Cohort Profile Update: The Brazilian Longitudinal Study of Adult Health Musculoskeletal (ELSA-Brasil MSK) cohort

Job stress and chronic low back pain: incidence, number of episodes, and severity in a 4-year follow-up of the ELSA-Brasil Musculoskeletal cohort

ABSTRACT

We investigated the association between job stress, as assessed by the effort-reward imbalance model, and the incidence of chronic low back pain (CLBP) over a 4-year period. A total of 1733 participants from the ELSA-Brasil Musculoskeletal cohort, who were free from LBP at baseline (2012-2014), were included. Episodes of LBP in the past 30 days, intensity, and the presence of disability were investigated in annual telephone follow-ups (2015-2018). Chronic LBP was defined as episodes of LBP lasting >3 months with at least moderate intensity. We analyzed the incidence of at least one episode of CLBP (yes/no), the number of CLBP episodes (0, 1, ≥2), and CLBP severity/disability (absent, nondisabling, severe/disabling). The association between these outcomes and tertiles of the effort-to-reward ratio, as well as each dimension of the effort-reward imbalance model, was investigated using multinomial logistic and Poisson regression models adjusting for sociodemographic and occupational variables. The cumulative incidence of CLBP over 4 years was 24.8%. High effort-reward imbalance increased the chances of experiencing multiple CLBP episodes and severe/disabling CLBP by 67% (95% confidence interval [CI]: 1.12-2.47) and 70% (95% CI: 1.14-2.53), respectively. High overcommitment increased the incidence of CLBP by 23% (95% CI: 1.01-1.50) and the chances of multiple CLBP episodes and severe/disabling CLBP by 67% (95% CI: 1.11-2.50) and 57% (95% CI: 1.05-2.34), respectively. These results indicate that exposure to job stress is associated with a higher incidence, a greater number of episodes, and increased severity of CLBP over a 4-year period. If this association is causal, measures aimed at reducing exposure to job stress are likely to alleviate the burden of CLBP.

Obesity and the risk of multiple or severe frequent knee pain episodes: a 4-year follow-up of the ELSA-Brasil MSK cohort

BACKGROUND/OBJECTIVE

Knee pain is an important health problem due to its high prevalence, negative impact on daily activities and quality of life, and societal burden. While the link between excess weight and knee pain has been well-documented in the literature, many studies are limited to patients with osteoarthritis or use cross-sectional data. This longitudinal study investigated whether overweight and obesity were associated with the frequency and severity of frequent knee pain (FKP) episodes over 4 years in civil servants enrolled in the ELSA-Brasil MSK cohort.

METHODS

Knee pain was assessed during baseline face-to-face interviews (2012-2014) and four yearly telephone follow-ups (2015-2019). Disabling FKP episodes or those of moderate to very severe intensity were classified as severe. Multinomial logistic regression models adjusted for confounders were used to test for associations in two participant groups: those with knee pain at baseline (prognosis cohort) and those without knee pain (incidence cohort).

RESULTS

A total of 2644 participants were included: 54.2% female, mean age 55.8 (SD 8.8) years. In the incidence cohort (n = 1896), obesity increased the risk of one (OR: 1.63; 95% CI 1.13-2.37) and multiple FKP episodes (OR: 2.61; 95% CI 1.71-3.97), as well as the risk of non-severe (OR: 1.72; 95% CI 1.04-2.84) and severe FKP episodes (OR: 2.10; 95% CI 1.50-2.95). In the prognosis cohort (n = 748), obesity increased the risk of multiple (OR: 2.54; 95% CI 1.60-4.05) and severe FKP episodes (OR: 2.31; 95% CI 1.49-3.59). Overweight presented the same trends but fell short of significance.

CONCLUSIONS

These results provide further support that overweight and obesity are important contributors to the incidence and worsening of FKP, and that weight management must be prioritized in multidisciplinary knee pain prevention and treatment programs to reduce the burden of musculoskeletal disorders.

Development of a convolutional neural network for diagnosing osteoarthritis, trained with knee radiographs from the ELSA-Brasil Musculoskeletal

Objective

To develop a convolutional neural network (CNN) model, trained with the Brazilian "Estudo Longitudinal de Saúde do Adulto Musculoesquelético" (ELSA-Brasil MSK, Longitudinal Study of Adult Health, Musculoskeletal) baseline radiographic examinations, for the automated classification of knee osteoarthritis.

Materials and Methods

This was a cross-sectional study carried out with 5,660 baseline posteroanterior knee radiographs from the ELSA-Brasil MSK database (5,660 baseline posteroanterior knee radiographs). The examinations were interpreted by a radiologist with specific training, and the calibration was as established previously.

Results

The CNN presented an area under the receiver operating characteristic curve of 0.866 (95% CI: 0.842-0.882). The model can be optimized to achieve, not simultaneously, maximum values of 0.907 for accuracy, 0.938 for sensitivity, and 0.994 for specificity.

Conclusion

The proposed CNN can be used as a screening tool, reducing the total number of examinations evaluated by the radiologists of the study, and as a double-reading tool, contributing to the reduction of possible interpretation errors.