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Background: Smoking is the leading behavioural risk factor for mortality globally, accounting for more than 175 million deaths and nearly 4·30 billion years of life lost (YLLs) from 1990 to 2021. The pace of decline in smoking prevalence has slowed in recent years for many countries, and although strategies have recently been proposed to achieve tobacco-free generations, none have been implemented to date. Assessing what could happen if current trends in smoking prevalence persist, and what could happen if additional smoking prevalence reductions occur, is important for communicating the effect of potential smoking policies. Methods: In this analysis, we use the Institute for Health Metrics and Evaluation's Future Health Scenarios platform to forecast the effects of three smoking prevalence scenarios on all-cause and cause-specific YLLs and life expectancy at birth until 2050. YLLs were computed for each scenario using the Global Burden of Disease Study 2021 reference life table and forecasts of cause-specific mortality under each scenario. The reference scenario forecasts what could occur if past smoking prevalence and other risk factor trends continue, the Tobacco Smoking Elimination as of 2023 (Elimination-2023) scenario quantifies the maximum potential future health benefits from assuming zero percent smoking prevalence from 2023 onwards, whereas the Tobacco Smoking Elimination by 2050 (Elimination-2050) scenario provides estimates for countries considering policies to steadily reduce smoking prevalence to 5%. Together, these scenarios underscore the magnitude of health benefits that could be reached by 2050 if countries take decisive action to eliminate smoking. The 95% uncertainty interval (UI) of estimates is based on the 2·5th and 97·5th percentile of draws that were carried through the multistage computational framework. Findings: Global age-standardised smoking prevalence was estimated to be 28·5% (95% UI 27·9–29·1) among males and 5·96% (5·76–6·21) among females in 2022. In the reference scenario, smoking prevalence declined by 25·9% (25·2–26·6) among males, and 30·0% (26·1–32·1) among females from 2022 to 2050. Under this scenario, we forecast a cumulative 29·3 billion (95% UI 26·8–32·4) overall YLLs among males and 22·2 billion (20·1–24·6) YLLs among females over this period. Life expectancy at birth under this scenario would increase from 73·6 years (95% UI 72·8–74·4) in 2022 to 78·3 years (75·9–80·3) in 2050. Under our Elimination-2023 scenario, we forecast 2·04 billion (95% UI 1·90–2·21) fewer cumulative YLLs by 2050 compared with the reference scenario, and life expectancy at birth would increase to 77·6 years (95% UI 75·1–79·6) among males and 81·0 years (78·5–83·1) among females. Under our Elimination-2050 scenario, we forecast 735 million (675–808) and 141 million (131–154) cumulative YLLs would be avoided among males and females, respectively. Life expectancy in 2050 would increase to 77·1 years (95% UI 74·6–79·0) among males and 80·8 years (78·3–82·9) among females. Interpretation: Existing tobacco policies must be maintained if smoking prevalence is to continue to decline as forecast by the reference scenario. In addition, substantial smoking-attributable burden can be avoided by accelerating the pace of smoking elimination. Implementation of new tobacco control policies are crucial in avoiding additional smoking-attributable burden in the coming decades and to ensure that the gains won over the past three decades are not lost. Funding: Bloomberg Philanthropies and the Bill & Melinda Gates Foundation.
Bryazka, D., Reitsma, M.B., Abate, Y.H., Abd Al Magied, A.H.A., Abdelkader, A., Abdollahi, A., et al. (2024). Forecasting the effects of smoking prevalence scenarios on years of life lost and life expectancy from 2022 to 2050: a systematic analysis for the Global Burden of Disease Study 2021. THE LANCET PUBLIC HEALTH, 9(10), e729-e744 [10.1016/S2468-2667(24)00166-X].
Forecasting the effects of smoking prevalence scenarios on years of life lost and life expectancy from 2022 to 2050: a systematic analysis for the Global Burden of Disease Study 2021
Bryazka D.;Reitsma M. B.;Abate Y. H.;Abd Al Magied A. H. A.;Abdelkader A.;Abdollahi A.;Abdoun M.;Abdulkader R. S.;Abeldano Zuniga R. A.;Abhilash E. S.;Abiodun O. O.;Abiodun O.;Aboagye R. G.;Abreu L. G.;Abtahi D.;Abualruz H.;Abubakar B.;Abu-Rmeileh N. M.;Aburuz S.;Abu-Zaid A.;Adane M. M.;Adebiyi A. O.;Adegboye O. A.;Adekanmbi V.;Adewuyi H. O.;Adnani Q. E. S.;Adzigbli L. A.;Afaghi S.;Afolabi A. A.;Afzal M. S.;Afzal S.;Agodi A.;Agyemang-Duah W.;Ahinkorah B. O.;Ahlstrom A. J.;Ahmad A.;Ahmad D.;Ahmad M. M.;Ahmad S.;Ahmad S.;Ahmadi A.;Ahmed A.;Ahmed A.;Ahmed H.;Ahmed M. B.;Ahmed S.;Ajami M.;Akkaif M. A.;Akter E.;Al Awaidy S.;Al Hasan S. M.;Al-Ajlouni Y.;Al-Aly Z.;Alam K.;Alam Z.;Aldhaleei W. A.;Algammal A. M.;Al-Gheethi A. A. S.;Alhabib K. F.;Alhalaiqa F. N.;Al-Hanawi M. K.;Ali A.;Ali M. U.;Ali R.;Ali S. S.;Ali W.;Alif S. M.;Aljunid S. M.;Alla F.;Allebeck P.;Almahmeed W.;Al-Marwani S.;Al-Maweri S.;Alomari M. A.;Alqahtani J. S.;Alqutaibi A. Y.;Al-Raddadi R. M. M.;Alrousan S. M.;Alsakarneh S.;Alshahrani N. Z.;Altaany Z.;Altaf A.;Alvis-Guzman N.;Al-Wardat M.;Al-Worafi Y. M.;Aly H.;Aly S.;Alyahya M. S. I.;Alzoubi K. H.;Al-Zyoud W. A.;Amani R.;Amin T. T.;Amiri S.;Amu H.;Amul G. G. H.;Amusa G. A.;Anand T.;Anderlini D.;Anderson D. B.;Anderson J. A.;Andrei C. L.;Andrei T.;Ansari M. T.;Anuoluwa I. A.;Anvari S.;Anwar S. L.;Anyasodor A. E.;Arabloo J.;Arafa E. A.;Aravkin A. Y.;Areda D.;Aregawi B. B.;Aremu O.;Artamonov A. A.;Asgedom A. A.;Asghari-Jafarabadi M.;Ashemo M. Y.;Ashraf T.;Astell-Burt T.;Athari S. S.;Atorkey P.;Atreya A.;Aujayeb A.;Awotidebe A. W.;Ayano G.;Aychiluhm S. B.;Azadnajafabad S.;Azzam A. Y.;Babu G. R.;Bahrami Taghanaki P.;Bahramian S.;Bai R.;Bakkannavar S. M.;Balakrishnan S.;Bam K.;Banach M.;Bandyopadhyay S.;Baran M. F.;Barchitta M.;Bardhan M.;Barker-Collo S. L.;Barrow A.;Bashiru H. A.;Basiru A.;Bastan M. -M.;Basu S.;Basu S.;Batra K.;Bayati M.;Behnoush A. H.;Bell S. L.;Belo L.;Beneke A. A.;Bennett D. A.;Bensenor I. M.;Beran A.;Bermudez A. N. C.;Beyene H. B.;Bhagat D. S.;Bhagavathula A. S.;Bhala N.;Bhardwaj N.;Bhardwaj P.;Bhaskar S.;Bhat A. N.;Bhattacharjee N. V.;Bhattacharjee P.;Bhatti J. S.;Bilgin C.;Biswas A.;Biswas B.;Boachie M. K.;Bogale E. K.;Bora Basara B.;Borhany H.;Bosoka S. A.;Bouaoud S.;Boyko E. J.;Brenner H.;Brunoni A. R.;Bugiardini R.;Bulamu N. B.;Bustanji Y.;Butt Z. A.;Caetano dos Santos F. L.;Calina D.;Cao C.;Cao F.;Capodici A.;Cardenas R.;Carreras G.;Castaldelli-Maia J. M.;Cattaruzza M. S.;Caye A.;Cegolon L.;Cenko E.;Cerrai S.;Chakraborty S.;Chandika R. M.;Chandrasekar E. K.;Chattu V. K.;Chaudhary A. A.;Chaurasia A.;Chen A. -T.;Chen G.;Chen H.;Chen M. X.;Chen S.;Cheng K. J. G.;Chi G.;Chichagi F.;Chimoriya R.;Chirinos-Caceres J. L.;Chitheer A.;Chong B.;Chong C. L.;Chong Y. Y.;Chopra H.;Choudhari S. G.;Chu D. -T.;Chukwu I. S.;Chung S. -C.;Chutiyami M.;Conde J.;Corlateanu A.;Criqui M. H.;Cruz-Martins N.;da Silva A. G.;Dadras O.;Dai S.;Dai X.;Damiani G.;Dandona L.;Dandona R.;Darcho S. D.;Darvishi Cheshmeh Soltani R.;Das S.;Dash N. R.;Davletov K.;Debele A. T.;Debopadhaya S.;Demant D.;Desai H. D.;Devegowda D.;Dewan S. M. R.;Dhali A.;Dhane A. S.;Dhulipala V. R.;Do T. C.;Dodangeh M.;Doegah P. T.;Dohare S.;Dongarwar D.;D'Oria M.;Doshi O. P.;Doshi R. P.;Dowou R. K.;Dsouza A. C.;Dsouza H. L.;Dsouza V. S.;Duncan B. B.;Duraes A. R.;Dziedzic A. M.;E'mar A. R.;Ebrahimi A.;Ebrahimi N.;Ebrahimi Kalan M.;Edvardsson D.;Edvardsson K.;Efendi F.;Effendi D. E.;Eghbali F.;Ekholuenetale M.;El Arab R. A.;El Bayoumy I. F.;El Sayed I.;Elbarazi I.;Elhadi M.;El-Huneidi W.;Elmonem M. A.;ELNahas G.;Elsohaby I.;Eltaha C.;Eltahir M. E.;Emamverdi M.;Emeto T. I.;Erku D. A.;Etaee F.;Ezenwankwo E. F.;Fabin N.;Fagbamigbe A. F.;Fagbule O. F.;Faghani S.;Fahim A.;Fakhradiyev I. R.;Falzone L.;Farooque U.;Fatehizadeh A.;Fatima Z.;Fauk N. K.;Fazylov T.;Feizkhah A.;Fekadu G.;Feng X.;Ferrara P.;Ferreira N.;Feyisa B. R.;Filippidis F. T.;Fischer F.;Flor L. S.;Foigt N. 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K.;Shiferaw D.;Shimels T.;Shiri R.;Shittu A.;Shiue I.;Shivarov V.;Shorofi S. A.;Shrestha S.;Siddig E. E.;Silva J. P.;Singh A.;Singh B.;Singh H.;Singh J. A.;Singh P.;Singh P.;Singh S.;Singh V.;Sitas F.;Smith A. E.;Soboka M.;Solanki R.;Solmi M.;Soraneh S.;Soriano J. B.;Soyiri I. N.;Spartalis M.;Sreeramareddy C. T.;Stachteas P.;Stein D. J.;Steiropoulos P.;Stevanovic A.;Straif K.;Suleman M.;Sulo G.;Sun Z.;Suresh V.;Swain C. K.;Szarpak L.;T Y S. S.;Tabaee Damavandi P.;Tabatabaei Malazy O.;Tabatabaeizadeh S. -A.;Tabche C.;Tadakamadla J.;Tadakamadla S. K.;Taiba J.;Talaat I. M.;Talukder A.;Tampa M.;Tamuzi J. L. J.;Tan K. -K.;Tareke M.;Tarigan I. U.;Teimoori M.;Temsah M. -H.;Temsah R. M. H.;Teramoto M.;Terefa D. R.;Thangaraju P.;Thankappan K. R.;Thapar R.;Thayakaran R.;Thomas N. K.;Ticoalu J. H. V.;Tiwari K.;Topor-Madry R.;Tovani-Palone M. R.;Trabelsi K.;Tran A. T.;Tran N. H.;Tran T. H.;Tran Minh Duc N.;Trihandini I.;Tripathy J. P.;Truyen T. T. T. T.;Tsermpini E. E.;Tualeka A. R.;Udoakang A. J.;Udoh A.;Ullah A.;Ullah S.;Umair M.;Unim B.;Unnikrishnan B.;Usman J. S.;Vahdati S.;Vaithinathan A. G.;Van den Eynde J.;Vardavas C.;Vasankari T. J.;Vaziri S.;Vellingiri B.;Venketasubramanian N.;Verma M.;Villeneuve P. J.;Vinayak M.;Violante F. S.;Vladimirov S. K.;Volovat S. R.;Wadood A.;Waheed Y.;Walde M. T.;Wang S.;Wang Y.;Waqas M.;Wickramasinghe N. D.;Willeit P.;Wojewodzic M. W.;Wolde A. A.;Wonde T. E.;Xiao H.;Xu S.;Yadav M. K.;Yamagishi K.;Yang D.;Yang L.;Yano Y.;Yarahmadi A.;Yesodharan R.;Yezli S.;Yi X.;Yigit A.;Yin D.;Yon D. K.;Yonemoto N.;Yoon S. -J.;Yu C.;Yuan C. -W.;Zakham F.;Zeariya M. G. M.;Zhang H.;Zhang J.;Zhang L.;Zhong C. C.;Zhou S. C.;Zhu B.;Zielinska M.;Zoghi G.;Zyoud S. H.;Vollset S. E.;Gakidou E.
2024
Abstract
Background: Smoking is the leading behavioural risk factor for mortality globally, accounting for more than 175 million deaths and nearly 4·30 billion years of life lost (YLLs) from 1990 to 2021. The pace of decline in smoking prevalence has slowed in recent years for many countries, and although strategies have recently been proposed to achieve tobacco-free generations, none have been implemented to date. Assessing what could happen if current trends in smoking prevalence persist, and what could happen if additional smoking prevalence reductions occur, is important for communicating the effect of potential smoking policies. Methods: In this analysis, we use the Institute for Health Metrics and Evaluation's Future Health Scenarios platform to forecast the effects of three smoking prevalence scenarios on all-cause and cause-specific YLLs and life expectancy at birth until 2050. YLLs were computed for each scenario using the Global Burden of Disease Study 2021 reference life table and forecasts of cause-specific mortality under each scenario. The reference scenario forecasts what could occur if past smoking prevalence and other risk factor trends continue, the Tobacco Smoking Elimination as of 2023 (Elimination-2023) scenario quantifies the maximum potential future health benefits from assuming zero percent smoking prevalence from 2023 onwards, whereas the Tobacco Smoking Elimination by 2050 (Elimination-2050) scenario provides estimates for countries considering policies to steadily reduce smoking prevalence to 5%. Together, these scenarios underscore the magnitude of health benefits that could be reached by 2050 if countries take decisive action to eliminate smoking. The 95% uncertainty interval (UI) of estimates is based on the 2·5th and 97·5th percentile of draws that were carried through the multistage computational framework. Findings: Global age-standardised smoking prevalence was estimated to be 28·5% (95% UI 27·9–29·1) among males and 5·96% (5·76–6·21) among females in 2022. In the reference scenario, smoking prevalence declined by 25·9% (25·2–26·6) among males, and 30·0% (26·1–32·1) among females from 2022 to 2050. Under this scenario, we forecast a cumulative 29·3 billion (95% UI 26·8–32·4) overall YLLs among males and 22·2 billion (20·1–24·6) YLLs among females over this period. Life expectancy at birth under this scenario would increase from 73·6 years (95% UI 72·8–74·4) in 2022 to 78·3 years (75·9–80·3) in 2050. Under our Elimination-2023 scenario, we forecast 2·04 billion (95% UI 1·90–2·21) fewer cumulative YLLs by 2050 compared with the reference scenario, and life expectancy at birth would increase to 77·6 years (95% UI 75·1–79·6) among males and 81·0 years (78·5–83·1) among females. Under our Elimination-2050 scenario, we forecast 735 million (675–808) and 141 million (131–154) cumulative YLLs would be avoided among males and females, respectively. Life expectancy in 2050 would increase to 77·1 years (95% UI 74·6–79·0) among males and 80·8 years (78·3–82·9) among females. Interpretation: Existing tobacco policies must be maintained if smoking prevalence is to continue to decline as forecast by the reference scenario. In addition, substantial smoking-attributable burden can be avoided by accelerating the pace of smoking elimination. Implementation of new tobacco control policies are crucial in avoiding additional smoking-attributable burden in the coming decades and to ensure that the gains won over the past three decades are not lost. Funding: Bloomberg Philanthropies and the Bill & Melinda Gates Foundation.
Bryazka, D., Reitsma, M.B., Abate, Y.H., Abd Al Magied, A.H.A., Abdelkader, A., Abdollahi, A., et al. (2024). Forecasting the effects of smoking prevalence scenarios on years of life lost and life expectancy from 2022 to 2050: a systematic analysis for the Global Burden of Disease Study 2021. THE LANCET PUBLIC HEALTH, 9(10), e729-e744 [10.1016/S2468-2667(24)00166-X].
Bryazka, D.; Reitsma, M. B.; Abate, Y. H.; Abd Al Magied, A. H. A.; Abdelkader, A.; Abdollahi, A.; Abdoun, M.; Abdulkader, R. S.; Abeldano Zuniga, R. ...espandi
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simulazione ASN
Il report seguente simula gli indicatori relativi alla propria produzione scientifica in relazione alle soglie ASN 2023-2025 del proprio SC/SSD. Si ricorda che il superamento dei valori soglia (almeno 2 su 3) è requisito necessario ma non sufficiente al conseguimento dell'abilitazione. La simulazione si basa sui dati IRIS e sugli indicatori bibliometrici alla data indicata e non tiene conto di eventuali periodi di congedo obbligatorio, che in sede di domanda ASN danno diritto a incrementi percentuali dei valori. La simulazione può differire dall'esito di un’eventuale domanda ASN sia per errori di catalogazione e/o dati mancanti in IRIS, sia per la variabilità dei dati bibliometrici nel tempo. Si consideri che Anvur calcola i valori degli indicatori all'ultima data utile per la presentazione delle domande.
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