title: Predictive Malaria Risk and Uncertainty Mapping in Nchelenge District, Zambia: Evidence of Widespread, Persistent Risk and Implications for Targeted Interventions.
abstract: Malaria risk maps may be used to guide policy decisions on whether vector control interventions should be targeted and, if so, where. Active surveillance for malaria was conducted through household surveys in Nchelenge District, Zambia from April 2012 through December 2014. Households were enumerated based on satellite imagery and randomly selected for study enrollment. At each visit, participants were administered a questionnaire and a malaria rapid diagnostic test (RDT). Logistic regression models were used to construct spatial prediction risk maps and maps of risk uncertainty. A total of 461 households were visited, comprising 1,725 participants, of whom 48% were RDT positive. Several environmental features were associated with increased household malaria risk in a multivariable logistic regression model adjusting for seasonal variation. The model was validated using both internal and external evaluation measures to generate and assess root mean square error, as well as sensitivity and specificity for predicted risk. The final, validated model was used to predict and map malaria risk including a measure of risk uncertainty. Malaria risk in a high, perennial transmission setting is widespread but heterogeneous at a local scale, with seasonal variation. Targeting malaria control interventions may not be appropriate in this epidemiological setting.
authors.affiliation: Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland; Tropical Disease Research Centre, Ndola, Zambia jpincho1@jhu.edu.
authors.forename: Jessie
authors.initials: J
authors.lastname: Pinchoff
authors.affiliation: Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland; Tropical Disease Research Centre, Ndola, Zambia.
authors.forename: Mike
authors.initials: M
authors.lastname: Chaponda
authors.affiliation: Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland; Tropical Disease Research Centre, Ndola, Zambia.
authors.forename: Timothy
authors.initials: T
authors.lastname: Shields
authors.affiliation: Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland; Tropical Disease Research Centre, Ndola, Zambia.
authors.forename: James
authors.initials: J
authors.lastname: Lupiya
authors.affiliation: Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland; Tropical Disease Research Centre, Ndola, Zambia.
authors.forename: Tamaki
authors.initials: T
authors.lastname: Kobayashi
authors.affiliation: Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland; Tropical Disease Research Centre, Ndola, Zambia.
authors.forename: Modest
authors.initials: M
authors.lastname: Mulenga
authors.affiliation: Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland; Tropical Disease Research Centre, Ndola, Zambia.
authors.forename: William J
authors.initials: WJ
authors.lastname: Moss
authors.forename: Frank C
authors.initials: FC
authors.lastname: Curriero
mesh_terms: D000293:Adolescent; D003430:Cross-Sectional Studies; D004777:Environment; D005191:Family Characteristics; D005260:Female; D006801:Humans; D016015:Logistic Models; D008288:Malaria / Q000453:epidemiology* / Q000209:etiology / Q000517:prevention & control; D008297:Male; D009010:Monte Carlo Method; D018570:Risk Assessment / Q000379:methods*; D012307:Risk Factors; D063809:Satellite Imagery; D012621:Seasons; D011795:Surveys and Questionnaires; D035501:Uncertainty; D015024:Zambia / Q000453:epidemiology