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3 results on '"Malik Ara, Ibrar"'

1. Global, regional, and national burden of other musculoskeletal disorders, 1990–2020, and projections to 2050: a systematic analysis of the Global Burden of Disease Study 2021

Author

Universitat Politècnica de Catalunya. Doctorat en Automàtica, Robòtica i Visió, Barrero, Luís, Ferreira Soares Borges Lopes, Nuno Filipe, Ben Hassen, M., Heidari, Majid, Malik Ara, Ibrar, Neupane, Shova, Pawar, Suwil D., Sahebkar, Amirhossein, Shahabi, Shahriar, Zare, Iman, Vos, Tanja, Marateb, Hamid Reza, Universitat Politècnica de Catalunya. Doctorat en Automàtica, Robòtica i Visió, Barrero, Luís, Ferreira Soares Borges Lopes, Nuno Filipe, Ben Hassen, M., Heidari, Majid, Malik Ara, Ibrar, Neupane, Shova, Pawar, Suwil D., Sahebkar, Amirhossein, Shahabi, Shahriar, Zare, Iman, Vos, Tanja, and Marateb, Hamid Reza

Abstract

Background Musculoskeletal disorders include more than 150 different conditions affecting joints, muscles, bones, ligaments, tendons, and the spine. To capture all health loss from death and disability due to musculoskeletal disorders, the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) includes a residual musculoskeletal category for conditions other than osteoarthritis, rheumatoid arthritis, gout, low back pain, and neck pain. This category is called other musculoskeletal disorders and includes, for example, systemic lupus erythematosus and spondylopathies. We provide updated estimates of the prevalence, mortality, and disability attributable to other musculoskeletal disorders and forecasted prevalence to 2050. Methods Prevalence of other musculoskeletal disorders was estimated in 204 countries and territories from 1990 to 2020 using data from 68 sources across 23 countries from which subtraction of cases of rheumatoid arthritis, osteoarthritis, low back pain, neck pain, and gout from the total number of cases of musculoskeletal disorders was possible. Data were analysed with Bayesian meta-regression models to estimate prevalence by year, age, sex, and location. Years lived with disability (YLDs) were estimated from prevalence and disability weights. Mortality attributed to other musculoskeletal disorders was estimated using vital registration data. Prevalence was forecast to 2050 by regressing prevalence estimates from 1990 to 2020 with Socio-demographic Index as a predictor, then multiplying by population forecasts. Findings Globally, 494 million (95% uncertainty interval 431–564) people had other musculoskeletal disorders in 2020, an increase of 123·4% (116·9–129·3) in total cases from 221 million (192–253) in 1990. Cases of other musculoskeletal disorders are projected to increase by 115% (107–124) from 2020 to 2050, to an estimated 1060 million (95% UI 964–1170) prevalent cases in 2050; most regions were projected to have at least a 50% incr, Peer Reviewed, Postprint (published version)

Published
2023

2. Global, regional, and national burden of diabetes from 1990 to 2021, with projections of prevalence to 2050: a systematic analysis for the Global Burden of Disease Study 2021

Author

Universitat Politècnica de Catalunya. Doctorat en Enginyeria de la Construcció, Universitat Politècnica de Catalunya. Doctorat en Teoria i Història de l'Arquitectura, Universitat Politècnica de Catalunya. Doctorat en Automàtica, Robòtica i Visió, Universitat Politècnica de Catalunya. Doctorat en Enginyeria Civil, Universitat Politècnica de Catalunya. Doctorat en Enginyeria del Terreny, Universitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial, Universitat Politècnica de Catalunya. ANiComp - Anàlisi Numèrica i Computació Científica, Universitat Politècnica de Catalunya. BIOART - BIOsignal Analysis for Rehabilitation and Therapy, Mclaughlin, S, Dalton, Benjamin, Haidar Ahmad, Dima, Ahmad, Shahid, Ahmadi, Abolfaz, Aljahdali, Hani Moaiteq, Andrei, C.O., Al Badawi, Ahmad, Bagherieh, Sara, Bhaskar, Sonu, Cho, Wonjun, Chu, Dewei, Díaz Sánchez, Daniel, Feng, Xiaodong, Ben Hassen, M., Heidari, Majid, Hossain, Md Abid, Hosseinzadeh, Mohammad Reza, Ben-Yehuda, Muli, Hussain, Syed Asad, Kilic, Mukremin, Iqbal Begum, Awais, Jain, Rahul, Joseph, Augustine, Kebede, S, Koyanagi, Ai, Kumar, Haresh, Kundu, Swarup, Larsson, A., Lee, Wonyoung, Anchalee, Puengnim, Lopes, Gonçalo, Mahmoud, Malak Maher Fawzy, Cusmai, Adriel, Makris, K G, Malik Ara, Ibrar, Al Mamun, Abdullah, Martini, Séverine, Marzo Lafuente, Rafael, Mossialos, E, Mueller, Ute, Panagiotakos, Demosthenes, Patel, J M, Rahmani, Saman, Rashidi Meybodi, Mehdi, Riaz, Maryam, Salemsafi, Sanam, Sahebkar, Amirhossein, Salehi, Maryam, Salomón Arquedas, Juan Carlos, Sanabria, José A., Schlaich, Mike, Singh, Parvesh, Soleimani, Hesam, Patan, Krzysztof, Tyrovolas, Stefanos, Vaziri, Sam, Verma, Manish, Bozhevolnyi, Sergey I, Moniruzzaman, Shaikh, Zare, Iman, Zoladl, Mohammad, Zou, ZhiXiang, Vos, Tanja, Marateb, Hamid Reza, Universitat Politècnica de Catalunya. Doctorat en Enginyeria de la Construcció, Universitat Politècnica de Catalunya. Doctorat en Teoria i Història de l'Arquitectura, Universitat Politècnica de Catalunya. Doctorat en Automàtica, Robòtica i Visió, Universitat Politècnica de Catalunya. Doctorat en Enginyeria Civil, Universitat Politècnica de Catalunya. Doctorat en Enginyeria del Terreny, Universitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial, Universitat Politècnica de Catalunya. ANiComp - Anàlisi Numèrica i Computació Científica, Universitat Politècnica de Catalunya. BIOART - BIOsignal Analysis for Rehabilitation and Therapy, Mclaughlin, S, Dalton, Benjamin, Haidar Ahmad, Dima, Ahmad, Shahid, Ahmadi, Abolfaz, Aljahdali, Hani Moaiteq, Andrei, C.O., Al Badawi, Ahmad, Bagherieh, Sara, Bhaskar, Sonu, Cho, Wonjun, Chu, Dewei, Díaz Sánchez, Daniel, Feng, Xiaodong, Ben Hassen, M., Heidari, Majid, Hossain, Md Abid, Hosseinzadeh, Mohammad Reza, Ben-Yehuda, Muli, Hussain, Syed Asad, Kilic, Mukremin, Iqbal Begum, Awais, Jain, Rahul, Joseph, Augustine, Kebede, S, Koyanagi, Ai, Kumar, Haresh, Kundu, Swarup, Larsson, A., Lee, Wonyoung, Anchalee, Puengnim, Lopes, Gonçalo, Mahmoud, Malak Maher Fawzy, Cusmai, Adriel, Makris, K G, Malik Ara, Ibrar, Al Mamun, Abdullah, Martini, Séverine, Marzo Lafuente, Rafael, Mossialos, E, Mueller, Ute, Panagiotakos, Demosthenes, Patel, J M, Rahmani, Saman, Rashidi Meybodi, Mehdi, Riaz, Maryam, Salemsafi, Sanam, Sahebkar, Amirhossein, Salehi, Maryam, Salomón Arquedas, Juan Carlos, Sanabria, José A., Schlaich, Mike, Singh, Parvesh, Soleimani, Hesam, Patan, Krzysztof, Tyrovolas, Stefanos, Vaziri, Sam, Verma, Manish, Bozhevolnyi, Sergey I, Moniruzzaman, Shaikh, Zare, Iman, Zoladl, Mohammad, Zou, ZhiXiang, Vos, Tanja, and Marateb, Hamid Reza

Abstract

Background Diabetes is one of the leading causes of death and disability worldwide, and affects people regardless of country, age group, or sex. Using the most recent evidentiary and analytical framework from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD), we produced location-specific, age-specific, and sex-specific estimates of diabetes prevalence and burden from 1990 to 2021, the proportion of type 1 and type 2 diabetes in 2021, the proportion of the type 2 diabetes burden attributable to selected risk factors, and projections of diabetes prevalence through 2050. Methods Estimates of diabetes prevalence and burden were computed in 204 countries and territories, across 25 age groups, for males and females separately and combined; these estimates comprised lost years of healthy life, measured in disability-adjusted life-years (DALYs; defined as the sum of years of life lost [YLLs] and years lived with disability [YLDs]). We used the Cause of Death Ensemble model (CODEm) approach to estimate deaths due to diabetes, incorporating 25 666 location-years of data from vital registration and verbal autopsy reports in separate total (including both type 1 and type 2 diabetes) and type-specific models. Other forms of diabetes, including gestational and monogenic diabetes, were not explicitly modelled. Total and type 1 diabetes prevalence was estimated by use of a Bayesian meta-regression modelling tool, DisMod-MR 2.1, to analyse 1527 location-years of data from the scientific literature, survey microdata, and insurance claims; type 2 diabetes estimates were computed by subtracting type 1 diabetes from total estimates. Mortality and prevalence estimates, along with standard life expectancy and disability weights, were used to calculate YLLs, YLDs, and DALYs. When appropriate, we extrapolated estimates to a hypothetical population with a standardised age structure to allow comparison in populations with different age structures. We used the comparative, Peer Reviewed, Postprint (published version)

Published
2023

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