Approaches and impacts of digital health in HIV self-testing uptake & use among populations from low- and middle-income countries: a descriptive systematic review and meta-analysis

Introduction

Background

There are approximately 38 million people living with human immunodeficiency virus (PLHIV), with the majority of disease burden concentrated in low- and middle-income countries (LMICs) (1,2). Moreover, the Joint United Nations Programme on HIV/AIDS (UNAIDS) 95-95-95 HIV targets are at risk of not being achieved, and an estimated 20% of PLHIV globally are unaware of their infection status (3,4). This is despite efforts from LMIC stakeholders working to ensure HIV self-tests (HIVSTs) are integrated into national programming guidelines (5). Many factors contribute to this disparity and range from access to antiviral medications, the quality of the healthcare system infrastructures, the lack of robust disease monitoring systems, and socioeconomic implications surrounding HIV (6). These create barriers to accessible care for individuals with HIV in LMICs. HIV self-testing has been shown to be an impactful strategy on reducing the transmission of HIV, reducing stigmatization, and allows for individuals to test themselves and seek care with less reliance on healthcare systems and associated aspects (7-10). Many reviews have demonstrated that HIVSTs are widely acceptable in high income countries with abundant resources, yet there are still concerns about cost per kit, linkage to care, and the complexity of kit instructions (8,11-14). Given the resource differences in LMICs where HIVST programming could substantially be impacted by dHealth initiatives, there is controversy as to if delivery augmentation is an impactful public health approach.

Rationale and knowledge gap

It is known the use of digital technologies (i.e., cell phones, web-based interfaces, application trackers, etc.) in self-testing programming functions to increase accessibility to resources aimed at reducing HIV transmission and increasing the knowledge surrounding infection with HIV (15-17). Digital health (dHealth) is defined as digital technologies in the form of social media platforms, mobile phones, personal computers, devices that access the internet, or even artificial intelligence, that interface with individual or community populations to monitor and address a health need. Common dHealth modalities are telehealth, mobile health (mHealth), health information technology (IT), wearable devices, and even personalized medicine. Telehealth or telemedicine has been shown to be particularly useful for resource limited areas or for patients living in remote environments, like mHealth which relies on readily access to smartphones, computers, or tablets to provide health education, disease management, and opportunities for convenient patient engagement with health systems (18,19). dHealth modalities such as these are especially relevant in a rapidly advancing digital age, where personal cell phone use has surpassed five billion subscriptions worldwide (20). Nevertheless, the ability of dHealth programming to augment HIVST in LMICs is poorly understood and current strategies surrounding the use of dHealth with HIVST in these settings are very heterogeneous and best practices guidelines are poorly described.

Objective

The aim of this systematic review was to assess the dHealth modalities used and quantify the impacts of dHealth interventions in HIVST programming among populations in LMICs on the outcomes of testing uptake, testing use, and identification of PLHIV. We present this article in accordance with the PRISMA reporting checklist (available at https://jphe.amegroups.com/article/view/10.21037/jphe-24-56/rc).

Methods

Study design

The aim of this systematic review was to assess and quantify the impacts of dHealth interventions in HIVST programming among populations in LMICs on the outcomes of testing uptake, testing use, and identification of PLHIV. The eligibility criteria for the research question were defined a priori using the population, intervention, control, outcomes (PICO) framework (Table S1). Studies from January 1, 1990 to January 4, 2024 in English evaluating the impact of dHealth on HIV self-testing programming in LMICs were included. dHealth interventions included, but were not limited to mentions of mHealth, short messaging services (SMS), telehealth, the internet, and social media platforms. Randomized controlled trials (RCTs), observational studies, cross-sectional studies, cohort studies, and qualitative studies were included. Case reports, case series, editorial/opinion pieces, and review articles were excluded. The dates were restricted based on the earliest availability of HIV self-testing reported in the literature (21). LMICs were defined using the World Bank country classification from 2023–2024 (22). Outcomes of interest included uptake, use/completion of HIVST, and confirmation of disease. Outcomes related to disease prevention, knowledge or education-based outcomes, and health system outcomes were excluded.

Search strategy

A search strategy was conducted using Cochrane Collaboration Guidelines and iteratively refined with two distinct searches conducted: one on February 1, 2022, and the second on January 4, 2024 (23). Search terms were developed via literature review and guidance of a medical librarian. The syntax of the search terms was formatted using the polyglot search feature available through the Systematic Review Accelerator (24). Six electronic databases were systematically searched: PubMed, OVID: Global Health, Embase, CINAHL, Web of Science, and Cochrane Library. Free text terms and standardized MeSH headings/subheadings in the context of Boolean operators and appropriate search term truncation were utilized to optimize sensitivity for relevant literature while minimizing excess search results (Table S2). The search results were imported into Endnote™ and duplicate studies were removed. Eligible studies included all RCTs and observational studies. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) study selection flow diagram is shown in Figure 1. The protocol for this systematic review was prospectively registered on PROSPERO (registration No. CRD42022308472).

Figure 1 PRISMA study selection flow diagram (25). PRISMA, Preferred Reporting Items for Systematic Reviews and Meta-Analyses.

Study selection

The titles and abstracts from the search were independently screened by two independent investigators using Abstrackr™ (26). Abstrackr™ is a software platform that uses machine learning to predict the likelihood of relevance of each abstract based on prior screening decisions during the project, making the screening process more efficient as the most likely relevant abstracts are presented first. The full-text articles of abstracts deemed to be potentially eligible were subsequently screened for inclusion using the same pre-specified same criteria. A third reviewer adjudicated discrepancies between reviewers in the abstract and full-text screening phases. For eligible studies that met all inclusion criteria, primary authors were contacted to request any additional published or unpublished data relevant to the review.

Data processing

A data extraction form was created on Covidence™, a web-based systematic review software platform, to allow for standardization of the data extracted from the full-text articles (27). Extracted data items included first author name, study setting (e.g., country, world bank development index, funding source, sample size, publication year), participant characteristics (e.g., age, sex, race, key populations), study design (e.g., eligibility criteria, method of recruitment, loss to follow-up, risk of bias, study setting), intervention descriptions (frequency of interaction, dHealth modality), and outcomes. Data extraction was done independently by two separate reviewers and then cross-checked. Key populations were defined as people who were incarcerated, people who inject drugs (PWIDs), men who have sex with men (MSM), sex workers, or transgender persons. dHealth interventions were defined as either Telehealth/Telemedicine/Telemonitoring (videos or calls with a healthcare provider), online peer support groups, mass SMS to undefined groups, SMS to targeted groups, personal self-monitoring wearables or applications, social media-based applications (Whatsapp^TM, WeChat^TM, Facebook^TM), website-based, HIVST specific applications, digital vending machines, or any combination of dHealth interventions. The primary outcomes of interest included HIVST uptake and use, number of participants completing confirmatory HIV testing, and the prevalence of identifying PLHIV. HIVST was defined as the proportion of participants who accepted an HIV self-test amongst those selected to receive one. HIVST use was defined as the proportion of participants who reported use of an HIV self-test at least once amongst those who were selected to receive one. Identification of the prevalence of PLHIV was defined as the proportion of participants who were linked to confirmatory testing and reported reactive HIV results.

Data synthesis & risk of bias assessment

The Cochrane Risk of Bias Tool 2.0 was utilized to assess the strength of evidence and assess the risk of bias, which covers sequence generation, allocation concealment, binding, incomplete outcome data, and selective outcome reporting (28). For cohort studies, the Newcastle Ottawa Scale was used (29). Two researchers independently performed the assessment of quality and risk of bias in the individual studies and verified all assessments. Based on the initial literature review and perceived quality of evidence, a qualitative narrative synthesis of the data was performed. Data tables included study design features, study participant characteristics, descriptions of interventions, outcome results, risk of bias/methodological quality, outcomes, and results.

Statistical analysis

Quantitative analysis was performed using STATA v17.0 (30). Cohen’s kappa (κ) was assessed for inter-observer agreement amongst included reports (31). The characteristics of the study population amongst the included studies were described with standard summary statistics. HIVST uptake, use, and disease identification were collated and analyzed across included studies. For RCTs, analyses were performed consistent with the data provided from the source reports per-protocol. Proportional testing uptake and differences in testing uptake based on HIVST with dHealth programming were calculated for each report with corresponding 95% confidence intervals (CIs). Meta-analyzed pooled results for testing uptake, HIVST use, and identification of PLHIV were derived using random effects models with associated 95% CIs. For the efficacy outcome of testing uptake, risk ratios (RRs) were calculated utilizing data from RCTs. Forest plots were derived from the meta-analytical models with statistical heterogeneity evaluated using I² statistics. A sensitivity analysis with removal of reports with unique study designs and patient populations (as determined with data extraction, heterogeneity, and forest plot results) was done to determine if pooled estimates changed. As no included reports provided data on linkage to HIV care services no analyses pertaining to that efficacy outcome could be completed.

Results

Characteristics of included reports

The search strategy yielded 2,479 reports, with 771 duplicates removed. The remaining 1,708 titles and abstracts were screened, with 132 studies meeting inclusion for full text review. Of those, five studies met the inclusion criteria for final analysis (Figure 1). Three studies were RCTs (32-34), one study was a cohort study (35), and one was a cross-sectional study (36). Inter-observer agreement for report selection was very good (κ=0.86 95% CI: 0.73–0.95). Three studies were conducted in Africa (33,35,36), and two in China (32,34). The studies were representative of three upper-middle income countries (32,34,36), a lower-middle income country (35), and a low-income country (33). The cumulative sample across the three reports was 2,581 participants.

Description of dHealth modalities and recipient populations

Two studies exclusively focused on text messaging as their dHealth programming (33,35). One study designed a specific HIVST application (36). The remaining two studies had a combination of dHealth programming initiatives consisting of text messaging and social media-based (32) and social media-based and virtual online peer support groups (34). Specifically, these initiatives were study recruitment applications like Grindr^TM, and interventions such as WeChat^TM. Some studies used WeChat^TM for peer moderated discussion threads, whereas others used it as an application to submit pictures of HIVST results or provide educational material and videos. Participants in three of the studies were recruited solely in person (33,35,36), compared to a joint recruitment strategy of online ads and in-person (32), and in-person and app-based recruitment (34). All of the studies (32-34,36) occurred in the community, with the exception of one study occurring in an outpatient non-HIV clinic (35), and no data from any emergency care venues. Four studies (32,34-36) recruited participants age ≥18 years old as a component of the inclusion criteria, one study that focused specifically on the population of adolescent refugees aged 16–24 years old (33). Aside from the population of adolescent refugees, two other studies focused specifically on MSM (32,34). Specifically, these were generally younger (<30 years old) Chinese MSM from Hefei and Shandong Province, China where HIV testing rates among Chinese MSM (despite national programming efforts) remain low (Table 1).

HIVST uptake

Five studies comprising 2,581 participants provided data on HIVST uptake (i.e., whether the group of interest received/picked up the self-testing kit or completed initial instructions of use). The prevalence of HIVST uptake in three studies was universal amongst participants with 100% uptake and acceptance of HIVST across exposed and unexposed groups (32,35,36). A single RCT had nearly universal uptake, with only one participant in the control arm not completing HIVST uptake (33). Additionally, one RCT (34) had the lowest uptake of HIVST across both control (16.4%) and intervention arms (49.5%) (Figures 2,3). The pooled prevalence of HIVST uptake across all studies was 83.5% (95% CI: 81.4–85.6%) for the exposed groups and 66.81% (95% CI: 64.2–69.3%) for the unexposed groups.

Figure 2 Outcomes of interest amongst participants exposed to control. HIV, human immunodeficiency virus.

Figure 3 Outcomes of interest amongst participants exposed to dHealth intervention. HIV, human immunodeficiency virus; dHealth, digital health.

HIVST use

HIVST use was measured in five studies comprising 2,581 participants. This was defined as completion and submission of HIVST data in the appropriate fashion as outlined by the methodology of the study organizers. Among the participants that had up taken HIVST, the pooled prevalence of HIVST use was 48.8% for the exposed participants (95% CI: 45.8–51.9%) and 48.5% for the participants not exposed to a dHealth intervention (95% CI: 45.2–51.9%) (Figures 2,3). Pooled analysis shows no increase in HIVST amongst participants who received dHealth programming compared to HIVST alone [odds ratio (OR) =0.96, 95% CI: 0.31–2.99]. Significant heterogeneity was observed in these results (I²=96.6%) (Figure 4). A sensitivity analysis with removal of Logie et al. (33) as a potential outlier was performed due to the study design and unique patient population studied. This revealed a 1.5-fold increase in HIVST amongst participants randomized to receive dHealth programming as compared to HIVST alone (OR =1.51, 95% CI: 0.80–2.83), though significant heterogeneity was still observed among these results (I²=85.9%) (31) (Figure S1).

Figure 4 Forest plot of HIVST use based on dHealth exposure. CI, confidence interval; HIVST, human immunodeficiency virus self-test; dHealth, digital health.

Identification of PLHIV

Four studies comprising 1,646 participants provided data on the number of participants completing confirmatory testing, with one RCT not reporting outcome data (34). Five studies comprising 2,581 participants provided data on the number of participants identifying PLHIV. In the cohort study by Drake et al., of the participants that used an HIVST, 89.2% of participants in the exposed group, and 87.4% of participants in the unexposed group completed confirmatory testing (35). One participant in the exposed group, and two participants in the unexposed group reported positive HIV results (35). In Fischer et al., amongst those that used an HIVST, 100% of participants in the exposed group, and 0% of participants in the unexposed groups completed confirmatory testing (36). This resulted in identification of PLHIV in fourteen individuals (n=14), all within the exposed group, yielding a total disease prevalence in this group of 8.3%. For Zhu et al., 82% of participants who received an intervention during HIVST use completed confirmatory testing, compared to only 18% in the control arm (32). Nevertheless, no participants were identified as PLHIV in the intervention arm, and 2 participants were identified as PLHIV in the control arm for a total prevalence of disease detection in this group of 4%. In the RCT by Logie et al., only one participant in the intervention group out of the 53 that used an HIVST completed confirmatory testing (1.89%) (33). Zero participants completed confirmatory testing from the 126 that used HIVST in the control arm. In the one participant who completed confirmatory testing, HIV was detected leading to a skewed prevalence of disease of 100%. Finally, in the RCT by Lin et al., no data on the number of participants completing confirmatory testing was reported, however, it was noted that three participants from the control arm were identified as PLHIV (Figures 2,3) (34). Across these studies, the pooled prevalence of confirmatory testing was 82.5% in the intervention arm (95% CI: 78.7–86.2%) and 25.1% in the control arm (95% CI: 20.8–30.0%). The pooled prevalence of disease identification was conducted across four studies due to lack of missing data from Lin et al. and was 5.3% (95% CI: 3.1–8.5%) in the intervention arm and 4.4% in the control arm (95% CI: 1.2–10.7%) (34).

For the RCTs included in the review, the quality of evidence using the Cochrane Risk of Bias Tool was found to range from low (32) to moderate (33,34). For the observational studies, the quality of evidence using the Newcastle Ottawa Scale was low (35) and moderate (36) (Table 2).

Discussion

This systematic review and meta-analysis found that dHealth programming in LMICs did not significantly increase HIVST programming compared to standard of care, though the data across the five studies was highly heterogeneous. Even though implementation of dHealth strategies to increase HIVST falls in compliance with World Health Organization (WHO) recommendations to increase HIV testing access, more investigation is warranted as to what types of dHealth programming is effective across differing environments (37).

HIVST is perceived by users as convenient and leads to increased patient autonomy with discrete use as compared to conventional provider-based facility testing (9,10). Additional drivers of HIVST acceptability and uptake from qualitative research have shown benefit in HIVST to reduce stigma around testing and reduce opportunity costs related to interaction with facility-based health centers (9,13,38); many HIVST users also prefer the lack of blood draw required by oral fluid-based HIVST kits (39). The first documented use of a dHealth intervention to improve uptake, access, and linkage to treatment of HIV was conducted in 2014 among MSM in Los Angeles using the app, Grindr^TM, to promote a website distributing free HIVST kits (40). The potential success of this investigation has served as a catalyst to investigating other implications of various dHealth modalities in a variety of global settings, particularly in South Africa using primarily smartphone-based applications (14,36,41-43). A systematic review of 12 studies assessing HIVST and dHealth programming conducted in high-income countries (HIC) through America, Europe, and Asia showed that social media or apps had higher success in linkage to patient care at a rate of 80% to 100% compared to the 53–100% success of web-based platforms (42). Although the data from HIC has implications for dHealth interventions for HIVST in LMICs the current results illustrate that there is a need for further study to appropriately inform how to design programming which will be both effective and acceptable to populations where the largest burdens of HIV exist.

The uptake of HIVST across the five studies in this review was high, with pooled prevalence across both intervention and control groups ranging from 67–84%, except for the uptake studied in Lin et al. (34). Their results highlighted the suboptimal uptake of HIV testing in MSM populations in China, despite results from prior trials demonstrating that crowdsourced interventions, such as the one posited in their study, can increase individuals receiving HIV testing (44). The dHealth programming in Lin et al., was multilevel, comprised of SMS messages of HIV educational materials delivered through WeChat^TM to each individual participant, in conjunction with community level interventions of peer-led education and moderated discussions through a platform in WeChat known as WeChat moments (34). Though the uptake of HIVST was lower than the other studies in this review, there was an increase in HIVST uptake in the intervention arm of Lin et al. compared to standard HIVST alone, which despite scarce evidence of the effects of multilevel interventions for HIV testing among MSM, multilevel interventions are known to reduce sexual risk behaviors and increase access to antiretroviral treatment (ART) in other environments (34,45,46). A primary limitation of Lin et al. was the reporting bias amongst participants reporting HIVST uptake data which may partially explain the low HIVST uptake reported in the study (34).

HIVST use was considerably higher in the dHealth intervention arms across the included studies with Logie et al., serving as a notable exception to this (33). There was significant acceptability of HIVST acceptability amongst the adolescent refugee youth participants from Kampala, Uganda which aligned with previous studies of non-refugee Ugandians (47-49). The significant uptake and use of HIVST as reported to standard of care in this study was also in alignment with previous studies discussing the utility of such HIV testing methods among marginalized and vulnerable populations (11,15,50). There were minimal differences noted in Logie et al. between the HIVST and the HIVST+ (dHealth SMS intervention arm) (33). The poor use of HIVST kits in the HIVST+ arm may be in part to high loss to follow up rates reported in the study in part to the coronavirus disease 2019 (COVID-19) pandemic, and in part by the difficulties in studying a generally mobile population given the many diverse cultural backgrounds comprising the refugee population (51,52). The prevalence of confirmatory testing and identification of PLHIV were highly heterogenous and most studies included in this review failed to report such outcomes. The cross-section study design of Fischer et al. only describes users amongst the exposed group using the Ithaka mobile device and hence there was no participants reporting confirmatory testing outcomes in the unexposed group (36). Overall, these data do suggest that dHealth adjuncts have the potential to increase HIVST acceptability however the generalizability of the dHealth modalities is not clear and additional data are needed to identify the proper modalities for differing LMIC settings and populations of interest.

Limitations

This systematic review and meta-analysis performed a holistic and comprehensive assessment of the existing literature; however, limitations do exist. English language was an inclusion criterion, which could have resulted in selection bias for the studies that were screened for review. The risk of bias tools applied to the studies revealed that two of the five reports had low quality of evidence with the remaining reports having moderate quality evidence. Common themes across the studies included high risk for selection bias, reporting bias, lack of participant and personnel blinding, or inadequate description of blinding protocol, and attrition bias due to high rates of lost to follow up. Study environments are challenging in LMICs, especially during infectious disease outbreaks in resource-constrained settings. This is particularly exacerbated in the context of the COVID-19 pandemic that placed tremendous strain on LMICs and impacted the study design of at least Logie et al. (33). Additional limitations that exist across the included studies are a potential lack of representativeness of study samples as the reports came from key populations in five very distinct study settings, thus explaining the high observed heterogeneity. There are also general concerns about privacy and replicability of real world socioeconomical situations when it comes to access and use of digital technologies to augment HIVST. In the current review the geographic and sampling limitations, the high variability in quality and frequency of dHealth interventions, and the methodological heterogeneity of the source data, makes further investigation on the role of dHealth on HIVST in LMICs necessary (8).

Conclusions

This systematic review and meta-analysis demonstrates that dHealth interventions studied in LMICs did not significantly increase HIVST use. Given the limited number of reports identified and the heterogeneity in study designs and populations additional research is needed. The controversy as to if delivery augmentation of dHealth initiatives to increase HIVST uptake in LMICs an impactful public health approach remains unclear. The individualized and personal nature of HIVST makes implementation of a controlled, population wide intervention difficult to implement and study. Future investigations should aim to study LMIC populations in representative geographic areas with high HIV prevalence to provide a more robust methodology of dHealth programming. Future dHealth initiatives should be created with specific stakeholders from the LMIC populations to evaluate additional intersections of dHealth programming with HIVST in resource limited settings in an ever-increasing digital world.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the PRISMA reporting checklist. Available at https://jphe.amegroups.com/article/view/10.21037/jphe-24-56/rc

Peer Review File: Available at https://jphe.amegroups.com/article/view/10.21037/jphe-24-56/prf

Funding: None.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jphe.amegroups.com/article/view/10.21037/jphe-24-56/coif). A.R.A. serves as an unpaid editorial board member of Journal of Public Health and Emergency from August 2022 to July 2026. A.R.A. was supported by the National Institute of Allergy and Infectious Diseases (No. K23AI145411). J.S.S. was supported in part by the National Institute of Allergy and Infectious Diseases (No. R25AI140490). S.C.G. was supported by the NIH Fogarty Center (No. R33TW012211). S.C.G. also reported serving as the president of Global EM Academy, a professional society of emergency medicine. The other authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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