Highlights
-
•
Life expectancy is shorter in patients with tuberculosis than in those without tuberculosis.
-
•
Tuberculosis reduces median survival time, especially in patients with drug-resistant tuberculosis or TB/HIV coinfection.
-
•
Tuberculosis also affects patients in terms of years of potential life lost.
Keywords: Tuberculosis, Median survival time, Life expectancy, Years of potential life lost, Scoping review
Abstract
Objective
Tuberculosis significantly affects human health and longevity. We aimed to systematically synthesize studies to summarize the median survival time range and years of potential life lost (YPLL) among tuberculosis patients.
Methods
We searched Web of Science, PubMed, China National Knowledge Infrastructure, and Wanfang Data from inception to October 1, 2024. Studies reporting median survival time or YPLL in patients with drug-susceptible or drug-resistant tuberculosis were included, mostly involving adult participants. Two reviewers independently screened studies and extracted data. Any disagreements were resolved by consensus or by arbitration from a third reviewer.
Results
A total of 22 studies were incorporated into this analysis. Patients with multidrug resistant tuberculosis (MDR -TB) had a median survival of 1.9–7.6 years (indexed from diagnosis), compared with 2.9–6.5 years for extensively drug-resistant tuberculosis (XDR-TB) and 2.0–8.0 years for Non- MDR -TB. Notably, untreated MDR -TB patients presented with particularly unfavorable survival outcomes. Patients coinfected with HIV and tuberculosis (TB/HIV) consistently showed very short survival times, whereas individuals with pneumoconiosis but without tuberculosis had substantially longer survival compared to those with both conditions. Life expectancy estimates revealed that TB/HIV coinfection reduced life expectancy at age 30 to between 4.2 and 21.6 years, in stark contrast to over 35.0 years in individuals with HIV but without tuberculosis. Years of potential life lost analyses indicated that patients with active tuberculosis lost 4.9–15.8 years of potential life, compared to 1.3 years for those with latent tuberculosis infection (LTBI). TB/HIV coinfection further amplified this burden, with losses reaching up to 16.3 years. Using a fixed age cut-off of 69 years, patients with tuberculosis lost an average of 39.1 years of potential life compared with 24.5 years among non-TB controls.
Conclusion
Most studies suggest that tuberculosis shortens survival and increases potential years of life lost, particularly in patients with drug-resistant tuberculosis, TB/HIV coinfection, or untreated tuberculosis, although there is important variation by patient group and study method. Early diagnosis and appropriate treatment may help reduce life loss and improve life expectancy.
1. Introduction
Tuberculosis (TB) is a significant challenge for global public health, marked by high morbidity and mortality rates. According to WHO Global Tuberculosis Report [1], an estimated one-quarter of the world’s population is infected with Mycobacterium tuberculosis. The number of newly diagnosed tuberculosis cases worldwide reached 10.8 million in 2023, with an incidence rate of 134 per 100,000 population. In the same year, tuberculosis caused 1.25 million deaths, reclaiming its status as the deadliest single infectious disease, with fatalities nearly double those of HIV/AIDS. In comparison, treatment outcomes have improved—showing an 88% success rate for drug-susceptible tuberculosis and 68% for multidrug resistant tuberculosis (MDR -TB)—but the mortality rate remains critically high [1]. A systematic review reported that the standardized mortality ratio (SMR) for individuals undergoing tuberculosis treatment is nearly three times that of the general population, while untreated patients face an even greater risk of death [2].
Current research primarily evaluates mortality disparities between patients with tuberculosis and the general population using relative indicators like SMR and mortality rate ratios. An alternative method for assessing excess mortality involves estimating life expectancy and years of potential life lost (YPLL). YPLL, a widely used metric, quantifies the gap between the actual age at death and the expected remaining lifespan. As an intuitive and easily interpretable measure of premature mortality, life expectancy highlights the disproportionate impact of early deaths. Recently, many studies have investigated median survival time and YPLL in individuals with tuberculosis, with most findings indicating a significantly reduced lifespan compared to the general population [3,4].
The increase in life expectancy for patients with tuberculosis benefits global life expectancy. The Global Burden of Disease Study highlighted that reductions in TB-related deaths have positively impacted life expectancy across all regions [5]. Research from China reveals that between 1990 and 2019, effective treatment of respiratory infections and tuberculosis contributed to an increase of 1.86 years in healthy life expectancy, accounting for 17.73% of the total improvement [6]. The United Nations' Sustainable Development Goal (SDG) aims to achieve a 90% reduction in tuberculosis-related mortality by 2030, using 2015 as the baseline. However, this objective is increasingly seen as unattainable. If the target is not achieved, tuberculosis-related deaths could result in an average loss of 0.31 years of life lost at birth per person by 2030 [7].
The spread of drug-resistant pulmonary tuberculosis poses a significant barrier to achieving the End TB Strategy targets by 2035 [1]. Patients with drug-resistant tuberculosis face prolonged treatment regimens, complex multi-drug therapy, all of which significantly impact adherence and reduce the likelihood of optimal treatment outcomes [8]. Studies have shown that patients with drug-resistant tuberculosis face shorter survival times [9].
Evaluating tuberculosis's impact on patient survival, YPLL, and related risk factors is essential for designing effective prevention and control strategies. A simulation study conducted in 2020 estimated that approximately 155 million individuals worldwide had survived their first episode of tuberculosis disease, with a median survival time of 12 years (IQR 6–21 years) [10]. Markov models offer useful estimates, but their outcomes may differ from real-world survival. Validation with real-world data is essential to improve tuberculosis management strategies.
Notably, no scoping review has explicitly focused on the median survival time or YPLL in patients with tuberculosis. Scoping reviews are valuable for synthesizing evidence and mapping the breadth of literature on a given topic, mainly when studies exhibit high heterogeneity, making meta-analysis infeasible for pooling effect sizes (e.g., risk ratios or standardized mean differences) [11]. Therefore, we conducted a scoping review following the PRISMA guidelines to systematically search and integrate existing knowledge, laying the foundation for future researchers to assess the need for a comprehensive systematic review and meta-analysis.
This review aims to analyze current literature on median survival time and YPLL in patients with tuberculosis, assess the impact on life expectancy, and provide data to refine tuberculosis prevention and control efforts, ultimately enhancing patient longevity.
2. Methods
This scoping review was conducted and reported by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines in Supplementary Table S1 [11]. The review protocol is not registered.
2.1. Search strategy and study selection
We conducted a comprehensive literature search across Web of Science, PubMed, China National Knowledge Infrastructure, and Wanfang Data, covering publications from the inception of each database to August 1, 2023. We updated the search through October 1, 2024. The search strategies for each database are detailed in Supplementary Table S2. Results were exported to EndNote 20 for screening. Additionally, relevant reviews and reference lists of included studies were examined. A three-person team carried out the search process.
JX Ning first removed duplicate records in EndNote 20 and independently screened studies based on title and abstract. The results were then reviewed and consolidated through team discussions. Two reviewers (JX Ning and R Wang) independently screened all titles/abstracts and assessed full texts for eligibility. Any disagreements regarding study inclusion were resolved through discussion with Q Liu until a consensus was reached. No automated tools were used for full-text screening. JX Ning developed a custom Excel spreadsheet for data extraction (details provided in the next section). After full-text screening, data extraction was performed and cross-checked by JX Ning and R Wang.
2.2. Definitions and eligibility criteria
Studies were eligible if they reported the median survival time or YPLL in patients with tuberculosis. While studies including patients of all ages were considered, most included samples were adults. Both drug-susceptible and drug-resistant tuberculosis (including MDR-TB and XDR-TB) were included. The study was not limited to controlled studies but also included case series, prospective and retrospective cohort studies, cross-sectional studies and clinical trials.
Life Expectancy represents the estimated average number of additional years a group of people is expected to live, usually derived from life tables and serving as a summary measure of mortality [12]. Median survival time represents the time point at which 50% of the study cohort has died (i.e., when the survival function equals 0.5), most commonly estimated by Kaplan-Meier curve [13]. YPLL was estimated using two approaches: loss-of-remaining-life-expectancy, which subtracts age at death from the expected life expectancy for the age group, and cause-specific YPLL [14], which subtracts age at death from a fixed upper age limit (e.g., 69 years) [15].
2.3. Data extraction
We designed an Excel data extraction form to capture essential study variables. The following items were extracted using a customized Excel spreadsheet: authors, publication year, country, trial and control groups, male-to-female ratio, mean age, survival factors, median survival time, and YPLL (Table 1, Table 2). Two reviewers (JX Ning and R Wang) independently screened all titles/abstracts and assessed full texts for eligibility. The same reviewers also independently extracted data from the included studies using a standardized Excel sheet. Extracted variables included study characteristics, population demographics, treatment details, and survival outcomes. Discrepancies were resolved by discussion, with a third reviewer (Q Liu) adjudicating when necessary.
Table 1.
Median survival time in patients with TB.
| Author & year | Country | Study design | Method | Duration of follow-up | Test group | Control group | Male/female ratio in test group | Male/female ratio in control group | Mean age in test group (years) | Mean age in control group (years) | Median survival in test group (years) | Median survival in control group (years) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Sun et al. (2013) [33] | China | Retrospective cohort | Observed survival (KM curve) | 9 years per patient | MDR-TB(n = 52) | non-MDR-TB(n = 146) | 2.9:1 | 2.6:1 | 43.9 | 42.6 | 6.7 | 8.0 |
| Pan et al. (2016) [34] | China | Retrospective cohort | Observed survival (KM curve) | 2 years per patient | MDR-TB(n = 87) | non-MDR-TB(n = 613) | 1.6:1 | 1.3:1 | 45.7 | 48.9 | 1.9 | 2.0 |
| Peng et al. (2009) [35] | China | Retrospective cohort | Observed survival (KM curve) | NA | Pneumoconiosis with TB**(n = 880) | pneumoconiosis without TB**(n = 5,394) | NA | NA | NA | NA | 20.2 | 29.9 |
| Balabanova et al. (2011) [9] | Lithuania | Retrospective cohort | Observed survival (KM curve) | 4089.3 person-years | XDR-TB(n = 71) | MDR-TB(n = 1,736) | 3.7:1 | 4:1 | NA | NA | 2.9 | 4.0 |
| Subgroup 1 | MDR/XDR-TB with HIV positive(n = 25) | MDR/XDR-TB with HIV negative(n = 285) | NA | NA | NA | NA | 1.9 | 4.9 | ||||
| Subgroup 2 | Primary MDR-TB(n#) | Acquired MDR-TB(n#) | NA | NA | NA | NA | 4.2 | 3.7 | ||||
| Subgroup 3 | Primary XDR-TB(n#) | Acquired XDR-TB(n#) | NA | NA | NA | NA | 2.7 | 2.9 | ||||
| Drobniewski et al. (2002) [39] | UK | Cohort study | Observed survival (KM curve) | NA | MDR-TB(n = 82) | 2.6:1 | >15 | 3.8 | ||||
| Subgroup 1 | immunocompromised individuals (n = 32) | immunocompetent cases (n = 48) | NA | NA | NA | NA | 2.4 | 4.3 | ||||
| Subgroup 2 | patients treated (n = 62) | patients not treated (n = 13) | NA | NA | NA | NA | 5.6 | 1.6 | ||||
| Getachew et al. (2013) [44] | Ethiopia | Retrospective study | Observed survival (KM curve) | 1.28 years | MDR-TB(n = 188) | 0.86:1 | 27 | 9.7 | ||||
| TB with HIV positive(n = 155) | TB with HIV negative(n = 33) | 4.4 | 11 | |||||||||
| Kim et al. (2010)[40] | Korea | Cohort study | Observed survival (KM curve) | 5–8 years per patient | MDR-TB（n = 1,407） | 2.8:1 | 42.9 | 7.6 | ||||
| Subgroup | XDR-TB (n = 149) | NA | NA | 6.3 | ||||||||
| Balabanova et al. (2016) [41] | Germany | Prospective cohort | Observed survival (KM curve) | 3 years per patient | MDR-TB（n = 737） | 3.7:1 | >15 | 5.9 | ||||
| Subgroup | TB/HIV coinfection(n = 20) | NA | NA | 1.9 | ||||||||
| Wang et al. (2017)[47] | China | Cohort study | Observed survival (KM curve) | 2409 person-years | MDR-TB（n = 552） | 3:1 | 46.08 | 7.5 | ||||
| Subgroup | patients treated (n = 366) | patients not treated (n = 196) | NA | NA | 8.5 | 5.0 |
TB: tuberculosis; MDR-TB: multidrug-resistant; XDR-TB: extensively drug-resistant; HIV: Human Immunodeficiency Virus; KM curve: Kaplan-Meier curve; TB**: Tuberculosis drug susceptibility not reported; n#: Sample size not reported; NA: Not Applicable.
Table 2.
Life expectancy in patients with TB.
| Author & year | Country | Study design | Method | Duration of follow-up | Test group | Control group | Male/female ratio in test group | Male/female ratio in control group | Mean age in test group (years) | Mean age in control group (years) | Life expectancy in the Test group (years) | Life expectancy in the control group (years) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Mirzaei et al. (2018) [38] |
Iran | Retrospective cohort | Model-based (Life table) | 3.4 years per patient | TB/HIV coinfection **(n = 1,136) | HIV without TB**(n = 29,380) | NA | NA | >20 | >20 | 21.6 | 36.5 |
| Han et al. (2017) [37] |
China | Retrospective study | Model-based (Life table) | NA | CWP with TB** (n = 36) | CWP without TB** (n = 459) | NA | NA | NA | NA | 34.1 | 37.9 |
| Bohlbro et al. (2023) [4] |
Guinea-Bissau | Observational study | Observed (KM curve) | 3.7 years per patient | patients with drug-susceptible TB (n = 2,278) | non-TB (n = 169,760) | 1.9:1 | 0.8:1 | 32 | 30 | 10.7 | 35.8 |
| Subgroup 1 | Smear-positive (n = 1737) | Smear-negative (n = 570) | NA | NA | NA | NA | 11.7 | 6.8 | ||||
| Subgroup 2 | TB/HIV coinfection (n = 358) | Only TB (n = 1483) | NA | NA | NA | NA | 4.2 | 20.9 | ||||
| Subgroup 3 | Treatment Successful (n = 1703) | Unsuccessful (n = 604) | NA | NA | NA | NA | 17.0 | 4.5 | ||||
| Subgroup 4 | Females (n = 1511) | Males (n = 796) | NA | NA | NA | NA | 8.7 | 11.6 | ||||
| Subgroup 5 | Non-smokers (n = 1798) | Smokers (n = 499) | NA | NA | NA | NA | 10.5 | 11.3 | ||||
| Subgroup 6 | Without alcohol addiction (n = 560) | Alcohol addiction (n = 201) | NA | NA | NA | NA | 12.8 | 8.7 |
TB: tuberculosis; CWP: Coal Workers' Pneumoconiosis; HIV: Human Immunodeficiency Virus; KM curve: Kaplan-Meier curve; TB**: Tuberculosis drug susceptibility not reported; NA: Not Applicable.
2.4. Data analysis
We summarized and analyzed all included studies. Data were extracted and summarized in tabular form with descriptive notes outlining the median survival time, life expectancy and YPLL of patients with tuberculosis (Table 1, Table 2, Table 3, Table 4). Factors influencing tuberculosis patients' survival were summarized (Table 5).
Table 3.
The median survival time from diagnosis to death of patients with TB in the included studies.
| Author & year | Country | Study design | Period of data collection | Duration of follow-up | Size | Male/female ratio | Mean Age | Mortality rate | Death toll | Median survival time in the death |
|---|---|---|---|---|---|---|---|---|---|---|
| Kang et al. (2015)[46] |
China | Cohort study | 2001–2012 | 3181.40 person-years | TB(n = 4,747) | 2.2:1 | NA | 3.08 /100 person-years | NA | 22 days |
| Shenoi et al. (2012) [36] |
South Africa | Retrospective cohort | 2005–2008 | NA | XDR-TB deaths within 180 days of diagnosis(n = 73) | 0.5:1 | 34 | NA | 68 | 34 days |
| Zhou et al. (2018) [45] |
China | Retrospective study | 2015–2017 | NA | TB/HIV coinfection (n = 142) | 4.9:1 | 43.36 | 18. 31% | 26 | 4.6 months |
| Zhu et al. (2017)[48] |
China | Retrospective study | 2008–2013 | NA | TB/HIV coinfection (n = 400) | NA | NA | 2.00% | 128 | 1.6 years |
| Garin et al. (1997)[49] |
Central African Republic | Prospective cohort | 1993–1995 | 24 months | TB/HIV coinfection (n = 139) | NA | 30 | 58.00% | NA | 1.3 years |
TB: tuberculosis; XDR-TB: extensively drug-resistant; HIV: Human Immunodeficiency Virus.
Table 4.
Potential years of life lost in patients with TB.
| Author & year | Country | Study design | Type of YPLL | Duration of follow-up | Test group | Control group | Male/female ratio in test group | Male/female ratio in control group | Mean age in test group (years) | Mean age in control group (years) | YPLL in the Test group (years) | YPLL in the control group (years) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Wang et al. (2007)[14] | China | Retrospective study | YPLL# | NA | TB | NA | NA | 15.8 | ||||
| Selvaraju et al. (2021)[15] | India | Matched Cohort Study | YPLL* | NA | TB(n = 2,895) | non-TB(n = 9,402) | 2.6:1 | 2.7:1 | 15–64 | 15–64 | 39.1 | 24.5 |
| Hoger et al. (2014)[42] | USA | Retrospective study | YPLL# | 6–16 years per patient | TB(n = 3,933) | LTBI(n = 9,166) | 1.7:1 | 1.3:1 | NA | NA | 4.9 | 1.3 |
| Subgroup | TB/HIV coinfection (n = 20) | 16.3 | ||||||||||
| Wang et al. (2013)[43] | China | Retrospective cohort | YPLL# | 2–6 years per patient | TB(n = 4,271) | 2.9:1 | 54.5 | 7.2 | ||||
| Subgroup | Male TB | Feman TB | 5.3 | 8.2 | ||||||||
| Lee-Rodriguez et al. (2020)[3] | USA | Retrospective study | YPLL# | 17,17 person-years | Active TB(n = 2,522) | Non- TB(n = 100,88) | 1.3:1 | 1.3:1 | NA | NA | 7.0 |
YPLL: years of potential life lost; TB: tuberculosis; LTBI: latent TB infection; Active TB: patients with microbiologically confirmed; HIV: Human Immunodeficiency Virus; YPLL#: loss of remaining life expectancy was calculated based on national or local life tables; YPLL*: Cause-specific YPLL was defined with a fixed age cut-off (69 years); NA: Not Applicable.
Table 5.
Factors affecting death in tuberculosis patients.
| Author & year | Country | Morbidity and mortality in the test group | Control group morbidity and mortality | Factors affecting death in tuberculosis patients |
|---|---|---|---|---|
| Sun et al. (2013)[33] |
China | 43.0％ (37/86) | 21.6％ (32/14) | Age, multidrug-resistant status, relapse to treatment, previous hospitalization, and treatment duration > 1 year. |
| Pan et al. (2016)[34] |
China | 9.6%（5/52） | 2.9%（18/613） | Age, multidrug-resistant status, most extended duration of previous treatment > 1 year, more than three anti-TB treatments. |
| Shenoi et al. (2012)[36] |
South Africa | NA | NA | Negative AFB smear, a lower laboratory index of routine findings, CD4 > 200 cells/mm, and receipt of antiretroviral therapy. |
| Balabanova et al. (2011)[9] |
Lithuania | NA | NA | Older age, rural living, alcohol use for alcoholic versus moderate use, unemployment, lower education levels for primary level versus tertiary level, cavitary disease, HIV positivity, and being smear positive. |
| Bohlbro et al. (2023)[4] |
Guinea-Bissau | 79.7 per 1000 person-years (95% CI: 73.0–87.0) | 5.3 per 1000 person-years (95% CI: 5.2–5.4) | Smear-negativity, HIV infection, and high score severity class. |
| Drobniewski et al. (2002)[39] |
UK | 74%（20/27） | NA | Immunocompromised status, failure to culture the bacterium in 30 days or to apply appropriate three-drug treatment, and age. |
| Kim et al. (2010)[40] |
Korea | 10.2% (144 /1407) |
NA | The definition of pre–XDR-TB. |
| Balabanova et al. (2016)[41] |
Germany | 10.94/1000(95% CI 9.61–12.47) | NA | Older age, male gender, alcohol abuse, retirement, comorbidities, extrapulmonary involvement, and TB/HIV coinfection. |
| Selvaraju et al. (2021)[15] |
India | 5.9%（95% CI: 5.53–6.23） | 1.4%（95% CI: 1.3–1.5） | Individuals aged > 50 years, those underweight (<40 kg), those with treatment failures, or those lost to follow-up. |
| Hoger et al. (2014)[42] |
USA | 22% | 4% | White and Hispanic. |
| Wangr et al. (2013)[43] |
China | 2.5%. | NA | Psychopathy, chronic bronchitis, cancer, and the presence of multiple diseases. |
| Zhou et al. (2018)[45] |
China | 18. 31%（26/142） | NA | Age ≥ 40 years at HIV diagnosis, other routes of infection, low first CD4 + T-lymphocyte counts, and lack of antiviral therapy. |
| Kang et al. (2015)[46] |
China | 3.08 per 100 person-years | NA | Advanced age, smear-positive patients, and retreatment patients. |
| Wang et al. (2017)[47] |
China | 157/593 | NA | Age at diagnosis, educational level, presence of diabetes, other serious diseases, number of previous treatments, and inclusion in multidrug-resistant regimens. |
| Zhu et al. (2017)[48] |
China | 2.00% (128／400) | NA | Transmission and Age. |
| KTL et al. (2023)[55] |
Bangui | 58% | 20% | HIV-seropositivity, older age, failure to complete the entire treatment regimen, and a low CD4 cell count. |
TB: tuberculosis; XDR-TB: extensively drug-resistant; HIV: Human Immunodeficiency Virus; AFB: Acid-Fast Bacillus; NA: Not Applicable.
2.5. Patient and public involvement
This study does not involve human participants. It was not appropriate to involve patients or the public in the design, or conduct, or reporting, or dissemination plans of our research.
3. Result
3.1. Study selection
Our database search yielded 4,058 records, and the search terms for each database listed in Supplementary Table S2. After removing 497 duplicates, 171 records comprising meta-analyses, animal studies, and case reports were excluded. A total of 3,390 records were screened based on titles and abstracts, narrowing to 187 for full-text review. Among these, 17 records were excluded due to the unavailability of full text [[16], [17], [18], [19], [20], [21], [22], [23], [24], [25], [26], [27], [28], [29], [30], [31], [32]], and 148 were excluded as they did not report survival time or YPLL for patients with tuberculosis. Ultimately, 22 studies were included in the scoping review [3,4,9,14,15,[33], [34], [35], [36], [37], [38], [39], [40], [41], [42], [43], [44], [45], [46], [47], [48], [49]]. The flowchart of the research selection process is shown in Fig. 1.
Fig. 1.
PRISMA-ScR flow diagram of this study.
3.2. Study characteristics
The 22 studies included in this review, published between 1997 and 2023, originated from 12 low-, middle-, and high-income countries, reflecting a broad temporal and geographical distribution. Most of the studies were conducted in middle-income countries, with only three from low-income nations and six from high-income countries. 13 studies (59%) were conducted in high-burden countries with tuberculosis, while the remaining were from countries with medium or low tuberculosis burdens. Nearly half (12/22, 52%) of the studies were conducted in Asia, with ten studies from China, two from the United States, and the others from South Africa, Lithuania, Iran, Guinea-Bissau, the United Kingdom, South Korea, Germany, India, Central African Republic, and Ethiopia.
3.3. Median survival time and life expectancy in tuberculosis
The majority of studies employed observed survival analyses utilizing Kaplan-Meier curve with or without Cox regression, while a smaller subset utilized life table methodologies. Among these, 9 studies [9,[39], [40], [41],44] reported the median survival time and 3 studies [4,37,38] documented life expectancy following diagnosis or treatment, as summarized in Table 1, Table 2. Presented in Table 1 are nine studies from seven countries (China, Lithuania, the UK, Ethiopia, South Korea, and Germany), all of which utilized the Kaplan-Meier curve for survival analysis. The data collectively provide median survival times for a cohort of over 11,000 tuberculosis patients worldwide. The three studies summarized in Table 2, from Iran, China, and Guinea-Bissau, used model-based (life table) or observed (Kaplan-Meier curve) methods for life expectancy analysis, featuring a combined cohort of over 190,000 participants. In all categories, survival outcomes demonstrated significant variation based on drug resistance pattern, comorbidity status, and the starting point of follow-up.
3.4. Median survival time stratified by resistance pattern and comorbidity status
Among patients with MDR-TB, eight studies (n = 4,723, 6 countries) reported median survival ranging from 1.9 to 7.6 years when indexed from diagnosis, and extending to 9.7 years when indexed from treatment initiation. For patients with XDR-TB, two studies (n = 315, 2 countries) reported shorter survival, ranging from 2.9 to 6.5 years (diagnosis baseline). In contrast, non-MDR-TB groups (2 studies, n = 759, China) exhibited longer survival, ranging from 2.0 to 8.0 years. Treated patients with MDR-TB demonstrated median survival of 5.6–8.5 years, compared to 1.6–45.0 years for untreated counterparts (Supplementary Table S3).
Patients with TB/HIV coinfection consistently demonstrated markedly reduced survival across three studies from distinct countries, with median survival of 1.9 years from diagnosis and 4.4 years from treatment initiation. Among patients with pneumoconiosis, survival outcomes varied substantially according to tuberculosis status: those without concurrent tuberculosis exhibited a median survival of 29.9 years, compared to 20.2 years among patients with concomitant pneumoconiosis and tuberculosis (Supplementary Table S3).
3.5. Median survival time from TB diagnosis to death
Five studies [36,45,46,48,49] reported the median survival time from tuberculosis diagnosis to death, ranging from 22 days to 1.6 years (Table 2). Among patients with TB/HIV coinfection, the median survival from tuberculosis diagnosis to death ranged from 4.58 months to 1.6 years [45,48,49].
3.6. Life expectancy estimates
Three studies provided life expectancy estimations utilizing either life table or Kaplan-Meier curve, as detailed in Supplementary Table S4, which categorizes expected lifespan by drug resistance pattern, major comorbidities, and temporal reference points. Among patients with TB/HIV coinfection, life expectancy at age 30 demonstrated substantial geographic variation, ranging from 4.2 years in Guinea-Bissau (Kaplan-Meier curve) to 21.6 years in Iran (life table). For comparison, HIV-positive patients without tuberculosis exhibited life expectancy at age 30 ranging from 35.8 to 36.5 years. Among coal workers' pneumoconiosis patients, life expectancy was 37.9 years for those without tuberculosis compared to 34.1 years for those with concurrent tuberculosis (life table).
Patients with drug-susceptible tuberculosis demonstrated a life expectancy of 10.7 years. Successful drug-susceptible tuberculosis treatment substantially improved life expectancy to 17 years, contrasting sharply with only 4.5 years for treatment-failed cases. Notable gender-specific disparities were identified, with male patients with drug-susceptible tuberculosis exhibiting a life expectancy of 11.6 years compared to 8.7 years for female patients (Supplementary Table S4).
3.7. YPLL in patients with TB
Cause-specific YPLL was defined with a fixed age cut-off (69 years), whereas loss of remaining life expectancy was calculated based on national or local life tables in our included studies. Across studies reporting loss of remaining life expectancy, tuberculosis patients were found to lose between 4.9 and 15.8 years of potential life, while latent tuberculosis infection individuals had a YPLL of 1.3 years. Patients with TB/HIV coinfection substantially aggravated the burden, losing up to 16.3 years. Gender-specific analyses indicated that male tuberculosis patients lost 5.3 years and female patients 8.2 years. Compared to non-tuberculosis patients, those with bacteriologically confirmed tuberculosis had a YPLL of 7.0 years. In addition, one study applying a cause-specific YPLL definition (cut-off at 69 years) showed that tuberculosis patients lost an average of 39.1 years, whereas the figure was 24.5 years for non-tuberculosis controls. None of these studies reported drug resistance pattern, so the results are not discussed.
3.8. Factors affecting the survival of patients with TB
15 of the 22 studies we included described factors affecting mortality in patients with tuberculosis, as shown in Table 4 [4,9,14,15,[33], [34], [35], [36],[38], [39], [40], [41], [42], [43]]. The mortality rate of patients with tuberculosis ranged from 5.9% to 74%. Risk factors affecting tuberculosis patient survival included age (>40 years), gender (male), body weight (<40 kg), multidrug-resistant status (MDR, XDR, pre-XDR), HIV co-infection (especially in patients without antiretroviral therapy or with CD4 counts < 200), retreatment, history of hospitalization and treatment duration > 1 year, inappropriate treatment regimens (e.g., lack of Fluoroquinolones or Injectables) or treatment failure, treatment delays (>4 weeks), socioeconomic status (illiteracy, unemployment, low education level, rural residence), comorbidities (including immunosuppression, chronic bronchitis, diabetes, cancer, and other severe illnesses), smoking, alcohol consumption, poor treatment adherence or loss to follow-up, sputum smear positivity, cavitary lesions, and a history of psychiatric disorders.
The heterogeneity of treatment regimens and variations in the implementation of directly observed therapy (DOT) may also have affected patient survival. Of the 22 included studies, only three studies (from Guinea-Bissau, India, and South Africa) explicitly reported on DOT-related practices. The level of supervision differed among these studies: some utilized fully supervised treatment (India, South Africa), while others involved partially self-administered regimens (Guinea-Bissau), as summarized in Supplementary Table 5. Additionally, six studies (from Guinea-Bissau, Eastern Europe, the United Kingdom, Lithuania, India, and South Africa) provided detailed descriptions of the anti-tuberculosis treatment regimens. As presented in Supplementary Table 5, differences in treatment strategies, such as the use of injectable agents versus fully oral protocols, may have been significant determinants of survival.
4. Discussion
This review summarizes the impact of tuberculosis on patient survival time and YPLL, with particular attention to the survival outcomes of patients with MDR-TB/XDR-TB. Multiple factors influence the survival time of patients with tuberculosis. Drug resistance and treatment history are critical determinants of survival in patients with tuberculosis. Evidence shows that those with MDR-TB and especially XDR-TB have substantially shorter survival than non-MDR populations, underscoring the severe prognostic impact of drug resistance. Multiple studies indicate that 75% of deaths among patients with MDR-TB occur within the first 95 days of treatment, with nearly all deaths concentrated within the first six months [50,51]. This high mortality phase is closely associated with the severity of infection, drug toxicity, and comorbid conditions [50]. Standardized treatment, improved patient adherence, regular follow-ups, and the prevention of drug resistance are essential for increasing survival rates and improving prognosis.
Furthermore, drug selection has a significant impact on the survival outcomes of patients with MDR-TB. Recently, novel drugs such as Bedaquiline (BDQ) and Delamanid (DLM) have been widely employed in treating multidrug-resistant tuberculosis, showing promise in reducing drug-resistance-associated morbidity and mortality while improving survival time for patients with tuberculosis. Studies have demonstrated that DLM regimens achieve an incremental effectiveness of 13.96 quality-adjusted life-years (QALYs), representing an increase of 2.44 QALYs compared to the background regimen. In contrast, BDQ regimens improve 1.14 QALYs [52]. Another Markov decision model analysis across 5,000 parameter sets found that, with one exception, Bedaquiline could minimize the total years of life lost and maximize life expectancy for nearly all patients with MDR-TB [53].
Across studies using the loss-of-remaining-life-expectancy approach, active tuberculosis patients lost between 4.9 and 15.8 years of potential life, whereas individuals with LTBI lost only 1.3 years. This pronounced gap highlights the excess burden once LTBI progresses to active disease and underscores the importance of preventive strategies. According to the WHO, LTBI has a 5%–10% chance of developing active tuberculosis. Once LTBI progresses to active pulmonary tuberculosis, it significantly jeopardizes patient survival. Preventive treatment for high-risk populations is an effective strategy to halt the progression of tuberculosis [54]. A study in Denmark found that a total of 186,469 life years were saved by preventing tuberculosis, with an average of 6.71 life years saved per tuberculosis patient. Preventing tuberculosis by screening and treating LTBI reduces mortality and YPLL loss due to active tuberculosis [55].
TB/HIV coinfection and other comorbidities are critical risk factors influencing tuberculosis patient survival. TB/HIV coinfection substantially shortened survival compared with tuberculosis alone, underscoring the compounding effect of dual disease burden. Studies show that eliminating HIV/AIDS as a cause of death could significantly extend life expectancy for TB/HIV coinfection. Among males aged 15–19, the potential gain in life expectancy is as high as 5.7 years, while for females of the same age group, it is 6.4 years. Also, in the 15–19 age group, eliminating tuberculosis as a cause of death would extend life expectancy by 1.7 years for males and 1.5 years for females [56]. HIV/AIDS can significantly impair the immune system, leading to poor health outcomes and prognosis. Multinational studies indicate that patients with HIV infection face a 5.7-fold higher risk of mortality during MDR-TB treatment compared to those without HIV [50,57]. Reducing tuberculosis incidence and minimizing drug resistance in HIV/AIDS patients is therefore crucial.
Preventive treatment with isoniazid (INH) has been shown to significantly lower tuberculosis incidence and extend life expectancy in HIV-infected individuals [[58], [59], [60]]. For instance, the WHO report showed that the INH regimens reduced tuberculosis incidence by 51% – 62%, compared with no isoniazid preventive therapy (IPT) [61]. Furthermore, a 12-month INH regimen improved the five-year survival rate by 9% and extended life expectancy by 8.7 months [62].
Studies also reveal that clinical comorbidities negatively impact the prognosis of patients with tuberculosis, increasing mortality risks. The YPLL to tuberculosis alone was 7.07 years, and the YPLL persons who developed chronic obstructive pulmonary disease (COPD) after tuberculosis was 11.93 years per person [55]. Past studies have shown that INH preventive therapy is often recommended for patients with comorbid conditions. Among different age groups and patients with severe illnesses, such as chronic heart failure (CHF) or COPD, preventive INH treatment significantly reduces TB-related deaths and improves life expectancy [63,64]. Recent studies, however, have indicated that rifampicin is safer than isoniazid, with adverse events not being linked to advanced age [65,66].
Socioeconomic factors affect the survival of patients with tuberculosis. Globalization is seen as a factor that could increase tuberculosis transmission and deaths, but studies show minimal impact on tuberculosis-related life expectancy loss. Instead, more vigorous global health efforts, better infrastructure, and preventive treatments can significantly reduce tuberculosis mortality [67]. In high-burden tuberculosis countries, reducing the prevalence will necessitate a multi-faceted strategy, including more funding, stronger health systems, improved tuberculosis surveillance, and preventive measures targeting alcohol consumption, tobacco use, and diabetes [68].
This study has certain limitations. This scoping review was conducted without a registered protocol, which may limit transparency and reproducibility. Differences in analytic methods may partly explain the wide variation in reported survival outcomes. Observed survival analyses (e.g., Kaplan-Meier curve) are susceptible to loss to follow-up, competing risks, whereas model-based estimates rely on assumptions and extrapolations that may introduce bias. Although the treatment era represents an important contextual factor, only a small proportion of the included studies reported this information in sufficient detail, constraining our ability to undertake further stratified analyses. Our review primarily focused on patients with tuberculosis aged 15 years and older, with limited consideration given to survival time and YPLL among children and adolescents. This population, with underdeveloped immune systems, may exhibit distinct disease characteristics that warrant further investigation.
Despite the quality limitations of the studies reviewed, the aggregated data provided valuable insights, and our analysis expanded the work of others. To the best of our knowledge, this is the first scoping review of median survival time and YPLL for patients with tuberculosis, covering a wide range of diagnoses. We summarised data on survival time, YPLL, and risk factors for patients with tuberculosis, providing new information on survival associations that are sufficient to inform treatment policies and thus help improve tuberculosis management strategies.
5. Conclusion
Most studies suggest that tuberculosis shortens survival and increases potential years of life lost, particularly in patients with drug-resistant tuberculosis, TB/HIV coinfection, or untreated tuberculosis, although there is important variation by patient group and study method. Early diagnosis and appropriate treatment may help reduce life loss and improve life expectancy.
Ethics statements
This review included no patient identifiers and therefore did not require institutional review board approval.
8. Patient consent statement
The study did not involve human patients.
CRediT authorship contribution statement
Jingxian Ning: Writing – original draft, Data curation, Conceptualization. Rong Wang: Writing – original draft, Formal analysis, Conceptualization. Yuchen Pan: Methodology, Formal analysis. Xinru Fei: Writing – original draft, Validation, Supervision. Wenxin Jiang: Investigation, Formal analysis, Conceptualization. Leonardo Martinez: Writing – review & editing, Validation. Limei Zhu: Resources, Project administration, Methodology. Zhihua Qin: Writing – review & editing, Methodology. Qiao Liu: Writing – review & editing, Funding acquisition, Formal analysis, Conceptualization.
Funding
This study was supported by the National Nature Science Foundation of China (82574173, 82003516), Jiangsu Provincial Medical Key Discipline (ZDXK202250) and the Medical Scientific Research General Project of Jiangsu Health Commission (M2020020). The funders had no involvement in the study design, data collection or analysis, publication decision, or manuscript preparation.
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Footnotes
Supplementary data to this article can be found online at https://doi.org/10.1016/j.jctube.2025.100568.
Contributor Information
Zhihua Qin, Email: ntqzhua@163.com.
Qiao Liu, Email: liuqiaonjmu@163.com.
Appendix A. Supplementary data
The following are the Supplementary data to this article:
References
- 1.World Health Organization (2024) Global tuberculosis report 2024. https://www.who.int/teams/global-tuberculosis-programme/tb-reports/global-tuberculosis-report-2024.
- 2.Romanowski K., Baumann B., Basham C.A., Ahmad Khan F., Fox G.J., Johnston J.C. Long-term all-cause mortality in people treated for tuberculosis: a systematic review and meta-analysis. Lancet Infect Dis. Oct 2019;19(10):1129–1137. doi: 10.1016/s1473-3099(19)30309-3. [DOI] [PubMed] [Google Scholar]
- 3.Lee-Rodriguez C, Wada PY, Hung YY, Skarbinski J. Association of Mortality and Years of Potential Life Lost With Active Tuberculosis in the United States. JAMA network open. Sep 1 2020;3(9):e2014481. doi:10.1001/jamanetworkopen.2020.14481. [DOI] [PMC free article] [PubMed]
- 4.Bohlbro A.S., Hønge B.L., Engell-Sørensen T., et al. Life expectancy among patients with pulmonary tuberculosis is less than one-third of life expectancy in the background population in Guinea-Bissau-an observational study. Int J Epidemiol. 2023;52(4):1112–1123. doi: 10.1093/ije/dyad039. [DOI] [PubMed] [Google Scholar]
- 5.Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990-2021: a systematic analysis for the Global Burden of Disease Study 2021. Lancet (London, England). May 18 2024;403(10440):2100-2132. doi:10.1016/s0140-6736(24)00367-2. [DOI] [PMC free article] [PubMed]
- 6.Chen L., Wang L., Qian Y., Chen H. Changes and trend disparities in life expectancy and health-adjusted life expectancy attributed to disability and mortality from 1990 to 2019 in China. Front Public Health. 2022;10 doi: 10.3389/fpubh.2022.925114. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Silva S., Arinaminpathy N., Atun R., Goosby E., Reid M. Economic impact of tuberculosis mortality in 120 countries and the cost of not achieving the Sustainable Development Goals tuberculosis targets: a full-income analysis. Lancet Glob Health. Oct 2021;9(10):e1372–e1379. doi: 10.1016/s2214-109x(21)00299-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Matteelli A, Lovatti S, Rossi B, Rossi L. Update on MDR-TB preventive therapy towards the global TB elimination. International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases. Feb 22 2025:107849. doi:10.1016/j.ijid.2025.107849. [DOI] [PubMed]
- 9.Balabanova Y., Radiulyte B., Davidaviciene E., et al. Survival of drug resistant tuberculosis patients in Lithuania: retrospective national cohort study. BMJ Open. 2011;1(2) doi: 10.1136/bmjopen-2011-000351. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Dodd P.J., Yuen C.M., Jayasooriya S.M., van der Zalm M.M., Seddon J.A. Quantifying the global number of tuberculosis survivors: a modelling study. Lancet Infect Dis. Jul 2021;21(7):984–992. doi: 10.1016/s1473-3099(20)30919-1. [DOI] [PubMed] [Google Scholar]
- 11.Tricco A.C., Lillie E., Zarin W., et al. PRISMA extension for scoping reviews (PRISMA-ScR): Checklist and explanation. Ann Intern Med. 2018;169(7):467–473. doi: 10.7326/m18-0850. [DOI] [PubMed] [Google Scholar]
- 12.Modig K, Rau R, Ahlbom A. Life expectancy: what does it measure? BMJ open. Jul 22 2020;10(7):e035932. doi:10.1136/bmjopen-2019-035932. [DOI] [PMC free article] [PubMed]
- 13.Canudas-Romo V. Three measures of longevity: time trends and record values. Demography. May 2010;47(2):299–312. doi: 10.1353/dem.0.0098. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Wang J.S., He X.X., Jin S.G. Burden of disease analysis for tuberculosis chinese. Journal of AntiTuberculosis. 2007;29(3):219–221. [Google Scholar]
- 15.Selvaraju S., Thiruvengadam K., Watson B., et al. Long-term survival of treated tuberculosis patients in comparison to a general population in south india: a matched cohort study. Int J infectious diseases : IJID : official publication Int Soc Infectious Diseases. Sep 2021;110:385–393. doi: 10.1016/j.ijid.2021.07.067. [DOI] [PubMed] [Google Scholar]
- 16.Kempen WV. [Fate and life expectancy of children with active tuberculosis]. Beitrage zur Klinik der Tuberkulose und spezifischen Tuberkulose-Forschung. 1951;106(2):153-7. Schicksal und Lebenseiwartung der offentuberkulösen Jugendlichen. [PubMed]
- 17.Toledo Vila Hde J, Padilla Pérez O, Vidal García I, Rodríguez Avila I. [Tuberculosis mortality in Cuba, 1987-1993]. Revista cubana de medicina tropical. 1996;48(3):209-13. Mortalidad por tuberculosis en Cuba, 1987-1993. [PubMed]
- 18.Bradshaw B.S., Smith D.P. Decline of tuberculosis mortality in an urban Mexican-origin population, 1935-1984. Social biology Spring-Summer. 1997;44(1–2):25–41. [PubMed] [Google Scholar]
- 19.Jankauskas R., Urbanavicius A. Diseases in European historical populations and their effects on individuals and society. Coll Antropol. Dec 1998;22(2):465–476. [PubMed] [Google Scholar]
- 20.Canelo JA, Sardón MA, Pardo MM, León IL, González Mdel C. [Premature death from infectious diseases in Spain, 1908-1995]. Revista panamericana de salud publica = Pan American journal of public health. Oct 2002;12(4):262-8. Mortalidad prematura por enfermedades infecciosas en España, 1908-1995. doi:10.1590/s1020-49892002001000007. [DOI] [PubMed]
- 21.Boerma J.T., Nunn A.J., Whitworth J.A. Mortality impact of the AIDS epidemic: evidence from community studies in less developed countries. AIDS (London, England) 1998;12(Suppl 1):S3–S. [PubMed] [Google Scholar]
- 22.Klonowicz S. [Mortality from tuberculosis and the average life expectancy in Poland]. Pneumonologia polska. May 1976;44(5):421-8. Umieralność na gruźlice a przecietne trwanie źycia w Polsce. [PubMed]
- 23.Mortensen J, Lange P, Viskum K. [Life expectancy and prognosis for employment after contracting tuberculosis]. Lebensversicherungs Medizin. Sep 1 1987;39(5):149-51. Lebenserwartung und Prognose zur Erwerbstätigkeit nach Erkrankung an Tuberkulose. [PubMed]
- 24.Rickards BR, Slack FH, Arms BL. Longevity of B. Tuberculosis in Sputum. American journal of public hygiene. Aug 1909;19(3):586-94. [PMC free article] [PubMed]
- 25.Bottner H, Schlegel B. [Longevity of transfused erythrocytes in tuberculosis]. Beitrage zur Klinik der Tuberkulose und spezifischen Tuberkulose-Forschung. 1954;111(1-2):155-7. Die Lebensdauer übertragener Erythrocyten bei Kranken mit Tuberkulose. [PubMed]
- 26.Karlin M. [Tuberculosis as a disease of civilization and longevity]. Priroda, clovek in zdravje. Aug-Sep 1950;5(8-9):226-30. Izvzem6si tuberkulozo nara6s6cajotako imenovane bolezni civiliziranih narodov, ker se je podalj6sala 6zivljenjska doba. [PubMed]
- 27.Myers JA, Hartig H. Tuberculosis lingers as the life span lengthens. Part II. Geriatrics. Dec 1959;14:801-11. [PubMed]
- 28.Myers J.A., Hartig H. Tuberculosis lingers as the life span lengthens. I Geriatrics. Nov 1959;14:709–716. [PubMed] [Google Scholar]
- 29.De Cancella LC. [Effects of pulmonary tuberculosis on the duration of life]. Medicina contemporanea (Lisbon, Portugal). Sep 1958;76(9):403-8. Influence de la tuberculose pulmonaire sur la durée de la vie. [PubMed]
- 30.Pontali E., Akkerman O.W., Zenner D., Migliori G.B. Post-TB lung disease: keep going beyond TB! The Int J tuberculosis and lung disease : The official J Int Union against Tuberculosis and Lung Disease. 2024;28(3):113–114. doi: 10.5588/ijtld.23.0588. [DOI] [PubMed] [Google Scholar]
- 31.Nkereuwem E., Togun T., Kampmann B. Making a case for investing in post-tuberculosis lung health in children. Lancet Respir Med. Jun 2022;10(6):536–537. doi: 10.1016/s2213-2600(22)00102-3. [DOI] [PubMed] [Google Scholar]
- 32.Control of infectious diseases. MMWR Morbidity and mortality weekly report. Jul 30 1999;48(29):621-9. [PubMed]
- 33.Sun Y.N., Wang G.J., Zhen X.A., et al. The long term(9-year)survival of multidrug-resistant tuberculosis patients compared to non—multi-drug—resistant tuberculosis patients in Henan province. Chin J Epidemiol. 2013;34(2):133–136. [PubMed] [Google Scholar]
- 34.Pan L., Chu N.H. Medical information Comparative analysis of survival between multidrug-resistant tuberculosis patients and non-multidrug-resistant patients in Ningxia. China Medical Information. 2017;30(2):54–56. [Google Scholar]
- 35.Peng J.J., Xu L.L., Qu J., Jia X.D., Li Z.W., Wang S.R. Survival analysis of pneumoconiosis complicated with tuberculosis from 1953 to 2005 in shanghai. J Environ Occup Med. 2009;26(03):224–227. In Chinese. [Google Scholar]
- 36.Shenoi S.V., Brooks R.P., Barbour R., et al. Survival from XDR-TB is associated with modifiable clinical characteristics in rural South Africa. PLoS One. 2012;7(3) doi: 10.1371/journal.pone.0031786. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Han L, Gao Q, Yang J, et al. Survival Analysis of Coal Workers' Pneumoconiosis (CWP) Patients in a State-Owned Mine in the East of China from 1963 to 2014. International journal of environmental research and public health. May 6 2017;14(5)doi:10.3390/ijerph14050489. [DOI] [PMC free article] [PubMed]
- 38.Mirzaei M., Farhadian M., Poorolajal J., Afasr Kazerooni P., Tayeri K., Mohammadi Y. Life expectancy of HIV-positive patients after diagnosis in Iran from 1986 to 2016: a retrospective cohort study at national and sub-national levels. Epidemiology and health. 2018;40 doi: 10.4178/epih.e2018053. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.Drobniewski F., Eltringham I., Graham C., Magee J.G., Smith E.G., Watt B. A national study of clinical and laboratory factors affecting the survival of patients with multiple drug resistant tuberculosis in the UK. Thorax. Sep 2002;57(9):810–816. doi: 10.1136/thorax.57.9.810. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40.Kim D.H., Kim H.J., Park S.K., et al. Treatment outcomes and survival based on drug resistance patterns in multidrug-resistant tuberculosis. Am J Respir Crit Care Med. 2010;182(1):113–119. doi: 10.1164/rccm.200911-1656OC. [DOI] [PubMed] [Google Scholar]
- 41.Balabanova Y., Ignatyeva O., Fiebig L., et al. Survival of patients with multidrug-resistant TB in Eastern Europe: what makes a difference? Thorax. Sep 2016;71(9):854–861. doi: 10.1136/thoraxjnl-2015-207638. [DOI] [PubMed] [Google Scholar]
- 42.Hoger S., Lykens K., Beavers S.F., Katz D., Miller T.L. Longevity loss among cured tuberculosis patients and the potential value of prevention. The Int J tuberculosis and lung disease : The official J Inte Union against Tuberculosis and Lung Disease. Nov 2014;18(11):1347–1352. doi: 10.5588/ijtld.14.0242. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43.Wang W.B., Zhao Q., Yuan Z.A., Jiang W.L., Liu M.L., Xu B. Deaths of tuberculosis patients in urban China: a retrospective cohort study. The Int J tuberculosis and lung disease : The official J Int Union against Tuberculosis Lung Disease. Apr 2013;17(4):493–498. doi: 10.5588/ijtld.12.0400. [DOI] [PubMed] [Google Scholar]
- 44.T. G, A. B, B. W. Survival and predictors of mortality among patients under multi-drug resistant tuberculosis treatment in Ethiopia: St. Peter's specialized tuberculosis hospital, Ethiopia. Ethiopian Health and Nutrition Research Institute (EHNRI), Mekelle University, College of Health Science (MU, CHS), Ethiopia. 2013;Vol.4(No.2):772-783.
- 45.Zhou H, Wang WP, FYang. Observation of Clinical Characteristics and Survival Time of Patients with HIV Infection Combined with Pulmonary Tuberculosis and Analysis of Influencing Death Factors. Med & Pharm J Chin PLA. 2018;30(06):38-41. (In Chinese).
- 46.Kang G.Q., Li K.F. Historical cohort study on the mortality pattern and the related risk factors resulting in pulmonary tuberculosis. Health Research. 2015;6:611–613. [Google Scholar]
- 47.Wang J.J., Zhou M.L., Chen X., Chen C., Wu G., Zuo Y.P. Historical cohort study on the mortality pattern and the related risk factors resulting in pulmonary tuberculosis. 2017::6. [Google Scholar]
- 48.Zhu G.J., Qin C.W., Xu Z.W., Xu Y.X., Liang X.J., Huang Y.X. Analysis of factors in Guangxi Guigang City of HIV／TB patient survival time and influence. J Med Pest Control. 2017;33(05):485–487. In Chinese. [Google Scholar]
- 49.Garin B, Glaziou P, Kassa-Kelembho E, Yassibanda S, Mbelesso P, Morvan J. High mortality rates among patients with tuberculosis in Bangui, Central African Republic. Lancet (London, England). Nov 1 1997;350(9087):1298. doi:10.1016/s0140-6736(05)62475-0. [DOI] [PubMed]
- 50.Muluneh MA, Zeru AB, Derseh BT, Molla Kebede A. Survival Status and Predictors of Mortality among Multidrug-Resistant Tuberculosis Patients in Saint Peter's Specialized Hospital, Addis Ababa, Ethiopia. The Canadian journal of infectious diseases & medical microbiology = Journal canadien des maladies infectieuses et de la microbiologie medicale. 2021;2021:6696199. doi:10.1155/2021/6696199. [DOI] [PMC free article] [PubMed]
- 51.Alene K.A., Viney K., McBryde E.S., Tsegaye A.T., Clements A.C. Treatment outcomes in patients with multidrug-resistant tuberculosis in north-west Ethiopia. Tropical Med Int health : TM & IH. Mar 2017;22(3):351–362. doi: 10.1111/tmi.12826. [DOI] [PubMed] [Google Scholar]
- 52.Byun J.H., Park J.A., Kang H.R., Shin J.Y., Lee E.K. Comparison of effectiveness between delamanid and bedaquiline among patients with multidrug-resistant tuberculosis: a markov model simulation study. Clin Drug Invest. Nov 2016;36(11):957–968. doi: 10.1007/s40261-016-0443-6. [DOI] [PubMed] [Google Scholar]
- 53.Kunkel A., Cobelens F.G., Cohen T. Tradeoffs in introduction policies for the anti-tuberculosis drug bedaquiline: a model-based analysis. PLoS Med. Oct 2016;13(10) doi: 10.1371/journal.pmed.1002142. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 54.Huang C.C., Becerra M.C., Calderon R., et al. Isoniazid preventive therapy in contacts of multidrug-resistant tuberculosis. Am J Respir Crit Care Med. 2020;202(8):1159–1168. doi: 10.1164/rccm.201908-1576OC. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 55.Sy K.T.L., Horváth-Puhó E., Sorensen H.T., et al. Burden of chronic obstructive pulmonary disease attributable to tuberculosis: A microsimulation study. Am J Epidemiol. Jun 2023;192(6):908–915. doi: 10.1093/aje/kwad042. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 56.Chisumpa V.H., Odimegwu C.O., Saikia N. Adult mortality in sub-Saharan Africa: cross-sectional study of causes of death in Zambia. Tropical Med Int health : TM & IH. Oct 2019;24(10):1208–1220. doi: 10.1111/tmi.13302. [DOI] [PubMed] [Google Scholar]
- 57.Molie T, Teklemariam Z, Klinkenberg E, et al. Intensive phase treatment outcome and associated factors among patients treated for multi drug resistant tuberculosis in Ethiopia: a retrospective cohort study. BMC infectious diseases. Sep 18 2019;19(1):818. doi:10.1186/s12879-019-4411-7. [DOI] [PMC free article] [PubMed]
- 58.Bell J.C., Rose D.N., Sacks H.S. Tuberculosis preventive therapy for HIV-infected people in sub-Saharan Africa is cost-effective. AIDS (London, England) 1999;13(12):1549–1556. doi: 10.1097/00002030-199908200-00016. [DOI] [PubMed] [Google Scholar]
- 59.Diel R., Nienhaus A., Schaberg T. Cost-effectiveness of isoniazid chemoprevention in close contacts. Eur Respir J. Sep 2005;26(3):465–473. doi: 10.1183/09031936.05.00047805. [DOI] [PubMed] [Google Scholar]
- 60.Jordan TJ, Lewit EM, Reichman LB. Isoniazid preventive therapy for tuberculosis. Decision analysis considering ethnicity and gender. The American review of respiratory disease. Dec 1991;144(6):1357-doi:10.1164/ajrccm/144.6.1357. [DOI] [PubMed]
- 61.Pho M.T., Swaminathan S., Kumarasamy N., et al. The cost-effectiveness of tuberculosis preventive therapy for HIV-infected individuals in southern India: a trial-based analysis. PLoS One. 2012;7(4) doi: 10.1371/journal.pone.0036001. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 62.Rose D.N. Short-course prophylaxis against tuberculosis in HIV-infected persons. a decision and cost-effectiveness analysis. Ann Intern Med. 1998;129(10):779–786. doi: 10.7326/0003-4819-129-10-199811150-00005. [DOI] [PubMed] [Google Scholar]
- 63.Sarasin F.P., Perrier A., Rochat T. Isoniazid preventive therapy for pulmonary tuberculosis sequelae: which patients up to which age? Tubercle and lung disease : The official J Int Union against Tuberculosis and Lung Disease. Oct 1995;76(5):394–400. doi: 10.1016/0962-8479(95)90004-7. [DOI] [PubMed] [Google Scholar]
- 64.Salpeter S.R., Sanders G.D., Salpeter E.E., Owens D.K. Monitored isoniazid prophylaxis for low-risk tuberculin reactors older than 35 years of age: a risk-benefit and cost-effectiveness analysis. Ann Intern Med. 1997;127(12):1051–1061. doi: 10.7326/0003-4819-127-12-199712150-00001. [DOI] [PubMed] [Google Scholar]
- 65.Campbell J.R., Trajman A., Cook V.J., et al. Adverse events in adults with latent tuberculosis infection receiving daily rifampicin or isoniazid: post-hoc safety analysis of two randomised controlled trials. Lancet Infect Dis. Mar 2020;20(3):318–329. doi: 10.1016/s1473-3099(19)30575-4. [DOI] [PubMed] [Google Scholar]
- 66.Bastos M.L., Campbell J.R., Oxlade O., et al. Health system costs of treating latent tuberculosis infection with four months of rifampin versus nine months of isoniazid in different settings. Ann Intern Med. 2020;173(3):169–178. doi: 10.7326/m19-3741. [DOI] [PubMed] [Google Scholar]
- 67.Sriram S., Albadrani M. Globalization and life lost due to tuberculosis: evidence from a multi-country study. F1000Research. 2021;10:1251. doi: 10.12688/f1000research.74445.1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 68.Ogbo F.A., Ogeleka P., Okoro A., et al. Tuberculosis disease burden and attributable risk factors in Nigeria, 1990-2016. Trop Med Health. 2018;46:34. doi: 10.1186/s41182-018-0114-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.