| アブストラクト | BACKGROUND: Accurate needs-based capitation is key to effective and equitable primary care funding. Most capitation schemes use only basic demographic and area characteristics. OBJECTIVE: We developed capitation weights for general practices in England using morbidity indicators recorded in primary and secondary care. METHODS: We used primary care records from the Clinical Practice Research Datalink (CPRD) linked with Hospital Episode Statistics (HES) for 12,667,755 patients registered with 1397 general practices on 1 April 2018. Using linear regression models, we estimated the effects on cost-weighted clinical appointments of patient age and gender, ethnicity, area-level deprivation, new registration, and morbidity (four sets of indicators covering 20 to 209 conditions). We included practice fixed-effects to adjust for differences in capacity and productivity. We applied the coefficients on patient characteristics as need-weights to data available nationally and we calculated weighted-patients for all 6892 practices in England. RESULTS: Most patients (71 %) had at least one appointment per-year. The average annual workload per-patient was pound110, with large variations across patients (range pound0- pound882) and practices ( pound47- pound179). Workload increased with age and with deprivation, but their direct effects halved when including morbidity in the model. Including morbidity widened the range of weighted-patient between practices at the 5th and 95th percentiles (from 0.84-1.14 to 0.79-1.16) and in the least and most deprived deciles (from 0.96-1.04 to 0.95-1.06). CONCLUSION: Needs-based capitation weights accounting for morbidity and adjusting for unexplained variations in practice capacity and productivity increase workload differentiation and direct resources toward practices in more deprived areas. |
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| ジャーナル名 | Health policy (Amsterdam, Netherlands) |
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| Pubmed追加日 | 2025/7/29 |
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| 投稿者 | Anselmi, Laura; Wang, Shaolin; Lau, Yiu-Shing; Anderson, Michael; Kontopantelis, Evangelos; Sutton, Matt |
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| 組織名 | Health Organisation, Policy and Economics (HOPE), The University of Manchester,;UK. Electronic address: laura.anselmi@manchester.ac.uk.;UK.;Division of Informatics, Imaging and Data Sciences, The University of Manchester, |
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| Pubmed リンク | https://www.ncbi.nlm.nih.gov/pubmed/40716164/ |
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