Spatial distribution of individuals with symptoms of depression in a periurban area in Lima

An example from Peru

CRONICAS Cohort Study Group

Research output: Contribution to journalArticle

3 Citations (Scopus)

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.

Original languageEnglish (US)
Pages (from-to)93-99
Number of pages7
JournalAnnals of Epidemiology
Volume26
Issue number2
DOIs
StatePublished - 2016
Externally publishedYes

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Peru
Depression
Social Adjustment
Censuses
Cluster Analysis
Epidemiologic Studies
Confidence Intervals
Equipment and Supplies

Keywords

  • Depression
  • Hotspot
  • Mental health
  • Peru
  • Spatial clustering

ASJC Scopus subject areas

  • Epidemiology

Cite this

Spatial distribution of individuals with symptoms of depression in a periurban area in Lima : An example from Peru. / CRONICAS Cohort Study Group.

In: Annals of Epidemiology, Vol. 26, No. 2, 2016, p. 93-99.

Research output: Contribution to journalArticle

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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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author = "{CRONICAS Cohort Study Group} and Paulo Ruiz-Grosso and Miranda, {J. Jaime} and Gilman, {Robert H.} and Walker, {Blake Byron} and Gabriel Carrasco-Escobar and Marco Varela-Gaona and Francisco Diez-Canseco and Luis Huicho and William Checkley and Antonio Bernabe-Ortiz and Casas, {Juan P.} and Smith, {George Davey} and Shah Ebrahim and Ra{\'u}l Gamboa and Germ{\'a}n M{\'a}laga and Montori, {Victor Manuel} and Liam Smeeth and Diette, {Gregory B.} and Fabiola Le{\'o}n-Velarde and Mar{\'i}a Rivera and Wise, {Robert A.} and Garc{\'i}a, {H{\'e}ctor H.} and Katherine Sacksteder",
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T1 - Spatial distribution of individuals with symptoms of depression in a periurban area in Lima

T2 - An example from Peru

AU - CRONICAS Cohort Study Group

AU - Ruiz-Grosso, Paulo

AU - Miranda, J. Jaime

AU - Gilman, Robert H.

AU - Walker, Blake Byron

AU - Carrasco-Escobar, Gabriel

AU - Varela-Gaona, Marco

AU - Diez-Canseco, Francisco

AU - Huicho, Luis

AU - Checkley, William

AU - Bernabe-Ortiz, Antonio

AU - Casas, Juan P.

AU - Smith, George Davey

AU - Ebrahim, Shah

AU - Gamboa, Raúl

AU - Málaga, Germán

AU - Montori, Victor Manuel

AU - Smeeth, Liam

AU - Diette, Gregory B.

AU - León-Velarde, Fabiola

AU - Rivera, María

AU - Wise, Robert A.

AU - García, Héctor H.

AU - Sacksteder, Katherine

PY - 2016

Y1 - 2016

N2 - 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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KW - Depression

KW - Hotspot

KW - Mental health

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KW - Spatial clustering

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