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Título:
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Spatial distribution of individuals with symptoms of depression in a periurban area in Lima: an example from Peru
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Autores:
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Ruiz-Grosso, Paulo ;
Miranda, J. Jaime ;
Gilman, Robert H. ;
Walker, Blake Byron ;
Carrasco-Escobar, Gabriel ;
Varela-Gaona, Marco ;
Diez-Canseco, Francisco ;
Huicho, Luis ;
Checkley, William ;
Bernabé-Ortiz, Antonio
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Tipo de documento:
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texto impreso
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Editorial:
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Elsevier, 2019-02-06T14:52:12Z
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Nota general:
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info:eu-repo/semantics/restrictedAccess
https://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
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Idiomas:
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Inglés
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Palabras clave:
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Editados por otras instituciones
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Artículos
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Artículos en revistas indizadas
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Resumen:
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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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En línea:
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http://doi.org/10.1016/j.annepidem.2015.11.002
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