Adjusting primary-care funding by deprivation: a cross-sectional study of lower-super-output-areas in England.

IF 2.5 Q2 PRIMARY HEALTH CARE BJGP Open Pub Date : 2024-08-19 DOI:10.3399/BJGPO.2024.0185
Ian Holdroyd, Cameron Appel, Efthalia Massou, John Ford
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Abstract

Background: Previous research has called for General Practice (GP) funding to be adjusted by deprivation data. However, there is no evidence that this would better meet clinical need.

Aim: We assessed 1. how accurately the capitation formula (Carr Hill), and total GP funding predicts clinical need and 2. whether adjusting by the Index of Multiple Deprivation score (IMD), improves accuracy.

Design & setting: Cross-sectional analysis of 32 844 Lower-Super-Output-Areas in England in 2021-2022. Sensitivity analysis used data from 2015-2019.

Method: Weighted average Carr-Hill Index (CHI), total GP funding and five measures of clinical need were calculated for each LSOA. For both CHI and total funding, four sets of generalised linear models were calculated for each outcome measure: unadjusted; Age-adjusted; IMD-adjusted; and age and IMD adjusted. Adjusted R2 assessed model accuracy.

Results: In unadjusted models, CHI was a better predictor than total-funding of Combined Morbidity Index (CMI) (R2=49.81%,29.31% respectively), combined diagnosed and undiagnosed morbidity (R2=43.52%,21.9%), emergency admissions (R2=32.75%,16.95%). Total-funding was a better predictor than capitation of GP appointments per patient (R2=28.5%, 22.5% respectively) and age and sex standardised mortality rates (R2=0.42%,0.37%).. Adjusting for age and IMD improved all ten models (R2=62.2%,53.2%,48.6%,38.5%,40.5%, 32.8%, 29.1%,34.6%, 25.2%,25.2% respectively). All age and IMD adjusted models significantly outperformed age-adjusted models (p<0.001). Sensitivity analysis confirmed findings.

Conclusion: Adjusting capitation or total-funding by IMD would increase funding efficiency, especially for long term outcomes, such as mortality. However, adjusting for IMD without age could have unwanted consequences.

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按贫困程度调整初级保健资金:对英格兰较低产出地区的横断面研究。
背景:先前的研究呼吁根据贫困数据调整全科医生(GP)经费。目的:我们评估了:1.按人头计算公式(卡尔希尔)和全科医生总经费预测临床需求的准确性;2.根据多重贫困指数(IMD)进行调整是否能提高准确性:对 2021-2022 年英格兰 32 844 个较低-超级产出地区进行横截面分析。敏感性分析使用的是 2015-2019 年的数据:计算每个低等收入区的加权平均卡尔-希尔指数(CHI)、全科医生总经费和五项临床需求指标。对于CHI和资金总额,为每项结果指标计算了四组广义线性模型:未调整模型;年龄调整模型;IMD调整模型;年龄和IMD调整模型。调整后的 R2 用于评估模型的准确性:在未经调整的模型中,CHI 比资金总额更能预测综合发病率指数(CMI)(R2=49.81%,分别为 29.31%)、综合诊断和未诊断发病率(R2=43.52%,分别为 21.9%)、急诊入院率(R2=32.75%,分别为 16.95%)。与按人头计算的全科医生预约率(R2=28.5%,22.5%)以及年龄和性别标准化死亡率(R2=0.42%,0.37%)相比,资金总额是更好的预测指标。对年龄和 IMD 进行调整后,所有十个模型都有所改善(R2=62.2%、53.2%、48.6%、38.5%、40.5%、32.8%、29.1%、34.6%、25.2%、25.2%)。所有年龄和 IMD 调整模型均明显优于年龄调整模型(p 结论:根据 IMD 调整按人头付费或供资总额将提高供资效率,尤其是对死亡率等长期结果而言。然而,不按年龄调整 IMD 可能会产生不必要的后果。
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来源期刊
BJGP Open
BJGP Open Medicine-Family Practice
CiteScore
5.00
自引率
0.00%
发文量
181
审稿时长
22 weeks
期刊最新文献
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