Spatial distribution of individuals with symptoms of depression in a periurban area in Lima: An example from Peru
- CRONICAS Cohort Study Group,
- Paulo Ruiz-Grosso(Author),
- J. Jaime Miranda(Author),
- Robert H. Gilman(Author),
- Blake Byron Walker(Author),
- Gabriel Carrasco-Escobar(Author)
- Universidad Peruana Cayetano Heredia,
- Universidad Peruana Cayetano Heredia,
- Johns Hopkins University,
- Asociación Benéfica PRISMA,
- Simon Fraser University,
Open access
Publication Information
Output type
Original language
EnglishPages from-to (Number of pages)
Pages 93-99.e2Journal (Volume, Issue Number)
Annals of Epidemiology (Volume 26, Issue 2)Publication milestones
- Published - 2016
Publication status
ISSN
1047-2797Publication IDs
- Scopus: 84961771307
- PubMed: 26654102
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.
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- SDG 3 Good Health and Well
