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Abstract
Background A recurrent tuberculosis (TB) episode results from exogenous reinfection or relapse after cure. The use of genotyping allows the distinction between both.
Methods We did a systematic review and meta-analysis, using four databases to search for studies in English, French and Spanish published between 1 January 1980 and 30 September 2020 that assessed recurrences after TB treatment success and/or differentiated relapses from reinfections using genotyping. We calculated person years of follow-up and performed random-effects model meta-analysis for estimating pooled recurrent TB incidence rates and proportions of relapses and reinfections. We performed subgroup analyses by clinical–epidemiological factors and by methodological study characteristics.
Findings The pooled recurrent TB incidence rate was 2.26 per 100 person years at risk (95% CI 1.87 to 2.73; 145 studies). Heterogeneity was high (I2=98%). Stratified pooled recurrence rates increased from 1.47 (95% CI 0.87 to 2.46) to 4.10 (95% CI 2.67 to 6.28) per 100 person years for studies conducted in low versus high TB incidence settings. Background HIV prevalence, treatment drug regimen, sample size and duration of follow-up contributed too. The pooled proportion of relapses was 70% (95% CI 63% to 77%; I²=85%; 48 studies). Heterogeneity was determined by background TB incidence, as demonstrated by pooled proportions of 83% (95% CI 75% to 89%) versus 59% (95% CI 42% to 74%) relapse for studies from settings with low versus high TB incidence, respectively.
Interpretation The risk of recurrent TB is substantial and relapse is consistently the most frequent form of recurrence. Notwithstanding, with increasing background TB incidence the proportion of reinfections increases and the predominance of relapses among recurrences decreases.
PROSPERO registration number CRD42018077867
- tuberculosis
- clinical epidemiology
- respiratory infection
Data availability statement
All data relevant to the study are included in the article or uploaded as supplementary information.
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Key messages
What is the key question?
What is the incidence of recurrent TB and the proportions of relapses and reinfections?
What is the bottom line?
The risk of recurrent TB is substantial and relapse is consistently the most frequent form of recurrence, yet, as background incidence increases, the proportion of reinfections also increases and the predominance of relapses among recurrences decreases.
Why read on?
Understanding of the frequency of recurrent TB, relapses and reinfections will help to implement better post treatment follow-up and to reduce TB burden.
Introduction
The proportion of people with TB that are successfully treated is a key indicator to monitor progress of the End TB Strategy.1 The standardised four-drug regimen for drug susceptible pulmonary TB has shown favourable outcome at the end of treatment in over 92% of cases in clinical trials2–4 and in TB programmes at least 85% of patients receiving it are successfully treated (cured or treatment completed).5 6 After being cured, some individuals can develop a recurrent episode of TB, as a result of an endogenous reactivation of the first infection or of an exogenous new infection.7–9 Historically,9 it was not possible to differentiate relapses from reinfections in recurrent episodes because they are clinical indistinguishable. Moreover, reinfection was considered to be rare.10 The advent of molecular techniques using DNA markers8 permits comparison of the genotype of strains isolated in the first and recurrent TB episode. Episodes sharing the same strain are classified as relapses while those with two different strains are classified as reinfections.
The incidence of recurrent TB and the proportion of reinfections and relapses can guide TB control. Furthermore, these relative frequencies depend on the background transmission rate besides the effectiveness of treatment. While relapses generally predominate, studies conducted in high TB incidence populations usually identify a larger proportion of cases due to reinfection than studies in countries where the burden of TB disease is lower.10–12 Notwithstanding, a higher than expected incidence of relapse calls for an evaluation of the efficacy of the first-episode treatment regimen.13 On the other hand, if reinfection rates are higher than expected, reducing the risk of transmission is fundamental.
Two systematic reviews published in 2003 investigated recurrent TB episodes. One review7 found, without providing a pooled estimate, that the proportion of reinfections ranged between 0% and 100%. The other13 analysed the influence of HIV-status and rifampin-based treatment on recurrence and found a recurrent TB rate of 1.9 cases per 100 person years among HIV negative and 4.5 per 100 person years among HIV-positive individuals and recurrence rates that increased with decreasing duration of therapy. A 2007 systematic review14 pooled recurrent TB rates across 10 controlled trials and found a rate of 2.3 per 100 person years at 12 months after treatment completion. In the 13 years that passed since this last review was published, the scientific literature on recurrences has boomed alongside the markedly increased use of genotyping methods. This raises not only the need to review anew the phenomenon of TB recurrences but offers also the opportunity to more comprehensively look into the difference between relapses and reinfections because new genotyping techniques, including whole-genome sequencing (WGS), allow enhanced resolution to differentiate between Mycobacterium tuberculosis strains.8 15 We conducted a systematic review of the literature published in the last four decades to assess the recurrent TB incidence rate and the proportion of recurrent TB cases due to reinfection and relapse.
Methods
Search strategy and selection criteria
We included clinical trials, longitudinal studies and studies using TB programme databases that reported recurrent TB rates and/or the proportion of reinfections and relapses, published between 1 January 1980 and 30 September 2020, in English, French and Spanish. We searched Medline/PubMed, Cochrane, Literatura Latinoamericana y del Caribe en Ciencias de la Salud (LILACS) and Scientific Electronic Library Online (SciELO). Literature search strategies for Medline/PubMed used medical subject headings (MeSH) and text words related to TB recurrence. For all databases, key search terms included variations on the concept “tuberculosis AND (recurrence OR reinfection OR relapse OR reactivation)”. The search strategy is fully described in online supplemental appendix 1. We also reviewed references of included articles. In addition to studies using epidemiological designs, we included peer-reviewed studies reporting data from TB programme databases.
Supplemental material
Duplicity of studies was identified using Covidence software, Veritas Health Innovation (available at www.covidence.org) or manually, for example, when the titles were in two different languages. When two studies reported on the same group of subjects but used different molecular techniques, the study whose technique had the best discriminatory power was included. When two studies analysed recurrent episodes in the same population over the same time period, full texts were reviewed and the study with less restricted exclusion criteria was withheld in order to capture more recurrences.
Titles and abstracts of retrieved studies were screened independently by two authors (VV and SR) to identify studies that met the inclusion criteria. Disagreements were resolved through discussion with a third author (LO). If abstracts were not available or did not provide sufficient information to decide on eligibility, the study was included in the full-text review. Our inclusion criteria were studies reporting on recurrent TB defined as a new diagnosis of TB after a successfully treated TB episode. Studies had to define treatment success as cured (bacteriologically negative at month 5) or treatment completion. All TB regimens were included. For recurrent TB incidence, our first outcome, we selected studies that followed up successfully treated patients to detect recurrences. For proportions of relapses and reinfections, our second outcome, we also included studies exploiting national or subnational TB programme databases, which do not report follow-up data for all patients.
Data extraction and analysis
The two primary reviewers (VV and SR) extracted data independently into a Google Form prepared for the study that had been tested on five randomly selected studies. Discrepancies in data extraction were resolved through discussion with a senior researcher (LO). Data extracted included study characteristics, number of TB cases who successfully completed treatment, type of follow-up (active, where patients are sought after to determine the presence or absence of a recurrent TB episode vs passive or routine TB register based, where patients self-present at health facilities), duration of follow-up in years, number of recurrent episodes, number of relapses and reinfections, test used to diagnose a recurrent TB episode, and molecular techniques used. We assessed the risk of bias using a modified Newcastle Ottawa Scale16 for both outcomes. We adapted this tool for all studies, regardless of study design, to evaluate the risk of bias in their estimations of the incidence of recurrences, as well as of the proportions of relapses and reinfections. The nature of and rationale for modifying some items of the scale is detailed in online supplemental appendix 2.
Pooled recurrent TB incidence
The recurrent TB incidence rate was calculated by dividing the number of recurrent TB episodes by the total person years at risk. The person years at risk contributed by the patients with a successfully treated first TB episode was extracted as reported by the study or, depending on the reported data, calculated as detailed in online supplemental appendix 3.
The meta-analysis of recurrent TB incidence was done with RStudio V.1.1.463 using the meta package and the command metarate. Rates were expressed as recurrences per 100 person years. We used a DerSimonian-Laird random effects model for meta-analysis in view of the heterogeneity between studies. We tested for heterogeneity by using the I2 statistic with p<0.05 as threshold. Results were displayed in forest plots. We evaluated the risk of publication bias by assessing symmetry in a funnel plot and performing Egger’s test.
We performed subgroup analyses of the recurrent TB rate pooled by clinical–epidemiological factors related to study setting and by methodological study characteristics. Background TB incidence was based on the country-level TB incidence per 100 000 population provided by the WHO17 for the year of study or at midterm in case of follow-up >1 year. Before 2000, estimated incidence is not available and we used the case notification rate provided by WHO. We classified countries’ incidence as low if <30 per 100 000 population, moderate if between 30 and 100, medium if between 100 and 300 and high if >300. Regarding type of follow-up, clinical trials were considered to have ‘active follow-up’ unless otherwise specified, and observational studies were classified based on the follow-up description in the methods. Type of treatment regimen, took into account the most used regimen in the study (if more than one) and was classified in three subgroups (six or more month rifampicin, <6 months of rifampicin and drug resistant TB regimens). Based on UNAIDS or government publications, background HIV prevalence, was dichotomised as low if <1% in the general population and high otherwise. For studies conducted before 1990, we categorised the country as low level unless it was recognised as contributing early to HIV epidemic.
We explored reasons for heterogeneity by estimating stratified incidence rate ratios of recurrent TB. First, we used univariate meta-regression with the variables listed above and obtained R2 statistics, which reflect the proportion of between-studies variance explained by each variable. Subsequently, we performed multivariate meta-regression. We included in an initial model all covariates with p<0.2 in the univariate analysis, as well as potential confounding variables that were statistically non-significant. Using backward stepwise selection, we eliminated non-significant (p>0.05 in likelihood ratio test) and non-confounding variables from the model.
Pooled proportions of reinfections and relapses
We describe the frequency of use over time of molecular technique to differentiate relapses from reinfections in the included studies. To calculate the proportions of reinfections and relapses, we divided the number of reinfections and relapses by the number of recurrent TB episodes with DNA fingerprinting results. Meta-analysis was performed as described above. In subgroup analyses, we also used the variables specified above, except drug regimen (information not available for studies using TB programme databases) and, additionally, the proportion of study patients with DNA fingerprinting results available for the first and recurrent TB episode (dichotomised with the upper tertile as cut-off), and the molecular method used. We calculated crude and adjusted ORs for reinfection in the different study subgroups and used meta-regression to assess heterogeneity. We followed the multivariate modelling strategy described above.
We adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines in our study. The PRISMA checklist can be found in online supplemental appendix 4. We evaluated the certainty of evidence of our results using the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) approach.18
Results
The search yielded 4884 articles. The selection process in shown in figure 1. We identified 488 articles for retrieving the full text. Thirty-nine full-text articles were not available and 273 were excluded. A total of 176 studies met the inclusion criteria. There were 111 cohort studies, 48 clinical trials and 17 studies using a TB programme database. Sufficient data on patient follow-up were available in 145 studies, which were included in the meta-analysis of recurrent TB incidence rate. Fourty-eight studies could be included in the meta-analysis of the proportion of reinfections and relapses, of which 24 were also included in the former analysis. The characteristics of all studies are outlined in the online supplemental appendix 5.
Flowchart of study selection.
Molecular techniques used to discriminate TB reinfection and relapse
Of the studies reporting on reinfections and relapses, 30 used IS6110-restriction fragment length polymorphism analysis (IS6110 RFLP) to differentiate strains, 19 spoligotyping, 19 mycobacterial interspersed repetitive unit-variable number tandem repeat typing, two polymorphic GC-rich repetitive sequence genotyping, one double repetitive element-PCR and six WGS. IS6110-RFLP was the only method used before 2000. Twenty-one studies used more than one method; the most frequent combination was IS6110-RFLP with spoligotyping. Figure 2 shows the use of novel molecular techniques over time.
Frequency of use of molecular techniques to discriminate tuberculosis reinfections and relapses, 1990-2020. Size of the circle is proportional to the number of studies. DRE-PCR, double repetitive element-PCR; PGRS, polymorphic GC-rich repetitive sequence genotyping; RFLP, restriction fragment length polymorphism; WGS, whole-genome sequencing.
Recurrent TB incidence rate
The pooled estimate of the recurrent TB incidence across 145 studies was 2.26 per 100 person years at risk (95% CI 1.87 to 2.73). Heterogeneity was significant (I2=98%). Figure 3 shows the forest plot for this analysis. Details on the studies, including the follow-up information used to calculate person years at risk, are shown in online supplemental table S2). The mean follow-up after successful treatment was 2.3 years.
Forest plot of the recurrent tuberculosis incidence rate.
In univariate stratified analysis, background TB incidence, background HIV prevalence, sample size and study design were important sources of heterogeneity (table 1). Recurrence rates were an order of 10, 15, 25 and over 50 times higher than background rates in high, median, moderate and low TB incidence settings, respectively. After adjusting, we found that the rate of recurrent TB in studies conducted in high incidence settings was more than twice higher than the rate in studies from low background TB incidence, and that studies with less than 6 months of rifampicin treatment had almost twice the recurrent TB incidence rate found in studies with 6 or more months of rifampicin therapy.
Univariate subgroup analysis and multivariate meta-regression of the incidence rate of TB recurrence
Forest plots for each subgroup analysis are shown in online supplemental figures 1–8. The funnel plot in online supplemental figure S9 shows mild asymmetry, pointing at a possible publication bias, but Egger’s test is not significant (p=0.79).Quality assessment for studies included in this outcome is presented on online supplemental table S3. Twenty-four studies were of good quality, 84 fair and 37 poor quality.
Proportion of reinfections and relapses in recurrent TB episodes
For studies that differentiate between reinfections and relapses, the overall pooled proportion of relapses was 70% (95% CI 63% to 77%) and of reinfection 30% (95% CI 23% to 37%). Heterogeneity was high (I2=85%) Figure 4 presents a forest plot of this analysis. Detailed data of the included studies are shown in online supplemental table S2.
Forest plot of the proportion of reinfections among recurrent tuberculosis episodes.
Table 2 shows subgroup analyses of the proportion of relapses and reinfections and meta-regression results. When we assessed potential sources of heterogeneity, we found background TB incidence to significantly and substantially contribute, while study design and quality, sample size, background HIV prevalence and molecular method used had a moderate influence. In multivariate analysis, only background TB incidence had a statistically significant—positive and strong—effect on the OR for reinfections, but HIV prevalence, study design, molecular method used, sample size and study quality, all acted as confounders.
Univariate subgroup analysis of proportion of relapses and reinfections and multivariate meta-regression of the OR for reinfection
Subgroup analysis forest plots are shown in online supplemental figures S10–S17. The funnel plot for this outcome (online supplemental figure S18) is not asymmetric and Egger’s test is not significant (p=0.06). Quality assessment for studies included in this outcome is presented in online supplemental table S4. Seven studies were graded good quality, 22 fair and 19 poor quality.
Extrapolating the pooled proportion of reinfections and relapses from the studies included in this analysis to the pooled recurrent TB rate estimated from the studies included in the corresponding analysis yields a rate of relapses of 1.60 per 100 person years and a rate of reinfection of 0.68 per 100 person years.
We evaluate the results using the GRADE approach and reported the evidence profile table (table 3), indicating that certainty is low for both outcomes and the reasons for it.
Grading of Recommendations, Assessment, Development and Evaluation evidence profile
Discussion
We found, across 145 studies, a pooled incidence rate of recurrent TB after successful treatment of 2.26 (95% CI 1.87 to 2.73) per 100 person years for a mean follow-up of 2.3 years. The recurrent TB incidence rate increased as the background TB incidence increased and was systematically manifold higher than the background rate, dramatically so in low and medium TB incidence settings. Relapses were the most common mechanism of recurrence across 48 studies, accounting for a pooled estimate of 70% (95%CI 63% to 77%) recurrent episodes. With increasing background TB incidence, the proportion of reinfections increased and the predominance of relapses among recurrences decreased.
Our pooled estimate of recurrent TB incidence rate is in line with two previous systematic reviews. In 2007, Panjabi et al,14 excluding studies in HIV-positive-cured patients with TB, reported across 14 observational studies and 18 clinical trials a median recurrence rate for the 12 months after completing standard treatment regimens of 2.58 (0.98 to 11.90) per 100 person years. In 2003, Korenromp et al13 found across 47 prospective studies reporting TB recurrences after cure and a recurrent rate of 1.9 (1.2 to 2.7) and 4.5 (3.2 to 5.8) per 100 person to years among HIV-negative and HIV-positive patients, respectively.
Recurrent TB is determined by a wide array of factors. We could extract from the included studies information on several of them. Clinical–epidemiological ones, background TB incidence, HIV prevalence and treatment regimens contribute to the heterogeneity of the pooled recurrent TB incidence rate. Methodological factors such as study sample size and planned follow-up duration also play an important role. Factors we had no information on may be an additional source of heterogeneity, adherence to treatment for instance, for relapse, or socioeconomic status related overcrowding for reinfection. This may in itself explain the mild asymmetry observed in the funnel plot, but despite 12/145 reviewed articles reporting zero events, we cannot completely exclude publication bias from unpublished small studies finding no recurrences.
Our study confirms the intuitive notion that the recurrent TB and background TB incidence are correlated, which has previously been reported. Korenromp et al13 also found that recurrent TB rates increased with 0.14 per 100 person years for every increase in background TB incidence of 100 per 100 000 person years. Panjabi et al14 reported a median recurrent TB rate of 7.85 compared with 1.78 per 100 person years in high (>100 cases per 100 000) versus low incidence settings. Furthermore, our results point at higher recurrent TB rates in high HIV prevalence settings which is also consistent with the earlier reviews.13 HIV infection has been recognised as a strong risk factor for recurrence due to immunosuppression or continuous exposure to health services.
It is also not surprising that recurrent TB rates were higher in studies with planned follow-up below 1 year. However, type of follow-up nor study design were significantly associated with recurrences in multivariate analysis possibly for lack of power. Notwithstanding, recurrent TB can be better identified if populations at risk are closely followed up and higher recurrent TB incidence rates were found among clinical trials compared with observational studies.14 Studies using TB programme databases can miss recurrent TB episodes if only patients attending the same health facilities or entered in the same information system are counted.
We identified relapses as the most common mechanism of recurrence, also in line with previous reviews,7 13 but found quite some heterogeneity among studies. Background TB incidence was the most important source. Other factors such as background HIV prevalence, study design and quality, sample size and molecular methods used confounded, but were not independent significant determinants of heterogeneity. However, it cannot be excluded that this may be partially due to meta-regression having relatively limited power and the small number of studies included for this outcome. Still, the funnel plot for this outcome did not show asymmetry by visual inspection and Egger’s test was not significant.
The proportion of reinfections increased with background TB incidence, which is most likely the result of continuous exposure to a more infectious environment after treatment completion. Akin to this, an increased relative frequency of reinfection has been reported in low incidence settings among people visiting high burden countries19 and in frequent contact with health facilities.20
Length of follow-up has also been found to determine the reinfections ratio.21 The risk of relapse decreases over time and they occur predominantly in the first year after treatment completion, while late recurrences tend to be reinfections. Therefore, short follow-up periods spuriously increases this ratio. We see an extreme example of it in two contrasting studies from India and South Africa. Velayutham et al22 identified 97% relapses in the first year after treatment completion, while Marx et al21 found 51% reinfections up to 13 years after treatment. In our review, stratified analysis showed only a discrete difference in pooled relapse proportion between studies with up to 2 years and 2 or more years of intended follow-up.
Our review has several limitations. Differences between definitions of recurrent TB were frequent and few studies reported death after treatment. The former could entail random misclassification, the latter, if due to TB recurrence, could result in underestimated recurrence rates. Furthermore, most studies did not report laboratory quality standards assessment, while recurrences diagnosed on isolated positive cultures caused by cross contamination will lead to overestimated reinfection frequencies. We could also not sort out the presence of mixed infections, which alter the interpretation of genotyping. Besides, recent literature8 has described the possibility of reinfection with the same strain, which is more frequent with small numbers circulating, and leads to misclassification as relapse. Finally, since WHO recommended implementing Xpert in 2010, more patients with bacteriologically confirmed TB are diagnosed and treated. However, cure could become difficult to verify in these patients. Also, augmented use of more sensitive diagnostic techniques will lead to increased detection of recurrent TB. On the other hand, for recurrences diagnosed with molecular assays, differentiation between relapse and reinfection will be virtually excluded. These various possible effects, at programme level and for research, should be scrutinised as empirical evidence for them becomes available.
Our results have implications for clinical management and public health strategies. First of all, patients who have successfully completed TB treatment should be made aware that the risk of developing a subsequent episode is higher than the risk of suffering a first one, and be sensitised to promptly seek care if symptoms reappear. Second, TB programmes could routinely monitor the full continuum of TB care23–25 and follow-up treated patients for a period of one or more years for timely detecting recurrent episodes and preventing long-term mortality.26 However, the best strategies to ensure that all patients are actively followed up beyond treatment completion are yet to be determined, but could be designed studying patients’ pathways after cure.27 Third, our study clarifies the relative contributions of relapse and reinfection. It demonstrates relapses are the predominant form of recurrences in all settings, which should prompt TB programmes to implement measures that enhance effective treatment of a first episode as among others assure strict adherence and provide universal drug susceptibility testing to ensure adequate drug regimens.
To make follow-up for recurrence more practical, future studies could identify biomedical factors associated with relapse and socioenvironmental ones associated with reinfection, so these can be addressed or guide care after cure. Romanowski et al28 already found that despite poor predictive ability, cavitary disease and 2-month smear positivity could be used as markers for higher risk of relapse. If risk factors for reinfection would be identified, they should likewise condition post-treatment follow-up, particularly in high TB incidence settings, which endure a higher proportion of reinfections. Modelling studies could further explore the potential impact on TB control of such improved detection of TB recurrences.
Falling prices of molecular technologies permit more widespread implementation of genotyping. Regardless of background incidence, TB control programmes should be encouraged to monitor the relative contributions of relapse and reinfection towards TB recurrence. This will permit to gain deeper understanding of the local dynamics of this health problem, to plan for, balance and adjust efforts at curbing its occurrence, and to improve individual patient long-term outcomes.
Data availability statement
All data relevant to the study are included in the article or uploaded as supplementary information.
Ethics statements
Patient consent for publication
Ethics approval
Universidad Peruana Cayetano Heredia Ethics Committee (FWA00000525) waived ethical approval.
References
Supplementary materials
Supplementary Data
This web only file has been produced by the BMJ Publishing Group from an electronic file supplied by the author(s) and has not been edited for content.
Footnotes
Contributors LO, CS and VV conceived the presented idea, initiated the project and were responsible for the design of the protocol. SR and VV did the review, data extraction, synthesis of results and quality assessment of studies. PVdS, LO, SR and VV contributed to the analysis of the results. All authors discussed the results and contributed to the final manuscript.
Funding The study received financial support from Institutional Collaboration Framework Agreement IV Institute of Tropical Medicine Antwerp, Belgium - Instituto de Medicina Tropical Alexander von Humboldt, Universidad Peruana Cayetano Heredia. LO is supported by an Emerging Global Leader Award from the Fogarty International Center at the National Institutes of Health (K43TW011137). The funders had no role in the design and conduction of this study.
Competing interests None declared.
Provenance and peer review Not commissioned; externally peer reviewed.