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Open Access Highly Accessed Research

Population, behavioural and environmental drivers of malaria prevalence in the Democratic Republic of Congo

Jane P Messina12*, Steve M Taylor34, Steven R Meshnick3, Andrew M Linke5, Antoinette K Tshefu6, Benjamin Atua7, Kashamuka Mwandagalirwa8 and Michael Emch12*

Author Affiliations

1 Department of Geography, University of North Carolina, Chapel Hill, NC, USA

2 Carolina Population Center, University of North Carolina, Chapel Hill, NC, USA

3 Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA

4 Division of Infectious Diseases and International Health, Duke University Medical Center, Durham, NC, USA

5 Department of Geography, University of Colorado at Boulder, Boulder, CO, USA

6 Ecole de Santé Publique, Faculté de Médecine, Université de Kinshasa, République Démocratique du Congo

7 Programme National de Lutte contre le Paludisme (PNLP), Kinshasa, République Démocratique du Congo

8 Kinshasa General Hospital (HGK), Kinshasa-Gombe, DRC

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Malaria Journal 2011, 10:161  doi:10.1186/1475-2875-10-161

Published: 9 June 2011

Abstract

Background

Malaria is highly endemic in the Democratic Republic of Congo (DRC), but the limits and intensity of transmission within the country are unknown. It is important to discern these patterns as well as the drivers which may underlie them in order for effective prevention measures to be carried out.

Methods

By applying high-throughput PCR analyses on leftover dried blood spots from the 2007 Demographic and Health Survey (DHS) for the DRC, prevalence estimates were generated and ecological drivers of malaria were explored using spatial statistical analyses and multilevel modelling.

Results

Of the 7,746 respondents, 2268 (29.3%) were parasitaemic; prevalence ranged from 0-82% within geographically-defined survey clusters. Regional variation in these rates was mapped using the inverse-distance weighting spatial interpolation technique. Males were more likely to be parasitaemic than older people or females (p < 0.0001), while wealthier people were at a lower risk (p < 0.001). Increased community use of bed nets (p = 0.001) and community wealth (p < 0.05) were protective against malaria at the community level but not at the individual level. Paradoxically, the number of battle events since 1994 surrounding one's community was negatively associated with malaria risk (p < 0.0001).

Conclusions

This research demonstrates the feasibility of using population-based behavioural and molecular surveillance in conjunction with DHS data and geographic methods to study endemic infectious diseases. This study provides the most accurate population-based estimates to date of where illness from malaria occurs in the DRC and what factors contribute to the estimated spatial patterns. This study suggests that spatial information and analyses can enable the DRC government to focus its control efforts against malaria.