PUBH 6331 Week 8 Distribution Analysis Example

Free Drugs, Costly Cure: A Distribution Analysis of Who Gains, Who Pays and Who Never Enrolls in a Donor-Funded Free Tuberculosis Treatment Program in a Composite Low-Income Country

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Walden University

PUBH 6331: Global Health and Social Justice

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Month Day, Year

Free Drugs, Costly Cure: A Distribution Analysis of Who Gains, Who Pays and Who Never Enrolls in a Donor-Funded Free Tuberculosis Treatment Program in a Composite Low-Income Country

The Program and the Pattern

In Tamesi, a composite low-income country in West Africa, a donor-funded national program has made tuberculosis diagnosis and treatment free at every public clinic since 2018. The program's reports show rising treatment success among enrolled patients. This paper asks a different question: how are the program's benefits and costs distributed across income groups, and who is left outside it? The pattern it traces is well known in tuberculosis research. A program can make every drug free and still leave the poorest patients paying the most, through transport, lost wages, food and months of clinic visits.

What "Free" Leaves Out

Pooling patient cost surveys from poorer countries, Tanimura and colleagues reported that the average total cost to patients was equivalent to 58% of their annual individual income and 39% of annual household income (Tanimura et al., 2014). Only about a fifth of the total, on average, was direct medical cost. Another fifth was direct nonmedical cost, such as travel and food, and the remaining three fifths was lost income. Half of all costs were incurred before treatment even began, during the search for a diagnosis (Tanimura et al., 2014). Making drugs and clinic visits free addresses the smallest part of the burden.

Who Gains

The program's main benefit, a free cure, goes to people who are diagnosed and enroll. Enrolled patients in all income groups gain, and the gain is real: without treatment, tuberculosis is often fatal. In Tamesi's reports, more than 85% of enrolled patients complete treatment. The benefit is large for everyone who reaches it.

Who Pays

The costs that remain fall unevenly. A cohort study in Lima's shantytowns measured all tuberculosis-related costs, including lost income, as a share of each household's annual income. Poorer households spent less in absolute terms but more as a share of income: 48% of annual income in the poorest households compared with 27% in the least poor. Thirty-nine percent of households faced costs of at least 20% of annual income, the threshold the study defined as catastrophic because it was most strongly linked to poor treatment outcomes (Wingfield et al., 2014). In that cohort, catastrophic costs were independently associated with death, treatment abandonment or failure.

Tamesi's program design adds to this burden in two ways. Patients in the first two months must attend clinic daily for observed treatment, and rural clinics are often a long trip from villages. For a farmer or market trader, two months of daily visits can mean losing a season's income. The program provides no transport or food support.

Table 1

Distribution of Program Benefits and Remaining Costs by Income Group

Note. Pattern based on Tanimura et al. (2014) and Wingfield et al. (2014); Tamesi is composite.

Who Never Enrolls

The people most absent from the program's success figures are those never diagnosed. Long distances, the cost of the first visits and the habit of trying cheaper remedies first delay diagnosis most among the poorest. Because half of costs fall before treatment, some households exhaust their savings before they reach a clinic that could diagnose them. They do not appear as failures in the program's reports because they never appear at all.

Reading the Distribution

The program is progressive in one sense: it removes a fee that would weigh most heavily on the poor. It is regressive in another: the costs it leaves in place fall hardest on the poorest households, who are also the households diagnosis reaches last. The World Health Organization's End TB Strategy set as one of its targets that no tuberculosis-affected household should face catastrophic costs (World Health Organization [WHO], 2015). That target reflects exactly this pattern: free treatment is necessary but not sufficient for fairness.

What Would Change the Distribution

Three changes would shift costs away from the poorest. Community-based or video-observed treatment would cut daily travel. Cash or food support during treatment would replace part of lost income, which the Tanimura review identified as the largest financial risk for patients. Active case finding in the poorest rural areas would reach people before they spend their savings searching for a diagnosis. Each change has a cost, and the donor would need to fund social protection as well as medicine.

The Donor's Share of Responsibility

The donor that funds the drugs has shaped what the program measures and what it leaves out. Its grant agreement pays for medicines and laboratory supplies and rewards treatment success, which pushes the program to count what happens inside clinics. A donor concerned with fairness could fund transport vouchers or food support alongside drugs and could require cost surveys as a reporting condition. That would not shift the main duty from the government, but it would stop the donor's own rules from hiding the costs that fall on the poorest patients.

What the Program Should Measure

Tamesi's program reports treatment success, a measure of what happens to people who enroll. It does not measure what enrollment costs them or who never arrives. Adding a patient cost survey, interviewing a sample of patients during and after treatment about direct costs and lost income, would let the program track the share of households facing catastrophic costs, the indicator the End TB target relies on. Comparing case notifications with estimated incidence by district would show where people are being missed. Neither measure is expensive compared with the drugs. Without them, the program can report success while the poorest households carry the hidden costs and the undiagnosed remain invisible.

Conclusion

Judged by its own figures, Tamesi's program succeeds. Judged by who pays and who is reached, it leaves the heaviest burdens on the poorest. A justice analysis does not dismiss the cure the program provides; it asks that the program count the costs patients carry and the people it never sees.

References

Tanimura, T., Jaramillo, E., Weil, D., Raviglione, M., & Lönnroth, K. (2014). Financial burden for tuberculosis patients in low- and middle-income countries: A systematic review. European Respiratory Journal, 43(6), 1763-1775. https://doi.org/10.1183/09031936.00193413

Wingfield, T., Boccia, D., Tovar, M., Gavino, A., Zevallos, K., Montoya, R., Lönnroth, K., & Evans, C. A. (2014). Defining catastrophic costs and comparing their importance for adverse tuberculosis outcome with multi-drug resistance: A prospective cohort study, Peru. PLOS Medicine, 11(7), Article e1001675. https://doi.org/10.1371/journal.pmed.1001675

World Health Organization. (2015). The End TB Strategy. https://www.who.int/publications/i/item/WHO-HTM-TB-2015.19