Longevity Research Initiative  ·  Bayesian Causal Atlas

Tuberculosis: A Structural Causal Analysis

A Judea Pearl Structural Causal Model for the world's leading infectious killer — and the disease with arguably the largest single causal effect in medicine: untreated active TB kills roughly 70% over ten years, while a 6-month drug regimen cures ~85–90%. The model is a cascade — prevent, treat by resistance profile, support — and the headline lever for drug-resistant TB is a regimen swap that nearly doubles survival. All effect sizes are real published values; aggregation is an upper-bound ceiling.

do-calculus treatment cascade backdoor adjustment PAF (Levin) E-values Pareto set
Read first — scope & honesty. This is a quantitative research instrument, not medical advice. TB treatment is a public-health responsibility: regimen choice depends on drug-susceptibility testing, and incomplete or incorrect treatment breeds resistance. The model's structure: untreated case-fatality is the anchor, the regimen converts it to a treated outcome, and resistance status determines which regimen applies. Drug-susceptibility testing, regimen selection, and adherence support belong with a TB program and clinician.

I. Experiment — Question, endpoint, states

Causal question. Across TB interventions with published mortality, cure, and progression effects, what is the identifiable causal contribution of each to a reduction in TB death — conditional on disease state (latent, active drug-susceptible, active drug-resistant) and HIV status — after backdoor confounding control?

StatePopulationEndpointAnchor (untreated)Decisive lever
Latent TBInfected, not yet diseased (~1 in 4 humans ever infected)Progression to active TB~10% lifetime (30–50% if HIV+)TB preventive therapy; BCG
Active DS-TBDrug-susceptible active disease10-yr case fatality~70% smear+; ~20% smear−6-month first-line regimen (HRZE/HR)
Active MDR/RR-TBRifampicin/multidrug-resistant diseaseTreatment success vs deathUntreated ≥ DS-TB; old SoC success only 52%BPaLM (6-month, all-oral) — 89% success

HIV co-infection raises both progression and case fatality; antiretroviral therapy is modelled as a modifier. ~10.8 million cases and ~1.23 million deaths per year; the world's leading cause of death from a single infectious agent.

II. Method — The structural causal model

II.1 The causal DAG

TB is a cascade: infection may progress to disease; disease, treated with a regimen matched to the resistance profile, resolves or kills. Two do(·) points dominate — prevention (stopping progression) and effective treatment (converting a ~70% killer into a ~90% cure). Resistance status is the switch that decides which regimen works.

Latent infection~1 in 4 ever TPT / BCGdo(prevent) Active TB diseaseDS or MDR/RR Regimen by resistancedo(treat) TB deathcure vs fatality HIV · malnutrition · diabetes · delayconfounders / risk modifiers ~70% untreated → ~10% treated (DS)

II.2 Estimators & assumptions

OperationMethodImplementation here
Confounding controlBackdoor criterion (Pearl)Regimen success rates from RCTs/cohorts; untreated CFR from pre-chemotherapy natural-history studies
State conditioningdo(·) effect varies by stateThe same regimen has different effects on latent (prevention) vs active (cure) vs resistant (regimen-dependent) TB
HIV modificationEffect-measure modifierHIV raises progression and case fatality; ART is modelled as a partial reversal (labelled approximate)
Population impactAttributable fraction (Levin)Most TB deaths are attributable to lack of timely effective treatment — a treatment-access PAF, not a biology PAF
RobustnessE-value (VanderWeele & Ding)Computed for the dominant treatment effect
Minimum-effective setPareto frontier (threshold slider)Interventions ranked by relative mortality / progression reduction (Section IV)

III. Result — Verified effect sizes (the evidence base)

Intervention / stateEffectSource / grade
Untreated active TB (smear+), 10-yr~70% case fatality (53–86%)Tiemersma 2011, PLOS One; WHO B
Untreated active TB (smear−, culture+)~20% case fatalityTiemersma 2011; WHO B
Standard 6-month regimen (DS-TB)~85% treatment successWHO global; cohort 74–95% A
BPaLM (MDR/RR-TB, 6-month all-oral)89% success vs 52% old SoCTB-PRACTECAL; ZeNix; Nix-TB 90% A
TB preventive therapy (latent)~60% reduction in progression (RR ≈ 0.40)Cochrane IPT/3HP A
BCG vaccination~50% vs disease; ~73% vs severe childhood TBTrunz 2006 meta-analysis B
ART in HIV-associated TB~56–68% mortality reduction (low CD4)SAPIT / CAMELIA A

The defining fact: the gap between treated and untreated active TB is one of the largest causal effects in all of medicine — roughly 70% mortality collapses to single digits. For resistant TB the decisive modern lever is the regimen itself: BPaLM raised success from 52% to 89%.

Interactive engine — mortality by state, resistance & HIV

Tuberculosis SCM engine
Disease state
Treatment
A=RCT (HRZE, BPaLM, TPT) · B=historical MDR SoC / variable BCG. Below-tier regimens disabled; falls back to No-treatment.
70% = smear-positive 10-yr CFR (Tiemersma); ~20% for smear-negative culture-positive.
Outcome (no treatment)
death / progression
Outcome (this care)
modelled
Absolute reduction
percentage points
Relative reduction
vs no treatment

IV. Pareto minimum-effective set

TB interventions ranked by relative mortality / progression reduction versus their no-treatment baseline. Drag the threshold to set a minimum: interventions at or above the line form the minimum-effective set. Effective treatment of active disease dominates everything — but prevention reaches people before they ever reach the clinic.

V. Curiosity — second-order implications

The largest effect in medicine

Few interventions move an outcome from ~70% mortality to single digits. TB treatment does — which means most TB deaths are not failures of biology but failures of access, diagnosis, and completion. The do(·) is a functioning health system.

Resistance is iatrogenic

MDR-TB is partly created by incomplete treatment. BPaLM's 89% success is transformative, but the upstream lever is preventing resistance through complete first-line cure — a causal loop the model makes visible.

Prevention scales differently

Treating active TB saves a life already at risk; TPT and BCG act on people before disease. With ~1.2 million deaths a year, even modest preventive-therapy coverage in latent and HIV-positive populations compounds over time.

HIV is the multiplier

HIV converts latent TB from a ~10% lifetime risk into a yearly one and raises case fatality; integrated TB–HIV care with ART is therefore not an add-on but a core mortality lever.

VI. Antithesis — where this analysis could be wrong

ChallengeWhy it threatens the conclusionMitigation here
Untreated CFR is historicalThe ~70% figure comes from pre-chemotherapy-era cohorts; populations and comorbidity differ todayGraded B; presented as the anchor with a user-adjustable range (20–86%)
Success ≠ survival"Treatment success" bundles cure + completion; death is only part of the unfavorable fractionTreated outcome modelled as the death component, not 1 − success, and labelled
HIV/ART multipliers are approximateThe exact mortality multiplier for HIV and its reversal by ART vary by CD4 and timingLabelled approximate; ART benefit anchored to the SAPIT/CAMELIA range, flagged
BCG efficacy variesBCG protection ranges widely by latitude and prior exposureReported as a range (≈50% disease, ≈73% severe childhood), graded B
Programmatic vs trialTrial success (BPaLM 89%) exceeds routine programmatic outcomesFramed as an upper-bound ceiling; old-SoC 52% shown alongside as the real-world contrast

Appendix A — Computation reference

active untreated = anchorCFR × hivMult ; latent untreated = progRisk × hivProgMult

treated death: DS standard ≈ 0.05 ; MDR BPaLM ≈ 0.06 ; MDR old SoC ≈ 0.15 (×hivMult×artMult)

latent + TPT: progression × 0.40 (≈60% reduction)

hivMult = HIV ? (ART ? 1.15 : 1.6) : 1.0 ; hivProgMult = HIV ? (ART ? 1.8 : 4.0) : 1.0

ARR = untreated − treated ; RRR = ARR / untreated ; E-value(RR) = RR* + √(RR*·(RR*−1)), RR* = 1/RR

Worked example: active DS-TB, smear+ anchor 70%, standard regimen, HIV− → untreated 70%, treated 5%, ARR 65 pp, RRR 93%. MDR-TB old SoC death 15% vs BPaLM 6% → modernising the regimen alone averts 9 deaths per 100 treated.

Appendix B — References

1. Tiemersma EW, van der Werf MJ, Borgdorff MW, et al. Natural History of Tuberculosis: Duration and Fatality of Untreated Pulmonary Tuberculosis in HIV-Negative Patients. PLoS One 2011;6:e17601. Smear+ 10-yr case fatality ~70% (53–86%); smear− culture+ ~20%.

2. WHO Global Tuberculosis Report (treatment). Standard 6-month regimen (2HRZE/4HR) for drug-susceptible TB; global treatment success ~85%. ~10.8 million cases, ~1.23 million deaths/yr.

3. Nyang'wa B-T, et al. (TB-PRACTECAL). NEJM 2022. BPaLM 6-month all-oral regimen: 89% treatment success vs 52% standard of care for MDR/RR-TB.

4. Conradie F, et al. (Nix-TB / ZeNix). NEJM 2020/2022. BPaL favourable outcome ~90% (95/107 Nix-TB); ZeNix 84–91% with reduced linezolid.

5. Cochrane reviews of TB preventive therapy (isoniazid; 3HP rifapentine): ~60% reduction in progression from latent to active TB (RR ≈ 0.40).

6. Trunz BB, Fine PEM, Dye C. Effect of BCG vaccination on childhood tuberculous meningitis and miliary tuberculosis worldwide: a meta-analysis. Lancet 2006. ~73% protection against TB meningitis; ~50% against disease overall.

7. SAPIT (Abdool Karim, NEJM 2010) and CAMELIA (Blanc, NEJM 2011): integrating ART with TB treatment reduces mortality in HIV-associated TB, particularly at low CD4 (~56–68%).

Longevity Research Initiative — Bayesian Causal Atlas. SCM methodology after Judea Pearl; E-values after VanderWeele & Ding. All effect sizes are published values cited above; none were fabricated. Outputs are upper-bound ceilings for research, not clinical guidance. Report generated .