The results of this analysis build on the findings of the Malaria Atlas Project (MAP), which previously defined the spatial limits of malaria within all endemic countries,20,21
to present a more detailed empirical description of malaria risk in Bangladesh. The findings indicate that malaria in the country exhibits a unique spatial pattern, with varying prevalence levels and significant and justifiable relationships to environmental variables. Importantly, there exist areas of relatively high transmission that should not be neglected because of their remoteness.
The presentation of spatial variation in disease risk is one of the most important functions of spatial analysis,52
with a diagnostic semi-variogram the first step in this process. For malaria in Bangladesh, the observed spatial autocorrelation at distances up to 50 km is approximately one-half of that observed in Kenya17
but about 10 times that observed on a small island in Vanuatu.11
The practical implication of this is that malaria control in Bangladesh will need to be targeted at a finer spatial scale than in Kenya but not with the same precision as that in Vanuatu.
The observed variation in risk can then be formally included in the geostatistical model to generate a continuous prediction surface, with this surface being the enabling link between the original point-prevalence survey and instructive outputs to guide the control program. For example, if the program managers decide that they will first target populations living in areas with prevalences above 5%, the predicted prevalence map shown in clearly demarcates their targeted area. The calculations in are then able to inform program managers of the number of bed nets, for example, that need to be procured to cover 80% of the population in this area. The number of cases averted, however, will depend on which 80% of the population is provided with bed nets: those living closest to settlements > 50,000 people or those living in the highest risk areas. This has important implications in meeting the first key objective of the control program, which is to effectively diagnosis and treat 80% of estimated malaria cases.
Targeting those higher endemicity regions located within forested areas will become particularly important as Bangladesh scales up control operations with the forest foci thought to serve as reservoirs from which the lowland and floodplain areas are reinfected.23
Movement of people to and from the forest not only provides a constant flow of malaria parasites from the forest to rural communities53
but also exposes immunologically naive individuals to high levels of transmission.23
To what extent the flow of malaria from the hilly, forested regions maintains transmission in the unforested, lowland areas is an important issue that warrants further research.
An additional challenge for the national control program is the flow of drug-resistant malaria across the border from Myanmar.54
Areas of elevated transmission along the border region with Myanmar, which can be identified from the risk maps (), present strategic locales for both the surveillance of imported malaria cases and drug-resistant malaria. Any attempt to control malaria in Bangladesh is jeopardized by unmonitored migration across the border.
An earlier study identified the most influential environmental parameters for the distribution of An. dirus
to be forest cover, altitude, rainfall, and temperature.55
The current analysis replicated the positive associations with forest cover, altitude, and temperature but did not find a significant relationship with rainfall, possibly because of high and relatively consistent levels of rainfall across the study region over the monsoonal period. The positive association between malaria risk and increasing elevation, although unusual for some vector species, is to be expected for An. dirus
with higher altitudes providing denser forest cover and thus, a more favorable ecological environment.56
As GIS and geostatistical techniques become more sophisticated, there is a need to foster better links between malaria-program managers and researchers such that the most useful data are generated and that these data are accessible and usable outside the research community. Strong research-program links in Kenya led to the development of a 2009 malaria-risk map for Kenya, which is now the cornerstone for planning the scale up of malaria interventions such as ITNs and malaria diagnostics in the country beyond 2009.17
Additionally, risk maps and geo-spatial data are increasingly being used to support malaria elimination in the southwest Pacific.11
With closer ties to national programs, the results of model-based geostatistics can be better used.
In conclusion, geographic targeting has tremendous potential to enhance the effectiveness of the national malaria-control program in Bangladesh. In Bangladesh, the areas of relatively high malaria transmission in the hilly, forested region present an immediate starting point for malaria-control activities. Targeting this area will ensure that interventions reach those most susceptible and potentially, control the flow of malaria to lowland low-risk areas. Furthermore, the presented PfPR2–10 estimates may provide baseline information against which epidemiological changes can be compared as the malaria-control program in Bangladesh is scaled up.