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.

Background Cameroon has witnessed a 131,000 increase in malaria cases, according to a recent report addressing the malaria burden and control strategies in endemic regions. Studies have illustrated the association between malaria cases and environmental factors in Cameroon but limited in addressing how these factors vary in space for timely interventions. Thus, we want to find the spatial variability between malaria hotspot cases and environmental predictors using Geographically weighted regression (GWR) spatial modelling technique. Methods The global Ordinary least squares(OLS) tool in the modelling spatial relationships tool in ArcGIS 10.3. was used to select candidate explanatory environmental variables for a properly specified GWR model. The local GWR model used the OLS candidate variables to examine, predict and explore the spatial variability between environmental factors and malaria hotspot cases generated from Getis-Ord Gi* statistical analysis. Spatial maps of mosquito bed net ownership and GWR outputs were also created for public health surveillance. Results The OLS candidate environmental variable coefficients were statistically significant for a properly specified GWR model (adjusted R2 = 22.3% and p < 0.01). The GWR model identified a strong association between malaria cases and rainfall, vegetation index, population density, and drought episodes in most hotspot areas and a weak correlation with aridity and proximity to water (adjusted R2= 24.3%). The mosquito bed nets analysis maps demonstrated an overall low coverage(<50%) of household ownership. Conclusion The generated GWR maps suggest that for policymakers to eliminate malaria in Cameroon by 2030, there should be the creation of outreach programs that will target malaria hotspots locations, intensify free insecticidal net distribution, allocate specific funding, establish vaccination campaigns and carry out further investigations in areas where the environmental variables showed strong spatial associations with malaria hotspot cases.

Background: There have been controversial debates on the relationship between socioeconomic status and the distribution of HIV in Cameroon. We aim to illustrate the vulnerability of socioeconomic disparities and the risk of getting HIV for public health interventions. Methods: Descriptive statistics was conducted to quantify the socioeconomic gradients of HIV. A Multilevel logistic regression model was used to study the relationship between socioeconomic factors and HIV. The effect of the factors was presented as odds ratios (OR), with 95% confidence intervals (CIs). P-value less than 0.05 was considered to be statistically significant. We further mapped HIV prevalence in ArcGIS to visualize the regional distribution of HIV.Results: HIV was significantly associated with age (p<0.001), sex (p<0.001) and varies significantly by geographic region (p<0.001), level of education (p=0.001), wealth status (p<0.001), religion (p=0.042), ethnicity (p<0.001) and residence (p=0.001). HIV positive participants were more likely to be women, people with higher educational level, live in urban areas, practice the protestant religious belief, belong to the ethnicity of Kako/Meka/Pygmy and distributed in the East, South, and Yaoundé regions. Age, sex, region, education level, and ethnicity were significantly associated with the odds of having HIV from the multilevel regression model. Conclusion: Our finding recommends for novel intervention programs that will target the various socioeconomic factors associated with the odds of having HIV for proper public health management of the disease in Cameroon.

Background: Studies have illustrated the association of malaria cases with environmental factors in Cameroon but limited in addressing how these factors vary in space for timely public health interventions. Thus, we want to find the spatial variability between malaria hotspot cases and environmental predictors using Geographically weighted regression (GWR) spatial modelling technique. Methods: The global Ordinary least squares(OLS) in the modelling spatial relationships tool in ArcGIS 10.3. was used to select candidate explanatory environmental variables for a properly specified GWR model. The local GWR model used the global OLS candidate variables to examine, predict and explore the spatial variability between environmental factors and malaria hotspot cases generated from Getis-Ord Gi* statistical analysis. Results: The OLS candidate environmental variable coefficients were statistically significant ( adjusted R 2 = 22.3% and p < 0.01) for a properly specified GWR model. The GWR model identified a strong spatial association between malaria cases and rainfall, vegetation index, population density, and drought episodes in most hotspot areas and a weak correlation with aridity and proximity to water with an overall model performance of 0.243 (adjusted R 2 = 24.3%). Conclusion: The generated GWR maps suggest that for policymakers to eliminate malaria in Cameroon, there should be the creation of malaria outreach programs and further investigations in areas where the environmental variables showed strong spatial associations with malaria hotspot cases . Keywords: Geographically weighted regression, Ordinary least squares, malaria, spatial statistics, mapping, Geographical information systems.

Background: Studies have illustrated the association of malaria cases with environmental factors in Cameroon but limited in addressing how these factors vary in space for timely public health interventions. Thus, we want to find the spatial variability between malaria hotspot cases and environmental predictors using Geographically weighted regression (GWR) spatial modelling technique.Methods: The global Ordinary least squares (OLS) in the modelling spatial relationships tool in ArcGIS 10.3. was used to select candidate explanatory environmental variables for a properly specified GWR model. The local GWR model used the global OLS candidate variables to examine, predict and explore the spatial variability between environmental factors and malaria hotspot cases generated from Getis-Ord Gi* statistical analysis. Results: The OLS candidate environmental variable coefficients were statistically significant (adjusted R2 = 22.3% and p < 0.01) for a properly specified GWR model. The GWR model identified a strong spatial association between malaria cases and rainfall, vegetation index, population density, and drought episodes in most hotspot areas and a weak correlation with aridity and proximity to water with an overall model performance of 0.243 (adjusted R2= 24.3%).Conclusion: The generated GWR maps suggest that for policymakers to eliminate malaria in Cameroon, there should be the creation of malaria outreach programs and further investigations in areas where the environmental variables showed strong spatial associations with malaria hotspot cases.