys B Vounatsou P Raso G et al Urban farming and malariarisk factors in a medium-sized town in Cocircte drsquoIvoireAm J Trop Med Hyg 2006 75 1223ndash31
42 Pullan RL Bukirwa H Staedke SG Snow RW Brooker S Plasmodium infection and its risk factors in eastern Uganda Malar J 2010 4 9
43 Somi MF Butler JRG Is there evidence for dual causation betweenmalaria and socioeconomic status Findings from rural TanzaniaAm J Trop Med Hyg 2007 77 1020ndash27
44 Chuma JM Thiede M Rethinking the economic costs of malaria atthe household level evidence from applying a new analyticalframework in rural Kenya Malar J 2006 5 76ndash89
45 Onwujekwe O Hanson K Are malaria treatment expenditurescatastrophic to different socio-economic and geographic groups andhow do they cope with payment A study in southeast NigeriaTrop Med Int Health 2010 15 18ndash25
46 Sicuri E Davy C Marinelli M et al The economic cost tohouseholds of childhood malaria in Papua New Guinea a focus onintra-country variation Health Policy Plann 2012 27 339ndash47
47 Matovu F Goodman C How equitable is bed net ownership andutilisation in Tanzania A practical application of the principles ofhorizontal and vertical equity Malar J 2009 8 109ndash21
48 Gingrich CD Hanson K Marchant T Mulligan J-A Mponda HPrice subsidies and the market for mosquito nets in developingcountries a study of Tanzaniarsquos discount voucher schemeSoc Sci Med 2011 73 160ndash68
49 Ahmed SM Haque R Haque U Hossain A Knowledge on thetransmission prevention and treatment of malaria among twoendemic populations of Bangladesh and their health-seekingbehaviour Malar J 2009 8 173
50 Saaka M Oosthuizen J Beatty S Effect of joint iron and zincsupplementation on malarial infection and anaemiaEast Afr J Public Health 2009 6 55ndash62
51 Worrall E Basu S Hanson K Is malaria a disease of povertyA review of the literature Trop Med Int Health 2005 10 1047ndash59 52 Barat LM Palmer N Basu S Worrall E Hanson K Mills A Do
malaria control interventions reach the poor Am J Trop Med Hyg 2004 71 174ndash78
53 Sterne JAC Sutton AJ Ioannidis JPA et al Recommendationsfor examining and interpreting funnel plot asymmetry inmeta-analyses of randomised controlled trials BMJ 2011 343 4002
54 Garcia-Martin G Status of malaria eradication in the AmericasAm J Trop Med Hyg 1972 21 617ndash33
55 Bruce-Chwatt L de Zulueta J The rise and fall of malaria inEurope London Oxford University Press 1980
56 Sinden RE Malaria mosquitoes and the legacy of Ronald RossBull World Health Organ 2007 85 894ndash96
57 Keiser J Singer BH Utzinger J Reducing the burden of malariain different eco-epidemiological settings with environmentalmanagement a systematic review Lancet Infect Dis 20055 695ndash708
8132019 SE Dev as an Intervention Agst Malaria
httpslidepdfcomreaderfullse-dev-as-an-intervention-agst-malaria 1010
Articles
10 www thelancet com Published online June 19 2013 httpdx doi org10 1016S0140-6736(13)60851-X
58 Randolph SE Rogers DJ The arrival establishment and spread ofexotic diseases patterns and predictions Nat Rev Microbiol 2010
8 361ndash71 59 Hay S Guerra C Tatem A Noor A Snow R The global distribution
and population at risk of malaria past present and futureLancet Infect Dis 2004 4 327ndash36
60 Killeen GF Fillinger U Kiche I Gouagna LC Knols BGEradication of Anopheles gambiae from Brazil lessons for malariacontrol in Africa Lancet Infect Dis 2002 2 618ndash27
61 Jetten T Takken W Anophelism without malaria in Europea review of the ecology and distribution of the genus Anopheles in
Europe Wageningen Wageningen Agricultural University 1994 62 Millennium Villages Project httpwwwmillenniumvillagesorg
the-villages (accessed May 16 2013) 63 Garrett L Global health hits crisis point Nature 2012 482 7
8132019 SE Dev as an Intervention Agst Malaria
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Articles
wwwthelancetcom Published online June 19 2013 httpdxdoiorg101016S0140-6736(13)60851-X 5
used the generic inverse-variance method for the meta-
analysis in which weight is given to each study accordingto the inverse of the variance of the effect to minimiseuncertainty about the pooled effect estimates Bothoutcomes (P falciparum infection and clinical malaria)were combined in the analysis We allocated the includedstudies into four subgroups according to the measure ofsocioeconomic status used asset ownership householdwealth socioeconomic index or parentsrsquo occupations Wedid separate analyses for unadjusted and adjusted ORsMissing data were not problematic since meta-regressionof individual data was not done
Initially we did a fixed-effects meta-analysis but ifI sup2 was large (gt50) which suggests substantial hetero-geneity between studies we used random-effectsanalysis Random-effects analysis adjusts the standard
errors of each study estimate of effect to include a
measure of variation in the effects reported betweenstudies We produced forest plots to visually assess theORs and 95 CIs of each study and used funnel plots toassess publication bias (with study size as a function ofeffect size) We used Eggerrsquos linear regression method totest for funnel plot asymmetry (ie to quantify the biascaptured by the funnel plot)22 Analyses were done withStata 11 and RevMan 5
ResultsOur initial search yielded 6106 records of which4696 remained after removal of duplicates (figure 2)20 records met our inclusion criteria (table)23ndash42 and ofthese 15 contained the necessary data for inclusion in thequantitative analysis (meta-analysis) Five records were
Study
site
Study
design
n Participant s R ecru it -
ment of
participants
Exp os ure Outcome Control
group
Measure
of effect
Un-
adjusted
effect
(95 CI)
Ad justed
effect
(95 CI)
Factors adjusted
for
Reason for
exclusion
from
quan-
titative
analysis
(Continued from previous page)
Klinkenberg
et al39 2006
Ghana Cross-
sectional
1744 Aged 6ndash60
months
Randomly
sampled
from
communities
near (lt1000
m) and less
near
(gt1000 m)
agricultural
sites in Accra
Socio-
economic
status below
vs above mean
for the city
Pf PR None In-
suffi cient
infor-
mation
provided
NA NA NA Not
possible to
calculate
OR
Kreuels
et al40 2008
Ghana Cohort 535 Aged
2ndash4 months
Recruited
from nine
villages after
visiting
health centre
(children
with chronic
diseases
excluded)
Family does
not have good
financial
situation vs
family has
good financial
situation
Incidence of
clinical
malaria
(parasitaemia
plus fever)
None Incidence
rate ratio
1middot59
(1middot33ndash
1middot89)
1middot52
(1middot27ndash
1middot82)
Sex ethnicity
season of birth (dry
or rainy season)
sickle cell trait
motherrsquos education
motherrsquos
occupation
knowledge of
malaria and
protective measures
Not
possible to
calculate
OR
Matthys
et al41 2006
Cocircte
drsquoIvoire
Cross-
sectional
672 Aged
0ndash15 years
Selected
from
farming and
non-farming
households
Low vs high
socioeconomic
status
Pf PR None OR NA 2middot44
(0middot88ndash
10middot00)
Age agricultural
zone crops grown
irrigation overnight
stays in temporary
farm huts and
distance to
permanent ponds
and fish ponds
Bayesian
credible
intervals
reported
only
Pullan
et al42
2010
Uganda Cross-
sectional
1844 A ged
5ndash15 years
All residents
of four
villages asked
to
participate
with 78
successfully
enrolled
Decreasing
household
socioeconomic
status
Pf PR None OR NA 2middot27
(0middot88ndash
25middot00)
Ag e bednet use Bayesian
credible
intervals
reported
only
OR=odds ratio Pf PR=Plasmodium falciparum parasite rate RR=risk ratio Socioeconomic status analysed as a continuous variable
Table Studies included in the systematic review and meta-analysis
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excluded from the quantitative analysis either because
Bayesian credible intervals were reported (n=2) or becauseORs could not be calculated from the available data (n=3)
Despite substantial overlap between CIs for both unadjust-
ed and adjusted results high I sup2 values from fixed-effectsanalysis suggested substantial heterogeneity between
Figure 983091 Association between low s ocioeconomic status and clinical malaria or parasitaemia in chi ldren aged 0ndash15 years
Pooled effects from random-effects meta-analyses for unadjusted (A) and adjusted (B) results are shown Studies are divided into subgroups by measure ofsocioeconomic status used Error bars show 95 CIs df=degrees of freedom
Odds ratio(95 CI)
WeightLog(odds ratio)
SE
Socioeconomic indexAl-Taiar et al23 (2009)Custodio et al26 (2009)Pullan et al32 (2010)Ronald et al33 (2006)Slutsker et al34 (1996)Winskill et al36 (2011)Yanamoto et al37 (2010)
SubtotalHeterogeneity τsup2=0middot12 χsup2=24middot51 df=6 (p=0middot0004) I2=76Test for overall effect Z=2middot56 (p=0middot01)
0middot565314 0middot397341 0middot223144 1middot169381 0middot587787 0middot139262 ndash0middot756122
0middot191170 0middot210810 0middot271367 0middot255894 0middot166032 0middot102621 0middot435365
1middot76 (1middot21ndash2middot56) 1middot49 (0middot98ndash2middot25) 1middot25 (0middot73ndash2middot13) 3middot22 (1middot95ndash5middot32) 1middot80 (1middot30ndash2middot49) 1middot15 (0middot94ndash1middot41)
0middot47 (0middot20ndash1middot10) 1middot49 (1middot10ndash2middot01)
8middot4 8middot0 6middot6 7middot0 9middot1 10middot5
4middot0 53middot5
Asset ownership
Baragatti et al24 (2009)Ghebreyseus et al28 (2000)Koram et al29 (1995)Villamor et al35 (2003)
SubtotalHeterogeneity τ2=0middot09 χ2=7middot14 df=3 (p=0middot07) I2=58Test for overall effect Z=2middot76 (p=0middot006)
0middot727549 ndash0middot028422 0middot832909 0middot611533
0middot322571 0middot249721 0middot238911 0middot205363
2middot07 (1middot10ndash3middot90) 0middot97 (0middot60ndash1middot59) 2middot30 (1middot44ndash3middot67) 1middot84 (1middot23ndash2middot76) 1middot70 (1middot17ndash2middot48)
5middot6 7middot1 7middot3 8middot1 28middot2
Reduced odds of malaria Increased odds of malaria
10middot50middot20middot1 2 5 10
Household wealthClarke et al25 (2001)Gahutu et al27 (2011)
SubtotalHeterogeneity τ2=0middot01 χ2=1middot22 df=1 (p=0middot27) I2=18
Test for overall effect Z=3middot25 (p=0middot001)
0middot851030 0middot463734
0middot279963 0middot211706
2middot34 (1middot35ndash4middot05) 1middot59 (1middot05ndash2middot41) 1middot85 (1middot28ndash2middot68)
6middot4 8middot0 14middot4
Parentsrsquo occupationOngrsquoecha et al31 (2006)SubtotalHeterogeneity NA
Test for overall effect Z=3middot10 (p=0middot002)
Total (all measures of socioeconomic status)Heterogeneity τ2=0middot10 χ2=40middot38 df=13 (p=0middot0001) I2=68Test for overall effect Z=4middot76 (plt0middot00001)Test for subgroup differences χ2=4middot46 df=3 (p=0middot22) I2=32middot83
1middot347074 0middot434886 3middot85 (1middot64ndash9middot02) 3middot85 (1middot64ndash9middot02)
4middot0
4middot0
1middot66 (1middot35ndash2middot05) 100middot0
A
Odds ratio(95 CI)
WeightLog(odds ratio)
SE
Socioeconomic indexKrefis et al30 (2010)Ronald et al33 (2006)
SubtotalHeterogeneity τsup2=0middot26 χsup2=6middot01 df=1 (p=0middot01) I2=83Test for overall effect Z=2middot38 (p=0middot02)
0middot579818 1middot373716
0middot154177 0middot284873
1middot79 (1middot32ndash2middot42)
3middot95 (2middot26ndash6middot90) 2middot56 (1middot18ndash5middot56)
27middot4
19middot0 46middot3
Asset ownershipBaragatti et al24 (2009)Koram et al29 (1995)SubtotalHeterogeneity τ2=0middot00 χ2=0middot80 df=1 (p=0middot37) I2=0Test for overall effect Z=4middot10 (plt0middot0001)
0middot615186 0middot947789
0middot233766 0middot290486
1middot85 (1middot17ndash2middot93) 2middot58 (1middot46ndash4middot56) 2middot11 (1middot48ndash3middot01)
22middot1 18middot6 40middot7
Reduced odds of malaria Increased odds of malaria
10middot50middot2 2 5
Parentsrsquo occupationsOngrsquoecha et al31 (2006)SubtotalHeterogeneity NATest for overall effect Z=0middot21 (p=0middot83)
ndash0middot086178 0middot410929 0middot92 (0middot41ndash2middot05) 0middot92 (0middot41ndash2middot05)
12middot9 12middot9
Total (all measures of socioeconomic status)Heterogeneity τ2=0middot11 χ2=10middot72 df=4 (p=0middot03) I2=63Test for overall effect Z=3middot83 (plt0middot0001)Test for subgroup differences χ2=4middot02 df=2 (p=0middot13) I2=50middot3
2middot06 (1middot42ndash2middot97) 100middot0
B
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studies (unadjusted effect size I sup2=68 adjusted effect size
I sup2=63) Therefore random-effects analysis was usedThe meta-analysis was restricted to comparisonsbetween the highest (least poor) and lowest (poorest)socioeconomic groups Subgroup analysis suggested thatlow socioeconomic status was associated with increasedodds of malaria irrespective of the measure used forsocioeconomic status with the exception of one study inwhich parentsrsquo occupations were used31 we thereforejudged that to pool all results would be appropriate Inthe meta-analyses for both unadjusted and adjustedresults the odds of malaria infection were higher in thepoorest children than in the least poor children (figure 3)
Visual assessment of funnel plots (appendix p 8)showed that the studies were distributed fairly
symmetrically about the combined effect size whichsuggests little publication bias However Eggerrsquos test forbias suggested funnel plot asymmetry for the unadjustedresults (bias coeffi cient 1middot70 95 CI ndash0middot97 to 4middot37p=0∙191) which suggests that publication bias (delayedpublication or location bias) small-study effects selectiveoutcome reporting or selective analysis reporting mighthave been present A test for funnel plot asymmetry wasnot possible for the adjusted effects since only fivestudies were included in the meta-analysis Overallquality assessment scores for risk of bias in studiesincluded in the quantitative analysis ranged from two toseven out of a maximum of eight (appendix p 6ndash7)
DiscussionOur findings suggest that low socioeconomic status isassociated with roughly doubled odds of clinical malariaor parasitaemia in children compared with higher socio-economic status within a locality This conclusion is sup-ported by a similar size and direction of effect noted inthe five studies excluded from the meta-analysis Sinceour analysis represents a comparison of the very poorestchildren with the least poor children within highly im-poverished communities the difference in the odds ofmalaria in the poorest children would probably be evengreater if the studies were expanded to include childrenfrom wealthier backgrounds The association between
socioeconomic status and malaria is not definitiveevidence for the direction of causality since the pooresthouseholds are not only more susceptible to the diseasebut are also more vulnerable to its costs such that thedisease itself can induce poverty For example asignificant positive association between low socio-economic status and malaria parasitaemia has beenreported in Tanzania43 with causality in both directionsFindings from Kenya44 and Nigeria45 suggest that the costsof malaria treatment (as a proportion of non-food monthlyincome) and subsequent financial setbacks are greater forpoorer than for more wealthy households Costs also varygeographically in Kenya44 and Papua New Guinea46 therisk of clinical disease is greater in low-transmissiondistricts with subsequently greater loss of income
Wealth is probably protective against malaria since it
renders prophylaxis and treatment more affordable
47ndash49
andis positively associated with other beneficial factorsincluding better-educated parents (which improvesprophylaxis and treatment for children) increasedhousing quality (which reduces house entry by malaria-transmitting mosquitoes) and improved nutritionalstatus of children (which could increase their subsequentability to cope with malaria infection)50ndash52 Malaria andpoverty therefore constitute a vicious cycle for the pooresthouseholds exacerbating differences in health and wealth
A major limitation of our meta-analysis is that themeasurement of risk factors was done with varyingprecision in the included studies and although we didsubgroup and random-effects analyses these are unlikely
to have fully accounted for heterogeneity in study designAnother important limitation is the poor quality of thestudies included in the meta-analysis which results fromthe nature of the study question (since randomisation forsocioeconomic status would not be practically or ethicallypossible) However the consistency of results acrossstudies and settings suggests that the finding of increasedodds of malaria in children of low socioeconomic status isrobust For our systematic review the main limitationwas the language of the search In particular notincluding publications in Spanish probably excludedmuch data from South America such that our findingscannot be generalised to that region Eggerrsquos testsuggested the presence of some forest plot asymmetryhowever statistical tests for forest plot asymmetry tend tohave low power53 and asymmetry might not be attributableto publication biasmdashit might also have arisen from poorstudy quality leading to artificially inflated effects in thesmaller studies selective outcome or analysis reportingor chance Incomplete retrieval (four full-text studiescould not be retrieved) might also have introduced bias
On the basis of our findings we advocate thatdevelopment programmes should be an essentialcomponent of malaria control Malaria elimination inmany high-income countries was achieved withoutmalaria-specific interventions prevalence started to fall inEurope and North America as a by-product of improved
living conditions and increased wealth5455 and afterRonald Ross deduced the mode of malaria transmission56 in 1897 more specific interventions became possibleincluding habitat modification (permanent elimination ofbreeding sitesmdasheg by installing and maintaining drains)habitat manipulation (temporary creation of unfavourableconditions for the vectormdasheg by fluctuating the amountof water in reservoirs) and modifications to humanhabitation or behaviour to reduce human-vector contactsuch as mosquito-proofing of houses57 As a result mostof Europe and North America is now characterised byanophelism without malaria which is testament to theeffectiveness of these control efforts together with areduced innate receptivity to malaria transmission thatstems from advances in nutrition health care and
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8 wwwthelancetcom Published online June 19 2013 httpdxdoiorg101016S0140-6736(13)60851-X
development58 Similar environmental management
strategies together with larval control also helped toreduce malaria transmission in many developingcountries during the 20th century including ZanzibarIndonesia Malaysia the Panama Canal and the CopperBelt of Zambia5960 Thus as transmission today falls inmuch of sub-Saharan Africa and elsewhere developmentwill contribute to the reduction and elimination of thedisease Several specific development interventions couldcontribute to malaria control (appendix p 5) which mightbe similar to malaria-specific interventions in terms ofcosts (appendix pp 9ndash10) An excellent example of howsuch interventions can work in practice can be seen inKhartoum Sudan (appendix p 11)
This approach has three major constraints First
accurate costing of the extent to which specificdevelopment interventions contribute to malaria controlis diffi cult Whereas measuring the effect of housescreening is straightforward measuring that ofimproved education or raised incomes is not Secondthe effectiveness of a development intervention dependson both the nature and intensity of malaria transmissionFor example house screening is probably most effectivein areas of low to moderate transmission where vectorsfeed indoors Countries that have eliminated malariasince 1900 have largely been temperate subtropical orislands8 and the high malaria burden in manydeveloping countries is not merely a product of povertyRather the specific ecological requirements of both themalaria parasite and its mosquito vector help todetermine the range of the disease61 Interventions haveto be highly effective and development should not bethought of as a standalone strategy but as a complementto malaria-specific interventions such as LLINs indoorresidual spraying and larval source management Thirdeconomic development gives rise to broader socialenvironmental and ecological changes that might insome circumstances lead to an increase in the burden ofmalaria (appendix p 5) as has been seen in Sri Lanka(appendix p 12) Nonetheless these constraints shouldnot be treated as barriers to the use of socioeconomicdevelopment as an intervention against malaria
(appendix p 5)In addition to initiatives such as the Millennium Villages
project which is operating in 14 villages in ten Africancountries to examine the effects of socioeconomicdevelopment62 further research is needed to address someimportant questions and to galvanise specialists in bothhealth and development to work more closely together onmalaria control For example randomised controlled trialsshould be considered to assess the effectiveness ofsocioeconomic interventions (eg improved education andnutrition) against malaria in different settings We mustalso investigate the causal pathways that lead fromdevelopment to successful malaria control and vice versaand develop an understanding of the relation betweenmalaria control birth rates and population growth
That malaria control remains largely the preoccupation
of the health sector alone is a failing of both those whowork in health and those who work in internationaldevelopment The disease severely compromises socio-economic development and its control and eliminationwould improve economic prosperity worldwide Theeffectiveness of available drugs and insecticides formalaria control will ultimately deteriorate with theemergence of parasites resistant to antimalarials and ofvectors resistant to insecticides and the development andprocurement costs of replacements will be high Donorfatigue is also an ever-present threat to interventions suchas LLINs indoor residual spraying and intermittentpreventive treatment especially in view of the economicsituation since the 2007ndash08 financial crisis63 However
several specific development interventions could beintroduced to aid both economic development andmalaria control Increased wealth and improved standardsof living that stem directly from socioeconomicdevelopment could prove fundamental in ensuring thatmalaria transmission continues to fall in much of AsiaSouth America and Africa as it happened historically inEurope and North America Socioeconomic developmentcould prove to be a very effective and sustainableintervention against malaria in the long term
Contributors
SWL and RS conceived of the study SWL RS LST HL and JTdeveloped the study design and the outline of the report LST searchedthe scientific literature did the meta-analysis and prepared the first
draft of the report BW provided advice on the systematic review andmeta-analysis HTK contributed the case study from Sudan (appendix)All authors reviewed the final version of the report
Conflicts of interest
We declare that we have no conflicts of interest
Acknowledgments
This work was supported by the UK Department for InternationalDevelopment and the Malaria Centre at the London School of Hygiene ampTropical Medicine (LSHTM) SWL was supported by the Research andPolicy for Infectious Disease Dynamics (RAPIDD) programme of theScience and Technology Directorate US Department of Homeland Securitythe Fogarty International Center (US National Institutes of Health) and theBill amp Melinda Gates Foundation JT was supported in part by a grant fromthe UK Economic and Social Research Council to the STEPS Centre and theInstitute of Development Studies (University of Sussex Brighton UK)We are grateful to Frida Kasteng (LSHTM) for providing information about
the costs of malaria and development interventionsReferences 1 Gething PW Patil AP Smith DL et al A new world malaria map
Plasmodium falciparum endemicity in 2010 Malar J 2011 10 378 2 WHO World Malaria Report 2011 Geneva World Health
Organization 2011 3 Murray CJL Rosenfeld LC Lim SS et al Global malaria mortality
between 1980 and 2010 a systematic analysis Lancet 2012 379 413ndash31 4 Murray CJL Vos T Lozano R et al Disability-adjusted life years
(DALYs) for 291 diseases and injuries in 21 regions 1990mdash2010a systematic analysis for the Global Burden of Disease Study 2010Lancet 2012 380 2197ndash223
5 Sachs J Malaney P The economic and social burden of malariaNature 2002 415 680ndash85
6 UNDP International human development indicators httphdrundporgendatabuild (accessed April 24 2012)
7 Sachs JD Macroeconomics and health investing in health for
economic development Geneva World Health Organization 2001
8132019 SE Dev as an Intervention Agst Malaria
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Articles
wwwthelancetcom Published online June 19 2013 httpdxdoiorg101016S0140-6736(13)60851-X 9
8 Gallup J Sachs J The economic burden of malariaAm J Trop Med Hyg 2001 64 85ndash96
9 Mandelbaum-Schmid J HIVAIDS hunger and malaria are theworldrsquos most urgent problems say economistsBull World Health Organ 2004 82 554ndash55
10 Kidson C Indaratna K Ecology economics and political will thevicissitudes of malaria strategies in Asia Parassitologia 1998 40 39ndash46
11 UNDP Millennium Development Goal 6 focuses on combatingHIVAIDS malaria and other diseases httpwebundporgmdggoal6shtml (accessed April 21 2012)
12 OrsquoMeara WP Mangeni JN Steketee R Greenwood B Changes inthe burden of malaria in sub-Saharan Africa Lancet Infect Dis 201010 545ndash55
13 Dondorp AM Fairhurst RM Slutsker L et al The threat ofartemisinin-resistant malaria N Engl J Med 2011 365 1073ndash75
14 Ranson H NrsquoGuessan R Lines J et al Pyrethroid resistance inAfrican anopheline mosquitoes what are the implications formalaria control Trends Parasitol 2011 27 91ndash98
15 Cohen J Smith D Cotter C et al Malaria resurgence a systematic
review and assessment of its causes Malar J 2012 11 122 16 WHO WHO position statement on integrated vector management
Wkly Epidemiol Rec 2008 83 177ndash84 17 van den Berg H Mutero CM Ichimori K WHO guidance on
policy-making for integrated vector management Geneva WorldHealth Organization 2012
18 Stroup DF Berlin JA Morton SC et al Meta-analysis ofobservational studies in epidemiology JAMA 2000 283 2008ndash12
19 Modher D Liberati A Tetzlaff J Altman DG Group TP Preferredreporting items for systematic reviews and meta-analyses thePRISMA statement PLoS Med 2009 6 e1000097
20 Roll Back Malaria Malaria endemic countries Geneva The RBMPartnership 2010 httpwwwrbmwhointendemiccountrieshtml(accessed June 8 2011)
21 Wells G Shea B OrsquoConnell D et al The Newcastle-Ottawa Scale(NOS) for assessing the quality of nonrandomised studies inmeta-analyses Ottawa University of Ottawa 2011
22 Egger M Smith GD Schneider M Minder C Bias in meta-analysisdetected by a simple graphical test BMJ 1997 315 629ndash34
23 Al-Taiar A Assabri A Al-Habori M et al Socioeconomic andenvironmental factors important for acquiring non-severe malariain children in Yemen a case-control studyTrans R Soc Trop Med Hyg 2009 103 72ndash78
24 Baragatti M Fournet F Henry M-C et al Social and environmentalmalaria risk factors in urban areas of Ouagadougou Burkina FasoMalar J 2009 8 13
25 Clarke SE Boslashgh C Brown RC Pinder M Walraven GELLindsay SW Do untreated bednets protect against malariaTrans R Soc Trop Med Hyg 2001 95 457ndash62
26 Custodio E Descalzo MAacute Villamor E et al Nutritional andsocio-economic factors associated with Plasmodium falciparum infection in children from Equatorial Guinea results from anationally representative survey Malar J 2009 8 225
27 Gahutu J-B Steininger C Shyirambere C et al Prevalence and riskfactors of malaria among children in southern highland Rwanda
Malar J 2011 10 134 28 Ghebreyesus TA Haile M Witten KH et al Household risk factorsfor malaria among children in the Ethiopian HighlandsTrans R Soc Trop Med Hyg 2000 94 17ndash21
29 Koram KA Bennett S Adiamah JH Greenwood BM Socio-economicrisk factors for malaria in a peri-urban area of The GambiaTrans R Soc Trop Med Hyg 1995 89 146ndash50
30 Krefis AC Schwarz NG Nkrumah B et al Principal componentanalysis of socioeconomic factors and their association withmalaria in children from the Ashanti Region Ghana Malar J 20109 201
31 Ongrsquoecha JM Keller CC Were T et al Parasitemia anemia andmalarial anemia in infants and young children in a ruralholoendemic Plasmodium falciparum transmission area Am J Trop Med Hyg 2006 74 376ndash85
32 Pullan RL Kabatereine NB Bukirwa H Staedke SG Brooker SHeterogeneities and consequences of Plasmodium species andhookworm coinfection a population based study in Uganda
J Infect Dis 2011 203 406ndash17
33 Ronald LA Kenny SL Klinkenberg E et al Malaria and anaemiaamong children in two communities of Kumasi Ghana
a cross-sectional survey Malar J 2006 1 105 34 Slutsker L Khoromana CO Hightower AW et al Malaria infection
in infancy in rural Malawi Am J Trop Med Hyg 1996 55 71ndash76 35 Villamor E Fataki MR Mbise RL Fawzi WW Malaria parasitaemia
in relation to HIV status and vitamin A supplementation amongpre-school children Trop Med Int Health 2003 8 1051ndash61
36 Winskill P Rowland M Mtove G Malima R Kirby M Malaria riskfactors in north-east Tanzania Malar J 2011 10 98
37 Yamamoto S Louis VR Sie A Sauerborn R Household risk factorsfor clinical malaria in a semi-urban area of Burkina Fasoa case-control study Trans R Soc Trop Med Hyg 2010 104 61ndash65
38 Clark TD Greenhouse B Njama-Meya D et al Factors determiningthe heterogeneity of malaria incidence in children in KampalaUganda J Infect Dis 2008 198 393ndash400
39 Klinkenberg E McCall P Wilson M et al Urban malaria andanaemia in children a cross-sectional survey in two cities of GhanaTrop Med Int Health 2006 11 578ndash88
40 Kreuels B Kobbe R Adjei S et al Spatial variation of malariaincidence in young children from a geographically homogeneousarea with high endemicity J Infect Dis 2008 197 85ndash93
41 Matthys B Vounatsou P Raso G et al Urban farming and malariarisk factors in a medium-sized town in Cocircte drsquoIvoireAm J Trop Med Hyg 2006 75 1223ndash31
42 Pullan RL Bukirwa H Staedke SG Snow RW Brooker S Plasmodium infection and its risk factors in eastern Uganda Malar J 2010 4 9
43 Somi MF Butler JRG Is there evidence for dual causation betweenmalaria and socioeconomic status Findings from rural TanzaniaAm J Trop Med Hyg 2007 77 1020ndash27
44 Chuma JM Thiede M Rethinking the economic costs of malaria atthe household level evidence from applying a new analyticalframework in rural Kenya Malar J 2006 5 76ndash89
45 Onwujekwe O Hanson K Are malaria treatment expenditurescatastrophic to different socio-economic and geographic groups andhow do they cope with payment A study in southeast NigeriaTrop Med Int Health 2010 15 18ndash25
46 Sicuri E Davy C Marinelli M et al The economic cost tohouseholds of childhood malaria in Papua New Guinea a focus onintra-country variation Health Policy Plann 2012 27 339ndash47
47 Matovu F Goodman C How equitable is bed net ownership andutilisation in Tanzania A practical application of the principles ofhorizontal and vertical equity Malar J 2009 8 109ndash21
48 Gingrich CD Hanson K Marchant T Mulligan J-A Mponda HPrice subsidies and the market for mosquito nets in developingcountries a study of Tanzaniarsquos discount voucher schemeSoc Sci Med 2011 73 160ndash68
49 Ahmed SM Haque R Haque U Hossain A Knowledge on thetransmission prevention and treatment of malaria among twoendemic populations of Bangladesh and their health-seekingbehaviour Malar J 2009 8 173
50 Saaka M Oosthuizen J Beatty S Effect of joint iron and zincsupplementation on malarial infection and anaemiaEast Afr J Public Health 2009 6 55ndash62
51 Worrall E Basu S Hanson K Is malaria a disease of povertyA review of the literature Trop Med Int Health 2005 10 1047ndash59 52 Barat LM Palmer N Basu S Worrall E Hanson K Mills A Do
malaria control interventions reach the poor Am J Trop Med Hyg 2004 71 174ndash78
53 Sterne JAC Sutton AJ Ioannidis JPA et al Recommendationsfor examining and interpreting funnel plot asymmetry inmeta-analyses of randomised controlled trials BMJ 2011 343 4002
54 Garcia-Martin G Status of malaria eradication in the AmericasAm J Trop Med Hyg 1972 21 617ndash33
55 Bruce-Chwatt L de Zulueta J The rise and fall of malaria inEurope London Oxford University Press 1980
56 Sinden RE Malaria mosquitoes and the legacy of Ronald RossBull World Health Organ 2007 85 894ndash96
57 Keiser J Singer BH Utzinger J Reducing the burden of malariain different eco-epidemiological settings with environmentalmanagement a systematic review Lancet Infect Dis 20055 695ndash708
8132019 SE Dev as an Intervention Agst Malaria
httpslidepdfcomreaderfullse-dev-as-an-intervention-agst-malaria 1010
Articles
10 www thelancet com Published online June 19 2013 httpdx doi org10 1016S0140-6736(13)60851-X
58 Randolph SE Rogers DJ The arrival establishment and spread ofexotic diseases patterns and predictions Nat Rev Microbiol 2010
8 361ndash71 59 Hay S Guerra C Tatem A Noor A Snow R The global distribution
and population at risk of malaria past present and futureLancet Infect Dis 2004 4 327ndash36
60 Killeen GF Fillinger U Kiche I Gouagna LC Knols BGEradication of Anopheles gambiae from Brazil lessons for malariacontrol in Africa Lancet Infect Dis 2002 2 618ndash27
61 Jetten T Takken W Anophelism without malaria in Europea review of the ecology and distribution of the genus Anopheles in
Europe Wageningen Wageningen Agricultural University 1994 62 Millennium Villages Project httpwwwmillenniumvillagesorg
the-villages (accessed May 16 2013) 63 Garrett L Global health hits crisis point Nature 2012 482 7
8132019 SE Dev as an Intervention Agst Malaria
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Articles
6 wwwthelancetcom Published online June 19 2013 httpdxdoiorg101016S0140-6736(13)60851-X
excluded from the quantitative analysis either because
Bayesian credible intervals were reported (n=2) or becauseORs could not be calculated from the available data (n=3)
Despite substantial overlap between CIs for both unadjust-
ed and adjusted results high I sup2 values from fixed-effectsanalysis suggested substantial heterogeneity between
Figure 983091 Association between low s ocioeconomic status and clinical malaria or parasitaemia in chi ldren aged 0ndash15 years
Pooled effects from random-effects meta-analyses for unadjusted (A) and adjusted (B) results are shown Studies are divided into subgroups by measure ofsocioeconomic status used Error bars show 95 CIs df=degrees of freedom
Odds ratio(95 CI)
WeightLog(odds ratio)
SE
Socioeconomic indexAl-Taiar et al23 (2009)Custodio et al26 (2009)Pullan et al32 (2010)Ronald et al33 (2006)Slutsker et al34 (1996)Winskill et al36 (2011)Yanamoto et al37 (2010)
SubtotalHeterogeneity τsup2=0middot12 χsup2=24middot51 df=6 (p=0middot0004) I2=76Test for overall effect Z=2middot56 (p=0middot01)
0middot565314 0middot397341 0middot223144 1middot169381 0middot587787 0middot139262 ndash0middot756122
0middot191170 0middot210810 0middot271367 0middot255894 0middot166032 0middot102621 0middot435365
1middot76 (1middot21ndash2middot56) 1middot49 (0middot98ndash2middot25) 1middot25 (0middot73ndash2middot13) 3middot22 (1middot95ndash5middot32) 1middot80 (1middot30ndash2middot49) 1middot15 (0middot94ndash1middot41)
0middot47 (0middot20ndash1middot10) 1middot49 (1middot10ndash2middot01)
8middot4 8middot0 6middot6 7middot0 9middot1 10middot5
4middot0 53middot5
Asset ownership
Baragatti et al24 (2009)Ghebreyseus et al28 (2000)Koram et al29 (1995)Villamor et al35 (2003)
SubtotalHeterogeneity τ2=0middot09 χ2=7middot14 df=3 (p=0middot07) I2=58Test for overall effect Z=2middot76 (p=0middot006)
0middot727549 ndash0middot028422 0middot832909 0middot611533
0middot322571 0middot249721 0middot238911 0middot205363
2middot07 (1middot10ndash3middot90) 0middot97 (0middot60ndash1middot59) 2middot30 (1middot44ndash3middot67) 1middot84 (1middot23ndash2middot76) 1middot70 (1middot17ndash2middot48)
5middot6 7middot1 7middot3 8middot1 28middot2
Reduced odds of malaria Increased odds of malaria
10middot50middot20middot1 2 5 10
Household wealthClarke et al25 (2001)Gahutu et al27 (2011)
SubtotalHeterogeneity τ2=0middot01 χ2=1middot22 df=1 (p=0middot27) I2=18
Test for overall effect Z=3middot25 (p=0middot001)
0middot851030 0middot463734
0middot279963 0middot211706
2middot34 (1middot35ndash4middot05) 1middot59 (1middot05ndash2middot41) 1middot85 (1middot28ndash2middot68)
6middot4 8middot0 14middot4
Parentsrsquo occupationOngrsquoecha et al31 (2006)SubtotalHeterogeneity NA
Test for overall effect Z=3middot10 (p=0middot002)
Total (all measures of socioeconomic status)Heterogeneity τ2=0middot10 χ2=40middot38 df=13 (p=0middot0001) I2=68Test for overall effect Z=4middot76 (plt0middot00001)Test for subgroup differences χ2=4middot46 df=3 (p=0middot22) I2=32middot83
1middot347074 0middot434886 3middot85 (1middot64ndash9middot02) 3middot85 (1middot64ndash9middot02)
4middot0
4middot0
1middot66 (1middot35ndash2middot05) 100middot0
A
Odds ratio(95 CI)
WeightLog(odds ratio)
SE
Socioeconomic indexKrefis et al30 (2010)Ronald et al33 (2006)
SubtotalHeterogeneity τsup2=0middot26 χsup2=6middot01 df=1 (p=0middot01) I2=83Test for overall effect Z=2middot38 (p=0middot02)
0middot579818 1middot373716
0middot154177 0middot284873
1middot79 (1middot32ndash2middot42)
3middot95 (2middot26ndash6middot90) 2middot56 (1middot18ndash5middot56)
27middot4
19middot0 46middot3
Asset ownershipBaragatti et al24 (2009)Koram et al29 (1995)SubtotalHeterogeneity τ2=0middot00 χ2=0middot80 df=1 (p=0middot37) I2=0Test for overall effect Z=4middot10 (plt0middot0001)
0middot615186 0middot947789
0middot233766 0middot290486
1middot85 (1middot17ndash2middot93) 2middot58 (1middot46ndash4middot56) 2middot11 (1middot48ndash3middot01)
22middot1 18middot6 40middot7
Reduced odds of malaria Increased odds of malaria
10middot50middot2 2 5
Parentsrsquo occupationsOngrsquoecha et al31 (2006)SubtotalHeterogeneity NATest for overall effect Z=0middot21 (p=0middot83)
ndash0middot086178 0middot410929 0middot92 (0middot41ndash2middot05) 0middot92 (0middot41ndash2middot05)
12middot9 12middot9
Total (all measures of socioeconomic status)Heterogeneity τ2=0middot11 χ2=10middot72 df=4 (p=0middot03) I2=63Test for overall effect Z=3middot83 (plt0middot0001)Test for subgroup differences χ2=4middot02 df=2 (p=0middot13) I2=50middot3
2middot06 (1middot42ndash2middot97) 100middot0
B
8132019 SE Dev as an Intervention Agst Malaria
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Articles
wwwthelancetcom Published online June 19 2013 httpdxdoiorg101016S0140-6736(13)60851-X 7
studies (unadjusted effect size I sup2=68 adjusted effect size
I sup2=63) Therefore random-effects analysis was usedThe meta-analysis was restricted to comparisonsbetween the highest (least poor) and lowest (poorest)socioeconomic groups Subgroup analysis suggested thatlow socioeconomic status was associated with increasedodds of malaria irrespective of the measure used forsocioeconomic status with the exception of one study inwhich parentsrsquo occupations were used31 we thereforejudged that to pool all results would be appropriate Inthe meta-analyses for both unadjusted and adjustedresults the odds of malaria infection were higher in thepoorest children than in the least poor children (figure 3)
Visual assessment of funnel plots (appendix p 8)showed that the studies were distributed fairly
symmetrically about the combined effect size whichsuggests little publication bias However Eggerrsquos test forbias suggested funnel plot asymmetry for the unadjustedresults (bias coeffi cient 1middot70 95 CI ndash0middot97 to 4middot37p=0∙191) which suggests that publication bias (delayedpublication or location bias) small-study effects selectiveoutcome reporting or selective analysis reporting mighthave been present A test for funnel plot asymmetry wasnot possible for the adjusted effects since only fivestudies were included in the meta-analysis Overallquality assessment scores for risk of bias in studiesincluded in the quantitative analysis ranged from two toseven out of a maximum of eight (appendix p 6ndash7)
DiscussionOur findings suggest that low socioeconomic status isassociated with roughly doubled odds of clinical malariaor parasitaemia in children compared with higher socio-economic status within a locality This conclusion is sup-ported by a similar size and direction of effect noted inthe five studies excluded from the meta-analysis Sinceour analysis represents a comparison of the very poorestchildren with the least poor children within highly im-poverished communities the difference in the odds ofmalaria in the poorest children would probably be evengreater if the studies were expanded to include childrenfrom wealthier backgrounds The association between
socioeconomic status and malaria is not definitiveevidence for the direction of causality since the pooresthouseholds are not only more susceptible to the diseasebut are also more vulnerable to its costs such that thedisease itself can induce poverty For example asignificant positive association between low socio-economic status and malaria parasitaemia has beenreported in Tanzania43 with causality in both directionsFindings from Kenya44 and Nigeria45 suggest that the costsof malaria treatment (as a proportion of non-food monthlyincome) and subsequent financial setbacks are greater forpoorer than for more wealthy households Costs also varygeographically in Kenya44 and Papua New Guinea46 therisk of clinical disease is greater in low-transmissiondistricts with subsequently greater loss of income
Wealth is probably protective against malaria since it
renders prophylaxis and treatment more affordable
47ndash49
andis positively associated with other beneficial factorsincluding better-educated parents (which improvesprophylaxis and treatment for children) increasedhousing quality (which reduces house entry by malaria-transmitting mosquitoes) and improved nutritionalstatus of children (which could increase their subsequentability to cope with malaria infection)50ndash52 Malaria andpoverty therefore constitute a vicious cycle for the pooresthouseholds exacerbating differences in health and wealth
A major limitation of our meta-analysis is that themeasurement of risk factors was done with varyingprecision in the included studies and although we didsubgroup and random-effects analyses these are unlikely
to have fully accounted for heterogeneity in study designAnother important limitation is the poor quality of thestudies included in the meta-analysis which results fromthe nature of the study question (since randomisation forsocioeconomic status would not be practically or ethicallypossible) However the consistency of results acrossstudies and settings suggests that the finding of increasedodds of malaria in children of low socioeconomic status isrobust For our systematic review the main limitationwas the language of the search In particular notincluding publications in Spanish probably excludedmuch data from South America such that our findingscannot be generalised to that region Eggerrsquos testsuggested the presence of some forest plot asymmetryhowever statistical tests for forest plot asymmetry tend tohave low power53 and asymmetry might not be attributableto publication biasmdashit might also have arisen from poorstudy quality leading to artificially inflated effects in thesmaller studies selective outcome or analysis reportingor chance Incomplete retrieval (four full-text studiescould not be retrieved) might also have introduced bias
On the basis of our findings we advocate thatdevelopment programmes should be an essentialcomponent of malaria control Malaria elimination inmany high-income countries was achieved withoutmalaria-specific interventions prevalence started to fall inEurope and North America as a by-product of improved
living conditions and increased wealth5455 and afterRonald Ross deduced the mode of malaria transmission56 in 1897 more specific interventions became possibleincluding habitat modification (permanent elimination ofbreeding sitesmdasheg by installing and maintaining drains)habitat manipulation (temporary creation of unfavourableconditions for the vectormdasheg by fluctuating the amountof water in reservoirs) and modifications to humanhabitation or behaviour to reduce human-vector contactsuch as mosquito-proofing of houses57 As a result mostof Europe and North America is now characterised byanophelism without malaria which is testament to theeffectiveness of these control efforts together with areduced innate receptivity to malaria transmission thatstems from advances in nutrition health care and
8132019 SE Dev as an Intervention Agst Malaria
httpslidepdfcomreaderfullse-dev-as-an-intervention-agst-malaria 810
Articles
8 wwwthelancetcom Published online June 19 2013 httpdxdoiorg101016S0140-6736(13)60851-X
development58 Similar environmental management
strategies together with larval control also helped toreduce malaria transmission in many developingcountries during the 20th century including ZanzibarIndonesia Malaysia the Panama Canal and the CopperBelt of Zambia5960 Thus as transmission today falls inmuch of sub-Saharan Africa and elsewhere developmentwill contribute to the reduction and elimination of thedisease Several specific development interventions couldcontribute to malaria control (appendix p 5) which mightbe similar to malaria-specific interventions in terms ofcosts (appendix pp 9ndash10) An excellent example of howsuch interventions can work in practice can be seen inKhartoum Sudan (appendix p 11)
This approach has three major constraints First
accurate costing of the extent to which specificdevelopment interventions contribute to malaria controlis diffi cult Whereas measuring the effect of housescreening is straightforward measuring that ofimproved education or raised incomes is not Secondthe effectiveness of a development intervention dependson both the nature and intensity of malaria transmissionFor example house screening is probably most effectivein areas of low to moderate transmission where vectorsfeed indoors Countries that have eliminated malariasince 1900 have largely been temperate subtropical orislands8 and the high malaria burden in manydeveloping countries is not merely a product of povertyRather the specific ecological requirements of both themalaria parasite and its mosquito vector help todetermine the range of the disease61 Interventions haveto be highly effective and development should not bethought of as a standalone strategy but as a complementto malaria-specific interventions such as LLINs indoorresidual spraying and larval source management Thirdeconomic development gives rise to broader socialenvironmental and ecological changes that might insome circumstances lead to an increase in the burden ofmalaria (appendix p 5) as has been seen in Sri Lanka(appendix p 12) Nonetheless these constraints shouldnot be treated as barriers to the use of socioeconomicdevelopment as an intervention against malaria
(appendix p 5)In addition to initiatives such as the Millennium Villages
project which is operating in 14 villages in ten Africancountries to examine the effects of socioeconomicdevelopment62 further research is needed to address someimportant questions and to galvanise specialists in bothhealth and development to work more closely together onmalaria control For example randomised controlled trialsshould be considered to assess the effectiveness ofsocioeconomic interventions (eg improved education andnutrition) against malaria in different settings We mustalso investigate the causal pathways that lead fromdevelopment to successful malaria control and vice versaand develop an understanding of the relation betweenmalaria control birth rates and population growth
That malaria control remains largely the preoccupation
of the health sector alone is a failing of both those whowork in health and those who work in internationaldevelopment The disease severely compromises socio-economic development and its control and eliminationwould improve economic prosperity worldwide Theeffectiveness of available drugs and insecticides formalaria control will ultimately deteriorate with theemergence of parasites resistant to antimalarials and ofvectors resistant to insecticides and the development andprocurement costs of replacements will be high Donorfatigue is also an ever-present threat to interventions suchas LLINs indoor residual spraying and intermittentpreventive treatment especially in view of the economicsituation since the 2007ndash08 financial crisis63 However
several specific development interventions could beintroduced to aid both economic development andmalaria control Increased wealth and improved standardsof living that stem directly from socioeconomicdevelopment could prove fundamental in ensuring thatmalaria transmission continues to fall in much of AsiaSouth America and Africa as it happened historically inEurope and North America Socioeconomic developmentcould prove to be a very effective and sustainableintervention against malaria in the long term
Contributors
SWL and RS conceived of the study SWL RS LST HL and JTdeveloped the study design and the outline of the report LST searchedthe scientific literature did the meta-analysis and prepared the first
draft of the report BW provided advice on the systematic review andmeta-analysis HTK contributed the case study from Sudan (appendix)All authors reviewed the final version of the report
Conflicts of interest
We declare that we have no conflicts of interest
Acknowledgments
This work was supported by the UK Department for InternationalDevelopment and the Malaria Centre at the London School of Hygiene ampTropical Medicine (LSHTM) SWL was supported by the Research andPolicy for Infectious Disease Dynamics (RAPIDD) programme of theScience and Technology Directorate US Department of Homeland Securitythe Fogarty International Center (US National Institutes of Health) and theBill amp Melinda Gates Foundation JT was supported in part by a grant fromthe UK Economic and Social Research Council to the STEPS Centre and theInstitute of Development Studies (University of Sussex Brighton UK)We are grateful to Frida Kasteng (LSHTM) for providing information about
the costs of malaria and development interventionsReferences 1 Gething PW Patil AP Smith DL et al A new world malaria map
Plasmodium falciparum endemicity in 2010 Malar J 2011 10 378 2 WHO World Malaria Report 2011 Geneva World Health
Organization 2011 3 Murray CJL Rosenfeld LC Lim SS et al Global malaria mortality
between 1980 and 2010 a systematic analysis Lancet 2012 379 413ndash31 4 Murray CJL Vos T Lozano R et al Disability-adjusted life years
(DALYs) for 291 diseases and injuries in 21 regions 1990mdash2010a systematic analysis for the Global Burden of Disease Study 2010Lancet 2012 380 2197ndash223
5 Sachs J Malaney P The economic and social burden of malariaNature 2002 415 680ndash85
6 UNDP International human development indicators httphdrundporgendatabuild (accessed April 24 2012)
7 Sachs JD Macroeconomics and health investing in health for
economic development Geneva World Health Organization 2001
8132019 SE Dev as an Intervention Agst Malaria
httpslidepdfcomreaderfullse-dev-as-an-intervention-agst-malaria 910
Articles
wwwthelancetcom Published online June 19 2013 httpdxdoiorg101016S0140-6736(13)60851-X 9
8 Gallup J Sachs J The economic burden of malariaAm J Trop Med Hyg 2001 64 85ndash96
9 Mandelbaum-Schmid J HIVAIDS hunger and malaria are theworldrsquos most urgent problems say economistsBull World Health Organ 2004 82 554ndash55
10 Kidson C Indaratna K Ecology economics and political will thevicissitudes of malaria strategies in Asia Parassitologia 1998 40 39ndash46
11 UNDP Millennium Development Goal 6 focuses on combatingHIVAIDS malaria and other diseases httpwebundporgmdggoal6shtml (accessed April 21 2012)
12 OrsquoMeara WP Mangeni JN Steketee R Greenwood B Changes inthe burden of malaria in sub-Saharan Africa Lancet Infect Dis 201010 545ndash55
13 Dondorp AM Fairhurst RM Slutsker L et al The threat ofartemisinin-resistant malaria N Engl J Med 2011 365 1073ndash75
14 Ranson H NrsquoGuessan R Lines J et al Pyrethroid resistance inAfrican anopheline mosquitoes what are the implications formalaria control Trends Parasitol 2011 27 91ndash98
15 Cohen J Smith D Cotter C et al Malaria resurgence a systematic
review and assessment of its causes Malar J 2012 11 122 16 WHO WHO position statement on integrated vector management
Wkly Epidemiol Rec 2008 83 177ndash84 17 van den Berg H Mutero CM Ichimori K WHO guidance on
policy-making for integrated vector management Geneva WorldHealth Organization 2012
18 Stroup DF Berlin JA Morton SC et al Meta-analysis ofobservational studies in epidemiology JAMA 2000 283 2008ndash12
19 Modher D Liberati A Tetzlaff J Altman DG Group TP Preferredreporting items for systematic reviews and meta-analyses thePRISMA statement PLoS Med 2009 6 e1000097
20 Roll Back Malaria Malaria endemic countries Geneva The RBMPartnership 2010 httpwwwrbmwhointendemiccountrieshtml(accessed June 8 2011)
21 Wells G Shea B OrsquoConnell D et al The Newcastle-Ottawa Scale(NOS) for assessing the quality of nonrandomised studies inmeta-analyses Ottawa University of Ottawa 2011
22 Egger M Smith GD Schneider M Minder C Bias in meta-analysisdetected by a simple graphical test BMJ 1997 315 629ndash34
23 Al-Taiar A Assabri A Al-Habori M et al Socioeconomic andenvironmental factors important for acquiring non-severe malariain children in Yemen a case-control studyTrans R Soc Trop Med Hyg 2009 103 72ndash78
24 Baragatti M Fournet F Henry M-C et al Social and environmentalmalaria risk factors in urban areas of Ouagadougou Burkina FasoMalar J 2009 8 13
25 Clarke SE Boslashgh C Brown RC Pinder M Walraven GELLindsay SW Do untreated bednets protect against malariaTrans R Soc Trop Med Hyg 2001 95 457ndash62
26 Custodio E Descalzo MAacute Villamor E et al Nutritional andsocio-economic factors associated with Plasmodium falciparum infection in children from Equatorial Guinea results from anationally representative survey Malar J 2009 8 225
27 Gahutu J-B Steininger C Shyirambere C et al Prevalence and riskfactors of malaria among children in southern highland Rwanda
Malar J 2011 10 134 28 Ghebreyesus TA Haile M Witten KH et al Household risk factorsfor malaria among children in the Ethiopian HighlandsTrans R Soc Trop Med Hyg 2000 94 17ndash21
29 Koram KA Bennett S Adiamah JH Greenwood BM Socio-economicrisk factors for malaria in a peri-urban area of The GambiaTrans R Soc Trop Med Hyg 1995 89 146ndash50
30 Krefis AC Schwarz NG Nkrumah B et al Principal componentanalysis of socioeconomic factors and their association withmalaria in children from the Ashanti Region Ghana Malar J 20109 201
31 Ongrsquoecha JM Keller CC Were T et al Parasitemia anemia andmalarial anemia in infants and young children in a ruralholoendemic Plasmodium falciparum transmission area Am J Trop Med Hyg 2006 74 376ndash85
32 Pullan RL Kabatereine NB Bukirwa H Staedke SG Brooker SHeterogeneities and consequences of Plasmodium species andhookworm coinfection a population based study in Uganda
J Infect Dis 2011 203 406ndash17
33 Ronald LA Kenny SL Klinkenberg E et al Malaria and anaemiaamong children in two communities of Kumasi Ghana
a cross-sectional survey Malar J 2006 1 105 34 Slutsker L Khoromana CO Hightower AW et al Malaria infection
in infancy in rural Malawi Am J Trop Med Hyg 1996 55 71ndash76 35 Villamor E Fataki MR Mbise RL Fawzi WW Malaria parasitaemia
in relation to HIV status and vitamin A supplementation amongpre-school children Trop Med Int Health 2003 8 1051ndash61
36 Winskill P Rowland M Mtove G Malima R Kirby M Malaria riskfactors in north-east Tanzania Malar J 2011 10 98
37 Yamamoto S Louis VR Sie A Sauerborn R Household risk factorsfor clinical malaria in a semi-urban area of Burkina Fasoa case-control study Trans R Soc Trop Med Hyg 2010 104 61ndash65
38 Clark TD Greenhouse B Njama-Meya D et al Factors determiningthe heterogeneity of malaria incidence in children in KampalaUganda J Infect Dis 2008 198 393ndash400
39 Klinkenberg E McCall P Wilson M et al Urban malaria andanaemia in children a cross-sectional survey in two cities of GhanaTrop Med Int Health 2006 11 578ndash88
40 Kreuels B Kobbe R Adjei S et al Spatial variation of malariaincidence in young children from a geographically homogeneousarea with high endemicity J Infect Dis 2008 197 85ndash93
41 Matthys B Vounatsou P Raso G et al Urban farming and malariarisk factors in a medium-sized town in Cocircte drsquoIvoireAm J Trop Med Hyg 2006 75 1223ndash31
42 Pullan RL Bukirwa H Staedke SG Snow RW Brooker S Plasmodium infection and its risk factors in eastern Uganda Malar J 2010 4 9
43 Somi MF Butler JRG Is there evidence for dual causation betweenmalaria and socioeconomic status Findings from rural TanzaniaAm J Trop Med Hyg 2007 77 1020ndash27
44 Chuma JM Thiede M Rethinking the economic costs of malaria atthe household level evidence from applying a new analyticalframework in rural Kenya Malar J 2006 5 76ndash89
45 Onwujekwe O Hanson K Are malaria treatment expenditurescatastrophic to different socio-economic and geographic groups andhow do they cope with payment A study in southeast NigeriaTrop Med Int Health 2010 15 18ndash25
46 Sicuri E Davy C Marinelli M et al The economic cost tohouseholds of childhood malaria in Papua New Guinea a focus onintra-country variation Health Policy Plann 2012 27 339ndash47
47 Matovu F Goodman C How equitable is bed net ownership andutilisation in Tanzania A practical application of the principles ofhorizontal and vertical equity Malar J 2009 8 109ndash21
48 Gingrich CD Hanson K Marchant T Mulligan J-A Mponda HPrice subsidies and the market for mosquito nets in developingcountries a study of Tanzaniarsquos discount voucher schemeSoc Sci Med 2011 73 160ndash68
49 Ahmed SM Haque R Haque U Hossain A Knowledge on thetransmission prevention and treatment of malaria among twoendemic populations of Bangladesh and their health-seekingbehaviour Malar J 2009 8 173
50 Saaka M Oosthuizen J Beatty S Effect of joint iron and zincsupplementation on malarial infection and anaemiaEast Afr J Public Health 2009 6 55ndash62
51 Worrall E Basu S Hanson K Is malaria a disease of povertyA review of the literature Trop Med Int Health 2005 10 1047ndash59 52 Barat LM Palmer N Basu S Worrall E Hanson K Mills A Do
malaria control interventions reach the poor Am J Trop Med Hyg 2004 71 174ndash78
53 Sterne JAC Sutton AJ Ioannidis JPA et al Recommendationsfor examining and interpreting funnel plot asymmetry inmeta-analyses of randomised controlled trials BMJ 2011 343 4002
54 Garcia-Martin G Status of malaria eradication in the AmericasAm J Trop Med Hyg 1972 21 617ndash33
55 Bruce-Chwatt L de Zulueta J The rise and fall of malaria inEurope London Oxford University Press 1980
56 Sinden RE Malaria mosquitoes and the legacy of Ronald RossBull World Health Organ 2007 85 894ndash96
57 Keiser J Singer BH Utzinger J Reducing the burden of malariain different eco-epidemiological settings with environmentalmanagement a systematic review Lancet Infect Dis 20055 695ndash708
8132019 SE Dev as an Intervention Agst Malaria
httpslidepdfcomreaderfullse-dev-as-an-intervention-agst-malaria 1010
Articles
10 www thelancet com Published online June 19 2013 httpdx doi org10 1016S0140-6736(13)60851-X
58 Randolph SE Rogers DJ The arrival establishment and spread ofexotic diseases patterns and predictions Nat Rev Microbiol 2010
8 361ndash71 59 Hay S Guerra C Tatem A Noor A Snow R The global distribution
and population at risk of malaria past present and futureLancet Infect Dis 2004 4 327ndash36
60 Killeen GF Fillinger U Kiche I Gouagna LC Knols BGEradication of Anopheles gambiae from Brazil lessons for malariacontrol in Africa Lancet Infect Dis 2002 2 618ndash27
61 Jetten T Takken W Anophelism without malaria in Europea review of the ecology and distribution of the genus Anopheles in
Europe Wageningen Wageningen Agricultural University 1994 62 Millennium Villages Project httpwwwmillenniumvillagesorg
the-villages (accessed May 16 2013) 63 Garrett L Global health hits crisis point Nature 2012 482 7
8132019 SE Dev as an Intervention Agst Malaria
httpslidepdfcomreaderfullse-dev-as-an-intervention-agst-malaria 710
Articles
wwwthelancetcom Published online June 19 2013 httpdxdoiorg101016S0140-6736(13)60851-X 7
studies (unadjusted effect size I sup2=68 adjusted effect size
I sup2=63) Therefore random-effects analysis was usedThe meta-analysis was restricted to comparisonsbetween the highest (least poor) and lowest (poorest)socioeconomic groups Subgroup analysis suggested thatlow socioeconomic status was associated with increasedodds of malaria irrespective of the measure used forsocioeconomic status with the exception of one study inwhich parentsrsquo occupations were used31 we thereforejudged that to pool all results would be appropriate Inthe meta-analyses for both unadjusted and adjustedresults the odds of malaria infection were higher in thepoorest children than in the least poor children (figure 3)
Visual assessment of funnel plots (appendix p 8)showed that the studies were distributed fairly
symmetrically about the combined effect size whichsuggests little publication bias However Eggerrsquos test forbias suggested funnel plot asymmetry for the unadjustedresults (bias coeffi cient 1middot70 95 CI ndash0middot97 to 4middot37p=0∙191) which suggests that publication bias (delayedpublication or location bias) small-study effects selectiveoutcome reporting or selective analysis reporting mighthave been present A test for funnel plot asymmetry wasnot possible for the adjusted effects since only fivestudies were included in the meta-analysis Overallquality assessment scores for risk of bias in studiesincluded in the quantitative analysis ranged from two toseven out of a maximum of eight (appendix p 6ndash7)
DiscussionOur findings suggest that low socioeconomic status isassociated with roughly doubled odds of clinical malariaor parasitaemia in children compared with higher socio-economic status within a locality This conclusion is sup-ported by a similar size and direction of effect noted inthe five studies excluded from the meta-analysis Sinceour analysis represents a comparison of the very poorestchildren with the least poor children within highly im-poverished communities the difference in the odds ofmalaria in the poorest children would probably be evengreater if the studies were expanded to include childrenfrom wealthier backgrounds The association between
socioeconomic status and malaria is not definitiveevidence for the direction of causality since the pooresthouseholds are not only more susceptible to the diseasebut are also more vulnerable to its costs such that thedisease itself can induce poverty For example asignificant positive association between low socio-economic status and malaria parasitaemia has beenreported in Tanzania43 with causality in both directionsFindings from Kenya44 and Nigeria45 suggest that the costsof malaria treatment (as a proportion of non-food monthlyincome) and subsequent financial setbacks are greater forpoorer than for more wealthy households Costs also varygeographically in Kenya44 and Papua New Guinea46 therisk of clinical disease is greater in low-transmissiondistricts with subsequently greater loss of income
Wealth is probably protective against malaria since it
renders prophylaxis and treatment more affordable
47ndash49
andis positively associated with other beneficial factorsincluding better-educated parents (which improvesprophylaxis and treatment for children) increasedhousing quality (which reduces house entry by malaria-transmitting mosquitoes) and improved nutritionalstatus of children (which could increase their subsequentability to cope with malaria infection)50ndash52 Malaria andpoverty therefore constitute a vicious cycle for the pooresthouseholds exacerbating differences in health and wealth
A major limitation of our meta-analysis is that themeasurement of risk factors was done with varyingprecision in the included studies and although we didsubgroup and random-effects analyses these are unlikely
to have fully accounted for heterogeneity in study designAnother important limitation is the poor quality of thestudies included in the meta-analysis which results fromthe nature of the study question (since randomisation forsocioeconomic status would not be practically or ethicallypossible) However the consistency of results acrossstudies and settings suggests that the finding of increasedodds of malaria in children of low socioeconomic status isrobust For our systematic review the main limitationwas the language of the search In particular notincluding publications in Spanish probably excludedmuch data from South America such that our findingscannot be generalised to that region Eggerrsquos testsuggested the presence of some forest plot asymmetryhowever statistical tests for forest plot asymmetry tend tohave low power53 and asymmetry might not be attributableto publication biasmdashit might also have arisen from poorstudy quality leading to artificially inflated effects in thesmaller studies selective outcome or analysis reportingor chance Incomplete retrieval (four full-text studiescould not be retrieved) might also have introduced bias
On the basis of our findings we advocate thatdevelopment programmes should be an essentialcomponent of malaria control Malaria elimination inmany high-income countries was achieved withoutmalaria-specific interventions prevalence started to fall inEurope and North America as a by-product of improved
living conditions and increased wealth5455 and afterRonald Ross deduced the mode of malaria transmission56 in 1897 more specific interventions became possibleincluding habitat modification (permanent elimination ofbreeding sitesmdasheg by installing and maintaining drains)habitat manipulation (temporary creation of unfavourableconditions for the vectormdasheg by fluctuating the amountof water in reservoirs) and modifications to humanhabitation or behaviour to reduce human-vector contactsuch as mosquito-proofing of houses57 As a result mostof Europe and North America is now characterised byanophelism without malaria which is testament to theeffectiveness of these control efforts together with areduced innate receptivity to malaria transmission thatstems from advances in nutrition health care and
8132019 SE Dev as an Intervention Agst Malaria
httpslidepdfcomreaderfullse-dev-as-an-intervention-agst-malaria 810
Articles
8 wwwthelancetcom Published online June 19 2013 httpdxdoiorg101016S0140-6736(13)60851-X
development58 Similar environmental management
strategies together with larval control also helped toreduce malaria transmission in many developingcountries during the 20th century including ZanzibarIndonesia Malaysia the Panama Canal and the CopperBelt of Zambia5960 Thus as transmission today falls inmuch of sub-Saharan Africa and elsewhere developmentwill contribute to the reduction and elimination of thedisease Several specific development interventions couldcontribute to malaria control (appendix p 5) which mightbe similar to malaria-specific interventions in terms ofcosts (appendix pp 9ndash10) An excellent example of howsuch interventions can work in practice can be seen inKhartoum Sudan (appendix p 11)
This approach has three major constraints First
accurate costing of the extent to which specificdevelopment interventions contribute to malaria controlis diffi cult Whereas measuring the effect of housescreening is straightforward measuring that ofimproved education or raised incomes is not Secondthe effectiveness of a development intervention dependson both the nature and intensity of malaria transmissionFor example house screening is probably most effectivein areas of low to moderate transmission where vectorsfeed indoors Countries that have eliminated malariasince 1900 have largely been temperate subtropical orislands8 and the high malaria burden in manydeveloping countries is not merely a product of povertyRather the specific ecological requirements of both themalaria parasite and its mosquito vector help todetermine the range of the disease61 Interventions haveto be highly effective and development should not bethought of as a standalone strategy but as a complementto malaria-specific interventions such as LLINs indoorresidual spraying and larval source management Thirdeconomic development gives rise to broader socialenvironmental and ecological changes that might insome circumstances lead to an increase in the burden ofmalaria (appendix p 5) as has been seen in Sri Lanka(appendix p 12) Nonetheless these constraints shouldnot be treated as barriers to the use of socioeconomicdevelopment as an intervention against malaria
(appendix p 5)In addition to initiatives such as the Millennium Villages
project which is operating in 14 villages in ten Africancountries to examine the effects of socioeconomicdevelopment62 further research is needed to address someimportant questions and to galvanise specialists in bothhealth and development to work more closely together onmalaria control For example randomised controlled trialsshould be considered to assess the effectiveness ofsocioeconomic interventions (eg improved education andnutrition) against malaria in different settings We mustalso investigate the causal pathways that lead fromdevelopment to successful malaria control and vice versaand develop an understanding of the relation betweenmalaria control birth rates and population growth
That malaria control remains largely the preoccupation
of the health sector alone is a failing of both those whowork in health and those who work in internationaldevelopment The disease severely compromises socio-economic development and its control and eliminationwould improve economic prosperity worldwide Theeffectiveness of available drugs and insecticides formalaria control will ultimately deteriorate with theemergence of parasites resistant to antimalarials and ofvectors resistant to insecticides and the development andprocurement costs of replacements will be high Donorfatigue is also an ever-present threat to interventions suchas LLINs indoor residual spraying and intermittentpreventive treatment especially in view of the economicsituation since the 2007ndash08 financial crisis63 However
several specific development interventions could beintroduced to aid both economic development andmalaria control Increased wealth and improved standardsof living that stem directly from socioeconomicdevelopment could prove fundamental in ensuring thatmalaria transmission continues to fall in much of AsiaSouth America and Africa as it happened historically inEurope and North America Socioeconomic developmentcould prove to be a very effective and sustainableintervention against malaria in the long term
Contributors
SWL and RS conceived of the study SWL RS LST HL and JTdeveloped the study design and the outline of the report LST searchedthe scientific literature did the meta-analysis and prepared the first
draft of the report BW provided advice on the systematic review andmeta-analysis HTK contributed the case study from Sudan (appendix)All authors reviewed the final version of the report
Conflicts of interest
We declare that we have no conflicts of interest
Acknowledgments
This work was supported by the UK Department for InternationalDevelopment and the Malaria Centre at the London School of Hygiene ampTropical Medicine (LSHTM) SWL was supported by the Research andPolicy for Infectious Disease Dynamics (RAPIDD) programme of theScience and Technology Directorate US Department of Homeland Securitythe Fogarty International Center (US National Institutes of Health) and theBill amp Melinda Gates Foundation JT was supported in part by a grant fromthe UK Economic and Social Research Council to the STEPS Centre and theInstitute of Development Studies (University of Sussex Brighton UK)We are grateful to Frida Kasteng (LSHTM) for providing information about
the costs of malaria and development interventionsReferences 1 Gething PW Patil AP Smith DL et al A new world malaria map
Plasmodium falciparum endemicity in 2010 Malar J 2011 10 378 2 WHO World Malaria Report 2011 Geneva World Health
Organization 2011 3 Murray CJL Rosenfeld LC Lim SS et al Global malaria mortality
between 1980 and 2010 a systematic analysis Lancet 2012 379 413ndash31 4 Murray CJL Vos T Lozano R et al Disability-adjusted life years
(DALYs) for 291 diseases and injuries in 21 regions 1990mdash2010a systematic analysis for the Global Burden of Disease Study 2010Lancet 2012 380 2197ndash223
5 Sachs J Malaney P The economic and social burden of malariaNature 2002 415 680ndash85
6 UNDP International human development indicators httphdrundporgendatabuild (accessed April 24 2012)
7 Sachs JD Macroeconomics and health investing in health for
economic development Geneva World Health Organization 2001
8132019 SE Dev as an Intervention Agst Malaria
httpslidepdfcomreaderfullse-dev-as-an-intervention-agst-malaria 910
Articles
wwwthelancetcom Published online June 19 2013 httpdxdoiorg101016S0140-6736(13)60851-X 9
8 Gallup J Sachs J The economic burden of malariaAm J Trop Med Hyg 2001 64 85ndash96
9 Mandelbaum-Schmid J HIVAIDS hunger and malaria are theworldrsquos most urgent problems say economistsBull World Health Organ 2004 82 554ndash55
10 Kidson C Indaratna K Ecology economics and political will thevicissitudes of malaria strategies in Asia Parassitologia 1998 40 39ndash46
11 UNDP Millennium Development Goal 6 focuses on combatingHIVAIDS malaria and other diseases httpwebundporgmdggoal6shtml (accessed April 21 2012)
12 OrsquoMeara WP Mangeni JN Steketee R Greenwood B Changes inthe burden of malaria in sub-Saharan Africa Lancet Infect Dis 201010 545ndash55
13 Dondorp AM Fairhurst RM Slutsker L et al The threat ofartemisinin-resistant malaria N Engl J Med 2011 365 1073ndash75
14 Ranson H NrsquoGuessan R Lines J et al Pyrethroid resistance inAfrican anopheline mosquitoes what are the implications formalaria control Trends Parasitol 2011 27 91ndash98
15 Cohen J Smith D Cotter C et al Malaria resurgence a systematic
review and assessment of its causes Malar J 2012 11 122 16 WHO WHO position statement on integrated vector management
Wkly Epidemiol Rec 2008 83 177ndash84 17 van den Berg H Mutero CM Ichimori K WHO guidance on
policy-making for integrated vector management Geneva WorldHealth Organization 2012
18 Stroup DF Berlin JA Morton SC et al Meta-analysis ofobservational studies in epidemiology JAMA 2000 283 2008ndash12
19 Modher D Liberati A Tetzlaff J Altman DG Group TP Preferredreporting items for systematic reviews and meta-analyses thePRISMA statement PLoS Med 2009 6 e1000097
20 Roll Back Malaria Malaria endemic countries Geneva The RBMPartnership 2010 httpwwwrbmwhointendemiccountrieshtml(accessed June 8 2011)
21 Wells G Shea B OrsquoConnell D et al The Newcastle-Ottawa Scale(NOS) for assessing the quality of nonrandomised studies inmeta-analyses Ottawa University of Ottawa 2011
22 Egger M Smith GD Schneider M Minder C Bias in meta-analysisdetected by a simple graphical test BMJ 1997 315 629ndash34
23 Al-Taiar A Assabri A Al-Habori M et al Socioeconomic andenvironmental factors important for acquiring non-severe malariain children in Yemen a case-control studyTrans R Soc Trop Med Hyg 2009 103 72ndash78
24 Baragatti M Fournet F Henry M-C et al Social and environmentalmalaria risk factors in urban areas of Ouagadougou Burkina FasoMalar J 2009 8 13
25 Clarke SE Boslashgh C Brown RC Pinder M Walraven GELLindsay SW Do untreated bednets protect against malariaTrans R Soc Trop Med Hyg 2001 95 457ndash62
26 Custodio E Descalzo MAacute Villamor E et al Nutritional andsocio-economic factors associated with Plasmodium falciparum infection in children from Equatorial Guinea results from anationally representative survey Malar J 2009 8 225
27 Gahutu J-B Steininger C Shyirambere C et al Prevalence and riskfactors of malaria among children in southern highland Rwanda
Malar J 2011 10 134 28 Ghebreyesus TA Haile M Witten KH et al Household risk factorsfor malaria among children in the Ethiopian HighlandsTrans R Soc Trop Med Hyg 2000 94 17ndash21
29 Koram KA Bennett S Adiamah JH Greenwood BM Socio-economicrisk factors for malaria in a peri-urban area of The GambiaTrans R Soc Trop Med Hyg 1995 89 146ndash50
30 Krefis AC Schwarz NG Nkrumah B et al Principal componentanalysis of socioeconomic factors and their association withmalaria in children from the Ashanti Region Ghana Malar J 20109 201
31 Ongrsquoecha JM Keller CC Were T et al Parasitemia anemia andmalarial anemia in infants and young children in a ruralholoendemic Plasmodium falciparum transmission area Am J Trop Med Hyg 2006 74 376ndash85
32 Pullan RL Kabatereine NB Bukirwa H Staedke SG Brooker SHeterogeneities and consequences of Plasmodium species andhookworm coinfection a population based study in Uganda
J Infect Dis 2011 203 406ndash17
33 Ronald LA Kenny SL Klinkenberg E et al Malaria and anaemiaamong children in two communities of Kumasi Ghana
a cross-sectional survey Malar J 2006 1 105 34 Slutsker L Khoromana CO Hightower AW et al Malaria infection
in infancy in rural Malawi Am J Trop Med Hyg 1996 55 71ndash76 35 Villamor E Fataki MR Mbise RL Fawzi WW Malaria parasitaemia
in relation to HIV status and vitamin A supplementation amongpre-school children Trop Med Int Health 2003 8 1051ndash61
36 Winskill P Rowland M Mtove G Malima R Kirby M Malaria riskfactors in north-east Tanzania Malar J 2011 10 98
37 Yamamoto S Louis VR Sie A Sauerborn R Household risk factorsfor clinical malaria in a semi-urban area of Burkina Fasoa case-control study Trans R Soc Trop Med Hyg 2010 104 61ndash65
38 Clark TD Greenhouse B Njama-Meya D et al Factors determiningthe heterogeneity of malaria incidence in children in KampalaUganda J Infect Dis 2008 198 393ndash400
39 Klinkenberg E McCall P Wilson M et al Urban malaria andanaemia in children a cross-sectional survey in two cities of GhanaTrop Med Int Health 2006 11 578ndash88
40 Kreuels B Kobbe R Adjei S et al Spatial variation of malariaincidence in young children from a geographically homogeneousarea with high endemicity J Infect Dis 2008 197 85ndash93
41 Matthys B Vounatsou P Raso G et al Urban farming and malariarisk factors in a medium-sized town in Cocircte drsquoIvoireAm J Trop Med Hyg 2006 75 1223ndash31
42 Pullan RL Bukirwa H Staedke SG Snow RW Brooker S Plasmodium infection and its risk factors in eastern Uganda Malar J 2010 4 9
43 Somi MF Butler JRG Is there evidence for dual causation betweenmalaria and socioeconomic status Findings from rural TanzaniaAm J Trop Med Hyg 2007 77 1020ndash27
44 Chuma JM Thiede M Rethinking the economic costs of malaria atthe household level evidence from applying a new analyticalframework in rural Kenya Malar J 2006 5 76ndash89
45 Onwujekwe O Hanson K Are malaria treatment expenditurescatastrophic to different socio-economic and geographic groups andhow do they cope with payment A study in southeast NigeriaTrop Med Int Health 2010 15 18ndash25
46 Sicuri E Davy C Marinelli M et al The economic cost tohouseholds of childhood malaria in Papua New Guinea a focus onintra-country variation Health Policy Plann 2012 27 339ndash47
47 Matovu F Goodman C How equitable is bed net ownership andutilisation in Tanzania A practical application of the principles ofhorizontal and vertical equity Malar J 2009 8 109ndash21
48 Gingrich CD Hanson K Marchant T Mulligan J-A Mponda HPrice subsidies and the market for mosquito nets in developingcountries a study of Tanzaniarsquos discount voucher schemeSoc Sci Med 2011 73 160ndash68
49 Ahmed SM Haque R Haque U Hossain A Knowledge on thetransmission prevention and treatment of malaria among twoendemic populations of Bangladesh and their health-seekingbehaviour Malar J 2009 8 173
50 Saaka M Oosthuizen J Beatty S Effect of joint iron and zincsupplementation on malarial infection and anaemiaEast Afr J Public Health 2009 6 55ndash62
51 Worrall E Basu S Hanson K Is malaria a disease of povertyA review of the literature Trop Med Int Health 2005 10 1047ndash59 52 Barat LM Palmer N Basu S Worrall E Hanson K Mills A Do
malaria control interventions reach the poor Am J Trop Med Hyg 2004 71 174ndash78
53 Sterne JAC Sutton AJ Ioannidis JPA et al Recommendationsfor examining and interpreting funnel plot asymmetry inmeta-analyses of randomised controlled trials BMJ 2011 343 4002
54 Garcia-Martin G Status of malaria eradication in the AmericasAm J Trop Med Hyg 1972 21 617ndash33
55 Bruce-Chwatt L de Zulueta J The rise and fall of malaria inEurope London Oxford University Press 1980
56 Sinden RE Malaria mosquitoes and the legacy of Ronald RossBull World Health Organ 2007 85 894ndash96
57 Keiser J Singer BH Utzinger J Reducing the burden of malariain different eco-epidemiological settings with environmentalmanagement a systematic review Lancet Infect Dis 20055 695ndash708
8132019 SE Dev as an Intervention Agst Malaria
httpslidepdfcomreaderfullse-dev-as-an-intervention-agst-malaria 1010
Articles
10 www thelancet com Published online June 19 2013 httpdx doi org10 1016S0140-6736(13)60851-X
58 Randolph SE Rogers DJ The arrival establishment and spread ofexotic diseases patterns and predictions Nat Rev Microbiol 2010
8 361ndash71 59 Hay S Guerra C Tatem A Noor A Snow R The global distribution
and population at risk of malaria past present and futureLancet Infect Dis 2004 4 327ndash36
60 Killeen GF Fillinger U Kiche I Gouagna LC Knols BGEradication of Anopheles gambiae from Brazil lessons for malariacontrol in Africa Lancet Infect Dis 2002 2 618ndash27
61 Jetten T Takken W Anophelism without malaria in Europea review of the ecology and distribution of the genus Anopheles in
Europe Wageningen Wageningen Agricultural University 1994 62 Millennium Villages Project httpwwwmillenniumvillagesorg
the-villages (accessed May 16 2013) 63 Garrett L Global health hits crisis point Nature 2012 482 7
8132019 SE Dev as an Intervention Agst Malaria
httpslidepdfcomreaderfullse-dev-as-an-intervention-agst-malaria 810
Articles
8 wwwthelancetcom Published online June 19 2013 httpdxdoiorg101016S0140-6736(13)60851-X
development58 Similar environmental management
strategies together with larval control also helped toreduce malaria transmission in many developingcountries during the 20th century including ZanzibarIndonesia Malaysia the Panama Canal and the CopperBelt of Zambia5960 Thus as transmission today falls inmuch of sub-Saharan Africa and elsewhere developmentwill contribute to the reduction and elimination of thedisease Several specific development interventions couldcontribute to malaria control (appendix p 5) which mightbe similar to malaria-specific interventions in terms ofcosts (appendix pp 9ndash10) An excellent example of howsuch interventions can work in practice can be seen inKhartoum Sudan (appendix p 11)
This approach has three major constraints First
accurate costing of the extent to which specificdevelopment interventions contribute to malaria controlis diffi cult Whereas measuring the effect of housescreening is straightforward measuring that ofimproved education or raised incomes is not Secondthe effectiveness of a development intervention dependson both the nature and intensity of malaria transmissionFor example house screening is probably most effectivein areas of low to moderate transmission where vectorsfeed indoors Countries that have eliminated malariasince 1900 have largely been temperate subtropical orislands8 and the high malaria burden in manydeveloping countries is not merely a product of povertyRather the specific ecological requirements of both themalaria parasite and its mosquito vector help todetermine the range of the disease61 Interventions haveto be highly effective and development should not bethought of as a standalone strategy but as a complementto malaria-specific interventions such as LLINs indoorresidual spraying and larval source management Thirdeconomic development gives rise to broader socialenvironmental and ecological changes that might insome circumstances lead to an increase in the burden ofmalaria (appendix p 5) as has been seen in Sri Lanka(appendix p 12) Nonetheless these constraints shouldnot be treated as barriers to the use of socioeconomicdevelopment as an intervention against malaria
(appendix p 5)In addition to initiatives such as the Millennium Villages
project which is operating in 14 villages in ten Africancountries to examine the effects of socioeconomicdevelopment62 further research is needed to address someimportant questions and to galvanise specialists in bothhealth and development to work more closely together onmalaria control For example randomised controlled trialsshould be considered to assess the effectiveness ofsocioeconomic interventions (eg improved education andnutrition) against malaria in different settings We mustalso investigate the causal pathways that lead fromdevelopment to successful malaria control and vice versaand develop an understanding of the relation betweenmalaria control birth rates and population growth
That malaria control remains largely the preoccupation
of the health sector alone is a failing of both those whowork in health and those who work in internationaldevelopment The disease severely compromises socio-economic development and its control and eliminationwould improve economic prosperity worldwide Theeffectiveness of available drugs and insecticides formalaria control will ultimately deteriorate with theemergence of parasites resistant to antimalarials and ofvectors resistant to insecticides and the development andprocurement costs of replacements will be high Donorfatigue is also an ever-present threat to interventions suchas LLINs indoor residual spraying and intermittentpreventive treatment especially in view of the economicsituation since the 2007ndash08 financial crisis63 However
several specific development interventions could beintroduced to aid both economic development andmalaria control Increased wealth and improved standardsof living that stem directly from socioeconomicdevelopment could prove fundamental in ensuring thatmalaria transmission continues to fall in much of AsiaSouth America and Africa as it happened historically inEurope and North America Socioeconomic developmentcould prove to be a very effective and sustainableintervention against malaria in the long term
Contributors
SWL and RS conceived of the study SWL RS LST HL and JTdeveloped the study design and the outline of the report LST searchedthe scientific literature did the meta-analysis and prepared the first
draft of the report BW provided advice on the systematic review andmeta-analysis HTK contributed the case study from Sudan (appendix)All authors reviewed the final version of the report
Conflicts of interest
We declare that we have no conflicts of interest
Acknowledgments
This work was supported by the UK Department for InternationalDevelopment and the Malaria Centre at the London School of Hygiene ampTropical Medicine (LSHTM) SWL was supported by the Research andPolicy for Infectious Disease Dynamics (RAPIDD) programme of theScience and Technology Directorate US Department of Homeland Securitythe Fogarty International Center (US National Institutes of Health) and theBill amp Melinda Gates Foundation JT was supported in part by a grant fromthe UK Economic and Social Research Council to the STEPS Centre and theInstitute of Development Studies (University of Sussex Brighton UK)We are grateful to Frida Kasteng (LSHTM) for providing information about
the costs of malaria and development interventionsReferences 1 Gething PW Patil AP Smith DL et al A new world malaria map
Plasmodium falciparum endemicity in 2010 Malar J 2011 10 378 2 WHO World Malaria Report 2011 Geneva World Health
Organization 2011 3 Murray CJL Rosenfeld LC Lim SS et al Global malaria mortality
between 1980 and 2010 a systematic analysis Lancet 2012 379 413ndash31 4 Murray CJL Vos T Lozano R et al Disability-adjusted life years
(DALYs) for 291 diseases and injuries in 21 regions 1990mdash2010a systematic analysis for the Global Burden of Disease Study 2010Lancet 2012 380 2197ndash223
5 Sachs J Malaney P The economic and social burden of malariaNature 2002 415 680ndash85
6 UNDP International human development indicators httphdrundporgendatabuild (accessed April 24 2012)
7 Sachs JD Macroeconomics and health investing in health for
economic development Geneva World Health Organization 2001
8132019 SE Dev as an Intervention Agst Malaria
httpslidepdfcomreaderfullse-dev-as-an-intervention-agst-malaria 910
Articles
wwwthelancetcom Published online June 19 2013 httpdxdoiorg101016S0140-6736(13)60851-X 9
8 Gallup J Sachs J The economic burden of malariaAm J Trop Med Hyg 2001 64 85ndash96
9 Mandelbaum-Schmid J HIVAIDS hunger and malaria are theworldrsquos most urgent problems say economistsBull World Health Organ 2004 82 554ndash55
10 Kidson C Indaratna K Ecology economics and political will thevicissitudes of malaria strategies in Asia Parassitologia 1998 40 39ndash46
11 UNDP Millennium Development Goal 6 focuses on combatingHIVAIDS malaria and other diseases httpwebundporgmdggoal6shtml (accessed April 21 2012)
12 OrsquoMeara WP Mangeni JN Steketee R Greenwood B Changes inthe burden of malaria in sub-Saharan Africa Lancet Infect Dis 201010 545ndash55
13 Dondorp AM Fairhurst RM Slutsker L et al The threat ofartemisinin-resistant malaria N Engl J Med 2011 365 1073ndash75
14 Ranson H NrsquoGuessan R Lines J et al Pyrethroid resistance inAfrican anopheline mosquitoes what are the implications formalaria control Trends Parasitol 2011 27 91ndash98
15 Cohen J Smith D Cotter C et al Malaria resurgence a systematic
review and assessment of its causes Malar J 2012 11 122 16 WHO WHO position statement on integrated vector management
Wkly Epidemiol Rec 2008 83 177ndash84 17 van den Berg H Mutero CM Ichimori K WHO guidance on
policy-making for integrated vector management Geneva WorldHealth Organization 2012
18 Stroup DF Berlin JA Morton SC et al Meta-analysis ofobservational studies in epidemiology JAMA 2000 283 2008ndash12
19 Modher D Liberati A Tetzlaff J Altman DG Group TP Preferredreporting items for systematic reviews and meta-analyses thePRISMA statement PLoS Med 2009 6 e1000097
20 Roll Back Malaria Malaria endemic countries Geneva The RBMPartnership 2010 httpwwwrbmwhointendemiccountrieshtml(accessed June 8 2011)
21 Wells G Shea B OrsquoConnell D et al The Newcastle-Ottawa Scale(NOS) for assessing the quality of nonrandomised studies inmeta-analyses Ottawa University of Ottawa 2011
22 Egger M Smith GD Schneider M Minder C Bias in meta-analysisdetected by a simple graphical test BMJ 1997 315 629ndash34
23 Al-Taiar A Assabri A Al-Habori M et al Socioeconomic andenvironmental factors important for acquiring non-severe malariain children in Yemen a case-control studyTrans R Soc Trop Med Hyg 2009 103 72ndash78
24 Baragatti M Fournet F Henry M-C et al Social and environmentalmalaria risk factors in urban areas of Ouagadougou Burkina FasoMalar J 2009 8 13
25 Clarke SE Boslashgh C Brown RC Pinder M Walraven GELLindsay SW Do untreated bednets protect against malariaTrans R Soc Trop Med Hyg 2001 95 457ndash62
26 Custodio E Descalzo MAacute Villamor E et al Nutritional andsocio-economic factors associated with Plasmodium falciparum infection in children from Equatorial Guinea results from anationally representative survey Malar J 2009 8 225
27 Gahutu J-B Steininger C Shyirambere C et al Prevalence and riskfactors of malaria among children in southern highland Rwanda
Malar J 2011 10 134 28 Ghebreyesus TA Haile M Witten KH et al Household risk factorsfor malaria among children in the Ethiopian HighlandsTrans R Soc Trop Med Hyg 2000 94 17ndash21
29 Koram KA Bennett S Adiamah JH Greenwood BM Socio-economicrisk factors for malaria in a peri-urban area of The GambiaTrans R Soc Trop Med Hyg 1995 89 146ndash50
30 Krefis AC Schwarz NG Nkrumah B et al Principal componentanalysis of socioeconomic factors and their association withmalaria in children from the Ashanti Region Ghana Malar J 20109 201
31 Ongrsquoecha JM Keller CC Were T et al Parasitemia anemia andmalarial anemia in infants and young children in a ruralholoendemic Plasmodium falciparum transmission area Am J Trop Med Hyg 2006 74 376ndash85
32 Pullan RL Kabatereine NB Bukirwa H Staedke SG Brooker SHeterogeneities and consequences of Plasmodium species andhookworm coinfection a population based study in Uganda
J Infect Dis 2011 203 406ndash17
33 Ronald LA Kenny SL Klinkenberg E et al Malaria and anaemiaamong children in two communities of Kumasi Ghana
a cross-sectional survey Malar J 2006 1 105 34 Slutsker L Khoromana CO Hightower AW et al Malaria infection
in infancy in rural Malawi Am J Trop Med Hyg 1996 55 71ndash76 35 Villamor E Fataki MR Mbise RL Fawzi WW Malaria parasitaemia
in relation to HIV status and vitamin A supplementation amongpre-school children Trop Med Int Health 2003 8 1051ndash61
36 Winskill P Rowland M Mtove G Malima R Kirby M Malaria riskfactors in north-east Tanzania Malar J 2011 10 98
37 Yamamoto S Louis VR Sie A Sauerborn R Household risk factorsfor clinical malaria in a semi-urban area of Burkina Fasoa case-control study Trans R Soc Trop Med Hyg 2010 104 61ndash65
38 Clark TD Greenhouse B Njama-Meya D et al Factors determiningthe heterogeneity of malaria incidence in children in KampalaUganda J Infect Dis 2008 198 393ndash400
39 Klinkenberg E McCall P Wilson M et al Urban malaria andanaemia in children a cross-sectional survey in two cities of GhanaTrop Med Int Health 2006 11 578ndash88
40 Kreuels B Kobbe R Adjei S et al Spatial variation of malariaincidence in young children from a geographically homogeneousarea with high endemicity J Infect Dis 2008 197 85ndash93
41 Matthys B Vounatsou P Raso G et al Urban farming and malariarisk factors in a medium-sized town in Cocircte drsquoIvoireAm J Trop Med Hyg 2006 75 1223ndash31
42 Pullan RL Bukirwa H Staedke SG Snow RW Brooker S Plasmodium infection and its risk factors in eastern Uganda Malar J 2010 4 9
43 Somi MF Butler JRG Is there evidence for dual causation betweenmalaria and socioeconomic status Findings from rural TanzaniaAm J Trop Med Hyg 2007 77 1020ndash27
44 Chuma JM Thiede M Rethinking the economic costs of malaria atthe household level evidence from applying a new analyticalframework in rural Kenya Malar J 2006 5 76ndash89
45 Onwujekwe O Hanson K Are malaria treatment expenditurescatastrophic to different socio-economic and geographic groups andhow do they cope with payment A study in southeast NigeriaTrop Med Int Health 2010 15 18ndash25
46 Sicuri E Davy C Marinelli M et al The economic cost tohouseholds of childhood malaria in Papua New Guinea a focus onintra-country variation Health Policy Plann 2012 27 339ndash47
47 Matovu F Goodman C How equitable is bed net ownership andutilisation in Tanzania A practical application of the principles ofhorizontal and vertical equity Malar J 2009 8 109ndash21
48 Gingrich CD Hanson K Marchant T Mulligan J-A Mponda HPrice subsidies and the market for mosquito nets in developingcountries a study of Tanzaniarsquos discount voucher schemeSoc Sci Med 2011 73 160ndash68
49 Ahmed SM Haque R Haque U Hossain A Knowledge on thetransmission prevention and treatment of malaria among twoendemic populations of Bangladesh and their health-seekingbehaviour Malar J 2009 8 173
50 Saaka M Oosthuizen J Beatty S Effect of joint iron and zincsupplementation on malarial infection and anaemiaEast Afr J Public Health 2009 6 55ndash62
51 Worrall E Basu S Hanson K Is malaria a disease of povertyA review of the literature Trop Med Int Health 2005 10 1047ndash59 52 Barat LM Palmer N Basu S Worrall E Hanson K Mills A Do
malaria control interventions reach the poor Am J Trop Med Hyg 2004 71 174ndash78
53 Sterne JAC Sutton AJ Ioannidis JPA et al Recommendationsfor examining and interpreting funnel plot asymmetry inmeta-analyses of randomised controlled trials BMJ 2011 343 4002
54 Garcia-Martin G Status of malaria eradication in the AmericasAm J Trop Med Hyg 1972 21 617ndash33
55 Bruce-Chwatt L de Zulueta J The rise and fall of malaria inEurope London Oxford University Press 1980
56 Sinden RE Malaria mosquitoes and the legacy of Ronald RossBull World Health Organ 2007 85 894ndash96
57 Keiser J Singer BH Utzinger J Reducing the burden of malariain different eco-epidemiological settings with environmentalmanagement a systematic review Lancet Infect Dis 20055 695ndash708
8132019 SE Dev as an Intervention Agst Malaria
httpslidepdfcomreaderfullse-dev-as-an-intervention-agst-malaria 1010
Articles
10 www thelancet com Published online June 19 2013 httpdx doi org10 1016S0140-6736(13)60851-X
58 Randolph SE Rogers DJ The arrival establishment and spread ofexotic diseases patterns and predictions Nat Rev Microbiol 2010
8 361ndash71 59 Hay S Guerra C Tatem A Noor A Snow R The global distribution
and population at risk of malaria past present and futureLancet Infect Dis 2004 4 327ndash36
60 Killeen GF Fillinger U Kiche I Gouagna LC Knols BGEradication of Anopheles gambiae from Brazil lessons for malariacontrol in Africa Lancet Infect Dis 2002 2 618ndash27
61 Jetten T Takken W Anophelism without malaria in Europea review of the ecology and distribution of the genus Anopheles in
Europe Wageningen Wageningen Agricultural University 1994 62 Millennium Villages Project httpwwwmillenniumvillagesorg
the-villages (accessed May 16 2013) 63 Garrett L Global health hits crisis point Nature 2012 482 7
8132019 SE Dev as an Intervention Agst Malaria
httpslidepdfcomreaderfullse-dev-as-an-intervention-agst-malaria 910
Articles
wwwthelancetcom Published online June 19 2013 httpdxdoiorg101016S0140-6736(13)60851-X 9
8 Gallup J Sachs J The economic burden of malariaAm J Trop Med Hyg 2001 64 85ndash96
9 Mandelbaum-Schmid J HIVAIDS hunger and malaria are theworldrsquos most urgent problems say economistsBull World Health Organ 2004 82 554ndash55
10 Kidson C Indaratna K Ecology economics and political will thevicissitudes of malaria strategies in Asia Parassitologia 1998 40 39ndash46
11 UNDP Millennium Development Goal 6 focuses on combatingHIVAIDS malaria and other diseases httpwebundporgmdggoal6shtml (accessed April 21 2012)
12 OrsquoMeara WP Mangeni JN Steketee R Greenwood B Changes inthe burden of malaria in sub-Saharan Africa Lancet Infect Dis 201010 545ndash55
13 Dondorp AM Fairhurst RM Slutsker L et al The threat ofartemisinin-resistant malaria N Engl J Med 2011 365 1073ndash75
14 Ranson H NrsquoGuessan R Lines J et al Pyrethroid resistance inAfrican anopheline mosquitoes what are the implications formalaria control Trends Parasitol 2011 27 91ndash98
15 Cohen J Smith D Cotter C et al Malaria resurgence a systematic
review and assessment of its causes Malar J 2012 11 122 16 WHO WHO position statement on integrated vector management
Wkly Epidemiol Rec 2008 83 177ndash84 17 van den Berg H Mutero CM Ichimori K WHO guidance on
policy-making for integrated vector management Geneva WorldHealth Organization 2012
18 Stroup DF Berlin JA Morton SC et al Meta-analysis ofobservational studies in epidemiology JAMA 2000 283 2008ndash12
19 Modher D Liberati A Tetzlaff J Altman DG Group TP Preferredreporting items for systematic reviews and meta-analyses thePRISMA statement PLoS Med 2009 6 e1000097
20 Roll Back Malaria Malaria endemic countries Geneva The RBMPartnership 2010 httpwwwrbmwhointendemiccountrieshtml(accessed June 8 2011)
21 Wells G Shea B OrsquoConnell D et al The Newcastle-Ottawa Scale(NOS) for assessing the quality of nonrandomised studies inmeta-analyses Ottawa University of Ottawa 2011
22 Egger M Smith GD Schneider M Minder C Bias in meta-analysisdetected by a simple graphical test BMJ 1997 315 629ndash34
23 Al-Taiar A Assabri A Al-Habori M et al Socioeconomic andenvironmental factors important for acquiring non-severe malariain children in Yemen a case-control studyTrans R Soc Trop Med Hyg 2009 103 72ndash78
24 Baragatti M Fournet F Henry M-C et al Social and environmentalmalaria risk factors in urban areas of Ouagadougou Burkina FasoMalar J 2009 8 13
25 Clarke SE Boslashgh C Brown RC Pinder M Walraven GELLindsay SW Do untreated bednets protect against malariaTrans R Soc Trop Med Hyg 2001 95 457ndash62
26 Custodio E Descalzo MAacute Villamor E et al Nutritional andsocio-economic factors associated with Plasmodium falciparum infection in children from Equatorial Guinea results from anationally representative survey Malar J 2009 8 225
27 Gahutu J-B Steininger C Shyirambere C et al Prevalence and riskfactors of malaria among children in southern highland Rwanda
Malar J 2011 10 134 28 Ghebreyesus TA Haile M Witten KH et al Household risk factorsfor malaria among children in the Ethiopian HighlandsTrans R Soc Trop Med Hyg 2000 94 17ndash21
29 Koram KA Bennett S Adiamah JH Greenwood BM Socio-economicrisk factors for malaria in a peri-urban area of The GambiaTrans R Soc Trop Med Hyg 1995 89 146ndash50
30 Krefis AC Schwarz NG Nkrumah B et al Principal componentanalysis of socioeconomic factors and their association withmalaria in children from the Ashanti Region Ghana Malar J 20109 201
31 Ongrsquoecha JM Keller CC Were T et al Parasitemia anemia andmalarial anemia in infants and young children in a ruralholoendemic Plasmodium falciparum transmission area Am J Trop Med Hyg 2006 74 376ndash85
32 Pullan RL Kabatereine NB Bukirwa H Staedke SG Brooker SHeterogeneities and consequences of Plasmodium species andhookworm coinfection a population based study in Uganda
J Infect Dis 2011 203 406ndash17
33 Ronald LA Kenny SL Klinkenberg E et al Malaria and anaemiaamong children in two communities of Kumasi Ghana
a cross-sectional survey Malar J 2006 1 105 34 Slutsker L Khoromana CO Hightower AW et al Malaria infection
in infancy in rural Malawi Am J Trop Med Hyg 1996 55 71ndash76 35 Villamor E Fataki MR Mbise RL Fawzi WW Malaria parasitaemia
in relation to HIV status and vitamin A supplementation amongpre-school children Trop Med Int Health 2003 8 1051ndash61
36 Winskill P Rowland M Mtove G Malima R Kirby M Malaria riskfactors in north-east Tanzania Malar J 2011 10 98
37 Yamamoto S Louis VR Sie A Sauerborn R Household risk factorsfor clinical malaria in a semi-urban area of Burkina Fasoa case-control study Trans R Soc Trop Med Hyg 2010 104 61ndash65
38 Clark TD Greenhouse B Njama-Meya D et al Factors determiningthe heterogeneity of malaria incidence in children in KampalaUganda J Infect Dis 2008 198 393ndash400
39 Klinkenberg E McCall P Wilson M et al Urban malaria andanaemia in children a cross-sectional survey in two cities of GhanaTrop Med Int Health 2006 11 578ndash88
40 Kreuels B Kobbe R Adjei S et al Spatial variation of malariaincidence in young children from a geographically homogeneousarea with high endemicity J Infect Dis 2008 197 85ndash93
41 Matthys B Vounatsou P Raso G et al Urban farming and malariarisk factors in a medium-sized town in Cocircte drsquoIvoireAm J Trop Med Hyg 2006 75 1223ndash31
42 Pullan RL Bukirwa H Staedke SG Snow RW Brooker S Plasmodium infection and its risk factors in eastern Uganda Malar J 2010 4 9
43 Somi MF Butler JRG Is there evidence for dual causation betweenmalaria and socioeconomic status Findings from rural TanzaniaAm J Trop Med Hyg 2007 77 1020ndash27
44 Chuma JM Thiede M Rethinking the economic costs of malaria atthe household level evidence from applying a new analyticalframework in rural Kenya Malar J 2006 5 76ndash89
45 Onwujekwe O Hanson K Are malaria treatment expenditurescatastrophic to different socio-economic and geographic groups andhow do they cope with payment A study in southeast NigeriaTrop Med Int Health 2010 15 18ndash25
46 Sicuri E Davy C Marinelli M et al The economic cost tohouseholds of childhood malaria in Papua New Guinea a focus onintra-country variation Health Policy Plann 2012 27 339ndash47
47 Matovu F Goodman C How equitable is bed net ownership andutilisation in Tanzania A practical application of the principles ofhorizontal and vertical equity Malar J 2009 8 109ndash21
48 Gingrich CD Hanson K Marchant T Mulligan J-A Mponda HPrice subsidies and the market for mosquito nets in developingcountries a study of Tanzaniarsquos discount voucher schemeSoc Sci Med 2011 73 160ndash68
49 Ahmed SM Haque R Haque U Hossain A Knowledge on thetransmission prevention and treatment of malaria among twoendemic populations of Bangladesh and their health-seekingbehaviour Malar J 2009 8 173
50 Saaka M Oosthuizen J Beatty S Effect of joint iron and zincsupplementation on malarial infection and anaemiaEast Afr J Public Health 2009 6 55ndash62
51 Worrall E Basu S Hanson K Is malaria a disease of povertyA review of the literature Trop Med Int Health 2005 10 1047ndash59 52 Barat LM Palmer N Basu S Worrall E Hanson K Mills A Do
malaria control interventions reach the poor Am J Trop Med Hyg 2004 71 174ndash78
53 Sterne JAC Sutton AJ Ioannidis JPA et al Recommendationsfor examining and interpreting funnel plot asymmetry inmeta-analyses of randomised controlled trials BMJ 2011 343 4002
54 Garcia-Martin G Status of malaria eradication in the AmericasAm J Trop Med Hyg 1972 21 617ndash33
55 Bruce-Chwatt L de Zulueta J The rise and fall of malaria inEurope London Oxford University Press 1980
56 Sinden RE Malaria mosquitoes and the legacy of Ronald RossBull World Health Organ 2007 85 894ndash96
57 Keiser J Singer BH Utzinger J Reducing the burden of malariain different eco-epidemiological settings with environmentalmanagement a systematic review Lancet Infect Dis 20055 695ndash708
8132019 SE Dev as an Intervention Agst Malaria
httpslidepdfcomreaderfullse-dev-as-an-intervention-agst-malaria 1010
Articles
10 www thelancet com Published online June 19 2013 httpdx doi org10 1016S0140-6736(13)60851-X
58 Randolph SE Rogers DJ The arrival establishment and spread ofexotic diseases patterns and predictions Nat Rev Microbiol 2010
8 361ndash71 59 Hay S Guerra C Tatem A Noor A Snow R The global distribution
and population at risk of malaria past present and futureLancet Infect Dis 2004 4 327ndash36
60 Killeen GF Fillinger U Kiche I Gouagna LC Knols BGEradication of Anopheles gambiae from Brazil lessons for malariacontrol in Africa Lancet Infect Dis 2002 2 618ndash27
61 Jetten T Takken W Anophelism without malaria in Europea review of the ecology and distribution of the genus Anopheles in
Europe Wageningen Wageningen Agricultural University 1994 62 Millennium Villages Project httpwwwmillenniumvillagesorg
the-villages (accessed May 16 2013) 63 Garrett L Global health hits crisis point Nature 2012 482 7
8132019 SE Dev as an Intervention Agst Malaria
httpslidepdfcomreaderfullse-dev-as-an-intervention-agst-malaria 1010
Articles
10 www thelancet com Published online June 19 2013 httpdx doi org10 1016S0140-6736(13)60851-X
58 Randolph SE Rogers DJ The arrival establishment and spread ofexotic diseases patterns and predictions Nat Rev Microbiol 2010
8 361ndash71 59 Hay S Guerra C Tatem A Noor A Snow R The global distribution
and population at risk of malaria past present and futureLancet Infect Dis 2004 4 327ndash36
60 Killeen GF Fillinger U Kiche I Gouagna LC Knols BGEradication of Anopheles gambiae from Brazil lessons for malariacontrol in Africa Lancet Infect Dis 2002 2 618ndash27
61 Jetten T Takken W Anophelism without malaria in Europea review of the ecology and distribution of the genus Anopheles in
Europe Wageningen Wageningen Agricultural University 1994 62 Millennium Villages Project httpwwwmillenniumvillagesorg
the-villages (accessed May 16 2013) 63 Garrett L Global health hits crisis point Nature 2012 482 7