Predicting risk of hospitalisation: a retrospective population-based analysis in a paediatric population in Emilia-Romagna, Italy

Abstract

ObjectivesDevelop predictive models for a paediatric population that provide information for paediatricians and health authorities to identify children at risk of hospitalisation for conditions that may be impacted through improved patient care.DesignRetrospective healthcare utilisation analysis with multivariable logistic regression models.DataDemographic information linked with utilisation of health services in the years 2006–2014 was used to predict risk of hospitalisation or death in 2015 using a longitudinal administrative database of 527 458 children aged 1–13 years residing in the Regione Emilia-Romagna (RER), Italy, in 2014.Outcome measuresModels designed to predict risk of hospitalisation or death in 2015 for problems that are potentially avoidable were developed and evaluated using the C-statistic, for calibration to assess performance across levels of predicted risk, and in terms of their sensitivity, specificity and positive predictive value.ResultsOf the 527 458 children residing in RER in 2014, 6391 children (1.21%) were hospitalised for selected conditions or died in 2015. 49 486 children (9.4%) of the population were classified in the ‘At Higher Risk’ group using a threshold of predicted risk >2.5%. The observed risk of hospitalisation (5%) for the ‘At Higher Risk’ group was more than four times higher than the overall population. We observed a C-statistic of 0.78 indicating good model performance. The model was well calibrated across categories of predicted risk.ConclusionsIt is feasible to develop a population-based model using a longitudinal administrative database that identifies the risk of hospitalisation for a paediatric population. The results of this model, along with profiles of children identified as high risk, are being provided to the paediatricians and other healthcare professionals providing care to this population to aid in planning for care management and interventions that may reduce their patients’ likelihood of a preventable, high-cost hospitalisation.

Big Data Analytics to Reduce Preventable Hospitalizations—Using Real-World Data to Predict Ambulatory Care-Sensitive Conditions

“…The models developed in this publication achieved c-statistics comparable to Billings et al (0.780) [ 7 ] and Yi et al (0.805) [ 10 ], indicating a good model fit above the median of 0.68 of a systematic review of prediction models for rehospitalization [ 10 ]. However, perhaps due to the smaller sample size, the model performance did not reach that of Louis et al (0.856) [ 21 ] or Gao et al (0.833) [ 20 ]. In contrast to other studies in the field of hospital care, in this study we did not discriminate between emergency and elective admissions following the argument that an elective inpatient episode can also be a sign of unforeseen deterioration.…”

“…Specificity, on the other hand, relates to the number of individuals without an ACSH that are identified as such. Additional risk thresholds also used by Louis et al [ 21 ] were implemented. The category “high risk” includes individuals with a predicted probability of 15% to 24%; the category “very high risk” includes individuals with a predicted probability of 25% and higher to have an ACSH in the prediction year.…”

“…The outcome variable was defined similar to prior studies focusing on ACSH prediction [ 10 , 20 , 21 ]. It is the event of an individual being hospitalized with an ACSC in the prediction year.…”

“…This is comparable to an approach of the Veterans Health Administration providing patient-specific care assessment need scores based on data from the corporate data warehouse that can be accessed by healthcare providers and population health managers [ 13 ]. Several studies exist predicting (re-)hospitalizations in general [ 7 , 14 , 15 , 16 , 17 , 18 , 19 ], but only a few specifically predict ACSH in the context of the health systems of the USA, Canada and Italy [ 10 , 20 , 21 , 22 ]. While the methodologies are comparable to a certain extent, this study extends the context to Germany, which is on the one hand valuable since ACSH definitions are most often adapted to the specific health system characteristics and therefore models and results from other contexts cannot be directly transferred or put into practice.…”

Inequalities in avoidable hospitalisation in large urban areas: retrospective observational study in the metropolitan area of Milan

“…23 Although patients with higher morbidity are routinely found to be at greater risk of being hospitalised for ACSC, 24 25 there is evidence of an independent effect of better access to ambulatory care on rates of hospitalisation for ACSC. 26 27 After adjustments for different measures of health status, most studies support the conclusion that although hospital discharges for ACSC may reflect morbidity and health-seeking behaviours, there is international evidence in support of hospitalisations for ACSC as a measure of access to timely and effective ambulatory care in Australia, 28 Canada, 29 England, 30 France, 18 Italy [31][32][33] and many other countries.…”