Length of paediatric inpatient stay, socio-economic status and hospital configuration: a retrospective cohort study

BACKGROUND Variation in paediatric inpatient length of stay exists - whether this is driven by differences in patient characteristics or health service delivery is unclear. We will test the hypotheses that higher levels of deprivation are associated with prolonged length of stay and that differences in prolonged length of stay across 2 hospitals will be explained by demographic, clinical and process factors. METHODS This is a retrospective cohort study of 2889 children aged less than 16 years admitted from 1st April 2009 to 30th March 2010. Administrative data were used from two UK hospitals whose Accident and Emergency (A&E) departments were paediatric and adult physician led respectively. The main outcome was prolonged length of stay defined as greater than or equal to the mean (1.8 days). Sensitivity analyses defined prolonged length of stay as greater than the median (1 day). Demographic, clinical and process characteristics were examined. Socio-economic position was measured by Income Deprivation Affecting Children Index. Multivariable logistic and linear regression analyses were performed. RESULTS We did not find a consistent association between length of stay and socio-economic position, using a variety of definitions of length of stay. In contrast, adjusted for age, gender, socio-economic position, ethnicity, final diagnosis, number of hospital admissions, source of admission, and timing of admission, admission to the adult led A&E hospital was more strongly associated with prolonged length of stay (Odds Ratio 1.41, 95% Confidence Interval 1.16, 1.71). CONCLUSION Local variation in paediatric inpatient length of stay was not explained by demographic, clinical or process factors, but could have been due to residual confounding by medical complexity. Length of stay was not consistently associated with socio-economic position suggesting that length of stay is a function of health service not the determinants of health. Analyses of these types of data would be strengthened by measures of complexity and adverse events.