Fine-scale variation in malaria prevalence across ecological regions in Madagascar: a cross-sectional study

Abstract

Background Large-scale variation in ecological parameters across Madagascar is hypothesized to drive varying spatial patterns of malaria infection. However, to date, few studies of parasite prevalence with resolution at finer, sub-regional spatial scales are available. As a result, there is a poor understanding of how Madagascar’s diverse local ecologies link with variation in the distribution of infections at the community and household level. Efforts to preserve Madagascar’s ecological diversity often focus on improving livelihoods in rural communities near remaining forested areas but are limited by a lack of data on their infectious disease burden. Methods To investigate spatial variation in malaria prevalence at the sub-regional scale in Madagascar, we sampled 1476 households (7117 total individuals, all ages) from 31 rural communities divided among five ecologically distinct regions. The sampled regions range from tropical rainforest to semi-arid, spiny forest and include communities near protected areas including the Masoala, Makira, and Mikea forests. Malaria prevalence was estimated by rapid diagnostic test (RDT) cross-sectional surveys performed during malaria transmission seasons over 2013–2017. Results Indicative of localized hotspots, malaria prevalence varied more than 10-fold between nearby (< 50 km) communities in some cases. Prevalence was highest on average in the west coast region (Morombe district, average community prevalence 29.4%), situated near protected dry deciduous forest habitat. At the household level, communities in southeast Madagascar (Mananjary district) were observed with over 50% of households containing multiple infected individuals at the time of sampling. From simulations accounting for variation in household size and prevalence at the community level, we observed a significant excess of households with multiple infections in rural communities in southwest and southeast Madagascar, suggesting variation in risk within communities. Conclusions Our data suggest that the malaria infection burden experienced by rural communities in Madagascar varies greatly at smaller spatial scales (i.e., at the community and household level) and that the southeast and west coast ecological regions warrant further attention from disease control efforts. Conservation and development efforts in these regions may benefit from consideration of the high, and variable, malaria prevalences among communities in these areas.

Climatic, land-use and socio-economic factors can predict malaria dynamics at fine spatial scales relevant to local health actors: Evidence from rural Madagascar

“…Socio-ecological drivers of human malaria have been identified at global, national and subnational spatial scales [26][27][28], but it is currently unclear whether associations at large spatial PLOS GLOBAL PUBLIC HEALTH scales hold at smaller spatial scales relevant for malaria control efforts by local authorities [14]. Here, we used the rural district of Ifanadiana, in south-eastern Madagascar, to investigate the relationship between hypothesized social and environmental determinants of malaria and malaria incidence at a very fine spatial scale.…”

“…within a government district), where most control activities are actually implemented [13]. Malaria incidence can be highly heterogeneous at small spatial scales [14], so a key need to improve local strategies to curb malaria transmission is to better understand how disease risk varies at fine spatial and temporal scales, and whether there are predictable factors (environmental, socio-economic, climatic) driving this variability. This could help medical practitioners and local health programs anticipate resource needs or inform the implementation of targeted control activities.…”

“…High quality data on malaria incidence is limited in many countries. Studies attempting to predict malaria patterns often rely on surveys or active surveillance methods, which have limited geographic and temporal scope [14][15][16][17]. Routine health system data provide a more sustainable data source and are available in most countries, but the spatial resolution of such data is low when aggregate numbers of cases over a catchment area are reported, as is often the case, rather than the precise locations of cases [18].…”

Investigating inequalities in HIV testing in sub-Saharan Africa: insights from a spatial analysis of 25 countries

“…Spatial analysis at local scales has proved useful for the control and prevention of infectious diseases, by uncovering spatial variations in the transmission or the access to prevention [10–12]. Regarding HIV, it has helped identify high transmission areas [13] and understand access difficulties to healthcare facilities in underserved areas in Africa [14].…”

“…To the best of our knowledge, such a local analysis has only been performed in a single country, Malawi [9]. Spatial analysis at local scales has proved useful for the control and prevention of infectious diseases, by uncovering spatial variations in the transmission or the access to prevention [10][11][12].…”