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.
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.
Authors: Fay J Hosking; Iain M Carey; Sunil M Shah; Tess Harris; Stephen DeWilde; Carole Beighton; Derek G Cook Journal: Am J Public Health Date: 2016-06-16 Impact factor: 9.308
Authors: Fay J Hosking; Iain M Carey; Stephen DeWilde; Tess Harris; Carole Beighton; Derek G Cook Journal: Ann Fam Med Date: 2017-09 Impact factor: 5.166
Authors: Julián Benito-León; Elan D Louis; Verónica Puertas-Martín; Juan Pablo Romero; Michele Matarazzo; José Antonio Molina-Arjona; Cristina Domínguez-González; Álvaro Sánchez-Ferro Journal: J Neurol Sci Date: 2015-12-21 Impact factor: 3.181