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Global Burden of Bacterial Antimicrobial Resistance 1990–2021: A Systematic Analysis With Forecasts to 2050 Publisher Pubmed



Naghavi M1, 2 ; Vollset SE1, 2, 9 ; Ikuta KS1, 11 ; Swetschinski LR1 ; Gray AP1 ; Wool EE1 ; Robles Aguilar G12 ; Mestrovic T1, 20 ; Smith G1 ; Han C1 ; Hsu RL1 ; Chalek J1 ; Araki DT1, 21 ; Chung E1, 3 Show All Authors
Authors
  1. Naghavi M1, 2
  2. Vollset SE1, 2, 9
  3. Ikuta KS1, 11
  4. Swetschinski LR1
  5. Gray AP1
  6. Wool EE1
  7. Robles Aguilar G12
  8. Mestrovic T1, 20
  9. Smith G1
  10. Han C1
  11. Hsu RL1
  12. Chalek J1
  13. Araki DT1, 21
  14. Chung E1, 3
  15. Raggi C1
  16. Gershberg Hayoon A1
  17. Davis Weaver N1
  18. Lindstedt PA1
  19. Smith AE1
  20. Altay U10
  21. Bhattacharjee NV1
  22. Giannakis K9
  23. Fell F13
  24. Mcmanigal B12
  25. Ekapirat N12
  26. Mendes JA12
  27. Runghien T1, 12
  28. Srimokla O12, 14
  29. Abdelkader A22
  30. Abdelsalam S25
  31. Aboagye RG26
  32. Abolhassani H28, 35
  33. Abualruz H36
  34. Abubakar U37
  35. Abukhadijah HJ38
  36. Aburuz S39, 41
  37. Abuzaid A42, 43
  38. Achalapong S44
  39. Addo IY45, 51
  40. Adekanmbi V53
  41. Adeyeoluwa TE54, 57
  42. Adnani QES60
  43. Adzigbli LA27
  44. Afzal MS61
  45. Afzal S62, 63
  46. Agodi A64
  47. Ahlstrom AJ1, 4
  48. Ahmad A65
  49. Ahmad S66, 67
  50. Ahmad T68
  51. Ahmadi A69, 71
  52. Ahmed A74, 76
  53. Ahmed H78
  54. Ahmed I79, 81
  55. Ahmed M82
  56. Ahmed S84
  57. Ahmed SA85
  58. Akkaif MA86
  59. Al Awaidy S87, 88
  60. Al Thaher Y89, 91
  61. Alalalmeh SO23
  62. Albataineh MT92
  63. Aldhaleei WA93
  64. Algheethi AAS94, 95
  65. Alhaji NB96
  66. Ali A97
  67. Ali L84
  68. Ali SS98
  69. Ali W99
  70. Allel K15, 100
  71. Almarwani S103, 104
  72. Alrawashdeh A105
  73. Altaf A108
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  76. Alzoubi KH106, 114
  77. Alzyoud WA120
  78. Amos B122
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  80. Ancuceanu R125
  81. Andrews JR129
  82. Anil A132, 135
  83. Anuoluwa IA55
  84. Anvari S136
  85. Anyasodor AE138
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  88. Arafat M147
  89. Aravkin AY1, 2, 4
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  91. Aremu A150
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  93. Ashley EA12, 156
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  95. Athari SS159
  96. Atout MMW90
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  98. Azadnajafabad S29, 163
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  100. Aziz S169
  101. Azzam AY170, 171
  102. Babaei M30, 130
  103. Babin FX173
  104. Badar M174
  105. Baig AA175
  106. Bajcetic M176, 178
  107. Baker S179
  108. Bardhan M182
  109. Barqawi HJ115
  110. Basharat Z80
  111. Basiru A151
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  217. Hasan I165, 320
  218. Hasan RS321
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  220. Haselbeck AH323
  221. Hasnain MS327
  222. Hassan II236, 328, 329
  223. Hassan S237
  224. Hassan Zadeh Tabatabaei MS32
  225. Hayat K330, 331
  226. He J1
  227. Hegazi OE23
  228. Heidari M70
  229. Hezam K332, 333
  230. Holla R248
  231. Holm M324
  232. Hopkins H167
  233. Hossain MM334, 335
  234. Hosseinzadeh M336, 337
  235. Hostiuc S126, 338
  236. Hussein NR339
  237. Huy LD340, 341
  238. Ibanezprada ED343
  239. Ikiroma A344
  240. Ilic IM177
  241. Islam SMS47, 345
  242. Ismail F346, 347
  243. Ismail NE348, 349
  244. Iwu CD5
  245. Iwujaja CJ350, 351
  246. Jafarzadeh A352, 354
  247. Jaiteh F355
  248. Jalilzadeh Yengejeh R356
  249. Jamora RDG357, 358
  250. Javidnia J359
  251. Jawaid T360
  252. Jenney AWJ361
  253. Jeon HJ181, 326
  254. Jokar M362, 363
  255. Jomehzadeh N364, 365
  256. Joo T366, 367
  257. Joseph N368
  258. Kamal Z369, 370
  259. Kanmodi KK371, 372
  260. Kantar RS373, 374
  261. Kapisi JA121
  262. Karaye IM375, 376
  263. Khader YS107
  264. Khajuria H377
  265. Khalid N378
  266. Khamesipour F145, 379
  267. Khan A380
  268. Khan MJ381
  269. Khan MT369
  270. Khanal V382, 383
  271. Khidri FF384
  272. Khubchandani J385
  273. Khusuwan S44
  274. Kim MS386, 388
  275. Kisa A389, 390
  276. Korshunov VA200
  277. Krapp F391, 393
  278. Krumkamp R240
  279. Kuddus M394
  280. Kulimbet M395, 396
  281. Kumar D397
  282. Kumaran EAP12
  283. Kuttikkattu A398
  284. Kyu HH1, 2
  285. Landires I399, 400
  286. Lawal BK401
  287. Le TTT403
  288. Lederer IM404
  289. Lee M405
  290. Lee SW406
  291. Lepape A264
  292. Lerango TL407
  293. Ligade VS249
  294. Lim C1, 12, 408
  295. Lim SS2
  296. Limenh LW409
  297. Liu C411
  298. Liu X311, 387
  299. Liu X311, 387
  300. Loftus MJ416, 417
  301. Amin HIM418, 419
  302. Maass KL1
  303. Maharaj SB420, 421
  304. Mahmoud MA422
  305. Maikanticharalampous P423
  306. Makram OM172, 424
  307. Malhotra K425, 426
  308. Malik AA427
  309. Mandilara GD191
  310. Marks F180, 325
  311. Martinezguerra BA429
  312. Martorell M431, 432
  313. Masoumiasl H146
  314. Mathioudakis AG433, 434
  315. May J240, 435
  316. Mchugh TA1
  317. Meiring J436
  318. Meles HN301
  319. Melese A160, 438
  320. Melese EB410
  321. Minervini G439, 440
  322. Mohamed NS444, 445
  323. Mohammed S446, 449
  324. Mohan S219, 450
  325. Mokdad AH1, 2
  326. Monasta L451
  327. Moodi Ghalibaf A452
  328. Moore CE199
  329. Moradi Y453
  330. Mossialos E203, 455
  331. Mougin V1
  332. Mukoro GD456
  333. Mulita F457, 458
  334. Mullerpebody B460
  335. Murillozamora E461, 462
  336. Musa S447
  337. Musicha P463
  338. Musila LA192, 464
  339. Muthupandian S441, 465
  340. Nagarajan AJ466, 467
  341. Naghavi P468
  342. Nainu F469
  343. Nair TS470
  344. Najmuldeen HHR471
  345. Natto ZS428, 472
  346. Nauman J40, 473
  347. Nayak BP377
  348. Nchanji GT474, 475
  349. Ndishimye P476, 477
  350. Negoi I127, 478
  351. Negoi RI128, 479
  352. Nejadghaderi SA353, 480
  353. Nguyen QP1
  354. Noman EA48, 481
  355. Nwakanma DC166, 482
  356. Obrien S267
  357. Ochoa TJ392
  358. Odetokun IA152
  359. Ogundijo OA59
  360. Ojoakosile TR483
  361. Okeke SR50, 51
  362. Okonji OC278
  363. Olagunju AT484, 485
  364. Olivasmartinez A6, 430
  365. Olorukooba AA448
  366. Olwoch P122
  367. Onyedibe KI486
  368. Ortizbrizuela E429, 487
  369. Osuolale O488
  370. Ounchanum P44
  371. Oyeyemi OT56
  372. Padukudru PAM489
  373. Paredes JL391
  374. Parikh RR490
  375. Patel J269, 491
  376. Patil S442, 492
  377. Pawar S414
  378. Peleg AY416, 493
  379. Peprah P494
  380. Perdigao J495
  381. Perrone C18, 408
  382. Petcu IR496
  383. Phommasone K154
  384. Piracha ZZ497
  385. Poddighe D498, 499
  386. Pollard AJ19, 500
  387. Poluru R501
  388. Poncedeleon A429
  389. Puvvula J502
  390. Qamar FN322
  391. Qasim NH504
  392. Rafai CD244
  393. Raghav P133
  394. Rahbarnia L505
  395. Rahim F506, 507
  396. Rahimimovaghar V32
  397. Rahman M412
  398. Rahman MA508, 509
  399. Ramadan H510
  400. Ramasamy SK131
  401. Ramesh PS503
  402. Ramteke PW512, 513
  403. Rana RK514
  404. Rani U515
  405. Rashidi MM29, 72
  406. Rathish D516
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  423. Safaei M529
  424. Saghazadeh A28
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  438. Sedighi M454
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  442. Shahid S108, 109
  443. Shahwan MJ24
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  447. Shamshirgaran MA545
  448. Shamsi A24, 534
  449. Sharifan A34, 546
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  455. Sideroglou T552
  456. Sifuentesosornio J553
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  463. Soliman SSM119
  464. Soraneh S563
  465. Stoesser N12, 565
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  468. Szarpak L567, 568
  469. Sree Sudha TY569
  470. Tabatabai S73
  471. Tabche C204
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Source: The Lancet Published:2024


Abstract

Background: Antimicrobial resistance (AMR) poses an important global health challenge in the 21st century. A previous study has quantified the global and regional burden of AMR for 2019, followed with additional publications that provided more detailed estimates for several WHO regions by country. To date, there have been no studies that produce comprehensive estimates of AMR burden across locations that encompass historical trends and future forecasts. Methods: We estimated all-age and age-specific deaths and disability-adjusted life-years (DALYs) attributable to and associated with bacterial AMR for 22 pathogens, 84 pathogen–drug combinations, and 11 infectious syndromes in 204 countries and territories from 1990 to 2021. We collected and used multiple cause of death data, hospital discharge data, microbiology data, literature studies, single drug resistance profiles, pharmaceutical sales, antibiotic use surveys, mortality surveillance, linkage data, outpatient and inpatient insurance claims data, and previously published data, covering 520 million individual records or isolates and 19 513 study-location-years. We used statistical modelling to produce estimates of AMR burden for all locations, including those with no data. Our approach leverages the estimation of five broad component quantities: the number of deaths involving sepsis; the proportion of infectious deaths attributable to a given infectious syndrome; the proportion of infectious syndrome deaths attributable to a given pathogen; the percentage of a given pathogen resistant to an antibiotic of interest; and the excess risk of death or duration of an infection associated with this resistance. Using these components, we estimated disease burden attributable to and associated with AMR, which we define based on two counterfactuals; respectively, an alternative scenario in which all drug-resistant infections are replaced by drug-susceptible infections, and an alternative scenario in which all drug-resistant infections were replaced by no infection. Additionally, we produced global and regional forecasts of AMR burden until 2050 for three scenarios: a reference scenario that is a probabilistic forecast of the most likely future; a Gram-negative drug scenario that assumes future drug development that targets Gram-negative pathogens; and a better care scenario that assumes future improvements in health-care quality and access to appropriate antimicrobials. We present final estimates aggregated to the global, super-regional, and regional level. Findings: In 2021, we estimated 4·71 million (95% UI 4·23–5·19) deaths were associated with bacterial AMR, including 1·14 million (1·00–1·28) deaths attributable to bacterial AMR. Trends in AMR mortality over the past 31 years varied substantially by age and location. From 1990 to 2021, deaths from AMR decreased by more than 50% among children younger than 5 years yet increased by over 80% for adults 70 years and older. AMR mortality decreased for children younger than 5 years in all super-regions, whereas AMR mortality in people 5 years and older increased in all super-regions. For both deaths associated with and deaths attributable to AMR, meticillin-resistant Staphylococcus aureus increased the most globally (from 261 000 associated deaths [95% UI 150 000–372 000] and 57 200 attributable deaths [34 100–80 300] in 1990, to 550 000 associated deaths [500 000–600 000] and 130 000 attributable deaths [113 000–146 000] in 2021). Among Gram-negative bacteria, resistance to carbapenems increased more than any other antibiotic class, rising from 619 000 associated deaths (405 000–834 000) in 1990, to 1·03 million associated deaths (909 000–1·16 million) in 2021, and from 127 000 attributable deaths (82 100–171 000) in 1990, to 216 000 (168 000–264 000) attributable deaths in 2021. There was a notable decrease in non-COVID-related infectious disease in 2020 and 2021. Our forecasts show that an estimated 1·91 million (1·56–2·26) deaths attributable to AMR and 8·22 million (6·85–9·65) deaths associated with AMR could occur globally in 2050. Super-regions with the highest all-age AMR mortality rate in 2050 are forecasted to be south Asia and Latin America and the Caribbean. Increases in deaths attributable to AMR will be largest among those 70 years and older (65·9% [61·2–69·8] of all-age deaths attributable to AMR in 2050). In stark contrast to the strong increase in number of deaths due to AMR of 69·6% (51·5–89·2) from 2022 to 2050, the number of DALYs showed a much smaller increase of 9·4% (–6·9 to 29·0) to 46·5 million (37·7 to 57·3) in 2050. Under the better care scenario, across all age groups, 92·0 million deaths (82·8–102·0) could be cumulatively averted between 2025 and 2050, through better care of severe infections and improved access to antibiotics, and under the Gram-negative drug scenario, 11·1 million AMR deaths (9·08–13·2) could be averted through the development of a Gram-negative drug pipeline to prevent AMR deaths. Interpretation: This study presents the first comprehensive assessment of the global burden of AMR from 1990 to 2021, with results forecasted until 2050. Evaluating changing trends in AMR mortality across time and location is necessary to understand how this important global health threat is developing and prepares us to make informed decisions regarding interventions. Our findings show the importance of infection prevention, as shown by the reduction of AMR deaths in those younger than 5 years. Simultaneously, our results underscore the concerning trend of AMR burden among those older than 70 years, alongside a rapidly ageing global community. The opposing trends in the burden of AMR deaths between younger and older individuals explains the moderate future increase in global number of DALYs versus number of deaths. Given the high variability of AMR burden by location and age, it is important that interventions combine infection prevention, vaccination, minimisation of inappropriate antibiotic use in farming and humans, and research into new antibiotics to mitigate the number of AMR deaths that are forecasted for 2050. Funding: UK Department of Health and Social Care's Fleming Fund using UK aid, and the Wellcome Trust. © 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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