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. 2019 Dec 27;14(12):e0226507. doi: 10.1371/journal.pone.0226507

Factors predictive of the success of tuberculosis treatment: A systematic review with meta-analysis

Ninfa Marlen Chaves Torres 1,2,*,#, Jecxy Julieth Quijano Rodríguez 2,#, Pablo Sebastián Porras Andrade 3,#, María Belen Arriaga 4,5,‡, Eduardo Martins Netto 1,5,‡
Editor: Hasnain Seyed Ehtesham6
PMCID: PMC6934297  PMID: 31881023

Abstract

Objective

To produce pooled estimates of the global results of tuberculosis (TB) treatment and analyze the predictive factors of successful TB treatment.

Methods

Studies published between 2014 and 2019 that reported the results of the treatment of pulmonary TB and the factors that influenced these results. The quality of the studies was evaluated according to the Newcastle-Ottawa quality assessment scale. A random effects model was used to calculate the pooled odds ratio (OR) and 95% confidence interval (CI). This review was registered in the International Prospective Register of Systematic Reviews (PROSPERO) in February 2019 under number CRD42019121512.

Results

A total of 151 studies met the criteria for inclusion in this review. The success rate for the treatment of drug-sensitive TB in adults was 80.1% (95% CI: 78.4–81.7). America had the lowest treatment success rate, 75.9% (95% CI: 73.8–77.9), and Oceania had the highest, 83.9% (95% CI: 75.2–91.0). In children, the success rate was 84.8% (95% CI: 77.7–90.7); in patients coinfected with HIV, it was 71.0% (95% CI: 63.7–77.8), in patients with multidrug-resistant TB, it was 58.4% (95% CI: 51.4–64.6), in patients with and extensively drug-resistant TB it was 27.1% (12.7–44.5). Patients with negative sputum smears two months after treatment were almost three times more likely to be successfully treated (OR 2.7; 1.5–4.8), whereas patients younger than 65 years (OR 2.0; 1.7–2.4), nondrinkers (OR 2.0; 1.6–2.4) and HIV-negative patients (OR 1.9; 1.6–2.5 3) were two times more likely to be successfully treated.

Conclusion

The success of TB treatment at the global level was good, but was still below the defined threshold of 85%. Factors such as age, sex, alcohol consumption, smoking, lack of sputum conversion at two months of treatment and HIV affected the success of TB treatment.

Introduction

Tuberculosis (TB) remains the leading global cause of death by a single infectious agent; it caused approximately 1.6 million deaths in 2017. An estimated 10 million people developed the disease, of whom 6.4 million (64%) were notified[1]. Additionally, of the 558,000 estimated cases of rifampicin- and isoniazid-resistant TB (multidrug-resistant TB—MDR-TB)/rifampin-resistant TB (RR-TB), a total of 139,114 people (87%) received the second-line regimen, and the proportion of MDR-TB cases with extensively drug-resistant TB (XDR-TB) defined as MDR-TB plus resistance to at least one drug in both of the two most important classes of medicines in an MDR-TB regimen: fluoroquinolones and second-line injectable agents (amikacin, capreomycin or kanamycin) was 8.5% (95% CI: 6.2–11%) [1].

The innumerable efforts to end the global TB epidemic have resulted in remarkable developments in research focused on multiple aspects of the disease. Unfortunately, we should rely on poor diagnostic, therapeutic, and preventive options. However, it is estimated that with the current strategies for TB control, the goals of reducing the number of deaths by 95%, reducing the incidence rate by 90% and increasing the cure rate of patients receiving first-line treatment to 90% between 2015 and 2035 will not be reached without intensifying research and development[2]. It is also necessary to strengthen health systems’ ability to detect cases early and to improve the quality of care, diagnosis and treatment of people with TB[3].

TB treatment coverage is one of the ten priority indicators for achieving the goals of the End TB Strategy, and it has increased from 51% in 2013 to 70% in 2017[2,4]. However, the treatment success rate has decreased from 86% in 2013 to 82% in 2016; in MDR-TB/RR-TB and XDR-TB cases, the success rate remains low: 55% and 34% in 2015[1]. This situation could be related to the limited evaluation of treatment outcomes in countries with limited resources and to the presence of factors that affect the outcome of TB treatment. Exhaustive estimates of TB treatment outcomes are needed to improve the programmatic management of TB. Therefore, this review with meta-analysis was performed to produce pooled estimates of global TB treatment outcomes and to analyze the predictive factors of successful TB treatment.

Methods

This review was registered in the International Prospective Register of Systematic Reviews (PROSPERO) in February 2019 under number CRD42019121512.

Search strategy

PubMed, Medline, Embase, ProQuest, Scopus and Scielo were searched for publications of the last 6 years that is, published between January 2014 and November 2019 that reported the results of treatment for pulmonary tuberculosis and the factors that influenced these results. We also searched other sources, such as Google and Google Scholar, and bibliographies to obtain additional references. Our search contained the following terms: tuberculosis, predictive factors, risk factors and treatment outcomes (tuberculosis AND (risk factors OR associated factors OR predictive factors OR characteristics) AND (treatment results OR treatment outcome OR successful treatment OR unsuccessful treatment OR unfavorable outcome OR (poverty OR poor)) AND tuberculosis treatment results) in English, Spanish and Portuguese. Trying to include as many publications as possible about our topic of interest. Approval from the ethics committee was not required.

Data extraction and definitions

The step-by-step selection of the studies is described in Fig 1. All article titles and abstracts were evaluated by two investigators (JR and PP), including all the studies that reported quantitative measurements of the results of tuberculosis treatment, and these results were clearly described according to the WHO criteria. For cases of drug-sensitive TB, only studies that clearly described patients receiving the standard treatment for tuberculosis recommended by the WHO known as short-term treatment (6 months) that includes 4 drugs. Studies that reported exclusively on extrapulmonary tuberculosis and those that did not allow the adequate extraction of quantitative data were not included. The full text of articles identified as relevant by any of the reviewers was read.

Fig 1. PRISMA flow chart indicating the result of literature search.

Fig 1

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To determine which full-text articles met the inclusion criteria, two investigators (JR and PP) reviewed all full-text articles, and a third investigator (NC) reviewed a random selection of studies. In cases of disagreement, the two investigators discussed the article until they agreed. One investigator (JR) extracted data from all included studies. The second investigator (NC) independently extracted all numerical data regarding the estimation of the main effect to validate the first review. If data from the same cohort were included in several articles, the article with the most complete data was included. For each included study, detailed information was collected on the design, publication year, country, study population, sample size, definition and measurement of treatment outcomes and associated factors.

Validity assessment

The quality of the included studies was evaluated according to the Newcastle-Ottawa quality assessment scale[5]. It evaluates quality based on the content, design and ease of use of the data for meta-analysis. Two investigators (JR and PP) independently evaluated the quality of the studies, classifying each study as being of either good, acceptable or low quality.

Statistical methods and data synthesis

TB treatment outcome measures were evaluated as the percentage of successful and unsuccessful results among all patients who initiated anti-TB therapy. The results of treatment were defined according to WHO criteria[6]. Successful outcomes were those in which patients met the definition of 'cure' or ‘treatment completion’. Unsuccessful outcomes were those in which patients met the definitions of death, default, failure or transfer. The subgroup analysis was performed by continent (Africa, America, Asia, Europe and Oceania), people living with HIV, children (1 to 15 years of age) and MDR/XDR-TB.

The associations of different variables, such as age (<65 years—66 years or older), sex (male—female), area of residence (rural—urban), type of case (new—previously treated), form of TB (pulmonary—extrapulmonary), alcohol consumption (yes—no), smoking (yes—no), HIV status (positive—negative), diabetes (yes—no), baseline sputum smear (positive—negative), and sputum smear microscopy two months after treatment (positive—negative), with the TB treatment outcome were measured. It was not possible to test the association from nutritional status, of educational level or socioeconomic status with TB treatment because these variables were evaluated differently in the studies depending on the country or region of origin. The influence of the health facility providing treatment on the results of TB treatment was not addressed in the studies included in this review.

Because most of the studies did not examine the association between these variables and successful treatment outcome as the main effect, unadjusted odds ratios (OR) and 95% confidence intervals (CI) were calculated as estimates of this association. After a table of results was created for each of the analyses, a random effects model was used to calculate pooled ORs and 95% CIs as there were high levels of heterogeneity in the study populations. Statistical heterogeneity was assessed using the I2 statistic. Publication bias was assessed using a funnel plot. All statistical analyses were performed in MedCalc® version 19.03.

Results

As indicated in Fig 1, a total of 1,432 articles were identified. Of these, 992 were not duplicated; 807 of those were excluded after the title and abstract were evaluated, and 185 underwent a detailed review of the full text. A total of 151 studies with 1,550,449 patients with TB from 59 countries distributed among 5 continents met the criteria for inclusion in this review (Table 1).

Table 1. Distribution of patients with TB in the continents.

Continent No. Countries No. Studies % Pacientes %
Africa 15 47 31.1 164,091 10.6
America 6 20 13.2 716,992 46.2
Asia 19 58 38.4 357,163 23.0
Europe 12 18 11.9 302,557 19.5
Oceania 5 5 3.3 6,926 0.4
Intercontinental 3 3 2.0 2,740 0.2
Total 60 151 100.0 1,550,469 100.0
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In total, 95 of the 151 studies were retrospective cohort, 28 were cross-sectional, 25 were prospective cohort and 3 were case-control studies. Of the eligible studies, 91 reported treatment results in cases of TB in adults, 7 in children, 15 in patients coinfected with HIV and 38 in MDR/XDR-TB cases. These studies are detailed in Table 2.

Table 2. Characteristics of the included studies according to continent of origin and population studied.

Autor (Year) Country Study design n Success % Quality / newcastle
TB studies in Africa
  Adane K. et al (2018)[7] Ethiopia RC 422 395 93,6 L
  Ali M. et al (2017)[8] Somalia CS 385 315 81,8 L
  Amante T. et al (2015)[9] Ethiopia CC 976 646 66,2 L
  Budgell E. et al (2016)[10] South Africa RC 544 394 72,4 A
  Chidubem L. et al (2016)[11] Nigeria RC 555 479 86,3 L
  Ejeta E. et al (2018)[12] Ethiopia RC 4144 3532 85,2 G
  El-Shabrawy M. et al (2017)[13] Egypt RC 480 384 80,0 L
  Ershova V. et al (2014)[14] South Africa RC 741 617 83,3 L
  Esmael A. et al (2014)[15] Ethiopia RC 717 425 59,3 L
  Gebrezgabiher G. et al (2016) [16] Ethiopia RC 1537 1310 85,2 L
  Kosgei J. et al (2015)[17] Kenya RC 16056 14318 89,2 G
  Mbatchou B. et al (2016)[18] Cameroon RC 8902 6684 75,1 L
  Mhimbira F. et al (2016)[19] Tanzania RC 4835 4006 82,9 L
  Mlotshwa M. et al (2016) [20] South Africa RC 12742 10719 84,1 L
  Mugomeri E. et al (2017)[21] Lesotho RC 812 577 71,1 A
  Muluye A. et al (2018)[22] Ethiopia CS 995 905 91,0 L
  Nafae R. et al (2017)[23] Egypt RC 280 231 82,5 G
  Nanzaluka F. et al (2019)[24] Zambia RC 1724 985 57,1 L
  Nembot F. et al (2015)[25] Cameroon RC 1286 1119 87,0 L
  Oshi S. et al (2014)[26] Nigeria RC 1668 1268 76,0 G
  Peltzer K. et al (2014)[27] South Africa PC 1196 695 58,1 L
  Saleh A. et al (2017) [28] Yemen RC 273 227 83,2 A
  Ukwajaa N. et al (2014)[29] Nigeria RC 929 796 85,7 L
  Wondale B. et al (2017)[30] Ethiopia RC 1172 868 74,1 L
  Worku S. et al (2018)[31] Ethiopia CS 985 672 68,2 G
  Yoko J. et al (2017)[32] South Africa CS 229 176 76,9 L
  Zenebe T. et al (2016)[33] Ethiopia RC 380 320 84,2 L
  Zenebe Y. et al (2016)[34] Ethiopia CS 671 542 80,8 L
TB Studies in America
  Cailleaux M. et al (2017)[35] Brazil RC 174 146 83,9 L
  Calle A. et al (2016)[36] Colombia CS 837 645 77,1 A
  Djibuti M. et al (2014)[37] U.S PC 202 155 76,7 L
  Lackey B. et al (2015)[38] Peru PC 1233 1036 84,0 L
  Maciel E. et al (2015)[39] Brazil CS 318465 222186 69,8 G
  Magee M. et al (2015)[40] U.S PC 291 221 75,9 L
  Pereira J. Et al (2015)[41] Brazil RC 421 362 86,0 L
  Reis-Santos B. et al (2015)[42] Brazil CS 31578 23537 74,5 L
  Romanowski K. et al (2017)[43] Canada RC 165 144 87,3 L
  Silva M. et al (2017)[44] Brazil PC 220 172 78,2 L
  Snyder R. et al (2016)[45] Brazil RC 6601 3585 54,3 L
  Viana P. et al (2016)[46] Brazil CS 278674 204205 73,3 L
  Viegas A. et al (2016)[47] Brazil PC 83 64 77,1 A
TB studies in Asia
  Ahmad T. et al (2017)[48] Pakistan RC 493 468 94,9 L
  Ali K. et al (2015)[49] Iran RC 167 143 85,6 A
  Alqahtani S. et al (2017)[50] Saudi Arabia CS 1600 1338 83,6 L
  Atif M. et al (2014)[51] Malaysia RC 336 226 67,3 L
  Babalik A. et al (2014)[52] Turkey RC 23845 22412 94,0 L
  Chi C. et al (2015)[53] China PC 16345 13349 81,7 A
  Choi H. et al (2014)[54] Korea PC 669 335 50,1 L
  Gadoev J. et al (2015)[55] Uzbekistan RC 107380 89622 83,5 L
  Hongguang C. et al (2015)[56] China PC 1126 1066 94,7 L
  Jackson C. et al (2017)[57] India RC 8415 7148 84,9 A
  Khaing P. et al (2018)[58] Myanmar RC 13711 11066 80,7 L
  Khazaei S. et al (2016)[59] Iran CS 510 424 83,1 G
  Kwon Y. et al (2014)[60] Korea RC 2481 2333 94,0 L
  Liew S. et al (2015)[61] Malaysia RC 21582 16824 78,0 L
  Lin Y. et al (2017)[62] China CS 30277 26016 85,9 L
  Lo H. et al (2016)[63] Taiwan RC 766 544 71,0 L
  Lwin Z. et al (2017)[64] Myanmar RC 2975 2618 88,0 L
  Morishita F. et al (2017)[65] Philippines RC 612 548 89,5 L
  Mukhtar F. et al (2016)[66] Pakistan PC 614 434 70,7 L
  Mundra A. et al (2017)[67] India RC 510 418 82,0 L
  Mundra A. et al (2017)[68] India CC 275 187 68,0 L
  Ni W. et al (2016)[69] China RC 1447 1349 93,2 L
  Piparva K. (2016)[70] India RC 1340 1210 90,3 L
  Rahimy N. et al (2018)[71] Thailand RC 291 234 80,4 L
  Rao P. et al (2015)[72] India CS 862 718 83,3 L
  Sadykova L. et al (2019)[73] Kazakhstan RC 36926 26635 72,1 L
  Schwitters A. et al (2014)[74] Uganda CS 469 222 47,3 L
  Shahrezaei M. et al (2015)[75] Iran RC 2224 1827 82,1 L
  Thomas B. et al (2019)[76] India PC 455 374 82,2 A
  Wang X. et al (2015)[77] China CS 20396 18908 92,7 L
  Wang X. et al (2017)[78] China RC 395 296 74,9 L
  Wen Y. et al (2018)[79] China RC 22998 21851 95,0 G
  Xiao-chun H. et al (2017)[80] China RC 5663 3154 55,7 L
  Yoon Y. et al (2016)[81] Korea PC 661 512 77,5 L
  Zhang Q. et al (2014)[82] Kuwait RC 954 676 70,9 L
TB studies in Europe
  Aibana O. et al (2018)[83] Ukraine RC 296 193 65,2 L
  Cruz-Ferro E. et al (2014)[84] Spain CS 18660 16524 88,6 L
  Dias M. et al (2017)[85] Portugal RC 17655 14186 80,4 L
  Gaborit B. et al (2017)[86] France CC 134 112 83,6 L
  Holden I. et al (2019)[87] Denmark RC 1681 1353 80,5 A
  Karo B. et al (2015)[88] Union eropea RC 250864 196835 78,5 G
  Lucenko I. et al (2014)[89] Latvia RC 2476 2167 87,5 G
  Moreno-Gómez M. et al (2014)[90] Spain PC 146 62 42,5 L
  Priedeman M. et al (2018)[91] Ukraine RC 1618 1327 82,0 L
  Przybylski G. et al (2014)[92] Poland PC 2025 1813 89,5 G
  Rodríguez E. et al (2015)[93] Spain RC 5880 4703 80,0 L
TB studies in Oceania
  Alo A. et al (2014)[94] Fiji CS 395 322 81,5 L
  Itogo N. et al (2014)[95] Solomon Islands RC 4137 3779 91,3 L
  Scheelbeek P. et al (2014)[96] Indonesia RC 1582 1244 78,6 L
  Tagaro M. et al (2014)[97] Vanuatu RC 568 469 82,6 L
TB / HIV coinfection studies
  Agbor A. et al (2014)[98] Cameroon RC 337 205 60,8 L
  Ambadekar N. et al (2015)[99] India PC 11620 9731 83,7 L
  Belayneh M. et al (2015)[100] Ethiopia CS 342 242 70,8 L
  Do Prado T. et al (2016)[101] Brazil CS 68295 37445 54,8 L
  Engelbrecht M. et al (2017)[102] South Africa CS 66940 51668 77,2 L
  Jacobson K. et al (2015)[103] South Africa CS 657 540 82,2 L
  Lawal A. et al (2018)[104] Nigeria RC 1382 745 53,9 A
  Mahtab S. et al (2017)[105] South Africa CS 12672 8870 70,0 G
  Monge S. et al (2014)[106] Spain PC 271 216 79,7 G
  Parchure R. et al (2016)[107] India RC 769 450 58,5 G
  Sinshaw Y. et al (2017)[108] Ethiopia CS 308 238 77,3 G
  Tanue E. et al (2019)[109] Cameroon RC 1041 818 78,6 A
  Theingi P. et al (2017)[110] Myanmar RC 815 624 76,6 G
  Torrens A. et al (2016)[111] Brazil RC 7628 3664 48,0 L
  Wannheden C. et al (2014)[112] Sweden RC 127 109 85,8 G
TB / Children studies
  Alavi S. et al (2015)[113] Iran CS 177 144 81,4 L
  Flick R. et al (2016)[114] Malawi RC 371 228 61,5 G
  Hamid M. et al (2019)[115] Pakistan RC 1665 1421 85,3 A
  Laghari M. et al (2018)[116] Pakistan RC 2111 1950 92,4 L
  Ohene S. et al (2019)[117] Ghana RC 214 194 90,7 L
  Tilahun G. et al (2016)[118] Ethiopia RC 491 420 85,5 L
  Turkova A. et al (2016)[119] European Union, Thailand, Brazil CS 127 116 91,3 L
MDR/XDR-TB studies
  Addis K. et al (2017)[120] Ethiopia RC 242 154 63,6 L
  Aibana O. et al (2017)[121] Ukraine RC 378 65 17,2 L
  Altena R. et al (2015)[122] Netherlands CS 113 89 78,8 L
  Atif M. et al (2017)[123] Pakistan RC 80 48 60,0 L
  Bastard M. et al (2018)[124] Abkhazia, Armenia, Colombia, Kenya, Kyrgyzstan, Swaziland and Uzbekistan RC 1369 872 63,7 L
  Brust J. et al (2018)[125] South Africa PC 206 140 68,0 L
  Cegielski J. et al (2015)[126] Estonia, Latvia, Philippines, Peru, Russia, South Africa, South Korea, Taiwan, and Thailand PC 1244 722 58,0 L
  Chen Y. et al (2018)[127] China RC 284 194 68,3 G
  Chiang S. et al (2016)[128] Peru RC 232 163 70,3 L
  Demile B. eta al (2018)[129] Ethiopia CS 381 264 69,3 L
  Duraisamy K. et al (2014)[130] India RC 179 112 62,6 G
  Francis J. et al (2018)[131] Australia RC 244 161 66,0 G
  Heysell S. et al (2016)[132] Russia PC 98 51 52,0 L
  Ibrahim E. et al (2016)[133] Egypt RC 577 352 61,0 L
  Jagielski T. et al (2014)[134] Poland PC 46 8 17,4 L
  Janmeja A. et al (2018)[135] India RC 256 132 51,6 L
  Javaid A. et al (2016)[136] Pakistan RC 186 73 39,2 L
  Javaid A. et al (2017)[137] Pakistan RC 535 406 75,9 L
  Jensenius M. et al (2016)[138] Norway RC 89 45 50,6 L
  Kawatsu L. et al (2018)[139] Japan CS 172 98 57,0 L
  Khan M. et al (2015)[140] Pakistan RC 179 133 74,3 L
  Liu Q. et al(2018) [141] China RC 139 84 60,4 L
  Marais E. et al (2014)[142] South Africa RC 351 158 45,0 L
  Monserrat L. et al (2017)[143] Mexico RC 507 399 78,7 L
  Munoz-Torrico M. et al(2016)[144] Mexico RC 90 33 36,7 A
  Nair D. et al (2017)[145] India RC 788 469 59,5 L
  Pang Y. et al (2017)[146] China RC 29 7 24,1 L
  Parmar M. et al (2018)[147] India RC 3712 781 21,0 L
  Phuong N. et al (2016)[148] Viet Nam RC 1380 1008 73,0 L
  Schnippel K. et al (2015)[149] South Africa RC 10763 4227 39,3 L
  Trébucq A. et al (2018)[150] Central Africa PC 1006 821 81,6 L
  Udwadia Z. et al (2014)[151] India PC 78 53 67,9 L
  Verdecchia M. et al (2018)[152] Swaziland RC 174 131 75,3 L
  Viana P. et al (2018)[153] Brazil PC 257 139 54,1 G
  Villegas L. et al (2016)[154] Peru RC 1039 815 78,4 G
  Xu C. et al (2017)[155] China RC 1542 734 47,6 G
  Zhang L. et al (2017)[156] China PC 537 374 69,6 L
  Zhang Q. et al (2016)[157] China RC 160 88 55,0 L
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CC: Cases and controls study; CS: cross section study: PC: prospective cohort study; RP: retrospective cohort study; A: Acceptable; G: Good; L: Low.

Results of TB treatment

The success rate for the treatment of drug-sensitive TB in adults was 80.1% (95% CI: 78.4–81.7) (Fig 2). A high degree of heterogeneity (I2: 99.8%) was observed among the studies, but no publication bias was found in the funnel plot. Based on the subgroup analysis, America had the lowest treatment success rate at 75.9% (95% CI: 73.8–77.9) (S1 Fig), followed by Africa at 78.9% (95% CI: 75.5–82.2) (S2 Fig), Europe at 79.7% (95% CI: 76.2–83.0) (S3 Fig), Asia at 81.6% (95% CI: 78.5–84.5) (S4 Fig) and Oceania at 83.9% (95% CI: 75.2–91.0) (S5 Fig).

Fig 2. Pooled estimate of successful tuberculosis treatment outcome.

Fig 2

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The success rate was 84.8% in children (95% CI: 77.7–90.7) (S6 Fig), 71.0% in patients coinfected with HIV (95% CI: 63.7–77.8) (S7 Fig), 58.4% in patients with MDR-TB (95% CI: 51.4–64.6) (S8 Fig) and 27.1% (95% CI: 12.7–44.5) in patients with XDR-TB (Table 3). A high degree of heterogeneity (I2: 98%; I2: 99.8%; I2: 99.2%; I2: 84.3%, respectively), was observed in these subgroups, but there was no evidence of publication bias in the funnel plot.

Table 3. Success rate in patients with XDR-TB.

Autor (Year) n Success (%) 95% CI Weight (%)
Chen Y. et al (2018) 35 57,1 39,3 to 73,7 20,1
Pang Y. et al (2017) 29 24,1 10,3 to 43,5 19,4
Cegielski J. et al (2015) 58 29,3 18,1 to 42,7 21,4
Aibana O. et al (2017) 35 5,7 0,7 to 19,2 20,1
Javaid A. et al (2017) 26 23,1 8,9 to 43,6 19,1
Total (fixed effects) 183 27,5 21,3 to 34,5 100
Total (random effects) 183 27,1 12,7 to 44,5 100
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Predictors of TB treatment success

Patients who were smear-negative at two months of treatment were almost three times more likely to succeed in treatment (OR 2.7; 1.5–4.8) (Fig 3), whereas patients who were younger than 65 years (OR 2.0; 1.7–2.4) (Fig 4), nondrinkers (OR 2.0; 1.6–2.4) (Fig 5) and HIV-negative (OR 1.9; 1.6–2.3) were two times more likely to succeed in treatment. In contrast, diabetes, the TB form and positive baseline sputum smear did not influence the results of treatment (Table 4).

Fig 3. Pooled estimate to negative smear in the 2nd month as a factors predictive of favorable outcomes of tuberculosis treatment.

Fig 3

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Fig 4. Pooled estimate to age <65 years as a factors predictive of favorable outcomes of tuberculosis treatment.

Fig 4

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Fig 5. Pooled estimate to Non-alcoholic as a factors predictive of favorable outcomes of tuberculosis treatment.

Fig 5

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Table 4. Factors predictive of favorable outcomes of tuberculosis treatment.

Included studies Exposed Not exposed Odds ratio 95% CI
Negative smear in the 2nd month 9 8715/12233 4082/7077 2.7 1.5–4.8
Age <65 years 15 32384/36587 6937/8127 2.0 1.7–2.4
Non-alcoholic 9 18074/30334 4118/7609 2.0 1.6–2.4
HIV Negatives 35 51769/64024 43781/76845 1.9 1.6–2.3
New cases 34 86862/122085 17595/31183 1.6 1.5–1.6
No smoking 16 22336/26922 15092/19442 1.5 1.3–1.7
Urban residence 27 52617/78278 23016/28380 1.2 1.0–1.4
Female sex 61 74645/95809 128387/173218 1.2 1.1–1.3
No Diabetes 11 34836/53800 4161/5482 1.1 0.9–1.5
Pulmonary Tuberculosis 34 83906/118231 27509/42250 1.1 0.9–1.3
Positive smear on admission 15 39422/57407 31945/46381 1.0 08–1.2
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Discussion

This meta-analysis showed that the success rate for the treatment of drug-sensitive TB in adults was 80.1% (95% CI: 78.4–81.7); for those with associated HIV-TB, it was 71.0% (95% CI: 63.7–77.8), In patients with XDR-TB it was 27.1% (95% CI: 12.7–44.5) and for those with MDR-TB, it was 58.4% (95% CI: 51.4–64.6). These values did not differ significantly from those reported by the WHO for 2016 (82%, 77%, 34% and 55%, respectively)[1]. This result was expected considering that cases of TB require compulsory notification in most countries. A study in Europe (2005), reported a pooled estimate of successful outcomes of 74.4% (95% CI 71.0–77.9%), this lower estimate than the one reported in this review could be attributed to the fact that the studies analyzed in this work are prior to 2005 [158]. While, another study in Ethiopia, reported a global success rate of combined TB treatment of 86% (with a 95% CI: 83%-88%), higher than our estimate of successful treatment [159]. To XDR and MDR TB a review published in 2017 reported pooled treatment success of 26% and 60% respectively, which is not different from our results[160]. However, these results should be improved to cure 90% of patients. The results of tuberculosis treatment improve with the use of adherence interventions, such as patient education and counseling, incentives and enablers, psychological interventions, reminders and tracers, and digital health technologies. Therefore, tuberculosis control programs should keep in mind that in addition to prescribing tuberculosis medications, they need to include resources to help patients overcome individual challenges to complete treatment [161].

In children, the treatment success rate was 83.4% (95% CI: 71.0–92.9). The WHO annual Global Tuberculosis Report does not specify a treatment success rate for children. However, studies from varying countries published in 2016 reported success rates for the treatment of children with TB that were both lower and higher than that those estimated in this review, e.g., 61.5% in Malawi and 91.3% in the European Union[114,119]. Although in 2018 a review was published that calculated the success rate of 78% to MDR TB treatment in children[162], this is the first pooled estimate of treatment of drug-sensitive TB success in children at the global level in the last five years.

Sputum smear conversion in the second month of treatment was previously associated with treatment success[163]. This meta-analysis confirmed that a negative sputum smear at two months of treatment was a predictor of success (OR 2.7; 95% CI: 1.5–4.8). However, sputum smear non-conversion after two months of treatment continues to be controversial as a predictor of unfavorable outcomes due to its low sensitivity and specificity for identifying treatment failure[164]. Therefore, further studies are needed to clarify this controversy.

It was also confirmed that factors such as age <65 years (OR 2.0; 95% CI 1.7–2.4), female sex (OR 1.2; 95% CI 1.1–1.3) and a new case type favor the success of TB treatment, as reported in previous studies[8,9,17,48,68,69,86,138]. Not drinking alcohol was also a predictor of favorable treatment results (OR 2.0; 95% CI 1.6–2.4). Alcohol consumption has been associated with treatment failure and a predisposition toward adverse drug effects, either because those who consumed alcohol skipped more doses during TB treatment or because alcohol may affect the immune response against M. tuberculosis, leading to treatment failure or a late response to treatment[44,92,130].

Nonsmokers also had a higher probability of treatment success (OR 1.5; 95% CI: 1.3–1.7) according to a study conducted in China that suggested smoking adversely affects the bacteriological response to and the result of TB treatment[53]. In Malaysia, smoking was also identified as a risk factor for unfavorable treatment outcomes[61]. In Poland, smoking did not influence the results of TB treatment[92]. In Brazil patients with a history of smoking increase 2.1 (95% CI 1.1–4.1) times, but the possibility of failure in TB treatment. Moreover, having a larger age of 50 years shows that the possibility of failure increases 2.8 (95% CI 1.4–6.0)[165].

The HIV-TB association continues to be a challenge for public health. Studies have identified coinfection as a risk factor for unfavorable TB treatment results, and most have attributed these results to the high mortality in these patients[10,98–100,105,109]. We corroborated these results, showing that HIV-negative patients had a higher proportion of favorable treatment outcomes (OR 1.9; 95% CI 1.6–2.3), while in general, the treatment success rate in coinfected patients was low (70.5%).

In this review, diabetes did not influence treatment outcomes. Our results coincide with those reported in Georgia and Malaysia[40,61], although it was previously suggested that diabetes was associated with unfavorable TB treatment results[56,81].

Among the limitations of this study, it is necessary to mention that the use of observational studies for a meta-analysis could induce errors by finding false significant associations when combining small studies affected by confounding[166]. Additionally, it is known that the quality of a meta-analysis depends on the quality of the included studies; in most studies, the quality was classified as low, which may be associated with the fact that most of the studies were retrospective and based on mandatory notification systems, where it is difficult to control due to loss at follow-up and other confounding factors. The degree of heterogeneity was also high among the studies; therefore, the random effects method was used to obtain the pooled results. Finally, the methodological variations among the included studies could also compromise the results of the meta-analysis.

Conclusion

The study findings suggest that the rate of successful TB treatment at the global level is good but is still below the defined threshold of 85%. Factors such as age, sex, alcohol consumption, smoking, sputum smear non-conversion at two months of treatment and HIV affect the results of TB treatment.

Supporting information

S1 File. PRISMA 2009 checklist.

Predictors of TB treatment success.

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S1 Fig. Pooled estimate of successful tuberculosis treatment outcome in America.

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S2 Fig. Pooled estimate of successful tuberculosis treatment outcome in Africa.

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S3 Fig. Pooled estimate of successful tuberculosis treatment outcome in Europe.

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S4 Fig. Pooled estimate of successful tuberculosis treatment outcome in Asia.

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S5 Fig. Pooled estimate of successful tuberculosis treatment outcome in Oceania.

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S6 Fig. Pooled estimate of successful tuberculosis treatment outcome in children.

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S7 Fig. Pooled estimate of successful tuberculosis treatment outcome in patients coinfected with HIV.

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S8 Fig. Pooled estimate of successful MDR-TB treatment outcome.

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Data Availability

All relevant data are within the paper and its Supporting Information files.

Funding Statement

The author(s) received no specific funding for this work.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 File. PRISMA 2009 checklist.

Predictors of TB treatment success.

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S1 Fig. Pooled estimate of successful tuberculosis treatment outcome in America.

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S2 Fig. Pooled estimate of successful tuberculosis treatment outcome in Africa.

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S3 Fig. Pooled estimate of successful tuberculosis treatment outcome in Europe.

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S4 Fig. Pooled estimate of successful tuberculosis treatment outcome in Asia.

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S5 Fig. Pooled estimate of successful tuberculosis treatment outcome in Oceania.

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S6 Fig. Pooled estimate of successful tuberculosis treatment outcome in children.

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S7 Fig. Pooled estimate of successful tuberculosis treatment outcome in patients coinfected with HIV.

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S8 Fig. Pooled estimate of successful MDR-TB treatment outcome.

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Data Availability Statement

All relevant data are within the paper and its Supporting Information files.

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