Paulo Ruiz-Grosso1, J Jaime Miranda2, Robert H Gilman3, Blake Byron Walker4, Gabriel Carrasco-Escobar5, Marco Varela-Gaona6, Francisco Diez-Canseco6, Luis Huicho7, William Checkley8, Antonio Bernabe-Ortiz5. 1. CRONICAS Centre of Excellence in Chronic Diseases, Universidad Peruana Cayetano Heredia, Lima, Peru; Mental Health Working Group, Universidad Peruana Cayetano Heredia, Lima, Peru. 2. CRONICAS Centre of Excellence in Chronic Diseases, Universidad Peruana Cayetano Heredia, Lima, Peru; Department of Medicine, School of Medicine, Universidad Peruana Cayetano Heredia, Lima, Peru. Electronic address: Jaime.Miranda@upch.pe. 3. CRONICAS Centre of Excellence in Chronic Diseases, Universidad Peruana Cayetano Heredia, Lima, Peru; International Health Department, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD; Área de Investigación y Desarrollo, Asociación Benéfica PRISMA, Lima, Peru. 4. GIS and Health Informatics Laboratory, Department of Geography, Simon Fraser University, Burnaby, British Columbia, Canada. 5. CRONICAS Centre of Excellence in Chronic Diseases, Universidad Peruana Cayetano Heredia, Lima, Peru; School of Public Health and Administration, Universidad Peruana Cayetano Heredia, Lima, Peru. 6. CRONICAS Centre of Excellence in Chronic Diseases, Universidad Peruana Cayetano Heredia, Lima, Peru. 7. CRONICAS Centre of Excellence in Chronic Diseases, Universidad Peruana Cayetano Heredia, Lima, Peru; Department of Medicine, School of Medicine, Universidad Peruana Cayetano Heredia, Lima, Peru; Department of Pediatrics, Instituto Nacional de Salud del Niño, Lima, Peru; Department of Pediatrics, School of Medicine, Universidad Nacional Mayor de San Marcos, Lima, Peru. 8. CRONICAS Centre of Excellence in Chronic Diseases, Universidad Peruana Cayetano Heredia, Lima, Peru; International Health Department, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD; Área de Investigación y Desarrollo, Asociación Benéfica PRISMA, Lima, Peru; Division of Pulmonary and Critical Care, School of Medicine, Johns Hopkins University, Baltimore, MD.
Abstract
PURPOSE: To map the geographical distribution and spatial clustering of depressive symptoms cases in an area of Lima, Peru. METHODS: Presence of depressive symptoms suggesting a major depressive episode was assessed using a short version of the Center for Epidemiologic Studies Depression Scale. Data were obtained from a census conducted in 2010. One participant per selected household (aged 18 years and above, living more than 6 months in the area) was included. Residence latitude, longitude, and elevation were captured using a GPS device. The prevalence of depressive symptoms was estimated, and relative risks (RRs) were calculated to identify areas of significantly higher and lower geographical concentrations of depressive symptoms. RESULTS: Data from 7946 participants, 28.3% male, mean age 39.4 (SD, 13.9) years, were analyzed. The prevalence of depressive symptoms was 17.0% (95% confidence interval = 16.2%-17.8%). Three clusters with high prevalence of depressive symptoms (primary cluster: RR = 1.82; P = .003 and secondary: RR = 2.83; P = .004 and RR = 5.92; P = .01), and two clusters with significantly low prevalence (primary: RR = 0.23; P = .016 and secondary: RR = 0; P = .035), were identified. Further adjustment by potential confounders confirmed the high prevalence clusters but also identified newer ones. CONCLUSIONS: Screening strategies for depression, in combination with mapping techniques, may be useful tools to target interventions in resource-limited areas.
PURPOSE: To map the geographical distribution and spatial clustering of depressive symptoms cases in an area of Lima, Peru. METHODS: Presence of depressive symptoms suggesting a major depressive episode was assessed using a short version of the Center for Epidemiologic Studies Depression Scale. Data were obtained from a census conducted in 2010. One participant per selected household (aged 18 years and above, living more than 6 months in the area) was included. Residence latitude, longitude, and elevation were captured using a GPS device. The prevalence of depressive symptoms was estimated, and relative risks (RRs) were calculated to identify areas of significantly higher and lower geographical concentrations of depressive symptoms. RESULTS: Data from 7946 participants, 28.3% male, mean age 39.4 (SD, 13.9) years, were analyzed. The prevalence of depressive symptoms was 17.0% (95% confidence interval = 16.2%-17.8%). Three clusters with high prevalence of depressive symptoms (primary cluster: RR = 1.82; P = .003 and secondary: RR = 2.83; P = .004 and RR = 5.92; P = .01), and two clusters with significantly low prevalence (primary: RR = 0.23; P = .016 and secondary: RR = 0; P = .035), were identified. Further adjustment by potential confounders confirmed the high prevalence clusters but also identified newer ones. CONCLUSIONS: Screening strategies for depression, in combination with mapping techniques, may be useful tools to target interventions in resource-limited areas.
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