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A fairer funding formula for General Practice

The Carr-Hill formula, also known as the Global Sum Allocation Formula, is used in the UK to allocate funding to General Practitioner (GP) practices in primary care. It is designed to account for various factors that affect the workload and costs faced by GP practices, ensuring that resources are distributed fairly based on patient needs and other practice characteristics.

Components of the Carr-Hill Formula

  1. Patient Age and Sex: Different age and sex groups have varying healthcare needs. For example, elderly patients and young children typically require more medical attention than adults in their middle years. The formula adjusts funding based on the demographic profile of the practice’s patient list.
  2. Additional Needs: This considers factors such as morbidity and mortality rates, which indicate the general health of the patient population. Practices with higher rates of chronic illnesses or higher mortality rates receive more funding.
  3. Staff Costs: Geographic variations in the cost of employing staff are taken into account. Practices in areas with higher living costs receive additional funds to cover the higher salaries and expenses.
  4. Rurality and Remoteness: Practices in rural or remote areas face additional challenges, such as longer travel times for home visits and difficulties in recruiting staff. The formula provides extra funding to account for these factors.
  5. Market Forces Factor (MFF): This adjusts for regional variations in the cost of goods and services, including property and utilities.

How It Works

  • Practice List Size: Each practice’s funding starts with its list size (the number of registered patients). This is the base number to which various adjustments are applied.
  • Weighted Capitation: The base funding is adjusted using weights derived from the factors mentioned above. For example:Age-Sex Weighting: A practice with more elderly patients receives a higher weight.
  • Additional Needs Index: Practices in areas with higher deprivation or poor health indices receive higher funding.
  • Staff Market Forces: Higher funding for practices in areas with higher staff costs.
  • Adjustment for Unavoidable Costs: Additional adjustments are made for factors like rurality and MFF to ensure that practices in high-cost areas receive appropriate compensation.
  • Final Allocation: The final funding allocation for each practice is the sum of the weighted capitation adjusted for all relevant factors. This ensures that practices receive funding that reflects their specific patient demographics and local cost pressures.

Worked Example Calculation

  • Base List Size: 10,000 patients
  • Age-Sex Weighting: 1.2 (indicating an older population)
  • Additional Needs Index: 1.1 (indicating higher than average morbidity)
  • Staff Market Forces: 1.05 (indicating higher staff costs)
  • Rurality Adjustment: 1.03 (indicating a semi-rural practice)

The formula might look something like this:

  

Plugging in the numbers:

This means the practice would receive funding as if they were serving 14,278 patients, rather than the actual 10,000, reflecting the higher costs and workload associated with their patient population.

What might a new, fairer formula look like?

To create a new version of the Carr-Hill formula that better accounts for deprivation and rurality, we can propose the following steps and adjustments:

  1. Data Collection
    1. Patient Demographics: Age, sex, and health status (including chronic conditions).
    2. Deprivation Index: Use indices such as the Index of Multiple Deprivation (IMD) which considers income, employment, health deprivation, disability, education, skills, training, housing, and services.
    3. Rurality Index: Use measures of rurality such as the Rural Urban Classification, which considers population density and access to services.
  1. Model Adjustments

Modify the existing Carr-Hill formula by integrating deprivation and rurality as significant factors. The adjusted formula can be represented as:

  1. Adjustments Explained
  • Base Funding: The base amount allocated per patient before adjustments.
  • Age-Sex Adjustment: A factor based on patient demographics.
  • Health Status Adjustment: A factor based on the prevalence of chronic diseases and health needs.
  • Deprivation Adjustment: A new factor to account for socioeconomic status. This can be calculated using the IMD score:

  • Rurality Adjustment: A new factor to account for the challenges faced by rural practices. This can be calculated using the Rural Urban Classification:

  1. Determining Weights

Empirical data analysis and regression models can be used to determine the appropriate weights ()

This involves:

  1. Collecting Data: Historical funding data, patient outcomes, and demographic data.
  2. Regression Analysis: Performing multiple regression to estimate the impact of deprivation and rurality on healthcare costs and outcomes.
  • Weight Calculation: Setting the weights based on the regression coefficients to ensure the formula reflects real-world needs.

Worked Example Calculation

  • Base Funding: £100 per patient.
  • Age-Sex Adjustment: 1.2 (reflecting higher needs due to age/sex distribution).
  • Health Status Adjustment: 1.1 (reflecting higher prevalence of chronic conditions).
  • IMD Score: 30 (higher than the average IMD score of 20).
  • Rurality Score: 15 (higher than the average Rurality Score of 10).
  • Weight_d: 0.05 (empirically determined).
  • Weight_r: 0.03 (empirically determined).

This example demonstrates how the new formula can adjust funding based on deprivation and rurality alongside other factors.

 

Let's compare and contrast the old with the new...

Current Carr-Hill Formula

Strengths

    • Comprehensive Demographic Adjustments: Considers a wide range of patient demographics.
    • Morbidity and Mortality: Effectively reflects the impact of chronic conditions on healthcare costs.

Limitations

    • Deprivation Consideration: While it includes some socio-economic adjustments, it may not fully capture the impact of deprivation on healthcare needs.
    • Rurality: Does not explicitly adjust for rurality, potentially underfunding rural practices that face unique challenges.

New Adjusted Formula

Strengths

    • Explicit Deprivation Adjustment: Directly addresses socio-economic factors, providing better support for practices in deprived areas.
    • Rurality Adjustment: Recognises and compensates for the unique challenges of rural practices, such as travel distances and access to services.
    • Targeted Funding: Aims to ensure funding is more closely aligned with patient needs and practice challenges.

Limitations

    • Complexity: Introduces additional factors that require more data and calculations.
    • Weight Determination: Requires empirical data analysis to accurately determine the impact weights, which can be resource-intensive.

Worked Examples

To show how much a GP practice with 1000 patients would get paid using the original Carr-Hill formula, we need to go through the steps of the formula.  Let’s assume the following simplified adjustments and values for illustration purposes:

  • Base Funding per Patient: £100
  • Age-Sex Adjustment: 1.2
  • Morbidity and Mortality Adjustment: 1.1
  • Additional Needs Adjustment: 1.05
  • Market Forces Factor (MFF): 1.1

Therefore, a GP practice with 1000 patients would receive approximately £154,000 using the original Carr-Hill formula with the given adjustments.

 

To calculate how much a GP practice with 1000 patients would get paid using the new improved formula that includes adjustments for deprivation and rurality, we will follow similar steps as for the original Carr-Hill formula, but we will include the additional adjustments for deprivation and rurality.  Assume the following simplified adjustments and values for illustration purposes:

  • Base Funding per Patient: £100
  • Age-Sex Adjustment: 1.2
  • Health Status Adjustment: 1.1
  • Deprivation Adjustment: (IMD Score of 30, Average IMD Score of 20, Weight_d = 0.05)
  • Rurality Adjustment: (Rurality Score of 15, Average Rurality Score of 10, Weight_r = 0.03)

Therefore, a GP practice with 1000 patients would receive approximately £147,700 using the new improved formula with the given adjustments.

 

To perform the calculation for a GP practice with a more deprived and older population using the new improved formula, we will assume higher values for the deprivation and age-related adjustments.  Assume the following simplified adjustments and values for illustration purposes:

  • Base Funding: £100 per patient
  • Age and Sex Adjustment: Assume a higher value to reflect an older population (e.g., 1.5)
  • Health Status Adjustment: Assume a higher value due to older and potentially sicker population (e.g., 1.3)
  • Deprivation Adjustment: Higher IMD Score (e.g., 50 instead of 30), same Average IMD Score (20), Weight_d = 0.05
  • Rurality Adjustment: Assume same values as before for simplicity (Rurality Score = 15, Average Rurality Score = 10, Weight_r = 0.03)

Therefore, a GP practice with 1000 patients, a more deprived and older population, would receive approximately £204,700 using the new improved formula with the given adjustments.

Implementation considerations

Implementing a new primary care funding formula in England that includes adjustments for deprivation and rurality is expected to improve equity in healthcare delivery by directing more resources to areas with greater need. This approach can lead to better health outcomes and reduced disparities but requires careful planning, accurate data, and ongoing evaluation to ensure its effectiveness and sustainability. The overall cost of delivering primary care may increase initially due to the need for additional funding in high-need areas, but the long-term benefits of improved health outcomes and preventive care could offset these costs.

 

Increased Funding for Deprived and Rural Areas

Deprived Areas:

Higher Per Capita Funding: Practices in areas with higher deprivation indices would receive increased funding. This would allow for more resources to address the health disparities typically found in these areas.

Targeted Interventions: Additional funds could be used for community health programs, preventive care, and chronic disease management, which are often more prevalent in deprived populations.

Rural Areas:

Compensation for Higher Costs: Practices in rural areas, which face higher operational costs due to factors like travel distances and lower patient densities, would receive more funding.

Enhanced Access: Increased funding could improve access to services, reduce travel times for patients, and attract healthcare professionals to underserved rural areas.

 

Shift in Funding Distribution

Redistribution of Funds: There would likely be a shift in funding from relatively well-off urban areas to more deprived urban and rural areas. This redistribution aims to achieve equity by addressing the specific needs of different populations.

Budget Adjustments: Overall healthcare budgets might need to be adjusted to accommodate the increased funding for high-need areas. This might involve reallocating funds from other areas or increasing the total healthcare budget.

 

Impact on Healthcare Outcomes

Improved Health Equity: By targeting funds where they are most needed, the new formula could help reduce health disparities. Improved access to care and resources in deprived and rural areas can lead to better health outcomes.

Preventive Care and Long-Term Savings: Increased investment in preventive care in deprived areas can lead to long-term savings by reducing the incidence of chronic diseases and costly emergency interventions.

 

Operational Considerations

Implementation Costs: Transitioning to the new formula would incur administrative costs. Data collection, system updates, and training for practice managers and healthcare administrators would be necessary.

Monitoring and Evaluation: Continuous monitoring and evaluation would be needed to ensure the formula’s effectiveness and to make adjustments based on outcomes and feedback.

 

Stakeholder Impact

Healthcare Providers: GP practices in high-need areas would benefit from increased funding, potentially improving job satisfaction and reducing burnout due to better resourcing.

Patients: Patients in deprived and rural areas would experience improved access to care, potentially leading to better health outcomes and patient satisfaction.

Policy Makers: The new funding formula would require support and buy-in from policymakers to ensure successful implementation and ongoing adjustments.

 

Potential Challenges

Data Accuracy: Ensuring accurate and up-to-date data for deprivation and rurality indices is critical for the formula’s success. Inaccurate data could lead to misallocation of funds.

Resistance to Change: There might be resistance from practices that perceive themselves as losing out under the new formula. Effective communication and phased implementation could help mitigate this.