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Predicting disability retirement among Abu Dhabi police using multiple measure of sickness absence

BACKGROUND: Disability retirement has been investigated in the last two decades using predictors such as measures of sickness absence, psychological, social, and organizational work factors. The impact of various health-related and sickness measures on disability retirement across various occupation...

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Autor principal: Alkaabi, Faisal Almurbahani
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9270810/
https://www.ncbi.nlm.nih.gov/pubmed/35810280
http://dx.doi.org/10.1186/s12889-022-13713-9
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author Alkaabi, Faisal Almurbahani
author_facet Alkaabi, Faisal Almurbahani
author_sort Alkaabi, Faisal Almurbahani
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description BACKGROUND: Disability retirement has been investigated in the last two decades using predictors such as measures of sickness absence, psychological, social, and organizational work factors. The impact of various health-related and sickness measures on disability retirement across various occupational group reveal a significant relation. However, current literature lacks understanding in police personnel. METHODS: This study examines the roles of demographic and measures of sickness absence on disability retirement among police personnel in Abu Dhabi, UAE. The case–control design was used to predict disability retirement wherein controls were matched with cases according to age and gender from those who worked in the same administration as the case at baseline, to reduce the possible confounding influence of these variables. Conditional logistic regression models were used determine the odds-ratio of various measures of sickness absence in predicting disability retirement. RESULTS: Results indicate that increased number of spells, and number of days of sickness absence can predict disability retirements among police personnel in the UAE. Results indicate that odds ratios for disability retirement for the total exposure period increased from 1.76 (95% CI = 1.42-2.20) for spells of 4-7d to 2.47 (95%CI = 1.79-3.40) for spells of > 4 weeks. When compared with their married counterparts, non-married police employees had a statistically significant increase in odds of disability retirement of almost three fold (OR = 2.93, 95% CI = 1.55-5.56). Non-field and field police officers, on the other hand, had significantly reduced odds of disability retirement compared with admin/supportive staff (OR = 0.43 and 0.28 with 95% CI = 0.19-0.96 and 0.13-0.61 respectively). Odds ratios of disability retirement at end of the exposure period for the matching variables with those obtained after additionally adjusting for all demographic variables (model b), namely, marital status, occupation, employment grade and type, and educational level. The odds ratios of disability retirement remained significantly raised for the total number of days of sickness absence and for the number of spells of sickness absence for all spell types. CONCLUSIONS: Recommendation to reduce the number of future disability retirements among Abu Dhabi Police include structured problem-solving process addressed through stepwise meetings between the line-managers and the employee.
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spelling pubmed-92708102022-07-10 Predicting disability retirement among Abu Dhabi police using multiple measure of sickness absence Alkaabi, Faisal Almurbahani BMC Public Health Research BACKGROUND: Disability retirement has been investigated in the last two decades using predictors such as measures of sickness absence, psychological, social, and organizational work factors. The impact of various health-related and sickness measures on disability retirement across various occupational group reveal a significant relation. However, current literature lacks understanding in police personnel. METHODS: This study examines the roles of demographic and measures of sickness absence on disability retirement among police personnel in Abu Dhabi, UAE. The case–control design was used to predict disability retirement wherein controls were matched with cases according to age and gender from those who worked in the same administration as the case at baseline, to reduce the possible confounding influence of these variables. Conditional logistic regression models were used determine the odds-ratio of various measures of sickness absence in predicting disability retirement. RESULTS: Results indicate that increased number of spells, and number of days of sickness absence can predict disability retirements among police personnel in the UAE. Results indicate that odds ratios for disability retirement for the total exposure period increased from 1.76 (95% CI = 1.42-2.20) for spells of 4-7d to 2.47 (95%CI = 1.79-3.40) for spells of > 4 weeks. When compared with their married counterparts, non-married police employees had a statistically significant increase in odds of disability retirement of almost three fold (OR = 2.93, 95% CI = 1.55-5.56). Non-field and field police officers, on the other hand, had significantly reduced odds of disability retirement compared with admin/supportive staff (OR = 0.43 and 0.28 with 95% CI = 0.19-0.96 and 0.13-0.61 respectively). Odds ratios of disability retirement at end of the exposure period for the matching variables with those obtained after additionally adjusting for all demographic variables (model b), namely, marital status, occupation, employment grade and type, and educational level. The odds ratios of disability retirement remained significantly raised for the total number of days of sickness absence and for the number of spells of sickness absence for all spell types. CONCLUSIONS: Recommendation to reduce the number of future disability retirements among Abu Dhabi Police include structured problem-solving process addressed through stepwise meetings between the line-managers and the employee. BioMed Central 2022-07-09 /pmc/articles/PMC9270810/ /pubmed/35810280 http://dx.doi.org/10.1186/s12889-022-13713-9 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Alkaabi, Faisal Almurbahani
Predicting disability retirement among Abu Dhabi police using multiple measure of sickness absence
title Predicting disability retirement among Abu Dhabi police using multiple measure of sickness absence
title_full Predicting disability retirement among Abu Dhabi police using multiple measure of sickness absence
title_fullStr Predicting disability retirement among Abu Dhabi police using multiple measure of sickness absence
title_full_unstemmed Predicting disability retirement among Abu Dhabi police using multiple measure of sickness absence
title_short Predicting disability retirement among Abu Dhabi police using multiple measure of sickness absence
title_sort predicting disability retirement among abu dhabi police using multiple measure of sickness absence
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9270810/
https://www.ncbi.nlm.nih.gov/pubmed/35810280
http://dx.doi.org/10.1186/s12889-022-13713-9
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