标题：Small-area spatial statistical analysis of malaria clusters and hotspots in Cameroon;2000-2015
作者：Tewara, Marlvin Anemey; Mbah-Fongkimeh, Prisca Ngetemalah; Dayimu, Alimu; Kang, Fengling; Xue, Fuzhong
作者机构：[Tewara, Marlvin Anemey; Dayimu, Alimu; Kang, Fengling; Xue, Fuzhong] Shandong Univ, Sch Publ Hlth, Dept Epidemiol & Biostat, Cheeloo Coll Med, Jinan 更多
通讯作者地址：[Xue, FZ]Shandong Univ, Sch Publ Hlth, Dept Epidemiol & Biostat, Cheeloo Coll Med, Jinan 250012, Shandong, Peoples R China.
来源：BMC INFECTIOUS DISEASES
关键词：Spatial statistics; Urban-rural; Epidemiology; Hotspots; Clusters;; Malaria; Mapping
摘要：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.