The scale of the original problem is worth recalling. In 2015, a survey-based analysis across 16 countries in sub-Saharan Africa estimated that nearly half of people living with HIV in those countries did not know their status. Extrapolated across the region, that implied roughly 11 million people standing entirely outside the treatment cascade, unaware they needed antiretroviral therapy and capable of onward transmission at rates far higher than people whose infection was suppressed by medication. Self-testing, the authors contend, was precisely the technology positioned to reach this population, because it removed many of the barriers that keep people away from clinics: stigma, distance, waiting times, fear of being seen entering an HIV service, and the loss of a day’s income.

The epidemiological logic behind self-testing is straightforward. Antiretroviral therapy dramatically reduces infectiousness, and people who know their status can start treatment and achieve viral suppression. Every undiagnosed infection therefore represents both a personal health risk and a transmission risk to others. The UNAIDS 95-95-95 framework, which targets 95 percent of people living with HIV knowing their status, 95 percent of those diagnosed on treatment, and 95 percent of those on treatment virally suppressed, depends on the first 95 more than any other, because no one can be treated if they have not been found. Yet in 2025, an estimated 2.6 million people in sub-Saharan Africa and 4.9 million worldwide still did not know their HIV status.

The authors quantify the shortfall in procurement terms. The need for HIV self-testing in low- and middle-income countries was estimated at 177 million self-tests in 2020, rising to 192 million by 2025. Confirmed procurement over that same period reached only 21 million kits in total, an average of 5.3 million per year. That is roughly 3 percent of the estimated annual need. In other words, for every hundred self-tests that modeling suggested the region required, fewer than three were purchased. The gap between need and supply was not marginal; it was nearly total.

Why did a cheap, simple, WHO-endorsed technology fail to reach the market at scale? The analysis identifies four interlocking structural failures. The first is what the authors call research capture. Instead of population-scale deployment, the field invested heavily in demonstration projects, small studies designed to show that self-testing could work. Such studies accumulated evidence of feasibility and acceptability year after year, but they substituted for, rather than preceded, real distribution. A technology that had already been endorsed by WHO did not need another pilot; it needed procurement, supply chains, and delivery systems. The demonstration-project cycle, the authors argue, consumed a decade that should have been spent scaling.

The second failure is a paradox embedded in early implementation. Many programs introduced self-testing in supervised or provider-assisted forms, in which a health worker remained present while the client performed the test. This arrangement, intended to reassure regulators and funders, effectively reconstituted the very clinic barriers self-testing was designed to bypass. If a person must still travel to a facility, wait for a provider, and test under observation, the private, convenient, stigma-free experience that defines self-testing disappears. Supervised delivery, the authors suggest, blunted the technology’s core advantage during the years when scale was most urgently needed.

The third failure concerns procurement conservatism. Donors and national programs were slow to commit volume, and without predictable demand, manufacturers had little incentive to invest in production capacity or price reduction. Procurement behaves like a market signal: guaranteed volumes attract suppliers, drive competition, and lower unit costs, while hesitant, fragmented purchasing keeps prices high and supply chains thin. By buying cautiously, the global health system suppressed the very market development that would have made self-testing cheap and abundant. The result was a self-reinforcing loop of low demand, high prices, and continued hesitation.

The fourth failure is one of accountability. The authors found no publicly accessible, Africa-level series of HIV self-testing commodity volumes, which means the scale of the missed opportunity cannot even be measured from published data. Without transparent reporting of how many kits are bought and distributed, no one can hold programs, donors, or manufacturers to account, and no one can detect shortfalls in time to correct them. The absence of a simple volume series, the authors argue, is itself a policy failure, because what is not measured cannot be managed, and what cannot be measured cannot be missed publicly.

The consequences of the delay, while not formally modeled in the analysis, are likely to be severe. Each year that millions of people remained undiagnosed translated into untreated infection, avoidable illness and death, and onward transmission. The authors characterize the burden as potentially millions of illnesses, deaths, and transmissions that could have been avoided had self-testing been deployed at the scale the need estimates demanded. They are careful to note that this counterfactual has not been quantified with formal modeling, but the arithmetic of the treatment cascade makes the direction of the effect unambiguous: people outside the cascade are sicker and more infectious than people within it.

The paper closes with four concrete prescriptions. First, mandatory public reporting of HIV self-testing commodity volumes, so that procurement and distribution become visible and auditable. Second, a dedicated Africa-specific distribution target of at least 50 million kits per year, embedded within the UNAIDS 95-95-95 framework, to convert the first 95 from an aspiration into a supply commitment. Third, community-based distribution as the default delivery model for the general population, moving self-tests out of clinics and into the places where undiagnosed people actually live, work, and socialize. Fourth, explicit self-testing volume commitments written into the 2026 to 2030 memoranda of understanding and work plans of the Global Health Sector Strategies on HIV, so that the next decade begins with binding obligations rather than another round of pilots. Whether the global response absorbs these lessons, the authors suggest, will determine whether the remaining millions finally learn their status, or whether the diagnostic gap closes as slowly and as late as it opened.

Subject of Research: HIV self-testing scale-up and the global undiagnosed HIV gap in sub-Saharan Africa

Article Title: HIV self-testing and the global diagnostic gap: Addressing a decade of missed opportunity

Article References: Granich, R., Gupta, S., Munyangaju, I., & Ruffner, M. (2026). HIV self-testing and the global diagnostic gap: Addressing a decade of missed opportunity. PLOS Medicine, 23(9), e1005241. https://doi.org/10.1371/journal.pmed.1005241

Image Credits: AI Generated

DOI: 10.1371/journal.pmed.1005241

Keywords: HIV self-testing, HIV diagnosis, sub-Saharan Africa, UNAIDS 95-95-95, procurement, global health, PLOS Medicine, undiagnosed HIV, community-based distribution, World Health Organization, antiretroviral therapy, diagnostic gap

The research team, led by Geoffrey Mariga Mariita of Great Lakes University of Kisumu together with colleagues from the USAID DUMISHA Afya Project, interviewed 300 adult patients aged 18 years and older who were enrolled in antiretroviral therapy care at Level 4 and Level 5 public health facilities in Busia County. Rather than relying on convenience sampling, the investigators used probability-proportional-to-size sampling combined with systematic random selection, a design that gives larger facilities a proportionally greater chance of contributing participants and reduces the risk that a single clinic’s idiosyncrasies dominate the results. Structured interviews were conducted in Kiswahili and covered five domains: socio-demographic characteristics, cultural factors, psychosocial factors, health-service factors, and socio-economic factors.

A methodological strength of the study lies in its definition of the primary outcome. Adherence status was not self-reported, which is notoriously prone to social desirability bias, but instead derived from Electronic Medical Record pharmacy pick-up records. A patient was classified as non-adherent if a scheduled medication pick-up was missed by more than 28 days within the preceding six months. Viral load results and pill counts were abstracted as secondary corroborating indicators, allowing the researchers to triangulate their classification against clinical and behavioral measures. This objective anchor matters, because much of the adherence literature in sub-Saharan Africa rests on patient recall, which can overestimate adherence substantially.

The headline result is, on its face, encouraging. Of the 300 participants, 272, or 90.7 percent, were classified as adherent, while only 28, or 9.3 percent, fell into the non-adherent category. The cohort was 60 percent female and 40 percent male, a distribution consistent with the demographics of HIV care in much of eastern Africa. A non-adherence rate below ten percent, measured against a strict pharmacy-based definition, suggests that the treatment system in Busia County is reaching most patients effectively, even in a border region characterized by population movement, cross-border trade, and the logistical complications that mobility brings to chronic disease care.

Yet the study’s central analytical contribution lies in identifying which of the many candidate factors actually predicted non-adherence. The researchers first assessed bivariate associations using chi-square tests, then built a multivariable logistic regression model to estimate independent predictors, and finally cross-checked those estimates against a mixed-effects model that accounted for clustering of patients within facilities. This dual-model strategy is important: patients at the same hospital share providers, drug stock levels, and local norms, and ignoring that clustering can produce misleadingly narrow confidence intervals. Only two variables survived both modeling approaches.

The first was sex. Female participants had significantly lower odds of non-adherence than male participants, with a logistic regression odds ratio of 0.48 and a 95 percent confidence interval of 0.23 to 1.00, at a p-value of 0.049. The facility-clustered mixed-effects model confirmed the association, reporting a coefficient of negative 0.091 with a p-value of 0.035. In practical terms, men in this cohort were roughly twice as likely as women to miss a scheduled pharmacy pick-up by more than a month. This pattern echoes a broader and increasingly recognized phenomenon in global HIV care: men across sub-Saharan Africa are less likely to test for HIV, initiate treatment later, and disengage from care more often than women, partly because clinic services and outreach programs have historically been designed around antenatal and maternal health touchpoints that women encounter routinely.

The second independent predictor was educational attainment. Higher education was associated with lower odds of non-adherence, with an odds ratio of 0.56, a 95 percent confidence interval of 0.35 to 0.89, and a p-value of 0.015. The mixed-effects model again corroborated the finding, with a coefficient of 0.064 and a p-value of 0.012. The mechanism is plausibly multifaceted: education correlates with health literacy, the ability to navigate appointment systems, comprehension of dosing instructions, and economic stability, all of which can smooth the practical friction of lifelong daily medication. Whatever the precise pathway, the result implies that adherence support materials and counseling delivered at a single literacy level may leave the least-educated patients behind.

Equally notable is the list of factors that did not independently predict adherence. Marital status, social support, cultural beliefs, religious beliefs, stigma, patients’ ratings of health services, and difficulty balancing antiretroviral drugs with a prescribed diet all failed to reach statistical significance in either model. This null result is a caution against over-reading smaller studies: the authors themselves note that the most plausible explanation is limited statistical power, since only 28 patients were non-adherent, leaving a small subgroup in which even moderately sized effects are hard to detect. In other words, the absence of significance for stigma or social support should not be interpreted as evidence that these factors are irrelevant, but rather that this dataset cannot resolve their contribution. The finding nonetheless challenges the assumption, common in program design, that psychosocial and cultural variables are always the dominant drivers of adherence in rural African settings.

The context of Busia County sharpens the significance of these results. Kenya has made substantial national progress toward the UNAIDS 95-95-95 targets, which call for 95 percent of people living with HIV to know their status, 95 percent of those diagnosed to be on sustained treatment, and 95 percent of those on treatment to achieve viral suppression. National aggregates, however, can conceal pockets of vulnerability, and border counties with high population mobility are precisely the places where local evidence has been thinnest. By grounding adherence measurement in pharmacy records across multiple public facilities, including Busia County Referral Hospital, Khunyangu Sub-County Hospital, Port Victoria Hospital, and Teso North Sub-County Hospital, the study provides the kind of locally actionable data that national dashboards cannot supply.

The authors’ recommendations follow directly from their two robust findings. They call for sex-responsive adherence support, meaning interventions deliberately designed to reach and retain men, who emerge from this cohort as the higher-risk group, and for literacy-appropriate counseling and materials that meet patients across the full educational spectrum. They also urge adequately powered follow-up research, ideally with larger samples or longitudinal designs, to clarify whether the commonly cited barriers that fell out of significance in Busia, such as stigma and social support, exert real effects that this study was underpowered to detect. Ethical oversight was thorough, with approval from the Great Lakes University of Kisumu Scientific and Ethical Review Committee and the National Commission for Science, Technology and Innovation, and written informed consent from all participants. The study received no dedicated funding, and the authors declare no competing interests. As antiretroviral therapy programs worldwide pivot from simply getting patients onto treatment to keeping them virally suppressed for decades, evidence of this granularity, drawn from the borderlands where health systems are tested hardest, will be essential for targeting the last stubborn gaps in the HIV response.

Subject of Research: Determinants of adherence to antiretroviral therapy among HIV patients in Busia County, Kenya

Article Title: Determinants of adherence to antiretroviral therapy among patients in Busia, Kenya

Article References: Mariita, G. M., Ashiono, E., Florence, T. Z., Charles, W., Kimuli, D., & Nyanchoka, V. (2026). Determinants of adherence to antiretroviral therapy among patients in Busia, Kenya. BMC Health Services Research. https://doi.org/10.1186/s12913-026-15739-w

Image Credits: AI Generated

DOI: 10.1186/s12913-026-15739-w

Keywords: antiretroviral therapy, HIV, adherence, Kenya, Busia County, viral suppression, pharmacy refill records, health services research, sex differences, educational attainment, UNAIDS 95-95-95, cross-sectional study