A paper from Thanzi researchers and collaborators Jessica Ochalek, Gerald Manthalu and Pete Smith, Squaring the cube: towards an operational model of optimal universal health coverage, helps to inform key policy questions in reaching Universal Health Coverage. Their paper provides a method for trading-off the benefits and costs of expanding the breadth of coverage (i.e., how much of the population is covered) and the depth of coverage (i.e., what interventions are covered) that takes account of the additional cost of reaching poorer, underserved populations.
Most research for UHC to date has focused on developing methods to inform what treatments to provide (i.e., the depth of coverage). See e.g., a method that was developed in 2018 by Thanzi researchers in collaboration with colleagues in the Malawi Ministry of Health. Such methods typically assume the interventions included in the package are to be made available to the full population, and so implicitly ignores the breadth aspect of coverage (i.e., who is covered). However, reaching the full population may require overcoming challenges, such as drug supply chain bottlenecks and lack of infrastructure in remote areas, that make reaching underserved populations more costly.
To address this, the authors apply a theoretical mathematical programming approach adapted from a framework suggested by Smith (2005, 2013) that accounts for these trade-offs to data on the costs and health benefits at different levels of coverage for healthcare interventions for a hypothetical East African country. The application also illustrates how equity concerns can be incorporated.
Applying their approach requires data on the costs and benefits for each intervention and coverage level under consideration and the size of the patient population that stands to benefit from each intervention. Implicitly, the high costs of reaching disadvantaged populations are likely to be an important reason for the low levels of coverage found in many countries, and yet little data exists to quantify unit cost variations, which would allow decision-makers to make informed decisions about the trade-off between breadth and depth of coverage. The provision of data on variations in unit costs and health benefits at the country level is therefore a priority for future empirical research in this area.
The policy choice of whether or not to tolerate some element of ‘unmet need’ in a system of UHC is an agonizing one, with profound consequences for those who are denied treatment. However, the illustrative application in the paper shows on the other hand that insistence on pursuing high population coverage levels without regard to the implications for the size of the benefits package also has serious opportunity costs, in the form of the health benefits from treatments foregone because they could not be included in the benefits package. The methods described in Squaring the cube: towards an operational model of optimal universal health coverage offer the opportunity to help decision-makers become more aware of the structure of the coverage problem they confront, the data that are needed to inform analytic models, and the population health consequences of their coverage choices.
By: Jessica Ochalek, Gerald Manthalu and Pete Smith | September 2020