Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/288202 
Year of Publication: 
2023
Citation: 
[Journal:] Health Economics [ISSN:] 1099-1050 [Volume:] 33 [Issue:] 2 [Publisher:] Wiley [Place:] Hoboken, NJ [Year:] 2023 [Pages:] 229-247
Publisher: 
Wiley, Hoboken, NJ
Abstract: 
We investigated the impact of an integrated care initiative in a socially deprived urban area in Germany. Using administrative data, we empirically assessed the causal effect of its two sub‐interventions, which differed by the extent to which their instruments targeted the supply and demand side of healthcare provision. We addressed confounding using propensity score matching via the Super Learner machine learning algorithm. For our baseline model, we used a two‐way fixed‐effects difference‐in‐differences approach to identify causal effects. We then employed difference‐in‐differences analyses within an event‐study framework to explore the heterogeneity of treatment effects over time, allowing us to disentangle the effects of the sub‐interventions and improve causal interpretation and generalizability. The initiative led to a significant increase in hospital and emergency admissions and non‐hospital outpatient visits, as well as inpatient, non‐hospital outpatient, and total costs. Increased utilization may indicate that the intervention improved access to care or identified unmet need.
Subjects: 
difference‐in‐differences
evaluation
event study
health care utilization and costs
integrated care
socially deprived urban area
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by-nc-nd Logo
Document Type: 
Article
Document Version: 
Published Version

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