Evaluating the impact of capitation funding top-up payments in primary care

Evaluating the impact of capitation funding top-up payments in primary care | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Evaluating the impact of capitation funding top-up payments in primary care Sarah Opie-Martin, Freya Tracey, Emma Whitfield, Melissa Co, Jake Beech, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7509545/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 08 Jan, 2026 Read the published version in BMC Primary Care → Version 1 posted 10 You are reading this latest preprint version Abstract Background Capitation payments account for approximately half of core funding for General Practitioner (GP) practices in England, allocated via the Global Sum Allocation (‘Carr-Hill’) formula. The formula has not been updated since 2004 and lacks adjustments for clinical diagnoses, patient communication difficulties, and deprivation which are factors known to influence workload and health outcomes. In July 2021, Leicester, Leicestershire, and Rutland (LLR) Integrated Care Board introduced the Health Equity Payment (HEP), a top-up funding scheme based on a locally developed formula incorporating these additional factors. Method: We conducted a retrospective observational study using national public data to evaluate the impact of HEP between July 2021 and April 2023. Practices receiving HEP were matched to similar practices outside LLR using Genetic Matching on demographics, disease prevalence, and baseline outcomes. Seven outcomes were assessed: three patient experience measures from the GP Patient Survey, three staffing metrics (GP, nurse, and administrative full-time equivalents per 1000 weighted patients), and Quality and Outcomes Framework (QOF) achievement. Causal effects were estimated using doubly robust regression models with g-computation to estimate the average treatment effect. Results: Sixty-two LLR practices received HEP and were matched to 62 control practices. Practices receiving HEP achieved a 3.2 percentage point higher QOF score (95% CI: 0.5 to 5.9; p = 0.02) compared to controls. No statistically significant differences were found in patient experience or staffing outcomes. Sensitivity analyses confirmed robustness to alternative time periods and outcome specifications but revealed sensitivity to missing staffing data from atypical practices. Discussion: This study provides the first causal evaluation of a capitation funding model incorporating clinical and sociodemographic factors in England. The modest improvement in QOF achievement suggests that targeted funding could be linked to enhanced care quality. The absence of effects on staffing and patient experience may reflect data limitations, short follow-up, or heterogeneity in how funds were used. These findings provide the the first evidence that locally tailored funding models could address inequalities in primary care provision and inform ongoing national reviews of the general practice capitation funding. Primary care healthcare funding health inequalities Figures Figure 1 Figure 2 Figure 3 Introduction In England, capitation payments make up approximately 50% of core General Practitioner (GP) practice funding and are allocated through the Global Sum Allocation formula, also known as the Carr-Hill formula ( 1 ). The remaining funding includes fee-for-service, pay-for-performance, premises costs and local services payments. Capitation-based funding recognises the difficulties of reimbursing GPs for the complex range of tasks done in general practice and the holistic, ongoing and often preventive nature of the care they provide ( 2 ). To ensure general practice is effective and that funding is efficient, capitation payments need to be accurately distributed based on patient need and other local cost pressures. The Carr-Hill formula aims to reimburse practice running costs by modelling workload and calculating fixed costs related to geographical location, known as the ‘market forces factor’ ( 3 ). Key drivers of practice workload included in the Carr-Hill formula are patient age and sex, additional patient needs measured as limiting longstanding illness and standardised mortality ratio for people under 65; and list turnover ( 3 ). The methodology and underlying datasets that inform the Carr-Hill formula have not been updated since 2004, drawing concern that the formula does not effectively estimate primary care workload, particularly as increasing numbers of patients live with multiple morbidity ( 4 ). Some of the other factors that may affect practice workload include patient diagnosis, level of deprivation and communication difficulties, but these are not considered in the formula ( 5 , 6 ). Studies indicate modelling the effect of incorporating clinical diagnostic information or practice-level deprivation alongside age and sex adjustment into capitation models is a better predictor of primary care cost and resource utilisation than those that use age and sex only ( 5 , 7 , 8 ). The most deprived areas, identified using the Index of Multiple Deprivation (IMD), have fewest GPs per capita, and performance is worse on key quality measures ( 9 ). The Carr-Hill formula is often cited as a key driver of these inequalities although other payment sources, such as dispensing and Quality Outcomes Framework (QOF) performance payments, may be more important drivers of overall inequality in payments ( 10 ). Incorporating data on clinical diagnoses into the capitation formula, whether by individual or by groups of conditions, may capture some of the increased costs of socio-economic deprivation such as earlier onset of multimorbidity and higher consultation rates, however it may also reimburse less deprived areas with older populations ( 6 , 11 , 12 ). Further adjustment for increased severity of disease in deprived areas and costs associated with delivering population health management, eg vaccination programmes, which may take more resource to provide the same coverage in more deprived areas, may also be needed ( 13 , 14 ). In July 2021 Leicester, Leicestershire, and Rutland (LLR) Integrated Care Board (ICB) started providing ‘Health Equity Payments’ (HEP) to practices, consistent with the ICB mandate to address inequalities. The payments were calculated using a local formula aiming to better reflect patient needs and practice deprivation than the Carr-Hill formula. The local formula used recorded clinical diagnoses of the practice’s registered population (categorised into groups using the Johns Hopkins Adjusted Clinical Groups (ACG) system), communication needs of the practice’s registered population, and the practice’s list turnover to estimate expected practice appointment supply. The total patient list size was also multiplied by the IMD 2019 score of the practice, as calculated using the address of registered patient population. This was designed to provide a proxy for wider determinants of need not captured by clinical coding. The ICB total of the Carr-Hill allocated monies and other core primary care funding streams was calculated to provide the overall funding to the practices in LLR and is referred to as 'monies in scope'. Top-up funding eligibility and funding amounts were determined by separately comparing each practice’s share of the total ICB general practice deprivation and expected appointment supply (deprivation and appointment supply share) with its share of total ICB general practice monies in scope (funding share). Practices received a fixed payment based on their existing Carr-Hill funding amount and a top-up payment if their appointment supply or deprivation share exceeded their funding share. Of the monies in scope not allocated as a fixed amount of existing Carr-Hill payments, 90% of the remaining monies in scope are redistributed according to the practice's share of total appointment supply, and 10% are redistributed according to deprivation score. Details of eligible funding streams and weighting of each workload component can be found in Table S1 . Top-up payments came from other funding streams within the ICB, so no practices lost funding because of the scheme. Aim This study aimed to examine the effect of providing extra funding to practices based on clinical need and sociodemographic characteristics. We considered seven key outcomes spanning patient experience, quality of care and staffing, and used a causal analysis framework to evaluate impact. Methods Intervention Fixed monthly payments based on the HEP capitation formula were provided to GP practices between July 2021 and April 2023. Thereafter annually updated data were used to recalculate the workload components. The eligibility for, and size of the top-up payments were recalculated based on these new workload estimates. This study evaluated the effect of payments given in the period July 2021 to April 2023. The payment amounts ranged from £390 to £120,000 with an average payment of £48,000 per practice, per year, equating to an average 5% (range 0.06%-13%) funding increase as a proportion of core monies in scope (details of monies in scope can be found in Table S1 ). No restrictions were placed on how the payments are used and practices are not required to report how payments are spent but practices were informed of the new funding stream. Data sources Practice-level Organisational Data Service (ODS) codes were used to link national open-source data on: demographics of registered populations ( 15 , 16 ) staffing ( 17 ) patient experience ( 18 ) funding( 1 ) Quality and Outcomes Framework (QOF) scores( 19 ). The specific variables used are detailed below. Data on the HEP funding status for the 2021/22 funding round of practices within LLR and the size of the payment as a percentage of their global sum payment were provided by the ICB. Demographic and population health covariate definitions Age and sex of registered patients are taken directly from datasets on registered patients. IMD score, % of local population who were recorded as white ethnicity, % of local population who did not speak English as a first language, and % of local population living in a rural area were converted from Office of National Statistics Lower-level Super Output Area (LSOA) level to GP practice level. Using methods previously described, an average score for each practice was calculated, weighted by the number of registered patients in each general practice resident in each LSOA ( 9 ). Every practice was assigned an IMD quintile derived from the IMD scores of all included practices in England after removing practices as described in the exclusion criteria, as on 1st April 2021. The ICB to which each GP practice was affiliated was determined from the November 2024 ‘Patients Registered at a GP Practice’ dataset ( 15 ). As the ICS structure was introduced in July 2022, this ICS assignment was fixed retrospectively across all time points. The Carr-Hill Index of each practice was calculated by dividing the number of Carr-Hill weighted patients by the number of registered patients as described previously ( 20 ). Practice partner count was obtained by summing senior partner and partner FTE. Practices were grouped into four categories based on additional funding received as a percentage of the practice’s annual Carr-Hill funding: Category 1 (> 9.6%), Category 2 (6.4–9.6%), Category 3 (3.2–6.4%), and Category 4 (0–3.2%). Prevalence of the following QOF conditions: asthma, hypertension, chronic kidney disease, chronic obstructive pulmonary disease, coronary heart disease and diabetes mellitus were extracted. Baseline and outcome covariate definitions The study examined seven outcomes related to self-reported patient experience of primary care, practice staffing per capita and achievement of points in the QOF. The outcomes were chosen to reflect the heterogenous use of funding and from input on important outcomes from stakeholders in primary care. Patient experience We used data from the General Practice Patient Survey (GPPS), with baseline data drawn from the 2022 wave (reflecting patient experiences in 2021) and outcome data from the 2024 wave (reflecting experiences in 2023). We focused on three survey questions, extracting the proportion of respondents at each practice who gave positive responses: “Overall, how would you describe your experience of contacting your GP practice?” – responses: ‘Good’ or ‘Very Good’ “Overall, how would you describe your experience of your GP practice?” – responses: ‘Good’ or ‘Very Good’ “Thinking about your last appointment, were your needs met?” – responses: ‘Yes, definitely’ or ‘Yes, to some extent’ Staffing We used data on staffing in general practice with June 2021 used for baseline data, corresponding to the month before the first HEP payments are made, and April 2023 is used for the outcome period. We focussed on three staff groups expressed as the full time equivalent (FTE) staff per 1000 Carr-Hill weighted patients: GP staff (excluding training grades as these were assumed not to be employable using HEP funding) Nurses Administrative staff Quality of care We extracted Quality and Outcomes Framework (QOF) achievement scores, using the 2018/19 financial year as the baseline. This year was selected because QOF reporting was disrupted during the COVID-19 pandemic. The 2022/23 financial year was used as the outcome period. QOF achievement was calculated as the proportion of QOF points achieved by each practice, relative to the total number of QOF points available in that year. This provides a standardised measure of performance across practices and time periods. Study population General practices in LLR ICB were compared to a counterfactual group of practices selected from the rest of England. We excluded practices that met any of the following criteria at any point during the period April 2018 – June 2024: Missing list size data or list size below 500 patients. Opened or closed during the study period. Large change in list size, indicating a merger or other major organisational change – defined as a month-on-month change in list size of more than 100 patients making up at least 10% of the practice population. More than 60% practice population registered as male (as these are often specialist prison or army services). Practices operating under a Personalised Medical Services (PMS) contract (there were very few in LLR but more in the rest of England, these types of services were often inappropriately matched to practices with other types of contract). Complete data on all matching variables and outcome measures. Statistical analysis To analyse the causal effect of the top-up payment on our chosen outcomes we took a ‘doubly robust’ matching and regression approach ( 21 ). We used Genetic Matching methods to create a sample of matched practices from outside LLR with similar characteristics to funded practices within LLR ( 22 ). We were unable to determine which practices outside of LLR would have been eligible for top-up funding according to the HEP scheme. We therefore matched GP practices on a panel of practice characteristics identified through discussions with stakeholders, indicated in Table 1 . Demographic and population health covariates, as well as all outcome variables at baseline were used to match treated practices to control practices. Each intervention practice was matched to one control practice, without replacement. Table 1 Comparison of characteristics between practices in LLR ICB that receive the health equity payment, matched controls, unmatched controls and all practices (treated, matched and unmatched) at baseline. 1 = mean (SD), 2 = N (%). All variables in the table were matched on. Pre-intervention outcome measure, treatment status, Carr-Hill index and percentage of male patients were controlled for in the regression analysis. 9 practices with missing data in matching or outcome variables are excluded from the table due to risk of disclosure. Total (N = 4005) Treated (N = 62) Matched control (N = 62) Unmatched control (N = 3872) List size 1 8700 (5000) 9290 (4990) 9550 (4730) 8680 (5000) Percentage of registered patients are male 1 50.1 (1.82) 50.0 (1.65) 50.2 (1.56) 50.1 (1.83) Percentage of registered patients are > 65 years old 1 17.4 (6.65) 16.2 (4.88) 15.9 (5.51) 17.5 (6.69) IMD quintile based on registered patient place of residence 2 1 787 (19.7%) 7 (11.3%) 6 (9.7%) 773 (20.0%) 2 748 (18.7%) 15 (24.2%) 14 (22.6%) 712 (18.4%) 3 813 (20.3%) 6 (9.7%) 7 (11.3%) 800 (20.7%) 4 810 (20.2%) 19 (30.6%) 20 (32.3%) 770 (19.9%) 5 847 (21.1%) 15 (24.2%) 15 (24.2%) 817 (21.1%) Percentage of registered patients living in rural areas 1 20.9 (35.2) 11.2 (23.1) 9.68 (21.9) 21.3 (35.5) Percentage of white people living in LSOAs of registered patients 1 80.4 (20.8) 68.6 (29.2) 70.5 (29.3) 80.9 (20.3) Percentage of people with English as a second language living in LSOAs on registered patients 1 11.9 (12.5) 18.3 (18.8) 17.9 (18.3) 11.7 (12.1) Carr-Hill Index 1 1.02 (0.103) 0.948 (0.0525) 0.956 (0.0710) 1.02 (0.103) Missing 8 (0.2%) 0 (0%) 0 (0%) 8 (0.2%) FTE GPs (excluding training grade) per 1000 weighted patients 1 0.467 (0.187) 0.499 (0.161) 0.493 (0.129) 0.467 (0.188) Missing 46 (1.1%) 0 (0%) 0 (0%) 46 (1.2%) FTE nurses per 1000 weighted patients 1 0.261 (0.142) 0.246 (0.114) 0.241 (0.109) 0.261 (0.143) Missing 134 (3.3%) 0 (0%) 0 (0%) 129 (3.3%) FTE admin per 1000 weighted patients 1 1.12 (0.320) 1.21 (0.308) 1.20 (0.239) 1.12 (0.321) Missing 37 (0.9%) 0 (0%) 0 (0%) 35 (0.9%) Single partner practices 2 Multi-partner 3095 (77.3%) 49 (79.0%) 48 (77.4%) 2995 (77.4%) Single partner 910 (22.7%) 13 (21.0%) 14 (22.6%) 877 (22.6%) Contract type 2 APMS 140 (3.5%) 5 (8.1%) 5 (8.1%) 128 (3.3%) GMS 3865 (96.5%) 57 (91.9%) 57 (91.9%) 3744 (96.7%) Percentage of respondents reporting good overall experience of booking an appointment 1 59.2 (16.3) 53.0 (12.7) 52.1 (12.8) 59.4 (16.4) Missing 1 (0.0%) 0 (0%) 0 (0%) 1 (0.0%) Percentage of respondents reporting good overall experience of their GP practice 1 74.1 (13.2) 69.6 (11.7) 69.1 (10.9) 74.3 (13.2) Percentage of respondents reporting yes overall to whether their needs were met at their last appointment 1 91.1 (5.61) 89.1 (5.68) 89.6 (4.54) 91.2 (5.60) Percentage of total QOF points achieved 1 96.8 (5.45) 97.6 (3.49) 97.9 (2.03) 96.8 (5.51) Missing 27 (0.7%) 0 (0%) 0 (0%) 27 (0.7%) Prevalence of asthma 1 0.0659 (0.0139) 0.0662 (0.0133) 0.0660 (0.0100) 0.0660 (0.0140) Missing 3 (0.1%) 0 (0%) 0 (0%) 3 (0.1%) Prevalence of chronic kidney disease 1 0.0418 (0.0204) 0.0352 (0.0168) 0.0355 (0.0169) 0.0420 (0.0205) Missing 3 (0.1%) 0 (0%) 0 (0%) 3 (0.1%) Prevalence of chronic obstructive pulmonary disease 1 0.0195 (0.00898) 0.0172 (0.00682) 0.0166 (0.00762) 0.0196 (0.00902) Missing 3 (0.1%) 0 (0%) 0 (0%) 3 (0.1%) Prevalence of coronary heart disease 1 0.0318 (0.0103) 0.0284 (0.00527) 0.0288 (0.00626) 0.0319 (0.0104) Missing 3 (0.1%) 0 (0%) 0 (0%) 3 (0.1%) Prevalence of diabetes mellitus 1 0.0764 (0.0214) 0.0908 (0.0246) 0.0893 (0.0268) 0.0759 (0.0210) Missing 3 (0.1%) 0 (0%) 0 (0%) 3 (0.1%) Prevalence of hypertension 1 0.147 (0.0373) 0.149 (0.0233) 0.150 (0.0251) 0.147 (0.0376) Missing 3 (0.1%) 0 (0%) 0 (0%) 3 (0.1%) Quasibinomial regression was used to model the relationship between the intervention and proportional outcomes (QOF achievement and patient experience) with quasipoisson regression used for staffing outcomes ( 23 ). In addition to a binary treatment variable, pre-intervention outcome values and key variables with an absolute Standardised Mean Difference (SMD) greater than 0.1 were included in the regression model, and interacted with the treatment variable. The average treatment effect in the treated (ATT), representing the difference in expected outcomes between treated and control practices, was estimated using g-computation with cluster-robust standard errors. A series of sensitivity analyses were performed to examine the robustness of our results to different data periods, specifications of outcome variables, missing data and regression model designs. Details of these sensitivity analyses are described in the supplementary information. All analyses were performed using R version 4.3.1 using the MatchIt and marginaleffects packages ( 24 , 25 ). Results A total of 62 practices in LLR received funding from the HEP scheme from July 2021 to April 2023. Outside of LLR, 3934 practices were identified from which 62 matched practices were selected. Details of the exclusion of LLR and national practices are shown in Supplementary Fig. 1. Practices that received the Health Equity Payment in 2021–2023 are similar to practices in LLR that did not receive payments with respect to practice size, gender ratio of registered patients and staffing (Supplementary Table 2). Notable differences are urban/rural practice populations (9.82% living in rural areas in HEP practices compared to 32.5% not receiving payment), white population (65% compared to 75%), and percentage of people with English as a second language (21% compared to 15%). These variables are used when matching to practices in England. In the treated and matched controls these values are similar, as shown in Table 1 . [INSERT Table 1 HERE] After matching, control practices were similar to HEP practices on observed characteristics, with an absolute SMD less than 0.1 for 20 out of 23 matching variables, as shown in Fig. 1 . [INSERT FIGURE 1 HERE] As shown in Fig. 2 , the 62 matched control practices are similar to HEP practices in the pre-intervention period with only QOF showing a deviation in the post-intervention period. [INSERT FIGURE 2 HERE] Practices receiving top-up payments had a 3.2 percentage point (95% CI: 0.5% to 5.9%; p = 0.02) higher QOF achievement than control practices (Fig. 3 ). Predicted QOF attainment in the treated group is 94% compared to 91% in the matched controls. We did not find any statistically significant differences in patient experience or staffing outcomes, as shown in Fig. 3 . [INSERT FIGURE 3 HERE] Sensitivity analyses demonstrated that our findings are robust to a range of analytical decisions, including using the 2024 wave of GPPS instead of 2023 as the outcome period, using only the proportion of respondents rating their experience as ‘very good’, and not adjusting for high SMD variables in the outcome regression model (see supplementary information). However, our findings were sensitive to including practices with missing nurse or administrative staff data. After matching including these practices, we find the HEP is associated with significantly lower patient experience. QOF achievement, though higher in HEP practices, was no longer statistically significant (see supplementary information). Discussion Summary Calls for reform of general practice funding in England has been ongoing for many years. This study is, to our knowledge, the first to evaluate the impact of an intervention to improve the fairness of funding based on perceived need. Specifically, we looked at the impact of Leicester, Leicestershire and Rutland (LLR) ICB allocating additional funding to practices with higher levels of illness (as documented by diagnoses in the primary care electronic health record), patient communication needs and levels of socioeconomic deprivation. This was intended to reflect the greater staff workload in these settings than is thought to be captured in the current Carr-Hill GP funding formula. We found that the HEP led to a modest improvement in the quality of primary care in practices receiving top-up payments, as measured by QOF achievement, compared to a counterfactual group of similar practices outside the ICB which did not receive additional funding. We did not detect an effect of the intervention on the selected patient experience measures from GPPS or on GP, nurse or administrative staffing. The mechanism by which improvements in quality of care were achieved could not be explored in our analysis. Qualitative research undertaken in a subset of the practices included in our study indicates that payments were used by some practices to increase the capacity of cervical screening and childhood vaccination programmes (Greenstock et al, (forthcoming)). There was no detected effect on GP, nursing or admin roles despite the companion qualitative work indicating some practices did spend money on additional staffing. This may be due to the range of different staff roles payments were used to fund. The spending of payments on staff, whether through recruiting new staff or additional working hours for existing staff may not have been recorded in the NHS general practice workforce datasets we have used. Staff employed as a result of the scheme may also be recorded in primary care network-level datasets that we did not use as they couldn’t be attributed to a specific practice. Some practices may not have wanted to commit funding to staffing with no guarantee of long-term funding. In the case of both staffing and patient experience measures the lack of a detected effect in the data may also be due to the short-term nature of this evaluation. Strengths and limitations This study uses robust causal inference methodology to assess the effect of the Health Equity Payment on a range of relevant primary care outcomes. Drawing on a large pool of practices outside of LLR ICB, we were able to construct a cohort of control practices that was very similar to intervention practices across an extensive range of relevant practice characteristics, mitigating opportunities for omitted variable bias. We have investigated a broad range of outcomes using publicly available data to reflect the variety of ways practices may choose to spend the funding. Despite this approach, our analysis may not have captured some of the potential effects of the programme. Our analysis was planned, conducted and interpreted with input from primary care policy practitioners and practicing GPs. Following a mixed methods approach, a series of semi-structured interviews was conducted concurrently with GPs or operational staff, both with business management responsibilities at practices who received HEP with interim findings communicated between analytical teams (Greenstock et al. (forthcoming)). To aid transparency and reproducibility, we have published all code used in the analysis on GitHub. All data, except which practices received HEP, are publicly available. The timepoints chosen for our baseline and outcome measures may not be optimal to measure the impact of the intervention; however our findings are robust to using alternative time periods where available. The time periods used are limited by the frequency of data publication and the disruption of the Covid-19 pandemic to services and data collection. Although the Covid-19 pandemic could have affected practices differently we have matched on variables that may be expected to correlate with the impact of Covid-19 on a practice such as deprivation, list size and urban/rural location. It is also possible that Covid-19 had a larger effect on our outcome measures than the funding provided. Our outcomes were measured after the major pandemic restrictions in the UK were lifted, so the effect of the pandemic may be lessened at time of outcome measurement. The 2024 GPPS data used as a post-intervention outcome followed a different survey design to the baseline data from 2022. We selected survey questions that have identical or almost identical wording across waves and performed a sensitivity analysis using the 2023 survey, whose design is consistent with the 2022 survey, as an outcome measure and found our results to be consistent. Although our findings are generally robust, they are sensitive to the inclusion of four practices with missing staffing data. Here we find a statistically significant reduction in patient experience, and a non-significant increase in QOF achievement in intervention practices. Importantly, owing to missing staffing data, we are unable to ensure balance between control and intervention practices with respect to staffing in this case. Practices with missing data were small, urban, with younger, disproportionately male populations. They were more likely to have Alternative Provider Medical Services (APMS) contracts which have been associated with poorer care ( 26 ). Reporting of staffing data is mandatory and is done by almost all practices across England. Failure to do so may therefore indicate wider issues in the management of a practice, suggesting these practices are atypical of intervention practices. We were unable to match practices based on estimated workload as calculated using the LLR formula, meaning that we have used other variables to construct a counterfactual of practices that would otherwise have received the intervention. We have used a range of important demographic and practice characteristics to match practices based on stakeholder feedback. As we did not have information on the total amount of money received by a practice, we are unable to examine a dose-response relationship according to the size of the payment. The intervention was also limited to a single ICB, leading to a relatively small sample size available for analysis and potential limitations to the generalisability of findings to other ICBs. Comparison with existing literature Evidence of the effect of different capitation payments on the quality of care is limited and mixed. Higher capitation payments have been associated with higher care quality as measured by practice inspections by the Care Quality Commission, but with no effect on QOF achievement ( 27 , 28 ). However, both studies were non-causal, cross-sectional studies of the impact of targeted funding to individual GP practices. Between June 2021 and April 2023, the Health Equity Payment was a unique scheme in one ICB in England. The Johns Hopkins ACG tool as a measure of population need has been used to calculate primary care capitation payments in Sweden, Chile, Spain and the US ( 7 , 29 – 31 ). These studies did not address outcomes such as patient experience, per capita staffing or care quality. Implications for research and/or practice With the announcement in June 2025 of a government review of the Carr-Hill formula, this study makes an important and timely contribution to evidence surrounding the reform of general practice funding ( 32 ). The HEP is one way of defining an alternative capitation workload formula, and top-up payments allocated using this system may have caused improvements in quality of care. These findings were observed in the first years after the introduction of the payment, and it remains to be seen if they persist in the longer term. This study demonstrates the role ICBs can play in designing and implementing locally tailored funding solutions to address the needs of their practices and patients. Frimley ICB began to implement a similar funding programme in 2024 ( 33 ). Aided by access to patient-level primary care data, LLR ICB have been able to identify and focus on specific areas, such as communication needs, in an ICB with a high proportion of people speaking English as a second language. This study provides early evidence of how locally determined funding based on an area’s unique characteristics could impact care quality in a way that a national formula may not achieve. Declarations Funding The Health Foundation Ethical approval This project is a service evaluation. Competing interests We declare no competing interests. Author contributions All authors have made substantial contributions to the conception and design of the work. EW was responsible for data acquisition and cleaning, SO-M ran the main data analysis and prepared figures, JMC prepared tables. All authors were involved in interpretation of data. SO-M and JMC wrote the main manuscript text. All authors have reviewed and approved the manuscript. Acknowledgements EW acknowledges the receipt of a studentship award from the Health Data Research UK-The Alan Turing Institute Wellcome PhD Programme in Health Data Science (Grant Ref: 218529/Z/19/Z). The authors would like to acknowledge the support of David Shepherd from Leicester, Leicestershire and Rutland ICB for descriptions of the top-up payments and the local context. We’d also like to thank, from the Health Foundation, Stefano Conti (Senior Statistician) for input into the statistical methods, Liz Crellin (Data Manager) for data sourcing and Catriona Callan (Primary Care Fellow) for help designing the study. Data availability Data on which practices in the LLR ICS received funding and the amount of funding received as a percentage of core funding were provided by the LLR ICB to the study team for the sole purpose of the evaluation and are not publicly available. All other data used in this study are publicly available through links provided in the supplementary information. Consent for publication Not applicable Ethics approval and consent to participate This study is an evaluation at GP practice-level using publicly available data. References NHS England. https://digital.nhs.uk/data-and-information/publications/statistical/nhs-payments-to-general-practice. 2023. NHS Payments to General Practice. The Health Foundation. 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Aplicación de grupos clínicos ajustados como herramienta de ajuste de riesgo: evaluación en la distribución de recursos en programa de enfermedades crónicas. Rev Med Chil. 2014;142:153–60. Orueta JF, Urraca J, Berraondo I, Darpón J, Aurrekoetxea JJ. Adjusted Clinical Groups (ACGs) explain the utilization of primary care in Spain based on information registered in the medical records: A cross-sectional study. Health Policy (New York) [Internet]. 2006;76(1):38–48. Available from: https://www.sciencedirect.com/science/article/pii/S0168851005001053 Anell A, Dackehag M, Dietrichson J. Does risk-adjusted payment influence primary care providers’ decision on where to set up practices? BMC Health Serv Res [Internet]. 2018;18(1):179. Available from: https://doi.org/10.1186/s12913-018-2983-3 Department for Health and Social Care. https://www.gov.uk/government/speeches/health-and-social-care-secretary-speech-on-health-inequalities. 2025. Health and Social Care Secretary speech on health inequalities. Pulse. ‘Population need’ GP funding overhaul would cost just £333m, according to ICB modelling. https://www.pulsetoday.co.uk/news/practice-personal-finance/population-need-gp-funding-overhaul-would-cost-just-333m-according-to-icb-modelling/. 2025; Additional Declarations No competing interests reported. Supplementary Files causalpapersupplementaryinformation.docx Cite Share Download PDF Status: Published Journal Publication published 08 Jan, 2026 Read the published version in BMC Primary Care → Version 1 posted Editorial decision: Revision requested 22 Oct, 2025 Reviews received at journal 21 Oct, 2025 Reviewers agreed at journal 15 Oct, 2025 Reviews received at journal 07 Oct, 2025 Reviewers agreed at journal 07 Oct, 2025 Reviewers invited by journal 06 Oct, 2025 Editor assigned by journal 06 Oct, 2025 Editor invited by journal 16 Sep, 2025 Submission checks completed at journal 15 Sep, 2025 First submitted to journal 15 Sep, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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15:27:37","extension":"html","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":112430,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7509545/v1/ddf1b0859090e2499cee4083.html"},{"id":92274741,"identity":"43eea14c-c6fb-49b9-a6af-87133858cfcb","added_by":"auto","created_at":"2025-09-26 15:27:36","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":619408,"visible":true,"origin":"","legend":"\u003cp\u003eLove plot showing standardised mean differences of the overall population sample and the matched population sample.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7509545/v1/9abcd61d80e6fc629479c650.png"},{"id":92274907,"identity":"3dbfed4b-e744-4f28-8928-168d0a5f33c9","added_by":"auto","created_at":"2025-09-26 15:27:44","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":812341,"visible":true,"origin":"","legend":"\u003cp\u003ePlot showing crude comparisons of outcome measures between treated practices and matched controls.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7509545/v1/71123bdfb33ea80f7a38bc3e.png"},{"id":92274817,"identity":"8659dedd-96be-4457-878f-58b6b3951c24","added_by":"auto","created_at":"2025-09-26 15:27:40","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":403183,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot showing results of quasi-linear regression comparing outcomes in treated practices and matched controls.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7509545/v1/4f30ca1e5e0efa896db8790e.png"},{"id":100069345,"identity":"4d1b5156-2d60-49ed-8995-878213d443d9","added_by":"auto","created_at":"2026-01-12 16:13:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2677527,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7509545/v1/6aa70792-e493-489f-bf89-195d9ebf34c8.pdf"},{"id":92274763,"identity":"52edc910-368d-4ee0-a8b6-17a8e88211f0","added_by":"auto","created_at":"2025-09-26 15:27:37","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":187928,"visible":true,"origin":"","legend":"","description":"","filename":"causalpapersupplementaryinformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-7509545/v1/99eae5e984fc3d69f0574d82.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Evaluating the impact of capitation funding top-up payments in primary care","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn England, capitation payments make up approximately 50% of core General Practitioner (GP) practice funding and are allocated through the Global Sum Allocation formula, also known as the Carr-Hill formula (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). The remaining funding includes fee-for-service, pay-for-performance, premises costs and local services payments. Capitation-based funding recognises the difficulties of reimbursing GPs for the complex range of tasks done in general practice and the holistic, ongoing and often preventive nature of the care they provide (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). To ensure general practice is effective and that funding is efficient, capitation payments need to be accurately distributed based on patient need and other local cost pressures.\u003c/p\u003e\u003cp\u003eThe Carr-Hill formula aims to reimburse practice running costs by modelling workload and calculating fixed costs related to geographical location, known as the ‘market forces factor’ (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Key drivers of practice workload included in the Carr-Hill formula are patient age and sex, additional patient needs measured as limiting longstanding illness and standardised mortality ratio for people under 65; and list turnover (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). The methodology and underlying datasets that inform the Carr-Hill formula have not been updated since 2004, drawing concern that the formula does not effectively estimate primary care workload, particularly as increasing numbers of patients live with multiple morbidity (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSome of the other factors that may affect practice workload include patient diagnosis, level of deprivation and communication difficulties, but these are not considered in the formula (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Studies indicate modelling the effect of incorporating clinical diagnostic information or practice-level deprivation alongside age and sex adjustment into capitation models is a better predictor of primary care cost and resource utilisation than those that use age and sex only (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe most deprived areas, identified using the Index of Multiple Deprivation (IMD), have fewest GPs per capita, and performance is worse on key quality measures (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). The Carr-Hill formula is often cited as a key driver of these inequalities although other payment sources, such as dispensing and Quality Outcomes Framework (QOF) performance payments, may be more important drivers of overall inequality in payments (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Incorporating data on clinical diagnoses into the capitation formula, whether by individual or by groups of conditions, may capture some of the increased costs of socio-economic deprivation such as earlier onset of multimorbidity and higher consultation rates, however it may also reimburse less deprived areas with older populations (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Further adjustment for increased severity of disease in deprived areas and costs associated with delivering population health management, eg vaccination programmes, which may take more resource to provide the same coverage in more deprived areas, may also be needed (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn July 2021 Leicester, Leicestershire, and Rutland (LLR) Integrated Care Board (ICB) started providing ‘Health Equity Payments’ (HEP) to practices, consistent with the ICB mandate to address inequalities. The payments were calculated using a local formula aiming to better reflect patient needs and practice deprivation than the Carr-Hill formula. The local formula used recorded clinical diagnoses of the practice’s registered population (categorised into groups using the Johns Hopkins Adjusted Clinical Groups (ACG) system), communication needs of the practice’s registered population, and the practice’s list turnover to estimate expected practice appointment supply. The total patient list size was also multiplied by the IMD 2019 score of the practice, as calculated using the address of registered patient population. This was designed to provide a proxy for wider determinants of need not captured by clinical coding. The ICB total of the Carr-Hill allocated monies and other core primary care funding streams was calculated to provide the overall funding to the practices in LLR and is referred to as 'monies in scope'.\u003c/p\u003e\u003cp\u003eTop-up funding eligibility and funding amounts were determined by separately comparing each practice’s share of the total ICB general practice deprivation and expected appointment supply (deprivation and appointment supply share) with its share of total ICB general practice monies in scope (funding share). Practices received a fixed payment based on their existing Carr-Hill funding amount and a top-up payment if their appointment supply or deprivation share exceeded their funding share. Of the monies in scope not allocated as a fixed amount of existing Carr-Hill payments, 90% of the remaining monies in scope are redistributed according to the practice's share of total appointment supply, and 10% are redistributed according to deprivation score. Details of eligible funding streams and weighting of each workload component can be found in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eTop-up payments came from other funding streams within the ICB, so no practices lost funding because of the scheme.\u003c/p\u003e\n\u003ch3\u003eAim\u003c/h3\u003e\n\u003cp\u003eThis study aimed to examine the effect of providing extra funding to practices based on clinical need and sociodemographic characteristics. We considered seven key outcomes spanning patient experience, quality of care and staffing, and used a causal analysis framework to evaluate impact.\u003c/p\u003e"},{"header":"Methods","content":"\u003ch2\u003eIntervention\u003c/h2\u003e\u003cp\u003eFixed monthly payments based on the HEP capitation formula were provided to GP practices between July 2021 and April 2023. Thereafter annually updated data were used to recalculate the workload components. The eligibility for, and size of the top-up payments were recalculated based on these new workload estimates. This study evaluated the effect of payments given in the period July 2021 to April 2023. The payment amounts ranged from £390 to £120,000 with an average payment of £48,000 per practice, per year, equating to an average 5% (range 0.06%-13%) funding increase as a proportion of core monies in scope (details of monies in scope can be found in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). No restrictions were placed on how the payments are used and practices are not required to report how payments are spent but practices were informed of the new funding stream.\u003c/p\u003e\n\u003ch3\u003eData sources\u003c/h3\u003e\n\u003cp\u003ePractice-level Organisational Data Service (ODS) codes were used to link national open-source data on:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003edemographics of registered populations (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e)\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003estaffing (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e)\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003epatient experience (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e)\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003efunding(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eQuality and Outcomes Framework (QOF) scores(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e).\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eThe specific variables used are detailed below. Data on the HEP funding status for the 2021/22 funding round of practices within LLR and the size of the payment as a percentage of their global sum payment were provided by the ICB.\u003c/p\u003e\n\u003ch3\u003eDemographic and population health covariate definitions\u003c/h3\u003e\n\u003cp\u003eAge and sex of registered patients are taken directly from datasets on registered patients. IMD score, % of local population who were recorded as white ethnicity, % of local population who did not speak English as a first language, and % of local population living in a rural area were converted from Office of National Statistics Lower-level Super Output Area (LSOA) level to GP practice level. Using methods previously described, an average score for each practice was calculated, weighted by the number of registered patients in each general practice resident in each LSOA (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eEvery practice was assigned an IMD quintile derived from the IMD scores of all included practices in England after removing practices as described in the exclusion criteria, as on 1st April 2021. The ICB to which each GP practice was affiliated was determined from the November 2024 \u0026lsquo;Patients Registered at a GP Practice\u0026rsquo; dataset (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). As the ICS structure was introduced in July 2022, this ICS assignment was fixed retrospectively across all time points.\u003c/p\u003e\u003cp\u003eThe Carr-Hill Index of each practice was calculated by dividing the number of Carr-Hill weighted patients by the number of registered patients as described previously (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Practice partner count was obtained by summing senior partner and partner FTE. Practices were grouped into four categories based on additional funding received as a percentage of the practice\u0026rsquo;s annual Carr-Hill funding: Category 1 (\u0026gt;\u0026thinsp;9.6%), Category 2 (6.4\u0026ndash;9.6%), Category 3 (3.2\u0026ndash;6.4%), and Category 4 (0\u0026ndash;3.2%).\u003c/p\u003e\u003cp\u003ePrevalence of the following QOF conditions: asthma, hypertension, chronic kidney disease, chronic obstructive pulmonary disease, coronary heart disease and diabetes mellitus were extracted.\u003c/p\u003e\n\u003ch3\u003eBaseline and outcome covariate definitions\u003c/h3\u003e\n\u003cp\u003eThe study examined seven outcomes related to self-reported patient experience of primary care, practice staffing per capita and achievement of points in the QOF. The outcomes were chosen to reflect the heterogenous use of funding and from input on important outcomes from stakeholders in primary care.\u003c/p\u003e\n\u003ch3\u003ePatient experience\u003c/h3\u003e\n\u003cp\u003eWe used data from the General Practice Patient Survey (GPPS), with baseline data drawn from the 2022 wave (reflecting patient experiences in 2021) and outcome data from the 2024 wave (reflecting experiences in 2023). We focused on three survey questions, extracting the proportion of respondents at each practice who gave positive responses:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u0026ldquo;Overall, how would you describe your experience of contacting your GP practice?\u0026rdquo; \u0026ndash; responses: \u0026lsquo;Good\u0026rsquo; or \u0026lsquo;Very Good\u0026rsquo;\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u0026ldquo;Overall, how would you describe your experience of your GP practice?\u0026rdquo; \u0026ndash; responses: \u0026lsquo;Good\u0026rsquo; or \u0026lsquo;Very Good\u0026rsquo;\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u0026ldquo;Thinking about your last appointment, were your needs met?\u0026rdquo; \u0026ndash; responses: \u0026lsquo;Yes, definitely\u0026rsquo; or \u0026lsquo;Yes, to some extent\u0026rsquo;\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eStaffing\u003c/h2\u003e\u003cp\u003eWe used data on staffing in general practice with June 2021 used for baseline data, corresponding to the month before the first HEP payments are made, and April 2023 is used for the outcome period. We focussed on three staff groups expressed as the full time equivalent (FTE) staff per 1000 Carr-Hill weighted patients:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eGP staff (excluding training grades as these were assumed not to be employable using HEP funding)\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eNurses\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eAdministrative staff\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eQuality of care\u003c/h3\u003e\n\u003cp\u003eWe extracted Quality and Outcomes Framework (QOF) achievement scores, using the 2018/19 financial year as the baseline. This year was selected because QOF reporting was disrupted during the COVID-19 pandemic. The 2022/23 financial year was used as the outcome period. QOF achievement was calculated as the proportion of QOF points achieved by each practice, relative to the total number of QOF points available in that year. This provides a standardised measure of performance across practices and time periods.\u003c/p\u003e\n\u003ch3\u003eStudy population\u003c/h3\u003e\n\u003cp\u003eGeneral practices in LLR ICB were compared to a counterfactual group of practices selected from the rest of England. We excluded practices that met any of the following criteria at any point during the period April 2018 \u0026ndash; June 2024:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eMissing list size data or list size below 500 patients.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eOpened or closed during the study period.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eLarge change in list size, indicating a merger or other major organisational change \u0026ndash; defined as a month-on-month change in list size of more than 100 patients making up at least 10% of the practice population.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eMore than 60% practice population registered as male (as these are often specialist prison or army services).\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003ePractices operating under a Personalised Medical Services (PMS) contract (there were very few in LLR but more in the rest of England, these types of services were often inappropriately matched to practices with other types of contract).\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eComplete data on all matching variables and outcome measures.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eTo analyse the causal effect of the top-up payment on our chosen outcomes we took a \u0026lsquo;doubly robust\u0026rsquo; matching and regression approach (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). We used Genetic Matching methods to create a sample of matched practices from outside LLR with similar characteristics to funded practices within LLR (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWe were unable to determine which practices outside of LLR would have been eligible for top-up funding according to the HEP scheme. We therefore matched GP practices on a panel of practice characteristics identified through discussions with stakeholders, indicated in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Demographic and population health covariates, as well as all outcome variables at baseline were used to match treated practices to control practices. Each intervention practice was matched to one control practice, without replacement.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparison of characteristics between practices in LLR ICB that receive the health equity payment, matched controls, unmatched controls and all practices (treated, matched and unmatched) at baseline. \u003csup\u003e1\u003c/sup\u003e = mean (SD), \u003csup\u003e2\u003c/sup\u003e = N (%). All variables in the table were matched on. Pre-intervention outcome measure, treatment status, Carr-Hill index and percentage of male patients were controlled for in the regression analysis. 9 practices with missing data in matching or outcome variables are excluded from the table due to risk of disclosure.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003cp\u003e(N\u0026thinsp;=\u0026thinsp;4005)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTreated\u003c/p\u003e\u003cp\u003e(N\u0026thinsp;=\u0026thinsp;62)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMatched control\u003c/p\u003e\u003cp\u003e(N\u0026thinsp;=\u0026thinsp;62)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eUnmatched control\u003c/p\u003e\u003cp\u003e(N\u0026thinsp;=\u0026thinsp;3872)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eList size \u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8700 (5000)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9290 (4990)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9550 (4730)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8680 (5000)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003ePercentage of registered patients are male \u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e50.1 (1.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e50.0 (1.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e50.2 (1.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e50.1 (1.83)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003ePercentage of registered patients are \u0026gt;\u0026thinsp;65 years old \u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17.4 (6.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16.2 (4.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e15.9 (5.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e17.5 (6.69)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eIMD quintile based on registered patient place of residence \u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e787 (19.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7 (11.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6 (9.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e773 (20.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e748 (18.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15 (24.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e14 (22.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e712 (18.4%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e813 (20.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6 (9.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7 (11.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e800 (20.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e810 (20.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e19 (30.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e20 (32.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e770 (19.9%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e847 (21.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15 (24.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e15 (24.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e817 (21.1%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003ePercentage of registered patients living in rural areas \u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20.9 (35.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11.2 (23.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9.68 (21.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e21.3 (35.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003ePercentage of white people living in LSOAs of registered patients \u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e80.4 (20.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e68.6 (29.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e70.5 (29.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e80.9 (20.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003ePercentage of people with English as a second language living in LSOAs on registered patients \u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11.9 (12.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18.3 (18.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e17.9 (18.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e11.7 (12.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eCarr-Hill Index \u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.02 (0.103)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.948 (0.0525)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.956 (0.0710)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.02 (0.103)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eMissing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8 (0.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8 (0.2%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eFTE GPs (excluding training grade) per 1000 weighted patients \u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.467 (0.187)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.499 (0.161)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.493 (0.129)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.467 (0.188)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eMissing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e46 (1.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e46 (1.2%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eFTE nurses per 1000 weighted patients \u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.261 (0.142)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.246 (0.114)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.241 (0.109)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.261 (0.143)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eMissing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e134 (3.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e129 (3.3%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eFTE admin per 1000 weighted patients \u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.12 (0.320)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.21 (0.308)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.20 (0.239)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.12 (0.321)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eMissing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e37 (0.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e35 (0.9%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eSingle partner practices \u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMulti-partner\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3095 (77.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e49 (79.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e48 (77.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2995 (77.4%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSingle partner\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e910 (22.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13 (21.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e14 (22.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e877 (22.6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eContract type \u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAPMS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e140 (3.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5 (8.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5 (8.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e128 (3.3%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGMS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3865 (96.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e57 (91.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e57 (91.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3744 (96.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003ePercentage of respondents reporting good overall experience of booking an appointment \u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e59.2 (16.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e53.0 (12.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e52.1 (12.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e59.4 (16.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eMissing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (0.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1 (0.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003ePercentage of respondents reporting good overall experience of their GP practice \u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e74.1 (13.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e69.6 (11.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e69.1 (10.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e74.3 (13.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003ePercentage of respondents reporting yes overall to whether their needs were met at their last appointment \u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e91.1 (5.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e89.1 (5.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e89.6 (4.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e91.2 (5.60)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003ePercentage of total QOF points achieved \u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e96.8 (5.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e97.6 (3.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e97.9 (2.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e96.8 (5.51)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eMissing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27 (0.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e27 (0.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003ePrevalence of asthma \u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0659 (0.0139)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0662 (0.0133)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0660 (0.0100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0660 (0.0140)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eMissing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3 (0.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3 (0.1%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003ePrevalence of chronic kidney disease \u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0418 (0.0204)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0352 (0.0168)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0355 (0.0169)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0420 (0.0205)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eMissing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3 (0.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3 (0.1%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003ePrevalence of chronic obstructive pulmonary disease \u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0195 (0.00898)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0172 (0.00682)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0166 (0.00762)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0196 (0.00902)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eMissing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3 (0.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3 (0.1%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003ePrevalence of coronary heart disease \u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0318 (0.0103)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0284 (0.00527)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0288 (0.00626)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0319 (0.0104)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eMissing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3 (0.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3 (0.1%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003ePrevalence of diabetes mellitus \u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0764 (0.0214)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0908 (0.0246)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0893 (0.0268)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0759 (0.0210)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eMissing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3 (0.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3 (0.1%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003ePrevalence of hypertension \u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.147 (0.0373)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.149 (0.0233)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.150 (0.0251)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.147 (0.0376)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eMissing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3 (0.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3 (0.1%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eQuasibinomial regression was used to model the relationship between the intervention and proportional outcomes (QOF achievement and patient experience) with quasipoisson regression used for staffing outcomes (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). In addition to a binary treatment variable, pre-intervention outcome values and key variables with an absolute Standardised Mean Difference (SMD) greater than 0.1 were included in the regression model, and interacted with the treatment variable. The average treatment effect in the treated (ATT), representing the difference in expected outcomes between treated and control practices, was estimated using g-computation with cluster-robust standard errors.\u003c/p\u003e\u003cp\u003eA series of sensitivity analyses were performed to examine the robustness of our results to different data periods, specifications of outcome variables, missing data and regression model designs. Details of these sensitivity analyses are described in the supplementary information.\u003c/p\u003e\u003cp\u003eAll analyses were performed using R version 4.3.1 using the \u003cem\u003eMatchIt\u003c/em\u003e and \u003cem\u003emarginaleffects\u003c/em\u003e packages (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 62 practices in LLR received funding from the HEP scheme from July 2021 to April 2023. Outside of LLR, 3934 practices were identified from which 62 matched practices were selected. Details of the exclusion of LLR and national practices are shown in Supplementary Fig.\u0026nbsp;1.\u003c/p\u003e\u003cp\u003ePractices that received the Health Equity Payment in 2021\u0026ndash;2023 are similar to practices in LLR that did not receive payments with respect to practice size, gender ratio of registered patients and staffing (Supplementary Table\u0026nbsp;2). Notable differences are urban/rural practice populations (9.82% living in rural areas in HEP practices compared to 32.5% not receiving payment), white population (65% compared to 75%), and percentage of people with English as a second language (21% compared to 15%). These variables are used when matching to practices in England. In the treated and matched controls these values are similar, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e[INSERT Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e HERE]\u003c/p\u003e\u003cp\u003eAfter matching, control practices were similar to HEP practices on observed characteristics, with an absolute SMD less than 0.1 for 20 out of 23 matching variables, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e[INSERT FIGURE \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e HERE]\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the 62 matched control practices are similar to HEP practices in the pre-intervention period with only QOF showing a deviation in the post-intervention period.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e[INSERT FIGURE \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e HERE]\u003c/p\u003e\u003cp\u003ePractices receiving top-up payments had a 3.2 percentage point (95% CI: 0.5% to 5.9%; p\u0026thinsp;=\u0026thinsp;0.02) higher QOF achievement than control practices (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Predicted QOF attainment in the treated group is 94% compared to 91% in the matched controls. We did not find any statistically significant differences in patient experience or staffing outcomes, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e[INSERT FIGURE \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e HERE]\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eSensitivity analyses demonstrated that our findings are robust to a range of analytical decisions, including using the 2024 wave of GPPS instead of 2023 as the outcome period, using only the proportion of respondents rating their experience as \u0026lsquo;very good\u0026rsquo;, and not adjusting for high SMD variables in the outcome regression model (see supplementary information). However, our findings were sensitive to including practices with missing nurse or administrative staff data. After matching including these practices, we find the HEP is associated with significantly lower patient experience. QOF achievement, though higher in HEP practices, was no longer statistically significant (see supplementary information).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eSummary\u003c/h2\u003e\u003cp\u003eCalls for reform of general practice funding in England has been ongoing for many years. This study is, to our knowledge, the first to evaluate the impact of an intervention to improve the fairness of funding based on perceived need. Specifically, we looked at the impact of Leicester, Leicestershire and Rutland (LLR) ICB allocating additional funding to practices with higher levels of illness (as documented by diagnoses in the primary care electronic health record), patient communication needs and levels of socioeconomic deprivation. This was intended to reflect the greater staff workload in these settings than is thought to be captured in the current Carr-Hill GP funding formula.\u003c/p\u003e\u003cp\u003e We found that the HEP led to a modest improvement in the quality of primary care in practices receiving top-up payments, as measured by QOF achievement, compared to a counterfactual group of similar practices outside the ICB which did not receive additional funding. We did not detect an effect of the intervention on the selected patient experience measures from GPPS or on GP, nurse or administrative staffing.\u003c/p\u003e\u003cp\u003eThe mechanism by which improvements in quality of care were achieved could not be explored in our analysis. Qualitative research undertaken in a subset of the practices included in our study indicates that payments were used by some practices to increase the capacity of cervical screening and childhood vaccination programmes (Greenstock et al, (forthcoming)).\u003c/p\u003e\u003cp\u003eThere was no detected effect on GP, nursing or admin roles despite the companion qualitative work indicating some practices did spend money on additional staffing. This may be due to the range of different staff roles payments were used to fund. The spending of payments on staff, whether through recruiting new staff or additional working hours for existing staff may not have been recorded in the NHS general practice workforce datasets we have used. Staff employed as a result of the scheme may also be recorded in primary care network-level datasets that we did not use as they couldn\u0026rsquo;t be attributed to a specific practice. Some practices may not have wanted to commit funding to staffing with no guarantee of long-term funding. In the case of both staffing and patient experience measures the lack of a detected effect in the data may also be due to the short-term nature of this evaluation.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eStrengths and limitations\u003c/h2\u003e\u003cp\u003eThis study uses robust causal inference methodology to assess the effect of the Health Equity Payment on a range of relevant primary care outcomes. Drawing on a large pool of practices outside of LLR ICB, we were able to construct a cohort of control practices that was very similar to intervention practices across an extensive range of relevant practice characteristics, mitigating opportunities for omitted variable bias. We have investigated a broad range of outcomes using publicly available data to reflect the variety of ways practices may choose to spend the funding. Despite this approach, our analysis may not have captured some of the potential effects of the programme.\u003c/p\u003e\u003cp\u003eOur analysis was planned, conducted and interpreted with input from primary care policy practitioners and practicing GPs. Following a mixed methods approach, a series of semi-structured interviews was conducted concurrently with GPs or operational staff, both with business management responsibilities at practices who received HEP with interim findings communicated between analytical teams (Greenstock et al. (forthcoming)). To aid transparency and reproducibility, we have published all code used in the analysis on GitHub. All data, except which practices received HEP, are publicly available.\u003c/p\u003e\u003cp\u003eThe timepoints chosen for our baseline and outcome measures may not be optimal to measure the impact of the intervention; however our findings are robust to using alternative time periods where available. The time periods used are limited by the frequency of data publication and the disruption of the Covid-19 pandemic to services and data collection. Although the Covid-19 pandemic could have affected practices differently we have matched on variables that may be expected to correlate with the impact of Covid-19 on a practice such as deprivation, list size and urban/rural location. It is also possible that Covid-19 had a larger effect on our outcome measures than the funding provided. Our outcomes were measured after the major pandemic restrictions in the UK were lifted, so the effect of the pandemic may be lessened at time of outcome measurement.\u003c/p\u003e\u003cp\u003e The 2024 GPPS data used as a post-intervention outcome followed a different survey design to the baseline data from 2022. We selected survey questions that have identical or almost identical wording across waves and performed a sensitivity analysis using the 2023 survey, whose design is consistent with the 2022 survey, as an outcome measure and found our results to be consistent.\u003c/p\u003e\u003cp\u003eAlthough our findings are generally robust, they are sensitive to the inclusion of four practices with missing staffing data. Here we find a statistically significant reduction in patient experience, and a non-significant increase in QOF achievement in intervention practices. Importantly, owing to missing staffing data, we are unable to ensure balance between control and intervention practices with respect to staffing in this case. Practices with missing data were small, urban, with younger, disproportionately male populations. They were more likely to have Alternative Provider Medical Services (APMS) contracts which have been associated with poorer care (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Reporting of staffing data is mandatory and is done by almost all practices across England. Failure to do so may therefore indicate wider issues in the management of a practice, suggesting these practices are atypical of intervention practices.\u003c/p\u003e\u003cp\u003eWe were unable to match practices based on estimated workload as calculated using the LLR formula, meaning that we have used other variables to construct a counterfactual of practices that would otherwise have received the intervention. We have used a range of important demographic and practice characteristics to match practices based on stakeholder feedback. As we did not have information on the total amount of money received by a practice, we are unable to examine a dose-response relationship according to the size of the payment. The intervention was also limited to a single ICB, leading to a relatively small sample size available for analysis and potential limitations to the generalisability of findings to other ICBs.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eComparison with existing literature\u003c/h2\u003e\u003cp\u003eEvidence of the effect of different capitation payments on the quality of care is limited and mixed. Higher capitation payments have been associated with higher care quality as measured by practice inspections by the Care Quality Commission, but with no effect on QOF achievement (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). However, both studies were non-causal, cross-sectional studies of the impact of targeted funding to individual GP practices.\u003c/p\u003e\u003cp\u003eBetween June 2021 and April 2023, the Health Equity Payment was a unique scheme in one ICB in England. The Johns Hopkins ACG tool as a measure of population need has been used to calculate primary care capitation payments in Sweden, Chile, Spain and the US (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). These studies did not address outcomes such as patient experience, per capita staffing or care quality.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eImplications for research and/or practice\u003c/h2\u003e\u003cp\u003eWith the announcement in June 2025 of a government review of the Carr-Hill formula, this study makes an important and timely contribution to evidence surrounding the reform of general practice funding (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). The HEP is one way of defining an alternative capitation workload formula, and top-up payments allocated using this system may have caused improvements in quality of care. These findings were observed in the first years after the introduction of the payment, and it remains to be seen if they persist in the longer term.\u003c/p\u003e\u003cp\u003eThis study demonstrates the role ICBs can play in designing and implementing locally tailored funding solutions to address the needs of their practices and patients. Frimley ICB began to implement a similar funding programme in 2024 (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Aided by access to patient-level primary care data, LLR ICB have been able to identify and focus on specific areas, such as communication needs, in an ICB with a high proportion of people speaking English as a second language. This study provides early evidence of how locally determined funding based on an area\u0026rsquo;s unique characteristics could impact care quality in a way that a national formula may not achieve.\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\n\u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003eFunding\u003c/p\u003e\n\u003cp\u003eThe Health Foundation\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eEthical approval\u003c/h3\u003e\n\u003cp\u003eThis project is a service evaluation.\u003c/p\u003e\n\u003ch3\u003eCompeting interests\u003c/h3\u003e\n\u003cp\u003eWe declare no competing interests.\u003c/p\u003e\n\u003ch3\u003eAuthor contributions\u003c/h3\u003e\n\u003cp\u003eAll authors have made substantial contributions to the conception and design of the work. EW was responsible for data acquisition and cleaning, SO-M ran the main data analysis and prepared figures, JMC prepared tables. All authors were involved in interpretation of data. SO-M and JMC wrote the main manuscript text. All authors have reviewed and approved the manuscript.\u003c/p\u003e\n\u003ch3\u003eAcknowledgements\u003c/h3\u003e\n\u003cp\u003eEW acknowledges the receipt of a studentship award from the Health Data Research UK-The Alan Turing Institute Wellcome PhD Programme in Health Data Science (Grant Ref: 218529/Z/19/Z). The authors would like to acknowledge the support of David Shepherd from Leicester, Leicestershire and Rutland ICB for descriptions of the top-up payments and the local context. We\u0026rsquo;d also like to thank, from the Health Foundation, Stefano Conti (Senior Statistician) for input into the statistical methods, Liz Crellin (Data Manager) for data sourcing and Catriona Callan (Primary Care Fellow) for help designing the study.\u003c/p\u003e\n\u003ch3\u003eData availability\u003c/h3\u003e\n\u003cp\u003eData on which practices in the LLR ICS received funding and the amount of funding received as a percentage of core funding were provided by the LLR ICB to the study team for the sole purpose of the evaluation and are not publicly available. All other data used in this study are publicly available through links provided in the supplementary information.\u003c/p\u003e\n\u003ch3\u003eConsent for publication\u003c/h3\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Ethics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eThis study is an evaluation at GP practice-level using publicly available data.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eNHS England. https://digital.nhs.uk/data-and-information/publications/statistical/nhs-payments-to-general-practice. 2023. NHS Payments to General Practice. \u003c/li\u003e\n\u003cli\u003eThe Health Foundation. The future of funding for general practice. In 2024. \u003c/li\u003e\n\u003cli\u003eBritish Medical Association. https://www.bma.org.uk/advice-and-support/gp-practices/funding-and-contracts/global-sum-allocation-formula. 2024. 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Aplicaci\u0026oacute;n de grupos cl\u0026iacute;nicos ajustados como herramienta de ajuste de riesgo: evaluaci\u0026oacute;n en la distribuci\u0026oacute;n de recursos en programa de enfermedades cr\u0026oacute;nicas. Rev Med Chil. 2014;142:153\u0026ndash;60. \u003c/li\u003e\n\u003cli\u003eOrueta JF, Urraca J, Berraondo I, Darp\u0026oacute;n J, Aurrekoetxea JJ. Adjusted Clinical Groups (ACGs) explain the utilization of primary care in Spain based on information registered in the medical records: A cross-sectional study. Health Policy (New York) [Internet]. 2006;76(1):38\u0026ndash;48. Available from: https://www.sciencedirect.com/science/article/pii/S0168851005001053\u003c/li\u003e\n\u003cli\u003eAnell A, Dackehag M, Dietrichson J. Does risk-adjusted payment influence primary care providers\u0026rsquo; decision on where to set up practices? BMC Health Serv Res [Internet]. 2018;18(1):179. Available from: https://doi.org/10.1186/s12913-018-2983-3\u003c/li\u003e\n\u003cli\u003eDepartment for Health and Social Care. https://www.gov.uk/government/speeches/health-and-social-care-secretary-speech-on-health-inequalities. 2025. Health and Social Care Secretary speech on health inequalities. \u003c/li\u003e\n\u003cli\u003ePulse. \u0026lsquo;Population need\u0026rsquo; GP funding overhaul would cost just \u0026pound;333m, according to ICB modelling. https://www.pulsetoday.co.uk/news/practice-personal-finance/population-need-gp-funding-overhaul-would-cost-just-333m-according-to-icb-modelling/. 2025; \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-primary-care","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"famp","sideBox":"Learn more about [BMC Primary Care](https://bmcprimcare.biomedcentral.com/)","snPcode":"","submissionUrl":"https://author-welcome.nature.com/12875","title":"BMC Primary Care","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Primary care, healthcare funding, health inequalities","lastPublishedDoi":"10.21203/rs.3.rs-7509545/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7509545/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eCapitation payments account for approximately half of core funding for General Practitioner (GP) practices in England, allocated via the Global Sum Allocation (\u0026lsquo;Carr-Hill\u0026rsquo;) formula. The formula has not been updated since 2004 and lacks adjustments for clinical diagnoses, patient communication difficulties, and deprivation which are factors known to influence workload and health outcomes. In July 2021, Leicester, Leicestershire, and Rutland (LLR) Integrated Care Board introduced the Health Equity Payment (HEP), a top-up funding scheme based on a locally developed formula incorporating these additional factors.\u003c/p\u003e\u003ch2\u003eMethod:\u003c/h2\u003e\u003cp\u003eWe conducted a retrospective observational study using national public data to evaluate the impact of HEP between July 2021 and April 2023. Practices receiving HEP were matched to similar practices outside LLR using Genetic Matching on demographics, disease prevalence, and baseline outcomes. Seven outcomes were assessed: three patient experience measures from the GP Patient Survey, three staffing metrics (GP, nurse, and administrative full-time equivalents per 1000 weighted patients), and Quality and Outcomes Framework (QOF) achievement. Causal effects were estimated using doubly robust regression models with g-computation to estimate the average treatment effect.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e\u003cp\u003eSixty-two LLR practices received HEP and were matched to 62 control practices. Practices receiving HEP achieved a 3.2 percentage point higher QOF score (95% CI: 0.5 to 5.9; p\u0026thinsp;=\u0026thinsp;0.02) compared to controls. No statistically significant differences were found in patient experience or staffing outcomes. Sensitivity analyses confirmed robustness to alternative time periods and outcome specifications but revealed sensitivity to missing staffing data from atypical practices.\u003c/p\u003e\u003ch2\u003eDiscussion:\u003c/h2\u003e\u003cp\u003eThis study provides the first causal evaluation of a capitation funding model incorporating clinical and sociodemographic factors in England. The modest improvement in QOF achievement suggests that targeted funding could be linked to enhanced care quality. The absence of effects on staffing and patient experience may reflect data limitations, short follow-up, or heterogeneity in how funds were used. These findings provide the the first evidence that locally tailored funding models could address inequalities in primary care provision and inform ongoing national reviews of the general practice capitation funding.\u003c/p\u003e","manuscriptTitle":"Evaluating the impact of capitation funding top-up payments in primary care","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-26 15:06:55","doi":"10.21203/rs.3.rs-7509545/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-22T06:34:56+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-21T17:00:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"326860353931441869983468305010906239075","date":"2025-10-15T15:03:39+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-07T14:38:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"44028976592561849334305954217565250435","date":"2025-10-07T08:44:57+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-06T13:15:19+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-06T10:30:52+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-16T10:24:24+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-15T15:34:37+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Primary Care","date":"2025-09-15T15:31:09+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-primary-care","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"famp","sideBox":"Learn more about [BMC Primary Care](https://bmcprimcare.biomedcentral.com/)","snPcode":"","submissionUrl":"https://author-welcome.nature.com/12875","title":"BMC Primary Care","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ccea3dc2-8342-49f9-976a-6d7cd25e5318","owner":[],"postedDate":"September 26th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-01-12T16:04:59+00:00","versionOfRecord":{"articleIdentity":"rs-7509545","link":"https://doi.org/10.1186/s12875-025-03136-x","journal":{"identity":"bmc-primary-care","isVorOnly":false,"title":"BMC Primary Care"},"publishedOn":"2026-01-08 15:58:37","publishedOnDateReadable":"January 8th, 2026"},"versionCreatedAt":"2025-09-26 15:06:55","video":"","vorDoi":"10.1186/s12875-025-03136-x","vorDoiUrl":"https://doi.org/10.1186/s12875-025-03136-x","workflowStages":[]},"version":"v1","identity":"rs-7509545","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7509545","identity":"rs-7509545","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}