Spatial clustering and risk factors of malaria infections in Bata district, Equatorial Guinea

Abstract

BackgroundThe transmission of malaria is intense in the majority of the countries of sub-Saharan Africa, particularly in those that are located along the Equatorial strip. The present study aimed to describe the current distribution of malaria prevalence among children and its environment-related factors as well as to detect malaria spatial clusters in the district of Bata, in Equatorial Guinea.MethodsFrom June to August 2013 a representative cross-sectional survey using a multistage, stratified, cluster-selected sample was carried out of children in urban and rural areas of Bata District. All children were tested for malaria using rapid diagnostic tests (RDTs). Results were linked to each household by global position system data. Two cluster analysis methods were used: hot spot analysis using the Getis-Ord Gi statistic, and the SaTScan™ spatial statistic estimates, based on the assumption of a Poisson distribution to detect spatial clusters. In addition, univariate associations and Poisson regression model were used to explore the association between malaria prevalence at household level with different environmental factors.ResultsA total of 1416 children aged 2 months to 15 years living in 417 households were included in this study. Malaria prevalence by RDTs was 47.53%, being highest in the age group 6–15 years (63.24%, p < 0.001). Those children living in rural areas were there malaria risk is greater (65.81%) (p < 0.001). Malaria prevalence was higher in those houses located <1 km from a river and <3 km to a forest (IRR: 1.31; 95% CI 1.13–1.51 and IRR: 1.44; 95% CI 1.25–1.66, respectively). Poisson regression analysis also showed a decrease in malaria prevalence with altitude (IRR: 0.73; 95% CI 0.62–0.86). A significant cluster inland of the district, in rural areas has been found.ConclusionsThis study reveals a high prevalence of RDT-based malaria among children in Bata district. Those households situated in inland rural areas, near to a river, a green area and/or at low altitude were a risk factor for malaria. Spatial tools can help policy makers to promote new recommendations for malaria control.

Improving the efficiency of reactive case detection for malaria elimination in southern Zambia: a cross-sectional study

“…Early studies in Choma District, Zambia found that ownership of cattle reduced the risk of P. falciparum infection by 87% while others have found less conclusive evidence [36,37]. For elevation, however, it has been clearly shown that increased elevation offers protection against malaria infection [13,20,24,26,38]. Since index and secondary households in this study were located only <300 metres from each other and variation in elevation was minimal, it is unlikely that the elevation would influence malaria risk at this spatial scale.…”

“…During the dry season, as water accumulates into smaller pools, they become ideal larval development sites. These streams can also serve as important markers for nearby areas with similar high water table harbouring larvae [13,26,38,44]. And as these streams can be challenging to locate depending on size and season, spatial risk maps with topographical measures, such as CI and TWI, can offer guidance to CHWs to possibly reach clusters of asymptomatic carriers otherwise missed during regular RCD screening.…”

Improving the efficiency of reactive case detection for malaria elimination in southern Zambia: A cross-sectional study

“…36,37 For elevation, however, it has been clearly shown that increased elevation offers protection against malaria infection. 13,20,24,26,38 Since index and secondary households in this study were located only <300 meters from each other and variation in elevation was minimal, it is unlikely that the elevation would influence malaria risk at this scale. Distance from the index household marginally increased the probability of finding positive secondary households (OR: 1.24, 95% CI: 0.98 -1.58), in contrast to other studies.…”

“…26 Running head: Improving efficiency of reactive case detection Although no associations were found with the other environmental risk factors such as distance to main road, elevation, season, and number and presence of animal pens, non-parametric comparisons between positive and negative secondary households exhibited a trend of increased malaria risk for these risk factors. 19,20,21,26,36,37,38 The risk associated with animal pens varies in the literature depending on vector behavior. An.…”

“…Table 3). Although no statistically significant differences were detected for the other environmental risk factors, positive secondary households Running head: Improving efficiency of reactive case detection exhibited a trend previously associated with increased malaria risk: 19,20,21,26,36,37,38 they were at lower elevation from the index household (median: -0.7, IQR: -8.9 -9.4 m) (P = 0.52); further away from the main road (median: 9,266.6 m, IQR: 4,130.4 -17,353.7 m) (P = 0.28); and more likely to have an animal pen (64.4%) (P = 0.69). If an animal pen was present, they were also closer to it than negative secondary households (median: 36.9 m, IQR: 20.6 -50.5 m) (P = 0.43) ( Table 3).…”