Margaret Carrel1, Seungwon Kim2, Melchior Kashamuka Mwandagalirwa3, Nono Mvuama4, Joseph A Bala5, Marthe Nkalani6, Georges Kihuma7, Joseph Atibu8, Alpha Oumar Diallo9, Varun Goel10, Kyaw L Thwai11, Jonathan J Juliano12, Michael Emch13, Antoinette Tshefu14, Jonathan B Parr15. 1. Department of Geographical & Sustainability Sciences, 305 Jessup Hall, University of Iowa, Iowa City, IA, 52245, USA. Electronic address: margaret-carrel@uiowa.edu. 2. Department of Geographical & Sustainability Sciences, 305 Jessup Hall, University of Iowa, Iowa City, IA, 52245, USA. Electronic address: seungwon-kim-1@uiowa.edu. 3. Department of Epidemiology, CB7435, McGavran-Greenberg Hall, University of North Carolina-Chapel Hill, Chapel Hill, NC, 27599, USA; Ecole de Sante Publique, Faculte de Medecine, University of Kinshasa, Kinshasa, Democratic Republic of the Congo, USA. Electronic address: mwandaga@email.unc.edu. 4. Ecole de Sante Publique, Faculte de Medecine, University of Kinshasa, Kinshasa, Democratic Republic of the Congo, USA. Electronic address: donmvuama@yahoo.fr. 5. Ecole de Sante Publique, Faculte de Medecine, University of Kinshasa, Kinshasa, Democratic Republic of the Congo, USA. Electronic address: jalexandrebala@yahoo.fr. 6. Ecole de Sante Publique, Faculte de Medecine, University of Kinshasa, Kinshasa, Democratic Republic of the Congo, USA. Electronic address: marthenkalani@gmail.com. 7. Ecole de Sante Publique, Faculte de Medecine, University of Kinshasa, Kinshasa, Democratic Republic of the Congo, USA. Electronic address: georgeskihuma@gmail.com. 8. Ecole de Sante Publique, Faculte de Medecine, University of Kinshasa, Kinshasa, Democratic Republic of the Congo, USA. Electronic address: fejef576@gmail.com. 9. Department of Epidemiology, CB7435, McGavran-Greenberg Hall, University of North Carolina-Chapel Hill, Chapel Hill, NC, 27599, USA. Electronic address: oumar.diallo@unc.edu. 10. Department of Geography, CB3220, University of North Carolina-Chapel Hill, Chapel Hill, NC, 27599, USA. Electronic address: varung@live.unc.edu. 11. Department of Epidemiology, CB7435, McGavran-Greenberg Hall, University of North Carolina-Chapel Hill, Chapel Hill, NC, 27599, USA. Electronic address: thwai@email.unc.edu. 12. Department of Epidemiology, CB7435, McGavran-Greenberg Hall, University of North Carolina-Chapel Hill, Chapel Hill, NC, 27599, USA; Division of Infectious Diseases, School of Medicine, CB#7030, 130 Mason Farm Road, University of North Carolina-Chapel Hill, Chapel Hill, NC, 27599, USA. Electronic address: jonathan_juliano@med.unc.edu. 13. Department of Geography, CB3220, University of North Carolina-Chapel Hill, Chapel Hill, NC, 27599, USA. Electronic address: emch@unc.edu. 14. Ecole de Sante Publique, Faculte de Medecine, University of Kinshasa, Kinshasa, Democratic Republic of the Congo, USA. Electronic address: antotshe@yahoo.com. 15. Division of Infectious Diseases, School of Medicine, CB#7030, 130 Mason Farm Road, University of North Carolina-Chapel Hill, Chapel Hill, NC, 27599, USA. Electronic address: jonathan_parr@med.unc.edu.
Abstract
BACKGROUND: The Democratic Republic of the Congo (DRC) remains one of the countries most impacted by malaria despite decades of control efforts, including multiple mass insecticide treated net (ITN) distribution campaigns. The multi-scalar and complex nature of malaria necessitates an understanding of malaria risk factors over time and at multiple levels (e.g., individual, household, community). Surveillance of households in both rural and urban settings over time, coupled with detailed behavioral and geographic data, enables the detection of seasonal trends in malaria prevalence and malaria-associated behaviors as well as the assessment of how the local environments within and surrounding an individual's household impact malaria outcomes. METHODS: Participants from seven sites in Kinshasa Province, DRC were followed for over two years. Demographic, behavioral, and spatial information was gathered from enrolled households. Malaria was assessed using both rapid diagnostic tests (RDT) and polymerase chain reaction (PCR) and seasonal trends were assessed. Hierarchical regression modeling tested associations between behavioral and environmental factors and positive RDT and PCR outcomes at individual, household and neighborhood scales. RESULTS: Among 1591 enrolled participants, malaria prevalence did not consistently vary seasonally across the sites but did vary by age and ITN usage. Malaria was highest and ITN usage lowest in children ages 6-15 years across study visits and seasons. Having another member of the household test positive for malaria significantly increased the risk of an individual having malaria [RDT: OR = 4.158 (2.86-6.05); PCR: OR = 3.37 (2.41-4.71)], as did higher malaria prevalence in the 250 m neighborhood around the household [RDT: OR = 2.711 (1.42-5.17); PCR: OR = 4.056 (2.3-7.16)]. Presence of water within close proximity to the household was also associated with malaria outcomes. CONCLUSIONS: Taken together, these findings suggest that targeting non-traditional age groups, children >5 years old and teenagers, and deploying household- and neighborhood-focused interventions may be effective strategies for improving malaria outcomes in high-burden countries like the DRC.
BACKGROUND: The Democratic Republic of the Congo (DRC) remains one of the countries most impacted by malaria despite decades of control efforts, including multiple mass insecticide treated net (ITN) distribution campaigns. The multi-scalar and complex nature of malaria necessitates an understanding of malaria risk factors over time and at multiple levels (e.g., individual, household, community). Surveillance of households in both rural and urban settings over time, coupled with detailed behavioral and geographic data, enables the detection of seasonal trends in malaria prevalence and malaria-associated behaviors as well as the assessment of how the local environments within and surrounding an individual's household impact malaria outcomes. METHODS: Participants from seven sites in Kinshasa Province, DRC were followed for over two years. Demographic, behavioral, and spatial information was gathered from enrolled households. Malaria was assessed using both rapid diagnostic tests (RDT) and polymerase chain reaction (PCR) and seasonal trends were assessed. Hierarchical regression modeling tested associations between behavioral and environmental factors and positive RDT and PCR outcomes at individual, household and neighborhood scales. RESULTS: Among 1591 enrolled participants, malaria prevalence did not consistently vary seasonally across the sites but did vary by age and ITN usage. Malaria was highest and ITN usage lowest in children ages 6-15 years across study visits and seasons. Having another member of the household test positive for malaria significantly increased the risk of an individual having malaria [RDT: OR = 4.158 (2.86-6.05); PCR: OR = 3.37 (2.41-4.71)], as did higher malaria prevalence in the 250 m neighborhood around the household [RDT: OR = 2.711 (1.42-5.17); PCR: OR = 4.056 (2.3-7.16)]. Presence of water within close proximity to the household was also associated with malaria outcomes. CONCLUSIONS: Taken together, these findings suggest that targeting non-traditional age groups, children >5 years old and teenagers, and deploying household- and neighborhood-focused interventions may be effective strategies for improving malaria outcomes in high-burden countries like the DRC.
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