Today marks World Tuberculosis (TB) Day – a celebration of the anniversary of Robert Koch’s announcement that he had discovered the Mycobacterium tuberculosis (MTB) bacteria, which causes the disease. Today, 139 years after this announcement and in the context of the COVID pandemic, TB is still the world’s deadliest infectious killer causing nearly 4,000 deaths each day. In honour of World TB Day, I thought I’d do a special ‘TB Economics Research Round-Up’, highlighting some of the key developments from the last year in the world of TB Economics.
One of the major challenges for TB is the emergence of drug-resistant TB. Last year saw the publication of the long-awaited economic evaluation of the STREAM trial, investigating shortened drug regimens for multi-drug resistant (MDR) TB. STREAM was the first in a wide landscape of forthcoming trials investigating shortened MDR regimens, including TB PRACTECAL, which is also planning an economic evaluation. It perhaps does not come as a huge surprise that the shortened regimens in STREAM were less costly than longer regimens. However, it is interesting that a large part of the cost savings from shortened regimens was in the form of patients getting back to work faster, thereby having less income loss due to their TB. Pharmacoeconomic guidelines, particularly those in high-income countries, often treat analysis from the societal perspective as supplemental. There is no universally recognised framework for including productivity loss in economic analysis. Impacts on productivity and poverty are globally recognised goals of TB programmes and their funders, so, unlike other diseases, these types of costs are commonly considered in economic evaluations and their application to TB policy.
Alongside this, the WHO has in the last few years supported efforts to estimate catastrophic costs due to TB with national patient costing surveys. This has also been done using a common methodology, and since 2016 results have been published for 20 countries, including papers from Uganda, Lao PDR, and Zimbabwe published in 2020. Several more surveys are ongoing, including the development of a survey instrument for the UK. This work is an important data source for estimating the prevalence of catastrophic costs from TB, including both income loss and out of pocket payments. It provides insights into the impact of infectious disease more generally.
Another methodological innovation from TB economics in the last year is in the recognition that it is sometimes the whole health system that needs intervention (and hence incurs incremental costs), rather than just introduction of new technologies or drugs, where rapid increases in the provision of new services are required to control infectious disease. The economics of such non-marginal interventions can be challenging but often shines in being more realistic in informing decision-makers. An interesting paper from Bozzani et al. shows that taking health system constraints into account changes the cost-effectiveness ranking of intervention options. The limiting factor for most interventions was the availability of human resources; additional investment for relaxation of human resource constraints substantially increased the ICER for the modelled interventions. The approach required substantial additional data collection, and engagement with stakeholders, on the constraints to rapid scale-up and may be relevant to other economic evaluations that address the rapid scale-up of non-marginal interventions.
This year, several papers have demonstrated the potential for infectious disease models to illustrate the cost-effectiveness of hypothetical interventions and to inform investment in new technologies and services. An economic evaluation of novel TB vaccination shows potential for substantial reduction of MDR TB burden. An analysis of the cost-effectiveness of post-treatment follow-up examinations found that they could be cost-saving compared to current control efforts. However, models aren’t always robust where there isn’t sufficient good-quality data underpinning them. A summary of the state of the art in methods to determine the value of active TB case finding (ACF) emphasises the importance of empirical data on yield, timing, and costs of case detection. These can all vary substantially across settings for an intervention like ACF, making it challenging to identify optimal case finding approaches from an economic perspective.
Readers of the blog who work exclusively in high-income settings will be shocked to hear that one of the major limitations in health economics work in low- and middle-income countries is simply getting access to high-quality, relevant, and appropriate unit cost data. This is especially true for TB, which has, up until now, faced a dearth of quality unit cost estimates, making budgeting and planning very challenging for decision-makers. This is finally being addressed by a few major efforts to produce high-quality data globally, including the Value TB study, which has produced a full set of unit cost estimates for a wide array of TB interventions from five countries (Kenya, Ethiopia, India, Georgia, Philippines), and which is now freely available online. These costs were estimated using a common methodology published as the Costing Guidelines for Tuberculosis Interventions.
Finally, no research round-up this year would be complete without reference to COVID-19. The pandemic is of particular concern to the TB scientific community. The diversion of resources, disruption to health systems, and increased poverty resulting from the pandemic are predicted to lead to a 20% increase in TB incidence in the next five years. Many of those working in TB have been asked to switch their attention to COVID-19 in the last year. Several such efforts have used TB unit cost data to inform rapid policy and decision-making around COVID-19 interventions, both locally and globally. An interesting commentary also highlights the overlapping nature of the socioeconomic determinants (and consequences) of TB and COVID-19. It makes suggestions for policy solutions that could address the poverty impact of both these diseases.
- Marco Verch (CC-BY 2.0)