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Subnational Mapping of Hiv Incidence and Mortality Among Individuals Aged 15–49 Years in Sub-Saharan Africa, 2000–18: A Modelling Study Publisher Pubmed



Sartorius B1, 4 ; Vanderheide JD8 ; Yang M5 ; Goosmann EA5 ; Hon J5 ; Haeuser E5 ; Cork MA5 ; Perkins S5 ; Jahagirdar D5 ; Schaeffer LE9, 10 ; Serfes AL5 ; Legrand KE5 ; Abbastabar H11 ; Abebo ZH18 Show All Authors
Authors
  1. Sartorius B1, 4
  2. Vanderheide JD8
  3. Yang M5
  4. Goosmann EA5
  5. Hon J5
  6. Haeuser E5
  7. Cork MA5
  8. Perkins S5
  9. Jahagirdar D5
  10. Schaeffer LE9, 10
  11. Serfes AL5
  12. Legrand KE5
  13. Abbastabar H11
  14. Abebo ZH18
  15. Abosetugn AE23
  16. Abugharbieh E24
  17. Accrombessi MMK25, 27
  18. Adebayo OM28
  19. Adegbosin AE32
  20. Adekanmbi V33
  21. Adetokunboh OO37, 38
  22. Adeyinka DA39, 40
  23. Ahinkorah BO41
  24. Ahmadi K42
  25. Ahmed MB43, 44
  26. Akalu Y45
  27. Akinyemi OO29, 57
  28. Akinyemi RO58, 62
  29. Aklilu A19
  30. Akunna CJ63, 64
  31. Alahdab F65
  32. Alaly Z66, 67
  33. Alam N68, 69
  34. Alamneh AA70
  35. Alanzi TM72
  36. Alemu BW20, 75
  37. Alhassan RK76
  38. Ali T79
  39. Alipour V83, 84
  40. Amini S90
  41. Ancuceanu R93
  42. Ansari F94, 99
  43. Anteneh ZA100
  44. Anvari D107, 111
  45. Anwer R112
  46. Appiah SCY113, 114
  47. Arabloo J83
  48. Asemahagn MA101
  49. Asghari Jafarabadi M95, 115
  50. Asmare WN116
  51. Atnafu DD102
  52. Atout MMW118
  53. Atreya A119
  54. Ausloos M120, 122
  55. Awedew AF123
  56. Ayala Quintanilla BP126
  57. Ayanore MA77
  58. Aynalem YA127
  59. Ayza MA129
  60. Azari S83
  61. Azene ZN46
  62. Babar ZUD134
  63. Baig AA135
  64. Balakrishnan S80
  65. Banach M136, 137
  66. Barnighausen TW138, 139
  67. Basu S140, 143
  68. Bayati M148
  69. Bedi N149, 150
  70. Bekuma TT151
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  74. Bhattacharyya K160, 161
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  76. Bibi S165
  77. Bikbov B166
  78. Birhan TA47
  79. Bitew ZW167
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  81. Boloor A171
  82. Brady OJ26
  83. Bragazzi NL175
  84. Briko AN176
  85. Briko NI177
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  88. Cardenas R181
  89. Carvalho F182
  90. Charan J158
  91. Chatterjee S187
  92. Chattu SK188
  93. Chattu VK163, 189
  94. Chowdhury MAK190, 191
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  205. Kazemi Karyani A247
  206. Keiyoro PN303
  207. Kelkay B52
  208. Khalid N306
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  217. Kochhar S6, 315
  218. Kopec JA287, 316
  219. Kosen S317
  220. Koulmane Laxminarayana S318
  221. Koyanagi A319, 320
  222. Krishan K321
  223. Kuate Defo B322, 323
  224. Kugbey N78
  225. Kulkarni V172
  226. Kumar M304, 324
  227. Kumar N172
  228. Kurmi OP325, 326
  229. Kusuma D145, 328
  230. Kuupiel D253, 329
  231. Kyu HH4, 5
  232. La Vecchia C196
  233. Lal DK330
  234. Lam JO331
  235. Landires I332, 335
  236. Lasrado S336
  237. Lazarus JV337
  238. Lazzaratwood A5
  239. Lee PH121
  240. Leshargie CT338
  241. Li B339
  242. Liu X340
  243. Lopukhov PD177
  244. Amin HIM341, 342
  245. Madi D171
  246. Mahasha PW284
  247. Majeed A146
  248. Maleki A15, 343
  249. Maleki S248
  250. Mamun AA345
  251. Manafi N87, 346
  252. Mansournia MA13
  253. Martinsmelo FR347
  254. Masoumi SZ298
  255. Mayala BK5, 348
  256. Meharie BG349
  257. Meheretu HAA71, 101
  258. Meles HG133
  259. Melku M53
  260. Mendoza W350
  261. Mengesha EW104
  262. Meretoja TJ351
  263. Mersha AM21
  264. Mestrovic T352, 353
  265. Miller TR216, 354
  266. Mirica A122
  267. Mirzaeialavijeh M246
  268. Mohamad O355
  269. Mohammad Y356
  270. Mohammadianhafshejani A357
  271. Mohammed JA358
  272. Mohammed S138, 361
  273. Mohammed S138, 361
  274. Mokdad AH4, 5
  275. Mokonnon T364
  276. Molokhia M34
  277. Moradi M247
  278. Moradi Y344
  279. Moradzadeh R91
  280. Moraga P365
  281. Mosser JF5
  282. Munro SB5
  283. Mustafa G366, 367
  284. Muthupandian S131, 368
  285. Naderi M248
  286. Nagarajan AJ369, 370
  287. Naghavi M4, 5
  288. Naveed M371
  289. Nayak VC273
  290. Nazari J92
  291. Ndejjo R372
  292. Nepal S373
  293. Netsere HB54, 374
  294. Ngalesoni FN375
  295. Nguefacktsague G376
  296. Ngunjiri JW377
  297. Nigatu YT378, 379
  298. Nigussie SN117
  299. Nnaji CA170, 285
  300. Noubiap JJ380
  301. Nunezsamudio V333, 334
  302. Oancea B381
  303. Odukoya OO382, 383
  304. Ogbo FA384
  305. Oladimeji O385, 386
  306. Olagunju AT327, 387
  307. Olusanya BO388
  308. Olusanya JO388
  309. Omer MO261
  310. Omonisi AEE389, 390
  311. Onwujekwe OE391
  312. Orisakwe OE392
  313. Otstavnov N393
  314. Owolabi MO31, 61
  315. Mahesh PA394
  316. Padubidri JR272
  317. Pakhale S395
  318. Pana A122, 396
  319. Pandiperumal SR397
  320. Patel UK398
  321. Pathak M399
  322. Patton GC400, 402
  323. Pawar S403
  324. Peprah EK404
  325. Pokhrel KN405
  326. Postma MJ406, 407
  327. Pottoo FH74
  328. Pourjafar H408, 409
  329. Pribadi DRA410
  330. Quazi Syed Z411
  331. Rafiei A109, 110
  332. Rahim F16, 412
  333. Rahman MHU413
  334. Rahmani AM414, 415
  335. Ram P228
  336. Rana J416, 417
  337. Ranabhat CL418, 419
  338. Rao S173
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  348. Ribeiro AI186
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  361. Shahbaz M281
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  364. Sheikh A142, 442
  365. Shibuya K35
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  367. Shivakumar KM444
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  371. Skryabina AA449
  372. Soheili A450
  373. Soltani S247
  374. Somefun OD451
  375. Sorrie MB18
  376. Spurlock EE5
  377. Sufiyan MB362
  378. Taddele BW452
  379. Tadesse EG22
  380. Tamir Z125
  381. Tamiru AT52
  382. Tanser FC453, 454
  383. Taveira N455, 456
  384. Tehranibanihashemi A88, 89
  385. Tekalegn Y243
  386. Tesfay FH132, 230
  387. Tessema B55
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  389. Thakur B458
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  391. Topormadry R459, 460
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Source: The Lancet HIV Published:2021


Abstract

Background: High-resolution estimates of HIV burden across space and time provide an important tool for tracking and monitoring the progress of prevention and control efforts and assist with improving the precision and efficiency of targeting efforts. We aimed to assess HIV incidence and HIV mortality for all second-level administrative units across sub-Saharan Africa. Methods: In this modelling study, we developed a framework that used the geographically specific HIV prevalence data collected in seroprevalence surveys and antenatal care clinics to train a model that estimates HIV incidence and mortality among individuals aged 15–49 years. We used a model-based geostatistical framework to estimate HIV prevalence at the second administrative level in 44 countries in sub-Saharan Africa for 2000–18 and sought data on the number of individuals on antiretroviral therapy (ART) by second-level administrative unit. We then modified the Estimation and Projection Package (EPP) to use these HIV prevalence and treatment estimates to estimate HIV incidence and mortality by second-level administrative unit. Findings: The estimates suggest substantial variation in HIV incidence and mortality rates both between and within countries in sub-Saharan Africa, with 15 countries having a ten-times or greater difference in estimated HIV incidence between the second-level administrative units with the lowest and highest estimated incidence levels. Across all 44 countries in 2018, HIV incidence ranged from 2·8 (95% uncertainty interval 2·1–3·8) in Mauritania to 1585·9 (1369·4–1824·8) cases per 100 000 people in Lesotho and HIV mortality ranged from 0·8 (0·7–0·9) in Mauritania to 676·5 (513·6–888·0) deaths per 100 000 people in Lesotho. Variation in both incidence and mortality was substantially greater at the subnational level than at the national level and the highest estimated rates were accordingly higher. Among second-level administrative units, Guija District, Gaza Province, Mozambique, had the highest estimated HIV incidence (4661·7 [2544·8–8120·3]) cases per 100 000 people in 2018 and Inhassunge District, Zambezia Province, Mozambique, had the highest estimated HIV mortality rate (1163·0 [679·0–1866·8]) deaths per 100 000 people. Further, the rate of reduction in HIV incidence and mortality from 2000 to 2018, as well as the ratio of new infections to the number of people living with HIV was highly variable. Although most second-level administrative units had declines in the number of new cases (3316 [81·1%] of 4087 units) and number of deaths (3325 [81·4%]), nearly all appeared well short of the targeted 75% reduction in new cases and deaths between 2010 and 2020. Interpretation: Our estimates suggest that most second-level administrative units in sub-Saharan Africa are falling short of the targeted 75% reduction in new cases and deaths by 2020, which is further compounded by substantial within-country variability. These estimates will help decision makers and programme implementers expand access to ART and better target health resources to higher burden subnational areas. Funding: Bill & Melinda Gates Foundation. © 2021 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license
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