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CLINICAL SCIENCE: Epidemiology and Social

Undisclosed HIV infection and antiretroviral therapy use in the Kenya AIDS indicator survey 2012

relevance to national targets for HIV diagnosis and treatment

Kim, Andrea A.; Mukui, Irene; Young, Peter W.; Mirjahangir, Joy; Mwanyumba, Sophie; Wamicwe, Joyce; Bowen, Nancy; Wiesner, Lubbe; Ng’ang’a, Lucy; De Cock, Kevin M. for the KAIS Study Group

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doi: 10.1097/QAD.0000000000001227
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Reliable estimates of HIV diagnosis and access to antiretroviral therapy (ART) among persons living with HIV (PLHIV) are needed to monitor progress toward fast-track targets set by the Joint United Nations Programme on HIV/AIDS to control the HIV pandemic by 2030 [1]. National population-based HIV serosurveys provide insight on progress through collection of generalizable information on self-reported HIV status and coverage of HIV services among HIV-infected persons. In combination with biomarkers, this allows for the assessment of trends in the continuum of care among PLHIV, from HIV diagnosis, linkage to care, ART initiation, to viral suppression. Robust estimates of these indicators, however, rely on participants answering questions related to HIV status and access to services accurately during an interview.

The Kenya AIDS Indicator Survey (KAIS) 2012 was a national serosurvey that linked information on demographics, behavior, and access to health interventions to HIV-related biomarkers to monitor the impact of the national HIV response. The criteria for ART initiation at the time of the survey included persons with CD4+ cell counts 350 cells/μl or less, or, irrespective of CD4+ cell count, persons with WHO clinical stage 3/4 disease, persons with active tuberculosis coinfection, or persons with hepatitis B virus coinfection with evidence of liver disease [2]. In 2012, the national ART program reported that 549 000 HIV-infected adults were receiving ART, representing 78% of treatment-eligible adults living with HIV infection in Kenya [3].

In KAIS 2012, 23% of persons who reported HIV-positive status with no prior history of ART use were virally suppressed (unpublished data). This finding was surprising given that the control of viral replication in the absence of treatment is considered a rare event [4,5]. This phenomenon has also been documented in clinical studies that later confirmed that underreporting of true ART use among study participants resulted in higher than expected levels of viral suppression among PLHIV who were reportedly treatment naive [6–10]. The parallel finding in KAIS 2012 raised concerns that some respondents may have misreported their ART status resulting in an underestimate of ART coverage measured from the survey.

Testing HIV-positive blood for the presence of antiretroviral metabolites provides a direct biomedical measure of ART use that can be used to improve estimates of ART coverage, and, by extension, estimates of diagnosed HIV among PLHIV. Such data remain important for clinical trials that enroll participants based on no prior history of HIV treatment and population-based surveys designed to generalize levels of undiagnosed HIV, ART access, and viral suppression in the population. In this analysis we assess the level of undisclosed HIV infection and ART use among persons with the antiretroviral biomarker in KAIS 2012 and impact on national estimates of diagnosed HIV and ART coverage.


KAIS 2012 was a nationally representative household survey of persons aged 18 months to 64 years [11,12]. Survey respondents were administered face-to-face interviews that collected information on demographics, sexual behavior, HIV testing history, HIV status, and receipt of health interventions, including ART for persons who disclosed HIV-positive status. Venous blood samples were collected in CD4+ stabilization tubes, which were used to prepare dried blood spot (DBS) cards in a field laboratory. Respondents were offered on-site rapid HIV testing based on national guidelines [13], and results were returned immediately with counselling by trained staff in a private area in the home.

Blood samples were transported to a central laboratory where DBS were tested for HIV-1 antibodies using Vironostika HIV-1/2 UNIF II Plus O enzyme immunoassay (sensitivity and specificity = 100%) and Murex HIV.1.2.0 enzyme immunoassay (sensitivity = 100%; specificity = 99.5%) [14,15]. CD4+ cell counts were measured using the BD FACSCalibur flow cytometer (Becton Dickinson Biosciences, San Jose, California, USA). HIV-positive DBS were tested for HIV-1 RNA concentration (Abbott M2000 Real-Time HIV-1 Assay; Abbott Laboratories, Abbott Park, Illinois, USA). Undetectable viral load was defined as HIV-1 RNA concentration less than 1000 copies/ml. Remnant DBS samples were stored in −70°C freezers for future testing. Two years after survey completion, HIV-positive DBS were transferred to the University of Cape Town in South Africa where a qualitative antiretroviral assay was applied to test for nevirapine (NVP), efavirenz (EFV), lamivudine (3TC), and lopinavir (LPV) using liquid chromatography-tandem mass spectrometry [16]. The assay's lower limit of detection was 0.02 μg/ml. The number of days from ingestion to reaching this threshold was 12–28 days for EFV, 8–9 days for NVP, 1.5 days for 3TC, and 1.5–2.5 days for LPV. Samples that fell above this cut-off for any of the antiretroviral drugs tested were classified as having the antiretroviral biomarker present. Though validations studies assessing the performance of DBS on the qualitative assay have not been conducted, a study that compared the performance of quantitative measurement of antiretroviral drug levels in plasma and DBS using liquid chromatography-tandem mass spectrometry found no major differences [17]. In 2012, the first-line standardized ART regimen for Kenyan adults was tenofovir + 3TC + EFV or NVP and second-line regimen was zidovudine + 3TC + LPV/ritonavir [2]. For pregnant women or patients intolerant to tenofovir, the recommended first-line regimen was zidovudine + 3TC + EFV or NVP.


The primary outcomes for this analysis were undisclosed HIV infection and undisclosed ART use while on ART. We categorized self-reported HIV status into five categories: HIV-positive, HIV-negative, HIV-indeterminate, never tested for HIV, and unknown self-reported HIV status (i.e. persons who reported testing for HIV but did not provide a result). HIV-infected persons who reported HIV-positive status were classified as having disclosed HIV infection, whereas HIV-infected persons with the antiretroviral biomarker who reported HIV-negative, HIV-indeterminate, never tested for HIV, or had unknown self-reported status were classified as having undisclosed HIV infection while on ART. Persons who reported ART use, irrespective of antiretroviral test results, were classified as having disclosed ART use. Respondents with the antiretroviral biomarker who reported HIV-positive status with no history of ART use or who reported HIV-negative, HIV-indeterminate, never tested for HIV, or unknown self-reported status were classified as having undisclosed ART use while on ART.

Variables tested for potential associations with the outcomes of interest included demographic variables: sex, age, marital status, education, wealth, urban/rural residence, and region; behavioral variables: number of sexual partners in the past year, condom use, and knowledge of partner HIV status; and clinical variables: history of visiting a health provider in the past year, history of tuberculosis disease, undetectable viral load, and median CD4+ cell count.


This analysis was restricted to respondents aged 15–64 years who tested HIV-positive in the survey and had sufficient volume of blood available in stored DBS for antiretroviral testing. Using the antiretroviral test results as the reference standard, we calculated the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of self-reported ART status. The Rao–Scott χ2 test was used to measure associations between categorical variables and outcomes of interest in bivariate analysis. Medians and interquartile ranges were reported for continuous variables. The Wilcoxon rank sum test was used to measure the association between continuous variables and the outcome variables. We used PROC SURVEYLOGISTIC in SAS version 9.3 (SAS Institute, Inc., Cary, North Carolina, USA) to determine independent and significant correlates of undisclosed HIV infection and ART use among persons on ART in multivariate logistic regression. The model included variables that were associated with the outcomes of interest in bivariate analysis at a P value less than 0.2. Associations were considered statistically significant if the 95% confidence interval (CI) for the adjusted odds ratio (AOR) did not include 1.0. All estimates presented were weighted to account for sampling probability and survey nonresponse.

In a subanalysis, we compared national estimates of diagnosed HIV and ART coverage based on self-report alone; HIV and ART coverage based corrected for undisclosed HIV and ART use among persons on ART; and for comparison of ART coverage estimates only, estimates of treatment coverage based on national ART program data. We extrapolated population counts of the number of diagnosed PLHIV and the number of PLHIV on ART by applying nonnormalized survey weights based on the 2012 projected population data in the 2009 Kenya Population and Housing Census to the outcome variables [18].

For programmatic estimates of ART coverage, Spectrum version 5.31 (Avenir Health, Glastonbury, Connecticut, USA) was used to project estimates of the number of PLHIV aged 15 years and older from 1970 to 2020 [19]. The number of adult ART patients in 2012 was based on antiretroviral drug dispensing data from Kenya's Logistics Management Information System. Programmatic ART coverage was calculated by dividing the number of adult ART patients by the projected number of adult PLHIV. Plausibility bounds around programmatic ART coverage were calculated using an uncertainty analysis, which applied Monte Carlo simulations to estimate the impact of uncertainty in modeled HIV incidence and model assumptions [20,21]. A Z-test was conducted to test for differences in ART coverage by estimation method.


Overall, 648 of 11 626 respondents tested HIV-positive (Fig. 1). Of those, 559 (86.3%) had sufficient volume available in stored DBS for antiretroviral testing. No statistically significant differences in sex, age, urban/rural residence, region, marital status, education, or wealth were found among HIV-positive respondents with antiretroviral test results compared with HIV-positive respondents without these results. Among the 559 individuals with antiretroviral test results, 271 (47.7%; CI 41.8–53.6) reported HIV-positive status, 202 (35.9%; CI 31.0–40.9) reported HIV-negative status, seven (1.0%; CI 0–2.0) reported HIV-indeterminate status, 18 (3.1%; CI 1.5–4.8) had unknown self-reported status, and 61 (12.2%; CI 8.9–15.6) reported that they had never tested for HIV. Among respondents who disclosed HIV infection (n = 271), 186 (69.1%; CI 62.3–76.0) reported receiving ART.

Fig. 1
Fig. 1:
HIV-infected survey respondents aged 15–64 years by self-reported HIV status and detection of the ARV biomarker, Kenya AIDS Indicator Survey 2012.ART, antiretroviral therapy; ARV, antiretroviral.

Presence of the antiretroviral biomarker

At least one drug was detected in 235 of 559 specimens tested, representing 42.5% (CI 37.4–47.7) of HIV-positive persons. Among those, 3TC was detected in 94.5% (CI 91.3–97.8), NVP in 62.9% (CI 55.9–69.9), EFV in 34.0% (CI 27.1–40.9), and LPV in 3.9% (CI 0.8–7.0).

Among persons who disclosed HIV infection (n = 271), 180 (66.7%; CI 59.9–73.4) had detectable antiretrovirals (Fig. 2). Those who disclosed both HIV infection and ART use (n = 186) had antiretrovirals detected in 168 (90.7%; CI 86.1–95.2). Among persons who disclosed HIV infection but did not disclose using ART (n = 85), 12 (12.9%; CI 5.2–20.6) had antiretrovirals detected.

Fig. 2
Fig. 2:
Percentage of HIV-infected respondents aged 15–64 years with the antiretroviral biomarker by self-reported HIV status, Kenya AIDS Indicator Survey 2012.ARV, antiretroviral. Analysis restricted to 559 HIV-positive dried blood spot samples available for antiretroviral testing. Whiskers on bars represent 95% confidence intervals for presented estimates. Estimates may be unreliable due to denominator less than 25 observations and should be interpreted cautiously.

A total of 202 HIV-infected persons reported HIV-negative status. Of those, 38 (21.0%; CI 13.4–28.6) had the antiretroviral biomarker, and 26 (61.7%; CI 43.0–80.5) of those reported receiving their last HIV-negative result within the year preceding the survey. Among HIV-infected persons who reported never testing for HIV (n = 61), eight (12.8%; CI 3.3–22.3) had the antiretroviral biomarker present. A minority of persons reported that their last HIV test was indeterminate (n = 7) or reported an unknown HIV serostatus (n = 18). Of those, the antiretroviral biomarker was present in 13.4% (CI 0–41.1) and 46.5% (CI 20.4–72.7), respectively. Among persons who had the antiretroviral biomarker (n = 235), 18.3% (CI 12.7–24.0) did not disclose their HIV infection (n = 55) and 6.0% (CI 2.5–9.5) did not disclose ART use despite disclosing HIV infection (n = 12). Compared with results from antiretroviral testing, the sensitivity of self-reported ART use was 71%, the specificity was 94%, the PPV was 90%, and the NPV was 82%.

Characteristics of persons on antiretroviral therapy with undisclosed HIV infection

In bivariate analysis, male sex, younger age (15–24 years), living in urban residences, higher wealth, not visiting a health provider in the past year, and being virally suppressed were associated with undisclosed HIV infection while on ART at a P value less than 0.1 (Table 1). In multivariate analysis, compared with HIV-infected persons who disclosed their HIV infection HIV-positive status, persons aged 25–39 years (compared with aged 40–64 years: AOR 5.0; CI 1.1–22.0) and not visiting a health provider in the past year (AOR 6.1; CI 1.4–26.1) were associated with significantly higher adjusted odds of having undisclosed HIV infection while on ART.

Table 1
Table 1:
Demographic, behavioral, and clinical characteristics of HIV-infected persons aged 15–64 years by HIV disclosure status, Kenya 2012.

Characteristics of persons on antiretroviral therapy with undisclosed antiretroviral therapy use and impact on measures of viral suppression

In bivariate analysis, undisclosed ART use was associated with younger age (aged 15–24 years), higher wealth, and not visiting a health provider in the past year at a P value less than 1.0 (Table 2). In multivariate analysis, younger age (compared with aged 40–64 years: AOR 5.3; CI 1.4–19.8) and having higher wealth compared with lower wealth (AOR 3.1; CI 1.1–9.2) remained significantly associated with higher adjusted odds of undisclosed ART use while on ART.

Table 2
Table 2:
Demographic, behavioral, and clinical characteristics of HIV-infected persons aged 15–64 years on antiretroviral therapy by antiretroviral therapy disclosure status, Kenya 2012.

Viral suppression ranged from 22.6% (CI 17.1–28.1) among HIV-infected persons who reported no prior history of ART use to 76.2% (CI 69.4–83.0) among those who were reportedly taking ART (data not shown). After accounting for undisclosed ART use based on results of antiretroviral testing, viral suppression decreased to 10.4% (CI 6.4–14.4) among persons not on ART and increased to 80.4% (CI 74.7–86.1) among those on ART.

National estimates of HIV diagnosis and antiretroviral therapy coverage

The percentage of persons with diagnosed HIV increased from 46.9% (305/648, CI 41.3–52.4) based on self-report (population estimate: 558 000; CI 457 000–660 000) to 56.2% (360/648, CI 50.7–61.7) after correcting for undisclosed HIV infection among persons on ART (population estimate: 670 000; CI 561 000–779 000). ART coverage increased from 31.8% (205/648, CI 27.1–36.5) based on self-report (population estimate: 379 000; CI 304 000 -454 000) to 42.8% (272/648, CI 37.9–47.8) after correcting for undisclosed ART use among persons on ART (population estimate: 510 000; CI 425 000–596 000). In 2012, the national ART program reported that programmatic ART coverage was 47% (CI 40–53) based on a numerator of 549 000 adult patients who received ART in health facilities that year and a projected denominator of 1 157 000 adult PLHIV based on the Spectrum model. Programmatic ART coverage was statistically different from ART coverage based on self-report (P < 0.001) but similar to ART coverage adjusted for the antiretroviral biomarker (P = 0.315).


We confirm that substantial misreporting occurred when HIV-infected respondents were asked to report their HIV and ART status in KAIS 2012. Only 71% of persons with detectable antiretrovirals reported a history of ART use, resulting in an underestimation of national ART coverage by 11% points and diagnosed HIV by 9% points. The impact of this bias resulted in an estimated 131000 persons on ART and 112 000 persons with diagnosed HIV who were left unaccounted for in national estimates of coverage based exclusively on self-report [22].

Over 90% of persons who reported ART use in the interview had evidence of at least one antiretroviral drug in their blood, suggestive of high levels of treatment adherence among HIV-infected persons acknowledging ART use. The percentages of individual antiretroviral drugs detected in our sample were in alignment with the expected coverage of the same drugs in the national ART program in 2012. Approximately 95% of individuals with the antiretroviral biomarker had evidence of 3TC, a drug recommended in both first and second-line regimens for adults. Additionally, 96% of persons with the antiretroviral biomarker were receiving NVP or EFV, also recommended as first-line therapy for adults. Based on a systematic review of the literature, an estimated 16% of ART patients are expected to experience treatment failure after 1 year on treatment, requiring switch to second-line ART [23]. Though this estimate is higher than the percentage of second-line drug (LPV) detected in our sample, KAIS 2012 did not collect information on duration of ART use, treatment failure, and access to second-line therapy to confirm whether this level should have been higher.

Estimated viral suppression in the absence of treatment decreased by over 50%; from 23% based on self-report to 10% after correction through antiretroviral testing. A similar rate was reported in rural Uganda during a population-based health campaign in 2011, where 10% of persons who were not on ART were virally suppressed [24]. Though these rates of viremic control in the absence of treatment are high [4,5], they are not implausible given that the HIV epidemic in East Africa has existed for a long period resulting in more pressure to select those who have naturally lower viral loads as the duration of the HIV epidemic increases [25].

Compared with the antiretroviral biomarker, self-reported ART use had modest sensitivity and NPV, which was likely influenced by social desirability bias. Our findings also confirmed that self-reported serostatus was impacted by this bias. The risk of undisclosed HIV infection while on ART was highest among younger persons and individuals who had not visited a health provider in the past year. In contrast the risk of undisclosed ART use while on ART was highest among persons who were young or wealthy. Interestingly, 6% of persons with detectable antiretrovirals disclosed their HIV infection but did not disclose ART use. These results were unexpected given that the perceived level of stigma associated with acknowledging HIV infection is assumed to be higher than that of acknowledging ART use, particularly in settings where treatment is broadly promoted and accessible for most PLHIV.

Disclosure of sensitive health-related information is complex and may be influenced by a number of factors related to the respondent, interviewer, and interviewer mode. For example, respondents may feel embarrassed or fear judgement when asked sensitive questions; they may want to please the interviewer and report behaviors they perceive to be more socially acceptable; they may feel that their responses will not be kept private and respond inaccurately to protect their anonymity; they may believe what they report; or they may have misunderstood the question. Additionally, the interviewer may have reworded questions inaccurately or incorrectly coded responses. Every effort should be made to reduce reporting biases by ensuring that high-quality interviews that promote accurate reporting are delivered and that the mode of interview ensures that participants to feel at ease in their responses. Self-administered questionnaires or computer-assisted self-interviews that remove the interviewer could yield higher rates of accurate responses to sensitive questions, though measurement of these indicators may need to be simplified before consideration.

This analysis had the following limitations. Although estimates of ART coverage improved with inclusion of an antiretroviral biomarker, true population ART coverage may still be underestimated. The parent drugs selected for antiretroviral testing were based on the standardized treatment regimens available in public health facilities in Kenya. If a patient was on a regimen that did not include any of the four drugs tested, evidence of ART use would not have been detected. Additionally, given the short half-life of the antiretroviral drugs tested, the antiretroviral biomarker served as a marker of recent exposure to antiretrovirals and may have underestimated ART use if adherence was poor. Although we found that antiretroviral testing improved estimates of diagnosed HIV, a downward bias is still present by missing individuals who may be aware of their infection (but do not report it) and have not yet accessed ART. To understand the extent of this bias, 7% of persons with an HIV diagnosis in Kenya had not accessed care services in 2012, and of those in care, 22% had not yet accessed treatment. This limitation should be lessened in the future as treatment for all PLHIV is introduced irrespective of CD4+ cell count [26]. Finally, we were unable to compare reported ART use with antiretroviral biomarker results for HIV-infected persons who did not disclose HIV-positive status given that a history of ART use was collected only for persons who acknowledged that they were HIV-positive during the interview.

In conclusion, antiretroviral testing in a national population-based serosurvey in Kenya confirmed that self-reported information on HIV-positive status and ART use should not be used alone for generalizing estimates of diagnosed HIV and ART coverage. The change in viral suppression by ART status illustrates a further limitation of reliance on self-reported ART status. Not only did it result in an underestimate of diagnosed HIV and ART coverage at the population-level, the resulting misclassification at the individual level can potentially bias associations between known status, ART use, and other explanatory factors. We were encouraged to find the estimates of national ART coverage based on routine program data collected at ART facilities to be robust, a reassuring finding considering that program data are often criticized for poor data quality, though they remain the main source of information used by low and middle-income countries (LMIC) to plan, monitor, and evaluate treatment access. Still, much work is needed as most HIV treatment monitoring systems do not yet have the ability to link back to when an individual was diagnosed HIV-positive, limiting the opportunity to effectively monitor sentinel events along the cascade of HIV care for all PLHIV [27]. Until these systems are established and validated in LMIC, population-based serosurveys that include objective measures of ART use and biomarkers to measure viral suppression, serve as the best source of data for countries to monitor the reach and effectiveness of prevention and treatment interventions on achieving a successful treatment continuum. In settings where antiretroviral testing is not feasible, self-reported data on HIV and ART status remain informative [28] but should be supplemented with information that can confirm status, such as verification in a health record. Triangulation of biological, epidemiological, and programmatic data remains an essential approach to generate best-supported estimates of diagnosis and treatment targets in a country.


A.A.K., I.M., J.W., L.N., and K.D.C. conceived and designed the experiments. P.W.Y., S.M., and L.W. performed the experiments. A.A.K. analyzed the data and wrote the study. A.A.K., I.M., P.W.Y., J.M., S.M., J.W., N.B., L.W., L.N., and K.D.C. reviewed and approved the manuscript.

We would like to thank Wanjiru Waruiru from the University of California, San Francisco and Jennifer Norman and Karen Cohen from the University of Cape Town, South Africa for their support in facilitating antiretroviral testing of KAIS 2012 specimens. We would also like to thank the KAIS 2012 survey participants for their willingness to participate in the survey and the KAIS Study Group for their contribution to the design of the survey and collection of the data set. Members of the KAIS Study Group are: Willis Akhwale, Sehin Birhanu, John Bore, Angela Broad, Robert Buluma, Thomas Gachuki, Jennifer Galbraith, Anthony Gichangi, Beth Gikonyo, Margaret Gitau, Joshua Gitonga, Mike Grasso, Andrew Imbwaga, Malayah Harper, Muthoni Junghae, William Maina, Nicolas Muraguri, Mutua Kakinyi, Samuel Mwangi Kamiru, Nicholas Owenje Kandege, Lucy Kanyara, Yasuyo Kawamura, Timothy Kellogg, George Kichamu, Andrea Kim, Lucy Kimondo, Davies Kimanga, Elija Kinyanjui, Stephen Kipkerich, Dan Koros, Danson Kimutai Koske, Agneta Mbithi, Veronica Lee, Serenita Lewis, Ernest Makokha, Agneta Mbithi, Joy Mirjahangir, Ibrahim Mohamed, Rex Mpazanje, Nicolas Muraguri, Patrick Mureithi, Lilly Muthoni, James Muttunga, Jane Mwangi, Mary Mwangi, Sophie Mwanyumba, Francis Ndichu, Anne Ng’ang’a, James Ng’ang’a, John Gitahi Ng’ang’a, Lucy Ng’ang’a, Carol Ngare, Bernadette Ng’eno, Inviolata Njeri, David Njogu, Caleb Obada, Bernard Obasi, Macdonald Obudho, Edwin Ochieng, Linus Odawo, James Odek, Jacob Odhiambo, Samuel Ogola, David Ojakaa, James Kwach Ojwang, George Okumu, Patricia Oluoch, Tom Oluoch, Osborn Otieno, Kennedy Ochieng Omondi, Yakubu Owolabi, Boniface O.K’Oyugi, Bharat Parekh, George Rutherford, Sandra Schwarcz, Shahnaaz Sharrif, Victor Ssempijja, Lydia Tabuke, Yuko Takenaka, Mamo Umuro, Brian Eugene Wakhutu, Wanjiru Waruiru, Celia Wandera, John Wanyungu, Anthony Waruru, Paul Waweru, Larry Westerman, and Kelly Winter.

The second Kenya AIDS Indicator Survey was conducted by the National AIDS and STI Control Programme (NASCOP), Kenya National Bureau of Statistics, (KNBS), National Public Health Laboratory Services (NPHLS), National AIDS Control Council (NACC), National Council for Population and Development (NCPD), Kenya Medical Research Institute (KEMRI), U.S. Centers for Disease Control and Prevention (CDC/Kenya, CDC/Atlanta), United States Agency for International Development (USAID/Kenya), University of California, San Francisco (UCSF), the United States Agency for International Development (USAID/Kenya), Joint United Nations Team on HIV/AIDS, Japan International Cooperation Agency (JICA), the Elizabeth Glaser Paediatric AIDS Foundation (EGPAF), Liverpool Voluntary Counselling and Testing (LVCT), the African Medical and Research Foundation (AMREF), the World Bank, and the Global Fund.

This publication was made possible by support from the US President's Emergency Plan for AIDS Relief through cooperative agreements [#PS001805, GH000069, and PS001814] through the US Centers for Disease Control and Prevention, Division of Global HIV and Tuberculosis. This work was also funded in part by support from the Global Fund, World Bank, and the Joint United Nations Team for HIV/AIDS. Antiretroviral drug analysis reported in this publication was supported by the National Institute of Allergy and Infectious Diseases of the National Institutes of Health under Award Number UM1 AI068634, UM1 AI068636 and UM1 AI106701. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention or National Institutes of Health.

Conflicts of interest

There are no conflicts of interest.

A portion of this analysis was presented at the 2015 International AIDS Society Meeting in Vancouver, Canada, July 2015.


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antiretroviral therapy; HIV diagnosis; HIV surveillance; Kenya; self-reported data; sub-Saharan Africa; viral load

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