Use of past care markers in risk-adjustment: accounting for systematic differences across providers

Laura Anselmi (), Yiu-Shing Lau, Matt Sutton, Anna Everton, Rob Shaw and Stephen Lorrimer
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Laura Anselmi: University of Manchester
Yiu-Shing Lau: University of Manchester
Matt Sutton: University of Manchester
Anna Everton: NHS England and NHS Improvement
Rob Shaw: NHS England and NHS Improvement
Stephen Lorrimer: NHS England and NHS Improvement

The European Journal of Health Economics, 2022, vol. 23, issue 1, No 12, 133-151

Abstract: Abstract Risk-adjustment models are used to predict the cost of care for patients based on their observable characteristics, and to derive efficient and equitable budgets based on weighted capitation. Markers based on past care contacts can improve model fit, but their coefficients may be affected by provider variations in diagnostic, treatment and reporting quality. This is problematic when distinguishing need and supply influences on costs is required. We examine the extent of this bias in the national formula for mental health care using administrative records for 43.7 million adults registered with 7746 GP practices in England in 2015. We also illustrate a method to control for provider effects. A linear regression containing a rich set of individual, GP practice and area characteristics, and fixed effects for local health organisations, had goodness-of-fit equal to R2 = 0.007 at person level and R2 = 0.720 at GP practice level. The addition of past care markers changed substantially the coefficients on the other variables and increased the goodness-of-fit to R2 = 0.275 at person level and R2 = 0.815 at GP practice level. The further inclusion of provider effects affected the coefficients on GP practice and area variables and on local health organisation fixed effects, increasing goodness-of-fit at GP practice level to R2 = 0.848. With adequate supply controls, it is possible to estimate coefficients on past care markers that are stable and unbiased. Nonetheless, inconsistent reporting may affect need predictions and penalise populations served by underreporting providers.

Keywords: Risk-adjustment; Resource allocation; Weighted capitation; Need; Cost; Mental health (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s10198-021-01350-9

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