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Burden of Disease Scenarios by State in the Usa, 2022–50: A Forecasting Analysis for the Global Burden of Disease Study 2021 Publisher Pubmed



Mokdad AH1, 2 ; Bisignano C1 ; Hsu JM1 ; Bryazka D1 ; Cao S1 ; Bhattacharjee NV1 ; Dalton BE1 ; Lindstedt PA1 ; Smith AE1 ; Ababneh HS3 ; Abbasgholizadeh R4 ; Abdelkader A5 ; Abdi P6 ; Abiodun OO7 Show All Authors
Authors
  1. Mokdad AH1, 2
  2. Bisignano C1
  3. Hsu JM1
  4. Bryazka D1
  5. Cao S1
  6. Bhattacharjee NV1
  7. Dalton BE1
  8. Lindstedt PA1
  9. Smith AE1
  10. Ababneh HS3
  11. Abbasgholizadeh R4
  12. Abdelkader A5
  13. Abdi P6
  14. Abiodun OO7
  15. Aboagye RG8
  16. Abukhadijah HJ9
  17. Abuzaid A10, 11
  18. Acuna JM12, 13
  19. Addo IY14, 15
  20. Adekanmbi V16
  21. Adeyeoluwa TE17, 18
  22. Adzigbli LA19
  23. Afolabi AA20
  24. Afrashteh F21
  25. Agyemangduah W22
  26. Ahmad S23, 24
  27. Ahmadzade M25
  28. Ahmed A26, 27
  29. Ahmed A26, 27
  30. Ahmed SA30
  31. Akkaif MA31
  32. Akkala S32
  33. Akrami AE33, 34
  34. Al Awaidy S35, 36
  35. Al Hasan SM37
  36. Al Taani O38
  37. Al Zaabi OAM39
  38. Alahdab F40, 41
  39. Alajlouni Y42, 43
  40. Alaly Z44, 45
  41. Alam M46
  42. Aldhaleei WA47
  43. Algammal AM48
  44. Alhassan RK49
  45. Ali MU50, 51
  46. Ali R52
  47. Ali W53
  48. Alibraheem A54, 55
  49. Almustanyir S56, 57
  50. Alqahatni SA58, 59
  51. Alrawashdeh A60
  52. Alrifai RH61
  53. Alsabri MA62, 63
  54. Alshahrani NZ64
  55. Altawfiq JA65, 66
  56. Alwardat M67
  57. Aly H68
  58. Amindarolzarbi A69
  59. Amiri S70
  60. Anil A71, 72
  61. Anyasodor AE73
  62. Arabloo J74
  63. Arafat M75
  64. Aravkin AY1, 2, 76
  65. Ardekani A77
  66. Areda D78, 79
  67. Asghariahmadabad M80
  68. Ayanore MA81, 82
  69. Ayyoubzadeh SM83
  70. Azadnajafabad S84, 85
  71. Azhar GS86
  72. Aziz S87
  73. Azzam AY88, 89
  74. Babu GR90
  75. Baghdadi S91, 92
  76. Bahreini R93
  77. Bako AT94
  78. Barnighausen TW95, 96
  79. Bastan MM21, 97
  80. Basu S98, 99
  81. Batra K100
  82. Batra R101, 102
  83. Behnoush AH103, 104
  84. Bemanalizadeh M105, 106
  85. Benzian H107
  86. Bermudez ANC108, 109
  87. Bernstein RS110, 111
  88. Beyene KA112, 113
  89. Bhagavathula AS47, 114
  90. Bhala N115, 116
  91. Bharadwaj R117
  92. Bhargava A118
  93. Bhaskar S119, 120
  94. Bhat V121
  95. Bhuyan SS122
  96. Bodunrin AO123
  97. Boxe C124
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  104. Caetano Dos Santos FL135
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  107. Cembranel F139
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  117. Cho DY152
  118. Chong B153
  119. Choudhari SG154
  120. Chukwu IS155
  121. Chung E1, 156
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  123. Coker DC159
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  201. Hajmirzaian A253, 254
  202. Haq ZA255
  203. Hasaballah AI256
  204. Hasan I257
  205. Hasan MK258, 259
  206. Hasan SMM260
  207. Hasani H261
  208. Hasnain MS262
  209. Havmoeller RJ263
  210. Hay SI1, 2
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  212. Hebert JJ264, 265
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  216. Horita N270, 271
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  221. Hushmandi K278
  222. Hussain MA279, 280
  223. Huynh HH281
  224. Iftikhar PM282
  225. Ikiroma A283
  226. Islam MR284
  227. Islam SMS285, 286
  228. Iyasu AN287
  229. Jacob L288, 289
  230. Jairoun AA290
  231. Jaka S291
  232. Jakovljevic M292, 293
  233. Jalilzadeh Yengejeh R294
  234. Jamil S295, 296
  235. Javaheri T297
  236. Jeswani BM298
  237. Kalani R299
  238. Kamarajah SK300
  239. Kamireddy A301
  240. Kanmodi KK302, 303
  241. Kantar RS304, 305
  242. Karaye IM306, 307
  243. Katamreddy A308
  244. Kazemi F309
  245. Kazemian S310, 311
  246. Kempen JH312, 313
  247. Khamesipour F314, 315
  248. Khan A316
  249. Khan F317
  250. Khan MJ51
  251. Khanmohammadi S103, 318
  252. Khatab K319, 320
  253. Khatatbeh MM321
  254. Khorgamphar M322, 323
  255. Khormali M324
  256. Khosla AA23, 325
  257. Khosravi M326, 327
  258. Kim G328, 329
  259. Kim MS330, 331
  260. Kimokoti RW332
  261. Kisa A333, 334
  262. Kochhar S335, 336
  263. Koren G337
  264. Krishnamoorthy V338, 339
  265. Kuddus MA340, 341
  266. Kulimbet M342, 343
  267. Kulkarni V344
  268. Kumar A345
  269. Kumar R346
  270. Kumar V347, 348
  271. Kundu S349, 350
  272. Kurmi OP351, 352
  273. Kyei EF353
  274. Lan Q354
  275. Lansingh VC355, 356
  276. Le HH357, 358
  277. Le NHH357, 358
  278. Le TTT359
  279. Leasher JL360
  280. Lee M361
  281. Lee WC362
  282. Li W363
  283. Lim SS1, 2
  284. Lin J364
  285. Liu G365
  286. Liu RT366, 367
  287. Liu X368, 369
  288. Lopezgil JF370
  289. Lopukhov PD371
  290. Lucchetti G372
  291. Lunevicius R373, 374
  292. Lv L375
  293. Maaty DWS376
  294. Maharaj SB377, 378
  295. Mahmoudi E379
  296. Makram OM380, 381
  297. Malakan Rad E382
  298. Malasala S383
  299. Manla Y384
  300. Mansouri V385
  301. Manu E386
  302. Martinezpiedra R387
  303. Marzo RR388, 389
  304. Mathangasinghe Y390, 391
  305. Mathur M392
  306. Matozinhos FP393
  307. Mayeli M103
  308. Mcphail SM394, 395
  309. Mediratta RP396
  310. Mekene Meto T397
  311. Meles HN398
  312. Melese EB399, 400
  313. Meo SA401
  314. Mestrovic T1, 402
  315. Metanat P403
  316. Mhlanga L404, 405
  317. Michalek IM406, 407
  318. Miller TR408, 409
  319. Mini GK410, 411
  320. Mirarefin M412
  321. Moberg ME1
  322. Mohamed J413
  323. Mohamed NS414, 415
  324. Mohammad AM416
  325. Mohammadianhafshejani A417
  326. Mohammadzadeh I418
  327. Mohammed S95, 419
  328. Molavi Vardanjani H420
  329. Moni MA421, 422
  330. Moraga P423
  331. Morrison SD424
  332. Motappa R425
  333. Munkhsaikhan Y426
  334. Murillozamora E427, 428
  335. Mustafa A429
  336. Nafei A430
  337. Naghavi P431
  338. Naik G432
  339. Najafi MS433, 434
  340. Nanavaty DP435
  341. Nandu KTK436
  342. Nascimento GG437
  343. Naser AY438
  344. Nashwan AJ439
  345. Natto ZS440, 441
  346. Nduaguba SO442
  347. Nguyen DH443, 444
  348. Nguyen PT445
  349. Nguyen QP1
  350. Nguyen VT446
  351. Nikravangolsefid N447
  352. Niranjan V448, 449
  353. Noor STA450, 451
  354. Nugen F452, 453
  355. Nutor JJ454
  356. Nzoputam OJ455, 456
  357. Oancea B457
  358. Oduro MS458
  359. Ogundijo OA459
  360. Ogunsakin RE460
  361. Ojoakosile TR461
  362. Okeke SR15, 462
  363. Okonji OC463
  364. Olagunju AT464, 465
  365. Olorukooba AA466
  366. Olufadewa II467, 468
  367. Oluwafemi YD199
  368. Omar HA469, 470
  369. Opejin A471
  370. Ostroff SM1, 472
  371. Owolabi MO473, 474
  372. Ozair A475, 476
  373. P A MP477
  374. Panda SK478, 479
  375. Pandiperumal SR480, 481
  376. Parikh RR482
  377. Park S483
  378. Pashaei A484
  379. Patel P485
  380. Patil S486, 487
  381. Pawar S488
  382. Peprah EK489
  383. Pereira G490, 491
  384. Pham HN345, 492
  385. Philip AK493
  386. Phillips MR494, 495
  387. Pigeolet M496, 497
  388. Postma MJ498, 499
  389. Pourbabaki R500
  390. Prabhu D501
  391. Pradhan J502
  392. Pradhan PMS503, 665
  393. Puvvula J504
  394. Rafferty Q1
  395. Raggi C1
  396. Rahim MJ505, 506
  397. Rahimimovaghar V324
  398. Rahman MA507, 508
  399. Rahmanian M509
  400. Ramadan M510
  401. Ramasamy SK511
  402. Ramazanu S512, 513
  403. Ranabhat CL514, 515
  404. Rane A516, 517
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  415. Roy P533
  416. Runghien T1, 534
  417. Saad AMA535
  418. Sabet CJ267
  419. Saeed U536, 537
  420. Safari M538
  421. Sagoe D539
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  423. Saleh MA541, 542
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  426. Samy AM546, 547
  427. Sanabria J548, 549
  428. Saravanan A550, 551
  429. Saravi B552, 553
  430. Satpathy M554, 555
  431. Sawhney M556
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  433. Schuermans A559, 560
  434. Schumacher AE1
  435. Schwebel DC561
  436. Selvaraj S562
  437. Seylani A563
  438. Shafie M564
  439. Shahbandi A103
  440. Shahsavari HR565
  441. Shaikh MA566
  442. Shamim MA71
  443. Sharath M567
  444. Sharew NT568, 569
  445. Sharifan A570, 571
  446. Sharma A572
  447. Sharma M573
  448. Shayan M312, 574
  449. Sheikh A575, 576
  450. Shen J577
  451. Sherchan SP578, 579
  452. Shetty M580
  453. Shetty PH581
  454. Shetty PK582
  455. Shigematsu M583
  456. Shittu A584
  457. Shivarov V585, 586
  458. Shool S324, 587
  459. Shuval K588
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  462. Singh S71
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  464. Smith G1
  465. Solanki S595
  466. Soliman SSM596
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  469. Straif K530, 597
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  472. Swain CK531
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Source: The Lancet Published:2024


Abstract

Background: The capacity to anticipate future health issues is important for both policy makers and practitioners in the USA, as such insights can facilitate effective planning, investment, and implementation strategies. Forecasting trends in disease and injury burden is not only crucial for policy makers but also garners substantial interest from the general populace and leads to a better-informed public. Through the integration of new data sources, the refinement of methodologies, and the inclusion of additional causes, we have improved our previous forecasting efforts within the scope of the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) to produce forecasts at the state and national levels for the USA under various possible scenarios. Methods: We developed a comprehensive framework for forecasting life expectancy, healthy life expectancy (HALE), cause-specific mortality, and disability-adjusted life-years (DALYs) due to 359 causes of disease and injury burden from 2022 to 2050 for the USA and all 50 states and Washington, DC. Using the GBD 2021 Future Health Scenarios modelling framework, we forecasted drivers of disease, demographic drivers, risk factors, temperature and particulate matter, mortality and years of life lost (YLL), population, and non-fatal burden. In addition to a reference scenario (representing the most probable future trajectory), we explored various future scenarios and their potential impacts over the next several decades on human health. These alternative scenarios comprised four risk elimination scenarios (including safer environment, improved behavioural and metabolic risks, improved childhood nutrition and vaccination, and a combined scenario) and three USA-specific scenarios based on risk exposure or attributable burden in the best-performing US states (improved high adult BMI and high fasting plasma glucose [FPG], improved smoking, and improved drug use [encompassing opioids, cocaine, amphetamine, and others]). Findings: Life expectancy in the USA is projected to increase from 78·3 years (95% uncertainty interval 78·1–78·5) in 2022 to 79·9 years (79·5–80·2) in 2035, and to 80·4 years (79·8–81·0) in 2050 for all sexes combined. This increase is forecasted to be modest compared with that in other countries around the world, resulting in the USA declining in global rank over the 2022–50 forecasted period among the 204 countries and territories in GBD, from 49th to 66th. There is projected to be a decline in female life expectancy in West Virginia between 1990 and 2050, and little change in Arkansas and Oklahoma. Additionally, after 2023, we projected almost no change in female life expectancy in many states, notably in Oklahoma, South Dakota, Utah, Iowa, Maine, and Wisconsin. Female HALE is projected to decline between 1990 and 2050 in 20 states and to remain unchanged in three others. Drug use disorders and low back pain are projected to be the leading Level 3 causes of age-standardised DALYs in 2050. The age-standardised DALY rate due to drug use disorders is projected to increase considerably between 2022 and 2050 (19·5% [6·9–34·1]). Our combined risk elimination scenario shows that the USA could gain 3·8 additional years (3·6–4·0) of life expectancy and 4·1 additional years (3·9–4·3) of HALE in 2050 versus the reference scenario. Using our USA-specific scenarios, we forecasted that the USA could gain 0·4 additional years (0·3–0·6) of life expectancy and 0·6 additional years (0·5–0·8) of HALE in 2050 under the improved drug use scenario relative to the reference scenario. Life expectancy and HALE are likewise projected to be 0·4–0·5 years higher in 2050 under the improved adult BMI and FPG and improved smoking scenarios compared with the reference scenario. However, the increases in these scenarios would not substantially improve the USA's global ranking in 2050 (from 66th of 204 in life expectancy in the reference scenario to 63rd–64th in each of the three USA-specific scenarios), indicating that the USA's best-performing states are still lagging behind other countries in their rank throughout the forecasted period. Regardless, an estimated 12·4 million (11·3–13·5) deaths could be averted between 2022 and 2050 if the USA were to follow the combined scenario trajectory rather than the reference scenario. There would also be 1·4 million (0·7–2·2) fewer deaths over the 28-year forecasted period with improved adult BMI and FPG, 2·1 million (1·3–2·9) fewer deaths with improved exposure to smoking, and 1·2 million (0·9–1·5) fewer deaths with lower rates of drug use deaths. Interpretation: Our findings highlight the alarming trajectory of health challenges in the USA, which, if left unaddressed, could lead to a reversal of the health progress made over the past three decades for some US states and a decline in global health standing for all states. The evidence from our alternative scenarios along with other published studies suggests that through collaborative, evidence-based strategies, there are opportunities to change the trajectory of health outcomes in the USA, such as by investing in scientific innovation, health-care access, preventive health care, risk exposure reduction, and education. Our forecasts clearly show that the time to act is now, as the future of the country's health and wellbeing—as well as its prosperity and leadership position in science and innovation—are at stake. Funding: Bill & Melinda Gates Foundation. © 2024 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license
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