Effectiveness of a population-based integrated care model in reducing hospital activity: an interrupted time series analysis

Abstract

Data availability statement: Data may be obtained from a third party and are not publicly available. The NHS's data policy prevents sharing the data with anybody but the authors participating in the study. Any individual intending to access the data must first apply and be granted WSIC access.Copyright © Author(s) (or their employer(s)) 2022. Objectives First impact assessment analysis of an integrated care model (ICM) to reduce hospital activity in the London Borough of Hillingdon, UK. Methods We evaluated a population-based ICM consisting of multiple interventions based on self-management, multidisciplinary teams, case management and discharge management. The sample included 331 330 registered Hillingdon residents (at the time of data extraction) between October 2018 and July 2020. Longitudinal data was extracted from the Whole Systems Integrated Care database. Interrupted time series Poisson and Negative binomial regressions were used to examine changes in non-elective hospital admissions (NEL admissions), accident and emergency visits (A&E) and length of stay (LoS) at the hospital. Multiple imputations were used to replace missing data. Subgroup analysis of various groups with and without long-term conditions (LTC) was also conducted using the same models. Results In the whole registered population of Hillingdon at the time of data collection, gradual decline over time in NEL admissions (RR 0.91, 95% CI 0.90 to 0.92), A&E visits (RR 0.94, 95% CI 0.93 to 0.95) and LoS (RR 0.93, 95% CI 0.92 to 0.94) following an immediate increase during the first months of implementation in the three outcomes was observed. Subgroup analysis across different groups, including those with and without LTCs, showed similar effects. Sensitivity analysis did not show a notable change compared with the original analysis. Conclusion The Hillingdon ICM showed effectiveness in reducing NEL admissions, A&E visits and LoS. However, further investigations and analyses could confirm the results of this study and rule out the potential effects of some confounding events, such as the emergence of COVID-19 pandemic.Department of Health Sciences, Brunel University London