Assessment of seasonal and climatic effects Assessment of seasonal and climatic effects on the incidence and species composition of on the incidence and species composition of

malaria by using GIS methodsmalaria by using GIS methods

Ali-Akbar Haghdoost

Neal Alexander (supervisor)

Main objectives

1. Assessment of the feasibility of an early warning system based on ground climate and remote sensing data

2. Assessment of the interaction between Plasmodium spp from different points of view: meta-analysis, modelling, and extended analysis of a large epidemiological dataset

Feasibility of the early warning (1)0

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Jun 00Jun 98Jun 96Jun 94date

fitted value ppv

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Jun 00Jun 98Jun 96Jun 94date

fitted value ppf

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Jun 00Jun 98Jun 96Jun 94date

fitted value all species

num

ber of c

ase

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P. vivax P. falciparum

The fitted values of models based on seasonality, time trend and meteorological variables classified by species, observed numbers (dashes) and model estimated number (solid line)

Feasibility of the early warning (2)

• Main findings– Ground climate data explained around 80% of P. vivax

and 85% of P. falciparum variations one month ahead

– Comparing to the extrapolation of data from previous month, ground climate data improve the accuracies around 10%; but remote sensing data does not improve

– The ground climate data are freely available in the filed; therefore, it was concluded that the models based on ground climate data are feasible.

What is the interaction?

The difference between the observed number

of mixed infections in blood slides and the

expected number if infection with one species

is independent of infection with other species

Why the interaction is important?

• To know more about the pathogenesis of Plasmodium spp

• To know more about the immunity mechanisms against Plasmodium spp

• To estimate the impact of vaccine against one species on the other species

Positive interaction

1. Similarity in transmission routes

2. Higher susceptibility of a subgroup of people

Negative interaction

1.Suppression

2.Cross immunity

3.Differences in the biology of Plasmodium spp

4.Environmental factors

5.Missed mixed infections in blood slides

Background

Howard (2001) showed that the logarithm of odds ratio between P. falciparum and P. vivax changed in a wide rage from –5.08 (in Bangladesh) to 2.56 (in Sierra Leone).

He found that in Asian countries, the associations were largely negative; however, positive associations were seen in Tanzania, Papua New Guinea and USA.

Questions

• What is the overall association between species?

• How we can explain the differences between study findings?

Sections

1.Meta-analysis• To quantify the interaction between P. falciparum and

P. vivax

• To assess the source of the heterogeneities

2.Modelling the heterogeneity effect

3.To measure the association between Plasmodium spp in the Garki region of Sudan Savanna of west Africa

Meta-analysis (1)

Database number of citations

Medline: 1966-2001 395

Embase: 1980-2001 77

CAB-Health: 1973-2001 455

Merged database (excluding repeated citation) 829

Meta-analysis (2)

• Reviewing abstracts (829)– Non eligible papers 657 (72.2%)– Eligible papers 104 (12.5%)– Uncertain 68 ( 8.3%)

• Reviewing full texts of papers (172)– Eligible for meta-analysis 62 (36.1%)– Non eligible for meta-analysis 108 (63.3%)– Was not available (from China) 1 ( 0.6%)

Meta-analysis (3) Number of studies Percentage

Continent Asia Africa America

5246

83.96.49.7

Spatial span Villages District Province or larger

361610

58.125.816.1

Temporal span Month Season Year Greater than one year

26125

19

41.919.38.130.7

Age group Children All age groups or adults

557

8.191.9

Samples Febrile Normal

2636

41.958.1

Meta-analysis (4)

• Odds ratio

• .00167 • 100

• Overall (95% CI)

Minimum OR=0.02

Maximum OR= 10.9

Summary OR=0.6 (0.49-0.79)

Number of studies with OR<1=41

Number of studies with OR>1=32

Meta-analysis (5)

Subgroup (number of studies)

Odds ratio(95%CI)

Subgroup (number of studies) Odds ratio(95%CI)

Continent Asia (52) South America (6) Africa(4)

0.62(0.46-0.83)0.21(0.16-0.26)1.76(0.47-6.6)

Temporal Span Month(26) Season(12) Year or longer(24)

0.81(0.56-1.17)0.97(0.52-1.79)0.39(0.26-0.6)

Age group Children(5) Mixed(57)

1.38(0.31-6.08)0.56(0.43-0.75)

P. falciparum risk (%) <10(23) 10-14.99(10) ≥15(29)

1.06(0.54-2.1)0.75(0.42-1.35)0.4(0.28-0.57)

Subjects Normal(36) Febrile(26)

0.9(0.65-1.24)0.35(0.21-0.58)

P. vivax risk (%) <5(27) 5-9.99(18) ≥10(17)

1.43(0.98-2.1)0.49(0.32-0.75)0.25(0.13-0.5)

Spatial span A few villages(36) District(16) Larger than a district(10)

0.5(0.33-0.75)0.99(0.591-1.63)0.49(0.3-0.82)

Both species risk (%) <15(18) 15-29.99(22) ≥30(22)

2.51(1.66-3.8)0.5(0.36-0.7)0.32(0.22-0.47)

Meta-analysis (6)

Subgroup Tau square*

Model 1: no explanatory variable 0.91

Model2: explanatory variables were age group, subjects (febrile or normal), spatial and temporal span of studies and continent

1.18

Model3: the only explanatory variable was the frequencies of all species (all Plasmodium species considered together) and temporal span of studies

0.72

The results of meta-reg analysis

*a measure of between studies heterogeneity

Meta-analysis (7)

• Main findings:Main findings:

– The overall OR (between P. vivax and P. falciparum)

was less than 1

– There were negative associations (weaker) between

the prevalence of species and the overall OR

– There was a negative association between the temporal

span of studies and the overall OR

Modelling (1)

Positive associations between species mean that a

subgroup of people, in terms of time or space, has

higher infection risks for all species, i.e.,

heterogeneity in infection risksheterogeneity in infection risks within the

population.

Therefore, infection risk could be considered as a

confounder.

Modelling (2)

Main question:Main question:

Can the confounding effect of the heterogeneity in infection risks explain

OR as large as 11 by its own?

Modelling (3)

Model specification:– Population has been divided into low and high risk strata

– The OR between species in each stratum was 1

– The risk ratio of infection with species i in high risk versus low risk stratum (k1) was varied from 1 up to its

maximum possible values

– The ratio of the populations in low and high risk strata (m) was varied in a wide range (0.2-5)

– The prevalence of species were varied in a wide range from 0.05 to 0.8

Modelling (4)

1 3 5 7 9

11

13

15

16.6

13

5.80

15

30

45

60

75

90

105 OR

k2 k 1

90-105

75-90

60-75

45-60

30-45

15-30

0-15

• The impact of ki on the overall OR in the whole population

Modelling (5)

0

1

2

3

4

5

6

7

0.2 0.6 1 1.4 1.8 2.2 2.6 3 3.4 3.8 4.2 4.6 5

The ratio of populations in high and low risk groups

The impact of m on the overall OR in the whole population

Modelling (6)

• Greatest ORs were observed when the prevalence of species were equal

• By increasing the prevalence of species in low risk stratum, the overall OR was decreased

Modelling (7)

ConclusionConclusion

Just heterogeneity in infection risk can explain an OR as large as 11

Garki (1)

The Garki project was one of the largest epidemiological studies on malaria, with data comprised from more than 12,000 people in 23 surveys.

It was conducted in a highly endemic area in northern Nigeria from 1969 to 1976 by co-operation between the World Health Organisation (WHO) and the Nigerian government.

Garki (2)

The published results of the Garki data had not thoroughly explored the interactions between Plasmodium species, and that too had only approached this issue cross-sectionally using very simple methods.

Garki (3)

ObjectivesObjectives

To measure the associations between Plasmodium spp cross-sectionally and longitudinally; and assess the effects of:

repeated infections (i.e., within subject clustering)

Age

spatial and temporal distribution of individual species

Garki (4)

• Cross-sectional analysisCross-sectional analysis: the presence of P. falciparum in each survey was considered as a risk factor for the presence of the other species in the same survey

• Longitudinal analysisLongitudinal analysis: the presence of one species in each survey was considered as a risk factor for the presence of the other species in the following survey

Garki (5)

P. falciparum

P. ovale

43,713

1,37

2

12,761

9,588

703

32

435

Negative for all species (49,742)

P. malariae

Frequencies of single and mixed Plasmodium spp in 118,346 blood slides

Garki (6)

0

10

20

30

40

50

60

70

• 1 • 2 • 3• 4 • 5 • 6 • 7 • 8 • 9• 10• 11

Month

P. falciparum

P. malariae

P. ovale

Dry-hot Wet Dry-coolDry-cool

(%)

Jan Mar May July Sep Nov

Annual variation of Plasmodium spp prevalence, based on 6 years data

Garki (7)

Multi-level models showed that the risk of P.falciparum had the largest within person-variation, and also within and between village variations

Garki (8)

Age group<4 months

Number (%)4-7 monthsNumber (%)

8-12 monthsNumber (%)

1-9.9 yearNumber (%)

≥10 yearNumber (%)

P. falciparumOR

(95% CI)0.75

(0.64-0.9)2.52

(2.2-2.9)3.9

(3.41-4.56)11.68

(11.13-12.27)1-

OR for the whole first year: 2.1 (1.8-2.4)

P. malariaeOR

(95% CI)0.56

(0.39-0.8)1.31

(1.04-1.65)1.95

(1.6-2.37)5.9

(5.63-6.2)1-

OR for the whole first year: 1.3 (1.1-1.5)

P. ovaleOR

(95% CI)1

0.47-2.122.59

(1.68-4)2.2

(1.38-3.49)4.2

(3.72-4.75)1-

OR for the whole first year: 4.2 (3.6-5.0)

The risk of infection with Plasmodium spp classified by age

Garki (9)

P. malariaeOR (95% CI)

P. ovaleOR (95% CI)

All subjects

Age (year)<1 1-9>=10

SeasonDry and coolDry and hotWet

Rho=0.343.64(3.4-3.9)

6.25(2.63-14.82)2.32(1.70-3.16)3.97(3.24-4.85)

4.02(3.7-4.35)6.32(5.48-7.29)

3.58(3.3-3.9)

Rho=0.255.1 (4.33-6.0)

6.26(2.64-14.83)2.19(1.59-3.03)3.95(3.23-4.84)

5.53(4.6-6.68)3.94(2.18-7.12)3.76(2.78-5.07)

The associations of P. falciparum (as risk factor) with other species adjusted for intra-person clustering effect in cross-sectional analysis

Garki (10)

The associations between P. falciparum in a former survey with species in the latter survey, adjusted for intra-person clustering effect

P. falciparumOR (95% CI)

P. malariaeOR (95% CI)

P. ovaleOR (95% CI)

All subjects

Age (year)<1 1-9>=10

SeasonDry and coolDry and hotWet

Rho=0.731.9(1.9-2)

9.3(7.6-11.5)3.1(2.7-3.6)1.5(1.4-1.6)

4.3(3.9-4.6)9.8(9-10.6)4.3(3.9-4.6)

Rho=0.442.7(2.5-2.9)

11.6(6.8-20)2(1.7-2.3)1.8(1.7-2)

4.1(3.7-4.5)5.5(4.8-6.2)3.6(3.2-4.1)

Rho=0.343.6(3-4.4)

6.9(2.7-17.7)2.0(1.4-2.7)2.7(2.2-3.4)

2.6(2-3.5)4(2.8-5.7)

4.7(3.5-6.4)

Garki (11)

Why the ORs were greater in infants?Why the ORs were greater in infants?

– Heterogeneity in infection risk (as the source of positive associations depends on:

• The heterogeneity in exposure to mosquitoes• The heterogeneity in acquired protective immunity

– It is reasonable to assume a positive association between the strength of acquired immunity and exposure to mosquitoes in adults. Therefore, these two factors somehow decreased their impacts on the heterogeneity in infection risk in adults.

Garki (12)

The relationship between P. falciparum density and the risk of other species based on cross-sectional data

Density* 0 1-50 >50P. malariae 1 4.05 8.66

P. Ovale 1 4.05 8.73

* number of positive filed in 200 examined fields

Garki (13)

Latter surveyLatter survey

P. falciparum P. malariae P. ovale

Former SurveyFormer Survey

P. falciparum

OR(95% CI)Rho

1.9(1.9-2)0.73

2.63(2.5-2.9)0.44

3.6(3-4.4)0.34

P. malariae

OR(95% CI)Rho

1.7(1.5-2)0.22

2.7(2.5-2.9)0.33

2.6(2.2-3.0)0.03

P. ovale

OR(95% CI)Rho

1.9(1.3-2.8)0.22

2.8(2.3-3.4)0.29

5.3(3.9-7.2)0.17

The association between Plasmodium spp adjusted for intra-person clustering effect in cross-sectional analysis

Garki (14)

0.0001

0.001

0.01

0.1

1

>1 1-9 >=10 >1 1-9 >=10

Plasmodium malariae Plasmodium ovaleage group (year)

Daily conversion rates in logarithmic scale

pf negative acquisition rate pf negative clearance rate

pf positive acquisition rate pf positive clearance rate

Estimated daily clearance and acquisition rates of P. malariae and P. ovale classified by the presence of P. falciparum in the former survey

Garki (15): conclusion

• Cross-sectional analysis:– Suppression decreases the association between

species

• Longitudinal analysis:– Cross immunity, suppression and changing one’s

behaviour (such as the exposure risk to mosquitoes) after contracting the first infection decrease the association between species

Garki (16): conclusion

• P. falciparum suppress other species particularly P. malaria

• The suppression is not just due to the competition for host cells or nutrients. It is most probably due to heterologous immunity

• Low level of acquired immunity suppresses the other species; stronger immunity increases the clearance rate, and very strong immunity decreases the acquisition rate as well.

Summary (1)

• A very wide range of associations between Plasmodium spp was observed in meta-analysis which was partly explained by the prevalence of species and the temporal span of studies

• The heterogeneity in infection risk (due to heterogeneity in exposure risk or immunity) can explain the observed high ORs in meta-analysis

Summary (2)

• The ORs in longitudinal analysis of the Garki data was smaller than those in cross-sectional analysis

• The ORs in infants were less than others which can be explained by the heterogeneity in infection risk theory

• P. falciparum suppresses other species, probably via immunological pathways

• People obtained protective immunity after many infections; therefore, the frequency of species had direct association with the variation of infection risk within and between subjects and villages

Time for your comments

Thanks for you kind attention