TY - JOUR
T1 - Predictive malaria risk and uncertainty mapping in Nchelenge District, Zambia
T2 - Evidence of widespread, persistent risk and implications for targeted interventions
AU - Pinchoff, Jessie
AU - Chaponda, Mike
AU - Shields, Timothy
AU - Lupiya, James
AU - Kobayashi, Tamaki
AU - Mulenga, Modest
AU - Moss, William J.
AU - Curriero, Frank C.
N1 - Publisher Copyright:
© 2015 by The American Society of Tropical Medicine and Hygiene.
PY - 2015/12
Y1 - 2015/12
N2 - 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.
AB - 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.
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U2 - 10.4269/ajtmh.15-0283
DO - 10.4269/ajtmh.15-0283
M3 - Article
C2 - 26416106
AN - SCOPUS:84949661175
SN - 0002-9637
VL - 93
SP - 1260
EP - 1267
JO - American Journal of Tropical Medicine and Hygiene
JF - American Journal of Tropical Medicine and Hygiene
IS - 6
ER -