Using geo-spatial analysis for assessing the risk of hospital admissions due to community-acquired pneumonia in under-5 children and its association with socially vulnerable areas (Brazil)

Abstract

Background The concentration of under-5 child morbidity and mortality due to pneumonia in developing countries reflects the social inequities. This study aimed to map and assess the spatial risk for hospitalization due to Community-Acquired Pneumonia in children under 5 years of age and its association with vulnerable areas. Methods Ecological study in the city of Ribeirão Preto, state of São Paulo, Brazil. The study population consisted of hospitalized under-5 children, diagnosed with community-acquired pneumonia, in Ribeirão Preto-São Paulo-Brazil, from 2012 to 2013. Data were collected in different databases, by a trained team, between March 2012 and August 2013 and from the 2010 Demographic Census of the Brazilian Institute of Geography and Statistics. The 956 urban census tracts were considered as the units of analysis. The incidence of cases per 10,000 inhabitants was calculated by census tracts during the study period. For the identification of the spatial risk clusters, the Kernel density estimator and the Getis-Ord Gi* technique were performed. Generalized additive models were used to verify the association between areas with social vulnerability and the occurrence of childhood pneumonia. Results The study included 265 children under the age of five, hospitalized due to community-acquired pneumonia. A concentration of cases was identified in the regions with greater social vulnerability (low income, poor housing conditions and homelessness), as well as a lower occurrence of cases in the most developed and economically privileged area of the city. The majority of the children lived in territories served by traditional primary healthcare units, in which the health surveillance and family and community focus are limited. It is important to highlight that the tracts with the highest degrees of vulnerability, such as those identified as high vulnerability (urban) and very high vulnerability (subnormal urban clusters). Conclusions The results contribute to the comprehension of the social factors involved in child hospitalization due to pneumonia, based on the analysis of the spatial distribution. This approach revealed a strategic tool for diagnosing the disparities as well presenting evidences for the planning in health and strength health care system in achieving equity, welfare and social protection of children.

Spatial patterns and spatially-varying factors associated with childhood acute respiratory infection: data from Ethiopian demographic and health surveys (2005, 2011, and 2016)

“…Direct comparisons of the spatial variations and the impact of risk factors on ARI between studies are difficult because of differences in definitions of ARI, analysis methods, and study participants. However, the existence of spatial variations in hospitalization due to pneumonia has been noted among children younger than 5 years hospitalized for severe pneumonia in Brazil [ 28 ] and Bhutan [ 29 ]. Longitudinal data from Ethiopia between 2009 and 2017 shows that ARI is still a major cause of death in under five years children [ 30 ].…”

Trend, spatial distribution and predictors of childhood acute respiratory infection in Ethiopia: Evidence from Ethiopia’s 2005, 2011, and 2016 demographic and health survey

“…Direct comparisons of the spatial variations and the impact of risk factors on ARI between studies are di cult because of differences in de nitions of ARI, analysis methods, and study participants. However, the existence of spatial variations in hospitalization due to pneumonia has been noted among children younger than 5 years hospitalized for severe pneumonia in Brazil (29) and Bhutan (30). Longitudinal data from Ethiopia between 2009 and 2017 shows that ARI is still a major cause of death in under ve years children (31).…”

Applications of geospatial analyses in health research among homeless people: A systematic scoping review of available evidence

“…These same studies also used different statistical modeling approaches to identify the correlation between different variables and homelessness. Beyond the mapping and visualization, hotspot analysis using the kernel density functions is the most prominently used spatial analysis techniques in the reviewed articles 9,10,18,22,24,28-30,34,36 . However, a few studies also used global and local Moran’s I to identify the spatial autocorrelation between the locations and distribution 9,12 .…”