TIER-Plus — User Guide

A decision-support tool for trade-off analysis in HIV service planning

What is TIER-Plus

TIER-Plus is a planning tool for national HIV programme stakeholders to support annual budget allocation and resource prioritization decisions. It enables users to model how resource allocation across different HIV interventions—prevention, testing, treatment, and retention—translates into projected changes in key outcomes, including progress toward the 95-95-95 targets, new HIV acquisitions, mortality, and programme costs.

The tool uses the most recent programme data as a baseline reference point and allows country teams to systematically model the epidemiological and financial implications of scaling up or scaling down different interventions relative to current levels. Through an accessible, scenario-based platform, users can evaluate multiple allocation strategies side-by-side, compare their relative impact on outcomes, and make evidence-informed prioritization decisions that can be substantiated during planning and advocacy processes.

Important to know:

TIER-Plus is designed to inform and support annual planning deliberations within country-led processes. The tool provides indicative estimates of direction and relative magnitude of impact, which facilitates comparison across scenarios. All final budgetary allocations, epidemiological targets, and implementation specifications should be derived from country-led planning processes grounded in local epidemiological context and operational capacity. TIER-Plus functions as a complementary analytical resource and does not replace other validated modeling methodologies or country decision-making authority.

How to use the tool

1. Set the country context. Enter or confirm population and current 95-targets (diagnosed, on ART, suppressed) in the most recent reporting year.
2. Enter the baseline scenario. Populate each intervention with the volume and coverage of services delivered in the year prior (see What the baseline represents below).
3. Build a scenario. Adjust intervention volumes up or down from baseline. You can scale things in either direction.
4. Adjust assumptions. Model assumptions surrounding intervention effectiveness and cost can be adjusted from the defaults if required.
5. Compare. The output shows the estimated differences between each scenario and baseline. This includes the 95-targets, expected acquisitions, infant acquisitions, deaths and budget. Positive acquisitions averted and deaths averted mean the scenario outperforms baseline. Negative values mean it does worse — useful for testing budget-cut scenarios.
6. Iterate. Run several scenarios side-by-side to see where additional money buys the most impact and where cuts are least harmful.

What the baseline scenario coverage represents

Baseline coverage values should reflect what was delivered in the most recent year. It serves as a reference point against which alternative scenarios can be compared - i.e if we had the same implementation as last year what impact can we expect and how might this differ with alternative services. Additionally, baseline data is incorporated for model calibration, and hence serves an important purpose.

All numerical inputs are annual counts of people reached / units delivered, except where the input is explicitly described as a percentage.

Intervention definitions and data requirements

Prevention

• Oral PrEP / Lenacapavir: Number of individuals currently receiving and/or initiated on PrEP, entered either as a total or separately for FSW, MSM, and AGYW (15-24) and the general population.
• Condoms: Total number of condoms distributed in the year.
• VMMC: Voluntary medical male circumcisions performed in the year.
• Infant prophylaxis: Percentage of HIV-exposed infants receiving HIV prophylaxis (e.g. NVP) to reduce vertical transmission.

Testing and diagnosis

• Facility-based testing (general): Number of HIV tests performed at health facilities (excl. ANC).
• Community testing: Number of HIV tests performed in community settings.
• Index testing: Number of tests conducted among partners of newly-diagnosed people living with HIV.
• Key populations: Number of HIV tests performed among key populations or through STI services (excluding adolescents).
• Facility HIVST: Number of HIV self-tests distributed at facilities.
• Community HIVST: Number of HIV self-tests distributed in the community.
• Early Infant Diagnosis (EID): Percentage of HIV-exposed infants receiving HIV testing.
• ANC HIV testing: Percentage of pregnant women receiving ANC HIV testing.
• PNC HIV testing: Percentage of postpartum women (not known to be living with HIV) receiving PNC HIV testing.

Treatment, retention and monitoring

• Routine VL monitoring: Percentage of people on ART receiving routine viral load testing.
• ANC and PNC VL testing: Coverage of pregnant and postpartum women living with HIV who receive viral load testing.
• Multi-month dispensing (3-month, 6-month, 12-month): Percentage of stable ART clients enrolled in MMD (categories are mutually exclusive; the three must sum to ≤100%).
• Community ART pick-up: Percentage of MMD-enrolled clients receiving refills via community pickup instead of facility, applied equally across MMD-3/6/12. Has no effect when MMD enrolment is zero.
• Enhanced Adherence Counselling (EAC): Percentage of individuals identified as unsuppressed (through a recent viral load).
• Tracking and tracing: Outreach to people lost to follow-up to bring them back into care. Applied after DSD has already prevented some LTFU, against the remaining LTFU pool.
• Frequency of clinical visits for stable clients: The frequency at which stable ART clients must return for clinical visits, either every 6 months of every 12 months.

Advanced HIV disease

• CD4 testing: Coverage of CD4 testing among individuals initiating ART.
• AHD package: Number of AHD-diagnosed clients receiving the package of care.

What the tool does not do

• It does not project multi-year dynamics; each run is a single-year calculation.
• It does not model resistance, age structure, or sub-national heterogeneity.
• It does not optimise; it calculates the consequences of a chosen mix, leaving the decision to the user.
• It represents a complementary tool for country-led analysis and other validated modelling tools.

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Use the tool to compare directions and trade-offs. Use country processes for the exact numbers in your plan.