| Literature DB >> 34332552 |
Thiago S Torres1, Lara E Coelho2, Kelika A Konda3, E Hamid Vega-Ramirez4, Oliver A Elorreaga3, Dulce Diaz-Sosa4, Brenda Hoagland2, Cristina Pimenta5, Marcos Benedetti2, Beatriz Grinsztejn2, Carlos F Caceres3, Valdilea G Veloso2.
Abstract
BACKGROUND: Despite efforts to stop HIV epidemic in Latin America, new HIV cases continue to increase in the region especially among young MSM (YMSM). This study aims to assess if sociodemographic characteristics are associated with self-reported HIV positive status among YMSM from three Latin American countries.Entities:
Keywords: HIV prevalence; Latin America; Self-reported HIV status; Socioeconomic status; Young MSM
Year: 2021 PMID: 34332552 PMCID: PMC8325787 DOI: 10.1186/s12879-021-06455-3
Source DB: PubMed Journal: BMC Infect Dis ISSN: 1471-2334 Impact factor: 3.090
Fig. 1Study flow-chart. Brazil, Mexico, Peru, 2018
Characteristics of YMSM aged 18–24 years according to self-reported HIV positive status in Brazil, Mexico and Peru, 2018
| Brazil ( | Mexico ( | Peru ( | Total ( | |||||
|---|---|---|---|---|---|---|---|---|
| HIV- | HIV+ | HIV- | HIV+ | HIV- | HIV+ | HIV- | HIV+ | |
| 3905 | 326 (7.7) | 1596 | 147 (8.4) | 815 | 152 (15.7) | 6316 | 625 (8.9) | |
| Recruitment | ||||||||
| Apps | 3544 (90.8) | 304 (93.3) | 1440 (90.2) | 132 (89.8) | 346 (42.5) | 45 (29.6) | 5330 (84.4) | 481 (77.0) |
| 361 (9.2) | 22 (6.7) | 156 (9.8) | 15 (10.2) | 469 (57.5) | 107 (70.4) | 986 (15.6) | 144 (23.0) | |
| Race | ||||||||
| White | 1668 (43.4) | 118 (36.8) | N/A | 147 (18.8) | 30 (20.8) | 1815 (39.3) | 148 (31.8) | |
| Black | 734 (19.1) | 59 (18.4) | N/A | 14 (1.8) | 2 (1.4) | 748 (16.2) | 61 (13.1) | |
| | 1378 (35.9) | 135 (42.1) | N/A | 595 (76.3) | 108 (75.0) | 1973 (42.7) | 243 (52.3) | |
| Indigenous | 38 (1.0) | 7 (2.2) | N/A | 18 (2.3) | 4 (2.8) | 56 (1.2) | 11 (2.4) | |
| Asian | 24 (0.6) | 2 (0.6) | N/A | 6 (0.8) | 0 (0.0) | 30 (0.6) | 2 (0.4) | |
| Education | ||||||||
| ≤ Secondary education | 2566 (67.3) | 236 (73.5) | 645 (40.4) | 63 (42.9) | 289 (35.9) | 46 (31.5) | 3500 (56.3) | 345 (56.2) |
| > Secondary education | 1246 (32.7) | 85 (26.5) | 951 (59.6) | 84 (57.1) | 517 (64.1) | 100 (68.5) | 2714 (43.7) | 269 (43.8) |
| Income | ||||||||
| Low | 2670 (68.4) | 244 (74.8) | 775 (54.1) | 68 (51.1) | 425 (58.6) | 84 (61.8) | 3870 (63.8) | 396 (66.6) |
| Middle | 1026 (26.3) | 62 (19.0) | 567 (39.6) | 54 (40.6) | 281 (38.8) | 49 (36.0) | 1874 (30.9) | 165 (27.7) |
| High | 209 (5.4) | 20 (6.1) | 90 (6.3) | 11 (8.3) | 19 (2.6) | 3 (2.2) | 318 (5.2) | 34 (5.7) |
| Sexual Attraction | ||||||||
| Men | 3543 (90.7) | 312 (95.7) | 1380 (86.5) | 137 (93.2) | 641 (78.7) | 131 (86.2) | 5564 (88.1) | 580 (92.8) |
| Men and Women | 362 (9.3) | 14 (4.3) | 216 (13.5) | 10 (6.8) | 174 (21.3) | 21 (13.8) | 752 (11.9) | 45 (7.2) |
| Steady partner | ||||||||
| No | 3200 (82.6) | 251 (78.2) | 1241 (78.5) | 102 (70.8) | 588 (73.8) | 91 (60.7) | 5029 (80.4) | 444 (72.2) |
| Yes | 676 (17.4) | 70 (21.8) | 339 (21.5) | 42 (29.2) | 209 (26.2) | 59 (39.3) | 1224 (19.6) | 171 (27.8) |
| Last HIV test | ||||||||
| ≤ 1 year | 3351 (86.5) | 271 (84.7) | 1269 (80.6) | 110 (76.4) | 614 (76.0) | 110 (74.8) | 5234 (83.7) | 491 (80.4) |
| > 1 year | 523 (13.5) | 49 (15.3) | 305 (19.4) | 35 (23.6) | 194 (24.0) | 37 (25.2) | 1022 (16.3) | 120 (19.6) |
N/A Not applicable
Factors associated with self-reported HIV positive status in Brazil, Mexico and Peru, 2018
| Brazil ( | Mexico ( | Peru ( | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Bivariate models | Multivariate model | Bivariate models | Multivariate model | Bivariate models | Multivariate model | |||||||
| OR (95% CI) | aOR (95% CI) | OR (95% CI) | aOR (95% CI) | OR (95% CI) | aOR (95% CI) | |||||||
| Race | ||||||||||||
| White | Ref. | 0.146 | N/A | N/A | Ref. | 0.578 | Ref. | 0.513 | ||||
| Non-white | 1.20 (0.94–1.55) | N/A | N/A | 0.88 (0.57–1.39) | 0.85 (0.53–1.40) | |||||||
| Education | ||||||||||||
| ≤ Secondary education | 1.11 (0.78–1.55) | 0.564 | 1.13 (0.78–1.63) | 0.518 | 0.82 (0.56–1.19) | 0.312 | 0.79 (0.50–1.26) | 0.281 | ||||
| > Secondary education | Ref. | Ref. | Ref. | Ref. | Ref. | Ref. | ||||||
| Income | ||||||||||||
| Low | 0.89 (0.62–1.27) | 0.508 | 0.81 (0.56–1.18) | 0.273 | 1.14 (0.79–1.67) | 0.494 | ||||||
| Middle/High | Ref. | Ref. | Ref. | Ref. | Ref. | |||||||
| Sexual Attraction | ||||||||||||
| Men | 1.98 (1.04–4.27) | 0.056 | ||||||||||
| Men and Women | Ref. | Ref. | Ref. | Ref. | Ref. | |||||||
| Steady partner | ||||||||||||
| No | Ref. | Ref. | Ref. | Ref. | Ref. | |||||||
| Yes | 1.25 (0.93–1.65) | 0.127 | 1.42 (0.94–2.11) | 0.091 | ||||||||
N/A Not applicable. Regions within the countries were kept in the final models (data not shown). For Brazil, region was categorized according to geopolitical regions: North (7 states), Northeast (9 states), Central-West (3 states and Federal District), South (3 states), and Southeast (2 states). For Peru, regions were grouped according to their geographical characteristics and political division: Lima (Lima city and Callao), Coast (Lima region and other coastal cities), Sierra (cities of the northern, central, and southern highlands), and Jungle. For Mexico, Northwest (6 states); Northeast (3 states); West (4 states); East (4 states); North Center (5 states); South Central (2 states and Mexico City); South (7 states)