Setting

Zimbabwe is a landlocked, low-income country in Southern Africa located between Botswana, South Africa, Mozambique, and Zambia with an estimated population of 16,3 million and a human development index of 0.593, ranked number 174 globally out of 189 countries in 2022 [14,15]. The country is divided into two urban provinces, eight rural provinces and 62 districts.

The AIDS and TB Programme (ATP) is mandated to coordinate the development of HIV/AIDS health policies and set up national standards and guidelines as part of the national response to HIV in Zimbabwe. Four sub-units under ATP, namely, HIV Prevention, Care and Treatment, Prevention of Mother to Child Transmission (PMTCT) and Monitoring and Evaluation (M & E). These sub-units are delegated to ensure seamless yet specialised programming to ensure adequate response to the pandemic [16].

The HIV Prevention program oversees the activities of HIV Testing Services (HTS) activities with the ATP. Since 2016, the HTS programme has been pursuing targeted testing as an approach to reduce testing volumes, increase efficiency in HIV testing and enhance the identification of people living with HIV, to enrol them on life-long Antiretroviral Therapy (ART). Eligibility for HIV testing is done using a validated Screening algorithm [17]. In this algorithm, high-risk individuals are offered provider-delivered testing whilst those screened out are offered HIVST kits for self-screen. Following a negative test result, the patient is further screened for eligibility for combination prevention which includes PrEP.

Sampling

Multi-stage sampling was done to randomly select 4 out of 10 Zimbabwe provinces using the lottery method. Further, 3 districts per province were randomly selected resulting in 12 districts. All health facilities across the 12 districts were included in the study, resulting in a total of 36 health facilities. The sampling criteria were designed to ensure a balanced representation, incorporating a mix of high and low-volume facilities, as well as urban and rural sites (Table 1).

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Table 1. Study sites, Zimbabwe, 2024.

https://doi.org/10.1371/journal.pone.0302803.t001

Client population

All clients who were tested for HIV and documented in HIV Testing registers, at the 36 sampled facilities between 1 October 2023 to 31 December 2023 and aged 15 years and above, were included in the study.

Inclusion criteria

Individuals who underwent HIV testing within the specified study period.

Participants who provided sufficient demographic and clinical information necessary for analysis.

Exclusion criteria

Individuals with incomplete or missing data on key variables.

Participants whose testing data were not available in the DHIS2 database.

Sample size calculation

The sample size for this study was determined using statistical power analysis to ensure adequate power to detect significant differences between groups. The primary outcome measure was the effectiveness of the status-neutral approach in promoting linkage to care for both HIV-positive and HIV-negative individuals. This approach ensures that all individuals, regardless of their HIV status, are linked to appropriate health services. Using secondary data from existing HIV testing programs, our analysis aimed to evaluate how well the status-neutral approach facilitated continuous engagement in care for everyone tested, thereby improving overall health outcomes within the context of targeted HIV testing

The following assumptions and parameters were used

Effect Size: Based on previous studies, an effect size of 0.3 was considered clinically significant, Significance Level (α): A two-tailed significance level of 0.05 was used.

Power (1-β): To achieve a power of 80%, the sample size was calculated to detect the specified effect size, Proportion of Positive Cases: An estimated proportion of 10% HIV-positive cases was assumed based on historical data.

The sample size was calculated using the formula for comparing two proportions and using the above parameters and formula, the required sample size was calculated to be approximately 385 participants per group. To account for potential dropouts and incomplete data, an additional 10% was added, resulting in a final sample size of 424 participants per group.

However, the actual data set included 23,058 individuals who were tested for HIV within the study period. The larger sample size was due to the inclusion of all data available from the DHIS2 database, providing a more comprehensive analysis. This substantial increase in the sample size enhances the study’s statistical power and the generalizability of the findings, allowing for a more robust evaluation of the status-neutral approach’s effectiveness.

Data variables, sources of data and data collection

Data were extracted from District Health Information System version 2 (DHIS2) as an Excel report. The data was accessed on the 26^th of February 2024 for research purposes. The data was exported to EpiData Analysis version 2.2.2.186 (EpiData Association, Odense, Denmark) and Stata v14 (Stata Corporation College Station, Texas, USA) for further cleaning and analysis. The following variables were collected: HTS number, name of the facility, age, sex, screening for an HIV test, reason for an HIV test, HIV test result, linkage to post-test services (yes/no), and specific services linked to. To ensure privacy, patient names were not included in the final dataset used for analysis. Therefore, no personal identifiers were collected in the analysed data.

Variables Collected Screening for an HIV test with This variable refers to the initial assessment process conducted to determine an individual’s risk of HIV infection and the need for an HIV test. It includes questions about sexual behaviour, history of sexually transmitted infections, and other risk factors that might indicate the need for HIV testing. Linkage to post-test services: This variable indicates whether individuals who tested positive for HIV were connected to appropriate follow-up services. This includes referrals to healthcare facilities, counselling services, and support groups to ensure continuous engagement in care and treatment adherence.

Specific services linked to: These variables detail the specific types of post-test services that individuals were connected to. These services may include antiretroviral therapy (ART), prevention of mother-to-child transmission (PMTCT) programs, pre-exposure prophylaxis (PrEP) for those at high risk, and other medical and social support services aimed at improving health outcomes for people living with HIV.

Variables included in the adjusted models.

The adjusted models included the following variables based on their relevance and significance in the unadjusted analyses: Age, Sex, and High-risk behaviour: Individuals who reported behaviours such as multiple sexual partners, inconsistent condom use, or history of of of of of sexually transmitted infections, Screening for HIV test: The initial assessment process to determine the individual’s risk of HIV infection, Linkage to care and prevention: Whether individuals were successfully linked to post-test services, such as antiretroviral therapy (ART) for HIV-positive individuals or pre-exposure prophylaxis (PrEP) and other preventive measures for HIV-negative individuals, Testing district: Geographic location where the testing took place.

While the primary analysis of risk profiles used log-binomial regression, the variables in Table 3 were analysed using Poisson regression with robust error variance to approximate prevalence odds ratios. This approach ensures the reliability and stability of the estimates, aligning with the study design.

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Table 2. Clinical and demographic profile of patients, Zimbabwe, 2024. (N = 23,058).

https://doi.org/10.1371/journal.pone.0302803.t002

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Table 3. Factors associated with HIV Positivity among patients who tested for HIV, Zimbabwe, 2024. (N = 23,058).

https://doi.org/10.1371/journal.pone.0302803.t003

Analysis and statistics

Socio-demographic characteristics of participants were summarized using percentage for categorical data and mean (standard deviation) or median (interquartile range) for continuous data depending on whether they are normally distributed or not. The number and proportion with a 95% confidence interval were used to summarize all clients tested for HIV during the study period, the outcomes of the test and linkage to post-test services as documented in the respective registers.

To assess the risk profiles and estimate relative risks, we initially employed a log-binomial regression model. This model is appropriate for estimating relative risks directly when analysing binary outcomes. However, due to potential convergence issues often encountered with the log-binomial model, especially in cases of sparse data or rare outcomes, we also utilized Poisson regression with robust error variance. This method serves as an alternative approach, providing similar estimates to the log-binomial model and ensuring better convergence and stability in the results. Those variables with a p-value < 0.25 in the unadjusted analysis were included in adjusted models. The unadjusted and adjusted prevalence odds ratios at 5% significance levels (95%CI) were expressed as a measure of association.

The Poisson regression with robust error variance is widely used to approximate relative risks when the log-binomial model fails to converge. Consequently, while the primary analysis of risk profiles used the log-binomial regression, the variables in Table 3 were analysed using Poisson regression to ensure the reliability and stability of the estimates.

Ethics approval

Our study was exempted from ethical clearance because it utilized routinely collected data which was extracted from the electronic database (DHIS2). The data we received were de-identified, with all patient-identifying information removed before analysis. This ensured the privacy and confidentiality of the individuals’ information. As a result, no primary data were collected, and the use of de-identified data qualified the study for ethical exemption. Despite this exemption, we recognized the sensitivity of HIV-related data and implemented stringent privacy safeguards to protect participants.

Demographic characteristics

Out of the 23,058 patients tested for HIV, 55% (n = 12,698) were female. The majority of patients, 54.7% (n = 12,615), were within the age range of 25–49 years. Eligibility screening before testing was documented for 66.3% (n = 15,289) of the patients, while 33.7% were not screened. Among those tested, 63.5% (n = 14,650) underwent retesting, and 1,727 individuals tested positive, resulting in a positivity ratio of 7.5%. Across the districts, Gweru reported the highest number of tests conducted at 15.5% (n = 3,575), whereas Mangwe reported the lowest at 4.5% (n = 1,040) (Table 2).

Screening, testing outcomes and post-test linkages

Out of the 23,058 patients who attended the sampled healthcare facilities, 66.3% (N = 15,289) were screened for eligibility before testing. Among these screened patients, the positivity ratio was 8.5% (N = 1,296), with nearly all (N = 1,294, 99.8%) being enrolled into care. The positivity ratio among screened patients accounted for 75.1% of the total positivity observed in this study (1,296/1,727). In contrast, among the 7,769 patients (33.7%) who were not screened before testing, 431 (5.5%) tested positive, representing 24.9% of the overall positivity (431/1,727). Of the 7,338 patients (94.5%) who tested negative, 1,871 (25.5%) were linked to HIV prevention services (Fig 2).

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Fig 2. HIV Testing and Post-test Linkages, Zimbabwe, 2024.

https://doi.org/10.1371/journal.pone.0302803.g002

HIV positivity and linkage

The overall positivity ratio observed in this study was 7.5% (1,727/23,058). Individuals in the 45–49-year age group were 3.6 times more likely to test positive for HIV (adjusted 95% CI: 2.67, 4.90). Males had a 3.09 times higher likelihood of testing HIV positive in the adjusted analysis (adjusted 95% CI: 2.74, 3.49), with a positivity ratio of 8% (n = 912). Initial tests were 65% more likely to yield a positive HIV result (adjusted 95% CI: 1.43, 1.91). Additionally, patients who were screened before testing were 3.89 times more likely to be linked to at least one HIV prevention service (adjusted 95% CI: 3.05, 4.97) (Table 3).

Interpretation of key findings

This study offers significant insights into the effectiveness of status-neutral testing in targeted HIV testing in Zimbabwe.

Firstly, patients who underwent risk screening before HIV testing exhibited a higher positivity ratio of 8.5% compared to those who were not screened, who had a positivity ratio of 5.5%. The risk screening was conducted using a standardized algorithm, which is an integral part of the country’s standard service delivery for determining eligibility for an HIV test. This finding underscores the importance of pre-test risk assessment in identifying individuals at higher risk of HIV infection and enhancing the efficiency of targeted testing strategies [17].

Furthermore, screened patients accounted for 75% of the overall positivity observed in the study, highlighting the effectiveness of targeted testing in Zimbabwe and underscoring the urgent need for accessible and effective HIV screening options, particularly for high-risk populations. This finding suggests that a significant proportion of individuals screened through traditional methods are indeed HIV-positive, indicating that many high-risk individuals are still not being reached effectively by current screening efforts. This is particularly relevant in the context of a generalized epidemic and declining funding for HIV programs, which has led to sporadic stockouts of HIV testing commodities. The results align with previous studies that emphasize the importance of using algorithms to assess risk and prioritize clients for HIV testing while offering self-screening options to those at lower risk [17,23–25]. To remain on course of achieving and sustaining the targets for case identification, it is therefore imperative to effectively implement an algorithm that aids health workers in prioritizing patients who are most likely to test HIV positive

Secondly, 98.7% (n = 13,811) of the clients who tested HIV negative following risk screening were linked to HIV prevention services. In multivariate analysis, the probability of linkage among HIV-negative screened clients was 3.89 (adjusted 95% CI: 3.05, 4.97). This finding indicates that screening clients for testing facilitates focused HIV prevention linkage among those who test negative. Our findings are consistent with the status-neutral approach, which emphasizes equal importance on linkage to both prevention and treatment services. [6,7,26,27]. However, our approach differs from other studies that place risk assessment after testing rather than before. Conducting risk screening post-test increases the number of clients in the post-test stage with an unknown risk profile, necessitating additional risk screening. This increased workload, particularly in high-volume and high-frequency testing contexts, may compromise the thoroughness of the screening process [28,29].

This study highlights the utility of conducting risk screening before HIV testing within the status-neutral framework. This approach effectively complements the targeted testing strategy by reducing the frequency of testing that does not align with the individual’s risk profile. By embedding the status-neutral concept into targeted testing, we ensure that testing efforts are focused on those at higher risk, thereby optimizing resource use and improving the efficiency of HIV testing programs.

Implementing the status-neutral concept without integrating it into targeted testing deviates from the standardized retesting algorithm, which recommends a maximum testing frequency of once every three months for individuals at ongoing risk of HIV infection. This integration is crucial for maintaining adherence to established guidelines while enhancing the effectiveness of HIV prevention and care strategies [7,30].

Third, 63.5% of the clients who tested for HIV were retests (n = 14,650), and yet first tests were 65% more likely to test HIV positive (a95%CI: 1.43, 1.91), adjusted for age and sex. Most tests being retested may be suggestive of “over-testing” or high-risk perception, particularly given the low positivity ratios obtained [4]. Clients who test HIV negative at contact are retested annually if they fall into the general population category and retested 3 monthly if they are at ongoing risk for HIV transmissions, such as sero-different couples and those on PrEP, according to the national retesting algorithm [31].

Finally, our adjusted analysis revealed that men were 3.09 times more likely to test HIV positive (adjusted 95% CI: 2.74, 3.49), despite representing only 44.9% (10,360/23,058) of the tested population. This finding suggests that a smaller proportion of men undergo HIV testing, yet a higher percentage of those who do test positive. This observation is consistent with previous studies and widely available public information [32,33]. Additionally, men are less likely to adhere to HIV treatment and are more prone to unfavourable treatment outcomes, which can be attributed to various factors, including prevailing gender norms [31,34].

Implications for policy and practice

Targeted testing remains the cornerstone of HIV Testing Services (HTS) programming aimed at achieving epidemic control. When complemented with a status-neutral approach to HIV testing, it creates a dual strategy that prioritizes both testing and linkage to prevention and treatment services. This combined approach ensures that individuals who test positive are promptly linked to care, while those who test negative are connected to prevention services, thereby reducing the risk of new infections. Vigilant implementation of this concept is crucial to expedite epidemic control and enhance the overall effectiveness of HIV prevention and treatment efforts.

To effectively implement the status-neutral targeted HIV testing approach in Zimbabwe and beyond, several actionable steps and policy changes are necessary. Key stakeholders must also be identified to drive this initiative forward. Below are the recommended steps, policy adjustments, and potential barriers:

Actionable Steps:

1. i.. Policy Development and Alignment: Develop national policies that support the integration of the status-neutral approach with targeted HIV testing. These policies should align with existing health strategies and frameworks to ensure seamless implementation.
2. ii.. Stakeholder Engagement: Engage key stakeholders, including government health departments, non-governmental organizations (NGOs), community leaders, healthcare providers, and international partners such as the World Health Organization (WHO) and UNAIDS. Collaborative efforts are essential for resource mobilization, advocacy, and sustained support.
3. iii.. Capacity Building: Invest in the training and capacity building of healthcare providers to enhance their understanding and implementation of the integrated approach. Training programs should focus on the benefits, procedures, and best practices for combining status-neutral and targeted testing methods.
4. iv.. Resource Allocation: Ensure adequate resources are available for the implementation of the dual strategy. This includes funding for testing kits, infrastructure, personnel, and support services necessary for continuous engagement in care.
5. v.. Community Outreach and Education: Launch community outreach programs to raise awareness about the integrated approach. Educational campaigns should aim to reduce stigma, encourage testing, and promote the benefits of continuous care and targeted interventions.

Policy Adjustments:

1. i.. Regulatory Support: Implement regulatory frameworks that facilitate the integration of the status-neutral approach into existing HIV testing guidelines. This may involve revising existing protocols and ensuring compliance with new regulations.
2. ii.. Data Management and Monitoring: Establish robust data management systems to monitor the implementation and effectiveness of the integrated approach. Regular monitoring and evaluation will help identify gaps, measure progress, and inform policy adjustments.

Potential Barriers:

1. i.. Limited Resources: Insufficient funding and resources can impede the effective rollout of the integrated approach. Securing sustainable funding and resource allocation is critical.
2. ii.. Healthcare Infrastructure: Inadequate healthcare infrastructure, particularly in rural areas, can challenge the delivery of services. Investments in infrastructure improvements are necessary.
3. iii.. Data Privacy Concerns: Ensuring the confidentiality and security of patient data is paramount. Robust data protection measures must be implemented to build trust and encourage participation.

By addressing these steps, policies, stakeholders, and barriers, the integrated status-neutral targeted HIV testing approach can be effectively implemented, leading to improved health outcomes and enhanced HIV prevention and treatment efforts in Zimbabwe and beyond.