Dataset Information

Meeting the ambition of measuring the quality of hospitals' stroke care using routinely collected administrative data: a feasibility study.

ABSTRACT:

Objective

To examine the potential for using routinely collected administrative data to compare the quality and safety of stroke care at a hospital level, including evaluating any bias due to variations in coding practice.

Design

A retrospective cohort study of English hospitals' performance against six process and outcome indicators covering the acute care pathway. We used logistic regression to adjust the outcome measures for case mix.

Setting

Hospitals in England.

Participants

Stroke patients (ICD-10 I60-I64) admitted to English National Health Service public acute hospitals between April 2009 and March 2010, accounting for 91 936 admissions.

Main outcome measure

The quality and safety were measured using six indicators spanning the hospital care pathway, from timely access to brain scans to emergency readmissions following discharge after stroke.

Results

There were 182 occurrences of hospitals performing statistically differently from the national average at the 99.8% significance level across the six indicators. Differences in coding practice appeared to only partially explain the variation.

Conclusions

Hospital administrative data provide a practical and achievable method for evaluating aspects of stroke care across the acute pathway. However, without improvements in coding and further validation, it is unclear whether the cause of the variation is the quality of care or the result of different local care pathways and data coding accuracy.

SUBMITTER: Palmer WL

PROVIDER: S-EPMC3723302 | biostudies-literature | 2013 Sep

REPOSITORIES: biostudies-literature

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Publications

Meeting the ambition of measuring the quality of hospitals' stroke care using routinely collected administrative data: a feasibility study.

Palmer William L WL Bottle Alex A Davie Charlie C Vincent Charles A CA Aylin Paul P

International journal for quality in health care : journal of the International Society for Quality in Health Care 20130412 4

<h4>Objective</h4>To examine the potential for using routinely collected administrative data to compare the quality and safety of stroke care at a hospital level, including evaluating any bias due to variations in coding practice.<h4>Design</h4>A retrospective cohort study of English hospitals' performance against six process and outcome indicators covering the acute care pathway. We used logistic regression to adjust the outcome measures for case mix.<h4>Setting</h4>Hospitals in England.<h4>Par ...[more]

PMID: 23584363

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Project description:Introduction: Achieving equity in health care remains a challenge for health care systems worldwide and marked inequities in access and quality of care persist. Identifying health care equity indicators is an important first step in integrating the concept of equity into assessments of health care system performance, particularly in emergency care. Methods: We conducted a systematic review of administrative data-derived health care equity indicators and their association with socioeconomic determinants of health (SEDH) in emergency care settings. Following PRISMA-Equity reporting guidelines, Ovid MEDLINE, EMBASE, PubMed, and Web of Science were searched for relevant studies. The outcomes of interest were indicators of health care equity and the associated SEDH they examine. Results: Among 29 studies identified, 14 equity indicators were identified and grouped into four categories that reflect the patient emergency care pathway. Total emergency department (ED) visits and ambulatory care-sensitive condition-related ED visits were the two most frequently used equity indicators. The studies analyzed equity based on seven SEDH: social deprivation, income, education level, social class, insurance coverage, health literacy, and financial and nonfinancial barriers. Despite some conflicting results, all identified SEDH are associated with inequalities in access to and use of emergency care. Conclusion: The use of administrative data-derived indicators in combination with identified SEDH could improve the measurement of health care equity in emergency care settings across health care systems worldwide. Using a combination of indicators is likely to lead to a more comprehensive, well-rounded measurement of health care equity than using any one indicator in isolation. Although studies analyzed focused on emergency care settings, it seems possible to extrapolate these indicators to measure equity in other areas of the health care system. Further studies elucidating root causes of health inequities in and outside the health care system are needed.

Project description:BackgroundSuicide is a significant public health issue. Many risk prediction tools have been developed to estimate an individual's risk of suicide. Risk prediction models can go beyond individual risk assessment; one important application of risk prediction models is population health planning. Suicide is a result of the interaction among the risk and protective factors at the individual, health care system, and community levels. Thus, policy and decision makers can play an important role in suicide prevention. However, few prediction models for the population risk of suicide have been developed.ObjectiveThis study aims to develop and validate prediction models for the population risk of suicide using health administrative data, considering individual-, health system-, and community-level predictors.MethodsWe used a case-control study design to develop sex-specific risk prediction models for suicide, using the health administrative data in Quebec, Canada. The training data included all suicide cases (n=8899) that occurred from January 1, 2002, to December 31, 2010. The control group was a 1% random sample of living individuals in each year between January 1, 2002, and December 31, 2010 (n=645,590). Logistic regression was used to develop the prediction models based on individual-, health care system-, and community-level predictors. The developed model was converted into synthetic estimation models, which concerted the individual-level predictors into community-level predictors. The synthetic estimation models were directly applied to the validation data from January 1, 2011, to December 31, 2019. We assessed the performance of the synthetic estimation models with four indicators: the agreement between predicted and observed proportions of suicide, mean average error, root mean square error, and the proportion of correctly identified high-risk regions.ResultsThe sex-specific models based on individual data had good discrimination (male model: C=0.79; female model: C=0.85) and calibration (Brier score for male model 0.01; Brier score for female model 0.005). With the regression-based synthetic models applied in the validation data, the absolute differences between the synthetic risk estimates and observed suicide risk ranged from 0% to 0.001%. The root mean square errors were under 0.2. The synthetic estimation model for males correctly predicted 4 of 5 high-risk regions in 8 years, and the model for females correctly predicted 4 of 5 high-risk regions in 5 years.ConclusionsUsing linked health administrative databases, this study demonstrated the feasibility and the validity of developing prediction models for the population risk of suicide, incorporating individual-, health system-, and community-level variables. Synthetic estimation models built on routinely collected health administrative data can accurately predict the population risk of suicide. This effort can be enhanced by timely access to other critical information at the population level.

Dataset Information

Meeting the ambition of measuring the quality of hospitals' stroke care using routinely collected administrative data: a feasibility study.

Objective

Design

Setting

Participants

Main outcome measure

Results

Conclusions

Publications

Meeting the ambition of measuring the quality of hospitals' stroke care using routinely collected administrative data: a feasibility study.

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