Small-area spatial statistical analysis of malaria clusters and hotspots in Cameroon;2000–2015

Abstract

BackgroundMalaria prevalence in Cameroon is a major public health problem both at the regional and urban-rural geographic scale. In 2016, an estimated 1.6 million confirmed cases, and 18,738 cases were reported in health facilities and communities respectively, with about 8000 estimated deaths. Several studies have estimated malaria prevalence in Cameroon using the analytical techniques at the regional scale. We aimed at identifying malaria clusters and hotspots at the urban-rural geographic scale from the Demographic and Health Survey (DHS) data for households between 2000 and 2015 using ArcGIS for intervention programs.MethodsTo identify malaria hotspots and analyze the pattern of distribution, we used the optimized hotspots toolset and spatial autocorrelation respectively in ArcGIS 10.3 for desktop. We also used Pearson’s Correlation analysis to identify associative environmental factors using the R-software 3.4.1.ResultsThe spatial distribution of malaria showed statistically significant clustered pattern for the year 2000 and 2015 with Moran’s indexes 0.126 (P < 0.001) and 0.187 (P < 0.001) respectively. Meanwhile, the years 2005 and 2010 with Moran’s indexes 0.001 (P = 0.488) and 0.002 (P = 0.318) respectively, had a random malaria distribution pattern. There exist varying degrees of malaria clusters and statistically significant hotspots in the urban-rural areas of the 12 administrative regions. Malaria cases were associated with population density and some environmental covariates; rainfall, enhanced vegetation index and composite lights (P < 0.001).ConclusionThis study identified urban-rural areas with high and low malaria clusters and hotspots. Our maps can be used as supportive tools for effective malaria control and elimination, and investments in malaria programs and research, malaria prevention, diagnosis and treatment, surveillance, should pay more attention to urban-rural geographic scale.

Geographically weighted regression modelling of the spatial association between malaria cases and environmental factors in Cameroon.

“…Studies on the association of environmental factors with malaria cases in Cameroon have been illustrated (28,42). In this study, we demonstrated the ability of the GWR technique as a facilitatory tool to understand the spatial interaction of environmental factors with malaria hotspots.…”

“…Data for this study was obtained from the demographic and health survey (DHS) program website (https://dhsprogram.com) funded by the United States Agency for International Developments (USAID) following a written administrative clearance (30). The DHS program has been described elsewhere (28). Households are grouped as either an urban (city block or apartment building) or rural (village or group of villages) cluster points (figure 1) and displaced a distance up to 2 km for urbancity clusters and 5 km for rural clusters due to confidentiality.…”

“…A clinical case was defined as a malaria-attributable febrile episode (body temperature in excess of 37.5°C ), accompanied by headaches, nausea, excess sweating and/or fatigue censored by a 30-day window (6,27). Since the households are the variables to be analyzed, a malaria year as described in this study, is the average number of people per year who show clinical symptoms of Plasmodium falciparum malaria within the cells whose centroid falls within a radius of 10 km (for rural points) or 2 km (for urban points) (28). The environmental covariates data set used for this study are described in table 1.…”

Geographically weighted regression modelling of the spatial association between malaria cases and environmental factors in Cameroon

“…Data for this study was obtained from the demographic and health survey (DHS) program website (https://dhsprogram.com) funded by the United States Agency for International Developments (USAIDS) following a written administrative clearance (28). The DHS program has been described elsewhere (26). Households are grouped as either an urban(city block or apartment building) or rural (village or group of villages) cluster points and displaced a distance up to 2 km for urban-city clusters and 5 km for rural clusters due to confidentiality.…”

“…Knowing the spatial variation between malaria cases and environmental factors will aid in targeted funding and resource allocations, prevention programs such as insecticide-treated nets (ITN) distribution, clinical diagnosis, and treatments. The specific objective of this study is to identify ITN coverage in Cameroon and to find the spatial variability between malaria hotspot cases (26) and environmental predictors using the GWR spatial modelling technique.…”