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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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dc.contributor.author Ruiz-Grosso, Paulo
dc.contributor.author Miranda, J. Jaime
dc.contributor.author Gilman, Robert H.
dc.contributor.author Walker, Blake Byron
dc.contributor.author Carrasco-Escobar, Gabriel
dc.contributor.author Varela-Gaona, Marco
dc.contributor.author Diez-Canseco, Francisco
dc.contributor.author Huicho, Luis
dc.contributor.author Checkley, William
dc.contributor.author Bernabe-Ortiz, Antonio
dc.date.accessioned 2019-02-06T14:52:12Z
dc.date.available 2019-02-06T14:52:12Z
dc.date.issued 2015
dc.identifier.uri https://hdl.handle.net/20.500.12866/5269
dc.description.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. en_US
dc.language.iso eng
dc.publisher Elsevier
dc.relation.ispartof urn:issn:1873-2585
dc.rights info:eu-repo/semantics/restrictedAccess
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
dc.subject Peru en_US
dc.subject Adult en_US
dc.subject Female en_US
dc.subject Humans en_US
dc.subject Male en_US
dc.subject Middle Aged en_US
dc.subject Peru/epidemiology en_US
dc.subject Socioeconomic Factors en_US
dc.subject Altitude en_US
dc.subject Prevalence en_US
dc.subject Risk Factors en_US
dc.subject Spatial Analysis en_US
dc.subject Spatial clustering en_US
dc.subject Mental health en_US
dc.subject Depression en_US
dc.subject Hotspot en_US
dc.subject Residence Characteristics en_US
dc.subject Depression/epidemiology en_US
dc.subject Depressive Disorder, Major/epidemiology en_US
dc.title Spatial distribution of individuals with symptoms of depression in a periurban area in Lima: an example from Peru en_US
dc.type info:eu-repo/semantics/article
dc.identifier.doi https://doi.org/10.1016/j.annepidem.2015.11.002
dc.subject.ocde https://purl.org/pe-repo/ocde/ford#3.02.00 es_PE
dc.subject.ocde https://purl.org/pe-repo/ocde/ford#3.03.09


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