A census-weighted, spatially-stratified household sampling strategy for urban malaria epidemiology

Siri, José G.; Lindblade, Kim A.; Rosen, Daniel H.; Onyango, Bernard; Vulule, John; Slutsker, Laurence; Wilson, Mark L.

doi:10.1186/1475-2875-7-39

Cited by 12 publications

(15 citation statements)

References 24 publications

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“…The sampling strategy for the KAP survey is described in more detail in a companion paper [ 24 ]. Briefly, a spatially stratified sampling scheme was used to select 4,550 sampling points corresponding to households in 473 of 567 census enumeration areas (EAs), representing an intended 10% household sample, with probability of selection proportional to population density.…”

Section: Methodsmentioning

confidence: 99%

Quantitative urban classification for malaria epidemiology in sub-Saharan Africa

Siri

Lindblade

Rosen

et al. 2008

Malar J

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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.

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Section: Methodsmentioning

confidence: 99%

Quantitative urban classification for malaria epidemiology in sub-Saharan Africa

Siri

Lindblade

Rosen

et al. 2008

Malar J

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“…Data from a concurrent knowledge, attitudes, and practices (KAP) survey 40 indicates that approximately half the population would go to KDH in a health emergency, more than any other local facility. The U.S. Centers for Disease Control and Prevention (CDC), in conjunction with the Kenya Medical Research Institute (KEMRI), maintained a continuous surveillance system for malaria at KDH from 1996 through 2005, with ongoing collection of clinical and laboratory data during and prior to our study period.…”

Section: Methodsmentioning

confidence: 99%

“…Control children were chosen from among respondents to a concurrent citywide KAP survey. 40 To ensure comparability of case and control children, eligibility for controls was restricted to children within the observed case age range (i.e., ≤ 7 years of age). The criteria for inclusion as a control included residing within the study area, no history of malaria or serious illness within the previous 30 days, and indicating KDH as the first choice for treatment in the event of severe illness in the household.…”

Section: Methodsmentioning

confidence: 99%

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

Siri¹,

Wilson²,

Murray³

et al. 2010

The American Society of Tropical Medicine and Hygiene

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Abstract. The epidemiology of malaria in urban environments is poorly characterized, yet increasingly problematic. We conducted an unmatched case-control study of risk factors for malarial anemia with high parasitemia in urban Kisumu, Kenya, from June 2002 through February 2003. Cases (n = 80) were hospital patients with a hemoglobin level £ 8 g/dL and a Plasmodium parasite density ≥ 10,000/μL. Controls (n = 826) were healthy respondents to a concurrent citywide knowledge, attitude, and practice survey. Children who reported spending at least one night per month in a rural area were especially at risk (35% of cases; odds ratio = 9.3, 95% confidence interval [CI] = 4.4-19.7, P < 0.0001), and use of mosquito coils, bed net ownership, and house construction were non-significant, potentially indicating that malaria exposure during rural travel comprises an important element of risk. Control of severe malaria in an urban setting may be complicated by Plasmodium infections acquired elsewhere. Epidemiologic studies of urban malaria in low transmission settings should take travel history into account.

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“…Dhar and Datta [ 9 ] developed methodology based on inverse distance weighting method to reduce redundancy in monitoring network. Siri et al [ 10 ] establish a sampling scheme for generating samples simultaneously. They prove that GPS units and a pseudo-sampling frame are more effective than old sampling methods.…”

Section: Introductionmentioning

confidence: 99%

Spatial Prediction and Optimized Sampling Design for Sodium Concentration in Groundwater

et al. 2016

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Sodium is an integral part of water, and its excessive amount in drinking water causes high blood pressure and hypertension. In the present paper, spatial distribution of sodium concentration in drinking water is modeled and optimized sampling designs for selecting sampling locations is calculated for three divisions in Punjab, Pakistan. Universal kriging and Bayesian universal kriging are used to predict the sodium concentrations. Spatial simulated annealing is used to generate optimized sampling designs. Different estimation methods (i.e., maximum likelihood, restricted maximum likelihood, ordinary least squares, and weighted least squares) are used to estimate the parameters of the variogram model (i.e, exponential, Gaussian, spherical and cubic). It is concluded that Bayesian universal kriging fits better than universal kriging. It is also observed that the universal kriging predictor provides minimum mean universal kriging variance for both adding and deleting locations during sampling design.

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A census-weighted, spatially-stratified household sampling strategy for urban malaria epidemiology

Cited by 12 publications

References 24 publications

Quantitative urban classification for malaria epidemiology in sub-Saharan Africa

Quantitative urban classification for malaria epidemiology in sub-Saharan Africa

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

Spatial Prediction and Optimized Sampling Design for Sodium Concentration in Groundwater

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