When a health authority decides whether to add a new technology to the health system, the clinical question comes first. The financial one comes immediately after: how much will it cost, who will it affect, and at what point in the budget cycle will the spending be felt.

The budget impact analysis, known as BIA by its abbreviation, answers that question with method. It estimates the financial consequences of adopting a health technology within a specific system, payer, or health plan, over a defined horizon. ISPOR recognizes it as a central piece of economic evaluation alongside cost-effectiveness analysis [1].

In Latin America, this discipline has enormous practical importance. Resources are limited, budgets are usually closed in advance, and out-of-pocket spending continues to weigh on households. In 2019, 32.4 percent of health spending in Latin America and the Caribbean was paid directly by patients, well above the OECD average [3]. A well-built BIA can speed up a decision. A weak one can leave a technology stuck for months.

What a budget impact analysis is and why it matters

A BIA estimates the net cost of adding a new health technology, comparing a current scenario with a future scenario in which that technology enters the system. Its main question is concrete: what happens to the budget if we approve this?

The analysis must reflect the decision maker's perspective. Modeling for a ministry of health, an EPS, a private insurer, or a public hospital requires different assumptions. Each payer has its own population, prices, operational constraints, and decision timelines. That is why ISPOR recommends clearly defining the perspective, the eligible population, the treatment mix, current costs, future costs, and model uncertainty [1].

It must also be transparent. The decision maker needs to understand which assumptions drive the result, what the data sources are, and how sensitive the budget is to changes in price, adoption, eligibility, or resource use.

The six steps of the budget impact model

The practical approach can be organized into six steps. The sequence seems simple, but each step demands methodological discipline.

1. Estimate the target population

The first step is to define who the eligible patients for the technology are. This means starting from the total population with the condition, applying clinical eligibility criteria, and adjusting for actual diagnosis, effective access to the system, and the proportion of patients who would receive treatment.

Example: if the model evaluates an immunosuppressant for kidney transplant, the starting point should be the transplanted and eligible population, not the general prevalence of kidney disease. The model should use the annual number of transplants, the indication criteria, the rate of patients who meet those criteria, and the expected adoption during each year of the horizon.

2. Select the time horizon

The horizon should align with the decision maker's budget cycle. In many Latin American systems, three to five years is usually reasonable. A short horizon can hide scale costs. One that is too long introduces uncertainty that is hard to defend.

Technologies with slow adoption, such as some gene therapies or complex devices, may require five years. Drugs with rapid adoption and clear therapeutic competition can be evaluated over three years, as long as the model shows the annual impact.

3. Identify the current and projected treatment mix

The model must show how the eligible population is treated today and how that pattern will change with the new technology. The key question is who the new option will displace.

If an oral anticoagulant is introduced, the model must estimate how many patients use warfarin, how many use direct anticoagulants, how many receive no treatment, and what proportion will migrate to the new alternative. This displacement analysis avoids a common mistake: assuming that all adoption comes from new patients.

4. Estimate current and future costs

The BIA must include the direct costs relevant to the payer: acquisition of the drug or device, administration, monitoring, management of adverse events, medical visits, hospitalizations, and other associated resources.

In Latin America, local prices matter more than international list prices. Many markets have confidential agreements, institutional discounts, or centralized purchasing. Using a visible but irrelevant price for the payer weakens the model.

5. Estimate changes in disease-related costs

A technology can increase acquisition spending and reduce other system costs. If it decreases hospitalizations, complications, or disease progression, those effects must be quantified with solid evidence.

Example: an antidiabetic that reduces cardiovascular events can generate savings from avoided hospitalizations. The model can incorporate them if there is robust clinical evidence and local resource use data. Speculative projections should be left out or entered as sensitivity scenarios.

6. Present the annual, cumulative, and per-scenario impact

The final presentation must show the impact year by year and the total cumulative figure. Decision makers approve budgets by period, so they need to see when the spending appears and how it evolves.

The analysis must also include sensitivity. Price, adoption, population size, and management costs can change the result. ISPOR recommends exploring alternative scenarios so the decision maker understands the plausible range of the financial impact [1].

Common mistakes that reduce the credibility of a BIA

Methodological mistakes tend to appear in the same places. It is worth detecting them before the payer does.

Overestimating the eligible population: using raw epidemiological data without applying real clinical criteria inflates the impact and undermines credibility.
Ignoring the competitive mix: modeling adoption as if all patients were new distorts the net impact.
Using international prices: list prices can be far from the real cost for the local payer.
Projecting savings without local evidence: transferring hospitalization reductions observed in another system can be weak if not adjusted to regional practice.
Hiding assumptions: a model that does not show its sources, formulas, and scenarios invites distrust.

BIA in Latin America: what the model must capture

The regional context demands more grounded models. Information is fragmented, prices can vary by institution, and access patterns are not homogeneous. In addition, out-of-pocket pharmaceutical spending remains a relevant component for many households; the World Bank documented that the value of medicine purchases in Latin American and Caribbean pharmacies rose from USD 34.3 billion in 2008 to USD 69.5 billion in 2017 [4].

A regional BIA must consider four operational dimensions.

Fragmented data: many countries lack centralized registries of unit costs and resource use.
Opaque prices: discounts, public purchasing, and price-volume agreements can change the real cost.
Unequal access: adoption can differ between urban and rural areas, public and private sectors, or high- and low-complexity institutions.
Rigid budget cycles: if the model arrives late to the decision calendar, it can lose usefulness even if it is technically sound.

How technology can speed up the process

Building a robust BIA still requires expert judgment. Automation does not replace that judgment, but it can reduce mechanical tasks: searching for epidemiological data, validating assumptions, calculating scenarios, updating prices, and generating outputs for different countries.

For market access teams operating in several countries, the advantage lies in adapting a central model without rebuilding it from scratch. The right platform allows changing population, costs, adoption curves, and health system structure, while maintaining traceability over every assumption.

Conclusion

A well-designed budget impact analysis is a decision tool. It shows how much it will cost to add a technology, when the spending will appear, and which variables can change the result.

In markets with pressured budgets, every assumption counts. A transparent, local, and defensible BIA shows that the team understands the payer's constraints and did the work needed to quantify the financial impact rigorously.

At Quantus, we help market access, HEOR, and medical affairs teams turn clinical and economic evidence into clear models for decision makers. If you want to build a BIA that is faster, more traceable, and more aligned with the reality of Latin America, write to us.

Sources

[1] Sullivan, S. D., Mauskopf, J. A., Augustovski, F., Caro, J. J., Lee, K. M., Minchin, M., Orlewska, E., Penna, P., Rodriguez Barrios, J. M., & Shau, W. Y. Budget Impact Analysis: Principles of Good Practice: Report of the ISPOR 2012 Budget Impact Analysis Good Practice II Task Force. Value in Health. 2014;17(1):5-14. Available at: https://www.ispor.org/heor-resources/good-practices/article/principles-of-good-practice-for-budget-impact-analysis-ii

[2] Mauskopf, J. A., Sullivan, S. D., Annemans, L., Caro, J., Mullins, C. D., Nuijten, M., Orlewska, E., Watkins, J., & Trueman, P. Principles of Good Practice for Budget Impact Analysis: Report of the ISPOR Task Force on Good Research Practices. Value in Health. 2007;10(5):336-347. Available at: https://www.ispor.org/heor-resources/good-practices/article/principles-of-good-practice-for-budget-impact-analysis

[3] OECD/The World Bank. Health at a Glance: Latin America and the Caribbean 2023. OECD Publishing. 2023. Available at: https://www.oecd.org/en/publications/health-at-a-glance-latin-america-and-the-caribbean-2023_532b0e2d-en.html

[4] Vargas, V., et al. Pharmaceuticals in Latin America and the Caribbean: Players, Access, and Innovation Across Diverse Models. World Bank. 2022. Available at: https://openknowledge.worldbank.org/entities/publication/e5fae256-f0c0-5400-9c14-b80387e182c8