Malaria risk factor assessment is a critical step in determining cost-effective intervention strategies and operational plans in a regional setting. We develop a multi-indicator multistep approach to model the malaria risks at the population level in western Kenya. We used a combination of cross-sectional seasonal malaria infection prevalence, vector density, and cohort surveillance of malaria incidence at the village level to classify villages into malaria risk groups through unsupervised classification. Generalized boosted multinomial logistics regression analysis was performed to determine village-level risk factors using environmental, biological, socioeconomic, and climatic features. Thirty-six villages in western Kenya were first classified into two to five operational groups based on different combinations of malaria risk indicators. Risk assessment indicated that altitude accounted for 45–65% of all importance value relative to all other factors; all other variable importance values were < 6% in all models. After adjusting by altitude, villages were classified into three groups within distinct geographic areas regardless of the combination of risk indicators. Risk analysis based on altitude-adjusted classification indicated that factors related to larval habitat abundance accounted for 63% of all importance value, followed by geographic features related to the ponding effect (17%), vegetation cover or greenness (15%), and the number of bed nets combined with February temperature (5%). These results suggest that altitude is the intrinsic factor in determining malaria transmission risk in western Kenya. Malaria vector larval habitat management, such as habitat reduction and larviciding, may be an important supplement to the current first-line vector control tools in the study area.
Address correspondence to Guofa Zhou, Program in Public Health, University of California, Rm. 3066, Hewitt Hall, Irvine, CA 92697. E-mail: firstname.lastname@example.org
Disclosure: The funder has no role in study design; collection, management, analysis, and interpretation of data; writing of the report; or the decision to submit the report for publication. The datasets supporting the conclusions of this article are included within the article (and its additional files). Ethical clearance was obtained from the Ethical Review Committee of Maseno University, Kenya (MSU/DRPI/MUERC/00778/19), and the Institutional Review Board (IRB) of the University of California, Irvine, USA (HS# 2017-3512). Written consent was obtained from all adult participants. Written assent for children (< 18 years of age) was obtained from the participants and their parents or guardians. Inclusion criteria were provision of informed consent (assent for children) and no reported chronic or acute illness other than malaria. Exclusion criteria were unwillingness to participate in the study, reported chronic or acute illness other than malaria, or age < 6 months.
Financial support: This study is funded by the National Institutes of Health (R01 A1050243, D43 TW01505, and U19 AI129326).
Authors’ addresses: Guofa Zhou, Daibin Zhong, Ming-Chieh Lee, Xiaoming Wang, and Guiyun Yan, Program in Public Health, University of California at Irvine, Irvine, CA, E-mails: email@example.com, firstname.lastname@example.org, email@example.com, firstname.lastname@example.org, and email@example.com. Harrysone E. Atieli, School of Public Health and Community Development, Maseno University, Kisumu, Kenya, E-mail: firstname.lastname@example.org. John I. Githure, International Center of Excellence in Malaria Research, Tom Mboya University College, Homabay, Kenya, E-mail: email@example.com. Andrew K. Githeko, Centre for Global Health Research, Kenya Medical Research Institute, Kisumu, Kenya, E-mail: firstname.lastname@example.org. James Kazura, Center for Global Health and Diseases, Case Western Reserve University, Cleveland, OH, E-mail: email@example.com.