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

Abstract

Purpose To map the geographical distribution and spatial clustering of depressive symptoms cases in an area of Lima, Peru. Methods Presence of major depressive episode was assessed using a short version of the Center for Epidemiologic Studies Depression Scale (CES-D). 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 (RR) 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, was analyzed. The prevalence of depressive symptoms was 17.0% (95% CI: 16.2%–17.8%). Three clusters with high prevalence of depressive symptoms (primary cluster: RR=1.82; p=0.003 and secondary: RR=2.83; p=0.004 and RR=5.92; p=0.01), and two clusters with significantly low prevalence (primary: RR=0.23; p=0.016 and secondary: RR=0; p=0.035), 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.

Vector Transmission Alone Fails to Explain the Potato Yellow Vein Virus Epidemic among Potato Crops in Colombia

“…Scan statistics are among the most widely used methods for spatial cluster detection. They have been successfully used to support scientific research in epidemiology ( Kulldorff et al, 2006 ; Ryan et al, 2006 ; Wand and Ramjee, 2010 ; Cuadros et al, 2013 ; Jones et al, 2013 ; Malleson and Andresen, 2015 ; Ruiz-Grosso et al, 2016 ). However, only few studies in plant pathology have implemented this methodology (e.g., Coulston and Riitters, 2003 ; Porcasi et al, 2006 ; Bayon et al, 2007 ).…”

Depression and obesity, data from a national administrative database study: Geographic evidence for an epidemiological overlap

“…The same type of analysis highlighted a high-prevalence cluster of diagnosed diabetes in the south of the United States which was correlated to educational and ethnic factors [17]. Other studies have described the epidemiological, psychological and social characteristics of MDD (Major Depressive Disorders) or obesity in different countries or regions [18–20], but no joint analysis of a potential obesity-depression spatial overlap is available.…”

Spatial structure of depression in South Africa: A longitudinal panel survey of a nationally representative sample of households

“…Individuals who reported being infected with TB had significantly higher odds of belonging to a hotspot of new cases of depression. This was after controlling for other important socio-economical and demographical risk factors of depression, identified in previous work in sub-Saharan African settings, as well as in studies assessing the geographical risk factors of depression at smaller scales 20,25 . Likewise, the clusters of new cases of depression overlapped with those of TB self-reporting (Fig.…”

“…Spatial epidemiology has emerged as a promising approach for understanding the distribution and patterns underlying the processes and drivers of an epidemic. This approach has been extensively used to study communicable and non-communicable diseases, and could provide important insights into mental health disorders, such as schizophrenia and depression 20–22 . In recent years, several studies have underscored the spatial structure of mental disorders that are partly driven by factors distributed unevenly in space 23,24 .…”