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Case-mix adjustments of patient reported experience measures in general practice: variations between Norwegian
Katrine Skyrud1, Øyvind Bjertnæs1, Rebecka Norman1
1Cluster of Health Services Research, Norwegian Institute of Public Health, Oslo, Norway.
Background:
Understanding patients' experiences with general practice is essential for improving services, and quality indicators are commonly used for that purpose. When reporting at the municipality level, factors beyond municipal control, such as age and country of birth, may influence results. Case-mix adjustment helps ensure fair comparisons. While several international studies have assessed case-mix adjustment for patient experience measures, few studies have examine the impact of case-mix adjustment at the municipality level.
Objective:
To identify relevant municipality-level variables for case-mix adjustment in Norway and measure their impact on aggregated patient experience scores.
Methods:
We analysed data from the 2023 national survey on patient experiences with the general practitioner. Linear mixed-effects regression was applied with municipalities as a random effect and patient age, sex, long-term conditions, country of birth, education and household income as fixed effects. Outcomes were municipality-level means for six measures: Assessment of general practitioner, Cooperation, Enablement, Accessibility, Practice, and Continuity.
Results:
Age, birth country and long-term conditions were consistently associated with patient experience measures. Intraclass correlations showed that birth country (4%) and education (2.6%) accounted for the greatest between-municipality variation. Case-mix adjustment most strongly influenced Accessibility and Practice scores. Age and birth country were the most influential adjustors.
Conclusion:
Patient characteristics-particularly age and country of birth-are systematically linked to patient reported experience measures, while sex and education are less important. Case-mix adjustment modestly improves fairness in municipality comparisons, underscoring the need for a selective, evidence-based approach when applying adjustment in quality measurement and research.
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