| Title: |
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 |
| Authors: |
Ong, KL; Stafford, LK; McLaughlin, SA; Boyko, EJ; Vollset, SE; Smith, AE; Dalton, BE; Duprey, J; Cruz, JA; Hagins, H; Lindstedt, PA; Aali, A; Abate, YH; Abate, MD; Abbasian, M; Abbasi-Kangevari, Z; Abbasi-Kangevari, M; ElHafeez, SA; Abd-Rabu, R; Abdulah, DM; Abdullah, AYM; Abedi, V; Abidi, H; Aboagye, RG; Abolhassani, H; Abu-Gharbieh, E; Abu-Zaid, A; Adane, TD; Adane, DE; Addo, IY; Adegboye, OA; Adekanmbi, V; Adepoju, AV; Adnani, QES; Afolabi, RF; Agarwal, G; Aghdam, ZB; Agudelo-Botero, M; Arriagada, CEA; Agyemang-Duah, W; Ahinkorah, BO; Ahmad, D; Ahmad, R; Ahmad, S; Ahmad, A; Ahmadi, A; Ahmadi, K; Ahmed, A; Ahmed, LA; Ahmed, SA; Ajami, M; Akinyemi, RO; Al Hamad, H; Al Hasan, SM; AL-Ahdal, TMA; Alalwan, TA; Al-Aly, Z; AlBataineh, MT; Alcalde-Rabanal, JE; Alemi, S; Ali, H; Alinia, T; Aljunid, SM; Almustanyir, S; Al-Raddadi, RM; Alvis-Guzman, N; Amare, F; Ameyaw, EK; Amiri, S; Amusa, GA; Andrei, CL; Anjana, RM; Ansar, A; Ansari, G; Ansari-Moghaddam, A; Anyasodor, AE; Arabloo, J; Aravkin, AY; Areda, D; Arifin, H; Arkew, M; Armocida, B; Arnlov, J; Artamonov, AA; Arulappan, J; Aruleba, RT; Arumugam, A; Aryan, Z; Asemu, MT; Asghari-Jafarabadi, M; Askari, E; Asmelash, D; Astell-Burt, T; Athar, M; Athari, SS; Atout, MMDW; Avila-Burgos, L; Awaisu, A; Azadnajafabad, S; Xu, Luna; Bhaskar, Sonu; Sundstorm, Johan; Feng, Xiaoqi; Huda, Md Nazmul; Berhe, Kidanemaryam; Dai-Keller, Zhaoli; Saddik, Basema |
| Source: |
urn:ISSN:0140-6736 ; urn:ISSN:1474-547X ; Lancet, 402, 10397, 203-234 |
| Publisher Information: |
Elsevier |
| Publication Year: |
2023 |
| Collection: |
UNSW Sydney (The University of New South Wales): UNSWorks |
| Subject Terms: |
32 Biomedical and Clinical Sciences; 3202 Clinical Sciences; 42 Health Sciences; Burden of Illness; Prevention; Diabetes; Social Determinants of Health; Health Disparities and Racial or Ethnic Minority Health Research; Aging; Health Disparities; Women's Health; Nutrition; 2.4 Surveillance and distribution; Metabolic and endocrine; 3 Good Health and Well Being; Male; Female; Humans; Aged; Prevalence; Global Burden of Disease; Quality-Adjusted Life Years; Diabetes Mellitus; Type 2; Type 1; Bayes Theorem; Life Expectancy; Risk Factors; Global Health; GBD 2021 Diabetes Collaborators |
| Description: |
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 risk assessment framework to estimate the ... |
| Document Type: |
article in journal/newspaper |
| File Description: |
application/pdf |
| Language: |
unknown |
| Relation: |
https://hdl.handle.net/1959.4/unsworks_85564 |
| DOI: |
10.1016/S0140-6736(23)01301-6 |
| Availability: |
https://hdl.handle.net/1959.4/unsworks_85564; https://unsworks.unsw.edu.au/bitstreams/e8ba4364-459e-4f2e-a397-dfe12d6d31d4/download; https://doi.org/10.1016/S0140-6736(23)01301-6 |
| Rights: |
open access ; https://purl.org/coar/access_right/c_abf2 ; CC BY ; https://creativecommons.org/licenses/by/4.0/ ; free_to_read |
| Accession Number: |
edsbas.96DA759B |
| Database: |
BASE |