Gregory A. RothpersName, George A. MensahpersName, Catherine O. JohnsonpersName, Giovanni AddoloratopersName, Enrico AmmiratipersName, Larry M. BaddourpersName, Noël C. BarengopersName, Andrea Z. BeatonpersName, Emelia J. BenjaminpersName, Catherine P. BenzigerpersName, Aimé BonnypersName, Michael BrauerpersName, Marianne BrodmannpersName, Thomas J. CahillpersName, Jonathan CarapetispersName, Alberico L. CatapanopersName, Sumeet S. ChughpersName, Leslie T. CooperpersName, Josef CoreshpersName, Michael CriquipersName, Nicole DeCleenepersName, Kim A. EaglepersName, Sophia Emmons-BellpersName, Valery L. FeiginpersName, Joaquim Fernández-SolàpersName, Gerry FowkespersName, Emmanuela GakidoupersName, Scott M. GrundypersName, Feng J. HepersName, George HowardpersName, Frank HupersName, Lesley InkerpersName, Ganesan KarthikeyanpersName, Nicholas KassebaumpersName, Walter KoroshetzpersName, Carl LaviepersName, Donald Lloyd-JonespersName, Hong S. LupersName, Antonio MirijellopersName, Awoke Misganaw TemesgenpersName, Ali MokdadpersName, Andrew E. MoranpersName, Paul MuntnerpersName, Jagat NarulapersName, Bruce NealpersName, Mpiko NtsekhepersName, Glaucia Moraes de OliveirapersName, Catherine OttopersName, Mayowa OwolabipersName, Michael PrattpersName, Sanjay RajagopalanpersName, Marissa ReitsmapersName, Antonio Luiz P. RibeiropersName, Nancy RigottipersName, Anthony RodgerspersName, Craig SablepersName, Saate ShakilpersName, Karen Sliwa-HahnlepersName, Benjamin StarkpersName, Johan SundströmpersName, Patrick TimpelpersName, Imad M. TleyjehpersName, Marco ValgimiglipersName, Theo VospersName, Paul K. WheltonpersName, Magdi YacoubpersName, Liesl ZuhlkepersName, Christopher MurraypersName, Valentin FusterpersName, Gregory A. RothpersName, George A. MensahpersName, Catherine O. JohnsonpersName, Giovanni AddoloratopersName, Enrico AmmiratipersName, Larry M. BaddourpersName, Noel C. BarengopersName, Andrea BeatonpersName, Emelia J. BenjaminpersName, Catherine P. BenzigerpersName, Aime BonnypersName, Michael BrauerpersName, Marianne BrodmannpersName, Thomas J. CahillpersName, Jonathan R. CarapetispersName, Alberico L. CatapanopersName, Sumeet ChughpersName, Leslie T. CooperpersName, Josef CoreshpersName, Michael H. CriquipersName, Nicole K. DeCleenepersName, Kim A. EaglepersName, Sophia Emmons-BellpersName, Valery L. FeiginpersName, Joaquim Fernández-SolapersName, F. Gerry R. FowkespersName, Emmanuela GakidoupersName, Scott M. GrundypersName, Feng J. HepersName, George HowardpersName, Frank HupersName, Lesley InkerpersName, Ganesan KarthikeyanpersName, Nicholas J. KassebaumpersName, Walter J. KoroshetzpersName, Carl LaviepersName, Donald Lloyd-JonespersName, Hong S. LupersName, Antonio MirijellopersName, Awoke T. MisganawpersName, Ali H. MokdadpersName, Andrew E. MoranpersName, Paul MuntnerpersName, Jagat NarulapersName, Bruce NealpersName, Mpiko NtsekhepersName, Gláucia M.M. OliveirapersName, Catherine M. OttopersName, Mayowa O. OwolabipersName, Michael PrattpersName, Sanjay RajagopalanpersName, Marissa B. ReitsmapersName, Antonio Luiz P. RibeiropersName, Nancy A. RigottipersName, Anthony RodgerspersName, Craig A. SablepersName, Saate S. ShakilpersName, Karen SliwapersName, Benjamin A. StarkpersName, Johan SundströmpersName, Patrick TimpelpersName, Imad I. TleyjehpersName, Marco ValgimiglipersName, Theo VospersName, Paul K. WheltonpersName, Magdi YacoubpersName, Liesl J. ZuhlkepersName, Mohsen Abbasi-KangevaripersName, Alireza AbdipersName, Aidin AbedipersName, Victor AboyanspersName, Woldu A. AbrhapersName, Eman Abu-GharbiehpersName, Abdelrahman I. AbushoukpersName, Dilaram AcharyapersName, Tim AdairpersName, Oladimeji M. AdebayopersName, Zanfina AdemipersName, Shailesh M. AdvanipersName, Khashayar AfsharipersName, Ashkan AfshinpersName, Gina AgarwalpersName, Pradyumna AgasthipersName, Sohail AhmadpersName, Sepideh AhmadipersName, Muktar B. AhmedpersName, Budi AjipersName, Yonas AkalupersName, Wuraola Akande-SholabipersName, Addis AklilupersName, Chisom J. AkunnapersName, Fares AlahdabpersName, Ayman Al-EyadhypersName, Khalid F. AlhabibpersName, Sheikh M. AlifpersName, Vahid AlipourpersName, Syed M. AljunidpersName, François AllapersName, Amir Almasi-HashianipersName, Sami AlmustanyirpersName, Rajaa M. Al-RaddadipersName, Adeladza K. AmegahpersName, Saeed AminipersName, Arya AminorroayapersName, Hubert AmupersName, Dickson A. AmugsipersName, Robert AncuceanupersName, Deanna AnderlinipersName, Tudorel AndreipersName, Catalina Liliana AndreipersName, Alireza Ansari-MoghaddampersName, Zelalem A. AntenehpersName, Ippazio Cosimo AntonazzopersName, Benny AntonypersName, Razique AnwerpersName, Lambert T. AppiahpersName, Jalal ArabloopersName, Johan ÄrnlövpersName, Kurnia D. ArtantipersName, Zerihun AtaropersName, Marcel AusloospersName, Leticia Avila-BurgospersName, Asma T. AwanpersName, Mamaru A. AwokepersName, Henok T. AyelepersName, Muluken A. AyzapersName, Samad AzaripersName, Darshan B. BpersName, Nafiseh BaheiraeipersName, Atif A. BaigpersName, Ahad BakhtiaripersName, Maciej BanachpersName, Palash C. BanikpersName, Emerson A. BaptistapersName, Miguel A. BarbozapersName, Lingkan BaruapersName, Sanjay BasupersName, Neeraj BedipersName, Yannick BéjotpersName, Derrick A. BennettpersName, Isabela M. BensenorpersName, Adam E. BermanpersName, Yihienew M. BezabihpersName, Akshaya S. BhagavathulapersName, Sonu BhaskarpersName, Krittika BhattacharyyapersName, Ali BijanipersName, Boris BikbovpersName, Mulugeta M. BirhanupersName, Archith BoloorpersName, Luisa C. BrantpersName, Hermann BrennerpersName, Nikolay I. BrikopersName, Zahid A. ButtpersName, Florentino Luciano Caetano dos SantospersName, Leah E. CahillpersName, Lucero Cahuana-HurtadopersName, Luis A. CámerapersName, Ismael R. Campos-NonatopersName, Carlos Cantu-BritopersName, Josip CarpersName, Juan J. CarreropersName, Felix CarvalhopersName, Carlos A. Castañeda-OrjuelapersName, Ferrán Catalá-LópezpersName, Ester CerinpersName, Jaykaran CharanpersName, Vijay Kumar ChattupersName, Simiao ChenpersName, Ken L. ChinpersName, Jee-Young J. ChoipersName, Dinh-Toi ChupersName, Sheng-Chia ChungpersName, Massimo CirillopersName, Sean CoffeypersName, Sara ContipersName, Vera M. CostapersName, David K. CundiffpersName, Omid DadraspersName, Baye DagnewpersName, Xiaochen DaipersName, Albertino A.M. DamascenopersName, Lalit DandonapersName, Rakhi DandonapersName, Kairat DavletovpersName, Vanessa De la Cruz-GóngorapersName, Fernando P. De la HozpersName, Jan-Walter De NevepersName, Edgar Denova-GutiérrezpersName, Meseret Derbew MollapersName, Behailu T. DersehpersName, Rupak DesaipersName, Günther DeuschlpersName, Samath D. DharmaratnepersName, Meghnath DhimalpersName, Raja Ram DhunganapersName, Mostafa DianatinasabpersName, Daniel DiazpersName, Shirin DjalaliniapersName, Klara DokovapersName, Abdel DouiripersName, Bruce B. DuncanpersName, Andre R. DuraespersName, Arielle W. EaganpersName, Sanam EbtehajpersName, Aziz EftekharipersName, Sahar EftekharzadehpersName, Michael EkholuenetalepersName, Nevine El NahaspersName, Islam Y. ElgendypersName, Muhammed ElhadipersName, Shaimaa I. El-JaafarypersName, Sadaf EsteghamatipersName, Atkilt E. EtissopersName, Oghenowede EyawopersName, Ibtihal FadhilpersName, Emerito Jose A. FaraonpersName, Pawan S. FarispersName, Medhat FarwatipersName, Farshad FarzadfarpersName, Eduarda FernandespersName, Carlota Fernandez PrendespersName, Pietro FerrarapersName, Irina FilippersName, Florian FischerpersName, David FloodpersName, Takeshi FukumotopersName, Mohamed M. GadpersName, Shilpa GaidhanepersName, Morsaleh GanjipersName, Jalaj GargpersName, Abadi K. GebrepersName, Birhan G. GebregiorgispersName, Kidane Z. GebregzabiherpersName, Gebreamlak G. GebremeskelpersName, Lemma GetacherpersName, Abera Getachew ObsapersName, Alireza GhajarpersName, Ahmad GhashghaeepersName, Nermin GhithpersName, Simona GiampaolipersName, Syed Amir GilanipersName, Paramjit S. GillpersName, Richard F. GillumpersName, Ekaterina V. GlushkovapersName, Elena V. GnedovskayapersName, Mahaveer GolechhapersName, Kebebe B. GonfapersName, Amir Hossein GoudarzianpersName, Alessandra C. GoulartpersName, Jenny S. GuadamuzpersName, Avirup GuhapersName, Yuming GuopersName, Rajeev GuptapersName, Vladimir HachinskipersName, Nima Hafezi-NejadpersName, Teklehaimanot G. HailepersName, Randah R. HamadehpersName, Samer HamidipersName, Graeme J. HankeypersName, Arief HargonopersName, Risky K. HartonopersName, Maryam HashemianpersName, Abdiwahab HashipersName, Shoaib HassanpersName, Hamid Y. HassenpersName, Rasmus J. HavmoellerpersName, Simon I. HaypersName, Khezar HayatpersName, Golnaz HeidaripersName, Claudiu HerteliupersName, Ramesh HollapersName, Mostafa HosseinipersName, Mehdi HosseinzadehpersName, Mihaela HostiucpersName, Sorin HostiucpersName, Mowafa HousehpersName, Junjie HuangpersName, Ayesha HumayunpersName, Ivo IavicolipersName, Charles U. IbenemepersName, Segun E. IbitoyepersName, Olayinka S. IlesanmipersName, Irena M. IlicpersName, Milena D. IlicpersName, Usman IqbalpersName, Seyed Sina N. IrvanipersName, Sheikh Mohammed Shariful IslampersName, Rakibul M. IslampersName, Hiroyasu IsopersName, Masao IwagamipersName, Vardhmaan JainpersName, Tahereh JavaheripersName, Sathish Kumar JayapalpersName, Shubha JayarampersName, Ranil JayawardenapersName, Panniyammakal JeemonpersName, Ravi P. JhapersName, Jost B. JonaspersName, Jitendra JonnagaddalapersName, Farahnaz JoukarpersName, Jacek J. JozwiakpersName, Mikk JürissonpersName, Ali KabirpersName, Tanvir KahlonpersName, Rizwan KalanipersName, Rohollah KalhorpersName, Ashwin KamathpersName, Ibrahim KamelpersName, Himal KandelpersName, Amit KandelpersName, André KarchpersName, Ayele Semachew KasapersName, Patrick D.M.C. KatotopersName, Gbenga A. KayodepersName, Yousef S. KhaderpersName, Mohammad KhammarniapersName, Muhammad S. KhanpersName, Md Nuruzzaman KhanpersName, Maseer KhanpersName, Ejaz A. KhanpersName, Khaled KhatabpersName, Gulam M.A. KibriapersName, Yun Jin KimpersName, Gyu Ri KimpersName, Ruth W. KimokotipersName, Sezer KisapersName, Adnan KisapersName, Mika KivimäkipersName, Dhaval KoltepersName, Ali KoolivandpersName, Vladimir A. KorshunovpersName, Sindhura Lakshmi Koulmane LaxminarayanapersName, Ai KoyanagipersName, Kewal KrishanpersName, Vijay KrishnamoorthypersName, Barthelemy Kuate DefopersName, Burcu Kucuk BicerpersName, Vaman KulkarnipersName, G. Anil KumarpersName, Nithin KumarpersName, Om P. KurmipersName, Dian KusumapersName, Gene F. KwanpersName, Carlo La VecchiapersName, Ben LaceypersName, Tea LallukkapersName, Qing LanpersName, Savita LasradopersName, Zohra S. LassipersName, Paolo LauriolapersName, Wayne R. LawrencepersName, Avula LaxmaiahpersName, Kate E. LeGrandpersName, Ming-Chieh LipersName, Bingyu LipersName, Shanshan LipersName, Stephen S. LimpersName, Lee-Ling LimpersName, Hualiang LinpersName, Ziqiang LinpersName, Ro-Ting LinpersName, Xuefeng LiupersName, Alan D. LopezpersName, Stefan LorkowskipersName, Paulo A. LotufopersName, Alessandra LugopersName, Nirmal K. MpersName, Fabiana MadottopersName, Morteza MahmoudipersName, Azeem MajeedpersName, Reza MalekzadehpersName, Ahmad A. MalikpersName, Abdullah A. MamunpersName, Navid ManafipersName, Mohammad Ali MansourniapersName, Lorenzo G. MantovanipersName, Santi MartinipersName, Manu R. MathurpersName, Giampiero MazzagliapersName, Suresh MehatapersName, Man Mohan MehndirattapersName, Toni MeierpersName, Ritesh G. MenezespersName, Atte MeretojapersName, Tomislav MestrovicpersName, Bartosz MiazgowskipersName, Tomasz MiazgowskipersName, Irmina Maria MichalekpersName, Ted R. MillerpersName, Erkin M. MirrakhimovpersName, Hamed MirzaeipersName, Babak MoazenpersName, Masoud MoghadaszadehpersName, Yousef MohammadpersName, Dara K. MohammadpersName, Shafiu MohammedpersName, Mohammed A. MohammedpersName, Yaser MokhayeripersName, Mariam MolokhiapersName, Ahmed A. MontasirpersName, Ghobad MoradipersName, Rahmatollah MoradzadehpersName, Paula MoragapersName, Lidia MorawskapersName, Ilais Moreno VelásquezpersName, Jakub MorzepersName, Sumaira MubarikpersName, Walter MuruetpersName, Kamarul Imran MusapersName, Ahamarshan J. NagarajanpersName, Mahdi NalinipersName, Vinay NangiapersName, Atta Abbas NaqvipersName, Sreenivas Narasimha SwamypersName, Bruno R. NascimentopersName, Vinod C. NayakpersName, Javad NazaripersName, Milad NazarzadehpersName, Ruxandra I. NegoipersName, Sandhya Neupane KandelpersName, Huong L.T. NguyenpersName, Molly R. NixonpersName, Bo NorrvingpersName, Jean Jacques NoubiappersName, Brice E. NouthepersName, Christoph NowakpersName, Oluwakemi O. OdukoyapersName, Felix A. OgbopersName, Andrew T. OlagunjupersName, Hans OrrupersName, Alberto OrtizpersName, Samuel M. OstroffpersName, Jagadish Rao PadubidripersName, Raffaele PalladinopersName, Adrian PanapersName, Songhomitra Panda-JonaspersName, Utsav ParekhpersName, Eun-Cheol ParkpersName, Mojtaba ParvizipersName, Fatemeh Pashazadeh KanpersName, Urvish K. PatelpersName, Mona PathakpersName, Rajan PaudelpersName, Veincent Christian F. PepitopersName, Arokiasamy PerianayagampersName, Norberto PericopersName, Hai Q. PhampersName, Thomas PilgrimpersName, Michael A. PiradovpersName, Farhad PishgarpersName, Vivek PodderpersName, Roman V. PolibinpersName, Akram PourshamspersName, Dimas R.A. PribadipersName, Navid RabieepersName, Mohammad RabieepersName, Amir RadfarpersName, Alireza RafieipersName, Fakher RahimpersName, Vafa Rahimi-MovagharpersName, Mohammad Hifz Ur RahmanpersName, Muhammad Aziz RahmanpersName, Amir Masoud RahmanipersName, Ivo RakovacpersName, Pradhum RampersName, Sudha RamalingampersName, Juwel RanapersName, Priyanga RanasinghepersName, Sowmya J. RaopersName, Priya RathipersName, Lal RawalpersName, Wasiq F. RawasiapersName, Reza RawassizadehpersName, Giuseppe RemuzzipersName, Andre M.N. RenzahopersName, Aziz RezapourpersName, Seyed Mohammad RiahipersName, Ross L. Roberts-ThomsonpersName, Leonardo RoeverpersName, Peter RohloffpersName, Michele RomolipersName, Gholamreza RoshandelpersName, Godfrey M. RwegererapersName, Seyedmohammad SaadatagahpersName, Maha M. Saber-AyadpersName, Siamak SabourpersName, Simona SaccopersName, Masoumeh SadeghipersName, Sahar Saeedi MoghaddampersName, Saeed SafaripersName, Amirhossein SahebkarpersName, Sana SalehipersName, Hamideh SalimzadehpersName, Mehrnoosh SamaeipersName, Abdallah M. SamypersName, Itamar S. SantospersName, Milena M. Santric-MilicevicpersName, Nizal SarrafzadeganpersName, Arash SarveazadpersName, Thirunavukkarasu SathishpersName, Monika SawhneypersName, Mete SaylanpersName, Maria I. SchmidtpersName, Aletta E. SchuttepersName, Subramanian SenthilkumaranpersName, Sadaf G. SepanloupersName, Feng ShapersName, Saeed ShahabipersName, Izza ShahidpersName, Masood A. ShaikhpersName, Mahdi ShamalipersName, Morteza ShamsizadehpersName, Md Shajedur Rahman ShawonpersName, Aziz SheikhpersName, Mika ShigematsupersName, Min-Jeong ShinpersName, Jae Il ShinpersName, Rahman ShiripersName, Ivy ShiuepersName, Kerem ShuvalpersName, Soraya SiabanipersName, Tariq J. SiddiqipersName, Diego A.S. SilvapersName, Jasvinder A. SinghpersName, Ambrish Singh MtechpersName, Valentin Y. SkryabinpersName, Anna A. SkryabinapersName, Amin SoheilipersName, Emma E. SpurlockpersName, Leo StockfeltpersName, Stefan StorteckypersName, Saverio StrangespersName, Rizwan Suliankatchi AbdulkaderpersName, Hooman TadbiripersName, Eyayou G. TadessepersName, Degena B. TadessepersName, Masih TajdinipersName, Md TariqujjamanpersName, Berhane F. TeklehaimanotpersName, Mohamad-Hani TemsahpersName, Ayenew K. TesemapersName, Bhaskar ThakurpersName, Kavumpurathu R. ThankappanpersName, Rekha ThaparpersName, Amanda G. ThriftpersName, Binod TimalsinapersName, Marcello TonellipersName, Mathilde TouvierpersName, Marcos R. Tovani-PalonepersName, Avnish TripathipersName, Jaya P. TripathypersName, Thomas C. TruelsenpersName, Guesh M. TsegaypersName, Gebiyaw W. TsegayepersName, Nikolaos TsilimparispersName, Biruk S. TusapersName, Stefanos TyrovolaspersName, Krishna Kishore UmapathipersName, Brigid UnimpersName, Bhaskaran UnnikrishnanpersName, Muhammad S. UsmanpersName, Muthiah VaduganathanpersName, Pascual R. ValdezpersName, Tommi J. VasankaripersName, Diana Z. VelazquezpersName, Narayanaswamy VenketasubramanianpersName, Giang T. VupersName, Isidora S. VujcicpersName, Yasir WaheedpersName, Yanzhong WangpersName, Fang WangpersName, Jingkai WeipersName, Robert G. WeintraubpersName, Abrha H. WeldemariampersName, Ronny WestermanpersName, Andrea S. WinklerpersName, Charles S. WiysongepersName, Charles D.A. WolfepersName, Befikadu Legesse WubishetpersName, Gelin XupersName, Ali YadollahpourpersName, Kazumasa YamagishipersName, Lijing L. YanpersName, Srikanth YandrapallipersName, Yuichiro YanopersName, Hiroshi YatsuyapersName, Tomas Y. YeheyispersName, Yigizie YeshawpersName, Christopher S. YilgwanpersName, Naohiro YonemotopersName, Chuanhua YupersName, Hasan YusefzadehpersName, Geevar ZachariahpersName, Sojib Bin ZamanpersName, Muhammed S. ZamanpersName, Maryam ZamanianpersName, Ramin ZandpersName, Alireza ZandifarpersName, Afshin ZarghipersName, Mikhail S. ZastrozhinpersName, Anasthasia ZastrozhinapersName, Zhi-Jiang ZhangpersName, Yunquan ZhangpersName, Wangjian ZhangpersName, Chenwen ZhongpersName, Zhiyong ZoupersName, Yves Miel H. ZunigapersName, Christopher J.L. MurraypersName, Valentin FusterpersName (2020date). “Global Burden of Cardiovascular Diseases and Risk Factors, 1990–2019.” Journal of the American College of Cardiologytitle·j.
Object of study — the global burden of cardiovascular diseases and their risk factors, 1990date to 2019date
Research question — how large total CVDterm·dom burden is, across 13 causes of cardiovascular death and 9 risk factors, in GBD 2019date estimates
Contribution — GBD 2019date estimates showing CVD as the leading cause of disease burden in the world
phenomenon · claim-type · cross-domain-transfer · mechanism · claim-relation · replication-status · limitation — grounded in the full text| phenomenon | rising global cardiovascular disease burden — “Prevalent cases of total CVD nearly doubled from 271 million (95% UI: 257 to 285 million) in 1990 to 523 million (95% UI: 497 to 550 million) in 2019” | |
| claim-type | existence — “Cardiovascular diseases remain the leading cause of disease burden in the world.” | |
| method — evidence | — “This paper reviews the magnitude of total CVD burden, including 13 underlying causes of cardiovascular death and 9 related risk factors, using estimates from the Global Burden of Disease (GBD) Study 2019.” | |
| object of study — evidence | — “This paper reviews the magnitude of total CVD burden, including 13 underlying causes of cardiovascular death and 9 related risk factors, using estimates from the Global Burden of Disease (GBD) Study 2019.” | |
| research question — evidence | — “This paper reviews the magnitude of total CVD burden, including 13 underlying causes of cardiovascular death and 9 related risk factors, using estimates from the Global Burden of Disease (GBD) Study 2019.” | |
| contribution — evidence | — “Cardiovascular diseases remain the leading cause of disease burden in the world.” | |
| mechanism | population growth and aging Falling age-standardized rates alongside rising counts are read as population growth and aging driving the rise in total CVD. — “From 1990 to 2019, large declines in the age-standardized rates of death, DALYs, and YLLs, together with small gradual reductions in age-standardized rates for prevalent cases and YLDs, suggest that population growth and aging are major drivers of the increase in total CVD ( Figure 1B ).” | mechanisms[0] |
| sense | burden - counted in DALYs, deaths, prevalent cases, YLLs and YLDs, as GBD 2019 estimates — “GBD 2019 is a multinational collaborative research study that estimates disease burden for every country in the world ( 1 , 4 ).” | senses[0] |
| sense | age-standardized rates - used to separate population growth and aging from change in the disease itself — “Comparing total numbers with age-standardized numbers suggests that global increases in AF/AFL are largely attributable to aging of the population and population growth.” | senses[1] |
| note | DALYs —operationalised-as→ YLLs — DOUBTFUL TYPE, found by an audit. The sentence says DALYs are the SUM of YLLs and YLDs, so YLLs is a component of DALYs - a part-of relation, not an operationalisation. Not retyped: the gate's part-of test refuses this clause, which carries no containment cue ('sum of' is not one). — “DALYs were calculated as the sum of YLLs, based on a reference maximum observed life expectancy, and YLDs based on standardized disability weights for each health state.” | concept_edges[0] |
| relation | DALYs —operationalised-as→ YLLs — calculated as the sum of — “DALYs were calculated as the sum of YLLs, based on a reference maximum observed life expectancy, and YLDs based on standardized disability weights for each health state.” | concept_edges[0] |
| weight | DALYs —operationalised-as→ YLLs — 0.9 — “DALYs were calculated as the sum of YLLs, based on a reference maximum observed life expectancy, and YLDs based on standardized disability weights for each health state.” | concept_edges[0] |
| note | DALYs —operationalised-as→ YLDs — DOUBTFUL TYPE, found by an audit. The sentence says DALYs are the SUM of YLLs and YLDs, so YLDs is a component of DALYs - a part-of relation, not an operationalisation. Not retyped: the gate's part-of test refuses this clause, which carries no containment cue ('sum of' is not one). — “DALYs were calculated as the sum of YLLs, based on a reference maximum observed life expectancy, and YLDs based on standardized disability weights for each health state.” | concept_edges[1] |
| relation | DALYs —operationalised-as→ YLDs — calculated as the sum of — “DALYs were calculated as the sum of YLLs, based on a reference maximum observed life expectancy, and YLDs based on standardized disability weights for each health state.” | concept_edges[1] |
| weight | DALYs —operationalised-as→ YLDs — 0.9 — “DALYs were calculated as the sum of YLLs, based on a reference maximum observed life expectancy, and YLDs based on standardized disability weights for each health state.” | concept_edges[1] |
| relation | High SBP —operationalised-as→ theoretical minimum risk exposure level — defined by — “High SBP is defined by GBD 2019 according to a theoretical minimum risk exposure level (TMREL) of ≥110 to 115 mm Hg, which is the level of exposure that minimizes risk at the population level.” | concept_edges[2] |
| weight | High SBP —operationalised-as→ theoretical minimum risk exposure level — 0.85 — “High SBP is defined by GBD 2019 according to a theoretical minimum risk exposure level (TMREL) of ≥110 to 115 mm Hg, which is the level of exposure that minimizes risk at the population level.” | concept_edges[2] |
| relation | Obesity —operationalised-as→ BMI — defined by — “Obesity, defined by elevated BMI (≥30 kg/m 2 ), has reached epidemic levels worldwide.” | concept_edges[3] |
| weight | Obesity —operationalised-as→ BMI — 0.9 — “Obesity, defined by elevated BMI (≥30 kg/m 2 ), has reached epidemic levels worldwide.” | concept_edges[3] |
| relation | LPA —operationalised-as→ metabolic equivalent minutes — defined as — “the total number of DALYs due to LPA, defined as <3,000 to 4,500 metabolic equivalent minutes per week” | concept_edges[4] |
| weight | LPA —operationalised-as→ metabolic equivalent minutes — 0.85 — “the total number of DALYs due to LPA, defined as <3,000 to 4,500 metabolic equivalent minutes per week” | concept_edges[4] |
| effect direction | population growth —causes→ total CVD — increases — “From 1990 to 2019, large declines in the age-standardized rates of death, DALYs, and YLLs, together with small gradual reductions in age-standardized rates for prevalent cases and YLDs, suggest that population growth and aging are major drivers of the increase in total CVD ( Figure 1B ).” — “suggest that population growth and aging are major drivers of the increase in total CVD” | concept_edges[5] |
| evidential status | population growth —causes→ total CVD — suggest — “From 1990 to 2019, large declines in the age-standardized rates of death, DALYs, and YLLs, together with small gradual reductions in age-standardized rates for prevalent cases and YLDs, suggest that population growth and aging are major drivers of the increase in total CVD ( Figure 1B ).” — “suggest that population growth and aging are major drivers of the increase in total CVD” | concept_edges[5] |
| relation | population growth —causes→ total CVD — major drivers of the increase — “From 1990 to 2019, large declines in the age-standardized rates of death, DALYs, and YLLs, together with small gradual reductions in age-standardized rates for prevalent cases and YLDs, suggest that population growth and aging are major drivers of the increase in total CVD ( Figure 1B ).” | concept_edges[5] |
| weight | population growth —causes→ total CVD — 0.75 — “From 1990 to 2019, large declines in the age-standardized rates of death, DALYs, and YLLs, together with small gradual reductions in age-standardized rates for prevalent cases and YLDs, suggest that population growth and aging are major drivers of the increase in total CVD ( Figure 1B ).” | concept_edges[5] |
| relation | MV prolapse —causes→ mitral regurgitation — can lead to — “The major cause of nonrheumatic degenerative mitral valve (MV) disease is MV prolapse ( 29 , 30 ). Untreated, this can lead to chronic mitral regurgitation, AF, and heart failure ( 31 ).” | concept_edges[6] |
| weight | MV prolapse —causes→ mitral regurgitation — 0.75 — “The major cause of nonrheumatic degenerative mitral valve (MV) disease is MV prolapse ( 29 , 30 ). Untreated, this can lead to chronic mitral regurgitation, AF, and heart failure ( 31 ).” | concept_edges[6] |
| relation | other cardiomyopathy —contrasted-with→ myocarditis — greater rise compared to — “The relatively greater rise of other cardiomyopathy compared to myocarditis prevalence with age is constant, from 1990 to 2019.” | concept_edges[7] |
| weight | other cardiomyopathy —contrasted-with→ myocarditis — 0.75 — “The relatively greater rise of other cardiomyopathy compared to myocarditis prevalence with age is constant, from 1990 to 2019.” | concept_edges[7] |
| relation | secondhand smoke —part-of→ Tobacco — including — “Tobacco, including primary smoking, secondhand smoke, and use of chewing tobacco, accounted for 8.71 million (95% UI: 8.12 to 9.31 million) deaths and 230 million (95% UI: 213 to 246 million) DALYs in 2019 ( Figure 21A ).” | concept_edges[8] |
| weight | secondhand smoke —part-of→ Tobacco — 0.75 — “Tobacco, including primary smoking, secondhand smoke, and use of chewing tobacco, accounted for 8.71 million (95% UI: 8.12 to 9.31 million) deaths and 230 million (95% UI: 213 to 246 million) DALYs in 2019 ( Figure 21A ).” | concept_edges[8] |
| replication | original | |
| limitation | Death certificates may misclassify the underlying cause of death. — “Death certificates, although an essential source of data for public health, may be misclassified given the difficulty of identifying an underlying cause of death.” | limitations[0] |
| limitation | Lower access to diagnostic technology lowers observed prevalence. — “Access to CVD diagnostic technologies is likely to influence estimates of some conditions, with lower access resulting in lower observed prevalence.” | limitations[1] |
| limitation | Uncertainty may be underestimated where no CVD data exist. — “However, uncertainty may be underestimated in locations where no data on CVD of any kind are available.” | limitations[2] |
| limitation | Common diagnostic tools such as ECG underestimate true prevalence, e.g. of AF. — “Some commonly used diagnostic tools—for example, electrocardiogram for AF—will underestimate true disease prevalence.” | limitations[3] |
finding · quantity · variable · definition · key-term — grounded in the full text| finding | Prevalent CVD cases nearly doubled, 271 million in 1990 to 523 million in 2019. — “Prevalent cases of total CVD nearly doubled from 271 million (95% UI: 257 to 285 million) in 1990 to 523 million (95% UI: 497 to 550 million) in 2019” | content_tags.findings[0] |
| finding | IHD reached 182 million DALYs and 9.14 million deaths in 2019. — “The total number of DALYs due to IHD has risen steadily since 1990, reaching 182 million (95% UI: 170 to 194 million) DALYs and 9.14 million (95% UI: 8.40 to 9.74 million) deaths in the year 2019 ( Figure 2A ).” | content_tags.findings[1] |
| finding | Stroke remains the second-leading cause of death, with rising DALYs. — “Stroke remains the second-leading cause of death, and stroke burden in terms of DALYs is increasing.” | content_tags.findings[2] |
| finding | Stroke DALY and death rates are higher in men; prevalence is higher in women. — “Age-standardized rates of DALYs and deaths due to stroke were substantially greater in men compared to women, but prevalence was greater in women, suggesting the possibility of greater risk of death and disability in men but better stroke survival in women.” | content_tags.findings[3] |
| finding | Global increases in AF/AFL are largely attributable to aging and population growth. — “Comparing total numbers with age-standardized numbers suggests that global increases in AF/AFL are largely attributable to aging of the population and population growth.” | content_tags.findings[4] |
| finding | Other cardiomyopathy rises more with age than myocarditis, constantly from 1990 to 2019. — “The relatively greater rise of other cardiomyopathy compared to myocarditis prevalence with age is constant, from 1990 to 2019.” | content_tags.findings[5] |
| finding | Unlike other CVDs, CHA burden after birth is reduced only by early diagnosis and cardiac surgery. — “Unlike other CVDs, prevention has limited ability to reduce the burden of CHA after birth; improvements in early diagnosis and access to cardiac surgery are the only solutions.” | content_tags.findings[6] |
| finding | HAP-attributable burden fell sharply, unlike ambient PM2.5. — “In contrast to ambient PM 2.5 , the burden attributable to HAP decreased sharply” | content_tags.findings[7] |
| quantity | 6.2 million — — “CVD was the cause of 6.2 million deaths occurring between the ages of 30 and 70 years in 2019.” | content_tags.quantities[0] |
| quantity | 33.5% and 6.8% — — “In 2019, global smoking prevalence among men was 33.5% (95% UI: 33.1% to 33.9%), and smoking prevalence among women was 6.8% (95% UI: 6.6% to 7.0%).” | content_tags.quantities[1] |
| quantity | 51.5% and 30.5% — — “In 2019, CVD accounted for 51.5% and 30.5% of the total DALYS attributable to PM 2.5 and HAP, respectively.” | content_tags.quantities[2] |
| variable | High SBP — “High SBP is defined by GBD 2019 according to a theoretical minimum risk exposure level (TMREL) of ≥110 to 115 mm Hg, which is the level of exposure that minimizes risk at the population level.” | content_tags.variables[0] |
| variable | LPA — “the total number of DALYs due to LPA, defined as <3,000 to 4,500 metabolic equivalent minutes per week” | content_tags.variables[1] |
| definition | DALYs — “DALYs represent the sum of years of life lost prematurely and years lived with disability” | content_tags.definitions[0] |
| definition | Hypertensive heart disease — “Hypertensive heart disease (HHD) was defined as symptomatic heart failure due to the direct and long-term effects of hypertension.” | content_tags.definitions[1] |
| definition | Cardiomyopathy — “Cardiomyopathy was defined as symptomatic heart failure due to primary myocardial disease or toxin exposure to the myocardium, such as alcohol.” | content_tags.definitions[2] |
| definition | Obesity — “Obesity, defined by elevated BMI (≥30 kg/m 2 ), has reached epidemic levels worldwide.” | content_tags.definitions[3] |
| definition | impaired kidney function — “GBD 2019 estimates use the term chronic kidney disease (CKD) to refer to the morbidity and mortality that can be directly attributed to all stages of CKD and the term impaired kidney function (IKF) to capture the additional risk of CKD from other associated conditions such as CVD and gout.” | content_tags.definitions[4] |
| key-terms | DALYs, YLLs, YLDs, ischemic heart disease, stroke, age-standardized, uncertainty interval, population growth, High SBP, BMI, air pollution, tobacco |
| concept | DALYs — the sum of years of life lost prematurely and years lived with disability — “DALYs represent the sum of years of life lost prematurely and years lived with disability” | concepts[0] |
| concept | YLLs — number of years of life lost (YLLs) — “DALYs were calculated as the sum of YLLs, based on a reference maximum observed life expectancy, and YLDs based on standardized disability weights for each health state.” | concepts[1] |
| concept | YLDs — number of years lived with disability (YLDs) — “DALYs were calculated as the sum of YLLs, based on a reference maximum observed life expectancy, and YLDs based on standardized disability weights for each health state.” | concepts[2] |
| concept | Population-attributable fractions — calculated independently by risk factor by using risk exposures, estimates of relative risk based on meta-analyses, and theoretical minimum risk levels — “Population-attributable fractions were calculated independently by risk factor by using risk exposures, estimates of relative risk based on meta-analyses, and theoretical minimum risk levels determined for each risk-outcome pair.” | concepts[3] |
| concept | Age standardization — performed via the direct method, applying a global age structure from the year 2019 — “Age standardization was performed via the direct method, applying a global age structure from the year 2019.” | concepts[4] |
| concept | Hypertensive heart disease — symptomatic heart failure due to the direct and long-term effects of hypertension — “Hypertensive heart disease (HHD) was defined as symptomatic heart failure due to the direct and long-term effects of hypertension.” | concepts[5] |
| concept | Cardiomyopathy — symptomatic heart failure due to primary myocardial disease or toxin exposure to the myocardium — “Cardiomyopathy was defined as symptomatic heart failure due to primary myocardial disease or toxin exposure to the myocardium, such as alcohol.” | concepts[6] |
| concept | peripheral artery disease — defined by an ankle brachial index of <0.9 — “Lower extremity peripheral artery disease (PAD) was defined by an ankle brachial index of <0.9.” | concepts[7] |
| concept | population growth — not defined in this paper's own words; with aging, named as a major driver of the rise in CVD counts — “From 1990 to 2019, large declines in the age-standardized rates of death, DALYs, and YLLs, together with small gradual reductions in age-standardized rates for prevalent cases and YLDs, suggest that population growth and aging are major drivers of the increase in total CVD ( Figure 1B ).” | concepts[8] |
| concept | total CVD — not defined in this paper's own words — “From 1990 to 2019, large declines in the age-standardized rates of death, DALYs, and YLLs, together with small gradual reductions in age-standardized rates for prevalent cases and YLDs, suggest that population growth and aging are major drivers of the increase in total CVD ( Figure 1B ).” | concepts[9] |
| concept | other cardiomyopathy — not defined in this paper's own words — “The relatively greater rise of other cardiomyopathy compared to myocarditis prevalence with age is constant, from 1990 to 2019.” | concepts[10] |
| concept | myocarditis — not defined in this paper's own words — “The relatively greater rise of other cardiomyopathy compared to myocarditis prevalence with age is constant, from 1990 to 2019.” | concepts[11] |
| concept | MV prolapse — not defined in this paper's own words; named as the major cause of degenerative mitral valve disease — “The major cause of nonrheumatic degenerative mitral valve (MV) disease is MV prolapse ( 29 , 30 ). Untreated, this can lead to chronic mitral regurgitation, AF, and heart failure ( 31 ).” | concepts[12] |
| concept | mitral regurgitation — not defined in this paper's own words — “The major cause of nonrheumatic degenerative mitral valve (MV) disease is MV prolapse ( 29 , 30 ). Untreated, this can lead to chronic mitral regurgitation, AF, and heart failure ( 31 ).” | concepts[13] |
| concept | High SBP — defined by GBD 2019 according to a theoretical minimum risk exposure level (TMREL) of ≥110 to 115 mm Hg — “High SBP is defined by GBD 2019 according to a theoretical minimum risk exposure level (TMREL) of ≥110 to 115 mm Hg, which is the level of exposure that minimizes risk at the population level.” | concepts[14] |
| concept | theoretical minimum risk exposure level — the level of exposure that minimizes risk at the population level — “High SBP is defined by GBD 2019 according to a theoretical minimum risk exposure level (TMREL) of ≥110 to 115 mm Hg, which is the level of exposure that minimizes risk at the population level.” | concepts[15] |
| concept | Obesity — defined by elevated BMI (≥30 kg/m 2 ) — “Obesity, defined by elevated BMI (≥30 kg/m 2 ), has reached epidemic levels worldwide.” | concepts[16] |
| concept | BMI — body mass index (BMI) — “Obesity, defined by elevated BMI (≥30 kg/m 2 ), has reached epidemic levels worldwide.” | concepts[17] |
| concept | LPA — defined as <3,000 to 4,500 metabolic equivalent minutes per week — “the total number of DALYs due to LPA, defined as <3,000 to 4,500 metabolic equivalent minutes per week” | concepts[18] |
| concept | metabolic equivalent minutes — not defined in this paper's own words — “the total number of DALYs due to LPA, defined as <3,000 to 4,500 metabolic equivalent minutes per week” | concepts[19] |
| concept | impaired kidney function — to capture the additional risk of CKD from other associated conditions such as CVD and gout — “GBD 2019 estimates use the term chronic kidney disease (CKD) to refer to the morbidity and mortality that can be directly attributed to all stages of CKD and the term impaired kidney function (IKF) to capture the additional risk of CKD from other associated conditions such as CVD and gout.” | concepts[20] |
| concept | chronic kidney disease — the morbidity and mortality that can be directly attributed to all stages of CKD — “GBD 2019 estimates use the term chronic kidney disease (CKD) to refer to the morbidity and mortality that can be directly attributed to all stages of CKD and the term impaired kidney function (IKF) to capture the additional risk of CKD from other associated conditions such as CVD and gout.” | concepts[21] |
| concept | Tobacco — including primary smoking, secondhand smoke, and use of chewing tobacco — “Tobacco, including primary smoking, secondhand smoke, and use of chewing tobacco, accounted for 8.71 million (95% UI: 8.12 to 9.31 million) deaths and 230 million (95% UI: 213 to 246 million) DALYs in 2019 ( Figure 21A ).” | concepts[22] |
| concept | secondhand smoke — not defined in this paper's own words — “Tobacco, including primary smoking, secondhand smoke, and use of chewing tobacco, accounted for 8.71 million (95% UI: 8.12 to 9.31 million) deaths and 230 million (95% UI: 213 to 246 million) DALYs in 2019 ( Figure 21A ).” | concepts[23] |
| concept | dietary risks — the sum of adverse effects of diets in which 15 food types are either underconsumed — “In GBD 2019, dietary risks comprise the sum of adverse effects of diets in which 15 food types are either underconsumed” | concepts[24] |
Full text — tagged
Global Burden of Cardiovascular Diseases and Risk Factors, 1990date–2019date Abstract Cardiovascular diseases (CVDs), principally ischemic heart diseaseterm·key (IHD) and stroketerm·key, are the leading cause of global mortality and a major contributor to disability. This paper reviewsterm·mth the magnitude of total CVDterm·dom burden, including 13 underlying causes of cardiovascular death and 9 related risk factors, using estimates from the Global Burden of Disease (GBD) Study 2019date. GBD, an ongoing multinational collaboration to provide comparable and consistent estimates of population health over time, used all available population-level data sources on incidence, prevalence, case fatality, mortality, and health risks to produce estimates for 204 countries and territories from 1990date to 2019date.
Prevalent cases of total CVDterm·dom nearly doubled from 271 million (95% uncertainty intervalterm·key [UI]: 257 to 285 million) in 1990date to 523 million (95% UI: 497 to 550 million) in 2019date, and the number of CVD deaths steadily increased from 12.1 million (95% UI:11.4 to 12.6 million) in 1990date, reaching 18.6 million (95% UI: 17.1 to 19.7 million) in 2019date. The global trends for disability-adjusted life years (DALYsterm·mth) and years of life lost also increased significantly, and years lived with disability doubled from 17.7 million (95% UI: 12.9 to 22.5 million) to 34.4 million (95% UI:24.9 to 43.6 million) over that period. The total number of DALYsterm·mth due to IHD has risen steadily since 1990date, reaching 182 million (95% UI: 170 to 194 million) DALYsterm·mth, 9.14 million (95% UI: 8.40 to 9.74 million) deaths in the year 2019date, and 197 million (95% UI: 178 to 220 million) prevalent cases of IHD in 2019date. The total number of DALYsterm·mth due to stroketerm·key has risen steadily since 1990date, reaching 143 million (95% UI: 133 to 153 million) DALYsterm·mth, 6.55 million (95% UI: 6.00 to 7.02 million) deaths in the year 2019date, and 101 million (95% UI: 93.2 to 111 million) prevalent cases of stroketerm·key in 2019date.
Cardiovascular diseases remain the leading cause of disease burden in the world. CVD burden continues its decades-long rise for almost all countries outside high-income countries, and alarmingly, the age-standardizedterm·key rate of CVD has begun to rise in some locations where it was previously declining in high-income countries. There is an urgent need to focus on implementing existing cost-effective policies and interventions if the world is to meet the targets for Sustainable Development Goal 3 and achieve a 30% reduction in premature mortality due to noncommunicable diseases.
Key Words: cardiovascular diseases, global health, health policy, population health Abbreviations and Acronyms: AC, alcoholic cardiomyopathyterm·dom; AF, atrial fibrillation; AFL, atrial flutter; BMIterm·mth, body mass index; CAVD, calcific aortic valve disease; CHA, congenital heart anomalies; CKD, chronic kidney diseaseterm·dom; CVD, cardiovascular disease; DALYsterm·mth, disability-adjusted life years; GBD, Global Burden of Diseases, Injuries, and Risk Factors Study; HAP, household air pollutionterm·key; HHD, hypertensive heart diseaseterm·dom; HICs, high-income countries; ICD, International Classification of Diseases; IHD, ischemic heart diseaseterm·key; IKF, impaired kidney functionterm·dom; IS, ischemic stroketerm·key; LDL, low-density lipoprotein; LMICs, low- and middle-income countries; LPAterm·dom, low physical activity; MV, mitral valve; PAD, peripheral artery diseaseterm·dom; PM, particulate matter; RHD, rheumatic heart disease; SBP, systolic blood pressure; SDI, sociodemographic index; TMREL, theoretical minimum risk exposure levelterm·mth; UI, uncertainty intervalterm·key; YLDsterm·mth, years lived with disability; YLLsterm·mth, years of life lost Cardiovascular disease (CVD) remains a major cause of premature mortality and rising health care costs ( 1 , 2 ). Cardiometabolic, behavioral, environmental, and social risk factors are major drivers of CVD. Consistent, comparable, and systematic analysis of long-term trends and patterns in global CVD are essential to guide public policy and provide benchmarks for decision makers. Beginning with ischemic heart diseaseterm·key (IHD) and stroketerm·key, this article provides information on the burden of CVD, including 13 underlying causes of cardiovascular death and 9 related risk factors at the global, regional, and national levels ( Supplemental Figure 1 ).
This paper explores CVD trends from 1990date to 2019date and examines the extent to which population growthterm·dom and aging explain the observed trends, sex differences, and regional patterns and how the epidemiology of the disease itself is changing. These trends show us where in the world CVD mortality and burden are increasing or declining and where progress has stalled ( 3 ). For each of the contributing causes of cardiovascular death and risk factors examined, we identify which regions and countries have the highest and lowest estimates of prevalent cases and number of deaths, as well as summary measures including number of years of life lost (YLLsterm·mth), number of years lived with disability (YLDsterm·mth), and the magnitude and temporal trends in disability-adjusted life years (DALYsterm·mth) ( 1 ). For each section, the article also addresses how the summary measures of health and disease discussed inform investments in cardiovascular research, their implications for clinical and public health practice, and implications for health system development and national and regional policy.
To improve accessibility across a wide range of audiences, we have structured the reviewterm·mth such that each section can be read independently for those most interested in a subset of causes or risks. Discussion pertinent to each topic is included within every section. This article is a collaborative effort involving the Journal of the American College of Cardiology , the National Heart, Lung, and Blood Institute, and the Institute for Health Metrics and Evaluation at the University of Washington designed to provide crucial population-level information that can guide action for CVD and risk factor prevention, treatment, and control ( 2 ).
Global Burden of Disease Study Methods GBD 2019date is a multinational collaborative research study that estimates disease burden for every country in the world ( 1 , 4 ). The study is an ongoing effort, updated annually, and is designed to allow for consistent comparison over time from 1990date to 2019date, by age and sex, and across locations. The study produces standard epidemiological measures such as incidence, prevalence, and death rates as well as summary measures of health, such as DALYsterm·mth. DALYsterm·mth represent the sum of years of life lost prematurely and years lived with disability; can be estimated from life tables, estimates of prevalence, and disability weights; and may be expressed as counts or rates. Annual updates to the study include new diseases, new data sources, and updates to methods. All results are available via the GBD Compare website ( 5 ), and all input data is identified via the Global Health Data Exchange website ( 6 ). The study is performed in compliance with Guidelines for Accurate and Transparent Health Estimates Reporting (GATHER) guidelines for reporting health estimates ( 1 ). Detailed methods of GBD 2019date are reported in the Supplemental Appendix and summarized here.
Each CVD cause and related health states were identified with standard case definitions. IHD represented acute myocardial infarction, chronic stable angina, chronic IHD, and heart failure due to IHD. Myocardial infarction was defined according to the Fourth Universal Definition of Myocardial Infarction and was adjusted to include out-of-hospital sudden cardiac death. Stable angina was defined according to the Rose Angina Questionnaire. Stroketerm·key was defined according to the World Health Organization definition and was estimated separately for 3 subcategories: 1) ischemic stroketerm·key (IS); 2) intracerebral hemorrhage; and 3) subarachnoid hemorrhage ( 7 ). Lower extremity peripheral artery diseaseterm·dom (PAD) was defined by an ankle brachial index of <0.9. Symptomatic PAD was defined as self-report of symptoms of claudication among those with an ankle brachial index of <0.9. Atrial fibrillation (AF) and atrial flutter (AFL) were defined by electrocardiogram. Hypertensive heart diseaseterm·dom (HHD) was defined as symptomatic heart failure due to the direct and long-term effects of hypertension. Cardiomyopathyterm·dom was defined as symptomatic heart failure due to primary myocardial disease or toxin exposure to the myocardium, such as alcohol. Acute myocarditisterm·dom was defined as an acute and time-limited condition due to myocardial inflammation using health system administrative data. Endocarditis and rheumatic heart disease (RHD) were defined by their clinical diagnosis. Estimates of RHD include cases identified by clinical history and physical examination, including auscultation or standard echocardiographic criteria for definite disease.
Mortality was estimated by using vital registration data coded to the International Classification of Disease (ICD) system or household mortality surveys known as verbal autopsy. Statistical methods were used to increase the comparability of mortality data sources, including the reclassification of codes that are nonspecific or unspecified, noise reduction algorithms, and Bayesian geospatial regression software (CODem, the cause of death ensemble model, Institute for Health Metrics and Evaluation, Seattle, Washington) that used location-specific covariates to create smoothed time trends for 204 countries and territories by borrowing strength over age, space, and time. GBD 2019date allowed for the production of estimates with uncertainty intervalsterm·key (UIs) for all locations in every year, even when data were sparse or missing. Disease incidence and prevalence were produced by using a broad range of population-representative data sources identified by literature reviewterm·mth and via study collaborations, including scientific reports of cohorts and registries, population surveys, microdata from registry and cohort studies, and health system administrative data. Consistent disease estimates were produced by using epidemiologic state-transition disease modeling software, DisMod-MR (Institute for Health Metrics and Evaluation), and Bayesian meta-regression software, MR-BRT (Institute for Health Metrics and Evaluation), that adjusted for study-level differences in measurement methods and case definitions. Risk factor exposures were estimated by using population-representative survey and surveillance data and geospatial Gaussian process regression models that borrowed strength across time and geography.
DALYsterm·mth were calculated as the sum of YLLsterm·mth, based on a reference maximum observed life expectancy, and YLDsterm·mth based on standardized disability weights for each health state. Population-attributable fractionsterm·mth were calculated independently by risk factor by using risk exposures, estimates of relative risk based on meta-analyses, and theoretical minimum risk levels determined for each risk-outcome pair. Adjustment was made for comorbidity by simulating 40,000 individuals in each age-sex-country-year exposed to the independent probability of acquiring conditions based on their prevalence. The 95% UIs reported for each estimate used 1,000 draws from the posterior distribution of models, reported as the 2.5th and 97.5th values of the distribution. Age standardizationterm·mth was performed via the direct method, applying a global age structure from the year 2019date. The University of Washington Institutional Reviewterm·mth Board Committee approved the Global Burden of Diseases, Injuries, and Risk Factors Study (STUDY00009060).
Cardiovascular Diseases Total CVDsterm·dom CVDs are common, have poor survival, and are increasing worldwide ( Central Illustration ). Prevalent cases of total CVDterm·dom nearly doubled from 271 million (95% UI: 257 to 285 million) in 1990date to 523 million (95% UI: 497 to 550 million) in 2019date, and the number of CVD deaths steadily increased from 12.1 million (95% UI: 11.4 to 12.6 million) in 1990date, reaching 18.6 million (95% UI: 17.1 to 19.7 million) in 2019date ( Figure 1A ). The global trends for DALYsterm·mth and YLLsterm·mth also increased significantly, and YLDsterm·mth doubled from 17.7 million (95% UI: 12.9 to 22.5 million) to 34.4 million (95% UI: 24.9 to 43.6 million) over that period.
Cardiovascular Disease Burden Across Time, Location, Cause, and Risk Factor Percent Change in Age-Standardizedterm·key CVD Death Rate from 2010-2019. Map of the percent change in age-standardizedterm·key CVD mortality rate from 2010 to 2019date. Number of CVD Deaths. Total number of deaths due to CVD by sex, 1990date to 2019date; total number of deaths due to CVD in 2010 and 2019date among the countries with the highest number of CVD deaths in 2019date. Proportion of CVD Deaths by Cause (2019date) . Proportion of total CVDterm·dom deaths in 2019date by underlying causes. CVD Burden Attributable to Modifiable Risk Factors . Comparison of the rankings of CVD DALYsterm·mth attributable to modifiable risk factors in 1990date and 2019date. CVD = cardiovascular disease; DALYsterm·mth = disability-adjusted life years; LDL = low-density lipoprotein.
Total Numbers and Rates of Cardiovascular Diseases (A) Total number of DALYsterm·mth, deaths, prevalent cases, YLDsterm·mth, and YLLsterm·mth due to cardiovascular diseases, 1990date to 2019date. Shaded region s represent 95% uncertainty intervalsterm·key. (B) Age-standardizedterm·key and all-ages DALYterm·mth, death, prevalence, YLDterm·mth, and YLLterm·mth rates of cardiovascular diseases, 1990date to 2019date. Shaded regions represent 95% uncertainty intervalsterm·key. DALYsterm·mth = disability-adjusted life years; YLDsterm·mth = years lived with disability; YLLsterm·mth = years of life lost.
At the country level, age-standardizedterm·key mortality rates for total CVDterm·dom were highest in Uzbekistan, Solomon Islands, and Tajikistan and were lowest in France, Peru, and Japan, where rates were 6-fold lower in 2019date. From 1990date to 2019date, large declines in the age-standardizedterm·key rates of death, DALYsterm·mth, and YLLsterm·mth, together with small gradual reductions in age-standardizedterm·key rates for prevalent cases and YLDsterm·mth, suggest that population growthterm·dom and aging are major drivers of the increase in total CVDterm·dom ( Figure 1B ).
In 2019date, total CVDterm·dom DALYsterm·mth were higher in men than women before age 80 to 84 years ( Supplemental Figure 2 ). After this age, the pattern reverses. The sex differences in DALYsterm·mth is most striking between ages 30 and 60 years (men greater) and age >80 years (women greater). The excess CVD deaths in women beginning at ages 80 to 84 years should focus attention to cause-specific mortality at older ages and have implications for secondary prevention strategies.
Among women, the age-standardizedterm·key rates for DALYsterm·mth were highest in Central Asia, Oceania, North Africa and the Middle East, and Eastern Europe; and lowest in High-Income Asia Pacific, Australasia, and Western Europe ( Supplemental Figure 3 ). Among men, age-standardizedterm·key rates for DALYsterm·mth were highest in Central Asia, Eastern Europe, and Oceania; and lowest in High-Income Asia Pacific, Australasia, Western Europe, and Andean Latin America. At the country level, the highest age-standardizedterm·key rates were estimated for many of the islands of Oceania, Uzbekistan, and Afghanistan, while the lowest rates for DALYsterm·mth were seen in Japan, France, and Israel ( Supplemental Figure 4 ). These regional and national differences in total CVDterm·dom burden and mortality reflect differences in prevalence of CVD risk factors as well as access to health care ( 8 ). Differences in access to effective primary and secondary prevention strategies may also play a role in differences in total CVDterm·dom burden, especially in low- and middle-income countries (LMICs) ( 9 ).
Global patterns of total CVDterm·dom have significant implications for clinical practice and public health policy development ( 10 ). Prevalent cases of total CVDterm·dom are likely to increase substantially as a result of population growthterm·dom and aging, especially in Northern Africa and Western Asia, Central and Southern Asia, Latin America and the Caribbean, and Eastern and Southeastern Asia, where the share of older persons is projected to double between 2019date and 2050 ( 11 , 12 ). Increased attention to promoting ideal cardiovascular health and healthy aging across the lifespan is necessary ( 13 ). Equally importantly, the time has come to implement feasible and affordable strategies for the prevention and control of CVD and to monitor results ( 14 ).
Ischemic heart diseaseterm·key The total number of DALYsterm·mth due to IHD has risen steadily since 1990date, reaching 182 million (95% UI: 170 to 194 million) DALYsterm·mth and 9.14 million (95% UI: 8.40 to 9.74 million) deaths in the year 2019date ( Figure 2A ). GBD 2019date estimated 197 million (95% UI: 178 to 220 million) prevalent cases of IHD in 2019date.
Total Numbers and Rates of Ischemic Heart Diseaseterm·key (A) Total number of DALYsterm·mth, deaths, prevalent cases, YLDsterm·mth, and YLLsterm·mth due to ischemic heart diseaseterm·key, 1990date to 2019date. Shaded regions represent 95% uncertainty intervalsterm·key. (B) Age-standardizedterm·key and all-ages DALYterm·mth, death, prevalence, YLDterm·mth, and YLLterm·mth rates of ischemic heart diseaseterm·key, 1990date to 2019date . Shaded regions represent 95% uncertainty intervalsterm·key. Abbreviations as in Figure 1 .
Global age-standardizedterm·key rates for DALYsterm·mth, deaths, and prevalent cases declined over this time period, indicating that, on average, global increases in IHD have been due to population growthterm·dom and aging, although even the age-standardizedterm·key death rate is estimated to be increasing in many locations across South, East, and Southeastern Asia, including China ( Figure 2B ). Increasing absolute numbers of incident and prevalent IHD cases in most countries mean that national health systems will need to address increasing demand for IHD-related preventive and therapeutic services as these trends continue.
At the global level, substantially more total DALYsterm·mth due to IHD were experienced by men than women. IHD DALYsterm·mth rose rapidly for men beginning at age 30 years ( Supplemental Figure 5 ). Men ages 45 to 49 years had almost as many DALYsterm·mth due to IHD as women ages 65 to 69 years.
Age-standardizedterm·key DALYterm·mth rates due to IHD were highest in Eastern Europe, Central Asia, Oceania, and the Middle East/North Africa regions ( Supplemental Figure 6 ). At the country level, extremely high rates were estimated for Uzbekistan, Ukraine, Tajikistan, and many of the islands of Oceania, with the lowest levels in Japan, Republic of Korea, and France ( Supplemental Figure 7 ). Higher exposure to risks including tobaccoterm·dom and excessive alcohol use and restricted access to preventive health care may partly explain these patterns.
IHD remains a major threat to public health, and overall burden is increasing globally. In some locations over the past 5 years, including parts of the United States and United Kingdom, age-standardizedterm·key IHD death rates are increasing, suggesting that long-term declines in IHD due to improved prevention and health care are no longer occurring in these locations. Health systems and countries need to focus on delivering effective interventions that will reverse these trends, including those that prevent and control diabetes, decrease obesityterm·dom and high cholesterol, improve diet and physical activity, reduce tobaccoterm·dom and excessive alcohol use, integrate “polypills” for elevated blood pressure and related conditions, improve pre- and in-hospital care for acute coronary syndrome, and improve survival and quality of life for those living with the long-term sequelae of IHD, including heart failure. Social and economic factors remain fundamental drivers of IHD, suggesting that multisectoral interventions are needed to eradicate this disease ( 15 ).
Stroketerm·key The total number of prevalent strokesterm·key, deaths, and DALYsterm·mth due to stroketerm·key increased steadily from 1990date, reaching 101 million (95% UI: 93.2 to 111 million) prevalent (85.3% [95% UI: 82.6% to 88.2%] increase) stroketerm·key survivors, 6.55 million (95% UI: 6.00 to 7.02 million) deaths from stroketerm·key (43.3% [95% UI: 31.0% to 55.4%] increase), and 143 million (95% UI: 133 to 153 million) DALYsterm·mth due to stroketerm·key (32.4% [95% UI: 22.0% to 42.2%] increase) in 2019date, with the bulk of the burden outside of the high-income world ( Supplemental Figures 8A and 9A ). Of 12.2 million (95% UI: 11.0 to 13.6 million) incident stroketerm·key cases, 7.63 million (95% UI: 6.57 to 8.96 million) (62.4%) were IS, 3.41 million (95% UI: 2.97 to 3.91 million) (27.9%) were intracerebral hemorrhages, and 1.18 million (95% UI: 1.01 to 1.39 million) (9.7%) were subarachnoid hemorrhages ( Supplemental Figures 10A, 11A, and 12A ).
Globally, age-standardizedterm·key rates for deaths and DALYsterm·mth due to stroketerm·key substantially declined over the same period of time ( Supplemental Figures 8B, 10B, 11B, and 12B ), suggesting that: 1) preventive measures are very effective at lowering risk of both ischemic and hemorrhagic stroketerm·key; and 2) on average, global increases in stroketerm·key burden have been largely due to population growthterm·dom and aging. Importantly, age-standardizedterm·key rates for prevalence of stroketerm·key survivors have increased since 1990date in several locations including in China, Indonesia, and parts of the United States. Age-standardizedterm·key death rates also increased in some locations, including Indonesia and the Philippines.
Age-standardizedterm·key rates of DALYsterm·mth and deaths due to stroketerm·key were substantially greater in men compared to women, but prevalence was greater in women, suggesting the possibility of greater risk of death and disability in men but better stroketerm·key survival in women. Similar patterns were observed in men and women with IS, intracerebral hemorrhage, and subarachnoid hemorrhage.
There is tremendous regional disparity in the burden of stroketerm·key. Age-standardizedterm·key rates of deaths and DALYsterm·mth due to stroketerm·key were highest in Oceania, Central Asia, East Asia, Southeast Asia, Eastern Europe, and sub-Saharan Africa ( Supplemental Figure 13 ), and prevalence of stroketerm·key survivors was highest in Oceania, Southeast Asia, East Asia, and the Middle East/North Africa regions ( Figure 3 ). These data suggest that applying preventive strategies, such as treatment of elevated blood pressure and cholesterol levels, can have major health benefits where stroketerm·key burden remains high, especially in those regions that did not show substantial decline over the past decades (i.e., Central Asia, Southern sub-Saharan Africa).
Map of Age-Standardizedterm·key Prevalence of Stroketerm·key Survivors in 2019date Stroketerm·key remains the second-leading cause of death, and stroketerm·key burden in terms of DALYsterm·mth is increasing. Primary stroketerm·key prevention strategies are not sufficiently effective as currently implemented in many countries. Large (15- to 20-fold) geographic variations in age-standardizedterm·key stroketerm·key DALYsterm·mth and mortality rates and moderate (4-fold) geographic variations in age-standardizedterm·key prevalence may be related to the variations in stroketerm·key risk factors or the quality of and/or access to preventative care, acute stroketerm·key care, and stroketerm·key rehabilitation across the globe. Global and national health systems need to focus on new approaches for delivering stroketerm·key prevention that can reverse these trends, including strategies to provide equal access to quality health care and prevention across all populations, and proven effective interventions that reduce mortality and improve outcomes post-stroke.
Hypertensive heart diseaseterm·dom The global prevalence of HHD has risen steadily over the last 3 decades, as have the total number of deaths, DALYsterm·mth, YLLsterm·mth, and YLDsterm·mth due to this disease. In 2019date, HHD was the main cause of 1.16 million (95% UI: 0.86 to 1.28 million) deaths and 21.5 million (95% UI: 16.4 to 23.9 million) DALYsterm·mth annually, with a global prevalence of 18.6 million (95% UI: 13.5 to 24.9 million) cases ( Supplemental Figure 14A ).
The age-standardizedterm·key prevalence and YLDsterm·mth of HHD per 100,000 persons have been constant over time, and corresponding age-standardizedterm·key rates of deaths, DALYsterm·mth, and YLLsterm·mth declined steadily until the mid-2000s and have leveled off since then ( Supplemental Figure 14B ). The trends for the absolute and age-standardizedterm·key estimates closely resemble those for high blood pressure. Taken together, the increasing global prevalence and rates of HHD can be explained by population growthterm·dom and aging, but the declining age-standardizedterm·key rates of adverse outcomes are similar to those for IHD and stroketerm·key and are likely an effect of improved secondary prevention.
The age distributions of DALYsterm·mth due to HHD are very similar in women and men until age 70 years ( Supplemental Figure 15 ). This contrasts to the age distributions for high blood pressure, in which men are overrepresented at younger ages. The similarities by sex in DALYsterm·mth calls for an equal attention to the clinical prevention and treatment of HHD in women and men.
The highest age-standardizedterm·key rates of DALYsterm·mth due to HHD are noted in Africa ( Figure 4 ). DALYsterm·mth due to HHD are particularly higher in Central sub-Saharan Africa, followed by most of Africa except Western sub-Saharan Africa, and Oceania. A similar geographic pattern exists for high blood pressure (with the exception of high rates of DALYsterm·mth in men from high blood pressure in Central Asia and Eastern Europe). An overrepresentation of DALYsterm·mth due to HHD in women can be noted in Central and Eastern sub-Saharan Africa ( Supplemental Figure 16 ). The significance of this is uncertain because no such peaks are noted for high blood pressure or other contributing factors, and it can represent regional differences in disease coding, including that of peripartum cardiomyopathyterm·dom. Inequalities in access to both primary and secondary prevention are a potential cause of these regional and sex differences.
Map of Age-Standardizedterm·key DALYsterm·mth Due to Hypertensive Heart Diseaseterm·dom in 2019date DALYsterm·mth = disability-adjusted life years.
The global prevalence of HHD and absolute rates of adverse outcomes are expected to continue to rise due to population growthterm·dom and aging. The age-standardizedterm·key rates of adverse outcomes due to HHD are no longer decreasing, and with continuing global increases in obesityterm·dom and diabetes, corresponding increases in left ventricular hypertrophy and other aspects of HHD are likely. Intensified global and regional efforts to lower blood pressure and control other risk factors are needed, for example with population-wide reductions in dietary sodium intake. Continued surveillance of inequalities in adverse outcomes due to high blood pressure are necessary to inform such efforts.
Congenital heart anomalies A total of 3.12 million (95% UI: 2.40 to 4.11 million) babies were born with congenital heart anomalies (CHA) in 2019date, representing 2,305.2 per 100,000 live births (95% UI: 1,772.9 to 3,039.2 per 100,000 live births), a total of 13.3 million (95% UI: 11.5 to 15.4 million) people were living with CHA, and CHA was the underlying cause of 217,000 deaths (95% UI: 177,000 to 262,000 deaths), of which 150,000 deaths (95% UI: 120,000 to 184,000 deaths) were in infants <1 year ( Figure 5A ). The all-ages rates of DALYsterm·mth, YLLsterm·mth, and YLDsterm·mth for CHA in 2019date were 241.6 per 100,000 (95% UI: 196.1 to 292.7 per 100,000), 234.0 per 100,000 (95% UI: 189.8 to 285.7 per 100,000), and 7.6 per 100,000 (95% UI: 3.7 to 12.7 per 100,000), respectively ( Figure 5B ).
Total Numbers and Rates of Congenital Heart Anomalies (A) Total number and number among children younger than 1 year of DALYsterm·mth, deaths, prevalent cases, YLDsterm·mth, and YLLsterm·mth due to congenital heart anomalies, 1990date to 2019date. Shaded regions represent 95% uncertainty intervalsterm·key. (B) Age-standardizedterm·key and all-ages DALYterm·mth, death, prevalence, YLDterm·mth, and YLLterm·mth rates of congenital heart anomalies, 1990date to 2019date. Shaded regions represent 95% uncertainty intervalsterm·key. Abbreviations as in Figure 1 .
Between 1990date and 2019date, the global CHA birth rate was largely unchanged, but the all-ages death rate due to CHA declined 60.4% (95% UI: –71.9% to –41.4%) from 7.1 per 100,000 (95% UI: 5.0 to 10.5 per 100,000) in 1990date to 2.8 per 100,000 (95% UI: 2.3 to 3.4 per 100,000) in 2019date. Age-standardizedterm·key prevalence, death, and DALYterm·mth rates remain lower than all-ages rates in low-income countries because of younger populations, higher birth rates of CHA, and much less access to the intervention that is needed to allow for the survival of children born with CHA.
The proportion of all infant deaths caused by CHA increased in all quintiles of the sociodemographic index (SDI) since 1990date except the highest because improvement in CHA mortality has lagged behind that of other causes, most notably infectious, respiratory, and diarrheal diseases ( 16 ). In low-SDI regions, CHA death rates have declined by 42.1% (95% UI: –61.9% to 12.1%) since 1990date, compared with declines of 71.3% (95% UI: –74.7% to –64.6%) in high-SDI subgroups. Unlike other CVDs, prevention has limited ability to reduce the burden of CHA after birth; improvements in early diagnosis and access to cardiac surgery are the only solutions.
The total number of global CHA births remained steady, global CHA deaths decreased by 42.7% (95% UI: –59.3% to –15.2%), and the global number of individuals living with CHA increased by 28.2% (95% UI: 26.3% to 30.1%). There were increases in the 15- to 49-year and the 50- to 69-year age groups, in which the number of individuals living with CHA grew by 41.6% (95% UI: 40.0% to 43.2%) and 117.3% (95% UI: 114.7% to 120.0%) to 4.21 million (95% UI: 3.67 to 4.76 million) and 1.31 million (95% UI: 1.13 to 1.51 million), respectively, reflecting a cohort effect from the time of birth. Most of the prevalence increase occurred outside of high-income countries (HICs) and was due to improvements in survival and population growthterm·dom. Health systems will be increasingly burdened with adolescents and adults needing care for their congenital heart conditions.
The very young age distribution in LMICs and large burden of YLLsterm·mth due to CHA should drive a global mandate for investment in data collection and resources to expand access to infant heart surgery in regions in most need. In low-income countries with very young populations, the prevalence and mortality from RHD parallel lack of access to cardiac surgery services for CHA, and pooling resources to care for both of these diseases makes a stronger case for investment in surgical services (that includes both in-country surgical personnel and nongovernmental organizations) than for either disease alone.
Rheumatic heart disease The prevalence of RHD has been rising steadily since 1990date, reaching 40.5 million (95% UI: 32.1 to 50.1 million) currently affected in 2019date ( Supplemental Figure 17A ). Deaths decreased until 2012 but have stabilized since then and even started increasing since 2017 (306,000 [95% UI: 259,000 to 340,000] in 2019date). DALYsterm·mth and YLLsterm·mth have slowly decreased to 10.7 million (95% UI: 9.21 to 12.1 million) and 8.68 million (95% UI: 7.43 to 9.77 million), respectively, in 2019date, whereas YLDsterm·mth have increased to 1.99 million (95% UI: 1.20 to 3.04 million).
Age-standardizedterm·key rates for RHD prevalence have closely tracked with all-age rates, but age-standardizedterm·key mortality has exceeded all-age mortality until the past few years, highlighting the differential mortality risk due to RHD observed in regions with differing age structures and levels of development ( Supplemental Figure 17B ). The narrowing of this differential tells a positive story about RHD care, although the dominance of China and India in the overall figures may mask persistent differentials in other high-prevalence countries and regions. The continuing increase in prevalence cannot be attributed to changes in age structure but likely reflects increased global awareness, the increasing availability of echocardiography for case definition, improved survival in some places, and the chronic nature of RHD.
Globally, the prevalence of RHD is estimated to peak between 20 and 29 years, remain relatively stable until 40 years, and then begin a steady decline, likely reflecting decreasing survival at older ages. Sex distribution is equal until the age of 15 years, after which women bear a higher burden in terms of prevalence across nearly all world regions. Higher rates of RHD in post-pubertal females are well described, but incompletely understood, and may reflect the interaction of biological, social, and environmental risk factors.
RHD burden continues to show substantial global heterogeneity ( Supplemental Figure 18 ). The highest age-standardizedterm·key DALYterm·mth rates are seen in Oceania and South Asia (627.4 per 100,000 [95% UI: 404.1 to 918.0 per 100,000] and 348.5 per 100,000 [95% UI: 272.4 to 412.2 per 100,000], respectively) with the lowest in the highest-income regions (<25 per 100,000). Furthermore, although some regions have shown large declines between 1990date and 2019date (Eastern and Central Europe, East Asia, Central Latin America, high-income Asia Pacific, and North Africa and the Middle East), there are also regions with only modest improvements and regions where very little improvement has been seen (Oceania, South Asia, the Caribbean, and sub-Saharan Africa). There is also substantial within-region national and subnational variability in RHD DALYsterm·mth, some examples of which include substantially higher RHD DALYsterm·mth in Guyana and Haiti compared to the rest of Latin America and the Caribbean and the heterogeneity seen in the subnational data on RHD DALYsterm·mth from India ( Figure 6 ).
Map of Age-Standardizedterm·key DALYsterm·mth Due to Rheumatic Heart Disease in 2019date DALYsterm·mth = disability-adjusted life years.
RHD burden is highest among the world’s most disadvantaged populations. The most marginalized and poorest populations regionally, nationally, and at a subnational level are not showing signs of improvement and continue to die early from RHD. Health systems in LMICs should support the recommendations of the 2018 World Health Assembly Global RHD Resolution with increased multisector investment in primary health care, improved sanitation and housing, infrastructure, secure medication supply chains, evidence-based RHD screening, prevention and management, and tertiary capacity to care for patients at the severe end of the RHD spectrum. RHD burden could be greatly decreased with an effective group A streptococcal vaccine, with several promising candidates currently in development ( 17 ). Future data collection is needed at subnational levels to identify and monitor populations with ongoing high RHD burdens.
Cardiomyopathyterm·dom and myocarditisterm·dom DALYsterm·mth due to cardiomyopathyterm·dom and myocarditisterm·dom have increased from 7.06 million (95% UI: 6.30 to 8.63 million) to 9.14 million (95% UI: 7.86 to 10.0 million) over the past 30 years, a pattern that is also seen in the rise of deaths from 238,000 (95% UI: 212,000 to 257,000) to 340,000 (95% UI: 285,000 to 371,000) ( Supplemental Figure 19A ). However, over the same period, the age-standardizedterm·key rate of death has decreased from 8.0 per 100,000 (95% UI: 6.4 to 8.6 per 100,000) to 5.6 per 100,000 (95% UI: 4.5 to 6.3 per 100,000) in men and 5.8 per 100,000 (95% UI: 4.4 to 6.4 per 100,000) to 3.3 per 100,000 (95% UI: 2.7 to 3.6 per 100,000) in women. The age-standardizedterm·key morbidity and mortality for men and women in 2019date remains different, at 6.5 per 100,000 YLDsterm·mth (95% UI: 4.3 to 9.3 per 100,000 YLDsterm·mth) and 4.2 per 100,000 YLDsterm·mth (95% UI: 2.8 to 6.0 per 100,000 YLDsterm·mth) and 148.9 per 100,000 YLLsterm·mth (95% UI: 120.2 to 168.7 per 100,000 YLLsterm·mth) and 71.4 per 100,000 YLLsterm·mth (95% UI: 61.0 to 79.9 per 100,000 YLLsterm·mth), respectively.
The prevalence and related mortality of cardiomyopathyterm·dom and myocarditisterm·dom increase throughout adulthood in both sexes, with a larger proportion of cases in men than in women. When myocarditisterm·dom alone is considered, a similar trend was observed. Referring to 2019date, in the age between 35 and 39 years, when myocarditisterm·dom can commonly occur ( 18 , 19 ), the rate of myocarditisterm·dom is 6.1 per 100,000 (95% UI: 4.2 to 8.7 per 100,000) in men and 4.4 per 100,000 (95% UI: 3.0 to 6.3 per 100,000) in women. Similar figures can be found in the range of ages between 20 and 44 years.
The increased prevalence associated with aging is more pronounced in cardiomyopathies than in myocarditisterm·dom. Approximately a 6-fold higher age-related increase of other cardiomyopathyterm·dom was observed in men between 35 and 39 years (10.8 per 100,000 [95% UI: 6.5 to 17.4 per 100,000]) versus those between 80 and 84 years (698.5 per 100,000 [95% UI: 429.6 to 1064.8 per 100,000]) compared with myocarditisterm·dom in men of 35 to 39 years (6.1 per 100,000 [95% UI: 4.2 to 8.7 per 100,00]) versus those of 80 to 84 years (63.0 per 100,000 [95% UI: 43.6 to 87.9 per 100,000]). The relatively greater rise of other cardiomyopathyterm·dom compared to myocarditisterm·dom prevalence with age is constant, from 1990date to 2019date.
Myocarditis-related mortality rate between 35 and 39 years was 0.2 per 100,000 (95% UI: 0.2 to 0.3 per 100,000) in men compared to 0.1 per 100,000 (95% UI: 0.1 to 0.2 per 100,000) in women in 2019date. Alternatively, myocarditisterm·dom resulted in death in 1 in 72 men (585 deaths per 42,200 incident cases) and 1 in 87 (324 deaths per 28,100 incident cases) women in this age bracket who were diagnosed in 2019date. Between ages 80 and 84 years, the rate was higher, at 1 death for every 19 incident cases of myocarditisterm·dom in men (1,800 deaths per 34,400 incident cases) and 1 death for every 15 incident cases of myocarditisterm·dom in women (2,260 deaths per 34,900 incident cases).
Marked regional variations in other cardiomyopathyterm·dom in age-standardizedterm·key DALYsterm·mth in both sexes were observed in 2019date ( Supplemental Figure 20 ). These ranged from 5 to 46 DALYsterm·mth per 100,000 in South Asia, East Asia, Andean Latin America, high-income Asia Pacific, and Central Latin America to 96 to 147 per 100,000 in Oceania, the Caribbean, high-income North America, and Western and Eastern sub-Saharan Africa to 230 to 271 per 100,000 in Central Asia, Eastern Europe, and Southern and Central sub-Saharan Africa. These regional variations may be explained by a higher prevalence of peripartum cardiomyopathyterm·dom in Africa, Chagas disease in Central and South America, and alcohol-related cardiomyopathyterm·dom in parts of Central Europe and Russia ( 20 ).
Although the morbidity and mortality rates from cardiomyopathies and myocarditisterm·dom collectively present a substantial global disease burden in 2019date, the regional differences in the burden ( Figure 7 ) suggest that public health interventions should be tailored to the specific etiologies of cardiomyopathyterm·dom to lower these rates in the future.
Map of Age-Standardizedterm·key DALYsterm·mth Due to Cardiomyopathyterm·dom and Myocarditisterm·dom in 2019date DALYsterm·mth = disability-adjusted life years.
Alcoholic cardiomyopathyterm·dom The global prevalence of alcoholic cardiomyopathyterm·dom (AC) estimated by GBD 2019date was 708,000 cases (95% UI: 545,000 to 924,000 cases), approximately 9.1 cases per 100,000 (95% UI: 7.0 to 11.9 cases per 100,000) ( Supplemental Figure 21A ). Globally, AC was responsible for 71,700 deaths (95% UI: 60,200 to 82,000 deaths), 2.38 million YLLsterm·mth (95% UI: 2.00 to 2.73 million YLLsterm·mth), and 60,100 YLDsterm·mth (95% UI: 38,500 to 88,300 YLDsterm·mth). The total number of DALYsterm·mth due to AC was 2.44 million (95% UI: 2.05 to 2.78 million). After a rapid increase from 1999, DALYsterm·mth, deaths, and YLLsterm·mth started decreasing from 2005 to 2019date. The global prevalence and YLDsterm·mth due to AC are increasing.
All-age and age-standardizedterm·key rates of DALYsterm·mth, deaths, prevalent cases, YLDsterm·mth, and YLLsterm·mth declined from 2005 to 2019date ( Supplemental Figure 21B ). This indicates that the global increase in AC prevalence is related, in part, to population growthterm·dom and aging. Several countries in East Asia and the Caribbean regions showed opposite trends, with increasing age-standardizedterm·key values for almost all indicators. Factors that might explain these regional differences remain incompletely understood.
At the global level, substantially more total DALYsterm·mth due to AC were experienced by men than women. DALYsterm·mth from AC rose rapidly among men beginning from age 25 years, being significantly higher in men than women across all ages ( Supplemental Figure 22 ). Women are generally considered more susceptible to alcohol-induced damages than men, which may reflect sex-specific differences in alcohol consumption, type, blood level, distribution, or metabolism. However, the higher level of alcohol consumption and the higher frequency of alcohol problems among men could justify the observed higher rate of DALYsterm·mth.
Age-standardizedterm·key DALYsterm·mth due to AC were higher in Central Europe as well as in the Caribbean (Cuba), followed by Australasia, North America, Central Asia, Western Europe, and tropical Latin America ( Figure 8 ). Extremely high rates were seen in Eastern Europe, particularly in Russia and Kyrgyzstan. The lowest levels were observed in sub-Saharan Africa, Andean Latin America, and North Africa and the Middle East. Geographic differences reflect drinking patterns among regions, for example, rates of binge drinking, as well as social and cultural behaviors, such as abstaining from alcohol.
Map of Age-Standardizedterm·key DALYsterm·mth Due to Alcoholic Cardiomyopathyterm·dom in 2019date DALYsterm·mth = disability-adjusted life years.
AC represents substantial morbidity and mortality among young and middle-age people, particularly men. The age-standardizedterm·key DALYsterm·mth are high in several countries, and the true burden of disease could be underestimated because alcohol consumption is often underreported ( 21 ). Additional policy support for public education and awareness is needed to reduce the harmful use of alcohol. Because alcohol negatively affects cardiovascular function, increased clinician emphasis on eliminating alcohol use among people with coexisting CVD is advisable. Research should be aimed at understanding the exact mechanisms of disease, susceptibility to alcohol damage, effective public health interventions, and treatments for AC.
AF and AFL The total number of DALYsterm·mth due to AF and AFL increased progressively from 3.79 million (95% UI: 2.96 to 4.83 million) in 1990date to 8.39 million (95% UI: 6.69 to 10.5 million) in 2019date ( Supplemental Figure 23A ). GBD 2019date estimated 59.7 million (95% UI: 45.7 to 75.3 million) prevalent cases of AF/AFL in 2019date, about a doubling compared to the prevalent cases in 1990date.
When standardized for age, prevalence of AF/AFL, DALYSterm·mth, and death rates did not display marked changes between 1990date and 2019date ( Supplemental Figure 23B ). The age-standardizedterm·key AF/AFL prevalence rate was 775.9 per 100,000 (95% UI: 592.4 to 990.8 per 100,000) in 1990date and 743.5 per 100,000 (95% UI: 571.2 to 938.3 per 100,000) in 2019date. Age-standardizedterm·key rates for DALYsterm·mth did not change substantially between 1990date, when they were 110.0 per 100,000 (95% UI: 87.7 to 139.2 per 100,000), to 2019date, when they were 107.1 per 100,000 (95% UI: 86.2 to 133.7 per 100,000). Similarly, the age-standardizedterm·key death rates per 100,000 were similar in 1990date at 4.3 per 100,000 (95% UI: 3.7 to 5.1 per 100,000) and 4.4 per 100,000 (95% UI: 3.7 to 5.0 per 100,000).
Comparing total numbers with age-standardizedterm·key numbers suggests that global increases in AF/AFL are largely attributable to aging of the population and population growthterm·dom.
Globally, under the age of 70 years, more total DALYsterm·mth due to AF were experienced by men than women ( Supplemental Figure 24 ). Between 70 and 74 years, men and women had similar DALYsterm·mth. However, at age 75 years and older, this trend was reversed, with higher total DALYsterm·mth experienced by women. These trends were observed in 1990date as well as 2019date. These findings point toward sex-specific, age-related targets for the prevention and management of AF/AFL.
Age-standardizedterm·key DALYterm·mth rates due to AF/AFL were highest in high-income North America, Australasia, Central Asia, and Europe and were observed to be lowest in the high-income Asia Pacific region ( Figure 9 ). Also, Andean and Central Latin America, the Caribbean, sub-Saharan Africa, and North Africa and the Middle East displayed low rates. However, because of a general lack of data from these regions, more detailed studies should be performed. The reasons for this significant regional variation may include increased ascertainment in high-income regions, differential control of risk factors, and genetic factors. A detailed comparison of potential differentiating features between highest DALYterm·mth rate versus lowest DALYterm·mth rate regions could yield valuable additional information.
Map of Age-Standardizedterm·key DALYsterm·mth Due to Atrial Fibrillation and Flutter in 2019date DALYsterm·mth = disability-adjusted life years.
AF/AFL are the most common arrhythmias and continue to make a progressive and substantial impact on public health at a global level. In most regions, prevalence rates are increasing, suggesting that more effort is needed to improve prevention and health care for AF/AFL at a global level. Health systems and countries will need to focus their efforts to reverse these trends by aggressive attention to the reduction of risk factors such as hypertension, diabetes, and obesityterm·dom; better treatment of individuals with IHD and heart failure; and improved access to medications for thromboembolism prophylaxis.
Aortic aneurysm The total number of YLLsterm·mth due to aortic aneurysm, including both thoracic and abdominal types, has increased steadily since 1990date, reaching 3.32 million YLLsterm·mth (95% UI: 3.11 to 3.52 million YLLsterm·mth) and 172,000 deaths (95% UI: 157,000 to 183,000 deaths) in 2019date ( Figure 10A ).
Total Numbers and Rates of Aortic Aneurysm (A) Total number of deaths and YLLsterm·mth due to aortic aneurysm, 1990date to 2019date. Shaded regions represent 95% uncertainty intervalsterm·key. (B) Age-standardizedterm·key and all-ages death and YLLterm·mth rates of aortic aneurysm, 1990date to 2019date. Shaded regions represent 95% uncertainty intervalsterm·key. YLLsterm·mth = years of life lost.
Age-standardizedterm·key rates for YLLsterm·mth and deaths declined over this period, indicating that, overall, global increases in aortic aneurysm have been due to population growthterm·dom and aging ( Figure 10B ). This likely reflects improvement in diagnosis and treatment of risk factors.
At the global level, men bore a substantially higher burden of YLLsterm·mth due to aortic aneurysm compared with women. YLLsterm·mth from aortic aneurysm rose most steeply in the sixth decade of life for men, peaking by age 70 years ( Supplemental Figure 25 ). Women younger than age 75 years accounted for less than one-half of YLLsterm·mth due to aortic aneurysm. The gap between men and women narrowed with increasing age, with YLLsterm·mth in women overtaking those of men at age 90 years. This could be explained by the generally longer lifespan of women compared with men. Despite the lower prevalence of abdominal aortic aneurysm than men, growth rate in women has been shown to be more rapid, rupture during surveillance 4 times more likely, and likelihood of fatal rupture 3 times as high, even after adjustment for age ( 22 , 23 , 24 ). For thoracic aortic aneurysms, women have a lower incidence but are more likely to have fatal consequences than men ( 25 ).
Age-standardizedterm·key YLLsterm·mth due to aortic aneurysm were highest in Eastern and Central Europe as well as Southern and Tropical Latin America, with extremely high levels noted in Montenegro and parts of Brazil ( Supplemental Figure 26 ). The lowest levels were observed in Andean Latin America, North Africa and the Middle East, and East Asia, including Mexico, Iraq, and China. Risk factors, including tobaccoterm·dom use and hypertension, as well as access to screening and preventive health care, may partially explain these patterns.
Aortic aneurysm remains a major public health issue, with the overall burden in terms of number of YLLsterm·mth and deaths increasing globally. Because age-standardizedterm·key rates for YLLsterm·mth and death due to aortic aneurysm declined from 1990date to 2019date, the increase may be attributable to population growthterm·dom and aging. However, because the risk factors for this disease remain common, most national health systems will need to address increasing demand for preventive care, including low-cost screening modalities for abdominal aneurysms in the high-burden regions mentioned earlier. The risk factors underlying the development of aortic aneurysm are common to other CVDs, such as IHD. As such, investing health system resources in these risk factors, including hypertension management and smoking cessation, could reduce the burden of other diseases in addition to aortic aneurysm. Combined with implementation of inexpensive screening technology, such as ultrasonography, where indicated, the morbidity and mortality due to aortic aneurysm can be significantly decreased globally.
Nonrheumatic valvular heart disease: calcific aortic valve disease Calcific aortic valve disease (CAVD) occurs commonly among older adults with a normal trileaflet aortic valve and with a greater frequency among those with a congenital bicuspid aortic valve. CAVD is clinically important because severe obstruction causes symptoms and left ventricular dysfunction warranting surgical or transcatheter valve replacement ( 26 ). The prevalence of CAVD increases with age and is >1,000 per 100,000 beyond the age of 75 years.
Globally, both the prevalence and age-standardizedterm·key prevalence of CAVD have increased steadily over the last 3 decades ( Supplemental Figure 27B ). In 2019date, the age-standardizedterm·key prevalence rose to approximately 116.3 cases per 100,000 (95% UI: 100.4 to 134.5 cases per 100,000) people, from about 45.5 cases per 100,000 (95% UI: 37.6 to 54.7 cases per 100,000) people in 1990date. This increase may be attributable to increased prevalence of atherosclerotic risk factors associated with the development and progression of CAVD ( 27 ). Although age-standardizedterm·key YLDsterm·mth due to CAVD have increased in parallel with prevalence, there has been no significant change in age-standardizedterm·key DALYsterm·mth, most likely because most of the DALYsterm·mth due to CAVD accrue from YLLsterm·mth, whereas deaths due to CAVD (and consequently, age-standardizedterm·key YLLsterm·mth) have not increased over the years. The ready availability of aortic valve replacement in regions of the world where CAVD is most prevalent may be an important check on mortality.
In HICs, the age-standardizedterm·key prevalence of CAVD is similar among both men and women, while the prevalence among men exceeds that among women to varying degrees in the other regions of the world. Population-based studies of the prevalence of nonrheumatic valvular heart disease remain rare, and further research is needed to understand if sex differences in prevalence are due to differences in disease burden, health-seeking behavior, or rates of diagnosis.
There is substantial heterogeneity among countries in age-standardizedterm·key prevalence and DALYsterm·mth due to CAVD ( Figure 11 ). Prevalence is low (<20 per 100,000) in several regions of the world, notably, sub-Saharan Africa (excluding the south), most of Asia (including South and Southeast Asia), North Africa and the Middle East, and Oceania. In contrast, age-standardizedterm·key prevalence is much higher (>200 per 100,000) in Australasia; high-income Asia Pacific and North America; and Central, Eastern, and Western Europe. The difference may reflect higher prevalence of other competing illnesses and lack of testing for CAVD in LMICs. Despite the low prevalence of CAVD in LMICs, DALYsterm·mth due to CAVD are disproportionately high, possibly reflecting the limited access to timely aortic valve replacement ( 28 ).
Age-Standardizedterm·key DALYsterm·mth Due to Nonrheumatic Calcific Aortic Valve Disease in 2019date by Region Age-standardizedterm·key DALYterm·mth rate of nonrheumatic calcific aortic valve disease by region and sex with 95% uncertainty intervalsterm·key, 2019date. DALYsterm·mth = disability-adjusted life years.
As populations age, CAVD becomes an increasingly important cause of morbidity and mortality. The increase in reported prevalence of CAVD may be a consequence of both an increase in the prevalence of risk factors for atherosclerosis and an increased awareness and investigation to detect disease. Although major advances have been made in the treatment of end-stage CAVD, research on ways to prevent the onset and progression of disease requires additional investments. In the populous regions of the world such as South Asia and Africa, despite the low prevalence of CAVD, the absolute number of people with CAVD is large. Countries will need to invest in health care infrastructure and health workers to provide timely valve interventions for these patients to reduce related morbidity and mortality.
Nonrheumatic valvular heart disease: degenerative mitral valve disease The major cause of nonrheumatic degenerative mitral valve (MV) disease is MV prolapseterm·dom ( 29 , 30 ). Untreated, this can lead to chronic mitral regurgitationterm·dom, AF, and heart failure ( 31 ). The total number of DALYsterm·mth due to degenerative MV disease has increased since 1990date and is responsible for 883,000 DALYsterm·mth (95% UI: 754,000 to 1,090,000 DALYsterm·mth) and 34,200 deaths (95% UI: 28,300 to 43,300 deaths) in 2019date ( Supplemental Figure 28A ). GBD 2019date estimated 24.2 million (95% UI: 23.1 to 25.4 million) prevalent cases of degenerative MV disease in 2019date.
Age-standardizedterm·key rates for DALYsterm·mth, deaths, and prevalent cases of degenerative MV disease declined over this time period, suggesting that the global increases in MV disease have been due to population growthterm·dom and aging ( Supplemental Figure 28B ). The regions with the largest reductions in age-standardizedterm·key DALYsterm·mth between 1990date and 2019date are high-income Asia Pacific, North Africa and the Middle East, and Australasia. However, for some regions—for example, in Eastern Europe and Central Asia—age-standardizedterm·key rates have not declined.
At the global level, substantially more total DALYsterm·mth due to MV disease were experienced by women than men. For almost all age groups, MV disease DALYsterm·mth were higher in women than men ( Figure 12 ). Globally, women and men had similar DALYsterm·mth in the 40- to 44-year age groups, but the levels diverged after age 65 years, when women had more than one-third more DALYsterm·mth due to MV disease than men, peaking at age 75 to 79 years. Prior studies have found that women with severe regurgitation have higher mortality and lower surgery rates than men, which may account for some of the increased DALYsterm·mth in older women ( 32 ).
DALYsterm·mth Due to Nonrheumatic Degenerative Mitral Valve Disease in 2019date by Age Number of DALYsterm·mth due to nonrheumatic degenerative mitral valve disease by age and sex with 95% uncertainty intervalsterm·key, 2019date. Ages younger than 15 years were removed from the figure because they are not modeled for this cause. DALYsterm·mth = disability-adjusted life years.
Age-standardizedterm·key DALYsterm·mth due to MV disease were highest for both men and women in Central Europe; specifically, high rates were seen in Serbia, Bosnia and Herzegovina, and Hungary, with the lowest levels seen in Southeast Asia, East Asia, and Andean Latin America ( Supplemental Figure 29 ). Women had higher DALYsterm·mth than men in most regions except Central Asia, Eastern Europe, Australasia, the Caribbean, and Western sub-Saharan Africa ( Supplemental Figure 30 ).
Degenerative MV disease continues to be a major threat to public health, and the overall burden in terms of number of DALYsterm·mth, deaths, and prevalent cases is increasing globally. Prevention of MV disease through reduction in risk factors remains a key public health issue. In addition, given an aging population in many regions, health systems should focus on improving access to diagnostic imaging (i.e., echocardiography) and subspecialty care for close follow-up. Improved access to surgical and percutaneous interventions is also needed to decrease mortality and long-term sequelae and improve quality of life for those with MV disease ( 33 ).
Endocarditis The total number of DALYsterm·mth due to endocarditis has risen steadily since 1990date, reaching 1.72 million (95% UI: 1.36 to 1.94 million) DALYsterm·mth and 66,300 deaths (95% UI: 46,200 to 75,900 deaths) in 2019date ( Figure 13A ). GBD 2019date estimated 1.09 million (95% UI: 0.913 to 1.30 million) incident cases of endocarditis in 2019date.
Total Numbers and Rates of Endocarditis (A) Total number of DALYsterm·mth, deaths, incident cases, YLDsterm·mth, and YLLsterm·mth due to endocarditis, 1990date to 2019date. Shaded regions represent 95% uncertainty intervalsterm·key. (B) Age-standardizedterm·key and all-ages DALYterm·mth, death, incidence, YLDterm·mth, and YLLterm·mth rates of endocarditis, 1990date to 2019date. Shaded regions represent 95% uncertainty intervalsterm·key. Abbreviations as in Figure 1 .
Age-standardizedterm·key rates show an increase in incidence, from 9.9 per 100,000 (95% UI: 8.2 to 11.8 per 100,000) to 13.8 per 100,000 (95% UI: 11.6 to 16.3 per 100,000), as well as deaths, from 0.7 per 100,000 (95% UI: 0.6 to 0.9 per 100,000) to 0.9 per 100,000 (95% UI: 0.6 to 1.0 per 100,000) ( Figure 13B ). Although there has been a slight increase in the DALYsterm·mth, YLLsterm·mth, and YLDsterm·mth caused by endocarditis across all ages, once standardized for age structure and growth, these metrics are largely static between 1990date and 2019date. The increase in death may be explained by an increasing proportion of endocarditis caused by virulent organisms such as staphylococci or complex infection in patients not fit for surgical intervention.
At the global level, more total DALYsterm·mth due to endocarditis were experienced by men than women in 2019date (973,000 DALYsterm·mth [95% UI: 736,000 to 1,120,000 DALYsterm·mth] versus 751,000 DALYsterm·mth [95% UI: 537,000 to 871,000 DALYsterm·mth]). DALYsterm·mth from endocarditis rose rapidly for both sexes from birth and reached a peak at age 55 to 59 years for men and age 65 to 69 years for women ( Supplemental Figure 31 ). Women ages 75 years and older had more DALYsterm·mth due to endocarditis than men in the same age group.
There is wide variation in the regional age-standardizedterm·key DALYterm·mth rate due to endocarditis ( Supplemental Figures 32 and 33 ). In 2019date, the highest rates were seen in Oceania, with 44.9 DALYsterm·mth per 100,000 (95% UI: 30.7 to 60.7 DALYsterm·mth per 100,000) (across men and women), followed by Southern Latin America (40.1 DALYsterm·mth per 100,000 [95% UI: 32.7 to 49.6 DALYsterm·mth per 100,000]) and Southeast Asia (38.6 DALYsterm·mth per 100,000 [95% UI: 31.6 to 53.8 DALYsterm·mth per 100,000]). There were also high rates in Eastern Europe (especially in men), the Caribbean, Tropical Latin America, and Africa. The lowest DALYsterm·mth for endocarditis were seen in Central and East Asia. In large part, areas with high age-standardizedterm·key DALYterm·mth rates for endocarditis mirror those areas with a high prevalence of RHD, for example, in Oceania, Southeast Asia, and sub-Saharan Africa.
The epidemiology of endocarditis, including predisposing conditions, is heterogeneous. Therefore, it is not surprising that the endocarditis-associated YLDsterm·mth and DALYsterm·mth vary considerably by location. Both the endocarditis-associated YLDsterm·mth and DALYsterm·mth increased in 76.2% and 52.4% of locations, respectively. Staphylococcus aureus has become the predominant endocarditis pathogen in many areas of the world and is extremely virulent and a well-recognized marker of worse outcomes ( 34 ). As a result, mounting human and health care burdens mandate that an individualized approach be developed for each location to better understand the unique epidemiology of endocarditis so that efforts can be focused on management and prevention of this life-threatening condition.
Peripheral artery diseaseterm·dom The global numbers of prevalent cases and deaths due to PAD have risen consistently each year since 1990date, resulting in a 2-fold increase to 113 million cases (95% UI: 99.2 to 128 million cases) and 74,100 deaths (95% UI: 41,200 to 128,000 deaths) in 2019date ( Supplemental Figure 34A ). Comparable trends in DALYsterm·mth, YLLsterm·mth, and YLDsterm·mth have resulted in 1.54 million (95% UI: 1.01 to 2.37 million), 1.04 million (95% UI: 0.604 to 1.78 million), and 501,000 (95% UI: 235,000 to 898,000) respectively in 2019date.
In keeping with the increase in numbers of prevalent cases, deaths, DALYSterm·mth, YLLsterm·mth, and YLDsterm·mth, rates per 100,000 persons of all ages increased between 1990date and 2019date ( Supplemental Figure 34B ). The trends in age-standardizedterm·key rates of deaths and YLLsterm·mth were flat from 1990date to 2019date, and prevalence, YLDsterm·mth, and DALYsterm·mth declined slightly. Overall, the PAD prevalence increase reflects population growthterm·dom rather than a major change in age-specific incidence. In both 1990date and 2019date, cross-sectional analyses show that the numbers of prevalent cases increased with age in both men and women up to 70 years of age and prevalence rates increased throughout the whole age spectrum. Numbers and rates of cases were higher in women than men at all ages. Deaths and DALYsterm·mth showed increases with age comparable to prevalence except that deaths and DALYsterm·mth were higher in men than women up to very old age.
The numbers of prevalent cases increased in both men and women at all ages by up to 2-fold since 1990date, but age-standardizedterm·key prevalence rates were slightly lower in 2019date than in 1990date. Numbers of deaths and DALYsterm·mth were higher in 2019date compared to 1990date in both men and women at all ages, but over the same period, age-standardizedterm·key death rates remained much the same, and DALYterm·mth rates fell slightly.
Age-standardizedterm·key DALYterm·mth rates varied by world region in 2019date ( Figure 14 ). Higher rates (>25 per 100,000 persons) were found in both men and women in Europe (western, central, and eastern), North America, Australasia, the Caribbean, and sub-Saharan Africa (central, eastern, and southern) ( Supplemental Figure 35 ). Especially high rates occurred in men in Eastern Europe and areas of sub-Saharan Africa. Lowest DALYterm·mth rates were found in Asia, Andean countries, West and North Africa and the Middle East. The pattern was similar in men and women, with most regions showing higher rates in men, but in regions with low overall rates, the sex differences were small. The burden of PAD is increasing not only in developed HICs but also in LMICs, where concomitant risk factors such as diabetes and obesityterm·dom are increasing.
Map of Age-Standardizedterm·key DALYsterm·mth Due to Peripheral Artery Diseaseterm·dom in 2019date DALYsterm·mth = disability-adjusted life years.
Despite decreases in PAD age-standardizedterm·key prevalence, deaths, DALYsterm·mth, YLDsterm·mth, and YLLsterm·mth, overall rates have increased because of worldwide increases in life expectancy. High DALYterm·mth rates of PAD in Eastern Europe and parts of Africa, particularly in men, require investigation. PAD is highly prevalent and is both underrecognized and inadequately studied.
Modifiable Risk Factors High systolic blood pressure With aging of the population and population growthterm·dom, the number of adults worldwide affected by high systolic blood pressure (SBP) increased from 2.18 billion (95% UI: 95% UI: 2.11 to 2.26 billion) to 4.06 billion (95% UI: 3.96 to 4.15 billion) from 1990date to 2019date ( Supplemental Table 1 ). High SBPterm·dom is defined by GBD 2019date according to a theoretical minimum risk exposure levelterm·mth (TMREL) of ≥110 to 115 mm Hg, which is the level of exposure that minimizes risk at the population level. In 2019date, there were 828 million (95% UI: 768 to 888 million) adults with SBP >140 mm Hg, a common threshold for treatment with pharmacotherapies. The prevalence of adults worldwide with high SBPterm·dom increased from 84,481.1 per 100,000 persons (95% UI: 81,641.5 to 87,579.2 per 100,000 persons) in 1990date to 88,971.1 per 100,000 persons (95% UI: 86,950.0 to 91,028.8 per 100,000 persons) in 2019date.
From 1990date to 2019date, the total number of DALYsterm·mth due to high SBPterm·dom increased from 154 million (95% UI: 139 to 169 million) to 235 million (95% UI: 211 to 261 million) ( Supplemental Figure 36A ). Additionally, between 1990date and 2019date, the number of deaths (6.79 million [95% UI: 6.07 to 7.50 million] to 10.8 million [95% UI: 9.51 to 12.1 million]), YLDsterm·mth (10.1 million [95% UI: 7.30 to 13.1 million] to 21.2 million [95% UI: 15.2 to 27.2 million]), and YLLsterm·mth (144 million [95% UI: 129 to 158 million] to 214 million [95% UI: 191 to 237 million]) attributed to high SBPterm·dom increased. The age-standardizedterm·key rates of DALYsterm·mth, deaths, and YLLsterm·mth attributed to high SBPterm·dom declined from 1990date to 2019date, whereas the age-standardizedterm·key rate of YLDsterm·mth did not change substantially over this time period ( Supplemental Figure 36B ). These data indicate that the increases in total number of disease events can be attributed to population growthterm·dom and aging. However, in some regions—for example, Oceania—the age-standardizedterm·key rates of DALYsterm·mth, deaths, YLDsterm·mth, and YLLsterm·mth increased between 1990date and 2019date.
Globally, DALYsterm·mth due to high SBPterm·dom in 2019date were higher among men compared with women from ages 15 to 19 through 70 to 74 years, but higher among women compared to men in those 80 to 84 years or older ( Figure 15 ). Age-standardizedterm·key DALYsterm·mth were higher among men compared with women in all regions of the world ( Supplemental Figure 37 ).
DALYsterm·mth Due to High Systolic Blood Pressure in 2019date by Age Number of DALYsterm·mth due to high systolic blood pressure by age and sex with 95% uncertainty intervalsterm·key, 2019date. Ages younger than 15 years were removed from the figure because they are not modeled for this risk. DALYsterm·mth = disability-adjusted life years.
In 2019date, there was an approximately 8-fold variation across regions in age-standardizedterm·key DALYsterm·mth attributed to high SBPterm·dom ( Supplemental Figure 38 ). Rates of DALYsterm·mth were lowest for both women and men in the high-income Asia Pacific region and highest in Central Asia. Marked variation was also noted for rates of death, YLLsterm·mth, and YLDsterm·mth. The highest age-standardizedterm·key rates of death and YLLsterm·mth were noted in Central Asia and Eastern Europe among men and in Central Asia, North Africa and the Middle East, and Central sub-Saharan Africa among women. The highest rate of YLDsterm·mth was noted in Central Asia, Central Europe, and Southeast Asia for men and in North Africa and the Middle East, East Asia, and Southeast Asia for women.
High SBPterm·dom is a major public health challenge, affecting approximately 9 of 10 adults worldwide, and is associated with high rates of DALYsterm·mth, death, YLDsterm·mth, and YLLsterm·mth. High SBPterm·dom and its adverse health consequences can be prevented by eating a heart-healthy diet that includes <1 teaspoon of salt per day and adequate potassium from fruits and vegetables, maintaining a normal weight, increasing physical activity, and avoiding unhealthy alcohol intake ( 35 ).
High fasting plasma glucose There are large differences between and within countries in the burden of high fasting plasma glucose, defined as above the TMREL of 4.8 to 5.4 mmol/l. The findings showed little improvement in age-standardizedterm·key mortality rates from high fasting plasma glucose and significant increases in age-standardizedterm·key YLDsterm·mth between 1990date and 2019date. Age-standardizedterm·key mortality due to high fasting plasma glucose actually increased from 1990date to 2005, with a downward trend thereafter.
Between 1990date and 2019date, a total of 134 million (95% UI: 107 to 171 million) deaths due to high fasting plasma glucose were recorded globally. The number of deaths increased from 2.91 million (95% UI: 2.34 to 3.75 million) in 1990date to 6.50 million (95% UI: 5.11 to 8.36 million) in 2019date ( Figure 16A ). The age-standardizedterm·key mortality rate due to high fasting plasma glucose increased between 1990date and 2005 from 84.2 per 100,000 deaths (95% UI: 65.9 to 111.1 per 100,000 deaths) to 89.2 per 100,000 deaths (95% UI: 70.2 to 115.9 per 100,000 deaths) then declined to 83.0 per 100,000 deaths (95% UI: 64.5 to 107.1 per 100,000 deaths) in 2019date ( Figure 16B ). Age-standardizedterm·key DALYsterm·mth followed the same patterns with a peak in 2005 and then a decline until 2019date. In contrast, age-standardizedterm·key DALYterm·mth rates were higher in 2019date at 2,104.3 per 100,000 (95% UI: 1,740.7 to 2,520.7 per 100,000) than in 1990date at 1,959.6 per 100,000 (95% UI: 1,638.7 to 2,362.4 per 100,000). Of course, this is the result of a sharp increase in age-standardizedterm·key YLDsterm·mth from 1990date to 2019date. Men had higher DALYterm·mth burden than women throughout the study period until the age of 80 years, after which women had a higher number of DALYsterm·mth ( Supplemental Figure 39 ). DALYsterm·mth due to high fasting plasma glucose increased with age, peaked at 70 years, and then declined.
Total Numbers and Rates of High Fasting Plasma Glucose (A) Total number of DALYsterm·mth, deaths, YLDsterm·mth, and YLLsterm·mth due to high fasting plasma glucose, 1990date to 2019date. Shaded regions represent 95% uncertainty intervalsterm·key. (B) Age-standardizedterm·key and all-ages DALYterm·mth, death, YLDterm·mth, and YLLterm·mth rates of high fasting plasma glucose, 1990date to 2019date. Shaded regions represent 95% uncertainty intervalsterm·key. Abbreviations as in Figure 1 .
Age-standardizedterm·key DALYsterm·mth due to high fasting plasma glucose were higher in Oceania, followed by Central Asia ( Supplemental Figure 40 ). There were large variations between countries in high fasting plasma glucose DALYterm·mth burden, with notably high rates in Uzbekistan, Afghanistan, Papua New Guinea, Egypt, and Oman ( Supplemental Figure 41 ). The highest age-standardizedterm·key DALYsterm·mth in 2019date were observed in Kiribati at 12,255.2 per 100,000 (95% UI: 9,799.7 to 15,073.0 per 100,000), and the highest age-standardizedterm·key death rates were also observed in Kiribati at 435.4 per 100,000 (95% UI: 347.0 to 540.5 per 100,000). Large variations were also observed within countries. For example, the age-standardizedterm·key DALYterm·mth rate varied in Brazil from 1,571.8 per 100,000 (95% UI: 1,293.6 to 1,909.1 per 100,000) in Minas Gerais to 3,286.5 per 100,000 (95% UI: 2,735.5 to 3,907.3 per 100,000) in Alagoas, a 70.6% difference in the country.
These population-based estimates of plasma glucose level help track the global rise in diabetes. GBD 2019date showed low rates of physical activity (LPAterm·dom) and poor diet in many countries. Both are major risk factors for high fasting plasma glucose and have led to caloric imbalance and higher rates of obesityterm·dom, which has rapidly increased globally since 1990date and affects most geographic areas. There is a need to develop and implement both public health policies and clinical programs to reduce health disparities and disease burden due to diabetes.
High low-density lipoprotein cholesterol The total number of DALYsterm·mth due to high low-density lipoprotein (LDL) cholesterol, defined by a TMREL of 0.7 to 1.3 mmol/l, has risen steadily since 1990date, reaching 98.6 million (95% UI: 80.3 to 119 million) DALYsterm·mth and 4.40 million (95% UI: 3.30 to 5.65 million) deaths in 2019date ( Supplemental Figure 42A ). Over the same period, the YLDsterm·mth and YLLsterm·mth rose to 5.71 million (95% UI: 3.68 to 8.27 million) and 92.9 million (95% UI: 75.6 to 111 million), respectively. This indicates that the direction of the global trend for high LDL is increasing.
Over the study period, the global all-age rates for DALYsterm·mth, deaths, and YLLsterm·mth remained relatively static and increased for YLDsterm·mth ( Supplemental Figure 42B ). During this time, the age-standardizedterm·key rates for the same measures all declined. This difference between all-age rates and age-standardizedterm·key rates suggests that although the global burden of LDL-related disease remains unacceptably high, on average, the observed increases in the burden of DALYsterm·mth in locations like China and India have been driven primarily by population growthterm·dom and aging. This trend is important for global health because it implies that there has been at least modest progress in reducing the burden of LDL-related disease globally. Age-standardizedterm·key DALYterm·mth rates attributable to elevated LDL cholesterol, however, are estimated to be increasing in some locations, including Pakistan and Saudi Arabia.
At the global level, significantly more total DALYsterm·mth from elevated LDL were experienced by men than women. LDL-related DALYsterm·mth increased rapidly for men beginning at age 30 years ( Supplemental Figure 43 ). Men ages 40 to 44 years had as many DALYsterm·mth due to LDL as women ages 60 to 64 and 80 to 84 years and more than women in all other age groups. In aging post-menopausal women, the LDL cholesterol levels are comparable to those of men of similar age. The difference in DALYsterm·mth by sex and age suggests that the burden of premature LDL-related disease in men younger than 65 years may need focused public health attention.
Age-standardizedterm·key DALYsterm·mth due to high LDL cholesterol were highest in Eastern Europe, North Africa and the Middle East, Oceania, and Central Asia ( Supplemental Figure 44 ). The lowest levels are present in high-income Asia Pacific, Australasia, Western Europe, and Andean Latin America and, by country, in the Republic of Korea, Japan, Rwanda, France, Israel, and Spain ( Figure 17 ). Potential explanations for these differences in regional patterns include the correspondingly LPAterm·dom, increased high body mass index (BMIterm·mth), dietary patterns, and increased tobaccoterm·dom use in the same regions and countries.
Map of Age-Standardizedterm·key DALYsterm·mth Due to High LDL Cholesterol in 2019date DALYsterm·mth = disability-adjusted life years; LDL = low-density lipoprotein.
High LDL cholesterol remains a major threat to public health, and the overall burden in terms of number of DALYsterm·mth, deaths, YLDsterm·mth, and YLLsterm·mth is increasing globally. In some locations, the risk associated with high LDL cholesterol is especially high and deserves immediate public health attention. Health systems and countries may need to focus on new approaches that can reverse these trends. These might include improved government policies on diet and tobaccoterm·dom, school physical activity programs, and, when needed, the use of lipid-lowering therapy in keeping with contemporary guidelines in which statins are the first choice. More global investment in research to address LDL cholesterol–related knowledge and therapeutic gaps is needed to address this persistent global health threat.
High BMIterm·mth Obesityterm·dom, defined by elevated BMIterm·mth (≥30 kg/m 2 ), has reached epidemic levels worldwide. Elevated BMIterm·mth worsens most of the CVD risk factors, including adverse effects on blood pressure, blood sugar, lipids, and inflammation and has adverse effects on cardiac structure and function. Not surprisingly, hypertension, coronary heart disease, heart failure, and AF are increased with obesityterm·dom ( 36 ).
Globally, 5.02 million (95% UI: 3.22 to 7.11 million) deaths and 160 million (95% UI: 106 to 219 million) DALYsterm·mth were attributed to high BMIterm·mth in 2019date ( Figure 18A ). High BMIterm·mth, defined as above a TMREL of 20 to 25 kg/m 2 for adults and above normal weight for children, contributed to more YLLsterm·mth (119 million [95% UI: 79.6 to 164 million]) than YLDsterm·mth (40.9 million [95% UI: 24.5 to 60.9 million]) in 2019date. Globally, between 1990date and 2019date, the absolute numbers of deaths attributable to high BMIterm·mth increased (2.20 million [95% UI: 1.21 to 3.43 million] to 5.02 million [95% UI: 3.22 to 7.11 million]), as did DALYsterm·mth (67.3 million [95% UI: 38.0 to 104 million] to 160 million [95% UI: 106 to 219 million]), YLDsterm·mth (12.9 million [95% UI: 6.9 to 21.0 million] to 40.9 million [95% UI: 24.5 to 60.9 million]), and YLLsterm·mth (54.4 million [95% UI: 30.2 to 84.4 million] to 119 million [95% UI: 79.6 to 164 million]).
Total Numbers and Rates of High Body Mass Index (A) Total number of DALYsterm·mth, deaths, YLDsterm·mth, and YLLsterm·mth due to high body mass index, 1990date to 2019date. Shaded regions represent 95% uncertainty intervalsterm·key. (B) Age-standardizedterm·key and all-ages DALYterm·mth, death, YLDterm·mth, and YLLterm·mth rates of high body mass index, 1990date to 2019date. Shaded regions represent 95% uncertainty intervalsterm·key. Abbreviations as in Figure 1 .
After standardization for population growthterm·dom and aging, rates of deaths attributable to high BMIterm·mth have increased only modestly from 1990date to 2019date (4.9% increase [95% UI: –7.3% to 24.6%]), DALYsterm·mth (18.0% [95% UI: 2.2% to 42.3%]), and YLLsterm·mth (8.3% [95% UI: –6.6% to 31.2%]), suggesting that population growthterm·dom and aging of the population have contributed substantially to the global trends in obesity-associated burden of disease ( Figure 18B ). YLDsterm·mth have increased more sharply (60.2% [95% UI: 41.3% to 90.2%]) from 1990date to 2019date. Since population growthterm·dom and aging from 1990date account for much of the absolute increases attributable to high BMIterm·mth, the focus may need to be on strategies targeting prevention of weight gain at younger ages to decrease the burden of all measures, including YLDsterm·mth.
Global DALYsterm·mth attributable to high BMIterm·mth are highest between ages 45 and 75 years and are slightly higher for men at younger and middle ages and for women at older ages ( Supplemental Figure 45 ).
Age-standardizedterm·key DALYterm·mth rates due to high BMIterm·mth are highest in Oceania, Central Asia, North Africa and the Middle East, Southern sub-Saharan Africa, Eastern Europe, Central Latin America, the Caribbean, and Central Europe ( Supplemental Figure 46 ). Among countries, Japan has the lowest and Kiribati the highest age-standardizedterm·key DALYsterm·mth in the world ( Supplemental Figure 47 ). The global findings are generally similar between men and women in most regions, but age-standardizedterm·key DALYsterm·mth are notably higher for men in Central Asia and Central and Eastern Europe.
Great efforts are needed to promote the prevention of obesityterm·dom and its progression to more severe forms ( 36 ). A multifactorial approach is required to promote improving dietary quality, especially reductions in simple sugars, complex carbohydrates, and total calories. Community prevention programs, like the Diabetes Prevention Program, duplicated in many programs ( 37 ), are needed ( 36 ). Optimizing efforts to promote physical activity and exercise training are needed, as well as reductions in sedentary behavior and making neighborhoods, communities, and workplaces more suitable to allow for these healthier lifestyle behaviors. Potential governmental involvement, such as mandating caloric/nutrition menu labeling and regulation of food ingredients, as well as controlling advertisements and certain forms of beverage and food item taxation, etc., could be instituted. Governmental intervention was very successful decades ago in the North Karelia Project in Finland ( 36 , 38 ), and potentially, similar efforts could be successful for obesityterm·dom intervention worldwide. Research is needed to identify effective methods of health promotion that will reduce obesityterm·dom and align all allied health professions toward this important goal ( 36 ).
Impaired kidney functionterm·dom GBD 2019date estimates use the term chronic kidney diseaseterm·dom (CKD) to refer to the morbidity and mortality that can be directly attributed to all stages of CKD and the term impaired kidney functionterm·dom (IKF) to capture the additional risk of CKD from other associated conditions such as CVD and gout. The DALYsterm·mth due to IKF nearly doubled from 1990date to 2019date, reaching 76.5 million (95% UI: 67.8 to 86.3 million) DALYsterm·mth and 3.16 million (95% UI: 2.72 to 3.62 million) deaths globally in 2019date ( Supplemental Figure 48A ). The estimated global prevalence of CKD is 9,011.9 per 100,000 (95% UI: 8,401.3 to 9,577.8 per 100,000) (697 million [95% UI: 650 to 741 million] people), 41.1% (95% UI: 38.9% to 43.4%) higher than in 1990date due to aging ( Supplemental Figures 49A and 49B ).
Age-standardizedterm·key DALYsterm·mth due to IKF declined slightly from 1,024.1 DALYsterm·mth per 100,000 people (95% UI: 898.4 to 1,154.8 DALYsterm·mth per 100,000 people) to 945.3 DALYsterm·mth per 100,000 people (95% UI: 836.3 to 1,066.8 DALYsterm·mth per 100,000 people) from 1990date to 2019date ( Supplemental Figure 48B ). This 7.7% (95% UI: –12.9% to –2.4%) decline in DALYsterm·mth lags behind the 39.3% (95% UI: –43.5% to –34.9%) decline in DALYsterm·mth for all risk factors combined. The age-standardizedterm·key rate per 100,000 people for YLDsterm·mth from IKF increased from 115.9 (95% UI: 84.7 to 150.5) to 139.4 (95% UI: 101.8 to 181.8) from 1990date to 2019date. Growth and aging of the global population resulted in an increase in all unadjusted disease burden measures for IKF.
CKD and its consequences generally rise markedly with age and are higher among men than women, but trends over time were similar across age and sex groups ( Supplemental Figures 50 and 51 ).
Geographically, the highest age-standardizedterm·key rates of DALYsterm·mth due to IKF are now seen in Central Latin America, Central Asia, the Caribbean, North Africa and the Middle East, Oceania, and Southeast Asia ( Figure 19 ). Men in Central Latin America have among the highest age-standardizedterm·key DALYsterm·mth at 2,091.2 per 100,000 people (95% UI: 1,791.6 to 2,424.8 per 100,000 people), double the global rate and 60% higher than in 1990date ( Supplemental Figure 52 ), which represents an epidemic of CKD of unknown origin that appears to be common in coastal lowlands, with repeated heat stress, pesticide, and heavy metal exposure implicated as potential causes ( 39 ).
Map of Age-Standardizedterm·key DALYsterm·mth Due to Impaired Kidney Functionterm·dom in 2019date DALYsterm·mth = disability-adjusted life years.
CKD is emerging as a growing global problem, with marked growth related to the aging population. Age-standardizedterm·key DALYsterm·mth for IKF have declined by only 7.7%, compared to 39.3% for all risk factors, and they increased in Central and Andean Latin America, the Caribbean, Central and Southeast Asia, Oceania, and Southern sub-Saharan Africa. Much of CKD remains undiagnosed and undertreated. Governments, health systems, physicians, and patient attention are needed to improve the diagnosis of CKD by measuring proteinuria and estimated glomerular filtration rate. There is also a need for earlier markers of kidney injury. Diabetes and hypertension are leading factors for CKD, its progression, and associated risk. Improving their prevention and control will reduce the burden of CKD. Therapeutic options for CKD are increasing with the proven benefits of renin-angiotensin system blockade in proteinuria and possibly sodium-glucose cotransporter-2 inhibitors and mineralocorticoid receptor antagonists. Trials are underway to test prevention of kidney events in patients with CKD without diabetes. A combination of population-based public health measures for increased CKD recognition and awareness and improved therapies to reduce risk is needed to reduce the burden of CKD.
Ambient and household air pollutionterm·key Air pollutionterm·key is the leading environmental risk factor for global health and the fourth largest risk factor for global mortality. Two main forms of air pollutionterm·key quantified in GBD 2019date contribute substantially to the burden of CVD: ambient particulate matter with an aerodynamic diameter smaller than 2.5 μm (PM 2.5 ) and household air pollutionterm·key (HAP) from the use of solid fuels for cooking. In 2019date, CVD accounted for 51.5% and 30.5% of the total DALYSterm·mth attributable to PM 2.5 and HAP, respectively.
Despite substantial reductions in ambient PM 2.5 concentrations in North America and Europe, levels remain high throughout most of the world ( 40 ). Global annual average population-weighted PM 2.5 levels increased slightly from 40.8 μg/m 3 in 1990date to 42.6 μg/m 3 in 2019date. However, the global disease burden attributable to ambient PM 2.5 increased from 70.5 million (95% UI: 47.3 to 98.9 million) DALYsterm·mth and 2.05 million (95% UI: 1.45 to 2.74 million) deaths in 1990date to 118 million (95% UI: 95.9 to 138 million) DALYsterm·mth and 4.14 million (95% UI: 3.45 to 4.80 million) deaths, respectively, in 2019date ( Supplemental Figure 53A ). Age-standardizedterm·key rates for DALYsterm·mth and deaths attributable to PM 2.5 have remained relatively constant between 1990date and 2019date, indicating that, on average, global increases in attributable disease have been due to population growthterm·dom and aging, especially in locations with high exposures ( Supplemental Figure 53B ). Age-standardizedterm·key DALYsterm·mth attributable to PM 2.5 were higher than the global mean in East, Central, and South Asia; North Africa and the Middle East; and Southern and Western sub-Saharan Africa ( Figure 20 ).
Map of Age-Standardizedterm·key DALYsterm·mth Due to Ambient Particulate Matter Pollution in 2019date DALYsterm·mth = disability-adjusted life years.
In contrast to ambient PM 2.5 , the burden attributable to HAP decreased sharply from 208 million (95% UI: 162 to 259 million) DALYsterm·mth and 4.36 million (95% UI: 3.33 to 5.40 million) deaths in 1990date to 91.5 million (95% UI: 67.0 to 119 million) DALYsterm·mth and 2.31 million (95% UI: 1.63 to 3.12 million) deaths in 2019date ( Supplemental Figure 54A ). Trends for both all-age and age-standardizedterm·key DALYsterm·mth and death rates attributable to HAP have also declined sharply, indicating the impact of exposure reduction ( Supplemental Figure 54B ). However, age-standardizedterm·key rates of DALYsterm·mth attributable to HAP remain high throughout much of sub-Saharan Africa, which has yet to experience the benefits of improved access to clean household energy sources seen in India, China, and much of Southeast Asia ( Supplemental Figure 55 ). The highest rates of DALYsterm·mth for HAP were in Oceania; Eastern, Western and Central sub-Saharan Africa; and South Asia, driven by differences in exposure.
DALYsterm·mth attributable to PM 2.5 and HAP were higher in men, driven by sex differences in rates of the diseases affected by air pollutionterm·key ( Supplemental Figures 56 and 57 ). These sex differences were less pronounced for HAP given that women tend to be more highly exposed. DALYsterm·mth attributable to HAP had the largest impacts in infancy and early childhood, whereas for PM 2.5 , it was between ages 30 and 90 years.
Increased attention to air pollutionterm·key as a CVD risk factor is warranted and will require concerted action and collaboration between government health and environmental agencies. This is especially relevant given high levels of exposure in many parts of the world and links between PM 2.5 and health risks at levels substantially below those of current regulatory levels ( 41 ). As demonstrated in North America and Western Europe, air quality management, coupled with regulation and enforcement, is effective at reducing ambient air pollutionterm·key and its disease burden ( 42 ). Similar approaches applied elsewhere, in combination with actions to reduce emissions of climate-forcing agents will be necessary. In the interim, personal-level interventions, such as the use of portable air cleaners, may be effective ( 43 ). Continued reductions in HAP exposure through programs to improve access to clean energy sources will lead to both health and climate benefits ( 44 ).
Tobaccoterm·dom Tobaccoterm·dom, including primary smoking, secondhand smoketerm·dom, and use of chewing tobaccoterm·dom, accounted for 8.71 million (95% UI: 8.12 to 9.31 million) deaths and 230 million (95% UI: 213 to 246 million) DALYsterm·mth in 2019date ( Figure 21A ). Of tobacco-attributable deaths, 36.7% were due to CVD. The number of YLLsterm·mth attributable to tobaccoterm·dom (194 million [95% UI: 180 to 209 million]) far exceeded the number of attributable YLDsterm·mth (36.1 million [95% UI: 27.0 to 46.0 million]), highlighting the outsized effect of tobaccoterm·dom on premature mortality.
Total Numbers and Rates of Tobaccoterm·dom (A) Total number of DALYsterm·mth, deaths, YLDsterm·mth, and YLLsterm·mth due to tobaccoterm·dom, 1990date to 2019date. Shaded regions represent 95% uncertainty intervalsterm·key. (B) Age-standardizedterm·key and all-ages DALYterm·mth, death, YLDterm·mth, and YLLterm·mth rates of tobaccoterm·dom, 1990date to 2019date. Shaded regions represent 95% uncertainty intervalsterm·key. Abbreviations as in Figure 1 .
Progress in reducing tobaccoterm·dom use and exposure has not kept pace with competing demographic forces, namely, population growthterm·dom and population aging. Population growthterm·dom, particularly in countries with a large population of tobaccoterm·dom users, has resulted in an increasing number of tobacco-attributable deaths and DALYsterm·mth since 1990date. When controlling for population growthterm·dom, decreases in both death and DALYterm·mth rates are observed. However, population aging has resulted in smaller decreases in all-age rates compared to age-standardizedterm·key rates ( Figure 21B ). With approximately 80% of current smokers living in LMICs, these demographic forces will continue to counter progress in tobaccoterm·dom control for years to come.
In 2019date, global smoking prevalence among men was 33.5% (95% UI: 33.1% to 33.9%), and smoking prevalence among women was 6.8% (95% UI: 6.6% to 7.0%). As a result of this difference, 75.4% of smoking attributable deaths occurred among men. The age pattern of tobaccoterm·dom use and exposure, combined with the age distribution of associated health outcomes, results in age-specific DALYsterm·mth peaking among men ages 60 to 64 years and among women ages 65 to 69 years ( Supplemental Figure 58 ).
The highest age-standardizedterm·key tobacco-attributable DALYterm·mth rates among men were observed in Eastern Europe, Central Asia, and Oceania ( Supplemental Figure 59 ). Among women, the highest rates were observed in Oceania, high-income North America, and Central Europe. Thirty percent of all current smokers live in China, and nearly one-third of tobacco-attributable deaths occurred in China in 2019date.
With more than 1 billion active tobaccoterm·dom users, the tobaccoterm·dom epidemic remains a central challenge to global health. The global disease burden of tobaccoterm·dom use continues to increase, attributable in large part to population growthterm·dom and falling most heavily on LMICs, where the large majority of tobaccoterm·dom users live. Effective tobaccoterm·dom control measures available to governments and private sector institutions include policies that increase taxes on tobaccoterm·dom, fund mass media education campaigns, ban tobaccoterm·dom marketing and sponsorship, require prominent warning labels on tobaccoterm·dom products, require environments to be smoke-free, and ensure access to and delivery of tobaccoterm·dom cessation treatments in health care systems. The evidence-based measures are included in the World Health Organization’s Framework Convention on Tobaccoterm·dom Control, a public health treaty that took effect in 2005 and has been ratified by 182 countries. Despite this global commitment, adoption of the Framework Convention on Tobaccoterm·dom Control measures has varied widely across countries. Increasing adoption and effective implementation of evidence-based tobaccoterm·dom control policies is a key worldwide public health priority. Looking ahead toward non-communicable disease targets set in the Sustainable Development Goals, accelerated progress in implementing proven policies and interventions is necessary to substantially reduce tobacco-attributable burden.
Dietary risksterm·dom In GBD 2019date, dietary risksterm·dom comprise the sum of adverse effects of diets in which 15 food types are either underconsumed (fruits, vegetables, legumes, whole grains, nuts and seeds, milk, fiber, calcium, omega-3 fatty acids from seafood, and polyunsaturated fatty acids) or overconsumed (red meat, processed meat, sugar-sweetened beverages, trans -fatty acids, and sodium).
CVDs are the primary consequence of these dietary risksterm·dom, with 7.94 million (95% UI: 6.47 to 9.76 million) annual deaths and 188 million (95% UI: 156 to 225 million) annual DALYsterm·mth attributed to dietary risksterm·dom ( Figure 22A ). Worldwide, the absolute disease burden caused by dietary risksterm·dom has risen for 30 years, regardless of how measured. Over the same period, age-standardizedterm·key rates have fallen for most measures, indicating the central role of population growthterm·dom and population aging on the observed increases ( Figure 22B ). The exception is age-standardizedterm·key rates of YLDsterm·mth caused by dietary risksterm·dom, which have grown in both absolute and proportional terms.
Total Numbers and Rates of Dietary Risksterm·dom (A) Total number of DALYsterm·mth, deaths, YLDsterm·mth, and YLLsterm·mth due to dietary risksterm·dom, 1990date to 2019date. Shaded regions represent 95% uncertainty intervalsterm·key. (B) Age-standardizedterm·key and all-ages DALYterm·mth, death, YLDterm·mth, and YLLterm·mth rates of dietary risksterm·dom, 1990date to 2019date. Shaded regions represent 95% uncertainty intervalsterm·key. Abbreviations as in Figure 1 .
Worldwide, the number of DALYsterm·mth caused by dietary risksterm·dom peaks between 55 and 70 years of age for both men and women, with a large excess of DALYsterm·mth among men compared to women in every age group to 80 years old ( Supplemental Figure 60 ). Age-standardizedterm·key rates of DALYsterm·mth caused by dietary risksterm·dom are highest in Central Asia, Oceania, and Eastern Europe and lowest in Australasia, HICs of the Asia Pacific, and Western Europe ( Supplemental Figures 61 and 62 ). Differences in age-standardizedterm·key DALYterm·mth rates among populations reflect a complex interplay between variations in diet quality, levels of exposure to different dietary risksterm·dom, and access to interventions targeting the downstream clinical effects of CVDs.
The detailed effects of many dietary risksterm·dom are poorly understood because assessments are hampered by the challenges of accurately quantifying exposures and separating the effects of each risk from those of important covariates. However, the effects of dietary risksterm·dom on CVD remain large whether assessed as individual dietary factors or measures of overall diet quality, and there is little doubt about the importance of dietary risksterm·dom as a cause of global disease burden.
Strong commercial interests driving the sales of unhealthy foods have made it challenging to persuade policy makers, clinicians, or community members to act decisively on diet quality. Easy access to healthy fruits, vegetables, and whole grains remains limited for much of the world. Many countries have adopted policies to limit consumption of added sugar, sodium, and harmful fats, but implementation has mostly been weak, and the impact on global health has been limited. Exceptions are regulatory and fiscal interventions such as soda taxes and beverage reformulation programs that have led to lower intakes of added sugars, proving the feasibility of determined intervention. Likewise, the removal of artificial trans -fat from the food supply in the United States has improved diet quality, although the amount of trans -fat in the global food supply remains high. Reliance on drug therapies targeting downstream metabolic risk factors caused by obesityterm·dom may be inadequate. Ill health caused by dietary risksterm·dom is anticipated to rise rapidly in LMICs over the coming years, and behavioral and policy interventions suited for implementation at scale in diverse settings around the world are urgently required.
Low Physical Activity LPAterm·dom is an important risk factor for IHD, stroketerm·key, diabetes, breast and colon cancers, and other noncommunicable diseases. The combination of increased risk for important causes of mortality and significant levels of LPAterm·dom in most countries has led to physical inactivity being characterized as a pandemic. LPAterm·dom is an important contributor of premature mortality, morbidity, and DALYsterm·mth for adults in most of the world.
Globally, the total number of DALYsterm·mth due to LPAterm·dom, defined as <3,000 to 4,500 metabolic equivalent minutesterm·mth per week, continuously increased from 8.61 million (95% UI: 4.28 to 15.9 million) in 1990date to 15.7 million (95% UI: 8.52 to 28.6 million) in 2019date ( Supplemental Figure 63A ). A linear trend was observed in the numbers of deaths due to LPAterm·dom, reaching 832,000 premature deaths (95% UI: 427,000 to 1,470,000 premature deaths) in 2019date. During the same time period, the estimated YLLsterm·mth grew steadily from 7.51 million (95% UI: 3.62 to 14.4 million) in 1990date to 12.7 million (95% UI: 6.58 to 23.8 million) YLLsterm·mth in 2019date.
Over time, the all-ages rate of DALYsterm·mth, deaths, and YLLsterm·mth due to LPAterm·dom has increased, and the age-standardizedterm·key rate has decreased ( Supplemental Figure 63B ). This pattern reflects increased burden due to growth and aging of the population, an effect that is removed by age standardizationterm·mth.
Total global DALYsterm·mth due to LPAterm·dom were similar for men and women until the 65- to 69-year age group, with men consistently higher ( Supplemental Figure 64 ). This pattern reversed in the 70- to 74-year age group as DALYsterm·mth for women surpassed those for men. At all ages beyond 75 years, women had more DALYsterm·mth than men, presumably driven by greater survival among women. LPAterm·dom DALYsterm·mth rose steadily for both men and women beginning at age 25 years, peaking for both sexes in the 80- to 84-year age group and declining thereafter.
Age-standardizedterm·key DALYsterm·mth due to LPAterm·dom were highly variable by both region and sex. The highest rates were seen for both men and women in North Africa and the Middle East, Oceania, tropical Latin America, and the Caribbean ( Figure 23 ), with high rates for men also observed in Central Asia ( Supplemental Figure 65 ). The lowest age-standardizedterm·key DALYsterm·mth for LPAterm·dom were seen for both men and women in high-income Asia Pacific, Southern Latin America, and Eastern sub−Saharan Africa; for men in high-income North America; and for women in Andean Latin America.
Map of Age-Standardizedterm·key DALYsterm·mth Due to Low Physical Activity in 2019date DALYsterm·mth = disability-adjusted life years.
LPAterm·dom remains a major threat to public health, and the overall burden is increasing globally even as age-standardizedterm·key rates are declining. Although few health systems have historically focused on LPAterm·dom, there is increased awareness and attention being paid to LPAterm·dom as exemplified by the 2018 World Health Organization Global Action Plan for Physical Activity. However, the factors that influence population physical activity largely reside outside of the health sector. Increasing physical activity will depend on effective partnerships and collaboration between health and sectors such as transportation, education, urban planning, workplace health strategies, and environmental protection.
Subnational Disease Burden GBD 2019date estimates disease burden within countries as well as between countries. Figure 24 shows the age-standardizedterm·key rate of YLLsterm·mth due to CVD for all countries, with Brazil, China, India, Indonesia, Japan, Kenya, Mexico, the United States, and the United Kingdom shown at the first subnational administrative level and Stockholm County shown separately from the rest of Sweden. Subnational disease estimation is made possible through collaboration with these governments. Subnational data reveal wide differences in DALYsterm·mth within countries. For example, in Kenya in 2019date, IHD DALYsterm·mth were estimated to vary from 342.7 per 100,000 (95% UI: 212.4 to 531.7 per 100,000) in Turkana to 1,215.4 per 100,000 (95% UI: 856.8 to 1,612.4 per 100,000) in Nyeri, a 112.0% difference within Kenya. IHD DALYsterm·mth in India ranged from 506.6 per 100,000 (95% UI: 379.6 to 819.0 per 100,000) in Mizoram to 5,413.2 per 100,000 (95% UI: 4,316.9 to 6,389.9 per 100,000) in Punjab, a 165.8% difference within the country. In Brazil, IHD DALYsterm·mth varied from 771.2 per 100,000 (95% UI: 679.4 to 866.3 per 100,000) in Amazonas to 2,416.2 per 100,000 (95% UI: 2,176.7 to 2,686.2 per 100,000) in the Rio de Janeiro state, a 103.2% difference within the country. Within the United States, there was a 99.6% difference in IHD DALYsterm·mth, ranging from 1,438.3 per 100,000 (95% UI: 1,210.7 to 1,699.0 per 100,000) in Utah to 4,293.1 per 100,000 (95% UI: 3,636.0 to 5,033.0 per 100,000) in West Virginia. In Mexico, they ranged from 890.5 per 100,000 (95% UI: 752.2 to 1,073.3 per 100,000) in Quintana Roo to 2,315.1 per 100,000 (95% UI: 1,932.1 to 2,716.3 per 100,000) in Chihuahua, an 88.9% difference within Mexico. Disease burden attributable to elevated levels of metabolic risk factors also varies significantly within countries.
Map of Age-Standardizedterm·key YLLsterm·mth Due to Cardiovascular Diseases in 2019date YLLsterm·mth = years of life lost.
Further analysis of the variation of CVD burden within countries will be vital to implementing health policies that reduce burden but also decrease disparities. The strategies to deal with these inequalities include: 1) targeting reduction in key modifiable risks including diet, tobaccoterm·dom, alcohol, physical activity, and obesityterm·dom; 2) improved access and quality of health care; and 3) a renewed focus on the social determinants of health. Addressing risk factors may prove to be an important initial opportunity to reduce disparities in parallel with broader efforts targeting social determinants, with potential indirect benefits for improving those social determinants, such as reduced financial hardship due to fewer acute cardiac events among working-age adults.
Several risks are increasing globally and warrant increased policy and health system attention, including high BMIterm·mth, blood pressure, unhealthy cholesterol, and poor diet. Targeting subnational locations where metabolic risks are rising rapidly may be an important approach for testing new interventions and delivering resources where they will have the biggest impact. There is often a gap between the identification of a problem, the location of a solution, and the translation of that solution to an entire population. We currently lack a systematic, scalable process for determining which interventions are most likely to succeed in a given location, and research funding should be directed at addressing this gap.
Study limitations Estimates from GBD 2019date should be interpreted in light of several limitations. Death certificates, although an essential source of data for public health, may be misclassified given the difficulty of identifying an underlying cause of death. GBD 2019date applies statistical methods to improve the comparability of health information coded using the ICD system and continuously evaluates, improves on, and reports the details of these methods. The use of different subnational population-based studies may lead to compositional bias of national estimates, although GBD 2019date adjusts variance and weighting to reflect this possibility. ICD system codes from administrative health facility data may represent a group of patients that differs from the standard case definition for a disease, although GBD 2019date applies adjustments to these data to account for differential access to health care and alternate case definitions. Access to CVD diagnostic technologies is likely to influence estimates of some conditions, with lower access resulting in lower observed prevalence. Some commonly used diagnostic tools—for example, electrocardiogram for AF—will underestimate true disease prevalence. Estimates are reported with an uncertainty intervalterm·key that should always be carefully considered, given that it represents both data sparsity and differences in sample size across data sources and study locations. However, uncertainty may be underestimated in locations where no data on CVD of any kind are available. Limited data on CVD and resulting wider uncertainty intervalsterm·key for parts of sub-Saharan Africa, Oceania, and Asia may restrict our ability to interpret trends in burden over time. An important use of GBD results is therefore to identify gaps in CVD health data and guide investments in future disease surveillance. In particular, better data are needed on the incidence of coronary events, cardiac arrest, and the severity and associated disability of chronic heart and vascular diseases. Comorbidity of CVD and the joint effects of multiple risk factors remains a topic where further investigation is also needed.
Conclusions and Recommendations CVDs remain the leading cause of disease burden in the world. CVD continues its decades-long rise for almost all countries outside the high-income world and, alarmingly, age-standardizedterm·key CVD rates have begun to rise in many locations where they were previously declining. There remains a large gap between identifying CVD as a problem, identifying the best package of solutions, and delivering them to the entire population.
GBD 2019date continues to be a platform that allows tracking and benchmarking of progress in the reduction of CVD burden. However, large investments are still needed to improve disease surveillance, including the expansion of robust vital registration systems, reliable electronic health records, and health surveys to all countries.
High rates of excess mortality are currently being observed because of the global severe acute respiratory syndrome coronavirus 2 pandemic, and much of this additional disease burden may be CVD due to the effects of both viral infection but also the unintended consequence of social distancing behaviors, such as changes in the delivery of health care ( 45 , 46 ). Further research in this area is urgently needed.
CVD was the cause of 6.2 million deaths occurring between the ages of 30 and 70 years in 2019date. There is a pressing need to focus on implementing existing cost-effective interventions and health policies if the world is to meet the targets for Sustainable Development Goal 3 and achieve at least a 30% reduction in premature mortality due to noncommunicable disease by 2030. In the face of a global viral pandemic, we still must emphasize global commitments to reduce the suffering and premature death caused by CVD, which limits healthy and sustainable development for every country in the world.
Author Disclosures This study was funded by the Bill and Melinda Gates Foundation. Dr. BenjaminpersName has received funding from the National Institutes of Health (NIH)/National Heart, Lung, and Blood Institute (NHLBI) (R01HL092577, 1R01HL128914) and American Heart Association (18SFRN34110082). Dr. BrauerpersName has received a grant from the Bill and Melinda Gates Foundation. Dr. CatapanopersName has received support from Fondazione Cariplo 2015-0524 and 2015-0564, H2020 REPROGRAM PHC-03-2015/667837-2, ERA NET ER-2017-2364981, GR-2011-02346974, Ministry of Health - Ricerca Corrente Multimedica; has received research grant/support from Sanofi, Sanofi Regeneron, Amgen, Mylan, Menarini, and Eli Lilly; has served on the speakers bureau for Akcea, Amgen, Sanofi, Esperion, Kowa, Novartis, Ionis Pharmaceuticals, Mylan, Menarini, Merck, Recordati, Regeneron, Daiichi-Sankyo, AstraZeneca, Aegerion, Amryt, and Sandoz; has received honoraria from Akcea, Amgen, Sanofi, Esperion, Kowa, Novartis, Ionis Pharmaceuticals, Mylan, Menarini, Merck, Recordati, Regeneron Daiichi-Sankyo, AstraZeneca, Aegerion, Amryt, and Sandoz; and has served as a consultant/on the Advisory Board for Akcea, Amgen, Sanofi, Esperion, Kowa, Novartis, Ionis Pharmaceuticals, Mylan, Menarini, Merck, Recordati, Regeneron Daiichi-Sankyo, Genzyme, Aegerion, and Sandoz. Dr. CoreshpersName has received funding from the NIH and National Kidney Foundation; and has served as an advisor to Healthy.io. Dr. FowkespersName has served on the Advisory Board for AstraZeneca. Dr. MuntnerpersName has received grants and personal fees from Amgen Inc. Dr. RibeiropersName has received partial support by CNPq (310679/2016-8 and 465518/2014-1) and by FAPEMIG (PPM-00428-17). Dr. ZuhlkepersName has received funding by the UK Medical Research Council (MRC) and the UK Department for International Development (DFID) under the MRC/DFID Concordat agreement and the National Research Foundation of South Africa. Dr. RigottipersName has served as a consultant to Achieve Life Sciences; and has received royalties from UpToDate, Inc. Dr. RodgerspersName is employed by The George Institute for Global Health (TGI) and seconded part time to George Medicines Pty Ltd (GM); TGI has submitted patent applications with respect to low-fixed-dose combination products for the treatment of cardiovascular or cardiometabolic disease and is listed as one of the inventors; George Health Enterprises Pty. Ltd. (GHE) and its subsidiary, GM, have received investment funds to develop fixed-dose combination products, including combinations of blood pressure-lowering drugs; GHE is the social enterprise arm of TGI (Dr. RodgerspersName does not have a direct financial interest in these patent applications or investments). Dr. SundströmpersName holds ownership in companies providing services to Itrim, Amgen, Janssen, Novo Nordisk, Eli Lilly, Boehringer Ingelheim, Bayer, Pfizer, and AstraZeneca, outside the submitted work. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose.
Acknowledgments This manuscript was produced as part of the GBD Collaborator Network and in accordance with the GBD protocol. Additional information can be found in the Supplemental Acknowledgments and Declarations .
Footnotes Contributor Information Gregory A. RothpersName, Email: rothg@uw.edu.
George A. MensahpersName, Email: george.mensahpersName@nih.gov.
GBD-NHLBI-JACC Global Burden of Cardiovascular Diseases Writing Group: Gregory A. RothpersName , George A. MensahpersName , Catherine O. JohnsonpersName , Giovanni AddoloratopersName , Enrico AmmiratipersName , Larry M. BaddourpersName , Noel C. BarengopersName , Andrea BeatonpersName , Emelia J. BenjaminpersName , Catherine P. BenzigerpersName , Aime BonnypersName , Michael BrauerpersName , Marianne BrodmannpersName , Thomas J. CahillpersName , Jonathan R. CarapetispersName , Alberico L. CatapanopersName , Sumeet ChughpersName , Leslie T. CooperpersName , Josef CoreshpersName , Michael H. CriquipersName , Nicole K. DeCleenepersName , KimpersName A. EaglepersName , Sophia Emmons-BellpersName , Valery L. FeiginpersName , Joaquim Fernández-SolapersName , F. Gerry R. FowkespersName , Emmanuela GakidoupersName , Scott M. GrundypersName , FengpersName J. He , George HowardpersName , FrankpersName Hu , Lesley InkerpersName , Ganesan KarthikeyanpersName , Nicholas J. KassebaumpersName , Walter J. KoroshetzpersName , Carl LaviepersName , Donald Lloyd-JonespersName , HongpersName S. Lu , Antonio MirijellopersName , AwokepersName T. MisganawpersName , Ali H. MokdadpersName , Andrew E. MoranpersName , Paul MuntnerpersName , Jagat NarulapersName , Bruce NealpersName , Mpiko NtsekhepersName , Gláucia M.M. OliveirapersName , Catherine M. OttopersName , Mayowa O. OwolabipersName , Michael PrattpersName , Sanjay RajagopalanpersName , Marissa B. ReitsmapersName , Antonio Luiz P. RibeiropersName , Nancy A. RigottipersName , Anthony RodgerspersName , Craig A. SablepersName , Saate S. ShakilpersName , Karen SliwapersName , BenjaminpersName A. StarkpersName , Johan SundströmpersName , Patrick TimpelpersName , Imad I. TleyjehpersName , Marco ValgimiglipersName , Theo VospersName , Paul K. WheltonpersName , Magdi YacoubpersName , Liesl J. ZuhlkepersName , Mohsen Abbasi-KangevaripersName , Alireza AbdipersName , Aidin AbedipersName , Victor AboyanspersName , Woldu A. AbrhapersName , Eman Abu-GharbiehpersName , Abdelrahman I. AbushoukpersName , Dilaram AcharyapersName , Tim AdairpersName , Oladimeji M. AdebayopersName , Zanfina AdemipersName , Shailesh M. AdvanipersName , Khashayar AfsharipersName , Ashkan AfshinpersName , Gina AgarwalpersName , Pradyumna AgasthipersName , Sohail AhmadpersName , Sepideh AhmadipersName , Muktar B. AhmedpersName , Budi AjipersName , Yonas AkalupersName , Wuraola Akande-SholabipersName , Addis AklilupersName , Chisom J. AkunnapersName , Fares AlahdabpersName , Ayman Al-EyadhypersName , Khalid F. AlhabibpersName , SheikhpersName M. AlifpersName , Vahid AlipourpersName , Syed M. AljunidpersName , François AllapersName , Amir Almasi-HashianipersName , Sami AlmustanyirpersName , Rajaa M. Al-RaddadipersName , Adeladza K. AmegahpersName , Saeed AminipersName , Arya AminorroayapersName , Hubert AmupersName , Dickson A. AmugsipersName , Robert AncuceanupersName , Deanna AnderlinipersName , Tudorel AndreipersName , Catalina Liliana AndreipersName , Alireza Ansari-MoghaddampersName , Zelalem A. AntenehpersName , Ippazio Cosimo AntonazzopersName , Benny AntonypersName , Razique AnwerpersName , Lambert T. AppiahpersName , Jalal ArabloopersName , Johan ÄrnlövpersName , Kurnia D. ArtantipersName , Zerihun AtaropersName , Marcel AusloospersName , Leticia Avila-BurgospersName , Asma T. AwanpersName , Mamaru A. AwokepersName , Henok T. AyelepersName , Muluken A. AyzapersName , Samad AzaripersName , DarshanpersName B. B , Nafiseh BaheiraeipersName , Atif A. BaigpersName , Ahad BakhtiaripersName , Maciej BanachpersName , Palash C. BanikpersName , Emerson A. BaptistapersName , Miguel A. BarbozapersName , Lingkan BaruapersName , Sanjay BasupersName , Neeraj BedipersName , Yannick BéjotpersName , Derrick A. BennettpersName , Isabela M. BensenorpersName , Adam E. BermanpersName , Yihienew M. BezabihpersName , Akshaya S. BhagavathulapersName , Sonu BhaskarpersName , Krittika BhattacharyyapersName , Ali BijanipersName , Boris BikbovpersName , Mulugeta M. BirhanupersName , Archith BoloorpersName , Luisa C. BrantpersName , Hermann BrennerpersName , Nikolay I. BrikopersName , Zahid A. ButtpersName , Florentino Luciano Caetano dos SantospersName , Leah E. CahillpersName , Lucero Cahuana-HurtadopersName , Luis A. CámerapersName , Ismael R. Campos-NonatopersName , Carlos Cantu-BritopersName , Josip CarpersName , Juan J. CarreropersName , Felix CarvalhopersName , Carlos A. Castañeda-OrjuelapersName , Ferrán Catalá-LópezpersName , Ester CerinpersName , Jaykaran CharanpersName , Vijay KumarpersName ChattupersName , Simiao ChenpersName , Ken L. ChinpersName , Jee-Young J. ChoipersName , Dinh-Toi ChupersName , Sheng-Chia ChungpersName , Massimo CirillopersName , Sean CoffeypersName , Sara ContipersName , Vera M. CostapersName , David K. CundiffpersName , Omid DadraspersName , Baye DagnewpersName , Xiaochen DaipersName , Albertino A.M. DamascenopersName , Lalit DandonapersName , Rakhi DandonapersName , Kairat DavletovpersName , Vanessa De la Cruz-GóngorapersName , Fernando P. De la HozpersName , Jan-Walter De NevepersName , Edgar Denova-GutiérrezpersName , Meseret Derbew MollapersName , Behailu T. DersehpersName , Rupak DesaipersName , Günther DeuschlpersName , Samath D. DharmaratnepersName , Meghnath DhimalpersName , Raja RampersName DhunganapersName , Mostafa DianatinasabpersName , Daniel DiazpersName , Shirin DjalaliniapersName , Klara DokovapersName , Abdel DouiripersName , Bruce B. DuncanpersName , Andre R. DuraespersName , Arielle W. EaganpersName , Sanam EbtehajpersName , Aziz EftekharipersName , Sahar EftekharzadehpersName , Michael EkholuenetalepersName , Nevine El NahaspersName , IslampersName Y. ElgendypersName , Muhammed ElhadipersName , Shaimaa I. El-JaafarypersName , Sadaf EsteghamatipersName , Atkilt E. EtissopersName , Oghenowede EyawopersName , Ibtihal FadhilpersName , Emerito Jose A. FaraonpersName , Pawan S. FarispersName , Medhat FarwatipersName , Farshad FarzadfarpersName , Eduarda FernandespersName , Carlota Fernandez PrendespersName , Pietro FerrarapersName , Irina FilippersName , Florian FischerpersName , David FloodpersName , Takeshi FukumotopersName , Mohamed M. GadpersName , Shilpa GaidhanepersName , Morsaleh GanjipersName , Jalaj GargpersName , Abadi K. GebrepersName , Birhan G. GebregiorgispersName , Kidane Z. GebregzabiherpersName , Gebreamlak G. GebremeskelpersName , Lemma GetacherpersName , Abera Getachew ObsapersName , Alireza GhajarpersName , AhmadpersName GhashghaeepersName , Nermin GhithpersName , Simona GiampaolipersName , Syed Amir GilanipersName , Paramjit S. GillpersName , Richard F. GillumpersName , Ekaterina V. GlushkovapersName , Elena V. GnedovskayapersName , Mahaveer GolechhapersName , Kebebe B. GonfapersName , Amir Hossein GoudarzianpersName , Alessandra C. GoulartpersName , Jenny S. GuadamuzpersName , Avirup GuhapersName , Yuming GuopersName , Rajeev GuptapersName , Vladimir HachinskipersName , Nima Hafezi-NejadpersName , TeklehaimanotpersName G. HailepersName , Randah R. HamadehpersName , Samer HamidipersName , Graeme J. HankeypersName , Arief HargonopersName , Risky K. HartonopersName , Maryam HashemianpersName , Abdiwahab HashipersName , Shoaib HassanpersName , Hamid Y. HassenpersName , Rasmus J. HavmoellerpersName , Simon I. HaypersName , Khezar HayatpersName , Golnaz HeidaripersName , Claudiu HerteliupersName , Ramesh HollapersName , Mostafa HosseinipersName , Mehdi HosseinzadehpersName , Mihaela HostiucpersName , Sorin HostiucpersName , Mowafa HousehpersName , Junjie HuangpersName , Ayesha HumayunpersName , Ivo IavicolipersName , Charles U. IbenemepersName , Segun E. IbitoyepersName , Olayinka S. IlesanmipersName , Irena M. IlicpersName , Milena D. IlicpersName , UsmanpersName IqbalpersName , Seyed Sina N. IrvanipersName , SheikhpersName MohammedpersName Shariful IslampersName , Rakibul M. IslampersName , Hiroyasu IsopersName , Masao IwagamipersName , Vardhmaan JainpersName , Tahereh JavaheripersName , SathishpersName KumarpersName JayapalpersName , Shubha JayarampersName , Ranil JayawardenapersName , Panniyammakal JeemonpersName , Ravi P. JhapersName , Jost B. JonaspersName , Jitendra JonnagaddalapersName , Farahnaz JoukarpersName , Jacek J. JozwiakpersName , Mikk JürissonpersName , Ali KabirpersName , Tanvir KahlonpersName , Rizwan KalanipersName , Rohollah KalhorpersName , Ashwin KamathpersName , Ibrahim KamelpersName , Himal KandelpersName , Amit KandelpersName , André KarchpersName , AyelepersName Semachew KasapersName , Patrick D.M.C. KatotopersName , Gbenga A. KayodepersName , Yousef S. KhaderpersName , MohammadpersName KhammarniapersName , Muhammad S. KhanpersName , Md Nuruzzaman KhanpersName , Maseer KhanpersName , Ejaz A. KhanpersName , Khaled KhatabpersName , Gulam M.A. KibriapersName , Yun Jin KimpersName , Gyu Ri KimpersName , Ruth W. KimokotipersName , Sezer KisapersName , Adnan KisapersName , Mika KivimäkipersName , Dhaval KoltepersName , Ali KoolivandpersName , Vladimir A. KorshunovpersName , Sindhura Lakshmi Koulmane LaxminarayanapersName , Ai KoyanagipersName , Kewal KrishanpersName , Vijay KrishnamoorthypersName , Barthelemy Kuate DefopersName , Burcu Kucuk BicerpersName , Vaman KulkarnipersName , G. Anil KumarpersName , Nithin KumarpersName , Om P. KurmipersName , Dian KusumapersName , Gene F. KwanpersName , Carlo La VecchiapersName , Ben LaceypersName , Tea LallukkapersName , Qing LanpersName , Savita LasradopersName , Zohra S. LassipersName , Paolo LauriolapersName , Wayne R. LawrencepersName , Avula LaxmaiahpersName , Kate E. LeGrandpersName , Ming-ChiehpersName Li , BingyupersName Li , ShanshanpersName Li , Stephen S. LimpersName , Lee-Ling LimpersName , Hualiang LinpersName , Ziqiang LinpersName , Ro-Ting LinpersName , Xuefeng LiupersName , Alan D. LopezpersName , Stefan LorkowskipersName , Paulo A. LotufopersName , Alessandra LugopersName , NirmalpersName K. M , Fabiana MadottopersName , Morteza MahmoudipersName , Azeem MajeedpersName , Reza MalekzadehpersName , AhmadpersName A. MalikpersName , Abdullah A. MamunpersName , Navid ManafipersName , MohammadpersName Ali MansourniapersName , Lorenzo G. MantovanipersName , Santi MartinipersName , Manu R. MathurpersName , Giampiero MazzagliapersName , Suresh MehatapersName , Man Mohan MehndirattapersName , Toni MeierpersName , Ritesh G. MenezespersName , Atte MeretojapersName , Tomislav MestrovicpersName , Bartosz MiazgowskipersName , Tomasz MiazgowskipersName , Irmina Maria MichalekpersName , Ted R. MillerpersName , Erkin M. MirrakhimovpersName , Hamed MirzaeipersName , Babak MoazenpersName , Masoud MoghadaszadehpersName , Yousef MohammadpersName , Dara K. MohammadpersName , Shafiu MohammedpersName , MohammedpersName A. MohammedpersName , Yaser MokhayeripersName , Mariam MolokhiapersName , AhmedpersName A. MontasirpersName , Ghobad MoradipersName , Rahmatollah MoradzadehpersName , Paula MoragapersName , Lidia MorawskapersName , Ilais Moreno VelásquezpersName , Jakub MorzepersName , Sumaira MubarikpersName , Walter MuruetpersName , Kamarul Imran MusapersName , Ahamarshan J. NagarajanpersName , Mahdi NalinipersName , Vinay NangiapersName , Atta Abbas NaqvipersName , Sreenivas Narasimha SwamypersName , Bruno R. NascimentopersName , Vinod C. NayakpersName , Javad NazaripersName , Milad NazarzadehpersName , Ruxandra I. NegoipersName , Sandhya Neupane KandelpersName , Huong L.T. NguyenpersName , Molly R. NixonpersName , Bo NorrvingpersName , Jean Jacques NoubiappersName , Brice E. NouthepersName , Christoph NowakpersName , Oluwakemi O. OdukoyapersName , Felix A. OgbopersName , Andrew T. OlagunjupersName , Hans OrrupersName , Alberto OrtizpersName , Samuel M. OstroffpersName , Jagadish RaopersName PadubidripersName , Raffaele PalladinopersName , Adrian PanapersName , Songhomitra Panda-JonaspersName , Utsav ParekhpersName , Eun-Cheol ParkpersName , Mojtaba ParvizipersName , Fatemeh Pashazadeh KanpersName , Urvish K. PatelpersName , Mona PathakpersName , Rajan PaudelpersName , Veincent Christian F. PepitopersName , Arokiasamy PerianayagampersName , Norberto PericopersName , Hai Q. PhampersName , Thomas PilgrimpersName , Michael A. PiradovpersName , Farhad PishgarpersName , Vivek PodderpersName , Roman V. PolibinpersName , Akram PourshamspersName , Dimas R.A. PribadipersName , Navid RabieepersName , MohammadpersName RabieepersName , Amir RadfarpersName , Alireza RafieipersName , Fakher RahimpersName , Vafa Rahimi-MovagharpersName , MohammadpersName Hifz Ur RahmanpersName , Muhammad Aziz RahmanpersName , Amir Masoud RahmanipersName , Ivo RakovacpersName , Pradhum RampersName , Sudha RamalingampersName , Juwel RanapersName , Priyanga RanasinghepersName , Sowmya J. RaopersName , Priya RathipersName , Lal RawalpersName , Wasiq F. RawasiapersName , Reza RawassizadehpersName , Giuseppe RemuzzipersName , Andre M.N. RenzahopersName , Aziz RezapourpersName , Seyed MohammadpersName RiahipersName , Ross L. Roberts-ThomsonpersName , Leonardo RoeverpersName , Peter RohloffpersName , Michele RomolipersName , Gholamreza RoshandelpersName , Godfrey M. RwegererapersName , Seyedmohammad SaadatagahpersName , Maha M. Saber-AyadpersName , Siamak SabourpersName , Simona SaccopersName , Masoumeh SadeghipersName , Sahar Saeedi MoghaddampersName , Saeed SafaripersName , Amirhossein SahebkarpersName , Sana SalehipersName , Hamideh SalimzadehpersName , Mehrnoosh SamaeipersName , Abdallah M. SamypersName , Itamar S. SantospersName , Milena M. Santric-MilicevicpersName , Nizal SarrafzadeganpersName , Arash SarveazadpersName , Thirunavukkarasu SathishpersName , Monika SawhneypersName , Mete SaylanpersName , Maria I. SchmidtpersName , Aletta E. SchuttepersName , Subramanian SenthilkumaranpersName , Sadaf G. SepanloupersName , FengpersName ShapersName , Saeed ShahabipersName , Izza ShahidpersName , Masood A. ShaikhpersName , Mahdi ShamalipersName , Morteza ShamsizadehpersName , Md Shajedur RahmanpersName ShawonpersName , Aziz SheikhpersName , Mika ShigematsupersName , Min-Jeong ShinpersName , Jae Il ShinpersName , RahmanpersName ShiripersName , Ivy ShiuepersName , Kerem ShuvalpersName , Soraya SiabanipersName , Tariq J. SiddiqipersName , Diego A.S. SilvapersName , Jasvinder A. SinghpersName , Ambrish SinghpersName MtechpersName , Valentin Y. SkryabinpersName , Anna A. SkryabinapersName , Amin SoheilipersName , Emma E. SpurlockpersName , Leo StockfeltpersName , Stefan StorteckypersName , Saverio StrangespersName , Rizwan Suliankatchi AbdulkaderpersName , Hooman TadbiripersName , Eyayou G. TadessepersName , Degena B. TadessepersName , Masih TajdinipersName , Md TariqujjamanpersName , Berhane F. TeklehaimanotpersName , Mohamad-Hani TemsahpersName , Ayenew K. TesemapersName , BhaskarpersName ThakurpersName , Kavumpurathu R. ThankappanpersName , Rekha ThaparpersName , Amanda G. ThriftpersName , Binod TimalsinapersName , Marcello TonellipersName , Mathilde TouvierpersName , Marcos R. Tovani-PalonepersName , Avnish TripathipersName , Jaya P. TripathypersName , Thomas C. TruelsenpersName , Guesh M. TsegaypersName , Gebiyaw W. TsegayepersName , Nikolaos TsilimparispersName , Biruk S. TusapersName , Stefanos TyrovolaspersName , Krishna Kishore UmapathipersName , Brigid UnimpersName , Bhaskaran UnnikrishnanpersName , Muhammad S. UsmanpersName , Muthiah VaduganathanpersName , Pascual R. ValdezpersName , Tommi J. VasankaripersName , Diana Z. VelazquezpersName , Narayanaswamy VenketasubramanianpersName , GiangpersName T. Vu , Isidora S. VujcicpersName , Yasir WaheedpersName , Yanzhong WangpersName , Fang WangpersName , Jingkai WeipersName , Robert G. WeintraubpersName , AbrhapersName H. WeldemariampersName , Ronny WestermanpersName , Andrea S. WinklerpersName , Charles S. WiysongepersName , Charles D.A. WolfepersName , Befikadu Legesse WubishetpersName , GelinpersName Xu , Ali YadollahpourpersName , Kazumasa YamagishipersName , Lijing L. YanpersName , Srikanth YandrapallipersName , Yuichiro YanopersName , Hiroshi YatsuyapersName , Tomas Y. YeheyispersName , Yigizie YeshawpersName , Christopher S. YilgwanpersName , Naohiro YonemotopersName , ChuanhuapersName Yu , Hasan YusefzadehpersName , Geevar ZachariahpersName , Sojib Bin ZamanpersName , Muhammed S. ZamanpersName , Maryam ZamanianpersName , Ramin ZandpersName , Alireza ZandifarpersName , AfshinpersName ZarghipersName , Mikhail S. ZastrozhinpersName , Anasthasia ZastrozhinapersName , Zhi-Jiang ZhangpersName , Yunquan ZhangpersName , Wangjian ZhangpersName , Chenwen ZhongpersName , Zhiyong ZoupersName , Yves Miel H. ZunigapersName , Christopher J.L. MurraypersName , and Valentin FusterpersName Appendix References Associated Data Supplementary Materials