Mass Screen-and-Treatment Programs for Tuberculosis

Published: March 2024

Summary

What does this program do?

Tuberculosis (TB) is a common cause of death in low- and middle-income countries. (More.)

Mass screen-and-treatment programs involve systematically testing a majority of individuals over a certain age within a particular region for both TB infection and active TB disease. Those found to have TB infection or active TB disease are then initiated on treatment.

Based on modeled estimates, we think that this approach could lead to long-term reductions in TB incidence by reducing transmission rates. (More.)

How cost-effective is it?

We think that mass screen-and-treatment programs for TB are moderately cost-effective, though less cost-effective than the range of programs we currently consider funding. As of March 2024, we estimate that a mass screen-and-treatment program in a medium-sized city in India would be around 5 times as cost-effective as unconditional cash transfers (GiveWell’s benchmark for comparing different programs). (More.)

A sketch of our preliminary cost-effectiveness analysis is below. We model three key benefits: reductions in mortality, morbidity, and treatment costs. We evaluate these benefits over a 20-year period. This analysis requires making assumptions about several uncertain parameters that could change cost-effectiveness substantially. These key parameters are in gray.

You can see our preliminary cost-effectiveness analysis for the program here and a simple version here.

We estimate that a mass screen-and-treatment program for tuberculosis is moderately cost-effective, but less cost-effective than our marginal funding opportunities. This is because although we think it would reduce both mortality and morbidity from TB and related causes, it requires screening the majority of the population (regardless of a person’s risk of TB) so the cost per death or case averted by the program remains relatively high. To break this down further:

• We expect the program to avert mortality and morbidity, both directly and indirectly.
  • We expect screening and treating people for TB to cause immediate, direct reductions in the prevalence of TB. Due to reductions in transmission, we also think that TB incidence and mortality will be lower over the longer-term (more).
  • TB disease also causes long-term health issues (such as respiratory problems) that persist even after the TB disease itself has been cured. By reducing the incidence of TB, we expect screen-and-treat programs to indirectly avert the morbidity and mortality associated with these health issues, known as post-TB sequelae. These indirect benefits are not estimated in the modeled evidence we draw on, so we apply upward adjustments to our estimate of the program’s effect size (more).
  • Since reductions in morbidity and mortality should reduce costs paid by patients and healthcare systems, we also include estimates for these benefits (more).
• Although we think the intervention would likely have a large impact over time, the costs of the program are relatively high. Our analysis assumes a cost of approximately $7 per person in the intervention area, though we are highly uncertain about this figure. This per-person cost is comparable to some of our Top Charity interventions, such as seasonal malaria chemoprevention (SMC). However, we expect that the program will only screen about half of the population, the population-wide TB burden in the modeled intervention area is comparatively low, and TB infections tend to occur later in life than malaria. We assign higher moral weight to averting the deaths of younger people, recognizing that averting these deaths saves more years of life. These factors make the program relatively expensive in terms of cost per unit of impact. (More.)
• We also have a number of reservations about taking the modeled evidence for the program’s impact at face value. We aren’t aware of direct evidence of one-time mass screen-and-treat TB programs that include testing for TB infection (more). Therefore, our estimates of the program’s impact largely derive from Shrestha et al. 2021, a paper that models the direct mortality effect of a one-time, community-wide screening campaign in a mid-sized city in India over 20 years. We think the paper likely overstates the impact of the program on mortality directly averted in practice because:
  • We think the marginal impact of active case-finding is likely lower than the paper’s effect size would suggest: over a 20-year period, the share of individuals seeking treatment for TB absent the program could increase and other healthcare improvements, not modeled in the paper, may occur. We apply a 30% downward adjustment to the model’s results to account for these factors. The magnitude of this adjustment is very uncertain. (More.)
  • The paper assumes a closed population, with no migration. We think that migration of individuals with TB into the intervention area could reduce the program’s effect on lowering transmission, and lead to a recurrence of TB for some individuals who have already been treated. We apply a 25% downward adjustment to reflect this possibility; this adjustment is a subjective guess. (More.)

Our main uncertainties about our estimate are:

• How the program may differ across different contexts. We expect that the per-person costs, and program benefits may vary substantially by location. This could be driven by differences in factors such as baseline tuberculosis prevalence rates, transmission rates, and screening methods. (More.)
• The difficulty of implementing the program. We think there is a chance that the model underestimates implementation risks, such as logistical issues, supply stock-outs, and refusal of the treatment by program participants. However, we do not currently make an adjustment to the model’s effect size to account for this, given the lack of evidence on real-world program implementation, and possible counterbalancing effects. (More.)
• "Unknown unknowns." Since the evidence for mass screen-and-treatment is modeled, rather than directly observed, we expect there may be unforeseen differences between the model and real-life implementation. We have also spent less time researching this program than the average program for which we have published a review. However, since these differences could be positive or negative, we do not adjust our cost-effectiveness estimate to account for this. (More.)

What is the program?

What is tuberculosis?

Tuberculosis (TB) is an infectious disease that usually affects people’s lungs.1 The World Health Organization (WHO) estimated that in 2021, around 10.6 million people fell ill with TB worldwide, and 1.6 million people died from the disease.2 The Institute for Health Metrics and Evaluation (IHME) estimated that TB accounted for 4% of all deaths in low- and middle-income countries in 2019.3 TB is spread from person to person through the air, when a person with TB disease coughs or sneezes.4 It takes one of two forms: TB infection, and TB disease, which occurs for a subset of those who have been infected with TB.5

TB infection is asymptomatic, and can only be detected using a tuberculin skin test or TB blood test. Unless the infection progresses to TB disease, those infected with TB do not feel sick, and cannot infect others. Approximately 5% of infections progress to TB disease during the first two years after infection. After this time, the risk of TB disease decreases significantly.6

Though TB disease typically affects people’s lungs, it can also affect other parts of the body, including the brain, kidneys, spine, or skin.7 However, a person who develops TB disease may also be asymptomatic, or the symptoms (such as a prolonged cough, fever, night sweats, chest pain, fatigue, and weight loss) may be mild for many months. The absence or delay of symptoms can lead to individuals with infectious TB not seeking treatment, resulting in the transmission of the disease to others.8

Without proper treatment, WHO estimates around 50% of HIV-negative people with TB, and nearly all HIV-positive people with TB, will die. With treatment, around 85% of TB cases are curable.9

What are mass screen-and-treatment programs?

Mass screen-and-treatment programs involve systematically testing a majority of individuals within a particular region over a certain age for both TB infection and disease. Those who test positive for TB are treated, either by the program or through referrals to external healthcare providers. The program involves a one-time campaign, rather than multiple rounds of screening or testing.10

In practice, the exact implementation of mass screen-and-treatment programs is likely to vary. In the empirical model which we rely on in order to generate our cost-effectiveness estimate (see below), the program screened people aged 15 years and above for TB disease using chest X-rays and rapid diagnostic tests, and tested the same adult population for TB infection using tuberculin skin testing. Children aged under 15 were also evaluated if they had been in contact with those identified with TB disease.11 In the model, those who test positive for TB are then provided with treatment. In practice, some people who test positive for TB could be treated through the program itself, while others (particularly those with more difficult to treat cases such as drug-resistant TB) may be referred for treatment with external healthcare providers.

Mass screen-and-treatment programs could lead to both an immediate reduction in the prevalence of TB, through the initial one-off campaign, and a sustained reduction in incidence over time, due to a reduction in longer-term transmission.12 To capture the latter effect, we evaluate the benefits of a mass screen-and-treatment program over a 20-year period.

Is the program cost-effective?

What do we estimate?

We have conducted a preliminary cost-effectiveness analysis of mass screen-and-treatment programs for TB. We estimate this program is likely to be moderately cost-effective, though less cost-effective than the range of programs we would currently consider funding.13 Our work at this stage is preliminary, and it is possible that a deeper review could cause us to update our view.

Note that our cost-effectiveness analyses are simplified models that do not take into account a number of factors. There are limitations to this kind of cost-effectiveness analysis, and we believe that cost-effectiveness estimates such as these should not be taken literally due to the significant uncertainty around them. We provide these estimates (a) for comparative purposes and (b) because working on them helps us ensure that we are thinking through as many of the relevant issues as possible.

How do we reach our estimate?

In short, our cost-effectiveness estimate is based on the following:

• Existing evidence from the literature: We base our estimate of the treatment effect on Shrestha et al. 2021, a paper that models the impact of a one-time, community-wide screening campaign in a mid-sized city in India over 20 years.14 We rely on this paper due to a lack of long-term direct evidence of large-scale programs of this kind. Shrestha et al. 2021 estimates the number of deaths averted from a screen-and-treat program, which we then adjust as follows:
  • Internal validity adjustment: We adjust the model’s results downward by 30%, to account for our guess that the benefits of active case-finding will be lower because people will counterfactually seek treatment and TB screening and treatment rates may counterfactually improve over the period of benefits. This adjustment is a guess, and we are uncertain about its magnitude.
  • External validity adjustments: We make adjustments in competing directions to account for the generalizability of this paper’s results in practice. We make a 74% upward adjustment to account for the fact that TB disease is additionally associated with long-term health issues, which a mass screen-and-treatment program will also avert. We also make a rough 25% downward adjustment, to account for our concern that the migration of people with TB into the intervention area would reduce the program’s effectiveness. This adjustment is highly uncertain.
• Other drivers of our estimate: In addition to our adjusted estimate of mortality averted, our cost-effectiveness estimate takes into account several other parameters:
  • Morbidity averted: Shrestha et al. 2021 also estimates the number of cases of TB disease either treated or prevented by the program. To estimate the total morbidity averted, we combine this with our guess about how ‘bad’ a case of TB is, and the average length of illness, along with the internal and external validity adjustments described above.
  • Cost of illness averted: We expect the program would lead to lower healthcare expenditure, proportional to the amount of illness we expect the program to avert.
• Program costs: We assume that the program would cost around $7 per person in the targeted population. This assumes that 70% of the target adult population is screened (as per Shrestha et al. 2021).15

We elaborate on each of these factors below.

Existing evidence from the literature

There is limited direct evidence of mass screen-and-treat programs for TB in the form that we focus on here (as described 16 but these programs differ substantially from mass screen-and-treatment, as they generally (a) screen solely for TB disease, not TB infection, (b) involve multiple rounds of testing, rather than one initial surge, and (c) often screen a smaller portion of the population.

Given this, our estimate of the impact of mass screen-and-treatment largely derives from Shrestha et al. 2021, a paper that models the effects of a one-time, community-wide screening campaign in a mid-sized city in India over 20 years. The paper’s model uses estimates of baseline prevalence, testing sensitivity, treatment efficacy, uptake, and other factors to estimate the number of deaths averted by the intervention. It projects that over 20 years, a mass screen-and-treat program could avert around 1,400 deaths per million people.17 We have not reviewed how the model works in detail. However, we have performed informal sense-checks based on studies of similar interventions, and these checks do not imply dramatically different results.18

We view this evidence as relatively weak, since it is modeled rather than directly observed. Though we use it as a basis for our cost-effectiveness estimate, we make a number of adjustments to account for our residual uncertainty about the model’s counterfactual assumptions (more) and its generalizability (more). We also incorporate benefits attributable to reductions in morbidity and averting the costs of illness (more).

General evidence on active case-finding programs

We conducted only a very shallow review of the evidence on other forms of active case-finding interventions.

A 2021 systematic review of active case-finding programs found mixed evidence from three RCTs that active case-finding leads to reductions in TB prevalence.19 (The review also reported on non-randomized evaluations and RCTs measuring TB notification rates, which we did not review.)

Of the three RCTs, two involved repeated rounds of active case-finding, whilst one involved community-based mobilization, rather than mass screening. Corbett et al. 2010 and Marks et al. 2019 evaluated multiple rounds of active case-finding, and found that these activities roughly halved TB prevalence.20 Ayles et al. 2013, which measured community-based mobilization, did not find a statistically significant effect.21 However, none of the three studies involved testing or treating cases of TB infection that had not progressed to TB disease.22 In theory, we would expect additionally preventively treating TB infection to result in a larger reduction in burden than treating TB disease alone, because a proportion of those with TB infection will eventually develop TB disease and transmit it to others.

Given the differences between mass screen-and-treatment programs as we are considering and the interventions discussed in the systematic review, we have not put weight on these findings. However, we do think it remains possible that a deeper analysis of the randomized and non-randomized literature on active case-finding interventions could cause us to update our opinion on mass screen-and-treatment programs.

Internal validity adjustment

We adjust the effect size from Shrestha et al. 2021 down by 30% to account for possible counterfactual treatment and improvements in healthcare services. The magnitude of this adjustment is a rough and subjective guess.

Active case-finding is theoretically effective at catching cases at an early stage (before serious symptoms of TB develop), and should thereby reduce transmission over time. However, at least some share of individuals with TB will seek early treatment absent active case-finding, and this share may increase over the period of benefits modeled. In part, our downward adjustment is designed to ensure that we do not credit to the program those instances of early treatment that would have occurred in any event.

Additionally, we would expect there to be improvements to healthcare services over the 20 year period that we evaluate the program’s effects, regardless of whether or not the program takes place. We think that these improvements could lead to increased rates of early TB case detection (which would reduce transmission, and lower mortality among affected individuals), improved treatment efficacy or adherence (lowering mortality risk), or lower TB incidence (such as might be possible if new vaccines are rolled out). These improvements could occur absent mass screen-and-treatment, so would reduce the counterfactual benefit of the program.

External validity adjustments

We make two adjustments to the effect size of Shrestha et al. 2021 to account for ways we expect the impact of the program to be different in practice compared to the model we use.

Post-tuberculosis sequelae

We adjust the effect size upward by 74% to account for averting additional deaths that we think can occur even after someone recovers from TB disease. We guess that this is an unmodeled upside to implementing a mass screen-and-treatment program.

Shrestha et al. 2021 models deaths averted by the program directly, meaning the deaths that would be attributable to TB disease. However, survivors of TB can suffer from a number of long-term health issues, known as ‘post-TB sequelae’, which include poor lung functioning.23 These post-tuberculosis health issues can themselves be fatal.24 By reducing the incidence of TB, we would expect mass screen-and-treatment programs to indirectly avert those deaths attributable to post-TB sequelae, in addition to averting the deaths caused by TB disease itself.

We are relatively uncertain about the size of this benefit. The 74% upward adjustment we apply is based on estimates from Menzies et al. 2021 of the percentage of overall deaths from TB attributed to post-TB sequelae, but we have not vetted this estimate in depth.25 We separately account for morbidity associated with post-TB sequelae (see below).

Migration

We adjust the effect size downward by 25% to account for the risk that migration could reduce the program’s impact. The magnitude of this adjustment is subjective, and we remain highly uncertain about it. Shrestha et al. 2021 assumes a closed population, with no migration.26 However, in practice we think that the migration of individuals with TB into the intervention area would lead to increases in TB transmission, thereby reducing the effect of the program. Since mass screen-and-treatment involves a one-off testing campaign that occurs at the program’s outset, the introduction and transmission of TB at a later stage could cause TB rates to increase again. Survivors of TB remain at elevated risk of contracting TB.27

To some extent, we think this effect may be offset by (a) reduced TB prevalence among emigrating individuals (so they are less likely to transmit TB in other places), and (b) the possibility that benefits could extend beyond 20 years if a program were successful. However, we have not explicitly modeled these competing dynamics.

Other drivers of our cost-effectiveness estimate

The modeled evidence for the impact of mass screen-and-treatment programs focuses on mortality. However, our cost-effectiveness estimate also takes into account two other forms of impact – morbidity averted and cost of illness averted – as well as the expected cost of the program itself.

Morbidity averted

Our estimate of morbidity averted is based on Shrestha et al. 2021’s estimate of the number of cases of TB disease that mass screen-and-treatment will either treat or prevent. We combine this with the average severity of the disability associated with TB disease, and the average length of TB disease.28

As with our estimate for mortality averted, we adjust the effect size downward to account for our concerns with internal validity and migration.29 We then adjust it upward by 397% to account for the aversion of morbidity associated with post-TB sequelae. This figure comes from the estimate in Menzies et al. 2021 of the percentage of years of healthy life lost due to disability (YLDs) from TB attributed to post-TB sequelae.30 As noted above, we have not reviewed this estimate in depth.

Under our current moral weights, we estimate that the program’s impact on averting morbidity constitutes around 31% of its total modeled impact.

Cost of illness averted

By reducing mortality and morbidity, we expect mass screen-and-treatment programs to lead to lower expenditure on healthcare (both for individuals and for governments), which we include in our cost-effectiveness model as an additional benefit of the program. We estimate this effect using GiveWell’s standard approach for costs of illness averted, which is explained in full here. This approach assumes that healthcare costs averted are roughly proportional to the disability burden of the disease in question (which we measure in disability-adjusted life years, or DALYs).

Under our current moral weights, we estimate that the program’s impact on averting costs of illness constitutes around 15% of its total modeled impact. However, this estimate is highly uncertain. We have not considered this in detail because we think this would be unlikely to meaningfully affect our bottom line assessment of cost-effectiveness.

The cost of the program

We assume that a mass screen-and-treatment program would cost approximately $7 per person in the intervention area, but this is highly uncertain.31 We base this figure on a range of estimates we found through a shallow literature review, while making subjective adjustments for (a) our belief that some costs cited in the literature reflect more expensive interventions than a typical mass screen-and-treatment program, and (b) our expectation that costs would likely be lower if the program is run at a larger scale.32 We discuss our cost-related uncertainties further below.

The per-person cost of the program is comparable to other programs GiveWell has reviewed, such as seasonal malaria chemoprevention (SMC).33 However, mass screen-and-treat TB programs are designed to screen a large share of the total population, while SMC specifically targets young children in areas where malaria is common. We view mass screen-and-treatment programs as relatively more expensive per unit of impact than SMC because (a) the population-wide burden of TB is low relative to malaria in the areas and age cohorts that the respective interventions aim to reach,34 and (b) the relatively lower weight GiveWell places on deaths and morbidity averted at older ages.35

What are our uncertainties?

Our assessment of mass screen-and-treatment programs is highly uncertain, since it is based on modeled estimates rather than direct evidence and the adjustments we have made to this data are ultimately subjective. We also continue to have a number of further uncertainties that do not currently affect our bottom-line estimate of cost-effectiveness, either because we think they point in competing directions and may cancel out, or because we do not know enough to be able to account for them adequately. In both cases, we think that learning more about these factors in future could affect our view of mass screen-and-treatment programs. Our core remaining uncertainties, beyond the inherently uncertain nature of our model as illustrated above, are:

• Context-specific inputs. We expect that the cost and benefits of the program are very likely to vary by location. Shrestha et al. 2021 models the program’s impact in an urban setting in one country (India), but these results may not generalize to places with different rates of TB prevalence and mortality, or to non-urban environments.36 For example, there is some evidence that TB incidence can be much higher in large cities than national-level data would indicate.37 Each of the uncertainties listed below could also differ substantially by context, so we would evaluate any given opportunity on a case-by-case basis.
• Implementation: We are unsure how on-the-ground program implementation would compare to the program modeled in Shrestha et al. 2021. We do not make an adjustment to the model's effect size to account for this because there is limited evidence about implementation of this program in practice. We are unsure about the direction of any adjustment because we think there could be a number of competing effects. These include:
  • Execution: The model in Shrestha et al. 2021 makes several simplifying assumptions in order to represent the impact of the program in a non-specific, mid-sized Indian city. As the authors note, this means that the model does not fully reflect the difficulty of implementing the program "in a specific (and inherently more complex) epidemiological setting."38 We therefore think there are likely to be several unmodeled program execution risks in practice, such as logistical issues, supply stock-outs, or the refusal of treatment by program participants.
  • Health system strengthening: The modeling paper asserts that health system strengthening activities, potentially facilitated by the initial screen-and-treatment process, may double program impact by reducing delays in treatment.39 We have not included the benefits of such possible improvements in our cost-effectiveness model, since we are unsure how likely it is that this strengthening would occur in practice, and the magnitude of additional funding it would require.
  • Differences between current and future implementation methods: the landscape of TB interventions continues to evolve rapidly. It is possible that the methodology evaluated in Shrestha et al. 2021 may soon become outdated, as better screen-and-treatment techniques are developed. It is also possible that additional evidence could establish a ‘best practice’ approach that differs from the method modeled.
• Screening and treatment protocols and associated costs: We are basing our estimates of costs on various related but not identical programs.40 The cost (and benefits) of a program could differ significantly depending on which ‘algorithm’ (i.e., the specific screening tests and methods) is used. We expect that by working with a given organization to understand their specific program and costs, we could reduce this uncertainty.
• Drug-resistant TB: Drug-resistant TB is treatable, but is very expensive and has large failure rates due to the necessity of an extended treatment period.41 By reducing overall TB transmission, we think that in the long term, mass screen-and-treat programs could avert a large share of the healthcare costs associated with drug-resistant TB. However, we also think it is plausible that drug-resistant TB may reduce the effectiveness of the initial screen-and-treatment process. Since drug-resistant TB represents a relatively small share of cases, we have not reviewed these competing effects in detail, or explored how they would impact our estimate.
• "Unknown unknowns": Our estimate of the impact of mass screen-and-treatment is based on modeled evidence, rather than direct, real-world observation. Unlike modeled evidence, direct evidence can surface concerns or opportunities that may not be forecastable in advance. For instance, there could be a population-level effect that only emerges as a result of community-wide treatment. This reduces our confidence in our cost-effectiveness estimate, but since such effects could be net positive or negative, it does not shift our estimate in any particular direction.
• Government costs: We do not account for leveraged spending by governments in our cost-effectiveness analysis. At face value, the program is expected to reduce the incidence of TB, so should lead to reductions in government healthcare spending on TB-related care. However, since the program would likely also lead to improvements in TB case detection, government healthcare costs could increase even while overall incidence falls. We are unsure of the magnitude of these competing effects.

Our process

To investigate mass screen-and-treatment programs for TB, we:

• Conducted a literature search for direct evidence of active tuberculosis case-finding programs.
• Identified a modeling paper that tries to estimate the impact of a program that closely resembles the proposed program.
• Built a cost-effectiveness analysis that combined the modeling paper’s impact estimates with our estimate of the program’s costs, along with adjustments for the long-term effects of TB, migration, internal validity, morbidity, and cost of illness averted.
• Sent our cost-effectiveness model to an independent TB expert (Dr. David Dowdy), and incorporated feedback.

Sources

• 1

  “Tuberculosis (TB) is an infectious disease that most often affects the lungs and is caused by a type of bacteria. It spreads through the air when infected people cough, sneeze or spit.” WHO, "Tuberculosis", 2023.

• 2
  • “An estimated global total of 10.6 million people (95% uncertainty interval [UI]: 9.9–11 million) fell ill with TB in 2021, equivalent to 134 cases (95% UI: 125–143) per 100 000 population (Table 2.1.1).” WHO, “TB Incidence”, 2022.
  • “In 2021, there were an estimated 1.4 million deaths among HIV-negative people (95% uncertainty interval [UI]: 1.3–1.5 million) and 187 000 (95% UI, 158 000–218 000) among HIV-positive people, for a combined total of 1.6 million.” WHO, “TB Mortality”, 2022.

• 3

  Global Burden of Disease Survey, 2019.

• 4

  “Tuberculosis (TB) is a disease caused by a germ called Mycobacterium tuberculosis that is spread from person to person through the air … When a person with infectious TB coughs or sneezes, droplet nuclei containing M. tuberculosis are expelled into the air. If another person inhales air containing these droplet nuclei, he or she may become infected." CDC, “The Difference Between Latent TB Infection and TB Disease”, 2014.

• 5

  Work on TB often refers to ‘active’ or ‘latent’ TB. In line with the WHO’s latest guidance, however, we use ‘TB infection’ and ‘TB disease’ in our report.

• 6
  • “Persons with latent TB infection do not feel sick and do not have any symptoms. They are infected with M. tuberculosis, but do not have TB disease. The only sign of TB infection is a positive reaction to the tuberculin skin test or TB blood test. Persons with latent TB infection are not infectious and cannot spread TB infection to others.” CDC, “The Difference Between Latent TB Infection and TB Disease”, 2014.
  • “The TB skin test is performed by injecting a small amount of fluid (called tuberculin) into the skin on the lower part of the arm … The result depends on the size of the raised, hard area or swelling.” CDC, “Testing for TB Infection”, 2016.
  • “TB blood tests are also called interferon-gamma release assays or IGRAs … A health care provider will draw a patient’s blood and send it to a laboratory for analysis and results.” CDC, “Testing for TB Infection”, 2016
  • “Following infection, the risk of developing TB disease is highest in the first 2 years (approximately 5%), after which it is much lower [...] The probability of developing TB disease is much higher among people living with HIV, and among people affected by risk factors such as undernutrition, diabetes, smoking and alcohol consumption”. WHO, "Global Tuberculosis Report 2023".

• 7
  • “TB usually affects the lungs, but it can also affect other parts of the body, such as the brain, the kidneys, or the spine.” CDC, “The Difference Between Latent TB Infection and TB Disease”, 2014
  • “The symptoms people get depend on where in the body TB becomes active. While TB usually affects the lungs, it also affects the kidneys, brain, spine and skin.” WHO, "Tuberculosis", 2023

• 8
  • “Unlike TB infection, when a person gets TB disease, they will have symptoms. These may be mild for many months, so it is easy to spread TB to others without knowing it. Common symptoms of TB:
    • prolonged cough (sometimes with blood)
    • chest pain
    • Weakness
    • Fatigue
    • weight loss
    • Fever
    • night sweats.”

  WHO, "Tuberculosis", 2023.

  • “When a person develops active TB (disease), the symptoms (cough, fever, night sweats, weight loss etc.) may be mild for many months. This can lead to delays in seeking care, and results in transmission of the bacteria to others.” WHO, “Tuberculosis”, 2018.
  • “Further progression leads to infectious disease (ie, with microbiologically positive sputum), which can be divided into subclinical disease (in which individuals do not report symptoms) and clinical disease (in which individuals report a prolonged cough or seek treatment due to their symptoms). A 2021 review of national tuberculosis prevalence surveys found that around 50% of people with prevalent infectious tuberculosis have subclinical disease, and, therefore, will not be diagnosed by policies that rely on reported symptoms.” Richards et al. 2023.

• 9

  “Without treatment, the death rate from TB disease is high (about 50%) (7). With treatments currently recommended by WHO (a 4–6 months course of anti-TB drugs), about 85% of people with TB can be cured.” WHO, "Global Tuberculosis Report 2023", p. 4.

• 10
  • “We conceptualized an intensive, city-wide intervention with two phases. The first phase (Fig. 3) comprised a one-time campaign to (a) screen the adult population, 15 years and above, for active TB (via a combination of chest X-ray and Xpert Ultra testing), with the treatment of persons identified with TB disease (active case finding, ACF) and evaluation of their child contacts, including treatment for active or latent TB (child contact tracing, CCT), and (b) test the same adult population (including adult household contacts) for latent TB, via tuberculin skin testing (TST), with the treatment of those with evidence of LTBI (preventive therapy, TPT).” Shrestha et al. 2021.
  • “One attractive approach is a 'surge/maintenance' strategy, involving an initial, time-limited phase of high-intensity intervention followed by a more sustained phase of health system strengthening that is facilitated by the initial 'surge.'" Shrestha et al. 2021.

• 11

  “We conceptualized an intensive, city-wide intervention with two phases. The first phase (Fig. 3) comprised a one-time campaign to (a) screen the adult population, 15 years and above, for active TB (via a combination of chest X-ray and Xpert Ultra testing), with the treatment of persons identified with TB disease (active case finding, ACF) and evaluation of their child contacts, including treatment for active or latent TB (child contact tracing, CCT), and (b) test the same adult population (including adult household contacts) for latent TB, via tuberculin skin testing (TST), with the treatment of those with evidence of LTBI (preventive therapy, TPT).” Shrestha et al. 2021.

• 12

  “If this one-time campaign was limited to only active case finding and related components, it achieved an immediate impact, but one that was not sustained … By contrast, when hypothetically limited to LTBI diagnosis and treatment (i.e., ignoring any effect on active TB), the immediate effects were small, but the longer-term impact was more pronounced”. Shrestha et al. 2021, p. 7. Since mass screen-and-treatment programs involve both components mentioned here, the program achieves both an immediate impact (predominantly caused by the active case finding) and a pronounced long-term effect (predominantly caused by diagnosing and treating TB infection).

• 13
  • For an example of the cost-effectiveness of our recommendations, see this page. As of January 2024, we estimate that the cost-effectiveness of opportunities we direct funding to is 10 times as cost-effective as unconditional cash transfers. Read more about how we use cost-effectiveness estimates in our grantmaking here.
  • Our cost-effectiveness analysis estimates that this program is around 5.1 times as cost-effective as unconditional cash transfers, though we expect this to depend on context, as we discuss here. See our cost-effectiveness analysis.

• 14
  • “We conceptualized the model to represent a medium-sized city in India—the country with the largest number of TB cases (more than 25% of the global burden)”
  • “This impact was achieved immediately … and persisted over time, lowering the TB incidence by 25.3% (24.1–26%) and TB mortality by 25.3% (24.2–26%) over the next 20 years following the intervention.”
  • “We conceptualized an intensive, city-wide intervention with two phases. The first phase (Fig. 3) comprised a one-time campaign to (a) screen the adult population, 15 years and above, for active TB (via a combination of chest X-ray and Xpert Ultra testing), with the treatment of persons identified with TB disease (active case finding, ACF) and evaluation of their child contacts, including treatment for active or latent TB (child contact tracing, CCT), and (b) test the same adult population (including adult household contacts) for latent TB, via tuberculin skin testing (TST), with the treatment of those with evidence of LTBI (preventive therapy, TPT).” Shrestha et al. 2021.

• 15

  “At baseline, we assumed that 70% of the adult population would undergo screening for active TB.” Shrestha et al. 2021.

• 16

  See here for more details.

• 17

  “[T]he cumulative impact of the one-time campaign was substantial and sustained: per 1 million population, a projected 5840 (4060–7650) cases would be averted by year 10 and 10,100 (6930–13,500) cases by year 20 (Fig. 4E, red lines); corresponding lives saved were 809 (612–1010) by year 10 and 1380 (1020–1750) by year 20 (Fig. 4F, red lines).”
  Shrestha et al. 2021

• 18
  • For example, Corbett et al (2010) and Marks et al (2019) find that active case-finding programs roughly halve TB prevalence. (See Table 4, Burke et al. 2021.)
  • Table 1 of Shrestha et al. 2021 estimates a baseline TB mortality of 24.75–41.25 deaths per 100,000 population. Assuming a baseline mortality of roughly 300 TB deaths per million and a program that reduces TB mortality by 50% (thereby assuming mortality reductions are proportional to prevalence reductions), we would guess that 150 deaths are averted per year of treatment (300 * 0.5 = 150). If this effect were to persist for 20 years, the program would avert 150 * 20 = 3,000 deaths. This is larger than the number of deaths averted estimated by Shrestha et al. 2021, but these programs involve several rounds of screening.

• 19

  “Two cluster-randomised trials compared the effects of active case-finding versus no active case-finding on tuberculosis prevalence in general populations (table 4). One further cluster-randomised trial allocated urban clusters in Zimbabwe to one of two types of active case-finding, and also evaluated change in tuberculosis prevalence before and after implementation of active case-finding, a non-randomised comparison.” Burke et al. 2021.

  From Table 4, Burke et al. 2021,

  • Corbett et al. 2010 found an adjusted risk ratio of 0.59 (0.40–0.89) on TB prevalence
  • Ayles et al. 2010 found an adjusted risk ratio of 1.09 (0.86–1.40) on TB prevalence
  • Marks et al. 2019 found an adjusted risk ratio of 0.55 (0.39–0.77) on TB prevalence

• 20

  “Clusters of neighbourhoods in the high-density residential suburbs of Harare, Zimbabwe, were randomised to receive six rounds of active case finding at 6-monthly intervals by either mobile van or door-to-door visits…The overall prevalence of culture-positive tuberculosis declined from 6·5 per 1000 adults (95% CI 5·1–8·3) to 3·7 per 1000 adults (2·6–5·0; adjusted risk ratio 0·59, 95% CI 0·40–0·89, p=0·0112).” Corbett et al. 2010.
  “Persons 15 years of age or older who resided in 60 intervention clusters (subcommunes) were screened for pulmonary tuberculosis, regardless of symptoms, annually for 3 years, beginning in 2014, by means of rapid nucleic acid amplification testing of spontaneously expectorated sputum samples…In the fourth-year prevalence survey, we tested 42,150 participants in the intervention group and 41,680 participants in the control group. A total of 53 participants in the intervention group (126 per 100,000 population) and 94 participants in the control group (226 per 100,000) had pulmonary tuberculosis, as confirmed by a positive nucleic acid amplification test for Mycobacterium tuberculosis (prevalence ratio, 0.56; 95% confidence interval [CI], 0.40 to 0.78; P<0.001).” Marks et al. 2019.

• 21

  “The Enhanced Case Finding (ECF) intervention had 4 main components;

  1. Community mobilisation was carried out in intervention communities using a mixture of methods including community drama, megaphone announcements, community meetings, leafleting with information about TB and the availability of the intervention and other community activities such as football matches, fashion shows etc. A rotating set of predefined messages were delivered during set time periods across all intervention communities but the activities through which they were delivered could vary depending on the local context.
  2. Establishment of an open access/fast track sputum collection point at the clinic that could be readily accessed by the community without having to wait to see a health care provider. Information about the availability of this service was included in the community mobilisation messaging.
  3. Sputum collection points were set up in the community in a rotating manner so that the whole community was covered. Each sputum collection point was established for a period of 2 weeks and then it moved on to the next location. Each location was visited 3 times per year. Community mobilisation activities and schools interventions were planned to coincide with the area where the sputum collection point was operating. For the open access/fast track and community sputum collection points, sputum was collected from any individual who wanted to provide it regardless of symptoms.
  4. A schools intervention was developed that included health talks, drama, drawing and writing competitions, quizzes, debates and other competitions to encourage children to spread messages about TB to their parents and the community. Activities that required the children to take home quizzes or drawings were especially considered as this directly involved other members of their households. In some schools anti-TB clubs were also established.” Ayles et al. 2013, Supplementary Appendix.

  “The adjusted prevalence ratio for the comparison of ECF versus non-ECF intervention groups was 1·09 (95% CI 0·86–1·40)” Ayles et al. 2013.

• 22
  • “Both active case-finding strategies used community workers to identify chronic cough (≥2 weeks) in the community, followed by collection of two sputum samples per adult for fluorescence microscopy but not culture.” Corbett et al. 2010. There was no mention of screening for cases of TB infection that have not progressed to disease.
  • “In our trial the intervention was limited to active case finding.” Marks et al. 2019. There was no mention of screening for cases of TB infection that have not progressed to disease.
  • “Of 64 463 randomly selected individuals, 894 individuals had active tuberculosis.” Ayles et al. 2013. There was no mention of screening for cases of TB infection that have not progressed to disease.

• 23
  • “There is an increasing body of evidence suggesting that after completion of TB treatment, despite clearance of TB bacilli, a significant proportion of TB survivors are left with post-TB sequelae, possibly due to damage of lung tissues during the period of active TB disease.” Maleche-Obimbo et al. 2022.
  • “The term post-tuberculosis describes the range of pathological conditions experienced by tuberculosis survivors. Pulmonary tuberculosis, the commonest disease presentation, causes progressive destruction of lung tissue, and this damage might not fully resolve after treatment. Although prompt treatment can minimise lung damage, many tuberculosis survivors experience residual lung pathology, with cross-sectional studies consistently demonstrating substantial pulmonary impairment—including chronic obstructive pulmonary disease (COPD), spirometric restriction, bronchiectasis, and pulmonary hypertension, as well as secondary non-tuberculosis lung infections—among tuberculosis survivors.” Menzies et al. 2021.

• 24

  “Although successful treatment prevents death, many tuberculosis survivors experience ongoing health problems following the disease episode, and there is increasing evidence of long-term disability and elevated mortality risks in this population.” Menzies et al. 2021.

• 25

  See Menzies et al. 2021, Table 2, row "Years of Life Lost, WHO high-burden," column "Percent increase with post-tuberculosis."

• 26

  “For simplicity and interpretability, we assumed a closed population with no immigration or emigration.” Shrestha et al. 2021.

• 27

  “People successfully completing treatment for tuberculosis remain at elevated risk for recurrent disease, either from relapse or reinfection.” Cudahy et al. 2020.

• 28
  • For the average severity of the disability associated with TB disease, we apply the TB disability weight from the 2019 Global Burden of Disease Study. See Menzies et al. 2021, Table 1, row "Disability weight for tuberculosis”.
  • Our figure for the average length of TB disease is drawn from Table 1 of Menzies et al. 2021, row "Duration of untreated tuberculosis". However, we mark this figure down slightly to account for the fact that tuberculosis is often treated.
  • We have not reviewed Menzies et al. 2021’s estimates in depth, and it is possible that further work would cause us to update our understanding of the morbidity benefits of averting TB disease.

• 29

  See GiveWell, CEA: TB mass screen and treatment, “CEA” tab, ‘Morbidity Averted’ section, 'Total validity adjustment' row.

• 30

  See Menzies et al. 2021, Table 2, row "Years lived with disability, WHO high-burden," column "Percent increase with post-tuberculosis."

• 31

  See GiveWell, CEA: TB mass screen and treatment, “CEA” tab, 'Cost per person' row. This is equivalent to around $13 per person screened, based on assumptions for the share of the population who are over 15 years old (73% adjustment) and for the share of population actually reached (70% adjustment). This means that around half of the population in the intervention area will be screened. (73% * 70% = 51%).

• 32

  See summary of papers used here. We apply a 50% downward adjustment to the cost estimates that were based on programs with universal sputum collection: based on discussions with an expert we think that these programs are likely to be significantly more expensive than a standard screen-and-treat program. We then average the costs of the different programs we identified. Finally, we apply a 25% downward adjustment to this average to account for our guess that programs would be cheaper when run at larger scale since various fixed costs wouldn’t scale.

• 33

  We estimate that it costs about $1.50 to deliver one round of SMC to a child, and there are typically four to five cycles of SMC delivery per year ($1.50 * 4 = $6.00 per person and $1.50 * 5 = $7.50 per person) Read more about our cost estimates for SMC here.

• 34

  For example, IHME estimates that tuberculosis accounts for 5% of deaths in India, and has a mortality rate of 30 deaths per 100,000 people annually (see here). In comparison, malaria accounts for 12% of deaths in children aged under 5 (i.e., those targeted by seasonal malaria chemoprevention) in Nigeria, and has a mortality rate of 285 deaths per 100,000 children annually (see here).

• 35

  GiveWell values different benefits using moral weights. See a summary of GiveWell’s moral weight approach (and age-based estimates) here.

• 36

  For an example of TB prevalence varying between urban and non-urban areas, see Qadeer et al. 2016, table 5.

• 37

  For example, the "National TB Prevalence Survey in India 2019-2021" indicated that TB prevalence in New Delhi was around twice as high as TB prevalence nationally: According to Figure 21, prevalence of TB in New Delhi is 747 per 100,000 population, compared to an average in India of 312 per 100,000.

• 38

  “We adopted a number of simplifying assumptions and data choices to broadly represent both an urban population-center in India and implementation of a comprehensive mass intervention; the projections created here are therefore not fully reflective of the impact of any specific intervention as implemented in a specific (and inherently more complex) epidemiological setting.” Shrestha et al. 2021.

• 39

  “The second phase of the intervention involved health system strengthening (HSS), modeled as a set of improvements to the TB health care system that could be facilitated by the improved infrastructure and lower TB burden achieved by the initial, one time intervention phase, potentially allowing them to be implemented and maintained at lower incremental cost than if implemented on their own. Such systemic improvements could include (i) communication and outreach efforts to increase awareness of TB, improve access to TB services, and destigmatize TB; (ii) patient database supporting adherence support mechanisms such as telecall-based monitoring, patient welfare support during treatment, and digital adherence technology (DAT) to improve linkage to care and retention; (iii) retention of trained staff and infrastructural improvements, including in testing and screening equipment (achieved through investment in the one-time intervention), to improve quality of care and facilitate contact investigation; and (iv) enhanced surveillance and reporting with real-time analysis to enhance outbreak response and follow-up. The impact of these health system strengthening activities was modeled in the reference scenario as a 50% reduction in time to TB treatment initiation once symptomatic and a 50% reduction in treatment non-success (see Additional File 1:S-4 for additional details) [38–41].” Shrestha et al. 2021.

  “Annual TB incidence could be reduced by 42.9% (37–52.3%) and mortality by 65.6% (59.3– 73.4%) at 10 years compared to no intervention (Fig. 5C, D, dark burgundy lines). Consequently, if the one-time campaign could catalyze health system strengthening, the total number of cases and deaths averted by year 10 increased to 7800 (5450–10,200; a 1.3-fold increase compared to the one-time intervention only) and 1710 (1290–2180; a 2.1-fold increase), respectively, per million population.” Shrestha et al. 2021.

• 40

  For example, some of the costing analyses we averaged over included the cost of treatment for TB, while others only included the cost of screening and referral:

  • The cost estimates we use from Brümmer et al. 2023 and Yadav et al. 2014 included treatment costs:
    • “Costs are expressed in 2023 US dollars and include treatment costs.” Brümmer et al. 2023.
    • “The total economic cost of the program was estimated to be $363,257 and are divided by category in Table 2. Of the total costs of the screening program, 72% were variable costs, determined by the number of patients screened, and the remaining 28% were fixed capital and salary costs. The cost of treating the 810 bacteriologically confirmed cases with community DOTS would be approximately $200,000 without discounting for defaulters.” Yadav et al. 2014.
  • The cost estimates from Baik et al. 2023 do not appear to include treatment costs: see Table 4 for a list of included costs.

  See GiveWell, CEA: TB mass screen and treatment, “Costs” tab for full workings.

• 41

  “156,000 persons with MDR-TB or RR-TB began treatment in 2018, but the latest data show that only 56% completed treatment successfully.” Mase and Chorba 2019.

  “The median cost per person treated for TB in 2022, from a provider perspective, was US$ 807 for drug-susceptible TB (Fig. 4.11)…The median cost per person treated for drug-resistant TB, from a provider perspective, was US$ 5047 in 2022 (Fig. 4.12). These amounts include all of the provider costs associated with treatment and TB programme-related costs. WHO, "Global Tuberculosis Report 2023", p. 4. Financing for TB prevention, diagnostic and treatment services’.