COVID-19 mortality among Massachusetts workers and the association with telework ability, 2020.

BACKGROUND: Working outside the home put some workers at risk for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) exposure and might partly explain elevated coronavirus disease 2019 (COVID-19) mortality rates in the first months of the pandemic in certain groups of Massachusetts workers. To further investigate this premise, we examined COVID-19 mortality among Massachusetts workers, with a specific focus on telework ability based on occupation. METHODS: COVID-19-associated deaths between January 1 and December 31, 2020 among Massachusetts residents aged 18-64 years were analyzed. Deaths were categorized into occupation-based quadrants (Q) of telework ability. Age-adjusted rates were calculated by key demographics, industry, occupation, and telework quadrant using American Community Survey workforce estimates as denominators. Rate ratios (RRs) and 95% confidence intervals comparing rates for quadrants with workers unlikely able to telework (Q2, Q3, Q4) to that among those likely able to telework (Q1) were calculated. RESULTS: The overall age-adjusted COVID-19-associated mortality rate was 26.4 deaths per 100,000 workers. Workers who were male, Black non-Hispanic, Hispanic, born outside the US, and with lower than a high school education level experienced the highest rates among their respective demographic groups. The rate varied by industry, occupation and telework quadrant. RRs comparing Q2, Q3, and Q4 to Q1 were 0.99 (95% confidence interval [CI]: 0.8-1.2), 3.2 (95% CI: 2.6-3.8) and 2.5 (95% CI: 2.0-3.0), respectively. CONCLUSION: Findings suggest a positive association between working on-site and COVID-19-associated mortality. Work-related factors likely contributed to COVID-19 among Massachusetts workers and should be considered in future studies of COVID-19 and similar diseases.

Bayesian decision support for coding occupational injury data.

INTRODUCTION: Studies on autocoding injury data have found that machine learning algorithms perform well for categories that occur frequently but often struggle with rare categories. Therefore, manual coding, although resource-intensive, cannot be eliminated. We propose a Bayesian decision support system to autocode a large portion of the data, filter cases for manual review, and assist human coders by presenting them top k prediction choices and a confusion matrix of predictions from Bayesian models. METHOD: We studied the prediction performance of Single-Word (SW) and Two-Word-Sequence (TW) Naïve Bayes models on a sample of data from the 2011 Survey of Occupational Injury and Illness (SOII). We used the agreement in prediction results of SW and TW models, and various prediction strength thresholds for autocoding and filtering cases for manual review. We also studied the sensitivity of the top k predictions of the SW model, TW model, and SW-TW combination, and then compared the accuracy of the manually assigned codes to SOII data with that of the proposed system. RESULTS: The accuracy of the proposed system, assuming well-trained coders reviewing a subset of only 26% of cases flagged for review, was estimated to be comparable (86.5%) to the accuracy of the original coding of the data set (range: 73%-86.8%). Overall, the TW model had higher sensitivity than the SW model, and the accuracy of the prediction results increased when the two models agreed, and for higher prediction strength thresholds. The sensitivity of the top five predictions was 93%. CONCLUSIONS: The proposed system seems promising for coding injury data as it offers comparable accuracy and less manual coding. PRACTICAL APPLICATIONS: Accurate and timely coded occupational injury data is useful for surveillance as well as prevention activities that aim to make workplaces safer.

Impact of differential injury reporting on the estimation of the total number of work-related amputations.

BACKGROUND: A capture-recapture analysis was performed to estimate the total number of work-related amputations. We examined the impact of misclassification due to differential injury reporting on the estimate of total number of work-related amputations. METHODS: Bureau of Labor Statistics' Survey of Occupational Injuries and Illnesses (SOII) samples and workers' compensation records (WC) were used to estimate the total number of work-related amputations. Some of the amputation cases in one data source matched with injuries other than amputations in the other data source. We performed sensitivity analyses reassigning such cases as matched amputations. RESULTS: Depending on how we treated the cases matched with other injuries, the total number of work-related amputations ranged from 276 to 442 cases, yielding dramatically different capture rates (35-87%). CONCLUSION: Due to differential classification, estimates of work-related amputations would be biased. Our findings highlight the importance of accurately reporting and classifying work-related injuries and illnesses.

Use of multiple data sources for surveillance of work-related amputations in Massachusetts, comparison with official estimates and implications for national surveillance.

BACKGROUND: Accurate surveillance of work-related injuries is needed at national and state levels. We used multiple sources for surveillance of work-related amputations, compared findings with Survey of Occupational Injuries and Illnesses (SOII) estimates, and assessed generalizability to national surveillance. METHODS: Three data sources were used to enumerate work-related amputations in Massachusetts, 2007-2008. SOII eligible amputations were compared with SOII estimates. RESULTS: 787 amputations were enumerated, 52% ascertained through hospital records only, exceeding the SOII estimate (n = 210). The estimated SOII undercount was 48% (95% CI: 36-61%). Additional amputations were reported in SOII as other injuries, accounting for about half the undercount. Proportionately more SOII estimated than multisource cases were in manufacturing and fewer in smaller establishments. CONCLUSION: Multisource surveillance enhanced our ability to document work-related amputations in Massachusetts. While not feasible to implement for work-related conditions nationwide, it is useful in states. Better understanding of potential biases in SOII is needed.