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Data and measurement issues in the analysis of health disparities.

ABSTRACT: OBJECTIVE: To describe measurement challenges and strategies in identifying and analyzing health disparities and inequities. METHODS: We discuss the limitations of existing data sources for measuring health disparities and inequities, describe current strategies to address those limitations, and explore the potential of emerging strategies. PRINCIPAL FINDINGS: Larger national sample sizes are necessary to identify disparities for major population subgroups. Collecting self-reported race and granular ethnicity data may reduce some measurement errors, but it raises other methodological questions. The assessment of health inequities presents particular challenges, requiring analysis of the interactive effects of multiple determinants of health. Indirect estimation and modeling methods are likely to be important tools for estimating health disparities and inequities for the foreseeable future. CONCLUSIONS: Interdisciplinary training and collaborative research models will be essential for future disparities research. Evaluation of evolving methodologies for assessing health disparities should be a priority for health services researchers in the next decade.

SUBMITTER: Bilheimer LT

PROVIDER: S-EPMC2965888 | biostudies-other | 2010 Oct

REPOSITORIES: biostudies-other

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Data and measurement issues in the analysis of health disparities.

Bilheimer Linda T LT Klein Richard J RJ

Health services research 20100802 5 Pt 2

<h4>Objective</h4>To describe measurement challenges and strategies in identifying and analyzing health disparities and inequities.<h4>Methods</h4>We discuss the limitations of existing data sources for measuring health disparities and inequities, describe current strategies to address those limitations, and explore the potential of emerging strategies.<h4>Principal findings</h4>Larger national sample sizes are necessary to identify disparities for major population subgroups. Collecting self-rep ...[more]

PMID: 21054368

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Project description:BackgroundThe promise of digital health is principally dependent on the ability to electronically capture data that can be analyzed to improve decision-making. However, the ability to effectively harness data has proven elusive, largely because of the quality of the data captured. Despite the importance of data quality (DQ), an agreed-upon DQ taxonomy evades literature. When consolidated frameworks are developed, the dimensions are often fragmented, without consideration of the interrelationships among the dimensions or their resultant impact.ObjectiveThe aim of this study was to develop a consolidated digital health DQ dimension and outcome (DQ-DO) framework to provide insights into 3 research questions: What are the dimensions of digital health DQ? How are the dimensions of digital health DQ related? and What are the impacts of digital health DQ?MethodsFollowing the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, a developmental systematic literature review was conducted of peer-reviewed literature focusing on digital health DQ in predominately hospital settings. A total of 227 relevant articles were retrieved and inductively analyzed to identify digital health DQ dimensions and outcomes. The inductive analysis was performed through open coding, constant comparison, and card sorting with subject matter experts to identify digital health DQ dimensions and digital health DQ outcomes. Subsequently, a computer-assisted analysis was performed and verified by DQ experts to identify the interrelationships among the DQ dimensions and relationships between DQ dimensions and outcomes. The analysis resulted in the development of the DQ-DO framework.ResultsThe digital health DQ-DO framework consists of 6 dimensions of DQ, namely accessibility, accuracy, completeness, consistency, contextual validity, and currency; interrelationships among the dimensions of digital health DQ, with consistency being the most influential dimension impacting all other digital health DQ dimensions; 5 digital health DQ outcomes, namely clinical, clinician, research-related, business process, and organizational outcomes; and relationships between the digital health DQ dimensions and DQ outcomes, with the consistency and accessibility dimensions impacting all DQ outcomes.ConclusionsThe DQ-DO framework developed in this study demonstrates the complexity of digital health DQ and the necessity for reducing digital health DQ issues. The framework further provides health care executives with holistic insights into DQ issues and resultant outcomes, which can help them prioritize which DQ-related problems to tackle first.

Project description:IMPORTANCE:There are more than 500 articles in the 2014 race-based healthcare disparities literature across a broad array of diseases and outcomes. However, unlike many other forms of research (e.g., clinical trials and systematic reviews), there are no required reporting guidelines when submitting results of disparities studies to journals. OBJECTIVE:This study describes the race-based healthcare disparities measurement literature in terms of study design, journal characteristics, generation of health disparities research, type of disparity measure used, and adherence to disparities measurement guidelines. METHODS:We searched three databases of peer-reviewed literature, PubMed, Ovid Medline, and JSTOR, for English language articles published in 2014 on racial/ethnic healthcare disparities. Studies must have quantitatively measured the difference in health outcomes between two racial/ethnic groups in order to be included. Our final sample included 266 studies from 167 medical and public health journals. FINDINGS:Only 7 % (n = 19) of articles reported both an absolute and relative measure of disparity; the majority of studies (64 %, n = 171) reported only a relative measure of effect. Most studies were published in clinical journals (74 %, n = 198), used secondary data (86 %, n = 229), and calculated black-white disparities (82 %, n = 218). The most common condition studied was cancer (25 %, n = 67), followed by a surgical procedure (18 %, n = 48). On average, articles in the sample only met 61 % of the applicable guidelines on reporting of disparities. CONCLUSIONS AND RELEVANCE:To be able to synthesize findings in the racial disparities literature (meta-analysis), there is a need for the use of consistent methods for quantifying disparities and reporting in the literature. A more consistent battery of measures and consistent reporting across studies may help speed our understanding of the origins and development of solutions to address healthcare disparities. Despite guidelines for best practices in reporting disparities, there is a lack of adherence in the current literature.

Project description:BackgroundAssessing disparities in injury is crucial for injury prevention and for evaluating injury prevention strategies, but efforts have been hampered by missing data. This study aimed to show the utility and reliability of the injury surveillance system as a trustworthy resource for examining disparities by generating multiple imputed companion datasets.MethodsWe employed data from the National Electronic Injury Surveillance System-All Injury Program (NEISS-AIP) for the period 2014-2018. A comprehensive simulation study was conducted to identify the appropriate strategy for addressing missing data limitations in NEISS-AIP. To evaluate the imputation performance more quantitatively, a new method based on Brier Skill Score (BSS) was developed to assess the accuracy of predictions by different approaches. We selected the multiple imputations by fully conditional specification (FCS MI) to generate the imputed companion data to NEISS-AIP 2014-2018. We further assessed health disparities systematically in nonfatal assault injuries treated in U.S. hospital emergency departments (EDs) by race and ethnicity, location of injury and sex.ResultsWe found for the first time that significantly higher age-adjusted nonfatal assault injury rates for ED visits per 100,000 population occurred among non-Hispanic Black persons (1306.8, 95% Confidence Interval [CI]: 660.1 - 1953.5), in public settings (286.3, 95% CI: 183.2 - 389.4) and for males (603.5, 95% CI: 409.4 - 797.5). We also observed similar trends in age-adjusted rates (AARs) by different subgroups for non-Hispanic Black persons, injuries occurring in public settings, and for males: AARs of nonfatal assault injury increased significantly from 2014 through 2017, then declined significantly in 2018.ConclusionsNonfatal assault injury imposes significant health care costs and productivity losses for millions of people each year. This study is the first to specifically look at health disparities in nonfatal assault injuries using multiply imputed companion data. Understanding how disparities differ by various groups may lead to the development of more effective initiatives to prevent such injury.

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Data and measurement issues in the analysis of health disparities.

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Data and measurement issues in the analysis of health disparities.

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