Mortality following workplace injury: Quantitative bias analysis

被引:4
|
作者
Busey, Andrew [1 ]
Asfaw, Abay [2 ]
Applebaum, Katie M. [3 ]
O'Leary, Paul K. [4 ]
Tripodis, Yorghos [5 ]
Fox, Matthew P. [6 ]
Stokes, Andrew C. [7 ]
Boden, Leslie I. [8 ]
机构
[1] NERA Econ Consulting, Boston, MA USA
[2] Natl Inst Occupat Safety & Hlth, Ctr Dis Control & Prevent, Washington, DC USA
[3] George Washington Univ, Sch Publ Hlth, Dept Environm & Occupat Hlth, Milken Inst, Washington, DC USA
[4] US Social Secur Adm, Off Retirement & Disabil Policy, Washington, DC USA
[5] Boston Univ, Dept Biostat, Sch Publ Hlth, 715 Albany St, Boston, MA 02118 USA
[6] Boston Univ, Dept Epidemiol & Global Hlth, Sch Publ Hlth, 715 Albany St, Boston, MA 02118 USA
[7] Boston Univ, Dept Global Hlth, Sch Publ Hlth, 715 Albany St, Boston, MA 02118 USA
[8] Boston Univ, Dept Environm Hlth, Sch Publ Hlth, 715 Albany St, Boston, MA 02118 USA
关键词
Epidemiological bias; Quantitative bias analysis; Confounding; Excess mortality; Occupational Safety; BODY-MASS-INDEX; ECONOMIC CONSEQUENCES; UNITED-STATES; EARNINGS; SMOKING; WORKERS; DISABILITY; ILLNESSES; OBESITY; IMPACT;
D O I
10.1016/j.annepidem.2021.09.015
中图分类号
R1 [预防医学、卫生学];
学科分类号
1004 ; 120402 ;
摘要
Purpose: Recent studies have shown increased all-cause mortality among workers following disabling workplace injury. These studies did not account for 2 potentially important confounders, smoking and obesity. We estimated injury-related mortality accounting for these factors. Methods: We followed workers receiving New Mexico workers' compensation benefits (1994-20 0 0) through 2013. Using data from the Panel Study of Income Dynamics, we derived the joint distribution of smoking status and obesity for workers with and without lost-time injuries. We conducted a quantitative bias analysis (QBA) to determine the adjusted relationship of injury and mortality. Results: We observed hazard ratios after adjusting for smoking and obesity of 1.13 for women (95% simulation interval (SI) 0.97 to 1.31) and 1.12 for men (95% SI 1.00 to 1.27). The estimated fully adjusted excess hazard was about half the estimates not adjusted for these factors. Conclusions: Using QBA to adjust for smoking and obesity reduced the estimated mortality hazard from lost-time injuries and widened the simulation interval. The adjusted estimate still showed more than a 10 percent increase for both women and men. The change in estimates reveals the importance of accounting for these confounders. Of course, the results depend on the methods and assumptions used. (c) 2021 Elsevier Inc. All rights reserved.
引用
收藏
页码:155 / 160
页数:6
相关论文
共 50 条
  • [1] WORKPLACE BIAS
    GAW, B
    CHEMICAL ENGINEERING PROGRESS, 1993, 89 (08) : 6 - 6
  • [2] WORKPLACE BIAS
    FARISS, RH
    CHEMICAL ENGINEERING PROGRESS, 1993, 89 (06) : 10 - &
  • [3] Factors influencing return to work following workplace injury
    Kenny, DT
    INTERNATIONAL JOURNAL OF PSYCHOLOGY, 1996, 31 (3-4) : 2226 - 2226
  • [4] Mortality following spinal cord injury
    Yeo, JD
    Walsh, J
    Rutkowski, S
    Soden, R
    Craven, M
    Middleton, J
    SPINAL CORD, 1998, 36 (05) : 329 - 336
  • [5] Mortality following spinal cord injury
    John D Yeo
    John Walsh
    Sue Rutkowski
    Ros Soden
    Mary Craven
    James Middleton
    Spinal Cord, 1998, 36 : 329 - 336
  • [6] Opioid-related Mortality in United States Death Certificate Data A Quantitative Bias Analysis With Expert Elicitation of Bias Parameters
    Goldsmith, Elizabeth S.
    Krebs, Erin E.
    Ramirez, Marizen R.
    MacLehose, Richard F.
    EPIDEMIOLOGY, 2023, 34 (03) : 421 - 429
  • [7] Analysis of outcomes following traumatic brain injury in older patients: incidence and mortality
    Chang, I. B.
    Ahn, S. K.
    Song, J. H.
    Park, S. H.
    EUROPEAN JOURNAL OF NEUROLOGY, 2010, 17 : 590 - 590
  • [8] Quantitative Bias Analysis in Regulatory Settings
    Lash, Timothy L.
    Fox, Matthew P.
    Cooney, Darryl
    Lu, Yun
    Forshee, Richard A.
    AMERICAN JOURNAL OF PUBLIC HEALTH, 2016, 106 (07) : 1227 - 1230
  • [9] Quantitative Bias Analysis for Collaborative Science
    Weuve, Jennifer
    Sagiv, Sharon K.
    Fox, Matthew P.
    EPIDEMIOLOGY, 2018, 29 (05) : 627 - 630
  • [10] Good practices for quantitative bias analysis
    Lash, Timothy L.
    Fox, Matthew P.
    MacLehose, Richard F.
    Maldonado, George
    McCandless, Lawrence C.
    Greenland, Sander
    INTERNATIONAL JOURNAL OF EPIDEMIOLOGY, 2014, 43 (06) : 1969 - 1985