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Are psychosocial factors associated with low back pain and work absence for low back pain in an occupational cohort?

Urquhart DM, Kelsall HL, Hoe VC, Cicuttini FM, Forbes AB, Sim MR

Clin J Pain, 2013 Dec;29(12):1015-20.
PMID: 23370089 DOI: 10.1097/AJP.0b013e31827ff0c0

OBJECTIVES: To examine the relationship between individual and work-related psychosocial factors and low back pain (LBP) and associated time off work in an occupational cohort.
METHODS: A self-administered questionnaire was completed by nurses working across 3 major public hospitals. Participants provided sociodemographic data and information on the occurrence of LBP, time off work, and psychosocial factors.
RESULTS: One thousand one hundred eleven participants (response rate 38.6%) were included in the study. Fifty-six percent of participants reported LBP in the previous year. When individual psychosocial factors were examined in the same model, the relationship between somatization and LBP persisted (OR 1.64; 95% confidence interval [CI], 1.35, 2.01). Low job security was also significantly associated with LBP independent of the other work-related factors (OR 0.82; 95% CI, 0.69, 0.98). Of those participants with LBP, 30% reported absence from work due to LBP. When absence from work was examined, negative beliefs (OR 0.97; 95% CI, 0.94, 1.00) and pain catastrophizing (OR 1.33; 95% CI, 1.04, 1.71) were independently associated with time off work, along with low job satisfaction (OR 0.71; 95% CI, 0.51, 0.97) and high job support (OR 1.35; 95% CI, 1.04, 1.75).
CONCLUSIONS: Somatization and low job security were found to be independently associated with occupational LBP, whereas negative beliefs, pain catastrophizing, reduced job satisfaction, and high job support were independently related to time off work. Longitudinal studies are needed to determine whether these individual and work-related psychosocial factors predict, or alternatively, are outcomes of pain and time off work associated with LBP.
A review on segmentation of knee articular cartilage: from conventional methods towards deep learning

Ebrahimkhani S, Jaward MH, Cicuttini FM, Dharmaratne A, Wang Y, de Herrera AGS

Artif Intell Med, 2020 06;106:101851.
PMID: 32593389 DOI: 10.1016/j.artmed.2020.101851

In this paper, we review the state-of-the-art approaches for knee articular cartilage segmentation from conventional techniques to deep learning (DL) based techniques. Knee articular cartilage segmentation on magnetic resonance (MR) images is of great importance in early diagnosis of osteoarthritis (OA). Besides, segmentation allows estimating the articular cartilage loss rate which is utilised in clinical practice for assessing the disease progression and morphological changes. It has been traditionally applied in quantifying longitudinal knee OA progression pattern to detect and assess the articular cartilage thickness and volume. Topics covered include various image processing algorithms and major features of different segmentation techniques, feature computations and the performance evaluation metrics. This paper is intended to provide researchers with a broad overview of the currently existing methods in the field, as well as to highlight the shortcomings and potential considerations in the application at clinical practice. The survey showed that state-of-the-art techniques based on DL outperform the other segmentation methods. The analysis of the existing methods reveals that integration of DL-based algorithms with other traditional model-based approaches has achieved the best results (mean Dice similarity coefficient (DSC) between 85.8% and 90%).
Fulltext Estimating global injuries morbidity and mortality: methods and data used in the Global Burden of Disease 2017 study

James SL, Castle CD, Dingels ZV, Fox JT, Hamilton EB, Liu Z, et al.

Inj Prev, 2020 Oct;26(Supp 1):i125-i153.
PMID: 32839249 DOI: 10.1136/injuryprev-2019-043531

BACKGROUND: While there is a long history of measuring death and disability from injuries, modern research methods must account for the wide spectrum of disability that can occur in an injury, and must provide estimates with sufficient demographic, geographical and temporal detail to be useful for policy makers. The Global Burden of Disease (GBD) 2017 study used methods to provide highly detailed estimates of global injury burden that meet these criteria.
METHODS: In this study, we report and discuss the methods used in GBD 2017 for injury morbidity and mortality burden estimation. In summary, these methods included estimating cause-specific mortality for every cause of injury, and then estimating incidence for every cause of injury. Non-fatal disability for each cause is then calculated based on the probabilities of suffering from different types of bodily injury experienced.
RESULTS: GBD 2017 produced morbidity and mortality estimates for 38 causes of injury. Estimates were produced in terms of incidence, prevalence, years lived with disability, cause-specific mortality, years of life lost and disability-adjusted life-years for a 28-year period for 22 age groups, 195 countries and both sexes.
CONCLUSIONS: GBD 2017 demonstrated a complex and sophisticated series of analytical steps using the largest known database of morbidity and mortality data on injuries. GBD 2017 results should be used to help inform injury prevention policy making and resource allocation. We also identify important avenues for improving injury burden estimation in the future.

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