Should measures of patient experience in primary care be adjusted for case mix? Evidence from the English General Practice Patient Survey

Cambridge Centre for Health Services Research, University of Cambridge, Cambridge, UK.
BMJ quality & safety (Impact Factor: 3.99). 05/2012; 21(8):634-40. DOI: 10.1136/bmjqs-2011-000737
Source: PubMed


Uncertainties exist about when and how best to adjust performance measures for case mix. Our aims are to quantify the impact of case-mix adjustment on practice-level scores in a national survey of patient experience, to identify why and when it may be useful to adjust for case mix, and to discuss unresolved policy issues regarding the use of case-mix adjustment in performance measurement in health care.
Secondary analysis of the 2009 English General Practice Patient Survey. Responses from 2 163 456 patients registered with 8267 primary care practices. Linear mixed effects models were used with practice included as a random effect and five case-mix variables (gender, age, race/ethnicity, deprivation, and self-reported health) as fixed effects.
Primary outcome was the impact of case-mix adjustment on practice-level means (adjusted minus unadjusted) and changes in practice percentile ranks for questions measuring patient experience in three domains of primary care: access; interpersonal care; anticipatory care planning, and overall satisfaction with primary care services.
Depending on the survey measure selected, case-mix adjustment changed the rank of between 0.4% and 29.8% of practices by more than 10 percentile points. Adjusting for case-mix resulted in large increases in score for a small number of practices and small decreases in score for a larger number of practices. Practices with younger patients, more ethnic minority patients and patients living in more socio-economically deprived areas were more likely to gain from case-mix adjustment. Age and race/ethnicity were the most influential adjustors.
While its effect is modest for most practices, case-mix adjustment corrects significant underestimation of scores for a small proportion of practices serving vulnerable patients and may reduce the risk that providers would 'cream-skim' by not enrolling patients from vulnerable socio-demographic groups.

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Available from: Georgios Lyratzopoulos
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    • "Relating case mix to funding strategies introduced a systematic bias of reporting more comorbidities, known as " upcoding " , for greater gains in several national health systems[12]. Such biases can change the relationship between patient profile and outcome across hospitals and would potentially lead to inaccurate or unfair provider comparisons and allocation of incentives[2,4,13141516. Different sources of information, employed by studies to verify consistency in hospital datasets , resulted in varying levels of agreement. "
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    ABSTRACT: Diagnostic data routinely collected for hospital admitted patients and used for case-mix adjustment in care provider comparisons and reimbursement are prone to biases. We aim to measure discrepancies, variations and associated factors in recorded chronic morbidities for hospital admitted patients in New South Wales (NSW), Australia. Of all admissions between July 2010 and June 2014 in all NSW public and private acute hospitals, admissions with over 24 hours stay and one or more of the chronic conditions of diabetes, smoking, hepatitis, HIV, and hypertension were included. The incidence of a non-recorded chronic condition in an admission occurring after the first admission with a recorded chronic condition (index admission) was considered as a discrepancy. Poisson models were employed to (i) derive adjusted discrepancy incidence rates (IR) and rate ratios (IRR) accounting for patient, admission, comorbidity and hospital characteristics and (ii) quantify variation in rates among hospitals. The discrepancy incidence rate was highest for hypertension (51% of 262,664 admissions), followed by hepatitis (37% of 12,107), smoking (33% of 548,965), HIV (27% of 1500) and diabetes (19% of 228,687). Adjusted rates for all conditions declined over the four-year period; with the sharpest drop of over 80% for diabetes (47.7% in 2010 vs. 7.3% in 2014), and 20% to 55% for the other conditions. Discrepancies were more common in private hospitals and smaller public hospitals. Inter-hospital differences were responsible for 1% (HIV) to 9.4% (smoking) of variation in adjusted discrepancy incidences, with an increasing trend for diabetes and HIV. Chronic conditions are recorded inconsistently in hospital administrative datasets, and hospitals contribute to the discrepancies. Adjustment for patterns and stratification in risk adjustments; and furthermore longitudinal accumulation of clinical data at patient level, refinement of clinical coding systems and standardisation of comorbidity recording across hospitals would enhance accuracy of datasets and validity of case-mix adjustment.
    Full-text · Article · Jan 2016 · PLoS ONE
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    • "Morbidity scores, designed to summarize comorbidity for individual patients, by summing scores for selected diseases, are widely used in research and service monitoring to adjust for baseline differences in patient groups or service providers [2]. In primary and ambulatory care, robust adjustment for case mix is important for valid interpretation of both observational research and routine health services outcome data [3]. A range of morbidity scores have been used, of which the Charlson index is the most well known [4]. "
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    ABSTRACT: 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.
    Full-text · Article · Feb 2013 · Journal of clinical epidemiology
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    ABSTRACT: Background/objectivesTo determine which aspects of primary care matter most to patients, we aim to identify those aspects of patient experience that show the strongest relationship with overall satisfaction and examine the extent to which these relationships vary by socio-demographic and health characteristics.Design/settingData from the 2009/10 English General Practice Patient Survey including 2 169 718 respondents registered with 8362 primary care practices.Measures/analysesLinear mixed-effects regression models (fixed effects adjusting for age, gender, ethnicity, deprivation, self-reported health, self-reported mental health condition and random practice effect) predicting overall satisfaction from six items covering four domains of care: access, helpfulness of receptionists, doctor communication and nurse communication. Additional models using interactions tested whether associations between patient experience and satisfaction varied by socio-demographic group.ResultsDoctor communication showed the strongest relationship with overall satisfaction (standardized coefficient 0.48, 95% CI = 0.48, 0.48), followed by the helpfulness of reception staff (standardized coefficient 0.22, 95% CI = 0.22, 0.22). Among six measures of patient experience, obtaining appointments in advance showed the weakest relationship with overall satisfaction (standardized coefficient 0.06, 95% CI = 0.05, 0.06). Interactions showed statistically significant but small variation in the importance of drivers across different patient groups.Conclusions For all patient groups, communication with the doctor is the most important driver of overall satisfaction with primary care in England, along with the helpfulness of receptionists. In contrast, and despite being a policy priority for government, measures of access, including the ability to obtain appointments, were poorly related to overall satisfaction.
    Full-text · Article · May 2013 · Health expectations: an international journal of public participation in health care and health policy
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