Spatial distribution of individuals with symptoms of depression in a periurban area in Lima: an example from Peru

Diez-Canseco Montero, Francisco; Gilman, Robert Hugh; Miranda, J. Jaime; Huicho Oriundo, Luis; Carrasco Escobar, Gabriel; Varela-Gaona, Marco; Walker, Blake Byron; Checkley, William; Ruiz-Grosso, Paulo; Bernabé Ortiz, Antonio

dc.contributor.author	Ruiz-Grosso, Paulo
dc.contributor.author	Miranda, J. Jaime
dc.contributor.author	Gilman, Robert Hugh
dc.contributor.author	Walker, Blake Byron
dc.contributor.author	Carrasco Escobar, Gabriel
dc.contributor.author	Varela-Gaona, Marco
dc.contributor.author	Diez-Canseco Montero, Francisco
dc.contributor.author	Huicho Oriundo, Luis
dc.contributor.author	Checkley, William
dc.contributor.author	Bernabé 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.ispartofseries	Annals of Epidemiology
dc.rights	info:eu-repo/semantics/restrictedAccess
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#3.03.09
dc.relation.issn	1873-2585