ArticleBMJ open2020
Examining the relationship between poverty and length of stay: a repeated cross-sectional study of paediatric hospitalisations in Chile.
Article in BMJ open, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
objectiveTo measure poverty-based disparities in inpatient length of stay for paediatric hospitalisations. In particular, this paper examines the relationship between municipality level poverty rates and length of stay, accounting for individual level characteristics.
designWe use patient discharge data to conduct a repeated cross-sectional study of the totality of paediatric hospitalisations in 15 regions of Chile, in the years 2011, 2013, 2015 and 2017.
settingAll hospital discharges in 15 regions of Chile.
participants1 033 222 discharges for children under the age of 15, between 2011 and 2017. OUTCOME MEASURES: Length of stay (LOS); LOS by type of insurance and type of hospital; hospitalisation rates; municipality-level average LOS.
resultsWe find that municipality level poverty rates are a significant predictor of LOS, even after controlling for individual and area level characteristics, including type of insurance. Children from municipalities in the poorest quintile have a LOS that is 14% shorter as compared with children from municipalities in the richest quintile. This relationship is stronger for publicly insured children: the decrease in LOS associated with the same poverty change is of 22%.
conclusionsThis paper shows that there is an association between municipality-level poverty rates and length of stay for paediatric hospitalisations in Chile. For the vast majority of the sample, and after controlling for individual level characteristics, an increase in the municipality level poverty rate is associated with a decrease in the length of stay. Further, there is a non-linearity in the relationship, where at the highest poverty rates, poverty and LOS are positively associated. These findings are robust after controlling for type of hospital (public vs private), type of insurance (public vs private), type of diagnosis, as well as year and region fixed effects.
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