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Multi-State Models for Estimating Risk of Low Back Pain with Associated Risk Factors

Abstract Number:

2034 

Submission Type:

Contributed Abstract 

Contributed Abstract Type:

Poster 

Participants:

Charles Ingulli (1), Elizabeth Malloy (2), Jay Kapellusch (3)

Institutions:

(1) N/A, N/A, (2) American University, Washington, D.C., (3) University of Wisconsin - Milwaukee, Milwaukee, Wisconsin

Co-Author(s):

Elizabeth Malloy  
American University
Jay Kapellusch  
University of Wisconsin - Milwaukee

First Author:

Charles Ingulli  
N/A

Presenting Author:

Charles Ingulli  
N/A

Abstract Text:

The study of Low Back Pain (LBP) and associated risk factors provides valuable insight into managing and preventing this illness through the implementation of multi-state models (MSMs) on transitions of individuals between distinct LBP states. Data comes from an LBP research consortium that compiled information from several longitudinal studies involving midwestern manufacturing workers. Repeated observations of individuals were made over approximately 6-years to determine changes in their LBP status: (1) LBP lasting for longer than 7 days, (2) LBP requiring medical care, and (3) LBP resulting in lost time from work. Several MSMs are considered to incorporate different combinations of case definitions as states. Particular interest is given to psychosocial risk factors to better understand their association with LBP transition probabilities. The probability of experiencing LBP is tripled on average for individuals who feel a lack of support and satisfaction in their jobs. An R package under development is used to assist with data preparation. The msm package is used for model fitting.

Keywords:

Multi-State Model|Low Back Pain|Psychosocial Factors|Longitudinal Data|R|Occupational Health

Sponsors:

Section on Statistics in Epidemiology

Tracks:

Miscellaneous

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I have read and understand that JSM participants must abide by the Participant Guidelines.

Yes

I understand that JSM participants must register and pay the appropriate registration fee by June 1, 2024. The registration fee is non-refundable.

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