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Models of the determinants of individuals' primary care costs can be used to set capitation payments to providers and to test for horizontal equity. We compare the ability of eight measures of patient morbidity and multimorbidity to predict future primary care costs and examine capitation payments based on them. The measures were derived from four morbidity descriptive systems: 17 chronic diseases in the Quality and Outcomes Framework (QOF); 17 chronic diseases in the Charlson scheme; 114 Expanded Diagnosis Clusters (EDCs); and 68 Adjusted Clinical Groups (ACGs). These were applied to patient records of 86,100 individuals in 174 English practices. For a given disease description system, counts of diseases and sets of disease dummy variables had similar explanatory power. The EDC measures performed best followed by the QOF and ACG measures. The Charlson measures had the worst performance but still improved markedly on models containing only age, gender, deprivation and practice effects. Comparisons of predictive power for different morbidity measures were similar for linear and exponential models, but the relative predictive power of the models varied with the morbidity measure. Capitation payments for an individual patient vary considerably with the different morbidity measures included in the cost model. Even for the best fitting model large differences between expected cost and capitation for some types of patient suggest incentives for patient selection. Models with any of the morbidity measures show higher cost for more deprived patients but the positive effect of deprivation on cost was smaller in better fitting models., (Copyright © 2014 The Authors. Published by Elsevier B.V. All rights reserved.)

Background: If treatments are used to modify a trait, then patients with high-risk genotypes for the trait should be found at higher frequency in treatment groups than in the general population. The frequency ratio of high- to low-risk genotypes treated should reflect the mean threshold above which the treatment is given in the population. As an example, we hypothesized that because APOE (apolipoprotein E) alleles affect the LDL cholesterol (LDLc) concentration, APOE genotype frequencies in statin takers should act as a proxy for the prevailing treatment threshold of LDLc., Methods: We used LDLc, statin usage, and APOE genotype data from the British Women's Heart and Health Study (n=2289; age, 60-79 years) and calculated the genotype ratio treatment index (GRTI) by dividing the proportion of ε3/ε2 or ε3/ε4 participants prescribed a statin by the proportion of ε3/ε3 participants prescribed a statin, both overall and according to socioeconomic class, geographic region, and coronary heart disease (CHD) status. Genotype-specific LDLc distributions were used to calculate the mean LDLc treatment threshold., Results: For genotype ε3/ε2, the GRTI was 0.52 (95% CI, 0.30-0.87) for statin takers overall, 0.22 (95% CI, 0.00-0.56) for those without CHD, and 0.69 (95% CI, 0.31-1.18) for those with CHD. The GRTIs for those without and with CHD backcalculate to LDLc thresholds of 5.65 mmol/L (95% CI, 5.50-5.82 mmol/L) and 4.39 mmol/L (95% CI, 4.21-4.59 mmol/L), respectively. Scotland and North England showed dissimilar GRTIs, which backcalculated to LDLc thresholds of 5.06 mmol/L (95% CI, 4.83-5.28 mmol/L) and 5.44 mmol/L (95% CI, 5.19-5.69 mmol/L), respectively, for all women., Conclusions: The findings illustrate how genotype frequencies can be a proxy for treatment thresholds used in clinical practice. Genome-wide studies have identified>500 disease-relevant polymorphisms. GRTIs from cost-efficient genotyping, in combination with phenotypic data, may have wide potential in health services research.