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Injuries, Illnesses, and Fatalities

Automated Coding of Injury and Illness Data

The Survey of Occupational Injuries and Illnesses (SOII) collects data from sampled establishments who are required to keep data on OSHA forms 300, 300A, and 301 or their equivalent. BLS uses the information provided on these forms to generate detailed statistics on the characteristics of cases involving injury or illness.

To generate these statistics, survey staff must convert text entries from the OSHA forms to standard codes used by BLS, as indicated in the table below:

OSHA field SOII Code Coding Taxonomy Used

Job title

Occupation Standard Occupational Classification

What was the employee doing just before the incident occurred?

Event or exposure Occupational Injury and Illness Classification System

What happened?

Nature of injury or illness and Event or exposure Occupational Injury and Illness Classification System

What was the injury or illness?

Nature of Injury or illness and Part of body Occupational Injury and Illness Classification System

What object or substance directly harmed the employee?

Source of injury or illness and Secondary Source of injury or illness Occupational Injury and Illness Classification System

The set of all fields, taken together, is considered the case "narrative." Prior to survey year 2014, BLS exclusively relied on humans to code cases. For survey year 2014, BLS began using machine learning to code a subset of cases. To use machine learning BLS first selected a learning algorithm and trained it on large quantities of previously coded SOII narratives. During this process, the algorithm calculated how strongly various features, such as words, pairs of words, and other items were associated with the codes that could be assigned. After training, BLS used the algorithm to estimate the best codes for each uncoded narrative and assigned those codes if the model’s confidence exceeded a predetermined threshold. For survey years 2014-2017 BLS used regularized multinomial logistic regression. Starting with survey year 2018, BLS switched to deep neural network architectures. For survey years 2018-2020, BLS used an architecture with character-level convolutional embeddings and Long-Short-Term-Memory recurrent layers (source code is available here), and BLS moved to using a transformer architecture starting with survey year 2021.

BLS use of autocoding SOII data has expanded over time. For survey year 2014, only 26 percent of occupation codes were assigned by machine learning. By survey year 2019, automatic coding expanded to include all six coding tasks (occupation, nature, part, source, secondary source, and event) with the model assigning approximately 85 percent of all codes. For survey year 2020, all cases mentioning ‘covid’ or ‘corona’ were manually coded due to their novel nature and prevalence, dropping the percentage of cases autocoded. Since then, COVID-19 cases were integrated into the autocoder training process, allowing for the automated coding of approximately 92 percent of codes for survey year 2021.

Starting with survey year 2021, BLS expanded collection of case data from all sampled establishments to include details for cases involving days of job transfer or restriction only. Previously BLS collected complete details only for cases involving days away from work. Biennial estimates of detailed case circumstances for cases involving days away from work, job transfer, or restriction covering survey years 2021-2022 will first be published in the fall of 2023. Estimates for case circumstances are not available for the first year of a biennial cycle (starting with survey year 2021); however, the chart illustrates the SOII autocoder performance for data collected annually.

View data

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Last Modified Date: February 9, 2023