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PA-769 Bayesian spatio-temporal analysis of malaria hotspot in Gabon from 2000 to 2015
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  1. Fabrice Mougeni1,2,
  2. Bertrand Lell2,3,
  3. Kandala Ngianga1,4,
  4. Tobias Chirwa1
  1. 1School of Public Health, University of the Witwatersrand, South Africa
  2. 2Centre de Recherches Médicales de Lambaréné, Gabon
  3. 3Department of Medicine I, Division of Infectious Diseases and Tropical Medicine, Medical University of Vienna, Austria
  4. 4Department of Epidemiology and Biostatistics, Western Centre for Public Health and Family Medicine, Schulich School of Medicine and Dentistry, Western University, Canada

Abstract

Background At the local level, malaria transmission persists through hotspots. Besides other known factors, the distribution of malaria hotspots may be shaped by environmental variables. However, research focusing on this aspect has been relatively scarce in Gabon. This underscores the need for further investigations to elucidate the specific environmental factors together with a specific intervention, that may contribute to the distribution of malaria hotspots, taking into account the spatio-temporal effect in Gabon.

Methods These data were part of the Demographic Health Survey program from 2000 to 2015. Hotspots of malaria prevalence for cluster of households were identified using the local Getis-Ord Gi* statistic. The effect of covariates on the outcome was assessed using a Bayesian space-time framework with a Binomial model, implemented in the Integrated Nested Laplace Approximation (INLA), using the Stochastic Partial Differential Equations approach (SPDE).

Results A total of 316 clusters were initially considered, out of which 257 clusters with known hotspot status were included in the analysis. Among these clusters, approximately thirty percent were persistent hotspot over time and concentrated in rural areas. Using a spatio-temporal model, association between malaria prevalence hotspot variation and two key factors was found: years and rainfall. Each additional year or amount of rainfall was associated with an increase in the odds of hotspot occurrence (adjusted posterior odds ratio [AOR]: 1.32, 95% confidence interval [CI]: 1.03–1.69 and AOR: 1.15, 95% CI: 1.02–1.30, respectively). Furthermore, the analysis found that clusters of households with high insecticide-treated net (ITN) coverage were less likely to be hotspots (0.19 (95% CI: 0.06–0.61)).

Conclusion These findings highlight the spatio-temporal dynamics of hotspots and the role of the rainfall, in influencing their occurrence. Moreover, the protective effect of high ITN coverage suggests the importance of targeted interventions in mitigating hotspot formation and malaria transmission.

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