Assessment of seasonal and climatic effects on the incidence and species composition of malaria 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) 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) Databasenumber of citations Medline: Embase: CAB-Health: Merged database ( excluding repeated citation ) 829

Meta-analysis (2) Reviewing abstracts (829) –Non eligible papers657 (72.2%) –Eligible papers104 (12.5%) –Uncertain 68 ( 8.3%) Reviewing full texts of papers (172) –Eligible for meta-analysis 62 (36.1%) –Non eligible for meta-analysis108 (63.3%) –Was not available (from China) 1 ( 0.6%)

Meta-analysis (3) Number of studiesPercentage Continent Asia Africa America Spatial span Villages District Province or larger Temporal span Month Season Year Greater than one year Age group Children All age groups or adults Samples Febrile Normal

Meta-analysis (4) Minimum OR=0.02 Maximum OR= 10.9 Summary OR=0.6 ( ) 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( ) 0.21( ) 1.76( ) Temporal Span Month(26) Season(12) Year or longer(24) 0.81( ) 0.97( ) 0.39( ) Age group Children(5) Mixed(57) 1.38( ) 0.56( ) P. falciparum risk (%) <10(23) (10) ≥15(29) 1.06( ) 0.75( ) 0.4( ) Subjects Normal(36) Febrile(26) 0.9( ) 0.35( ) P. vivax risk (%) <5(27) (18) ≥10(17) 1.43( ) 0.49( ) 0.25( ) Spatial span A few villages(36) District(16) Larger than a district(10 ) 0.5( ) 0.99( ) 0.49( ) Both species risk (%) <15(18) (22) ≥30(22) 2.51( ) 0.5( ) 0.32( )

Meta-analysis (6) Subgroup Tau square* Model 1: no explanatory variable0.91 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) heterogeneity in infection risks 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 risks within the population. Therefore, infection risk could be considered as a confounder.

Modelling (2) 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 (k 1 ) 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) The impact of k i on the overall OR in the whole population

Modelling (5) 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) Conclusion 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) Objectives 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, ,761 9, Negative for all species (49,742) P. malariae Frequencies of single and mixed Plasmodium spp in 118,346 blood slides

Garki (6) 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 months Number (%) 8-12 months Number (%) year Number (%) ≥10 year Number (%) P. falciparum OR (95% CI) 0.75 ( ) 2.52 ( ) 3.9 ( ) ( ) 1-1- OR for the whole first year: 2.1 ( ) P. malariae OR (95% CI) 0.56 ( ) 1.31 ( ) 1.95 ( ) 5.9 ( ) 1-1- OR for the whole first year: 1.3 ( ) P. ovale OR (95% CI) (1.68-4) 2.2 ( ) 4.2 ( ) 1-1- OR for the whole first year: 4.2 ( ) The risk of infection with Plasmodium spp classified by age

Garki (9) P. malariae OR (95% CI) P. ovale OR (95% CI) All subjects Age (year) <1 1-9 >=10 Season Dry and cool Dry and hot Wet Rho= ( ) 6.25( ) 2.32( ) 3.97( ) 4.02( ) 6.32( ) 3.58( ) Rho= ( ) 6.26( ) 2.19( ) 3.95( ) 5.53( ) 3.94( ) 3.76( ) 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. falciparum OR (95% CI) P. malariae OR (95% CI) P. ovale OR (95% CI) All subjects Age (year) <1 1-9 >=10 Season Dry and cool Dry and hot Wet Rho= (1.9-2) 9.3( ) 3.1( ) 1.5( ) 4.3( ) 9.8(9-10.6) 4.3( ) Rho= ( ) 11.6(6.8-20) 2( ) 1.8(1.7-2) 4.1( ) 5.5( ) 3.6( ) Rho= (3-4.4) 6.9( ) 2.0( ) 2.7( ) 2.6(2-3.5) 4( ) 4.7( )

Garki (11) 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*01-50>50 P. malariae P. Ovale * number of positive filed in 200 examined fields

Garki (13) Latter survey P. falciparumP. malariaeP. ovale Former Survey P. falciparum OR(95% CI) Rho 1.9(1.9-2) ( ) (3-4.4) 0.34 P. malariae OR(95% CI) Rho 1.7(1.5-2) ( ) ( ) 0.03 P. ovale OR(95% CI) Rho 1.9( ) ( ) ( ) 0.17 The association between Plasmodium spp adjusted for intra-person clustering effect in cross-sectional analysis

Garki (14) >11-9>=10>11-9>=10 Plasmodium malariae Plasmodium ovale age group (year) Daily conversion rates in logarithmic scale pf negative acquisition ratepf negative clearance rate pf positive acquisition ratepf 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

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