Background: Ratios of bacteriologically positive tuberculosis (TB) prevalence to notification rates are used to characterise typical durations of TB disease. However, this ignores the clinical spectrum of tuberculosis disease and potentially long infectious periods with minimal or no symptoms prior to care-seeking. Methods: We developed novel statistical models to estimate progression from initial bacteriological positivity including smear conversion, symptom onset and initial care-seeking. Case-detection ratios, TB incidence, durations, and other parameters were estimated by fitting the model to tuberculosis prevalence survey and notification data (one subnational and 11 national datasets) within a Bayesian framework using Markov chain Monte Carlo methods. Results: Analysis across 11 national datasets found asymptomatic tuberculosis durations in the range 4–8 months for African countries; three countries in Asia (Cambodia, Lao PDR, and Philippines) showed longer durations of > 1 year. For the six countries with relevant data, care-seeking typically began half-way between symptom onset and notification. For Kenya and Blantyre, Malawi, individual-level data were available. The sex-specific durations of asymptomatic bacteriologically-positive tuberculosis were 9.0 months (95% credible interval [CrI]: 7.2–11.2) for men and 8.1 months (95% CrI: 6.2–10.3) for women in Kenya, and 4.9 months (95% CrI: 2.6–7.9) for men and 3.5 months (95% CrI: 1.3–6.2) for women in Blantyre. Age-stratified analysis of data for Kenya showed no strong age-dependence in durations. For Blantyre, HIV-stratified analysis estimated an asymptomatic duration of 1.3 months (95% CrI: 0.3–3.0) for HIV-positive people, shorter than the 8.5 months (95% CrI: 5.0–12.7) for HIV-negative people. Additionally, case-detection ratios were higher for people living with HIV than HIV-negative people (93% vs 71%). Conclusion: Asymptomatic TB disease typically lasts around 6 months. We found no evidence of age-dependence, but much shorter durations among people living with HIV, and longer durations in some Asian settings. To eradicate TB transmission, greater gains may be achieved by proactively screening people without symptoms through active case finding interventions
Keywords: Tuberculosis; Sub-clinical tuberculosis; Bayesian statistics; Care-seeking; Epidemiology
Supplementary Information The online version contains supplementary material available at https://doi.org/10.1186/s12916-021-02128-9.
Population surveys of the prevalence of bacteriologically-positive tuberculosis (TB) disease are a key tool for understanding TB epidemiology and burden, and, when repeated over time, for monitoring the impacts of control efforts. (Bacteriologically-positive TB is TB that is diagnosed with a positive result to any bacteriological test: sputum smear, culture or Xpert.) Over the last decade, the World Health Organization (WHO) has encouraged and facilitated a series of nationally-representative TB prevalence surveys in priority countries [[
By comparing prevalence with notifications—usually as a prevalence-to-notification (P:N) ratio—one can estimate a typical timescale for prevalent TB, the inverse of which (the patient diagnostic rate) provides a measurable indicator of the effectiveness of case detection [[
Several prevalence surveys also record whether individuals with TB were symptomatic and some record whether individuals with TB had previously sought care for their symptoms [[
Many aspects of the natural history of TB disease prior to (or without) treatment remain very uncertain because ethical considerations mean we must rely on historic data from the pre-chemotherapy era. For example, how often and how quickly individuals with smear-negative TB progress to smear-positive disease is unclear. Similarly, while there are data to suggest a typical duration of around three years for untreated TB disease [[
We therefore sought, as our primary focus, to leverage prevalence survey data from a variety of settings to estimate the duration of asymptomatic TB disease and typical delays to care-seeking and notification. Hypothesising an influence of sex, age and HIV infection status on these quantities, our primary objectives also included stratified analyses where data allowed. We used a novel Bayesian framework within which we incorporated uncertainty, disease progression before detection, and trends in incidence. This approach also provided, as by-products, estimates of TB incidence, incidence trends, and case-detection ratios, which we also report as secondary outcomes.
We analysed data from eleven national and one sub-national setting with a TB prevalence survey conducted after 2010. We excluded TB prevalence surveys that relied solely on symptom screening to decide who to sample for bacteriological testing and restricted to the population aged 15 years or older.
The eleven national settings with recent TB prevalence surveys are Cambodia [[
Summaries of prevalence survey data are shown in the upper part of Table 1. The proportion of prevalent cases that were asymptomatic ranged from 30% in Malawi, to 70% in Cambodia, and the proportion of prevalent cases that were sputum smear-positive varied from 23% in Vietnam to 84% in Tanzania. Definitions used for symptoms, care-seeking, and raw counts extracted from the prevalence are summarised in Additional file 2.
Table 1 Epidemiological estimates. World Health Organization (WHO) case detection ratio (CDR), our CDR, proportions of cases dying and self-curing before treatment, rates of decline by setting
Cambodia Ethiopia Kenya Lao PDR Malawi Pakistan Philippines Uganda Tanzania Vietnam Zambia Population aged 15 and above, 2019, million 11.4 66.9 32 4.9 10.5 140.6 75.2 23.7 32.6 74.1 9.9 Survey year 2011 2010–2011 2016 2010–2011 2013 2010–2011 2016 2014–2015 2012 2017–2018 2013–2014 Prevalence in the survey year, per 100,000a 839 (748–933) 236 (193–281) 478 (425–532) 604 (528–683) 418 (348–491) 325 (291–360) 998 (908–1090) 395 (335–456) 305 (258–355) 358 (311–406) 575 (508–644) Case notification, 2019, per 100,000 247 (244–250) 150 (149–150) 238 (236–240) 138 (135–142) 146 (144–148) 201 (200–202) 488 (486–489) 238 (236–240) 212 (210–213) 136 (135–137) 340 (336–343) Case-detection ratio 57 (40.0–88) 69 (51.0–98) 63 (43.0–100) 57 (40.0–89) 56 (35.0–110) 64 (48.0–90) 63 (40.0–110) 65 (43.0–110) 53 (30.0–110) 57 (40.0–90) 58 (41.0–90) Incidence rate in 2019, per 100,000 387 (220–546) 208 (133–283) 385 (194–572) 206 (120–288) 218 (85–361) 351 (225–478) 705 (310–1102) 304 (135–473) 356 (98–617) 219 (127–312) 534 (302–766) Duration by P:N ratio, monthb 28.9 (25.8–32.3) 10.2 (8.4–12.3) 25.1 (22.5–28.0) 109.4 (95.1–125.6) 29.7 (25.1–34.9) 17.8 (15.9–19.9) 33.3 (30.5–36.4) 24.5 (21.0–28.5) 20.8 (17.7–24.2) 33.0 (28.6–37.3) 16.0 (14.2–18.0) Time bacteriologically positive, month 22.0 (18.9–25.8) 9.0 (7.5–10.8) 18.3 (16.1–21.0) 36.1 (29.9–45.3) 21.8 (18.4–25.9) 13.1 (11.5–14.8) 22.3 (19.6–25.6) 19.1 (16.4–22.2) 16.2 (14.0–18.8) 20.7 (17.8–24.5) 13.4 (11.8–15.2) Case-detection ratioc 67% (56–79%) 87% (80–94%) 69% (60–79%) 24% (19–31%) 63% (53–74%) 76% (68–84%) 53% (46–62%) 63% (53–72%) 68% (59–77%) 59% (50–70%) 79% (71–88%) Incidence rate in 2019, per 100,000 342 (285–403) 168 (156–183) 360 (312–414) 595 (449–743) 222 (187–265) 287 (259–319) 914 (782–1,055) 354 (305–412) 307 (271–352) 230 (194–272) 426 (382–474) Duration by P:N ratio, month 8.7 (7.1–10.5) 5.3 (4.0–6.7) 11.8 (9.9–13.8) 53.4 (43.6–64.1) 20.6 (16.7–24.9) 10.9 (9.4–12.4) 10.9 (9.2–12.7) 11.9 (9.4–14.6) 13.3 (10.7–16.0) 21.9 (18.6–25.6) 9.7 (8.2–11.3) Time without care-seeking, month 1.2 (0.6–1.9) N.A. 5.6 (4.6–6.8) N.A. 6.6 (4.8–8.6) N.A. 4.2 (3.3–5.2) 4.2 (3.0–5.6) 6.3 (4.9–7.8) N.A. 4.1 (3.3–5.1) Time bacteriologically positive, month 8.0 (6.7– 9.6) 4.8 (3.7–6.0) 9.5 (8.2–11.1) 21.6 (18.0–26.8) 15.8 (13.2–19.0) 8.2 (7.2–9.4) 9.2 (7.9–10.6) 10.4 (8.5–12.5) 10.9 (9.1–13.0) 14.2 (12.1–16.8) 8.5 (7.3– 9.8) Case-detection ratio 83% (77–89%) 92% (87–96%) 80% (73–86%) 36% (29–44%) 69% (59–79%) 82% (76–88%) 75% (69–81%) 75% (67–82%) 75% (67–82%) 66% (57–75%) 85% (78–91%) Incidence rate in 2019, per 100,000 270 (252–292) 160 (154–169) 309 (286–338) 396 (315–482) 203 (177–235) 265 (247–285) 643 (593–700) 296 (269–329) 277 (252–309) 206 (181–235) 397 (369–429) Annual decline rate, % per year 4.7% (4.4–5.0%) 7.3% (7.1–7.5%) 1.5% (1.3–1.6%) − 8.0% (− 8.5% to − 7.5%) 2.9% (2.6–3.2%) 1.6% (1.5–1.7%) − 8.3% (− 8.4% to − 8.2%) − 2.4% (− 2.7% to − 2.2%) − 1.3% (− 1.5% to − 1.1%) 1.0% (0.7–1.4%) 4.9% (4.7–5.1%) Asymptomatic phase over total duration, % 64% (58–69%) 47% (39–56%) 48% (42–53%) 40% (35–45%) 27% (21–35%) 37% (32–42%) 59% (53–65%) 45% (38–53%) 32% (26–40%) 31% (26–37%) 37% (31–43%) Symptomatic TB without care-seeking, % 15% (9–23%) N.A. 59% (51–67%) N.A. 42% (33–51%) N.A. 46% (38–54%) 41% (31–50%) 57% (48–67%) N.A. 48% (40–56%) Self-cure before case-detection 32% (20–42%) 14% (8–21%) 27% (16–36%) 50% (35–61%) 30% (19–41%) 20% (12–28%) 32% (20–42%) 27% (17–37%) 23% (14–33%) 30% (19–41%) 20% (12–28%) Deaths before case-detection 7% (6–9%) 3% (2–4%) 6% (5–7%) 19% (15–26%) 10% (8–12%) 6% (5–7%) 6% (5–7%) 8% (6–10%) 8% (6–9%) 12% (10–14%) 5% (4–6%)
The sub-national TB prevalence survey from Blantyre, Malawi, and the national survey from Kenya included individual data, allowing analysis by smear status, age, and HIV status [[
In 2019, a TB prevalence survey was carried out in Blantyre as part of a cluster-randomised trial of community-based TB screening interventions (ISRCTN11400592). Blantyre City was demarcated into 72 neighbourhood clusters, each with approximately 4000 adults aged 15 years or older. All households were enumerated and 115 households per cluster were chosen at random for participation into the prevalence survey, with the aim of recruiting 215 adults (≥ 15 years old) per neighbourhood. Adults from the randomly selected households were visited and invited to visit a study tent where TB symptom screening and digital chest radiograph (interpreted by radiographers and computer-assisted diagnostic software [Qure.ai version 2.0]) were done. Participants who had an abnormal chest X-ray or reported cough of any duration were asked to provide two spot sputum samples for Xpert, smear microscopy, and MGIT culture, and participants with positive results were linked to treatment.
Unless otherwise stated, we used the definition of TB symptoms and health-care seeking adopted by each TB prevalence survey (see Additional file 2). Asymptomatic TB was taken to be bacteriologically-positive pulmonary TB in those reporting no TB symptoms, and we assumed no health-care seeking for TB during this phase. Participants with prevalent TB who were already taking TB treatment were excluded. We assumed TB treatment initiation, TB confirmation, and case notification are identical events for the modelling.
We used TB notification data and treatment outcome data from the WHO TB database for the countries with national prevalence surveys [[
In order to take advantage of the finer-grained prevalence data for Kenya and Blantyre, Malawi, we sought correspondingly stratified notification data. For Kenya, we obtained data simultaneously stratified by HIV and age group from the National Tuberculosis Programme. For Blantyre, we obtained individual-level notification data from an enhanced surveillance system. We matched the age-groups of the Kenya data to the finest scale of WHO case notification data; for Blantyre, Malawi, with lower case counts, we used two age groups (15 to 49, and 50+ years).
In order to model mortality and project national numbers of prevalent cases to relate to notifications, we used age- and sex-specific background mortality rates and population sizes from 2019 World Population Prospect (WPP) data, using the mid-year population estimates [[
Additional file 2 details the extracted data and information from the prevalence surveys, and the demographic and notification data to replicate the analysis are available at Addition file 3, 4 and 5.
We developed three state transition models of TB case-detection to match available data, and fitted them to estimate parameters driving TB progression and care-seeking behaviour. Figure 1 shows that all models contain an asymptomatic phase and progress to the symptomatic phase. Model A presents a basic transition between asymptomatic/symptomatic phases; Model B divides the symptomatic phase by presence of care-seeking intentions, and Model C details the conversion between smear-negative and smear-positive and care-seeking by smear status. Model B is only applicable to the prevalence surveys that reported care-seeking behaviours. We did not combine Model B and C because the symptoms, smear status, and care-seeking behaviours prevalence surveys were not cross-tabulated in the published reports. We constructed a likelihood depending on these states for fitting to TB prevalence and case-notification data. For Kenya and Blantyre data with Model C, we incorporated proportional hazard models relating the rate of developing symptoms and the care-seeking rates by smear status to covariates of age, sex, and HIV status. The choice of model type used was determined by which data were available in the prevalence survey. These model structures follow the usual structures used in conventional ordinary differential equation TB models to describe the progression and detection or prevalent TB, e.g. the smear status-stratified model introduced by Dye et al. [[
Graph: Fig. 1 Diagrams of model structure. White boxes are states representing all bacteriologically-positive TB accessible to prevalence surveys, with structure matched to available data. Dashed arrows represent transitions out of active disease, either through self-cure or death. Models are additionally stratified by sex, and for Kenya and Blantyre, Malawi by age and HIV in addition
We calculated a single weighted-mean background mortality rate from WPP data in each country, using WHO TB notifications as weights. For the analyses with HIV stratification, we added HIV-related deaths for PLHIV from the UNAIDS database: 0.016 per year (
We assumed each state declined exponentially with a constant shared rate. The statistical models were constructed within a Bayesian framework; Additional file 1 details the mathematical formulation and priors. We fitted all models by Markov chain Monte Carlo (MCMC) using R with RStan. Inferences were based on 3000 samples from three chains. For each chain, we set at least 4000 burn-in steps and increased thinning to ensure the effective sample sizes are larger than 10% of sample sizes. Processed data, all source code, and detailed diagnostics for this analysis are available on Github as [https://github.com/TimeWz667/AsymTB].
For each setting, we used posterior samples to calculate: the TB incidence; the mean durations of asymptomatic disease, disease without case-seeking initiation (where applicable), and prevalent untreated disease; the case detection ratio (CDR; defined as the ratio of estimated incidence rates and observed notification rates) as the ratio of incidence and notification rates; and the proportion of TB cases reaching each stage of care (assuming unidirectional progression through states). Aggregated quantities were computed weighted by model-estimated incidence. We also output a joint posterior for the proportion of symptomatic cases initially smear-positive and the subsequent smear-conversion rate. We report means and 95% credible intervals (CrIs).
MCMC runs all converged with Gelman-Rubin R^2 statistics < 1.05. Supplementary data on convergence and parameter posteriors are presented in Additional file 1.
The national estimates for duration of asymptomatic disease typically ranged around 3–8 months. However, three countries in Asia (Lao PDR, Cambodia, Philippines) showed longer durations of over one year (see Fig. 2). These countries also had long total durations: 22 months for Cambodia and Philippines, and three years for Lao PDR. Only one country (Ethiopia) had a total duration lower than 12 months. In the seven countries where we could estimate the delay to care-seeking initiation, apart from the Philippines (16 months), delay varied between 1.2 (0.7–1.9) months for Cambodia and 6.6 (95% CrI: 4.8–8.6) months for Malawi. The asymptomatic phase represented between 27% and 63% of time as a prevalent case, and care-seeking occurred between 15% and 59% of the way between the first symptom developed and notification (or death or self-cure).
Graph: Fig. 2 Total time in months spent in each state during bacteriologically-positive TB disease. 'Not diagnosed' includes all states (white boxes) in Fig. 1. Median and 95% quantiles are shown as points and error bars, respectively. Posterior distributions are shown by coloured kernel density estimates
Figure 3 shows the joint posterior probability densities of smear conversion rates and the proportion of symptomatic cases that were smear-positive at symptom onset. Estimates of smear conversion rates were linearly related to the initial proportion of symptomatic cases that were smear-positive. Pooling weighted by TB notifications in 2019 (excluding Malawi and Vietnam as outliers) yielded a gradient of 0.239 years, and a pooled intercept of 53.4% smear-positive (for zero smear conversion rate).
Graph: Fig. 3 Smear-conversion rate and initial proportion smear-positive in symptomatic bacteriologically-positive TB disease. A joint posterior probability densities of initial proportion smear-positive (y-axis) and hazard of converting from smear-negative to smear-positive (x-axis) in symptomatic bacteriologically-positive TB by country, based on Model 3. B the correlation of percentage smear-positive at symptom onset and smear-type conversion rate. X-axis indicates the slopes of A estimated by linear regression (KHM = Cambodia, ETH = Ethiopia, KEN = Kenya, LAO = Lao People's Democratic Republic, MWI = Malawi, PAK = Pakistan, PHL = Philippines, TZA = United Republic of Tanzania, UGA = Uganda, VNM = Viet Nam, ZMB = Zambia) (C) joint probability density of initial proportion smear-positive (y-axis) and hazard of converting from smear-negative to smear-positive (x-axis) pooled by weights of notified cases in 2019 (excluding Malawi and Vietnam as outliers)
Table 1 shows the empirical estimates of total duration and total asymptomatic duration based on P:N ratios. Empirical estimates of duration systematically overestimate the durations (see also direct comparison of additional results in Additional file 1), because they implicitly assume all episodes of TB disease end in notification. Modelling self-cure and death leads to shorter estimates of duration and differences in estimated CDRs from WHO estimates. The proportions of incident TB who die or self-cure before being detected are shown in Table 1. Estimates of TB incidence are also distinct but comparable to WHO incidence estimates. We estimated rates of TB incidence change from a decline of 7.3% (95% CrI: 7.1–7.5%) per year in Ethiopia up to an increase of 8.3% (95% CrI: 8.2–8.4%) per year in the Philippines.
Figure 4 shows care cascade estimates for the year of national prevalence surveys, showing the proportion of incident TB cases that develop symptoms, begin seeking care, initiate treatment, and finally successfully complete treatment, assuming unidirectional progression. There were notable differences between countries, with the proportion initiating TB treatment ranging from 30% (95% CrI: 24–38%) in Lao PDR to 83% (95% CrI: 72–88%) in Ethiopia. Versions of these figures with cohort timings are shown in Additional file 1.
Graph: Fig. 4 Healthcare cascade. The values are the fraction of the incident cohort reaching each stage. The second bars may be missing if the TB prevalence survey did not report results on care-seeking behaviour
For Kenya and Blantyre, Malawi, we were able to examine results stratified by sex, age, and HIV-status (see Table 2). For Kenya we found total durations of 18.8 months (95% CrI: 16.1–21.8) for men and 15.4 months (95% CrI: 13.0–18.1) for women, and for Blantyre 9.9 months (95% CrI: 6.7–13.8) for men and 6.6 months (95% CrI: 3.5–10.2) for women, in line with previous work suggesting longer durations for men [[
Table 2 Covariate analysis for symptom development rate for Kenya and Blantyre. Asymptomatic, symptomatic and total TB duration by sex, age and HIV status for Kenya and Blantyre, Malawi (RR = risk ratio)
Asymptomatic duration, month Symptom onset rate after TB activation, per year Symptomatc duration, month Total duration, month Crude rate Crude RR Adjusted RR Kenya Overall 8.3 (6.8–10.1) 1.25 (1.05–1.50) 8.4 (7.1– 9.7) 16.7 (14.5–19.1) Sex Female 8.1 (6.2–10.3) 1.30 (1.01–1.69) Reference Reference 7.2 (5.8–8.8) 15.4 (13.0–18.1) Male 9.0 (7.2–11.2) 1.14 (0.93–1.40) 0.89 (0.63–1.20) 0.68 (0.50–0.89) 9.7 (8.2–11.5) 18.8 (16.1–21.8) Age (15,25) 9.5 (6.6–13.0) 1.11 (0.76–1.60) reference reference 10.4 (7.9–13.2) 20.0 (16.1–24.4) (25,35) 8.6 (6.3–11.4) 1.23 (0.90–1.66) 1.13 (0.75–1.67) 1.20 (0.77–1.69) 9.4 (7.5–11.5) 18.0 (15.0–21.6) (35,45) 7.1 (5.0– 9.7) 1.55 (1.08–2.22) 1.43 (0.87–2.17) 1.72 (1.10–2.54) 7.7 (5.9– 9.7) 14.7 (11.9–18.1) (45,55) 7.4 (4.8–10.7) 1.49 (0.94–2.31) 1.39 (0.80–2.43) 1.82 (0.95–3.17) 8.3 (6.0–11.1) 15.7 (12.4–19.7) (55,65) 9.6 (6.1–13.7) 1.10 (0.70–1.74) 1.02 (0.55–1.72) 1.23 (0.68–2.06) 5.2 (3.1–8.3) 14.8 (10.9–19.4) (65,Inf) 7.1 (4.6–10.2) 1.52 (0.97–2.35) 1.43 (0.80–2.55) 1.42 (0.89–2.34) 5.3 (3.4–7.7) 12.4 (9.4–16.0) HIV Non-HIV 9.8 (7.9–12.2) 1.03 (0.85–1.25) Reference Reference 9.9 (8.5–11.7) 19.7 (17.1–23.0) PLHIV 3.8 (2.4–5.6) 3.09 (1.96–4.80) 2.88 (1.82–4.54) 2.45 (1.45–3.68) 4.0 (2.5–5.7) 7.8 (5.8–10.2) Blantyre Overall 3.8 (2.3–5.8) 3.10 (1.86–4.98) 3.7 (2.3–5.4) 7.5 (5.4–10.1) Sex Female 3.5 (1.3–6.2) 3.90 (1.73–8.83) reference reference 3.1 (1.3–5.5) 6.6 (3.5–10.2) Male 4.9 (2.6–7.9) 2.46 (1.34–4.48) 0.82 (0.29–1.82) 0.81 (0.30–1.71) 5.0 (2.9–7.7) 9.9 (6.7–13.8) Age (15,50) 4.0 (2.3–6.2) 3.00 (1.74–5.03) reference reference 3.7 (2.1–5.6) 7.7 (5.3–10.3) (50,Inf) 3.5 (1.1–7.1) 4.17 (1.46–10.84) 1.53 (0.45–3.94) 1.41 (0.47–4.16) 4.6 (1.9–8.1) 8.1 (4.3–12.5) HIV Non-HIV 8.5 (5.0–12.7) 1.29 (0.76–2.16) reference reference 7.1 (4.3–10.8) 15.6 (11.3–20.8) PLHIV 1.3 (0.3–3.0) 12.79 (3.77–41.16) 5.33 (1.90–11.92) 4.81 (1.65–12.82) 2.4 (1.0–4.1) 3.7 (1.9–6.0)
RR risk ratio, PLHIV people living with HIV
Adjusted results for Kenya suggested longer durations asymptomatic in men than women; however, there were no clear differences in unadjusted results and results from Blantyre (see risk ratios in Table 2). There were also no clear patterns with respect to age. However, HIV status had a strong effect, with asymptomatic and symptomatic durations being shorter in PLHIV than those that were HIV negative. The total duration in PLHIV was 3.7 months (95% CrI: 1.9–6.0) for Blantyre and 7.8 months (95% CrI: 5.8–10.2) for Kenya. We found that CDRs were consistently higher in PLHIV than in those without HIV infection, even with higher deaths rates for PLHIV with TB assumed (Table 3).
Table 3 Epidemiological estimates by HIV status, Kenya and Blantyre. TB incidence, case detection ratio, and smear-stratified symptom duration by HIV status for Kenya and Blantyre, Malawi
Kenya Blantyre HIV status Non-HIV PLHIV Non-HIV PLHIV Background mortality μ μ + HIV-related mortality μ μ + HIV-related mortality excess mortality for symptomatic TB Smear−: 50%/10 years Smear+: 70%/10 years 5% per month Smear−: 50%/10 years Smear+: 70%/10 years 5% per month Time asymptomatic, month 10.35 (8.41–12.84) 3.81 (2.34–5.51) 3.22 (1.99–4.75) 8.79 (5.14–13.18) 1.32 (0.31–3.04) 1.15 (0.25–2.64) Time bacteriologically positive, month 20.90 (18.01–24.46) 7.79 (5.78– 9.99) 6.49 (4.94–8.11) 16.27 (11.59–21.53) 3.76 (1.81–6.07) 3.26 (1.76–5.03) Case-detection ratio 69% (59–81%) 84% (77–91%) 71% (63–80%) 76% (64–88%) 94% (88–98%) 84% (74–93%) Incidence rate in 2019, per 100,000 263 (221–306) 2025 (1873–2205) 2403 (2142–2715) 158 (132–192) 1742 (1618–1879) 1949 (1745–2217) Time bacteriologically positive, month 10.56 (8.86–12.48) 3.99 (2.52–5.56) 3.27 (2.16–4.40) 7.47 (4.53–11.05) 2.44 (1.00–4.25) 2.11 (0.95–3.49) Case-detection ratio 83% (75–91%) 90% (85–95%) 76% (67–84%) 88% (79–95%) 95% (90–98%) 85% (75–94%) Incidence rate in 2019, per 100,000 218 (198–240) 1882 (1791–2003) 2260 (2025–2542) 136 (121–155) 1712 (1604–1831) 1921 (1721–2177)
μ: background mortality based on the World Population Prospects, 2016 for Kenya and 2019 for Blantyre (Malawi) HIV-related mortality: AIDS-related deaths/PLHIV for aged 15 and above from the UNAIDS data 5% per month (0.61 per year): an extreme case for gold miner in pre-ART era [[
Across all mortality scenarios considered (Additional file 1-Section E), differences in duration were around one month, except for Lao PDR. The highest levels of assumed mortality led to up to a 7 percentage-point lower case-detection ratio than for the lowest assumed mortality. Sensitivity analysis assuming an extremely high excess TB mortality for PLHIV (Table 3) led to up to 15 percentage-point lower CDRs and correspondingly higher incidence rates. However, for both Kenya and Blantyre, the case-detection ratios were still not lower than that of HIV negative populations. Considering a broader range of symptoms resulted in shorter durations of the asymptomatic phase but longer time-spent on care-seeking given the TB prevalence survey and notification data. Estimates of rates of progression to symptoms were systematic under-estimates in the presence of symptom reversion, with an error that depended roughly linearly on reversion rate (with gradient of prevalent symptomatic TB over asymptomatic TB). The time asymptomatic and time symptomatic were both systematic over-estimates with symptom reversion.
By calibrating simple models of TB disease and care-seeking progression to prevalence survey and notification data, we were able to infer the typical duration of asymptomatic bacteriologically-positive TB to be around six months. However, there is a large variation between settings, with longer durations of asymptomatic disease in the included Asian settings. The asymptomatic phase typically comprised around half the total time before notification. For countries that reported care-seeking history in their prevalence surveys, we were able to estimate the average timing of initial care-seeking, finding this was approximately halfway between becoming symptomatic and ultimate diagnosis. We found limited evidence of age-dependence in overall durations, with a hint of longer durations in older age groups, but did find substantially shorter disease durations in people living with HIV. Our analysis of TB care-seeking, diagnosis and treatment outcome cascades also showed substantial differences between settings, and meaningful losses before the first symptom developed and care-seeking. Taken together, these findings suggest that important opportunities exist to identify people with tuberculosis earlier in their disease course through screening and community-based active case finding interventions, potentially improving individual outcomes and reducing transmission.
TB disease prior to care-seeking, including the asymptomatic phase, is beyond the reach of passive case-finding: improvements in diagnosis and retention at the clinic will not shorten these delays nor avert deaths during this phase. Cases that are truly asymptomatic can only be found by active approaches to screening based on exposure, chest X-ray or other novel rule-in tests - based in either the community or clinics. The symptomatic period prior to care seeking would be amenable to intervention by symptom-based screening approaches and potentially health-messaging or improved access to care. Understanding the duration of these phases and their contributions to transmission and mortality is therefore key to understanding the relative potential benefits of more active approaches to finding cases [[
Our method also generates estimates of TB incidence: using literature estimates of TB disease mortality and self-cure rates, we inferred the proportions that fail to reach notification. Our incidence estimates are similar to those of WHO [[
Our approach has quantified aspects of TB natural history that are potentially important but poorly characterised, relying on historical or anecdotal data. For example, mathematical models of TB transmission have from the outset often included progression in smear status [[
There has been an increased realisation that TB disease is less dichotomous and more dynamic than conventional paradigms have allowed [[
HIV-associated TB has long been known to have a more rapid TB disease progression [[
Comparing our model estimates of the delays from TB onset to notification with empirical estimates from P:N ratios, we find that empirical estimates systematically overestimate duration. This is because the empirical estimator implicitly assumes that all prevalent TB ends in notification, whereas in the model (and reality) self-cure and death are possible outcomes [[
The duration of symptomatic TB is necessarily shorter than the total duration. Empirical estimates of TB duration based on TB prevalence surveys that used symptoms as an entry point to bacteriological testing [[
A limitation of our approach is that it relies on self-report for defining whether individuals have symptoms or have begun seeking care. Stigma, fear of diagnosis, or expectations of treatment may all affect and influence participants' willingness to report symptoms, even if they are recognised; recognition of symptoms may itself be influenced by cultural and epidemiological factors [[
A strength of our approach is that we have adopted a rigorous Bayesian framework, which includes uncertainty in inputs and outputs and could easily be generalised for use on other national or subnational data, and potentially developed as a software package. Unlike most approaches to P:N analyses, we do not make an assumption of equilibrium. Our assumption is the slightly more general and realistic constant rate of decline, which is an additional output.
Active approaches to TB screening and case-finding should be considered in high TB burden settings, where up to 4 to 12 months with the infectious bacteriologically positive disease is spent without symptoms, and a comparable time again with symptoms before care-seeking initiation.
For sharing additional stratified TB notification data, we would like to thank the following staff at the Division of Tuberculosis, Leprosy and Lung Disease Program (DNTLD-P), Kenya: Elizabeth Onyango (head), Aiban Rono (head of M/E section), Martin Githiomi (ICT lead).
This work was conceived and initially drafted by CCK, PMcP, ELC and PJD; the formal analysis was developed and undertaken by CCK; PMcP, MK, RN, HRAF, MN, ELC and KH curated data and assisted in its interpretation; all authors contributed to editing the manuscript and had access to all the data. All authors read and approved the final manuscript.
This work was supported by the Wellcome Trust (206575/Z/17/Z to PM, 200901/Z/16/Z to ELC) and the UK MRC (MR/P022081/1 to PJD). This UK funded award (PJD) is part of the EDCTP2 programme supported by the European Union. KCH is supported by the European Research Council (757699) and UK FCDO ("Leaving no-one behind: transforming gendered pathways to health for TB"). This research has been partially funded by UK aid from the UK government (to KCH); however, the views expressed do not necessarily reflect the UK government's official policies.
The individual-level datasets of the Blantyre data generated and/or analysed during the current study are not publicly available to avoid potential deductive disclosure.
The aggregate datasets supporting the conclusions of this article are available in the TimeWz667/AsymTB repository, https://github.com/TimeWz667/AsymTB.git, and Additional files 2, 3, 4 and 5
The Blantyre, Malawi TB prevalence survey received ethical approval from the College of Medicine, University of Malawi, and from London School of Hygiene and Tropical Medicine.
The authors declare that they have no competing interests.
Graph: Additional file 1. Supplementary materials including: Sources of data; Model details; Inference on delay and duration, posterior distribution by country sensitivity analysis
Graph: Additional file 2. TB prevalence survey characteristics
Graph: Additional file 3. Extracted data, 11 included settings
Graph: Additional file 4. Extracted data, Blantyre Malawi
Graph: Additional file 5. Extracted data, Kenya. TB prevalence data were from Enos et al.
• CDR
- Case detection ratio
• CrIs
- Credible intervals
• MCMC
- Markov chain Monte Carlo
• PLHIV
- People living with HIV
• RR
- Risk ratio
• TB
- Tuberculosis
• WHO
- World Health Organization
• WPP
- World Population Prospect
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By Chu-Chang Ku; Peter MacPherson; McEwen Khundi; Rebecca H. Nzawa Soko; Helena R. A. Feasey; Marriott Nliwasa; Katherine C. Horton; Elizabeth L. Corbett and Peter J. Dodd
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