Exploratory spatial analysis of diabetes mortality and its relationship with the socioeconomic conditions of Colombian municipalities

Abstract

Type 2 diabetes mellitus prevalence has increased worldwide, especially in low- and middle-income countries. This study aimed to evaluate in the Colombian context whether the geographic variations of mortality due to type 2 diabetes mellitus are random or statistically significant and to measure the effect of the socioeconomic conditions of municipalities on these variations. An ecological study of trends for regions and subregions was undertaken considering two periods (2003-2009 and 2010-2016). The study population consisted of adults of 20 to 79 years old residing in each of Colombia’s municipalities during the period 2003-2016. Smoothed and standardized mortality rates were obtained by fitting a hierarchical Bayesian model, which considers the spatial structure of the data. Four socioeconomic variables related to municipal deprivation and development were included into the model to obtain their relative risk (RR) and 95% credible intervals. Mortality due to type 2 diabetes mellitus decrease between 2003 and 2016 in Colombia, both nationally and regionally. In addition, there was a clear positive association between mortality due to diabetes mellitus and the level of municipal development; the wealthiest and most developed municipalities had higher mortality risks.

Spatial association of socio-demographic, environmental factors and prevalence of diabetes mellitus in middle-aged and elderly people in Thailand

“…The connection between remotely sensed NTL imagery provides a straightforward method to analyze the relationship between urbanisation and human activities. Due to the exclusive capability of the US Defense Meteorological Satellite Program -Operational Linescan System (DMSP -OLS) to detect low levels of visible and near-infrared night-time radiance signals, the composed stable NTL data have been used for mapping urban areas, estimating the spatial expand trends of cities and measuring socioeconomic activities (Fan et al, 2014;Montoya-Betancur et al, 2020).…”

“…the spatial weight matrix; and N the number of spatial units.We used LISA to determine the local spatial autocorrelation patterns of the variables depicting locations with significant 05) outcomes (LISA significance maps) and classified these locations according to association type (LISA cluster maps) as done byMontoya-Betancur et al (2020). The selection of the spatial-weight matrix is one of the key factors contributing to the outcome of LISA computation; thus, its specification was carefully formulated in the present study for the 32 full-border provinces and 23 coastal ones, including the island province, Phuket.…”