The Joint United Nations Programme on HIV/AIDS (UNAIDS) has set ambitious “Fast Track” targets for adolescents and young adults (AYA) ages 10 to 24 years to accelerate progress toward reaching the 90–90-90 goals by 2030 (UNAIDS, 2016). However, fewer than 50% of AYA in sub-Saharan Africa know their HIV status (UNAIDS, 2017). In Kenya, only 36% of female and 27% of male adolescents ages 15–19 reported testing for HIV in the last year (National AIDS and STI Control Programme Kenya Ministry of Health, 2014). It is critical to increase uptake of HIV testing services (HTS) among AYA to increase early linkage to care, improve quality of life for AYA living with HIV, and reduce HIV transmission risk (Rosenberg et al., 2013).
Although the majority of AYA in sub-Saharan Africa have reported a desire to know their HIV status (Sam-Agudu, Folayan, & Ezeanolue, 2016), they face numerous barriers to testing, including fear of HIV stigma, distrust of health care providers, parental consent laws, and inconvenient testing-site hours (Kurth, Lally, Choko, Inwani, & Fortenberry, 2015; Sam-Agudu et al., 2016; Wachira et al., 2014; Wilson, Beima-Sofie et al., 2017). A potentially important barrier to HIV testing for AYA that has not been evaluated is cost, including cost of transport, lost wages, and childcare. Cost of seeking HIV testing has been cited as a major barrier to timely engagement in HTS and linkage to care in adult (Musheke et al., 2013) and pediatric populations (Wagner et al., 2018). This barrier has been especially prominent when individuals do not perceive themselves or their children to be sick or at risk for HIV (Wagner et al., 2018). While children rely entirely on adults to initiate and navigate HTS, AYA often prefer to seek HTS independent of their parents due to concerns about privacy and a growing sense of independence (Wilson, Beima-Sofie et al., 2017). However, it is not clear whether adolescents incur costs when seeking HTS and whether those costs are affordable, relative to their income, or available.
Additionally, cost-effectiveness analyses have been increasingly conducted to specifically evaluate AYA-focused HIV testing and prevention strategies (Inwani et al., 2017; Tierrablanca et al., 2018; Wilson, Mugo et al., 2017). Primary AYA-specific cost estimates can fill this key gap in the literature and enable cost-effectiveness analyses from the societal perspective.
We determined the costs incurred by AYA when seeking HTS at a Voluntary Counseling and Testing (VCT) clinic in Nairobi, Kenya. We further aimed to characterize the multiple sources of income and money that AYA have, and to compare the costs of HTS to AYA income. We present age- and sex-stratified results.
Methods
Our cross-sectional cost study was nested within the Developing Adolescent Strategies for HIV Testing (DASH) Study (Wagner et al., 2017). We recruited AYA participants from service delivery points that offered HTS at Kenyatta National Hospital, in Nairobi, Kenya. Kenyatta National Hospital is a national referral and teaching hospital that serves a mostly urban population. During April and May 2016, we recruited AYA ages 14 to 24 years who had completed HTS to participate in anonymous surveys, which included a set of cost questions. AYA who were newly diagnosed with HIV were transferred directly to HIV care services and were, therefore, excluded from our sample.
Data were collected anonymously with audio computer-assisted-self-interview using hand-held tablets and the Open Data Kit from the University of Washington (Hartung, Brunette, Lerer, Tseng, & Borriello 2010). Direct non-medical and indirect costs were estimated based on self-report and were reported in 2016 Kenya Shillings. Direct non-medical costs included AYA transportation, childcare, and food while attending clinic, as well as any costs incurred by AYA for a companion to accompany him/her to the clinic. Indirect costs included lost income or value from paid and unpaid work. We collected estimates of AYA income from paid work, including both salaried and unsalaried (casual) work; we also collected estimates of any money given to AYA by family members, friends, or boyfriends/girlfriends/partners, but these funds were not considered paid work and, therefore, were not valued as part of indirect costs. We collected estimates of AYA time spent doing unpaid work; the 2016 Kenyan gross domestic product (GDP) per capita ($121 USD/month) was used as a proxy for wages for unpaid work. Lost school time was estimated, but was not valued monetarily.
All cost estimates were converted to the same base year using the consumer price index from the Kenyan National Bureau of Statistics and converted to U.S. dollars using the exchange rates from the Central Bank of Kenya. All cost estimates are presented in 2016 U.S. dollars. Median and interquartile ranges (IQR) are presented for participants with non-zero costs in tables and text; tables additionally present 5th and 95th percentiles of costs for those with non-zero costs. Stratified sensitivity analyses were conducted to examine differences in costs incurred by men and women, as well as by adolescents (14–19 years) and young adults (20–24 years).
The study was approved by the University of Washington Institutional Review Board (UW IRB #48627) and the Kenyatta National Hospital Ethics and Research Committee (KNH ERC #P281/05/2015). AYA ages 18 to 24 and adolescents ages 14 to 17 who were not accompanied by a caregiver provided independent oral informed consent. Adolescents ages 14 to 17 who were accompanied by a caregiver provided oral informed assent and their caregivers provided oral informed consent for participation in the study.
Results
There were 189 AYA included in the analysis, 187 of whom had demographic information available. The median age was 20 (IQR: 18–22); 79 (42%) were ages 14 to 19 and 108 (58%) were ages 20 to 24; 122 (65%) were female. Males were generally older than females (median [IQR]: 21 [19, 23] vs. 19 [16, 22]). A majority had begun or completed university or polytechnic training (n = 112; 60%) or secondary education (n = 70; 37%), and 117 (95%) were enrolled in school (similar between age strata). A majority of AYA had presented to clinic originally for an HIV test (n = 129; 69%), but some had presented for general health information or counseling (n = 48; 26%), or another reason, including family planning or testing for other sexually transmitted infections (n = 10; 5%).
Most Adolescents Incurred Some Costs
Of the 189 adolescents, 121 (64%) reported non-zero costs associated with HIV testing; 118 (62%) had any direct non-medical costs and 19 (10%) had any indirect costs. Of those with non-zero costs, the median total cost was $1.97 (IQR: $0.79–3.95). Of those with non-zero costs, median direct non-medical costs were $1.73 (IQR: $0.69–2.96) and median indirect costs were $7.95 (IQR: $4.99–11.34; Table 1).
Table 1.
Direct Non-Medical and Indirect Costs for all Adolescents and Young Adults (N = 189)
| N = 189 | cost (USD)a | ||||||
|---|---|---|---|---|---|---|---|
| median | IQR | ||||||
| n | % | 5th | 25th | 75th | 95th | ||
| DIRECT COSTS | |||||||
| Any direct non-medical costs | 118 | 62% | 1.73 | 0.30 | 0.69 | 2.96 | 6.42 |
| Transportation of self | |||||||
| Paid own costs | 98c | 52% | 0.59 | 0.20 | 0.39 | 0.99 | 2.27 |
| Costs paid by another | 27 | 14% | -- | -- | -- | -- | -- |
| No costs | 64 | 34% | -- | -- | -- | -- | -- |
| Transportation of other person | |||||||
| Accompanied, did not pay transport for other person | 73 | 39% | -- | -- | -- | -- | -- |
| Paid another person’s fare | 20 | 11% | 0.49 | 0.30 | 0.30 | 0.84 | 1.33 |
| Not accompanied | 96 | 51% | -- | -- | -- | -- | -- |
| Mode of transport | |||||||
| Bus | 117 | 62% | |||||
| Walk | 65 | 34% | |||||
| Drove/was driven | 5 | 3% | |||||
| Taxi/motorbike | 2 | 1% | |||||
| Other costs | |||||||
| Any childcare or house help costs | 4 | 2% | -- d | -- d | -- d | -- d | -- d |
| Any food or drink outside the house | 79 | 42% | 1.97 | 0.49 | 0.99 | 1.97 | 4.93 |
| INDIRECT COSTS | |||||||
| Any indirect costs | 19 | 10% | 7.95 | 0.45 | 4.99 | 11.34 | 20.10 |
| Wages (monthly) | |||||||
| Had any paying jobb | 25 | 13% | |||||
| Had salaried job | 23 | 12% | 148.05 | 19.74 | 54.28 | 296.09 | 493.49 |
| Had non-salaried (casual labor) job | 15 | 8% | 49.35 | 1.97 | 19.74 | 98.70 | 197.39 |
| Did unpaid work | 86 | 46% | -- | -- | -- | -- | -- |
| Worked regularly on farm | 40 | 21% | -- | -- | -- | -- | -- |
| Had other source of income | 11 | 6% | 19.74 | 1.97 | 4.93 | 49.35 | 148.05 |
| Missed work for VCT | |||||||
| Full day | 9 | 5% | |||||
| Half day | 10 | 5% | |||||
| No work missed | 170 | 90% | |||||
| Missed school for VCT | |||||||
| Full day | 22 | 12% | |||||
| Half day | 56 | 30% | |||||
| No school missed | 111 | 59% | |||||
Note. VCT = voluntary counseling and testing; USD = U.S. dollars.
All medians and percentiles are among those with any costs
Percentages with income from different sources do not add to the percentage with any income because many had more than one source of income
Median and percentiles based on N = 95
Too few data points to estimate distribution Values were $2, $3, and $3 USD; 2 additionally gave food for housework or childcare
Data provided for costs where primary data were collected and more than 5 observations existed. Estimates based on GDP not shown, except for the total direct non-medical and total indirect costs rows, which contain both measured and estimated values combined.
Food and Transportation Costs
Seventy-nine (42%) AYA paid for food or drink outside of the home during their visits to voluntary testing and counseling (VCT; median: $1.97 [IQR: $0.99–1.97]). Ninety-eight AYA (52%) paid for their own transport (median: $0.59 [IQR: $0.39–0.99]), while 27 (14%) had their transportation costs paid by another person. Twenty (11%) paid the transport costs of a support person (median: $0.49 [IQR: $0.30–0.84]) and 73 (39%) were accompanied but did not pay the other person’s transport costs. Most AYA (62%) took the bus to the clinic, while 34% walked, and 4% drove or were driven in cars or on motorbikes. Few (n = 3; 2%) had any childcare costs (Table 1).
Few AYA Had Any Sources of Income or Money
Few AYA (n = 25 [13%]) had any paying job; 23 (12%) had a salaried job and 15 (8%) had a non-salaried (casual labor) job. Eighty-six (46%) did unpaid work and 40 (21%) worked regularly on a farm. Money received from another source, such as family or a partner, was uncommon; 11 (6%) received any money in this manner. The median monthly income from a salaried job was $148.05 (IQR: $54.28–296.09) and from a non-salaried job was $49.35 (IQR: $19.74–98.70). The median monthly amount of money from a family member or partner was $19.74 (IQR: $4.93–49.35; Table 1). Overall, 87% of adolescents reported no income or source of money; 66 (35%) reported no income or source of money and no direct costs, 98 (52%) reported no income or source of money but incurred direct costs. Of the 25 (13%) adolescents who had both income or a source of money and direct costs, these costs were 1% (IQR: 0.8–2.6%) of their monthly money.
Many AYA Miss School or Work to Seek HTS
Nineteen (10%) of AYA missed a half or full day of work to seek HTS. Seventy-eight (42%) missed a half or full day of school to seek HTS (Table 1).
Sex- and Age-Stratified Analyses
Males were more likely to report direct and indirect cost than females (78% vs. 53%, p = 0.0007; 15% vs. 7%, p = 0.009, respectively). Females were more likely to be accompanied but not responsible for paying their support person’s transportation (p < 0.001) and more likely to work regularly on a farm (p = 0.04). Young adults (20 to 24 years) were more likely to have indirect costs than adolescents (14 to 19 years; 14% vs. 5%, p = 0.05). Young adults were more likely to pay for their transport and be unaccompanied (p < 0.001) than adolescents (Tables 2a and 2b).
Table 2a.
Gender-Stratified Costs
| Males | cost (USD)a | Females | cost (USD)a | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| N = 65 | Mdn | IQR | N = 122 | Mdn | IQR | ||||||||||
| n | % | 5th | 25th | 75th | 95th | n | % | 5th | 25th | 75th | 95th | ||||
| DIRECT COSTS | DIRECT COSTS | DIRECT COSTS | |||||||||||||
| Any direct non-medical costs | 51 | 78% | 1.68 | 0.39 | 0.59 | 2.96 | 5.53 | 65 | 53% | 1.58 | 0.30 | 0.79 | 2.86 | 6.12 | |
| Transportation of self | |||||||||||||||
| Paid own costs | 41 | 63% | 0.69 | 0.30 | 0.49 | 1.18 | 1.97 | 55c | 45% | 0.59 | 0.20 | 0.30 | 0.99 | 2.37 | |
| Costs paid by another | 3 | 5% | -- | -- | -- | -- | -- | 24 | 20% | -- | -- | -- | -- | -- | |
| No costs | 21 | 32% | -- | -- | -- | -- | -- | 43 | 35% | -- | -- | -- | -- | -- | |
| Transportation of other person | |||||||||||||||
| Accompanied, did not pay transport for other person |
11 | 17% | -- | -- | -- | -- | -- | 61 | 50% | ||||||
| Paid another person’s fare | 7 | 11% | 0.59 | 0.30 | 0.30 | 1.18 | 1.48 | 12 | 10% | 0.44 | 0.30 | 0.30 | 0.59 | 0.99 | |
| Not accompanied | 47 | 72% | -- | -- | -- | -- | -- | 49 | 40% | ||||||
| Mode of transport | |||||||||||||||
| Bus | 42 | 65% | 73 | 60% | |||||||||||
| Walk | 19 | 29% | 46 | 38% | |||||||||||
| Drove / was driven | 3 | 5% | 2 | 2% | |||||||||||
| Taxi / motorbike | 1 | 2% | 1 | 1% | |||||||||||
| Other costs | |||||||||||||||
| Any childcare or house help costs | 1 | 2% | -- d | -- d | -- d | -- d | -- d | 2 | 2% | -- d | -- d | -- d | -- d | -- d | |
| Any food and drink outside the house | 32 | 49% | 1.48 | 0.49 | 0.99 | 1.97 | 4.93 | 45 | 37% | 1.97 | 0.49 | 0.99 | 1.97 | 4.93 | |
| INDIRECT COSTS | INDIRECT COSTS | INDIRECT COSTS | |||||||||||||
| Any indirect costs | 10 | 15% | 7.99 | 0.46 | 4.54 | 10.82 | 13.61 | 9 | 7% | 7.94 | 2.16 | 6.16 | 14.04 | 20.10 | |
| Wages (monthly) | |||||||||||||||
| Had any paying jobb | 11 | 17% | 13 | 11% | |||||||||||
| Had salaried job | 10 | 15% | 133.24 | 4.93 | 78.96 | 246.74 | 592.18 | 12 | 10% | 120.90 | 19.74 | 51.82 | 246.74 | 493.49 | |
| Had non-salaried (casual labor) job | 6 | 9% | 39.48 | 1.97 | 19.74 | 88.83 | 98.70 | 9 | 7% | 49.35 | 1.97 | 39.48 | 148.05 | 197.39 | |
| Did unpaid work | 29 | 45% | -- | -- | -- | -- | -- | 55 | 45% | -- | -- | -- | -- | -- | |
| Worked regularly on farm | 9 | 14% | -- | -- | -- | -- | -- | 30 | 25% | -- | -- | -- | -- | -- | |
| Had other source of income | 4 | 6% | 14.80 | 1.97 | 5.92 | 34.54 | 49.35 | 7 | 6% | 34.54 | 2.96 | 4.93 | 78.96 | 148.05 | |
| Missed work for VCT | |||||||||||||||
| Full day | 6 | 9% | 3 | 2% | |||||||||||
| Half day | 4 | 6% | 6 | 5% | |||||||||||
| No work missed | 55 | 85% | 113 | 93% | |||||||||||
| Missed school for VCT | |||||||||||||||
| Full day | 11 | 17% | 11 | 9% | |||||||||||
| Half day | 19 | 29% | 37 | 30% | |||||||||||
| No school missed | 35 | 54% | 74 | 61% | |||||||||||
Note. VCT = voluntary counseling and testing; USD = U.S. dollars.
All medians and percentiles are among those with any costs
Percentages with income from different sources do not add to the percentage with any income because many had more than one source of income
Median and percentiles based on N=52
Too few data points to estimate distribution Values were $2, $3, and $3USD; 2 additionally gave food for housework or childcare
Data provided for costs where primary data was collected and more than 5 observations existed. Estimates based on GDP not shown, except for the total direct non-medical and total indirect costs rows, which contain both measured and estimated values combined.
Table 2b.
Age-Stratified Costs
| 14–19 years | cost (USD)a | 20–24 years | cost (USD)a | |||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| N = 79 | median | IQR | N = 108 | median | IQR | |||||||||||
| n | % | 5th | 25th | 75th | 95th | n | % | 5th | 25th | 75th | 95th | |||||
| DIRECT COSTS | DIRECT COSTS | DIRECT COSTS | ||||||||||||||
| Any direct non-medical costs | 44 | 56% | 1.63 | 0.20 | 0.59 | 2.02 | 3.45 | 72 | 67% | 1.63 | 0.39 | 0.69 | 3.36 | 6.71 | ||
| Transportation of self | ||||||||||||||||
| Paid own costs | 33c | 42% | 0.69 | 0.20 | 0.44 | 1.28 | 2.27 | 63c | 58% | 0.59 | 0.30 | 0.39 | 0.99 | 1.97 | ||
| Costs paid by another | 23 | 29% | -- | -- | -- | -- | -- | 4 | 4% | -- | -- | -- | -- | -- | ||
| No costs | 23 | 29% | -- | -- | -- | -- | -- | 41 | 38% | -- | -- | -- | -- | -- | ||
| Transportation of other person | ||||||||||||||||
| Accompanied, did not pay transport for other person |
45 | 57% | -- | -- | -- | -- | -- | 27 | 25% | -- | -- | -- | -- | -- | ||
| Paid another person’s fare | 7 | 9% | 0.49 | 0.30 | 0.30 | 0.59 | 0.89 | 12 | 11% | 0.49 | 0.30 | 0.30 | 0.89 | 1.48 | ||
| Not accompanied | 27 | 34% | -- | -- | -- | -- | -- | 69 | 64% | -- | -- | -- | -- | -- | ||
| Mode of transport | ||||||||||||||||
| Bus | 57 | 72% | 58 | 54% | ||||||||||||
| Walk | 20 | 25% | 45 | 42% | ||||||||||||
| Drove / was driven | 2 | 3% | 3 | 3% | ||||||||||||
| Taxi / motorbike | 0 | 0% | 2 | 2% | ||||||||||||
| Other costs | ||||||||||||||||
| Any childcare or house help costs | 1 | 1% | -- d | -- d | -- d | -- d | -- d | 2 | 2% | -- d | -- d | -- d | -- d | -- d | ||
| Any food and drink outside the house | 27 | 34% | 1.48 | 0.49 | 0.99 | 1.97 | 2.96 | 50 | 46% | 1.97 | 0.49 | 0.99 | 2.47 | 4.93 | ||
| INDIRECT COSTS | INDIRECT COSTS | INDIRECT COSTS | ||||||||||||||
| Any indirect costs | 4 | 5% | 7.94 | 0.46 | 2.99 | 15.24 | 20.10 | 15 | 14% | 7.94 | 2.16 | 4.99 | 11.34 | 15.72 | ||
| Wages (monthly) | ||||||||||||||||
| Had any paying jobb | 6 | 8% | 18 | 17% | ||||||||||||
| Had salaried job | 5 | 6% | 118.44 | 4.93 | 19.74 | 197.39 | 197.39 | 17 | 16% | 148.05 | 44.41 | 74.02 | 296.09 | 592.18 | ||
| Had non-salaried (casual labor) job | 5 | 6% | 88.83 | 1.97 | 1.97 | 197.39 | 197.39 | 10 | 9% | 49.35 | 4.93 | 29.61 | 98.70 | 148.05 | ||
| Did unpaid work | 34 | 43% | -- | -- | -- | -- | -- | 50 | 46% | -- | -- | -- | -- | -- | ||
| Worked regularly on farm | 17 | 22% | -- | -- | -- | -- | -- | 22 | 20% | -- | -- | -- | -- | -- | ||
| Had other source of income | 4 | 5% | 26.15 | 1.97 | 2.47 | 64.15 | 78.96 | 7 | 6% | 19.74 | 4.93 | 9.87 | 49.35 | 148.05 | ||
| Missed work for VCT | ||||||||||||||||
| Full day | 3 | 4% | 6 | 6% | ||||||||||||
| Half day | 1 | 1% | 9 | 8% | ||||||||||||
| No work missed | 75 | 95% | 93 | 86% | ||||||||||||
| Missed school for VCT | ||||||||||||||||
| Full day | 14 | 18% | 8 | 7% | ||||||||||||
| Half day | 20 | 25% | 36 | 33% | ||||||||||||
| No school missed | 45 | 57% | 64 | 59% | ||||||||||||
Note. VCT = voluntary counseling and testing; USD = U.S. dollars.
All medians and percentiles are among those with any costs
Percentages with income from different sources do not add to the percentage with any income because many had more than one source of income
Median and percentiles based on N=32 (14–19 years), N=61 (20–24 years)
Too few data points to estimate distribution Values were $2, $3, and $3USD; 2 additionally gave food for housework or childcare
Data provided for costs where primary data was collected and more than 5 observations existed. Estimates based on GDP not shown, except for the total direct non-medical and total indirect costs rows, which contain both measured and estimated values combined.
Discussion
Despite the availability of free HTS for people in Kenya, we found that most AYA seeking HIV testing incurred direct non-medical costs, especially males and young adults. Nearly half of participants reported missing school to get testing. Our study highlighted the need to consider a wider range of costs beyond those borne by governments to inform optimization of HIV testing programs tailored to this priority population.
Studies on cost and cost-effectiveness of HIV testing strategies in Africa have used a health provider perspective (Aliyu et al., 2012; Grabbe et al., 2010; Menzies, Abang, et al., 2009; Menzies, Homsy, et al., 2009; Turner et al., 2015), which may have underestimated overall costs of testing from the societal perspective and overlooked important barriers to test uptake (Maheswaran et al., 2017). While the direct non-medical costs in our study were small (< $2 USD), largely from food and transport, this amount was not trivial in a population with limited financial independence (Jennings et al., 2017; Rassjo, Darj, Konde-Lule, & Olsson, 2007) and competing needs and interests (MacPhail, Pettifor, Moyo, & Rees, 2009; Musheke et al., 2013). Interventions to offset direct costs, such as small financial incentives, have been shown to substantially increase uptake of HIV testing for adolescents in Zimbabwe (Kranzer et al., 2018) and merit consideration for further testing.
In addition to direct costs, a large proportion of AYA reported missing school to test, and a few incurred opportunity costs due to missed work. Perceived opportunity costs of missing school or work have been reported to be important barriers to AYA seeking HIV testing (Jennings et al., 2017; Maheswaran et al., 2017; Musheke et al., 2013) and inflexible school schedules are a known barrier to adolescent engagement in HIV care (Freak-Poli, Wolfe, Wong, & Peeters, 2014). The low indirect costs due to missed work compared to other studies (Maheswaran et al., 2017) could be explained by the finding that most of our sample population was in school. Males reported comparably higher total costs than females, which likely reflected that a higher proportion of males were older and may have had more access to income and decision-making power (Jennings et al., 2017; Matovu et al., 2014). Further research is needed to explore potential gender differences between AYA in perceived and actual costs of testing to inform gender-sensitive strategies to increase testing uptake.
Strengths of our study included detailed data about different sources of income and indirect costs, large sample size, and primary data collection from adolescents younger than 18 years, a group that is underrepresented in the health economics literature (Eaton et al., 2015; Maheswaran et al., 2017). Importantly, these results could fill a key gap in the health economics literature by providing adolescent-specific cost estimates to model the cost-effectiveness of new HIV prevention and care interventions for this population. A limitation of our study was that the sample consisted of AYA who were already seeking HIV testing, and is likely not generalizable to those who do not access facility-based testing. In addition, the cost estimates reflect an urban setting, in a relatively educated population. Costs of HIV testing may differ for AYA in rural areas and with a lower level of education attainment.
Conclusion
The disproportionately low uptake of HIV testing by AYA in Africa signals the need to address key barriers to testing. We showed that, among AYA who accessed free HIV testing services in Kenya, most incurred costs and lacked income. These costs, paired with disruptions from school or work, may limit AYA engagement in routine testing and HIV detection. HIV testing strategies that address these costs, including financial incentives and self-testing, should be evaluated to help achieve the “Fast Track” targets for this population.
Acknowledgements
This research was funded by a 2014 developmental grant from the University of Washington/Fred Hutch Center for AIDS Research, an NIH funded program under award number AI027757 (PI: Holmes; PD: Kohler), which is supported by the following NIH Institutes and Centers: NIAID, NCI, NIMH, NIDA, NICHD, NHLBI, NIA, NIGMS, NIDDK. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Funding sources were not involved in the analyses or interpretation of data. ADW, KSW, JBB, CM, PMM, DCW, DB, GJS, PKK, and JAS were supported by P30 AI027757 (PI: Holmes; PD: Kohler).
The authors thank the DASH study participants and their families, without whom this research would not have been possible. We thank the Kenyatta National Hospital staff at the VCT, Youth Center, and the DASH administrative and data team for their dedication and support. We thank the Kenyan National AIDS and STI Control Programme (NASCOP) for valuable input during study design, conduct, and dissemination. We thank members of the UW Global Center for Integrated Health of Women, Adolescents and Children (Global WACh) and Kenya Research & Training Center (KRTC) for their support during the preparation of this article.
Footnotes
Disclosures
The authors report no real or perceived vested interests related to this article that could be construed as a conflict of interest.
Contributor Information
Anjuli D. Wagner, Department of Global Health, University of Washington, Seattle, Washington, USA..
Katherine S. Wilson, Department of Global Health, University of Washington, Seattle, Washington, USA..
Joseph B. Babigumira, Department of Global Health, University of Washington, Seattle, Washington, USA..
Cyrus Mugo, Kenyatta National Hospital, Nairobi, Kenya..
Peter M. Mutiti, VCT and HIV Prevention Unit/Youth Centre, Kenyatta National Hospital, Nairobi, Kenya..
Jillian Neary, Department of Global Health, University of Washington, Seattle, Washington, USA..
Dalton C. Wamalwa, Department of Paediatrics and Child Health, University of Nairobi, Nairobi, Kenya..
David Bukusi, Kenyatta National Hospital, Nairobi, Kenya..
Grace C. John-Stewart, Departments of Global Health, Epidemiology, Pediatrics, and Medicine, University of Washington, Seattle, Washington, USA..
Pamela K. Kohler, Departments of Global Health and Psychosocial and Community Health, University of Washington, Seattle, Washington, USA..
Jennifer A. Slyker, Departments of Global Health and Epidemiology, University of Washington, Seattle, Washington, USA..
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