Mitigating the impact of COVID-19 on tuberculosis burden

Each year 1.5 million people die of TB, now disruptions due to COVID-19 are predicted to have increased that number by a third. We use innovative mathematical models to evaluate the wider effects of health emergencies such as COVID-19 on TB burden, and how to mitigate that effect.

Dr Christopher Finn McQuaid, Assistant Professor In Infectious Disease Epidemiology, LSHTM

Early in the COVID-19 pandemic, modelling analyses predicted millions of additional people with TB disease and hundreds of thousands of additional TB deaths as a result of health service disruptions due to COVID-19. However, over a year later the wider impact of COVID-19 on TB burden remains unclear.

We need to know the long-term impact on TB burden of COVID-19-related health service disruptions (such as decreases in TB diagnosis, poorer treatment outcomes and a lack of TB prevention), in order to evaluate which intervention activities to prioritise for restoration efforts, and the resources required to do so. 

We also need to know the impact on TB burden of a major increase in vulnerability to TB due to COVID-19 (such as increases in rates of HIV and malnutrition) in order to better prioritise vulnerable groups for TB intervention activities. 

Finally, we need to know how TB infection transmission has changed due to COVID-19 (due to major changes in social contact patterns), in order to better prioritise TB intervention activities by location and setting. 

We use epidemiological and economic modelling to estimate the scale of these impacts on TB, identify cost-effective and feasible interventions to mitigate them, and translate these findings into policy and action.

The ultimate beneficiaries of our work are people living with TB in high TB burden countries. However, the primary target audience are those working in National TB Programmes (NTPs), who can use our results to make better informed decisions when allocating resources to different TB interventions, as well as international funders (such as the Global Fund) and advocacy groups (such as the Stop TB Partnership), who can use the results to better understand the resources needs of NTP and advocate for an increase in these at a local and global level.

We have a strong history of working with all of the above audience members. By working with NTPs in multiple countries with our user-friendly TIME model to increase their understanding of the modelling process, we have ensured that the modelling addresses the problems that are interested in and can better inform their decisions.

Through our position as leading the TB Modelling and Analysis Consortium, we play a crucial role in developing global TB modelling goods, as well as bringing together key TB funders. We currently hold frequent meetings with international funders, the World Health Organization and TB modellers to identify gaps specific to the impact of COVID-19 on TB.

There are three primary public goods that will result from this solution. 

The first and most important of these will be modelling evidence to support key decision making of National country TB Programme staff, in a number of Low and Middle-Income countries. This should lead to better informed decisions, better use of resources for TB services, and save lives.

The second of these is in the form of knowledge, through peer-reviewed publications on impacts of wider societal disruptions on TB burden, such as vulnerability to TB and TB-infection transmission.

The third of these is the extension to the free, user-friendly model to support TB decision-making. This software will be extended to include interventions to reduce transmission and social determinants. 

Our modelling and analysis activities will generate evidence outputs, which will better inform decisions, and therefore potentially better outcomes for those with TB.

We expect our solution to impact people with TB as National TB Programmes are equipped with evidence to make better informed decisions on which interventions to prioritise to mitigate the impact of COVID-19 on TB. In particular, this will benefit those most vulnerable to TB, who are more likely to be economically vulnerable and have other underlying risk factors and comorbidities (such as HIV, undernutrition and diabetes). Our solution will help NTPs to better direct interventions to these vulnerable populations.

The link between modelling activities and an improvement in evidence to support decisions can be found in multiple examples of our work, including in recent national strategic planning documents here (pp 69), here (pp59) and here (pp125).

In the first year, our solution will start to generate evidence in key high TB burden countries, which will affect those living with TB in those countries. Our solution will lead to better informed decisions, with the potential to prioritise interventions that will decrease TB incidence, mortality and patient costs.

Our solution will also enable advocacy to increase funding for the TB response, in particular highlighting the secondary impact of COVID-19 on TB burden and the widespread problem that TB still represents for millions globally, which has only been exacerbated.

Over three years, the data and extended TIME model created in our solution will be applied to multiple other high TB burden countries, to inform TB decision making to better manage the TB response. The Global Fund for TB, AIDS and Malaria already funds countries to apply the TIME model to inform their TB National Strategic Plans, and GFATM Funding Applications. 

Therefore we have a direct and automate route to scale our solution already in place.  

Given that 10 million people fall ill with TB every single year, and 1.5 million die, an improvement in decision-making in the highest TB burden countries has the potential to transform millions of lives across the globe.

We evaluate and monitor our impact through two key measures. The first of these is the production of academic publications, which add to our body of knowledge regarding TB epidemiology and economics. Specifically, the number of papers produced and the journals that these are published in is used as a measure of our impact. In 2020 this included 77 publications from within our group, including in highly regarded journals such as the Lancet (as well as Lancet Global Health, Infectious Diseases, Respiratory Medicine and others), New England Journal of Medicine and Nature Medicine.

The second of these is related, and measures the citation of these publications and other modelling work in the support of TB policy and decision-making. Specifically, this includes an explicit link to our work within policy documents (see above). In terms of national strategic planning, in the past 3 years alone we have seen at least 4 examples where our work has been explicitly cited. We have also identified 6 examples of our wider modelling work supporting global policy in the last 3 years.

Our current major barrier is a lack of funding to employ research staff to conduct the analysis and modelling work, to further develop the tools, and to engage with and support National TB Programmes (NTPs). We plan to overcome this barrier by applying for funding from sources such as the Trinity Challenge.

A secondary barrier is lack of time of NTP staff, whom are currently overworked responding to COVID-19. We will overcome this barrier by producing initial results which will highlight the utility of the work in supporting NTP decision-making, as well as enabling them to advocate for additional funding. This has been successful in the past.

A final barrier is lack of data regarding health service disruptions, increasing vulnerabilities and changing transmission. The first is currently being addressed by collection of data by NTPs. The second is similarly being addressed by data collation efforts by organisations such as the World Bank, with whom we plan to confer in order to obtain the data required. The last is being addressed by a limited number of contact surveys, as well as the collation of mobility data. We plan to conduct further analysis here to identify the implications of the data available.

The London School of Hygiene and Tropical Medicine

We are applying to The Trinity Challenge as a source of funding (specifically for staff time) to enable us to carry out model development, data collation and analysis and engagement with National TB Programmes.

The first barrier to our solution is lack of funding to employ staff to conduct model development work. Funding for the model in the past has focused on TB health service delivery, with little room for consideration of wider societal factors. With, amongst other issues, recent funding cuts to the overseas aid budget in the UK, The Trinity Challenge represents an opportunity for us to overcome this funding barrier.

The second barrier is lack of NTP staff time. Funding for the abovementioned staff will enable us to conduct initial modelling and engagement with NTPs, which has been successful in the past in highlighting the importance of modelling evidence and increasing the time which they are prepared to contribute to it.

The third barrier is a lack of data. Again this staff time will enable us to collate the necessary data where it is available, conduct analyses to better understand the implications of that data, and identify key data gaps to advocate for additional data collection exercises.

Primary partner organisations would be Avenir Health and KNCV Tuberculosis Foundation, with whom we have an established relationship and shared experience of supporting country-level TB modelling. These partners would provide support in development of the user-friendly tool interface (Avenir Health) and by enhancing links to National TB Programmes (KNCV).

Potential partners for this work from within The Trinity Challenge member organisations could include Imperial College London, where Dr Nimalan Arinaminpathy is a fellow TB modeller who would provide valuable insight into the impact of an increase in TB vulnerabilities, particularly in the Indian setting. Other potential partners include the London School of Economics, who could provide insight into the impact of changing economic indicators, as well as companies such as Google and Facebook, who could provide data and insight into changing mobility of populations. Lastly, in some instances it is possible that the Bill and Melinda Gates Foundation could provide links to additional TB-relevant data sources, although we already have a good working relationship well established here.