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papers

A new simple primary care morbidity score predicted mortality and better explains between practice variations than the Charlson index

Published: February 8, 2013
Category: Papers
Authors: Carey IM, Cook DG, DeWilde S, Harris T, Shah SM
Country: United Kingdom
Language: null
Type: Performance Analysis
Setting: Academic

J Clin Epidemiol 66:436-444.

Population Health Research Centre, Division of Population Health Sciences and Education, St George’s University of London, London, UK

OBJECTIVES: Adjustment for morbidity is important to ensure fair comparison of outcomes between patient groups and health care providers. The Quality and Outcomes Framework (QOF) in UK primary care offers potential for developing a standardized morbidity score for low-risk populations.

STUDY DESIGN AND SETTING: Retrospective cohort study of 653,780 patients aged 60 years or older registered with 375 practices in 2008 in a large primary care database (The Health Improvement Network). Half the practices were randomly selected to derive a morbidity score predicting 1-year mortality; the others assessed predictive performance.

RESULTS: Nine chronic conditions were robust copredictors (hazard ratio = ≥1.2) of mortality independent of age and sex, producing high predictive discrimination (c-statistic = 0.82). An individual’s QOF score explained more between practice variation in mortality than the Charlson index (46% vs. 32%). At practice level, mean QOF score was strongly correlated with practice standardized mortality ratios (r = 0.64), explaining more variation in practice death rates than the Charlson index.

CONCLUSION: A simple nine-item score derived from routine primary care recording provides a morbidity index highly predictive of mortality and between practice variation in older UK primary care populations. This has utility in research and health care outcome monitoring and can be easily implemented in other primary and ambulatory care settings.

PMID: 23395517

Morbidity Patterns,Mortality Prediction,Targeted Program,Resource Use,United Kingdom,Aged,80 and over,Chronic Disease/epidemiology,Cohort Studies,Gender,Middle Aged,Poverty Areas,Predictive Value of Tests,Proportional Hazards Models,Retrospective studies,United Kingdom/epidemiology

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