Predicting the onset of hypertension for workers: does including work characteristics improve risk predictive accuracy?

Research output: Contribution to journalArticlepeer-review

Abstract

Despite extensive evidence of work as a key social determinant of hypertension, risk prediction equations incorporating this information are lacking. Such limitations hinder clinicians’ ability to tailor patient care and comprehensively address hypertension risk factors. This study examined whether including work characteristics in hypertension risk equations improves their predictive accuracy. Using occupation ratings from the Occupational Information Network database, we measured job demand, job control, and supportiveness of supervisors and coworkers for occupations in the United States economy. We linked these occupation-based measures with the employment status and health data of participants in the Coronary Artery Risk Development in Young Adults (CARDIA) study. We fit logistic regression equations to estimate the probability of hypertension onset in five years among CARDIA participants with and without variables reflecting work characteristics. Based on the Harrell’s c- and Hosmer–Lemeshow’s goodness-of-fit statistics, we found that our logistic regression models that include work characteristics predict hypertension onset more accurately than those that do not incorporate these variables. We also found that the models that rely on occupation-based measures predict hypertension onset more accurately for White than Black participants, even after accounting for a sample size difference. Including other aspects of work, such as workers’ experience in the workplace, and other social determinants of health in risk equations may eliminate this discrepancy. Overall, our study showed that clinicians should examine workers’ work-related characteristics to tailor hypertension care plans appropriately.

Original languageEnglish (US)
JournalJournal of Human Hypertension
DOIs
StateAccepted/In press - 2022

Bibliographical note

Funding Information:
This research was funded by the Midwest Center for Occupational Health and Safety Pilot Project Research Training Program, supported by the National Institute for Occupational Safety and Health (NIOSH) under award number T42OH008434. The contents of this effort are solely the responsibility of the authors and do not necessarily represent the official views of NIOSH, the Centers for Disease Control and Prevention, or other associated entities.

Publisher Copyright:
© 2022, The Author(s), under exclusive licence to Springer Nature Limited.

PubMed: MeSH publication types

  • Journal Article

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