Socioeconomic inequities in treatment and prevention of

malaria in Tanga district, Tanzania

Presenter: Fred Matovu

Inaugural AfHEA Conference 10-12th March, 2009

Accra, Ghana

DeMTAP study site

Study site

Background

Malaria situation in Tanzania• Accounts for > 39% of the national disease

burden• Accounts for about 48% of under5 mortality

(facility –based data, 2005) • Leading diagnosis for outpatient visits• Major cause of mortality in Tanga (Lusingu, et al

2004).• Malaria prevalence higher among the poor

Effective remedies ITNs• Reduce Under5s death by 20%, saving 6 lives

for every 1000 under5 in SSA.• Reduce maternal mortality, anaemia & low birth

weight• Cost per DALY averted <$50• >40% protective efficacy against clinical malaria

(Ter Kuile, et al 2003) ACTs• Effective in malaria treatment

Accessibility to ITNs and ACTs

• The poor are less likely to use preventive measures (Webster et al, 2005; Worrall et al, 2007, 2005; Thwing et al, 2008 etc).

• RBM initiative emphasises improving ITN and ACT access for the poor

• The poor cannot afford ACTs without a subsidy (Wiseman, et al 2005; Whitty et al 2008)

Bednets in Tanga

By time of survey 2003-2005;• Nets were available from drug stores,

pharmacies and retail shops• A net cost about 3000/=Ts( US$ 3)• Insecticide for net treatment cost ~

0.20US$

• No subsidised nets (only a few distributed by Tanga Rotary club (very occasional)

Bednets in Tanga cont..

• After survey– Subsidised nets for pregnant women were

introduced mid-2006• Discounted voucher scheme of 75% of cost of ITN

– Subsidised nets distributed in an integrated child health campaign (CHC)

• Mass free distribution of nets to under-5s

– Net re-treatment campaigns under CHC

Malaria treatment

By survey time:• Sulfadoxine-pyrimethamine (SP) was 1st

line treatment– Retail price ranging 0.30-0.50 US per adult

dose• Other antimalarials included: quinine;

amodiaquine, artesunate, artemether-lumefantrine ( ALU)

Post-Survey• ALU is 1st line treatment (since 2006)

Study objectives

To analyse socioeconomic inequalities in:

1. Ownership and utilisation of bednets2. Obtaining AMs for reported fever

Sampling

• Simple random sampling was used to select wards, villages/streets and sub-villages

• 32 streets and 40 sub-villages were selected• 1603 households interviewed: (863 in rural and

740 in urban areas), Sept.03 - July 05• 16 FGDs: (8-mothers & 8- male household

heads), Dec 2006

Typical urban homestead

Typical rural homestead

Data collection process

Measurement: SES

• Education class: formal schooling of household head

1. None2. Lower primary (1-4 yrs)3. Upper primary (5-7yrs)4. Secondary (8-11 yrs)5. Post-secondary (12+ yrs)

• Asset-based wealth index (McKenzie, 2003)– PCA score for 14 household items (e.g. iron roof,

bicycle, iron bed, mattress etc)

Distribution of HH by education

.

17

3

1116

712

61

5257

5

21

12

1

17

9

0

10

20

30

40

50

60

70

Rural Urban Overall

%ag

e of

hou

seho

lds

by e

duca

tion

No education

1 - 4 yrs

5 - 7 yrs

8 - 11 yrs

12+ yrs

Distribution of HH by wealth index

34

4

20

28

11

202118

20

14

27

20

3

40

20

0

10

20

30

40

50

Rural Urban Overall

%of

hou

seho

lds

by w

ealth

Poorest

2nd quintile

3rd quintile

4th quintile

Least poor

Measurement 1: Net Ownership and Utilisation

• Household level – at least one net– Assumed all households in same “need”

• Individual level – slept under a net night before the survey (HH roster)– Assumed Under5s are in greater “need”

• ITNs: nets treated in past six months

Measurement 2: Utilisation of AMs

• Obtaining an AM at health provider visited– Perceived severe fevers and Under5s were

considered in greater need

• Health providers considered were:– Hospital– Health centre– Dispensary– Drug shop

Result1 : Distribution of at least one net by wealth quintiles

32

59

39

48

85

6860

92

83

70

88 8592 94 94

0

10

20

30

40

50

60

70

80

90

100

Rural Urban Overall

%ag

e ut

ilisa

tion

of a

t lea

st o

ne n

et b

y ho

useh

olds Poorest

2nd quintile

3rd quintile

4th quintile

Least poor

Result 2: Distribution of at least one ITNs by wealth quintiles

3

17

77

44

27

10

4434

19

42 3832

58 58

010

20304050

607080

90100

Rural Urban Overall

%ag

e ut

ilisa

tion

of tr

eate

d ne

ts b

y ho

useh

olds

Poorest

2nd quintile

3rd quintile

4th quintile

Least poor

Result 3: Concentration curves for utilisationof ITNs

Inequality in utilisation of treated nets by wealth quintiles

0

20

40

60

80

100

0 20 40 60 80 100

Cumulative proportion of households ranked by wealth

Cum

ulat

ive

prop

ortio

n of

us

ing

at le

ast o

ne tr

eate

d ne

t

Perfect equalityOverallRuralUrban

Result 4: Inequalities in bednet utilisation at household level

InterventionInequality

measure Rural Urban OverallUtilisation of ITNs Equity ratio:      

  Wealth 10.7 3.4 8.3

  Education class 8.5 6.3 12.4

  Conc. Index:

  Wealth 0.368 (3.67)* 0.093 (1.95) 0.276 (2.60)*

  Education class 0.276 (2.93)* 0.117(1.70) 0.234 (2.01)*

Utilisation of all nets Equity ratio:      

  Wealth 2.9 1.6 2.4

  Education class 2.3 1.2 2.0

  Conc. Index:

  Wealth 0.169 (3.98)* 0.027 (0.96) 0.138 (2.30)*

  Education class 0.108 (2.21)* 0.028 (2.36)* 0.089 (2.07)*

Result 5: Utilisation of any nets by age group

74%

86%

46%

68%

79%

31%

0

10

20

30

40

50

60

70

80

90

100

Rural Urban Overall

%ag

e ut

ilisa

tion

of a

ny n

et

Under5

Over5

Result 6: Utilisation of ITNs by age group

36%

47%

10%

35%

43%

7%

0

10

20

30

40

50

60

70

80

90

100

Rural Urban Overall

%ag

e ut

ilisa

tion

of a

trea

ted

net

Under5

Over5

Regression results for net use at HH level

Explanatory variableAll Nets

 ITNs

 

 Marginal/Average

effectsp-value

 Marginal/Average

effects   p-value

Family size 0.001 0.973 -0.002 0.656

Male -0.032 0.555 -0.01 0.859

Urban 0.228 <0.001* 0.205 <0.001*

Married 0.007 0.887 0.043 0.408

Sambaa 0.072 0.214 0.031 0.496

Digo -0.085 0.108 -0.102 0.012*

Bondei 0.079 0.248 -0.051 0.314

Other ethnic group 0.006 0.908 -0.078 0.048*

Age -0.002 0.097 -0.001 0.133

Using other prevention measures -0.232 <0.001* -0.006 0.81

Education 0.013 0.008* 0.02 <0.001*

Wealth 0.11 <0.001* 0.053 <0.001*

Education-squared -0.002 0.028* -0.001 0.073

Wealth -squared -0.008 <0.001* -0.004 <0.001*

Poor Road 0.012 0.698 -0.07 0.085

Market centre -0.016 0.764 -0.008 0.906

Constant - - - -

Regression results for net use: Indv levelExplanatory variable All Nets ITNs  Marginal/Average effects   p-value Marginal/Average effects   p-value

Family size -0.274 <0.001* -0.008 <0.001*

Urban 0.315 <0.001* 0.192 <0.001*

Education class 0.018 <0.001* 0.02 <0.001*

Married 0.031 0.051* 0.04 <0.001*

Poor road 0.005 0.785 -0.075 <0.001*

Market centre -0.072 0.009* -0.033 0.235

Under5 0.157 <0.001* 0.06 <0.001*

Male -0.056 <0.001* -0.02 0.023*

Using other prevention measure -0.182 <0.001* -0.004 0.684

Sambaa -0.087 0.003* 0.011 0.546

Digo -0.176 0.001* -0.086 0.001*

Bondei -0.05 0.15 -0.056 0.005*

Other ethnic group -0.09 0.001* -0.056 <0.001*

Wealth 0.091 0.001* 0.043 <0.001*

Wealth -squared -0.007 0.001* -0.004 <0.001*

Constant - - - -

Sources of treatment for reported fever

Treatment source Under5s Over5s Total p-value*  (n=339) (n=739) (n=1078)  Any treatment† 331(98%) 681(93%) 1012 (94%) <0.0001*

of which:        

Government facility 185 (57%) 264 (40%) 449 (45%) <0.0001*Private facility 26 (11%) 84 (14%) 110 (13%) 0.1632

Drug store 34 (13%) 156 (26%) 190 (22%) 0.0002*General shop 76 (18%) 182 (19%) 258 (19%) 0.4681

Traditional healer 13 (3%) 5 (0.5%) 18 (1%) 0.0054*

Other 6 (2%) 10 (1%) 16 (1%) 0.5856

Result 8: Proportion obtaining AMs and reporting severe fever

27 25 21 19 19

5059

67 67 66

0

20

40

60

80

100

Pooerest 2nd 3rd 4th Leastpoor

Proportion of Patients

Obt

aini

ng A

Ms

and

feve

r se

verit

y

Fever severity

Obtaining AMs

Inequalities in obtaining AMs

SES Measure Concentration index

  Rural Urban Overall

Wealth 0.092 (3.23) 0.005(0.33) 0.055 (2.62)

Education 0.085(2.06) 0.033(1.39) 0.064(1.81)

Result 9: Probability of obtaining an AM by treatment source

Explanatory variables Marginal Effects

 

Government Health units

Private health units Drug shop All providers

Urban 0.106* -0.001 0.014 0.052*

Poor road -0.086 -0.058* -0.043 -0.098*

Market centre -0.202* 0.01 0.046 -0.062

Education of household head 0.011 0.004 0.003 0.021*

Married 0.033 0.021 0.018 0.043

Sambaa -0.111 -0.016 0.022 -0.131

Digo -0.155* -0.016 -0.037 -0.150*

Bondei -0.175* -0.008 0.171* 0.012

Other ethnic group -0.170* 0 0.034 -0.104*

Male -0.064* 0.012 0.02 -0.041

Severe fever 0.058 0.014 -0.034 0.009

Wealth -0.013 0.011* -0.01 -0.001

Under5 0.180* -0.016 -0.078* 0.065*

Distance to facility 0.042* 0.004* 0.002 0.043*

Constant - - - -

Summary of findings 1: Nets

• Use of any net was higher in urban (90%) than rural areas (50%)

• Use of ITNs was higher in urban (48%) than rural (9%) areas

• A lot of nets in use were not treated• SES, urban location, small family size and

being under5 positively associated with net use

Summary of key findings 2: Nets

• Pro-rich inequalities in utilisation and ownership of any net and ITNs

• Inequalities were greater in rural areas• Lack of money was major barrier to net use• Some evidence of negative perceptions for use

of ITNs

Summary of findings 3: AMs

• Inequalities in obtaining AMs were pro-rich overall and in rural areas

• Drug shops + general shops were a major source of treatment ( >40%)

• Factors positively associated with obtaining AMs: Living in urban areas; education; short distance to facility; being under5

Policy implication• Need for community-wide treatment of all nets not treated

currently

• Need to promote greater access of ITNs and ACTs among the poor. For example

– Targeted intervention to reduce costs: discounted voucher schemes and mass ITN distribution

– Encourage use of LLINs and longer-lasting net treatment– Drug subsidy incl. at drug shops

• Public campaign to encourage net treatment and mitigate negative perceptions

• Monitoring equity outcomes on interventions to ensure the poorest of the poor benefit

Suggestion for future research • Equity analysis in monitoring and evaluation of

malaria control interventions

• ITNs inequality assessment following new strategies: discounted voucher scheme +mass free distribution of ITNs

Methodological• Using a range of inequality measures• Assessment of relevance of SES measure

Acknowledgements

• Gates Malaria Partnership, LSHTM– For funding the DeMTAP study– Training research fellowship

• AfHEA Secretariat – funding conference

• DeMTAP field staff, FGDs and survey participants