Time to return to work by occupational class after total hip or knee arthroplasty

Abstract

AimsFor the increasing number of working-age patients undergoing total hip or total knee arthroplasty (THA/TKA), return to work (RTW) after surgery is crucial. We investigated the association between occupational class and time to RTW after THA or TKA.MethodsData from the prospective multicentre Longitudinal Leiden Orthopaedics Outcomes of Osteoarthritis Study were used. Questionnaires were completed preoperatively and six and 12 months postoperatively. Time to RTW was defined as days from surgery until RTW (full or partial). Occupational class was preoperatively assessed and categorized into four categories according to the International Standard Classification of Occupations 2008 (blue-/white-collar, high-/low-skilled). Cox regression analyses were conducted separately for THA and TKA patients. Low-skilled blue-collar work was used as the reference category.ResultsA total of 360 THA and 276 TKA patients, preoperatively employed, were included. Patients were mainly high-skilled (THA 57%; TKA 41%) or low-skilled (THA 24%; TKA 38%) white-collar workers. Six months post-THA, RTW rates were 78% of low-skilled blue-collar workers compared to 83% to 86% within other occupational classes, increasing after 12 months to 87% to 90% in all occupational classes. Six months post-TKA, RTW rates were 58% of low-skilled and 64% of high-skilled blue-collar workers compared to 80% to 89% of white-collar workers, and after 12 months 79% of low-skilled blue-collar workers compared to 87% to 92% within other occupational classes. High-skilled white-collar workers (THA: hazard ratio (HR) 2.12 (95% confidence interval (CI) 1.32 to 3.40); TKA: HR 2.31 (95% CI 1.34 to 4.00)) and low-skilled white-collar workers (TKA: HR 1.82 (95% CI 1.04 to 3.18)) had a higher hazard to RTW within six months postoperatively.ConclusionClear differences existed in time to RTW among both THA and TKA patients in each of the groups studied. These findings may help guide tailored patient-specific information during preoperative consultation and advice postoperatively, as well as to create awareness among workers and their employers.Cite this article: Bone Joint J 2023;105-B(9):977–984.

Work-related support for employed and self-employed people with rheumatoid arthritis or axial spondyloarthritis: a cross-sectional online survey of patients

“…Items on work characteristics included employment type (employed/self-employed), job sector, including agricultural, craftmanship, transport, administrative, commercial, service and other size of company (categories of number of employees) and working hours (categories in hours/week), and were based on similar items used in national surveys [ 27 ]. Occupational class was determined according to the International Standard Classification of Occupations 2008 (ISCO-08) [ 34 ].The classification considers nine major occupational classes, which were further categorized, based on a previous study [ 35 ], into two categories: white-collar workers (i.e. managers, teachers, service and sales workers) and blue-collar workers (i.e.…”

Hip & Pelvis

“…The current study, conducted across various institutions in the Netherlands , aims to evaluate the time taken to return to work (RTW) after these surgeries. 2 It also investigates how occupational class impacts the RTW time. This is particularly relevant as joint replacement surgeries are no longer exclusive to the elderly; they are now common among individuals who are economically active and wish to maintain their lifestyle post-surgery.…”

A leap towards personalized orthopaedic surgery and the prediction of spinopelvic mechanics in total hip arthroplasty

“…Beyond the immediate clinical application, their work highlights a broader theme: the thriving role of personalized medicine and the utility of predictive models when planning and undertaking THA. We may be witnessing a technological revolution, in which data-driven models could redefine surgical planning and improve outcomes, 18 with artificial intelligence (AI) and machine learning potentially representing an avenue to further enhance their accuracy. 19,20 Alastruey-López et al 21 developed a computational tool integrating a 3D finite element model with an artificial neural network to predict impingement and instability in THA.…”