ArticleMedical care2025
Risk Adjustment in Capitation Payments to Primary Care Providers: Does It Matter How We Account for Patients' Socioeconomic Status?
Article in Medical care, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
2 citing papers in PubMed.
- Improving Primary Healthcare: Applying Grapevine (Healthcare (Basel, Switzerland) · 2026Article
- Ex ante evaluation of risk adjustment models for prospective provider payment: a conceptual framework and empirical application.The European journal of health economics : HEPAC : health economics in prevention and care · 2026Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundOne of the critical challenges with capitation payment to primary care providers is ensuring that the fixed payments are equitable and adjusted for expected care needs. Patients of lower socioeconomic status (SES) generally have higher health care need. Sweden developed a Care Needs Index, which is used in the capitation payments to primary care providers to account for patient SES.
objectivesWe aim to examine the potential value of collecting individual-level rather than geographic-level socioeconomic data to support an equitable payment to primary care providers. RESEARCH
designWe used data from 3 regional administrative care registers, which cover all consultations in publicly funded health care, and Statistics Sweden's registers covering individual background characteristics. We estimated linear regression models and evaluated the model fit using the adjusted R2 with the Care Needs Index at the individual and at the district level. The population consisted of the 3,490,943 individuals residing in the 3 study regions for whom we had complete data. MEASURES: The main outcome variable was the number of face-to-face consultations with a GP or a nurse at a primary care practice. We use the R2 to compare the predictive power of the models.
resultsThe share of the variation explained did not depend on whether the Care Needs Index was measured at the individual level or the small area level.
conclusionsSES explains very little variation in primary care visits, and there is no gain from having individual-level information about the individual's SES compared with having district-level information only.
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