Responses of Massachusetts hospitals to a state mandate to collect race, ethnicity and language data from patients: a qualitative study

A Scoping Review of Approaches to Improving Quality of Data Relating to Health Inequalities

Identifying and monitoring of health inequalities requires good-quality data. The aim of this work is to systematically review the evidence base on approaches taken within the healthcare context to improve the quality of data for the identification and monitoring of health inequalities and describe the evidence base on the effectiveness of such approaches or recommendations. Peer-reviewed scientific journal publications, as well as grey literature, were included in this review if they described approaches and/or made recommendations to improve data quality relating to the identification and monitoring of health inequalities. A thematic analysis was undertaken of included papers to identify themes, and a narrative synthesis approach was used to summarise findings. Fifty-seven papers were included describing a variety of approaches. These approaches were grouped under four themes: policy and legislation, wider actions that enable implementation of policies, data collection instruments and systems, and methodological approaches. Our findings indicate that a variety of mechanisms can be used to improve the quality of data on health inequalities at different stages (prior to, during, and after data collection). These findings can inform us of actions that can be taken by those working in local health and care services on approaches to improving the quality of data on health inequalities.

Missing Race and Ethnicity Data among COVID-19 Cases in Massachusetts

Infectious disease surveillance frequently lacks complete information on race and ethnicity, making it difficult to identify health inequities. Greater awareness of this issue has occurred due to the COVID-19 pandemic, during which inequities in cases, hospitalizations, and deaths were reported but with evidence of substantial missing demographic details. Although the problem of missing race and ethnicity data in COVID-19 cases has been well documented, neither its spatiotemporal variation nor its particular drivers have been characterized. Using individual-level data on confirmed COVID-19 cases in Massachusetts from March 2020 to February 2021, we show how missing race and ethnicity data: (1) varied over time, appearing to increase sharply during two different periods of rapid case growth; (2) differed substantially between towns, indicating a nonrandom distribution; and (3) was associated significantly with several individual- and town-level characteristics in a mixed-effects regression model, suggesting a combination of personal and infrastructural drivers of missing data that persisted despite state and federal data-collection mandates. We discuss how a variety of factors may contribute to persistent missing data but could potentially be mitigated in future contexts.

Information Loss in Harmonizing Granular Race and Ethnicity Data: Descriptive Study of Standards

BACKGROUND

Data standards for race and ethnicity have significant implications for health equity research.

OBJECTIVE

We aim to describe a challenge encountered when working with a multiple-race and ethnicity assessment in the Eastern Caribbean Health Outcomes Research Network (ECHORN), a research collaborative of Barbados, Puerto Rico, Trinidad and Tobago, and the US Virgin Islands.

METHODS

We examined the data standards guiding harmonization of race and ethnicity data for multiracial and multiethnic populations, using the Office of Management and Budget (OMB) Statistical Policy Directive No. 15.

RESULTS

Of 1211 participants in the ECHORN cohort study, 901 (74.40%) selected 1 racial category. Of those that selected 1 category, 13.0% (117/901) selected Caribbean; 6.4% (58/901), Puerto Rican or Boricua; and 13.5% (122/901), the mixed or multiracial category. A total of 17.84% (216/1211) of participants selected 2 or more categories, with 15.19% (184/1211) selecting 2 categories and 2.64% (32/1211) selecting 3 or more categories. With aggregation of ECHORN data into OMB categories, 27.91% (338/1211) of the participants can be placed in the "more than one race" category.

CONCLUSIONS

This analysis exposes the fundamental informatics challenges that current race and ethnicity data standards present to meaningful collection, organization, and dissemination of granular data about subgroup populations in diverse and marginalized communities. Current standards should reflect the science of measuring race and ethnicity and the need for multidisciplinary teams to improve evolving standards throughout the data life cycle.

A systematic review and meta-synthesis of policy intervention characteristics that influence the implementation of government-directed policy in the hospital setting: implications for infection prevention and control

Background

Government-directed policy plays an important role in the regulation and supervision of healthcare quality. Effective implementation of these policies has the potential to significantly improve clinical practice and patient outcomes, including the prevention of healthcare-associated infections. A systematic review of research describing the implementation of government-directed policy in the hospital setting was performed with the aim to identify policy intervention characteristics that influence implementation.

Methods

A systematic search of four electronic databases was undertaken to identify eligible articles published between 2007 and 2017. Studies were included if published in the English language and described the implementation of government-directed policy in a high-income country hospital setting. Data on policy and implementation were extracted for each article and interpretive syntheses performed.

Results

A total of 925 articles were retrieved and titles and abstracts reviewed, with 69 articles included after review of abstract and full text. Qualitative synthesis of implementation data showed three overarching themes related to intervention characteristics associated with implementation: clarity; infrastructure; and alignment.

Conclusion

Better understanding and consideration of policy intervention characteristics during development and planning will facilitate more effective implementation although research describing implementation of government-directed policy in the hospital setting is limited and of variable quality. The findings of this study provide guidance to staff tasked with the development or implementation of government-directed policy in the hospital setting, infection prevention and control professionals seeking to maximise the impact of policy on practice and improve patient outcomes.

A Qualitative Study of Spanish-Speakers' Experience with Dense Breast Notifications in a Massachusetts Safety-Net Hospital

BACKGROUND

Legislation requiring mammography facilities to notify women if they have dense breast tissue found on mammography has been enacted in 34 US states. The impact of dense breast notifications (DBNs) on women with limited English proficiency (LEP) is unknown.

OBJECTIVE

This study sought to understand Spanish-speaking women's experience receiving DBNs in a Massachusetts safety-net hospital.

DESIGN

Eligible women completed one audio-recorded, semi-structured interview via telephone with a native Spanish-speaking research assistant trained in qualitative methods. Interviews were professionally transcribed verbatim and translated. The translation was verified by a third reviewer to ensure fidelity with audio recordings.

PARTICIPANTS

Nineteen Spanish-speaking women ages 40-74 who received mammography with a normal result and recalled receiving a DBN.

APPROACH

Using the verified English transcripts, we conducted a content analysis to identify women's perceptions and actions related to receiving the notification. A structured codebook was developed. Transcripts were independently coded and assessed for agreement with a modification of Cohen's kappa. Content codes were grouped to build themes related to women's perceptions and actions after receiving a DBN.

KEY RESULTS

Nineteen Spanish-speaking women completed interviews. Nine reported not receiving the notification in their native language. Four key themes emerged: (1) The novelty of breast density contributed to notification-induced confusion; (2) women misinterpreted key messages in the notification; (3) varied actions were taken to seek further information; and (4) women held unrealized expectations and preferences for follow-up.

CONCLUSIONS

Not having previous knowledge of breast density and receiving notifications in English contributed to confusion about its meaning and inaccurate interpretations of key messages by Spanish speakers. Tools that promote understanding should be leveraged in seeking equity in risk-based breast cancer screening for women with dense breasts.

Facilitators and barriers of sociodemographic data collection in Canadian health care settings: a multisite case study evaluation

Background

Despite growing awareness of the importance of social determinants of health, research remains limited about the implementation of sociodemographic data collection in Canadian health care settings. Little is known about the salient contextual factors that enable or hinder collection and use of social information to improve quality of care in clinical settings. This study examines the perceptions and experiences of managers and care providers to better understand how to support organizational efforts to collect and use sociodemographic data to provide equity-oriented care.

Methods

Case studies of three diverse urban health care settings employed semi-structured individual and group interviews with managers and care providers respectively to explore their experiences with implementation. Data was analyzed separately and in context for each site as part of an individual case study. A thematic analysis of interview transcripts was performed with an inductive approach to coding of segments of the text. Constructs of the Consolidated Framework for Implementation Research (CFIR) were used as an analytical framework to structure the data to support cross case comparisons of facilitators and barriers to implementation across settings.

Results

Several perceived facilitators and barriers to implementation were identified that clustered around three CFIR domains: intervention, inner setting and characteristics of individuals. Macro level (outer setting) factors were relatively unexplored. Sites were motivated by their recognition of need for social information to improve quality of care. Organizational readiness for implementation was demonstrated by priorities that reflected concern for equity in care, leadership support and commitment to an inclusive process for stakeholder engagement. Barriers included perceived relevance of only a subset of sociodemographic questions to service delivery, staff capacity and comfort with data collection as well as adequate resources (funding and time).

Conclusion

Although system level mandates were underexplored, they may accelerate adoption and implementation of sociodemographic data collection in the presence of organizational readiness. Standardized tools integrated into information systems and workflows would support adequately trained personnel. More research is needed to understand important factors in rural health settings and with clinical application to inform care delivery pathways.

Electronic supplementary material

The online version of this article (10.1186/s12939-018-0903-0) contains supplementary material, which is available to authorized users.

Potential harms associated with routine collection of patient sociodemographic information: A rapid review

Background

Health systems are recommended to capture routine patient sociodemographic data as a key step in providing equitable person‐centred care. However, collection of this information has the potential to cause harm, especially for vulnerable or potentially disadvantaged patients.

Objective

To identify harms perceived or experienced by patients, their families, or health‐care providers from collection of sociodemographic information during routine health‐care visits and to identify best practices for when, by whom and how to collect this information.

Search Strategy

We searched OVID MEDLINE, PubMed “related articles” via NLM and healthevidence.org to the end of January 2018 and assessed reference lists and related citations of included studies.

Inclusion Criteria

We included studies reporting on harms of collecting patient sociodemographic information in health‐care settings.

Data Extraction and Synthesis

Data on study characteristics and types of harms were extracted and summarized narratively.

Main Results

Eighteen studies were included; 13 provided patient perceptions or experiences with the collection of these data and seven studies reported on provider perceptions. Five reported on patient recommendations for collecting sociodemographic information. Patients and providers reported similar potential harms which were grouped into the following themes: altered behaviour which may affect care‐seeking, data misuse or privacy concerns, discomfort, discrimination, offence or negative reactions, and quality of care. Patients suggested that sociodemographic information be collected face to face by a physician.

Discussion and Conclusions

Overall, patients support the collection of sociodemographic information. However, harms are possible, especially for some population subgroups. Harms may be mitigated by providing a rationale for the collection of this information.

Long-term retention in Office Based Opioid Treatment with buprenorphine

BACKGROUND

Guidelines recommend long-term treatment for opioid use disorder with buprenorphine; however, little is known about patients in long-term treatment. The aim of this study is to examine the prevalence and patient characteristics of long-term treatment retention (≥1year) in an Office Based Opioid Treatment (OBOT) program with buprenorphine.

METHODS

This is a retrospective cohort study of adults on buprenorphine from January 2002 to February 2014 in a large urban safety-net primary care OBOT program. The primary outcome was retention in OBOT for at least one continuous year. Potential predictors included age, race, psychiatric diagnoses, hepatitis C, employment, prior buprenorphine, ever heroin use, current cocaine, benzodiazepine and alcohol use on enrollment. Factors associated with ≥1year OBOT retention were identified using generalized estimating equation logistic regression models. Patients who re-enrolled in the program contributed repeated observations.

RESULTS

There were 1605 OBOT treatment periods among 1237 patients in this study. Almost half, 45% (717/1605), of all treatment periods were ≥1year and a majority, 53.7% (664/1237), of patients had at least one ≥1year period. In adjusted analyses, female gender (Adjusted Odds Ratio [AOR] 1.55, 95% CI [1.20, 2.00]) psychiatric diagnosis (AOR 1.75 [1.35, 2.27]) and age (AOR 1.19 per 10year increase [1.05, 1.34]) were associated with greater odds of ≥1year retention. Unemployment (AOR 0.72 [0.56, 0.92]), Hepatitis C (AOR 0.59 [0.45, 0.76]), black race/ethnicity (AOR 0.53 [0.36, 0.78]) and Hispanic race/ethnicity (AOR 0.66 [0.48, 0.92]) were associated with lower odds of ≥1year retention.

CONCLUSIONS

Over half of patients who presented to Office Based Opioid Treatment with buprenorphine were ultimately successfully retained for ≥1year. However, significant disparities in one-year treatment retention were observed, including poorer retention for patients who were younger, black, Hispanic, unemployed, or with hepatitis C.

Assessing race and ethnicity data quality across cancer registries and EMRs in two hospitals

BACKGROUND

Measurement of patient race/ethnicity in electronic health records is mandated and important for tracking health disparities.

OBJECTIVE

Characterize the quality of race/ethnicity data collection efforts.

METHODS

For all cancer patients diagnosed (2007-2010) at two hospitals, we extracted demographic data from five sources: 1) a university hospital cancer registry, 2) a university electronic medical record (EMR), 3) a community hospital cancer registry, 4) a community EMR, and 5) a joint clinical research registry. The patients whose data we examined (N = 17 834) contributed 41 025 entries (range: 2-5 per patient across sources), and the source comparisons generated 1-10 unique pairs per patient. We used generalized estimating equations, chi-squares tests, and kappas estimates to assess data availability and agreement.

RESULTS

Compared to sex and insurance status, race/ethnicity information was significantly less likely to be available (χ(2 )> 8043, P < .001), with variation across sources (χ(2 )> 10 589, P < .001). The university EMR had a high prevalence of "Unknown" values. Aggregate kappa estimates across the sources was 0.45 (95% confidence interval, 0.45-0.45; N = 31 276 unique pairs), but improved in sensitivity analyses that excluded the university EMR source (κ = 0.89). Race/ethnicity data were in complete agreement for only 6988 patients (39.2%). Pairs with a "Black" data value in one of the sources had the highest agreement (95.3%), whereas pairs with an "Other" value exhibited the lowest agreement across sources (11.1%).

DISCUSSION

Our findings suggest that high-quality race/ethnicity data are attainable. Many of the "errors" in race/ethnicity data are caused by missing or "Unknown" data values.

CONCLUSIONS

To facilitate transparent reporting of healthcare delivery outcomes by race/ethnicity, healthcare systems need to monitor and enforce race/ethnicity data collection standards.

How health care organizations are using data on patients' race and ethnicity to improve quality of care

CONTEXT

Racial and ethnic disparities in the quality of health care are well documented in the U.S. health care system. Reducing these disparities requires action by health care organizations. Collecting accurate data from patients about their race and ethnicity is an essential first step for health care organizations to take such action, but these data are not systematically collected and used for quality improvement purposes in the United States. This study explores the challenges encountered by health care organizations that attempted to collect and use these data to reduce disparities.

METHODS

Purposive sampling was used to identify eight health care organizations that collected race and ethnicity data to measure and reduce disparities in the quality and outcomes of health care. Staff, including senior managers and data analysts, were interviewed at each site, using a semi-structured interview format about the following themes: the challenges of collecting and collating accurate data from patients, how organizations defined a disparity and analyzed data, and the impact and uses of their findings.

FINDINGS

To collect accurate self-reported data on race and ethnicity from patients, most organizations had upgraded or modified their IT systems to capture data and trained staff to collect and input these data from patients. By stratifying nationally validated indicators of quality for hospitals and ambulatory care by race and ethnicity, most organizations had then used these data to identify disparities in the quality of care. In this process, organizations were taking different approaches to defining and measuring disparities. Through these various methods, all organizations had found some disparities, and some had invested in interventions designed to address them, such as extra staff, extended hours, or services in new locations.

CONCLUSION

If policymakers wish to hold health care organizations accountable for disparities in the quality of the care they deliver, common standards will be needed for organizations' data measurement, analysis, and use to guide systematic analysis and robust investment in potential solutions to reduce and eliminate disparities.