A Statistical Model to Estimate the Potential Risk of Work-related Stroke among Office Workers: A Methodological Description Article
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Abstract
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Teixeira LR, Pega F, Dzhambov AM, et al (2021). The effect of occupational exposure to noise on ischaemic heart disease, stroke and hypertension: A systematic review and meta-analysis from the WHO/ILO Joint Estimates of the Work-Related Burden of Disease and Injury. Environ Int, 154:106387.
2. Shariat A, Mohd Tamrin SB, Arumugam M, et al (2016). Musculoskeletal disorders and their relationship with physical activities among office workers: a review. MJPHM, 16(1):62-74.
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5. Schober P, Vetter TR (2021). Logistic regression in medical research. Anesth Analg, 132(2):365-366.
6. Gomila R (2021). Logistic or linear? Estimating causal effects of experimental treatments on binary outcomes using regression analysis. J Exp Psychol Gen, 150(4):700-709.
7. Kost S, Rheinbach O, Schaeben H (2021). Using logistic regression model selection towards interpretable machine learning in mineral prospectivity modeling. Geochemistry, 81(4):125826.
8. El Sanharawi M, Naudet F (2013). Understanding logistic regression. J Fr Ophtalmol, 36(8):710–5.
9. Bishop C (2006). Pattern recognition and machine learning. Springer; 205–215 p. 183.
10. Harris JK (2021). Primer on binary logistic regression. Fam Med Community Health, 9(Suppl 1):e001290.
2. Shariat A, Mohd Tamrin SB, Arumugam M, et al (2016). Musculoskeletal disorders and their relationship with physical activities among office workers: a review. MJPHM, 16(1):62-74.
3. Das A. Logistic regression. In: Encyclopedia of quality of life and well-being research (2024), Springer; p. 3985–6.
4. Lyu Y, Xu Q, Liu J (2024). Exploring the medical decision-making patterns and influencing factors among the general Chinese public: a binary logistic regression analysis. BMC Public Health, 24(1):887.
5. Schober P, Vetter TR (2021). Logistic regression in medical research. Anesth Analg, 132(2):365-366.
6. Gomila R (2021). Logistic or linear? Estimating causal effects of experimental treatments on binary outcomes using regression analysis. J Exp Psychol Gen, 150(4):700-709.
7. Kost S, Rheinbach O, Schaeben H (2021). Using logistic regression model selection towards interpretable machine learning in mineral prospectivity modeling. Geochemistry, 81(4):125826.
8. El Sanharawi M, Naudet F (2013). Understanding logistic regression. J Fr Ophtalmol, 36(8):710–5.
9. Bishop C (2006). Pattern recognition and machine learning. Springer; 205–215 p. 183.
10. Harris JK (2021). Primer on binary logistic regression. Fam Med Community Health, 9(Suppl 1):e001290.
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| Issue | Vol 54 No 10 (2025) | |
| Section | Letter to the Editor | |
| DOI | https://doi.org/10.18502/ijph.v54i10.20150 | |
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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
How to Cite
1.
Farghadan M, Shabbab Almutairi N, Shariat A, Anastasio A. A Statistical Model to Estimate the Potential Risk of Work-related Stroke among Office Workers: A Methodological Description Article. Iran J Public Health. 2025;54(10):2395-2397.
