Risk Adjustment in Capitation Payments to Primary Care Providers
Bibliographic record
Abstract
BACKGROUND: One 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. OBJECTIVES: We 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 DESIGN: We 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. RESULTS: The 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. CONCLUSIONS: SES 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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".