Quantitative urban classification for malaria epidemiology in sub-Saharan Africa

Abstract

BackgroundAlthough sub-Saharan Africa (SSA) is rapidly urbanizing, the terms used to classify urban ecotypes are poorly defined in the context of malaria epidemiology. Lack of clear definitions may cause misclassification error, which likely decreases the accuracy of continent-wide estimates of malaria burden, limits the generalizability of urban malaria studies, and makes identification of high-risk areas for targeted interventions within cities more difficult. Accordingly, clustering techniques were applied to a set of urbanization- and malaria-related variables in Kisumu, Kenya, to produce a quantitative classification of the urban environment for malaria research.MethodsSeven variables with a known or expected relationship with malaria in the context of urbanization were identified and measured at the census enumeration area (EA) level, using three sources: a) the results of a citywide knowledge, attitudes and practices (KAP) survey; b) a high-resolution multispectral satellite image; and c) national census data. Principal components analysis (PCA) was used to identify three factors explaining higher proportions of the combined variance than the original variables. A k-means clustering algorithm was applied to the EA-level factor scores to assign EAs to one of three categories: "urban," "peri-urban," or "semi-rural." The results were compared with classifications derived from two other approaches: a) administrative designation of urban/rural by the census or b) population density thresholds.ResultsUrban zones resulting from the clustering algorithm were more geographically coherent than those delineated by population density. Clustering distributed population more evenly among zones than either of the other methods and more accurately predicted variation in other variables related to urbanization, but not used for classification.ConclusionEffective urban malaria epidemiology and control would benefit from quantitative methods to identify and characterize urban areas. Cluster analysis techniques were used to classify Kisumu, Kenya, into levels of urbanization in a repeatable and unbiased manner, an approach that should permit more relevant comparisons among and within urban areas. To the extent that these divisions predict meaningful intra-urban differences in malaria epidemiology, they should inform targeted urban malaria interventions in cities across SSA.

Significance of Travel to Rural Areas as a Risk Factor for Malarial Anemia in an Urban Setting

“…Level of urbanization comprised an index encompassing a wide range of other socioeconomic and environmental variables, including NDVI, distance to town, population density, ownership of transportation and luxury items. 39 In addition, variables that met the Bonferroni significance criteria for multiple testing (i.e., P < 0.0016 for 32 individual risk factors) in bivariate analysis were included as potential risk factors in the multivariate model. Interaction terms were not considered because of the small sample size.…”

“…37 , 38 The study site was limited to the urban core of Kisumu and partitioned into three eco-epidemiologic zones (urban, peri-urban, and semi-rural) as described fully elsewhere. 39 Briefly, seven variables related to malaria and urbanization were summarized at the geographic scale of the national census enumeration area (EA) (encompassing, on average, approximately 100 households), and three factors accounting for a large proportion of the variation in this data identified through principal components analysis. This analysis is a mathematical procedure that takes a number of potentially correlated variables and transforms them into a smaller number of uncorrelated variables that account for much or most of the variation in the data.…”

Impact of Urban Agriculture on the Species Distribution and Insecticide Resistance Profile of &lt;i&gt;Anopheles gambiae s.s.&lt;/i&gt; and &lt;i&gt;Anopheles coluzzii&lt;/i&gt; in Accra Metropolis, Ghana

“…However, data collected throughout the decade indicate that urban malaria remains a major public health problem [7] [18] [19] [20] [21] [22]. It is therefore important to understand the factors that promote urban malaria to help formulate vector control strategies and help manage insecticide resistance in urban areas.…”

Distance Threshold for the Effect of Urban Agriculture on Elevated Self-reported Malaria Prevalence in Accra, Ghana

“…In this regard, the future work aims to fuse the strategies of both this study and the work of Klinkenberg and others 9. Neighborhood effects may contribute to the intra-urban variability in malaria seen in Accra, as recently shown in Kenya,27 and follow-up work will benefit from neighborhood characterization efforts already underway. The authors thereby hope to expand this work and uncover the patterns of urban malaria in Accra that to date have eluded local control efforts.…”