ArticleJournal of health economics2014
Keep it simple? Predicting primary health care costs with clinical morbidity measures.
Article in Journal of health economics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 3 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
33 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Promising algorithms to perilous applications: a systematic review of risk stratification tools for predicting healthcare utilisation.BMJ health & care informatics · 2024Pooled it
- Pooled it
- Impact of multimorbidity on healthcare costs and utilisation: a systematic review of the UK literature.The British journal of general practice : the journal of the Royal College of General Practitioners · 2021Pooled it
- Evaluating the impact of capitation funding top-up payments in primary care.BMC primary care · 2026Observational
- Risk Adjustment in Capitation Payments to Primary Care Providers: Does It Matter How We Account for Patients' Socioeconomic Status?Medical care · 2025Article
- Development and validation of a brief assessment of normative health and health-related social needs using the Simple Segmentation Tool.BMC health services research · 2025Article
- Inequalities in the prevalence recording of 205 chronic conditions recorded in primary and secondary care for 12 million patients in the English National Health Service.BMC medicine · 2024Article
- Validity of the Johns Hopkins Adjusted Clinical Groups system on the utilisation of healthcare services in Norway: a retrospective cross-sectional study.BMC health services research · 2024Article
- Predicting healthcare expenditure based on Adjusted Morbidity Groups to implement a needs-based capitation financing system.Health economics review · 2024Article
- Developing a prediction model to identify people with severe mental illness without regular contact to their GP - a study based on data from the Danish national registers.BMC psychiatry · 2024Article
- Short- and intermediate-term impact of DTC telemedicine consultations on subsequent healthcare consumption.The European journal of health economics : HEPAC : health economics in prevention and care · 2024Article
- Challenges in reaching patients with severe mental illness for trials in general practice-a convergent mixed methods study based on the SOFIA pilot trial.Pilot and feasibility studies · 2023Article
- Telehealth medication management and health care spending in a Medicare Accountable Care Organization.Journal of managed care & specialty pharmacy · 2023Article
- Improving multimorbidity measurement using individualized disease-specific quality of life impact assessments: predictive validity of a new comorbidity index.Health and quality of life outcomes · 2022Article
- Prediction of outpatient visits and expenditure under the Universal Coverage Scheme in Bangkok using subscriber's attributes: A random forest analysis.Public health in practice (Oxford, England) · 2021Article
- Performance of quantitative measures of multimorbidity: a population-based retrospective analysis.BMC public health · 2021Article
- A systematic review of risk stratification tools internationally used in primary care settings.Health science reports · 2021Review
- Prevalence of suspected COVID-19 infection in patients from ethnic minority populations: a cross-sectional study in primary care.The British journal of general practice : the journal of the Royal College of General Practitioners · 2020Article
- Predictors of late presentation to renal dialysis: a cohort study of linked primary and secondary care records in East London.BMJ open · 2019Article
- [Validity of adjusted morbidity groups with respect to clinical risk groups in the field of primary care].Atencion primaria · 2019Observational
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.