Abstract
Introduction
Sub‐Saharan Africa (SSA) carries the burden of the HIV epidemic, especially among adolescents and young people (AYP). Little is known about pre‐exposure prophylaxis (PrEP) uptake and preferences among AYP in SSA. We describe preferences for daily and on‐demand PrEP among AYP in South Africa, Uganda and Zimbabwe.
Methods
A cross‐sectional survey was conducted in 2019 among 13‐ to 24‐year olds, capturing socio‐demographics, HIV risk behaviours and preferences for daily or on‐demand PrEP. Logistic regression models were used to estimate odds ratios, adjusting for site, sex and age.
Results and discussion
A total of 1330 participants from Cape Town (n = 239), Johannesburg (n = 200), Entebbe (n = 491) and Chitungwiza (n = 400) were enrolled; 673 (51%) were male, and the median age was 19 years (interquartile range 17–22 years). Of 1287 participants expressing a preference, 60% indicated a preference for on‐demand PrEP with differences by site (p < 0.001), sex (p < 0.001) and age group (p = 0.003). On‐demand PrEP was most preferred in Entebbe (75%), among males (65%) versus females (54%) and in older participants (62% in 18‐ to 24‐year‐olds vs. 47% in 13‐ to 15‐year‐olds). After adjusting for site, sex and age group, preference for on‐demand PrEP decreased as sex frequency over the past month increased (p‐trend = 0.004) and varied with the number of partners in the last 6 months, being least popular among those reporting four or more partners (p = 0.02). Participants knowing further in advance that they were likely to have sex were more likely to prefer on‐demand PrEP (p‐trend = 0.02). Participants having a larger age gap with their most recent partner and participants whose last partner was a transactional sex partner or client were both less likely to prefer on‐demand compared to daily PrEP (p = 0.05 and p = 0.09, respectively). Participants who knew their most recent partner was living with HIV or who did not know the HIV status of their most recent partner were less likely to prefer on‐demand PrEP (p = 0.05).
Conclusions
Our data show that AYP in four SSA communities prefer on‐demand over daily PrEP options, with differences seen by site, age and sex. PrEP demand creation needs to be reviewed, optimized and tailored to socio‐demographic differences and designed in conjunction with AYP.
Keywords: Africa, HIV, on‐demand, oral pre‐exposure prophylaxis, young people
1. INTRODUCTION
Sub‐Saharan Africa (SSA) is home to 89% of the 1.75 million adolescents 10–19 years living with HIV worldwide, with approximately 150,000 new infections among this age group in 2020 [1, 2]. Given the social determinants of health in SSA, adolescents and young people (AYP) remain vulnerable to acquiring HIV through sexual transmission [3, 4].
Antiretroviral treatment prevents HIV acquisition through pre‐ and post‐exposure prophylaxis (PrEP and PEP, respectively). PrEP shows the efficacy of 86% with high adherence [5, 6], and is recommended for those at substantial risk of acquiring HIV. An on‐demand regimen, known as PrEP 2‐1‐1, is effective in men having sex with men (MSM) [7]. The on‐demand dosing is two pills 2–24 hours before sexual activity, one pill 24 hours after the first dose and one pill 24 hours after the second dose [7].
Currently, more than 100 countries have PrEP guidelines, with varying degrees of implementation [8, 9]. South Africa was the first country in SSA to rollout PrEP in June 2016 [10] with PrEP offered in 2018, to sexually active, HIV‐negative female AYP [10]. Uganda followed in November 2016 with PrEP available only through demonstration facilities [11]. Zimbabwe introduced PrEP in May 2016, in the private sector and demonstration projects for adolescent girls and young women [12].
A few trials included adolescents below 18 years [13], but those in young adults showed that less than one‐third had evidence of taking PrEP through plasma drug levels [14, 15]. The effectiveness of peer support and mobile technology on adherence is being investigated [16, 17], as well as trials for different delivery mechanisms and biomedical modalities [18]. Despite ongoing efforts, AYP continue to be vulnerable to HIV [19].
The Combined HIV Adolescent PrEP and Prevention Study (CHAPS) was a mixed‐methods study investigating daily and on‐demand PrEP among AYP in SSA [18]. Although on‐demand PrEP is presently recommended only for MSM [20], studies were conducted among adult key populations [21, 22], with a lack of research among heterosexual AYP [23, 24]. We investigated preferences for daily and on‐demand PrEP and its predictors, among AYP in South Africa, Uganda and Zimbabwe.
2. METHODS
2.1. Study sites
We conducted cross‐sectional surveys, between May and December 2019, at four sites in South Africa, Uganda and Zimbabwe. At this time, PrEP was available in South Africa but not in Uganda and Zimbabwe. There is a lack of data around PrEP uptake and preferences in all three countries among AYP, who contribute to the global HIV incidence [7, 14, 25, 26].
2.2. Participant sampling and procedures
Trained fieldworkers used a purposive community outreach strategy to recruit participants in highly populous informal peri‐urban communities, including informal settlements and areas with low‐cost government housing [27]. Participants were from comparable communities characterized by high unemployment, low household incomes, overcrowding, limited resources and service delivery [28].
In Zimbabwe and South Africa, participants were recruited in locations where young people meet. In Uganda, participants were approached in fishing communities through local leaders, project mobilizers and village health teams. We aimed to survey a target of 400 participants in each country stratified by age (13–15, 16–17 and 18–24 years in 1:2:4 ratio) and gender (male and female in 1:1 ratio). As the main study was descriptive, no formal sample size calculation was performed.
Eligible participants were 13–24 years, self‐reported sex in the past 6 months at screening (South Africa and Zimbabwe only) and were willing to undergo rapid HIV testing to confirm HIV status. Participants with a confirmed positive HIV test were supported and referred to healthcare facilities for care. Participants who were deemed eligible and tested HIV negative were enrolled.
2.3. Data collection procedures
Using Open Data Kit [29], fieldworkers administered a structured survey (available in English and local languages) using computer tablets. A description of daily and on‐demand PrEP was provided to ensure understanding about the choices in the survey. Following consent/assent procedures, participants responded to the interviewer‐administered survey in a confidential and convenient location.
3. MEASURES
3.1. Outcome variable
The main outcome was PrEP preference, assessed by: “At the moment, do you think you would prefer on‐demand or daily PrEP?” with response options on‐demand, daily, unsure and no preference. We also asked about PrEP‐related attitudes, including whether participants had heard of PrEP, would use PrEP, main reasons for preferring on‐demand PrEP or daily PrEP, respectively.
3.2. Exposure variables
Table 1 lists exposure variables: socio‐demographics, sexual risk behaviour and PrEP‐related disclosure.
Table 1.
Distribution of overall and site characteristics of AYP participating in the CHAPS survey
| Characteristic | Category | Cape Town (n = 239) | Johannesburg (n = 200) | Entebbe (n = 491) | Chitungwiza (n = 400) | Total (n = 1330) |
|---|---|---|---|---|---|---|
| Sex | Male | 124 (52%) | 99 (50%) | 250 (51%) | 200 (50%) | 673 (51%) |
| Female | 115 (48%) | 101 (51%) | 241 (49%) | 200 (50%) | 657 (49%) | |
| Age group, years | 13–15 | 37 (15%) | 21 (11%) | 52 (11%) | 40 (10%) | 150 (11%) |
| 16–17 | 44 (18%) | 33 (17%) | 83 (17%) | 80 (20%) | 240 (18%) | |
| 18–24 | 158 (66%) | 146 (73%) | 356 (73%) | 280 (70%) | 940 (71%) | |
| Highest education | Still studying | 141 (59%) | 126 (63%) | 226 (46%) | 203 (51%) | 696 (52%) |
| <Grade 7 | 1 (0.4%) | 0 (0%) | 118 (24%) | 9 (2.4%) | 128 (9.6%) | |
| Grade 7–12 | 87 (36%) | 67 (34%) | 136 (28%) | 169 (42%) | 459 (35%) | |
| Post‐school | 10 (4.2%) | 7 (4%) | 11 (2.2%) | 19 (4.8%) | 47 (3.5%) | |
| Sex frequency, past month a | At least daily | 18 (8.4%) | 11 (5.9%) | 8 (1.7%) | 15 (3.8%) | 52 (4.1%) |
| 2–3 times/week | 63 (29%) | 56 (30%) | 59 (12%) | 56 (14%) | 234 (18%) | |
| Once/week | 68 (32%) | 41 (22%) | 52 (11%) | 56 (14%) | 217 (17%) | |
| Once/month | 41 (19%) | 52 (27%) | 61 (13%) | 71 (18%) | 224 (18%) | |
| Never | 24 (11%) | 27 (15%) | 294 (62%) | 202 (51%) | 547 (43%) | |
| Advanced knowledge of last sexual encounter | <2 hours | 120 (50%) | 96 (48%) | 123 (37%) | 142 (51%) | 481 (46%) |
| 2–12 hours | 58 (24%) | 51 (26%) | 54 (16%) | 45 (16%) | 208 (20%) | |
| 13–24 hours | 15 (6.3%) | 28 (14%) | 31 (9.3%) | 17 (6.2%) | 91 (8.7%) | |
| >24 hours | 45 (19%) | 23 (12%) | 127 (38%) | 72 (26%) | 267 (26%) | |
| Number of partners, last 6 months a | 0 | 5 (2.2%) | 0 (0%) | 206 (42%) | 153 (38%) | 364 (28%) |
| 1 | 115 (50%) | 79 (41%) | 192 (39%) | 130 (33%) | 516 (39%) | |
| 2 | 59 (25%) | 51 (26%) | 47 (9.6%) | 53 (13%) | 210 (16%) | |
| 3 | 32 (14%) | 34 (17%) | 18 (3.7%) | 33 (8.3%) | 117 (8.9%) | |
| 4 or more | 21 (9.1%) | 31 (16%) | 28 (5.7%) | 31 (7.8%) | 111 (8.4%) | |
| Age of most recent partner a | >5 years younger | 2 (0.9%) | 1 (0.5%) | 1 (0.3%) | 10 (3.7%) | 14 (1.4%) |
| 1–5 years younger | 65 (28%) | 52 (27%) | 124 (38%) | 70 (26%) | 311 (30%) | |
| Same age | 72 (31%) | 44 (23%) | 33 (10%) | 61 (22%) | 210 (20%) | |
| 1–5 years older | 72 (31%) | 73 (37%) | 120 (37%) | 87 (32%) | 352 (34%) | |
| >5 years older | 21 (9.1%) | 25 (13%) | 48 (15%) | 44 (16%) | 138 (13%) | |
| Relationship with most recent partner a | Regular partner | 197 (83%) | 161 (81%) | 297 (89%) | 208 (75%) | 863 (83%) |
| Casual partner | 40 (17%) | 37 (19%) | 37 (11%) | 63 (23%) | 177 (17%) | |
| Transactional sex | 1 (0.4%) | 0 (0%) | 0 (0%) | 5 (1.8%) | 6 (0.6%) | |
| HIV status of most recent partner a | Positive | 3 (1.3%) | 0 (0%) | 2 (0.6%) | 1 (0.4%) | 6 (0.6%) |
| Negative | 134 (57%) | 101 (51%) | 192 (57%) | 176 (64%) | 603 (58%) | |
| Don't know | 98 (42%) | 97 (49%) | 141 (42%) | 99 (36%) | 435 (42%) | |
| Perceived change of acquiring HIV in next 3 months | No chance | 114 (48%) | 108 (54%) | 359 (73%) | 269 (67%) | 850 (64% |
| Some chance | 90 (38%) | 67 (34%) | 108 (22%) | 101 (25%) | 366 (28%) | |
| Moderate change | 28 (12%) | 17 (8.5%) | 20 (4.1%) | 23 (5.8%) | 88 (6.6%) | |
| High chance | 7 (2.9%) | 8 (4.0%) | 4 (0.8%) | 7 (1.8%) | 26 (2.0%) | |
| Had heard of PrEP a | No | 125 (53%) | 128 (64%) | 432 (88%) | 309 (77%) | 994 (75%) |
| Yes | 113 (47%) | 72 (36%) | 59 (12%) | 91 (23%) | 335 (25%) | |
| Would disclose PrEP use to partner | No | 63 (29%) | 58 (30%) | 144 (31%) | 165 (42%) | 430 (34%) |
| Yes | 157 (71%) | 137 (70%) | 325 (69%) | 228 (58%) | 847 (66%) |
Missing values for these variables.
3.3. Ethical considerations
Study procedures were approved per country requirements. Written informed consent was obtained from participant ≥18 years. Parental consent and participant assent were obtained for participants ≤17 years. Parental waivers were in place in Uganda, Zimbabwe and Cape Town. Participants were reimbursed for time and participation according per country requirements. To limit potential stigma, study sites collaborated with local community advisory boards.
3.4. Data analysis
Data were analysed in Stata version 15 (StataCorp, Texas, USA) [30]. Participants indicating preferences for daily/on‐demand PrEP were included for analysis. The outcome was PrEP preference: daily versus on‐demand. Descriptive statistics summarized the number and proportion of participants expressing a preference for daily versus on‐demand PrEP. Logistic regression models were fitted to generate crude and adjusted odds ratios (aOR)—adjusted for site, sex and age—and 95% confidence intervals (CI) for association between each exposure variable and the outcome, using daily PrEP as the reference group; p‐values were calculated from likelihood ratio tests. Tests for trend were conducted for ordered categorical exposures. Effect modification by site and sex was investigated using likelihood ratio tests.
4. RESULTS AND DISCUSSION
4.1. PrEP characteristics
A total of 1330 participants from Cape Town (n = 239), Johannesburg (n = 200), Entebbe (n = 491) and Chitungwiza (n = 400) participated in the survey; 673 (51%) were male, the median age was 19 years (interquartile range 17–22 years) and 699 (52%) were still studying. Of these, 43 stated that they had no preference for either daily or on‐demand PrEP. Of the remaining 1287 participants expressing a PrEP preference, 25% had heard of PrEP prior to taking the survey, 95% said that they would use PrEP and more than half (60%) preferred on‐demand to daily PrEP. In crude analysis, PrEP preference varied by site (p < 0.001), sex (p < 0.001) and age group (p = 0.003). On‐demand PrEP was most popular in Entebbe (75%) and least popular in Cape Town (32%) (p < 0.001), more popular among males than females (65% vs. 54%; p < 0.001) and more popular in 18‐ to 24‐year‐olds than 16‐ to 17‐ or 13‐ to 15‐year‐olds (62%; 57%; and 47%; p‐trend = 0.001).
Preference for on‐demand PrEP was associated with lower‐risk behaviours (Table 2). Preference for on‐demand PrEP decreased as sex frequency over the past month increased (p‐trend = 0.004) and varied with the number of recent partners, being least popular among those reporting four or more partners (p = 0.02). Participants who knew further in advance that they were likely to have sex were more likely to prefer on‐demand PrEP (p‐trend = 0.02). Participants who had a larger age gap with their most recent partner and participants whose last partner was a transactional sex partner were both less likely to prefer on‐demand PrEP (p = 0.05 and p = 0.09, respectively). Participants who knew that their most recent partner was living with HIV or who did not know the HIV status of their most recent partner were also less likely to prefer on‐demand PrEP (p = 0.05).
Table 2.
Factors associated with preference for on‐demand versus daily PrEP, after adjustment for site, sex and age group
| Characteristic | Category | Prefer daily | Prefer on‐demand | Total | Crude OR (95% CI) | p‐value | Adjusted OR (95% CI) | p‐value |
|---|---|---|---|---|---|---|---|---|
| Site | Cape Town | 146 (68%) | 68 (32%) | 214 | 0.16 (0.11, 0.22) | <0.001 | 0.15 (0.11, 0.22) | <0.001 |
| Johannesburg | 84 (46%) | 100 (54%) | 184 | 0.40 (0.28, 0.57) | 0.39 (0.28, 0.56) | |||
| Entebbe | 124 (25%) | 367 (75%) | 491 | Baseline | Baseline | |||
| Zimbabwe | 167 (42%) | 231 (58%) | 398 | 0.47 (0.35, 0.62) | 0.46 (0.35, 0.62) | |||
| Sex | Male | 225 (35%) | 422 (65%) | 647 | Baseline | <0.001 | Baseline | <0.001 |
| Female | 296 (46%) | 344 (54%) | 640 | 0.62 (0.50, 0.78) | 0.59 (0.47, 0.75) | |||
| Age group | 13–15 | 74 (53%) | 65 (47%) | 139 | 0.54 (0.38, 0.77) | 0.003 | 0.53 (0.36, 0.78) | 0.004 |
| 16–17 | 98 (43%) | 132 (57%) | 230 | 0.83 (0.62, 1.11) | 0.001 (trend) | 0.85 (0.62, 1.15) | 0.001 (trend) | |
| 18–24 | 349 (38%) | 569 (62%) | 918 | Baseline | Baseline | |||
| No. of partners, last 6 months | 0 | 141 (39%) | 222 (61%) | 363 | 1.06 (0.81, 1.40) | 0.41 | 0.69 (0.49, 0.97) | 0.02 |
| 1 | 200 (40%) | 296 (60%) | 496 | Baseline | 0.20 (trend) | Baseline | 0.85 (trend) | |
| 2 | 73 (37%) | 126 (63%) | 199 | 1.17 (0.83, 1.64) | 1.26 (0.87, 1.84) | |||
| 3 | 50 (46%) | 59 (54%) | 109 | 0.80 (0.53, 1.21) | 0.81 (0.51, 1.29) | |||
| 4 or more | 49 (45%) | 59 (54%) | 108 | 0.81 (0.54, 1.24) | 0.65 (0.41, 1.03) | |||
| Sex frequency past month | At least daily | 29 (59%) | 20 (41%) | 49 | 0.35 (0.20, 0.64) | <0.001 | 0.55 (0.29, 1.06) | 0.24 |
| 2–3 times a week | 108 (47%) | 120 (53%) | 228 | 0.57 (0.42, 0.78) | <0.001 (trend) | 0.72 (0.50, 1.05) | 0.04 (trend) | |
| Once a week | 89 (43%) | 117 (57%) | 206 | 0.68 (0.49, 0.94) | 0.95 (0.64, 1.39) | |||
| Once a month | 87 (41%) | 124 (59%) | 211 | 0.73 (0.53, 1.02) | 0.95 (0.66, 1.36) | |||
| Never | 184 (34%) | 358 (66%) | 542 | Baseline | Baseline | |||
| Last time had sex, how far in advance knew | <2 hours | 209 (46%) | 246 (54%) | 455 | Baseline | <0.001 | Baseline | 0.07 |
| 2–12 hours | 83 (41%) | 121 (59%) | 204 | 1.24 (0.89, 1.73) | <0.001 (trend) | 1.35 (0.94, 1.93) | 0.02 (trend) | |
| 13–24 hours | 34 (39%) | 53 (61%) | 87 | 1.32 (0.83, 2.12) | 1.09 (0.66, 1.81) | |||
| >24 hours | 76 (29%) | 183 (71%) | 259 | 2.05 (1.48, 2.83) | 1.56 (1.09, 2.22) | |||
| Age most recent partner | >5 years younger | 9 (64%) | 5 (36%) | 14 | 0.48 (0.15, 1.47) | <0.001 | 0.34 (0.10, 1.10) | 0.05 |
| 1–5 years younger | 91 (31%) | 205 (69%) | 296 | 1.93 (1.33, 2.81) | 1.25 (0.83, 1.90) | |||
| Same age | 91 (46%) | 106 (54%) | 197 | Baseline | Baseline | |||
| 1–5 years older | 135 (40%) | 206 (60%) | 341 | 1.31 (0.92, 1.87) | 1.40 (0.90, 2.17) | |||
| >5 years older | 66 (48%) | 71 (52%) | 137 | 0.92 (0.60, 1.43) | 0.94 (0.655 1.61) | |||
| Relationship, last partner | Regular partner | 336 (41%) | 494 (60%) | 830 | Baseline | 0.11 | Baseline | 0.09 |
| Casual partner | 61 (36%) | 107 (64%) | 168 | 1.19 (0.85, 1.68) | 1.11 (0.76, 1.62) | |||
| Transactional sex | 5 (83%) | 1 (17%) | 6 | 0.14 (0.02, 1.17) | 0.13 (0.01, 1.18) | |||
| HIV status recent partner | Negative | 227 (39%) | 357 (61%) | 584 | Baseline | 0.15 | Baseline | 0.05 |
| Positive | 4 (80%) | 1 (20%) | 5 | 0.16 (0.02, 1.43) | 0.16 (0.02, 1.58) | |||
| Don't know | 169 (41%) | 244 (59%) | 413 | 0.92 (0.71, 1.19) | 0.77 (0.57, 1.02) | |||
| Perceived chance of HIV next 3 months | No chance | 299 (36%) | 523 (64%) | 822 | Baseline | <0.001 | Baseline | 0.06 |
| Some chance | 160 (45%) | 194 (55%) | 354 | 0.69 (0.54, 0.89) | <0.001 (trend) | 0.77 (0.58, 1.01) | 0.006 (trend) | |
| Moderate chance | 47 (55%) | 39 (45%) | 86 | 0.47 (0.30, 0.74) | 0.64 (0.39, 1.03) | |||
| High chance | 15 (60%) | 10 (40%) | 25 | 0.38 (0.17, 0.86) | 0.50 (0.21, 1.20) | |||
| Would disclose PrEP to partner | No | 148 (36%) | 266 (64%) | 414 | Baseline | 0.03 | Baseline | 0.01 |
| Yes | 348 (42%) | 477 (58%) | 825 | 0.76 (0.60, 0.97) | 0.71 (0.55, 0.92) |
Participants perceiving a higher chance of acquiring HIV in the next 3 months and participants willing to disclose their PrEP usage to a partner were less likely to prefer on‐demand PrEP (p‐trend = 0.006, p = 0.01, respectively). There was no evidence of association with PrEP preference for any of the other exposures examined. Regarding effect modification, there was some suggestion that the association of age group with PrEP preference differed by site (Table 3). Younger participants in Cape Town were more likely to prefer on‐demand PrEP, while older participants from the other three sites were more likely to prefer daily PrEP. There was little evidence of effect modification by site or sex for any of the other associations seen.
Table 3.
Adjusted associations between participant characteristics and preference for on‐demand versus daily PrEP, overall and separately for each CHAPS survey setting
| Overall, all settings (n = 1287) | Cape Town (n = 214) | Johannesburg (n = 184) | Uganda (n = 491) | Zimbabwe (n = 398) | |||
|---|---|---|---|---|---|---|---|
| Characteristic | Category | Adjusted OR (95% CI) | Adjusted OR (95% CI) | Adjusted OR (95% CI) | Adjusted OR (95% CI) | Adjusted OR (95% CI) | Interaction p‐value a |
| Sex | Male | Baseline | Baseline | Baseline | Baseline | Baseline | 0.02 |
| Female | 0.59 (0.47, 0.75) | 0.82 (0.46, 1.46) | 0.67 (0.37, 1.21) | 0.80 (0.53, 1.22) | 0.34 (0.23, 0.52) | ||
| Age group | 13–15 | 0.53 (0.36, 0.78) | 1.86 (0.81, 4.23) | 0.71 (0.26, 1.91) | 0.37 (0.20, 0.68) | 0.32 (0.16, 0.66) | 0.01 |
| 16–17 | 0.85 (0.62, 1.15) | 1.79 (0.86, 3.72) | 0.57 (0.25, 1.30) | 0.59 (0.35, 1.01) | 0.93 (0.55, 1.57) | ||
| 18–24 | Baseline | Baseline | Baseline | Baseline | Baseline | ||
| Age of first sex | Per unit increase | 1.02 (0.96, 1.09) | 1.04 (0.89, 1.23) | 0.93 (0.79, 1.09) | 1.06 (0.96, 1.17) | 1.02 (0.88, 1.18) | 0.55 |
| Transactional sex, ever | No | Baseline | Baseline | Baseline | Baseline | Baseline | 0.14 |
| Yes | 0.92 (0.61, 1.38) | 0.46 (0.13, 1.69) | 1.75 (0.62, 4.97) | 0.77 (0.38, 1.53) | 0.99 (0.47, 2.07) | ||
| Forced sex, last 6 months | No | Baseline | Baseline | Baseline | Baseline | Baseline | 0.57 |
| Yes | 1.10 (0.65, 1.87) | 0.85 (0.21, 3.51) | 0.86 (0.21, 3.59) | 1.44 (0.61, 3.38) | 0.65 (0.23, 1.84) | ||
| Forced someone to have sex, last 6 months | No | Baseline | Baseline | Baseline | Baseline | Baseline | 0.74 |
| Yes | 0.98 (0.52, 1.85) | 0.53 (0.10, 2.70) | 1.08 (0.23, 5.05) | 1.53 (0.43, 5.42) | 0.75 (0.23, 2.44) | ||
| No. of partners, last 6 months | 0 | 0.69 (0.49, 0.97) | 1.56 (0.24, 9.95) | – | 0.78 (0.45, 1.33) | 0.74 (0.42, 1.30) | 0.70 |
| 1 | Baseline | Baseline | Baseline | Baseline | Baseline | ||
| 2 | 1.26 (0.87, 1.84) | 1.64 (0.78, 3.44) | 1.61 (0.74, 3.52) | 1.23 (0.53, 2.85) | 0.96 (0.47, 1.99) | ||
| 3 | 0.81 (0.51, 1.29) | 1.03 (0.38, 2.76) | 1.31 (0.53, 3.24) | 1.16 (0.31, 4.34) | 0.46 (0.20, 1.03) | ||
| 4 or more | 0.65 (0.41, 1.03) | 0.60 (0.17, 2.16) | 1.09 (0.43, 2.80) | 0.40 (0.16, 0.96) | 0.83 (0.34, 1.99) | ||
| Sex frequency past month | At least daily | 0.55 (0.29, 1.06) | 0.21 (0.05, 0.97) | 0.54 (0.11, 2.63) | 0.44 (0.10, 1.93) | 0.50 (0.16, 1.55) | 0.70 |
| 2–3 times a week | 0.72 (0.50, 1.05) | 0.40 (0.14, 1.13) | 0.35 (0.12, 1.05) | 0.76 (0.38, 1.52) | 0.76 (0.40, 1.47) | ||
| Once a week | 0.95 (0.64, 1.39) | 0.48 (0.18, 1.33) | 0.40 (0.13, 1.25) | 1.00 (0.47, 2.13) | 1.01 (0.52, 1.98) | ||
| Once a month | 0.95 (0.66, 1.36) | 0.37 (0.12, 1.14) | 0.38 (0.13, 1.15) | 1.22 (0.58, 2.55) | 0.88 (0.48, 1.61) | ||
| Never | Baseline | Baseline | Baseline | Baseline | Baseline | ||
| Last time had sex, how far in advance knew | <2 hours | Baseline | Baseline | Baseline | Baseline | Baseline | 0.74 |
| 2–12 hours | 1.35 (0.94, 1.93) | 1.72 (0.84, 3.52) | 1.26 (0.62, 2.58) | 0.89 (0.43, 1.87) | 1.70 (0.80, 3.63) | ||
| 13–24 hours | 1.09 (0.66, 1.81) | 0.40 (0.08, 1.94) | 0.97 (0.40, 2.36) | 1.92 (0.61, 6.00) | 0.96 (0.32, 2.88) | ||
| >24 hours | 1.56 (1.09, 2.22) | 1.64 (0.74, 3.63) | 1.16 (0.42, 3.22) | 1.37 (0.74, 2.53) | 1.87 (0.96, 3.62) | ||
| Current relationship status | Single | 1.04 (0.71, 1.54) | 2.16 (0.98, 4.76) | 1.25 (0.51, 3.02) | 0.75 (0.36, 1.56) | 0.72 (0.35, 1.49) | 0.61 |
| Boyfriend/girlfriend | Baseline | Baseline | Baseline | Baseline | Baseline | ||
| Other | 0.94 (0.60, 1.48) | 1.35 (0.32, 5.70) | 0.91 (0.05, 15.06) | 0.83 (0.43, 1.58) | 1.20 (0.55, 2.62) | ||
| Age gap, last partner | Same age | Baseline | Baseline | Baseline | Baseline | Baseline | 0.78 |
| 1–5 years gap | 1.30 (0.90, 1.87) | 1.38 (0.70, 2.70) | 1.02 (0.47, 2.21) | 1.95 (0.85, 4.50) | 1.38 (0.67, 2.85) | ||
| >5 years gap | 0.80 (0.49, 1.33) | 1.17 (0.37, 3.64) | 0.39 (0.12, 1.20) | 1.12 (0.39, 3.24) | 1.06 (0.43, 2.61) | ||
| Relationship, last partner | Regular sexual partner | Baseline | Baseline | Baseline | Baseline | Baseline | 0.09 |
| Other | 1.03 (0.71, 1.50) | 1.15 (0.52, 2.56) | 0.65 (0.30, 1.44) | 0.74 (0.32, 1.68) | 1.59 (0.82, 3.05) | ||
| Condom use, last sex | No | Baseline | Baseline | Baseline | Baseline | Baseline | 0.45 |
| Yes | 0.87 (0.66, 1.15) | 0.92 (0.50, 1.70) | 0.62 (0.33, 1.15) | 0.96 (0.55, 1.70) | 0.90 (0.54, 1.53) | ||
| HIV status, last partner | Negative | Baseline | Baseline | Baseline | Baseline | Baseline | 0.29 |
| Positive/don't know | 0.75 (0.56, 1.00) | 0.84 (0.45, 1.57) | 0.59 (0.31, 1.13) | 0.72 (0.42, 1.23) | 0.91 (0.52, 1.59) | ||
| Condom use past 6 months | Never | Baseline | Baseline | Baseline | Baseline | Baseline | 0.11 |
| Sometimes | 1.07 (0.78, 1.47) | 1.26 (0.57, 2.79) | 0.17 (0.05, 0.57) | 1.14 (0.63, 2.04) | 1.16 (0.67, 1.99) | ||
| Always | 1.06 (0.75, 1.50) | 0.97 (0.40, 2.31) | 0.18 (0.05, 0.59) | 0.97 (0.48, 1.96) | 1.24 (0.69, 2.21) | ||
| Risk taking | Avoid taking risks | Baseline | Baseline | Baseline | Baseline | Baseline | 0.78 |
| Somewhere in between | 0.95 (0.69, 1.29) | 1.25 (0.50, 3.09) | 1.33 (0.61, 2.89) | 0.89 (0.50, 1.60) | 1.59 (0.86, 2.92) | ||
| Take risks | 1.17 (0.84, 1.64) | 0.93 (0.49, 1.76) | 1.32 (0.60, 2.89) | 0.82 (0.39, 1.71) | 0.89 (0.54, 1.49) | ||
| Perceived chance of HIV, next 3 months | No chance | Baseline | Baseline | Baseline | Baseline | Baseline | 0.72 |
| Some chance | 0.77 (0.58, 1.01) | 0.85 (0.45, 1.62) | 0.79 (0.41, 1.52) | 0.58 (0.35, 0.95) | 0.75 (0.45, 1.24) | ||
| Moderate chance | 0.64 (0.39, 1.03) | 0.82 (0.32, 2.12) | 0.63 (0.21, 1.93) | 0.49 (0.18, 1.29) | 0.55 (0.22, 1.35) | ||
| High chance | 0.50 (0.21, 1.20) | 0.26 (0.03, 2.57) | 1.33 (0.29, 6.01) | 0.63 (0.06, 6.25) | 0.08 (0.01, 0.72) | ||
| Depression | No | Baseline | Baseline | Baseline | Baseline | Baseline | 0.97 |
| Yes | 0.84 (0.61, 1.17) | 0.93 (0.50, 1.74) | 0.93 (0.47, 1.83) | 0.67 (0.17, 2.66) | 0.86 (0.51, 1.43) | ||
| Anxiety | No | Baseline | Baseline | Baseline | Baseline | Baseline | 0.30 |
| Yes | 1.05 (0.75, 1.47) | 1.07 (0.57, 1.99) | 1.73 (0.85, 3.54) | 0.89 (0.24, 3.37) | 0.79 (0.46, 1.37) | ||
| PTSD symptoms | No | Baseline | Baseline | Baseline | Baseline | Baseline | 0.20 |
| Yes | 0.78 (0.58, 1.05) | 0.67 (0.34, 1.32) | 0.50 (0.24, 1.01) | 1.15 (0.64, 2.06) | 0.74 (0.42, 1.31) | ||
| Binge drinking | Never | Baseline | Baseline | Baseline | Baseline | Baseline | 0.06 |
| < Monthly | 1.12 (0.77, 1.65) | 1.15 (0.56, 2.39) | 0.67 (0.31, 1.44) | 1.04 (0.28, 3.85) | 1.44 (0.71, 2.90) | ||
| Monthly | 1.22 (0.81, 1.84) | 0.60 (0.26, 1.38) | 2.32 (0.96, 5.56) | 0.76 (0.29, 2.03) | 1.32 (0.58, 3.00) | ||
| ≥ Weekly | 1.04 (0.63, 1.70) | 1.03 (0.39, 2.71) | 0.66 (0.24, 1.79) | 1.63 (0.36, 7.48) | 1.44 (0.56, 3.72) | ||
| Drug use past 30 days | No | Baseline | Baseline | Baseline | Baseline | Baseline | 0.20 |
| Yes | 1.11 (0.76, 1.63) | 1.26 (0.60, 2.62) | 1.01 (0.52, 1.97) | 0.42 (0.13, 1.33) | 1.66 (0.74, 3/72) | ||
| Have heard of PrEP | No | Baseline | Baseline | Baseline | Baseline | Baseline | 0.62 |
| Yes | 1.00 (0.75, 1.33) | 0.76 (0.41, 1.41) | 0.87 (0.46, 1.65) | 1.36 (0.69, 2.67) | 1.01 (0.60, 1.67) | ||
| Would disclosure PrEP to partner | No | Baseline | Baseline | Baseline | Baseline | Baseline | 0.58 |
| Yes | 0.71 (0.55, 0.92) | 0.64 (0.33, 1.24) | 0.73 (0.37, 1.42) | 0.58 (0.36, 0.96) | 0.87 (0.57, 1.34) |
Result of test for interaction to assess whether associations between characteristics and preference for on‐demand versus daily PrEP differed between settings.
4.2. Reasons for PrEP preferences
The commonest reasons for preferring on‐demand PrEP were: I don't like taking tablets every day (77%) and I am not at risk most of the time (55%). The commonest reasons for preferring daily PrEP were: daily PrEP provides protection all the time (76%) and daily PrEP gives more protection than on‐demand (65%) (Table 4).
Table 4.
Reasons for PrEP preferences
| Characteristic | Prefer on‐demand | Prefer daily |
|---|---|---|
| Easiest PrEP option | ||
| Take two pills before sex and one after | 314 (41%) | 22 (4.2%) |
| Take two pills after you have sex | 97 (13%) | 16 (3.1%) |
| Take two pills before you have sex | 339 (44%) | 19 (3.6%) |
| Take a pill every day whether you are having sex or not | 16 (2.1%) | 464 (89%) |
| Pay for PrEP if same price as hot meal | ||
| No | 215 (28%) | 164 (32%) |
| Yes | 551 (72%) | 357 (69%) |
| If prefer on‐demand PrEP, why? | ||
|---|---|---|
| I don't like taking tablets everyday | ||
| No | 175 (23%) | 0 |
| Yes | 591 (77%) | 0 |
| I am not at risk most of the time so would not need PrEP everyday | ||
| No | 343 (45%) | 0 |
| Yes | 423 (55%) | 0 |
| Less tablets means less chance of getting side effects | ||
| No | 463 (60%) | 0 |
| Yes | 303 (40%) | 0 |
| Taking PrEP everyday may make people think that I have HIV | ||
| No | 419 (55%) | 0 |
| Yes | 347 (45%) | 0 |
| There will be less tablets than daily PrEP, so I will be able to store them more | ||
| No | 518 (68%) | 0 |
| Yes | 248 (32%) | 0 |
| It would be cheaper than taking everyday | ||
| No | 457 (60%) | 0 |
| Yes | 309 (40%) | 0 |
| Main reason for preferring on‐demand PrEP | ||
| I don't like taking tablets everyday | 300 (39%) | 0 |
| I am not at risk most of the time so would not need PrEP everyday | 135 (18%) | 0 |
| Less tablets means less chance of getting side effects | 77 (10%) | 0 |
| Taking PrEP everyday may make people think that I have HIV. On‐demand PrEP is different | 117 (15%) | 0 |
| There will be less tablets than daily PrEP, so I will be able to store them more easily | 30 (3.9%) | 0 |
| It would be cheaper than taking everyday | 34 (4.4%) | 0 |
| Not sure | 2 (0.3%) | 0 |
| Other | 71 (9.3%) | 0 |
| If prefer on daily PrEP, why? | ||
|---|---|---|
| I am at risk most of the time so I would need PrEP everyday | ||
| No | 0 | 373 (72%) |
| Yes | 0 | 148 (28%) |
| Daily PrEP provides protection all the time so I don't need to plan when I have | ||
| No | 0 | 124 (24%) |
| Yes | 0 | 397 (76%) |
| I think that daily PrEP gives more protection than on‐demand PrEP | ||
| No | 0 | 178 (34%) |
| Yes | 0 | 343 (66%) |
| I like the routine of daily tablets rather than having to remember PrEP just at | ||
| No | 0 | 273 (52%) |
| Yes | 0 | 248 (48%) |
| I do not plan sex; therefore, on‐demand PrEP would be difficult to take | ||
| No | 0 | 232 (45%) |
| Yes | 0 | 289 (56%) |
| To reduce the chance of getting side effects | ||
| No | 0 | 414 (80%) |
| Yes | 0 | 107 (21%) |
| Main reason for preferring daily PrEP | ||
| I am at risk most of the time so I would need PrEP everyday | 0 | 53 (10%) |
| Daily PrEP provides protection all the time so I don't need to plan when I have sex | 0 | 208 (40%) |
| I think that daily PrEP gives more protection than on‐demand PrEP | 0 | 115 (22%) |
| I like the routine of daily tablets rather than having to remember PrEP just at times of sex | 0 | 55 (11%) |
| I do not plan sex; therefore, on‐demand PrEP would be difficult to take | 0 | 66 (13%) |
| To reduce the chance of getting side effects | 0 | 18 (3.5%) |
| Other | 0 | 5 (1.0%) |
Our data show that AYP in SSA tend to prefer on‐demand over daily PrEP options, with on‐demand most preferred in Uganda, among males and participants 18‐ to 24‐year‐olds. These data support research suggesting that on‐demand PrEP may be preferred among AYP as the infrequent dosing makes it less burdensome and more discreet [31]. The difficulty of adhering to a strict dosing regimen and predicting when sex will occur might deter AYP from on‐demand PrEP.
Overall, while there has been considerable research into PrEP preferences both before and after its availability, showing similar findings to our study, the settings were near exclusive to MSM in the Global North [20, 32–41]. Our study provides insight into settings with the most substantial burden of the HIV epidemic, among a uniquely vulnerable group and where healthcare implementation has significant challenges. Similar findings were observed among MSM in developed countries in Australia, France and the United States, where less frequent sex and being likely to anticipate when sex will occur were the main reasons to opt for on‐demand PrEP [42, 43, 44].
Within our sample, on‐demand PrEP was more popular among males than females. Two studies among MSM in the United States and France showed a high preference for on‐demand PrEP [45]. In contrast, in Montreal, Belgium and the Netherlands, daily PrEP was preferred among MSM [34, 35, 46]. A daily regimen seemed easier to incorporate into a daily routine and did not require planning for sex [47].
AYP aged 18‐ to 24‐year‐olds in our study were more likely to prefer on‐demand PrEP compared to 13‐ to 15‐year‐olds. This might be because with age and experience, as well as natural psychosocial development, AYP tend to start thinking more about the future as opposed to the “here and now,” and relationships become more stable making planning sexual encounters easier, allowing on‐demand PrEP to be a more viable option.
We found that participants who knew further in advance that they were likely to have sex, and have sex less frequently, were more likely to prefer on‐demand PrEP. This might be because these circumstances are more predictable and/or planned, therefore, demanding a less frequent HIV prevention regimen. This is supported by a US study showing that AYP assigned male at birth who were in favour of on‐demand PrEP were having sex infrequently [31]. We also observed that a sexual partner's known or unknown HIV‐positive status was associated with a preference for daily PrEP. This is likely due to the added security that taking PrEP on a daily set schedule could provide someone if they know their partner is HIV positive or are unsure of their status. Likewise, in our study, we observed participants who perceived having a greater risk of contracting HIV preferred daily PrEP, which may also reflect the added sense of security of a regular PrEP regimen. Participants willing to disclose their PrEP use to their partners were also more likely to prefer daily PrEP. This could be because those willing to tell their partner about their PrEP use are likely to prefer a more frequent regimen as they do not have to hide their PrEP use.
A new finding from our study was that participants having a larger age gap with their most recent partner were more likely to prefer daily PrEP. There is no existing literature on the relationship between partner age gap and PrEP preference, but an increased partner age gap is an established risk factor for HIV [48, 49]. Therefore, it might be likely that those engaging in sexual activity with older partners are aware of the added risk and uncertainty, and thus prefer a more routine PrEP regimen to minimize this risk. However, the extent to which partner age gap is correlated with HIV risk is far from clear [50, 51].
Our study has limitations. As a cross‐sectional study, we cannot ascertain causality for PrEP preference. Furthermore, we asked hypothetical questions about PrEP preference without actual PrEP usage. The data are self‐reported but may have response bias in those where the survey was interviewer‐administered. We did not use random sampling. The sampling approach does not allow generalizability, and we had limited power to assess associations separately within each country. Although participants received monetary reimbursement for their time, it is possible that this might have increased willingness to participate in the study.
5. CONCLUSIONS
Our data show that AYP in four SSA communities prefer on‐demand over daily PrEP options, with differences by site, age and sex. PrEP demand could be optimized and tailored to socio‐demographic differences and co‐designed with AYP.
COMPETING INTERESTS
The authors declare no competing interests.
AUTHORS’ CONTRIBUTIONS
JJD, NA, ELW, HAW and JF conceived and designed the manuscript. SH, GT, MM, LSC, TGN, ASS and RM participated in data collection. ELW conducted data analysis and assisted with interpretation. JJD and NA interpreted and wrote the original manuscript draft; all authors revised and approved the final version of the paper.
FUNDING
The CHAPS study was funded by the European and Developing Countries Trial Partnership grant (EDCTP‐2) programme supported by the European Union (grant number RIA2016MC‐1616‐CHAPS). The work reported herein for Janan Janine Dietrich was made possible through funding by the South African Medical Research Council (SAMRC) through its Division of Research Capacity Development under the SAMRC Early Investigators Programme (for funding received from the South African National Treasury) as well as the CIPHER GROWING THE LEADERS OF TOMORROW grant from the International AIDS Society. Stefanie Hornschuh was supported by the Consortium for Advanced Research Training in Africa (CARTA). CARTA is jointly led by the African Population and Health Research Center and the University of the Witwatersrand and funded by the Carnegie Corporation of New York (Grant No. G‐19‐57145), Sida (Grant No:54100113), Uppsala Monitoring Center, Norwegian Agency for Development Cooperation (Norad), and by the Wellcome Trust [reference no. 107768/Z/15/Z] and the UK Foreign, Commonwealth & Development Office, with support from the Developing Excellence in Leadership, Training and Science in Africa (DELTAS Africa) programme.
DISCLAIMER
The content hereof is the sole responsibility of the authors and does not necessarily represent the official views of the funders.
ACKNOWLEDGEMENTS
We are particularly grateful to all the study participants (and parents) for the time and information they shared with us. We would like to recognize the study participants, their communities, the community advisory boards and the CHAPS study teams in Uganda, South Africa and Zimbabwe.
DATA AVAILABILITY STATEMENT
The analysis dataset will be made available upon request and accessed through the LSHTM Data Compass repository (https://datacompass.lshtm.ac.uk/).
REFERENCES
- 1. UNAIDS . UNAIDS Data 2020. Geneva, Switzerland: UNAIDS; 2020. [Google Scholar]
- 2. UNAIDS . Global HIV and AIDS Statistics ‐ Factsheet. Geneva, Switzerland: UNAIDS; 2021. [Google Scholar]
- 3. UNAIDS . Report on the Global HIV/AIDS Epidemic. Geneva, Switzerland: UNAIDS; 2000. [Google Scholar]
- 4. Kharsany ABM, Karim QA. HIV infection and AIDS in sub‐Saharan Africa: current status, challenges and opportunities. Open AIDS J. 2016;10:34–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5. Allen E, Gordon A, Krakower D, Hsu K. HIV preexposure prophylaxis for adolescents and young adults. Curr Opin Pediatr. 2017;29(4):399–406. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6. McCormack S, Dunn DT, Desai M, Dolling DI, Gafos M, Gilson R, et al. Pre‐exposure prophylaxis to prevent the acquisition of HIV‐1 infection (PROUD): effectiveness results from the pilot phase of a pragmatic open‐label randomised trial. Lancet. 2016;387(10013):53–60. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7. Molina JM, Capitant C, Spire B, Pialoux G, Cotte L, Charreau I, et al. On‐demand preexposure prophylaxis in men at high risk for HIV‐1 infection. N Engl J Med. 2015;373(23):2237–46. [DOI] [PubMed] [Google Scholar]
- 8. PrEP Watch . Global PrEP Tracker. 2021. [cited 2021 May 10]. Available at: https://www.prepwatch.org/in‐practice/global‐prep‐tracker/
- 9. WHO . Global data shows increasing PrEP use and widespread adoption of WHO PrEP recommendations. 2021. [cited 2021 May 17] Available at: https://www.who.int/news‐room/feature‐stories/detail/global‐data‐shows‐increasing‐prep‐use‐and‐widespread‐adoption‐of‐who‐prep‐recommendations
- 10. SANAC . Monitoring and evaluation plan for the national strategic plan on HIV, TB and STI (2017–2022). Pretoria, South Africa: SANAC; 2017. [Google Scholar]
- 11. Ministry of Health Uganda . Consolidated guidelines for the prevention and treatment of HIV and AIDS in Uganda. Kampala, Uganda: Government of Uganda; 2018. [Google Scholar]
- 12. Ministry of Health and Child Care, The National Medicine and Therapeutics Policy Advisory Committee, The AIDS and TB Directorate, Ministry of Health and Child Care . Guidelines for antiretroviral therapy for the prevention and treatment of HIV in Zimbabwe. Harare, Zimbabwe: The National Medicine and Therapeutics Policy Advisory Committee and the AIDS and TB Directorate, Ministry of Health and Child Care; 2016. [Google Scholar]
- 13. Gill K, Johnson L, Dietrich J, Myer L, Marcus R, Wallace M, et al. Acceptability, safety, and patterns of use of oral tenofovir disoproxil fumarate and emtricitabine for HIV pre‐exposure prophylaxis in South African adolescents: an open‐label single‐arm phase 2 trial. Lancet Child Adolesc Health. 2020;4(12):875–83. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14. Van Damme L, Corneli A, Ahmed K, Agot K, Lombaard J, Kapiga S, et al. Preexposure prophylaxis for HIV infection among African women. N Engl J Med. 2012;367(5):411–22. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15. Fonner VA, Dalglish SL, Kennedy CE, Baggaley R, O'Reilly KR, Koechlin FM, et al. Effectiveness and safety of oral HIV preexposure prophylaxis for all populations. AIDS. 2016;30(12):1973–83. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16. Abdulrahman SA, Rampal L, Ibrahim F, Radhakrishnan AP, Shahar HK, Othman N, et al. Mobile phone reminders and peer counseling improve adherence and treatment outcomes of patients on ART in Malaysia: a randomized clinical trial. PLoS One. 2017;12(5):e0177698. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17. Swendeman D, Arnold EM, Harris D, Fournier J, Comulada WS, Reback C, et al. Text‐messaging, online peer support group, and coaching strategies to optimize the HIV prevention continuum for youth: protocol for a randomized controlled trial. JMIR Res Protoc. 2019;8(8):e11165. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18. Nash S, Dietrich J, Ssemata AS, Herrera C, O'Hagan K, Else L, et al. Combined HIV Adolescent Prevention Study (CHAPS): comparison of HIV pre‐exposure prophylaxis regimens for adolescents in sub‐Saharan Africa‐study protocol for a mixed‐methods study including a randomised controlled trial. Trials. 2020;21(1):900. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19. Govender K, Masebo WGB, Nyamaruze P, Cowden RG, Schunter BT, Bains A. HIV prevention in adolescents and young people in the eastern and southern African region: a review of key challenges impeding actions for an effective response. Open AIDS J. 2018;12:53–67. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20. Molina JM, Charreau I, Spire B, Cotte L, Chas J, Capitant C, et al. Efficacy, safety, and effect on sexual behaviour of on‐demand pre‐exposure prophylaxis for HIV in men who have sex with men: an observational cohort study. Lancet HIV. 2017;4(9):e402–10. [DOI] [PubMed] [Google Scholar]
- 21. Eakle R, Weatherburn P, Bourne A. Understanding user perspectives of and preferences for oral PrEP for HIV prevention in the context of intervention scale‐up: a synthesis of evidence from sub‐Saharan Africa. J Int AIDS Soc. 2019;22(4):e25306. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22. Van der Elst EM, Mbogua J, Operario D, Mutua G, Kuo C, Mugo P, et al. High acceptability of HIV pre‐exposure prophylaxis but challenges in adherence and use: qualitative insights from a phase I trial of intermittent and daily PrEP in at‐risk populations in Kenya. AIDS Behav. 2013;17(6):2162–72. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23. Dietrich JJ, Atujuna M, Tshabalala G, Hornschuh S, Mulaudzi M, Koh M, et al. A qualitative study to identify critical attributes and attribute‐levels for a discrete choice experiment on oral pre‐exposure prophylaxis (PrEP) delivery among young people in Cape Town and Johannesburg, South Africa. BMC Health Serv Res. 2021;21(1):17. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24. CDC . On‐demand PrEP — if I am not at ongoing risk for getting HIV, can I take PrEP only when I'm at risk? 2022. [cited 2022 June 28]. Available at: https://www.cdc.gov/hiv/basics/prep/on‐demand‐prep.html
- 25. Baeten JM, Donnell D, Ndase P, Mugo NR, Campbell JD, Wangisi J, et al. Antiretroviral prophylaxis for HIV prevention in heterosexual men and women. N Engl J Med. 2012;367(5):399–410. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26. Marrazzo JM, Ramjee G, Richardson BA, Gomez K, Mgodi N, Nair G, et al. Tenofovir‐based preexposure prophylaxis for HIV infection among African women. N Engl J Med. 2015;372(6):509–18. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27. Smelser NJ, Baltes PB. International encyclopedia of the social & behavioral sciences. Amsterdam: Elsevier; 2001. [Google Scholar]
- 28. Pernegger L, Godehart S. Townships in the South African geographic landscape–physical and social legacies and challenges. Training for Township Renewal Initiative. South Africa. 2007. [Google Scholar]
- 29. ODK . Collect data anywhere. 2020. [cited 2021 May 17]. Available at: https://getodk.org/#features
- 30. Stata Corp . Stata release 15. 2021. [cited 2021 May 17]. Available at: https://www.stata.com/stata15/
- 31. Macapagal K, Nery‐Hurwit M, Matson M, Crosby S, Greene GJ. Perspectives on and preferences for on‐demand and long‐acting PrEP among sexual and gender minority adolescents assigned male at birth. Sex Res Social Policy. 2021;18(1):39–53. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32. Molina JM, Beniguel L, Rojas‐Castro D, Ghosn J, Algarte‐Genin M, Pialoux G, et al. Incidence of HIV‐infection in the ANRS Prévenir study in Paris region with daily or on‐demand PrEP with TDF/FTC. 22nd International AIDS Conference (AIDS 2018); 23–27 July 2018; Amsterdam, the Netherlands: International AIDS Society. [Google Scholar]
- 33. Molina JM, Pialoux G, Ohayon M, Cotte L, Valin L, Ghosn J, et al. One‐year experience with pre‐exposure prophylaxis (PrEP) implementation in France with TDF/FTC. 9th International AIDS Conference (AIDS 2017); 23–26 July 2017; Paris, France: International AIDS Society. [Google Scholar]
- 34. Reyniers T, Nöstlinger C, Laga M, De Baetselier I, Crucitti T, Wouters K, et al. Choosing between daily and event‐driven pre‐exposure prophylaxis: results of a Belgian PrEP Demonstration Project. J Acquir Immune Defic Syndr. 2018;79(2):186–94. [DOI] [PubMed] [Google Scholar]
- 35. Hoornenborg E, Achterbergh RC, van der Loeff MFS, Davidovich U, van der Helm JJ, Hogewoning A, et al. Men who have sex with men more often chose daily than event‐driven use of pre‐exposure prophylaxis: baseline analysis of a demonstration study in Amsterdam. J Int AIDS Soc. 2018;21(3):e25105. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36. Zimmermann HML, Jongen VW, Boyd A, Hoornenborg E, Prins M, de Vries HJC, et al. Decision‐making regarding condom use among daily and event‐driven users of preexposure prophylaxis in the Netherlands. AIDS. 2020;34(15):2295–304. [DOI] [PubMed] [Google Scholar]
- 37. Shao Y, Williamson C. The HIV‐1 epidemic: low‐ to middle‐income countries. Cold Spring Harb Perspect Med. 2012;2(3):a007187. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38. Adam PC, de Wit JB, Toskin I, Mathers BM, Nashkhoev M, Zablotska I, et al. Estimating levels of HIV testing, HIV prevention coverage, HIV knowledge, and condom use among men who have sex with men (MSM) in low‐income and middle‐income countries. J Acquir Immune Defic Syndr. 2009;52(Suppl 2):S143–51. [DOI] [PubMed] [Google Scholar]
- 39. Thapa S, Hannes K, Cargo M, Buve A, Peters S, Dauphin S, et al. Stigma reduction in relation to HIV test uptake in low‐ and middle‐income countries: a realist review. BMC Public Health. 2018;18(1):1277. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40. Han J, Bouey JZ, Wang L, Mi G, Chen Z, He Y, et al. PrEP uptake preferences among men who have sex with men in China: results from a National Internet Survey. J Int AIDS Soc. 2019;22(2):e25242. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41. Yi S, Tuot S, Mwai GW, Ngin C, Chhim K, Pal K, et al. Awareness and willingness to use HIV pre‐exposure prophylaxis among men who have sex with men in low‐ and middle‐income countries: a systematic review and meta‐analysis. J Int AIDS Soc. 2017;20(1):21580. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42. Cornelisse VJ, Lal L, Price B, Ryan KE, Bell C, Owen L, et al. Interest in switching to on‐demand HIV pre‐exposure prophylaxis (PrEP) among Australian users of daily PrEP: an online survey. Open Forum Infect Dis. 2019;6(7):ofz287. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43. Noret M, Balavoine S, Pintado C, Siguier M, Brun A, Bauer R, et al. Daily or on‐demand oral tenofovir disoproxil fumarate/emtricitabine for HIV pre‐exposure prophylaxis: experience from a hospital‐based clinic in France. AIDS. 2018;32(15):2161–9. [DOI] [PubMed] [Google Scholar]
- 44. Stack C, Oldenburg C, Mimiaga M, Elsesser SA, Krakower D, Novak DS, et al. Sexual behavior patterns and PrEP dosing preferences in a large sample of North American men who have sex with men. J Acquir Immune Defic Syndr. 2016;71(1):94–101. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 45. Beymer MR, Gildner JL, Holloway IW, Landovitz RJ. Acceptability of injectable and on‐demand pre‐exposure prophylaxis among an online sample of young men who have sex with men in California. LGBT Health. 2018;5(6):341–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 46. Greenwald ZR, Maheu‐Giroux M, Szabo J, Robin JAB, Boissonnault M, Nguyen V‐K, et al. Cohort profile: l'Actuel Pre‐Exposure Prophylaxis (PrEP) Cohort study in Montreal, Canada. BMJ Open. 2019;9(6):e028768. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 47. Chemnasiri T, Varangrat A, Amico KR, Chitwarakorn A, Dye BJ, Grant RM, et al. Facilitators and barriers affecting PrEP adherence among Thai men who have sex with men (MSM) in the HPTN 067/ADAPT Study. AIDS Care. 2020;32(2):249–54. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 48. Beauclair R, Helleringer S, Hens N, Delva W. Age differences between sexual partners, behavioural and demographic correlates, and HIV infection on Likoma Island, Malawi. Sci Rep. 2016;6(1):36121. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 49. Maughan‐Brown B, Kenyon C, Lurie MN. Partner age differences and concurrency in South Africa: implications for HIV‐infection risk among young women. AIDS Behav. 2014;18(12):2469–76. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 50. Harling G, Newell ML, Tanser F, Bärnighausen T. Partner age‐disparity and HIV incidence risk for older women in rural South Africa. AIDS Behav. 2015;19(7):1317–26. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 51. Harling G, Newell ML, Tanser F, Kawachi I, Subramanian SV, Bärnighausen T. Do age–disparate relationships drive HIV incidence in young women? Evidence from a population cohort in rural KwaZulu‐Natal, South Africa. J Acquir Immune Defic Syndr. 2014;66(4):443–51. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The analysis dataset will be made available upon request and accessed through the LSHTM Data Compass repository (https://datacompass.lshtm.ac.uk/).