Uptake and predictors of viral load testing and viral suppression among people receiving antiretroviral therapy in mainland Tanzania

This retrospective analysis of 70,000 HIV patients in mainland Tanzania (2017–2021) reveals that while viral suppression rates among tested individuals are high, significant gaps in testing uptake and treatment outcomes persist among young adults, males, adolescents, and populations in districts with high mobility, highlighting the need for targeted strategies to address geographic and demographic disparities.

The Gist

Imagine the fight against HIV as a massive, nationwide relay race. The runners are the people taking medication (antiretroviral therapy), and the baton they are passing is their health. The goal is to keep the virus so low in their blood that it's practically invisible—a state called "viral suppression."

But how do we know if the runners are doing well? We need to check their "pulse." In the medical world, this pulse check is called a Viral Load Test.

This paper is like a giant report card for the HIV relay race in Tanzania between 2017 and 2021. The researchers looked at the records of 70,000 runners to see who was getting their pulse checked and who was actually winning the race.

Here is the story of what they found, broken down simply:

1. The Big Picture: Good News, But Some Runners Are Missing

Overall, the team is doing pretty well.

• The Pulse Check: About 7 out of 10 runners (71%) got their viral load test at least once. That's a solid start, but it means 3 out of 10 never got checked. If you don't check the pulse, you don't know if the runner is struggling or if the medicine has stopped working.
• The Win Rate: Among the people who did get tested, 89% were winning the race (their virus was suppressed). This is excellent news! It means the medicine works very well when people stick to it.

2. Who Is Struggling to Get Checked? (The "Uptake" Problem)

Even though the medicine works, some groups are having a harder time showing up for their "pulse checks."

• The Young Adults (19–28): Think of this group as the "busy bees." They are often juggling jobs, school, and starting families. Because they are so busy, they are the least likely to get their viral load tested. They are the ones most likely to miss the bus to the clinic.
• The Men: Men are slightly less likely to get tested than women. It's like men are more likely to say, "I feel fine, I don't need a check-up," while women are more proactive about going.
• The "Mobile" Runners: The researchers found that in areas where people move around a lot (like farmers moving with seasons or herders moving with cattle), it's very hard to keep track of who needs a test. It's like trying to take attendance in a classroom where students keep swapping seats and leaving the building.

3. Who Is Winning the Race? (The "Suppression" Problem)

Once people do get tested, who is actually keeping the virus under control?

• The Kids and Teens: This is the saddest part of the story. Children and teenagers (ages 0–18) are the least likely to have their virus suppressed.
  • Why? Imagine a teenager trying to take medicine every day. They might forget because they are at school, or they might be embarrassed to take pills in front of friends. Sometimes, they rely on parents to give them the meds, and if the parents are busy, the teen might miss a dose. It's a tough age to manage a daily health routine.
• The Magic Pill (Dolutegravir): The researchers noticed a huge difference based on which medicine people were taking.
  • People taking the newer drug, Dolutegravir (DTG), were like runners with super-shoes. They were much more likely to win the race (91% suppression).
  • People on the older drugs (NNRTI) were like runners with heavy boots. They were still winning, but not as often (80% suppression).
  • The Lesson: Switching everyone to the "super-shoe" medicine is a winning strategy.

4. The Geography: Some Towns Are Doing Better Than Others

Tanzania is a big country, and the report card shows a patchwork of performance.

• The "Green Zones": Some regions (like Mbeya and Mwanza) have high testing rates and high win rates. These are the well-oiled machines where the system works smoothly.
• The "Red Zones": Other areas (like Manyara and Ruvuma) have lower testing rates.
• The "Mobile" Districts: The map showed that districts with lots of seasonal movement (people moving for work or grazing) had the biggest gaps. It's hard to keep a steady rhythm when the runners are constantly moving to a new track.

5. What Does This Mean for the Future?

The authors are saying: "We have a great engine (the medicine), but we need to fix the fuel lines (the testing system) for specific groups."

• For the Young: We need to make clinics more "youth-friendly." Maybe open them at different hours, make them less scary, or use apps to remind them to come in.
• For the Men: We need to find ways to get men to the clinic without them feeling like they are being nagged.
• For the Travelers: We need better ways to track people who move around, so they don't fall through the cracks when they cross from one town to another.
• For the Kids: We need to support the parents and caregivers who are managing the kids' medicine, and maybe create special support groups for teens.

The Bottom Line

Tanzania is running a very successful race against HIV. The medicine works, and most people are winning. But to finish the race and help everyone, the country needs to stop leaving the "busy young adults," the "struggling teens," and the "traveling runners" behind. If they can fix these gaps, they can get even closer to the goal of ending the epidemic.

Technical Summary

1. Problem Statement

Routine viral load (VL) monitoring is the gold standard for assessing antiretroviral therapy (ART) effectiveness and achieving the UNAIDS 95-95-95 targets. Despite expanded testing capacity in sub-Saharan Africa, significant gaps persist in monitoring coverage and equitable treatment outcomes.

• The Gap: While Tanzania has adopted routine VL monitoring, many patients miss recommended testing intervals (6 and 12 months post-initiation), delaying the detection of virological failure.
• Knowledge Deficit: Existing literature in Tanzania is largely regional or facility-based. There is a lack of comprehensive, national-level analyses examining VL uptake, suppression rates, and geographic heterogeneity across all regions and districts.
• Specific Concerns: Vulnerable populations (adolescents, young adults, men) and mobile populations often experience lower monitoring coverage and poorer outcomes, but the specific predictors and geographic patterns at a national scale were not well-defined.

2. Methodology

• Study Design: Retrospective observational analysis using routinely collected national HIV program data.
• Data Source: The Care and Treatment Clinic (CTC-2) electronic database managed by the National AIDS and Sexually Transmitted Infections Control Programme (NASHCoP).
• Study Period: January 1, 2017 – December 31, 2021.
• Study Population: 70,000 individuals receiving ART across all 26 mainland regions of Tanzania.
  • Inclusion Criteria: Valid demographic data, ART regimen history, clinic visit records, and geographic identifiers.
  • Exclusion: Records with missing key variables or incomplete geographic data.
• Key Variables & Definitions:
  • Viral Load Uptake: Presence of at least one documented VL test during the observation period.
  • Viral Suppression: VL result <1,000 copies/mL among those tested.
  • Adherence: Calculated via pharmacy refill records (percentage of days covered).
  • Regimens: Categorized by anchor drug (Dolutegravir/DTG, NNRTI, Protease Inhibitors/PI, Others).
• Statistical Analysis:
  • Descriptive statistics for demographics and treatment characteristics.
  • Multivariable Logistic Regression: To identify predictors of VL uptake and suppression, adjusting for age, gender, regimen, adherence, year of observation, and region.
  • Geospatial Analysis: Choropleth maps generated to visualize regional and district-level variations in uptake and adherence.

3. Key Contributions

• National Scale: First study to utilize a unified national dataset (70,000 patients) to map VL monitoring and suppression across all 26 regions and their districts in Tanzania.
• Geospatial Granularity: Moves beyond regional averages to identify specific districts with critical gaps, particularly those characterized by mobile populations and seasonal migration.
• Regimen Transition Analysis: Provides real-world evidence on the impact of the national transition from NNRTI-based to Dolutegravir (DTG)-based regimens on viral suppression rates.
• Differentiation of Outcomes: Distinguishes between uptake (getting tested) and suppression (achieving undetectable levels), revealing that different demographic groups face barriers to different stages of the care cascade.

4. Key Results

A. Overall Performance

• VL Uptake: 71.2% (49,820/70,000) of patients had at least one documented VL test. 28.8% had no recorded test.
• Viral Suppression: Among those tested, 88.7% (44,187/49,820) achieved suppression (<1,000 copies/mL).

B. Demographic Predictors

• Age:
  • Uptake: Lowest among young adults (19–28 years) (60.5%) compared to adolescents (75.3%) and older adults.
  • Suppression: Lowest among children (0–10 years) (68.3%) and adolescents (11–18 years) (69.0%), compared to adults (90%+).
• Gender: Males had lower VL uptake (69.3% vs 72.2% for females) and lower suppression rates (87.4% vs 89.4%).
• Adherence: Patients with adherence ≥85% had significantly higher odds of VL uptake (aOR 1.72) and suppression.

C. Treatment Regimens

• DTG vs. NNRTI: Patients on DTG-based regimens showed significantly higher suppression (91.0%) compared to NNRTI-based regimens (79.9%).
• Transition: The study captures the shift from NNRTI dominance (74.6% at start) to DTG dominance (73.5% at end of period).

D. Geographic Heterogeneity

• Regional Variation: VL uptake ranged from 62.4% (Manyara) to 76.6% (Mbeya). Suppression ranged from 78.0% (Ruvuma) to 93.7% (Geita).
• District-Level Clustering: Significant heterogeneity existed within regions. Districts with high mobility (e.g., pastoralist areas, border districts) showed lower monitoring coverage.
• Temporal Trend: VL uptake was highest for patients first observed in 2017 (82.6%) and declined sharply for those first observed in 2021 (34.4%), suggesting potential data capture issues or programmatic shifts in recent years.

E. Multivariable Predictors (Adjusted Odds Ratios)

• Predictors of Low Uptake: Young adulthood (19–28), male gender, NNRTI/PI regimens, and later years of observation (2021).
• Predictors of Low Suppression: Male gender, young adulthood (19–28), NNRTI/PI regimens, and lack of documented adherence data.

5. Significance and Implications

• Policy Guidance: The findings highlight that while overall suppression is high, the "leaks" in the cascade are demographic and geographic. Interventions must be targeted specifically at young adults (for testing uptake) and children/adolescents (for treatment adherence and suppression).
• Health System Strengthening: The geographic clustering of low uptake in mobile populations suggests that standard clinic-based monitoring is insufficient. Strategies such as differentiated service delivery (DSD), mobile testing, and improved specimen transport logistics are needed.
• Regimen Optimization: The superior performance of DTG-based regimens supports the continued and accelerated transition away from NNRTI-based therapies.
• Data Utilization: The study demonstrates the value of routine programmatic data (CTC-2) for real-time surveillance and identifying equity gaps without the need for costly new surveys.
• Future Directions: The authors recommend integrating pharmacy refill data with VL results to create predictive models for identifying patients at risk of disengagement or failure, particularly in resource-limited settings.

Conclusion: While Tanzania has made substantial progress in ART coverage and viral suppression, significant inequities remain. Achieving the 95-95-95 targets requires addressing the specific barriers faced by young adults, men, and children, as well as resolving geographic disparities in monitoring infrastructure.