Improving Occupational Health Disparity Research: Testing a method to estimate race and ethnicity in a working population

Methods for retrospectively improving race/ethnicity data quality: a scoping review

Improving race and ethnicity (hereafter, race/ethnicity) data quality is imperative to ensure underserved populations are represented in data sets used to identify health disparities and inform health care policy. We performed a scoping review of methods that retrospectively improve race/ethnicity classification in secondary data sets. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, searches were conducted in the MEDLINE, Embase, and Web of Science Core Collection databases in July 2022. A total of 2 441 abstracts were dually screened, 453 full-text articles were reviewed, and 120 articles were included. Study characteristics were extracted and described in a narrative analysis. Six main method types for improving race/ethnicity data were identified: expert review (n = 9; 8%), name lists (n = 27, 23%), name algorithms (n = 55, 46%), machine learning (n = 14, 12%), data linkage (n = 9, 8%), and other (n = 6, 5%). The main racial/ethnic groups targeted for classification were Asian (n = 56, 47%) and White (n = 51, 43%). Some form of validation evaluation was included in 86 articles (72%). We discuss the strengths and limitations of different method types and potential harms of identified methods. Innovative methods are needed to better identify racial/ethnic subgroups and further validation studies. Accurately collecting and reporting disaggregated data by race/ethnicity are critical to address the systematic missingness of relevant demographic data that can erroneously guide policymaking and hinder the effectiveness of health care practices and intervention.

Development and maturation of the occupational health services research field in the United States over the past 25 years: Challenges and opportunities for the future

Work is an important social determinant of health; unfortunately, work-related injuries remain prevalent, can have devastating impact on worker health, and can impose heavy economic burdens on workers and society. Occupational health services research (OHSR) underpins occupational health services policy and practice, focusing on health determinants, health services, healthcare delivery, and health systems affecting workers. The field of OHSR has undergone tremendous expansion in both definition and scope over the past 25 years. In this commentary, focusing on the US, we document the historical development and evolution of OHSR as a research field, describe current doctoral-level OHSR training, and discuss challenges and opportunities for the OHSR field. We also propose an updated definition for the OHSR field: Research and evaluation related to the determinants of worker health and well-being; to occupational injury and illness prevention and surveillance; to healthcare, health programs, and health policy affecting workers; and to the organization, access, quality, outcomes, and costs of occupational health services and related health systems. Researchers trained in OHSR are essential contributors to improvements in healthcare, health systems, and policy and programs to improve worker health and productivity, as well as equity and justice in job and employment conditions. We look forward to the continued growth of OHSR as a field and to the expansion of OHSR academic training opportunities.

The accuracy of race & ethnicity data in US based healthcare databases: A systematic review

BACKGROUND

The availability and accuracy of data on a patient's race/ethnicity varies across databases. Discrepancies in data quality can negatively impact attempts to study health disparities.

METHODS

We conducted a systematic review to organize information on the accuracy of race/ethnicity data stratified by database type and by specific race/ethnicity categories.

RESULTS

The review included 43 studies. Disease registries showed consistently high levels of data completeness and accuracy. EHRs frequently showed incomplete and/or inaccurate data on the race/ethnicity of patients. Databases had high levels of accurate data for White and Black patients but relatively high levels of misclassification and incomplete data for Hispanic/Latinx patients. Asians, Pacific Islanders, and AI/ANs are the most misclassified. Systems-based interventions to increase self-reported data showed improvement in data quality.

CONCLUSION

Data on race/ethnicity that is collected with the purpose of research and quality improvement appears most reliable. Data accuracy can vary by race/ethnicity status and better collection standards are needed.

Racial and ethnic disparities in workers' compensation claims rates

BACKGROUND

Workers of color experience a disproportionate share of work-related injuries and illnesses (WRII), however, most workers' compensation systems do not collect race and ethnicity information, making it difficult to monitor trends over time, or to investigate specific policies and procedures that maintain or could eliminate the unequal burden of WRII for workers of color. The purpose of this study is to apply a Bayesian method to Washington workers' compensation claims data to identify racial and ethnic disparities of WRII by industry and occupation, improving upon existing surveillance limitations. Measuring differences in risk for WRII will better inform prevention efforts and target prevention to those at increased risk.

METHODS

To estimate WRII by race/ethnicity, we applied the Bayesian Improved Surname Geocode (BISG) method to surname and residential address data among all Washington workers' compensation claims filed for injuries in 2013-2017. We then compare worker and injury characteristics by imputed race/ethnicity, and estimate rates of WRII by imputed race/ethnicity within industry and occupation.

RESULTS

Black/African Americans had the highest rates of WRII claims across all industry and occupational sectors. Hispanic/Latino WRII claimants also had higher rates than Whites and Asian/Pacific Islanders in almost all industry and occupational sectors. For accepted claims with both medical and non-medical compensation, Bodily reaction/overexertion injuries accounted for almost half of the claims during this reporting period.

DISCUSSION

The high rates of injury we report by racial/ethnic categories is a cause for major concern. Nearly all industry and occupation-specific rates of workers' compensation claims are higher for Black/African American and Hispanic/Latino workers compared to Whites. More work is needed to identify work-related, systemic, and individual characteristics.

The Contribution of First-name Information to the Accuracy of Racial-and-Ethnic Imputations Varies by Sex and Race-and-Ethnicity Among Medicare Beneficiaries

BACKGROUND

Data on race-and-ethnicity that are needed to measure health equity are often limited or missing. The importance of first name and sex in predicting race-and-ethnicity is not well understood.

OBJECTIVE

The objective of this study was to compare the contribution of first-name information to the accuracy of basic and more complex racial-and-ethnic imputations that incorporate surname information.

RESEARCH DESIGN

We imputed race-and-ethnicity in a sample of Medicare beneficiaries under 2 scenarios: (1) with only sparse predictors (name, address, sex) and (2) with a rich set (adding limited administrative race-and-ethnicity, demographics, and insurance).

SUBJECTS

A total of 284,627 Medicare beneficiaries who completed the 2014 Medicare Consumer Assessment of Healthcare Providers and Systems survey and reported race-and-ethnicity were included.

RESULTS

Hispanic, non-Hispanic Asian/Pacific Islander, and non-Hispanic White racial-and-ethnic imputations are more accurate for males than females under both sparse-predictor and rich-predictor scenarios; adding first-name information increases accuracy more for females than males. In contrast, imputations of non-Hispanic Black race-and-ethnicity are similarly accurate for females and males, and first names increase accuracy equally for each sex in both sparse-predictor and rich-predictor scenarios. For all 4 racial-and-ethnic groups, incorporating first-name information improves prediction accuracy more under the sparse-predictor scenario than under the rich-predictor scenario.

CONCLUSION

First-name information contributes more to the accuracy of racial-and-ethnic imputations in a sparse-predictor scenario than in a rich-predictor scenario and generally narrows sex gaps in accuracy of imputations.

Using advanced racial and ethnic identity demographics to improve surveillance of work-related conditions in an occupational clinic setting

Background

Although racial and ethnic identities are associated with a multitude of disparate medical outcomes, surveillance of these subpopulations in the occupational clinic setting could benefit enormously from a more detailed and nuanced recognition of racial and ethnic identity.

Methods

The research group designed a brief questionnaire to capture several dimensions of this identity and collected data from patients seen for work‐related conditions in four occupational medicine clinics from May 2019 through March 2020. Responses were used to calculate the sensitivity and specificity of extant racial/ethnic identity data within our electronic health records system, and were compared to participants' self‐reported industry and occupation, coded according to North American Industry Classification System and Standard Occupational Classification System listings.

Results

Our questionnaire permitted collection of data that defined our patients' specific racial/ethnic identity with far greater detail, identified patients with multiple ethnic identities, and elicited their preferred language. Response rate was excellent (94.2%, n = 773). Non‐White participants frequently selected a racial/ethnic subcategory (78.1%–92.2%). Using our race/ethnicity data as a referent, the electronic health record (EHR) had a high specificity (>87.1%), widely variable sensitivity (11.8%–82.2%), and poorer response rates (75.1% for race, 82.5% for ethnicity, as compared to 93.8% with our questionnaire). Additional analyses revealed some industries and occupations disproportionately populated by patients of particular racial/ethnic identities.

Conclusions

Our project demonstrates the usefulness of a questionnaire which more effectively identifies racial/ethnic subpopulations in an occupational medicine clinic, permitting far more detailed characterization of their occupations, industries, and diagnoses.

Heat related illness among workers in Washington State: A descriptive study using workers' compensation claims, 2006-2017

BACKGROUND

Heat related illness (HRI) places a significant burden on the health and safety of working populations and its impacts will likely increase with climate change. The aim of this study was to characterize the demographic and occupational characteristics of Washington workers who suffered from HRI from 2006 to 2017 using workers' compensation claims data.

METHODS

We used Washington workers' compensation data linked to weather station data to identify cases of work-related HRI. We utilized Occupational Injury and Illness Classification System codes, International Classification of Diseases 9/10 codes, and medical review to identify accepted and rejected Washington State (WA) workers' compensation claims for HRI from 2006 to 2017. We estimated rates of HRI by industry and evaluated patterns by ambient temperature.

RESULTS

We detected 918 confirmed Washington workers' compensation HRI claims from 2006 to 2017, 654 were accepted and 264 were rejected. Public Administration had the highest third quarter rate (131.3 per 100 000 full time employees [FTE]), followed by Agriculture, Forestry, Fishing, and Hunting (102.6 per 100 000 FTE). The median maximum daytime temperature was below the Washington heat rule threshold for 45% of the accepted HRI claims. Latinos were estimated to be overrepresented in HRI cases.

CONCLUSION

The WA heat rule threshold may not be adequately protecting workers and racial disparities are present in occupational HRI. Employers should take additional precautions to prevent HRI depending on the intensity of heat exposure. States without heat rules and with large industry sectors disproportionately affected by HRI should consider regulations to protect outdoor workers in the face of more frequent and extreme heat waves.

Coverage Gaps and Cost-Shifting for Work-Related Injury and Illness: Who Bears the Financial Burden?

The heavy economic burden of work-related injury/illness falls not only on employers and workers' compensation systems, but increasingly on health care systems, health and disability insurance, social safety net programs, and workers and their families. We present a flow diagram illustrating mechanisms responsible for the financial burden of occupational injury/illness borne by social safety net programs and by workers and their families, due to cost-shifting and gaps in workers' compensation coverage. This flow diagram depicts various pathways leading to coverage gaps that may shift the burden of occupational injury/illness-related health care and disability costs ultimately to workers, particularly the most socioeconomically vulnerable. We describe existing research and important research gaps linked to specific pathways in the flow diagram. This flow diagram was developed to facilitate more detailed and comprehensive research into the financial burden imposed by work-related injury/illness, in order to focus policy efforts where improvement is most needed.