Active pulmonary tuberculosis and coronavirus disease 2019: A systematic review and meta-analysis

Ashutosh Nath Aggarwal; Ritesh Agarwal; Sahajal Dhooria; Kuruswamy Thurai Prasad; Inderpaul Singh Sehgal; Valliappan Muthu

doi:10.1371/journal.pone.0259006

. 2021 Oct 21;16(10):e0259006. doi: 10.1371/journal.pone.0259006

Active pulmonary tuberculosis and coronavirus disease 2019: A systematic review and meta-analysis

Ashutosh Nath Aggarwal ^1,^*, Ritesh Agarwal ¹, Sahajal Dhooria ¹, Kuruswamy Thurai Prasad ¹, Inderpaul Singh Sehgal ¹, Valliappan Muthu ¹

Editor: Girish Chandra Bhatt²

PMCID: PMC8530351 PMID: 34673822

Abstract

Objective

The proportion of COVID-19 patients having active pulmonary tuberculosis, and its impact on COVID-19 related patient outcomes, is not clear. We conducted this systematic review to evaluate the proportion of patients with active pulmonary tuberculosis among COVID-19 patients, and to assess if comorbid pulmonary tuberculosis worsens clinical outcomes in these patients.

Methods

We queried the PubMed and Embase databases for studies providing data on (a) proportion of COVID-19 patients with active pulmonary tuberculosis or (b) severe disease, hospitalization, or mortality among COVID-19 patients with and without active pulmonary tuberculosis. We calculated the proportion of tuberculosis patients, and the relative risk (RR) for each reported outcome of interest. We used random-effects models to summarize our data.

Results

We retrieved 3,375 citations, and included 43 studies, in our review. The pooled estimate for proportion of active pulmonary tuberculosis was 1.07% (95% CI 0.81%-1.36%). COVID-19 patients with tuberculosis had a higher risk of mortality (summary RR 1.93, 95% CI 1.56–2.39, from 17 studies) and for severe COVID-19 disease (summary RR 1.46, 95% CI 1.05–2.02, from 20 studies), but not for hospitalization (summary RR 1.86, 95% CI 0.91–3.81, from four studies), as compared to COVID-19 patients without tuberculosis.

Conclusion

Active pulmonary tuberculosis is relatively common among COVID-19 patients and increases the risk of severe COVID-19 and COVID-19-related mortality.

Introduction

The ongoing coronavirus disease 2019 (COVID-19) pandemic is spreading relentlessly, and has affected more than 230 million people worldwide. COVID-19 is associated with worse outcomes in the elderly population, and those with comorbid health conditions such as obesity, diabetes mellitus, hypertension, and cardiovascular disorders [1–9].

Tuberculosis is a destructive pulmonary disease and therefore widely perceived to be associated with increased susceptibility to acquiring COVID-19, and poorer prognosis in patients having both diseases concurrently, especially among people living with human immunodeficiency virus infection (PLHIV). A note from World Health organization (WHO) also anticipated poorer outcomes in these patients [10]. However, the actual impact of tuberculosis on occurrence and clinical outcomes of COVID-19 is not clear. A case series from Italy reported a benign clinical course for patients having both infections [11]. An early meta-analysis of six Chinese studies found no association between tuberculosis and COVID‐19 severity or mortality [12]. Population based data from South Korea also did not suggest tuberculosis to be significantly associated with COVID-19-related mortality [13]. However, other investigators describe a disproportionately higher rate of adverse clinical outcomes among patients with tuberculosis and COVID-19 [14–16]. Tuberculosis was identified as the commonest comorbidity on verbal autopsy among 70 COVID-19 deaths, and in 10% of whole-body autopsies, in Zambia [17, 18]. Two meta-analyses suggest higher odds of underlying tuberculosis among patients with severe COVID-19 and those dying from COVID-19 [19, 20]. Due to these inconsistencies, we felt a need to perform a detailed analysis of the available evidence till date. Herein, we evaluate the frequency of concurrent active pulmonary tuberculosis among COVID-19 patients. We also assess if comorbid pulmonary tuberculosis increases the risk of severe disease, hospitalization, or mortality in COVID-19 patients.

Methods

We registered our systematic review protocol with the PROSPERO database (registration number CRD42021245835). We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and the Meta-analysis of Observational Studies in Epidemiology (MOOSE) recommendations for reporting our review [21, 22]. An approval from our Institutional Review Board was not necessary as we extracted only summary information from previously published articles.

Search strategy

We initially looked up the PubMed and EMBASE databases for publications indexed till March 31, 2021, and further updated our search on June 30, 2021. We queried the PubMed database using the following search string: (Tuberculosis OR Tubercular OR Tuberculous OR TB OR Mycobacterium OR Mycobacterial) AND (COVID-19 OR “COVID 19” OR COVID19 OR nCoV OR 2019nCoV OR 2019-nCoV OR CoV-2 OR “CoV 2” OR SARS-CoV-2 OR SARSCoV2). The Embase database was similarly searched. We further scanned the WHO compendium of tuberculosis/COVID-19 studies for any additional published studies [23]. We also examined the bibliographies of selected articles and recent reviews.

Selection of studies

After removing duplicate citations, two reviewers (ANA and RA) screened all the titles and abstracts. We omitted publications not reporting on COVID-19 or tuberculosis. We also excluded experimental, radiological or autopsy studies, case reports, letters to editor not describing original observations, reviews, guidelines, conference abstracts, editorials, and study protocols. Full texts of citations considered potentially suitable by either reviewer were assessed further.

We included a publication for data synthesis if it (a) included patients with COVID-19 confirmed by detection of novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA in respiratory specimens, or strongly suspected on clinical or radiological assessment if a confirmatory test was not available, (b) either described the frequency of patients having concurrent active pulmonary tuberculosis among COVID-19 patients, or reported on any of the following outcomes in COVID-19 patients with and without tuberculosis—severe COVID-19, hospital admission, or mortality. Severe COVID-19 was defined based on institutional or national guidelines, or as per the prevalent guidance from international professional bodies or the World Health Organization. If the same (or substantially overlapping) patient cohort was reported in two or more publications, we included the one describing the largest patient population. In case of any disagreement, consensus between the two reviewers determined study inclusion.

Data extraction and study quality

We obtained information on study design, location and healthcare setting, participant inclusion and exclusion criteria, period of patient enrollment, the source of patient information, and the outcomes reported, from all eligible studies. We used the Newcastle-Ottawa Scale (NOS) to assess methodological quality of studies [24]. We considered a study to be of good quality if its NOS score was seven or more (out of a maximum possible score of nine).

Statistical analysis

We estimated the percentage of active tuberculosis patients among those with COVID-19 disease in each study and calculated the corresponding 95% confidence interval (95% CI) by Clopper-Pearson exact method [25]. We also computed the relative risk (RR), and the corresponding 95% CI, for each predefined outcome from each study [26]. We employed a continuity correction of 0.5 for studies having ‘zero’ cell frequencies prior to these calculations.

We pooled our data using the DerSimonian-Laird random effects model to generate summary estimates [27]. Freeman-Tukey double arcsine transformation was used to summarize data on proportions [28]. We assessed between-study heterogeneity through the Higgins’ inconsistency index (I²), which was considered high for values greater than 0.75 [29]. The contribution of each study to overall heterogeneity, and its influence on the summary estimate, were assessed through the Baujat’s plot [30]. For searching the reasons for heterogeneity, we performed subgroup analyses for predefined covariates that included study location and setting, study design, COVID-19 diagnostic standards, description of criteria used to define active tuberculosis, national burden of tuberculosis, and the overall study quality. World Health Organization standards were used to refer to countries as high burden, and to extract country incidence estimates, for tuberculosis [31]. In a sensitivity analysis, the influence of each study on the summary estimate was also assessed by repeating meta-analysis after iteratively omitting one study at a time. Further, any influential study was identified using a battery of diagnostic tests using Studentized Residuals, Difference in Fits (DFFITS), Cooks Distance, Covariance Ratio, Tau Square, and the contribution of each study in the Q, H2 test statistics value and the weights assigned to these studies [32]. Publication bias was assessed through Eggers’ test and by visualizing contour-enhanced trim-and-fill funnel plots [33, 34]. We utilized the statistical softwares Stata (Intercooled edition 12.0, Stata Corp, USA) and R (version 4.1.1, R Foundation for Statistical Computing, Austria) for analyzing our data.

Results

We identified 3,375 publications from our literature search (Fig 1). We finally selected 43 studies, describing 236,863 patients with COVID-19, for data synthesis [35–77]. Thirty-three (76.7%) of them provided information on one or more of the adverse clinical outcomes of interest (Table 1). There were 30 (69.8%) publications from Asia, and 10 (23.3%) from Africa, with maximum contribution from China (22 studies) (Table 1). One (2.3%) study analyzed data from multiple countries [43]. All studies evaluated data from retrospective patient cohorts, except for four (9.3%) that collected the information prospectively [39, 40, 56, 69]. Six (14.0%) studies reported population-based data [35, 36, 42, 52, 59, 62], while the others were conducted in a hospital setting. Two (4.7%) studies also included COVID-19 patients based on high clinical or radiological suspicion [39, 44]. All others only studied patients with disease confirmed by the detection of SARS-CoV2 RNA in respiratory specimens. One (2.3%) study did not specify the inclusion criteria [46]. Only two (4.7%) studies specifically evaluated children [63, 71]; others included only adults or described a mixed population. Patient information was retrieved mainly from medical records at participating healthcare facilities, or from surveillance registries or insurance databases (Table 1). Most investigators reviewed patient records or used tuberculosis-related diagnostic codes in databases to identify patients having active tuberculosis (Table 1). Fourteen (32.6%) studies reported human immunodeficiency virus (HIV) seroprevalence in their patient cohorts [36, 37, 42, 48–50, 58–60, 62, 64, 67, 69, 71]. Of these, a single study from South Africa provided tuberculosis prevalence and outcome data based on HIV status [36]. Only six (14.0%) studies were considered high quality (S1 Table) [36, 42, 43, 52, 54, 58].

Table 1. Characteristics of included studies.

Author, year	Location	Study design	Setting	Inclusion criteria for COVID-19 patients	Exclusion criteria	Study period	Source of data	Tuberculosis definition	No. of COVID-19 patients	PLHIV	Information reported	NOS score
Al Kuwari HM, 2020 [35]	Qatar	Retrospective	General population	Confirmed disease	NS	Feb 28—Apr 18, 2020	National COVID database	ICD codes	5,685	NS	Proportion, severity	6
Boulle A, 2020 [36]	Western Cape, South Africa	Retrospective	General population	Confirmed disease in adult patients (> = 20 years)	NS	Mar 1—Jun 9, 2020	Health records database	Microbiological confirmation, anti-tubercular treatment, admission to tuberculosis hospital	22,308	17.8%	Proportion, hospitalization, mortality	8
Chen T, 2020 [37]	Wuhan, China	Retrospective	Inpatients	Confirmed disease	NS	Jan 1—Feb 10, 2020	Medical records	Medical records	203	1.0%	Proportion, mortality	6
Dai M, 2020 [38]	China	Retrospective	Inpatients	Confirmed disease in patients with at least two CT examinations and discharged by study end date	Poor quality CT scan images	Feb 5—Mar 8, 2020	Medical records	NS	73	NS	Proportion, severity	5
Du RH, 2020 [39]	Wuhan, China	Prospective	Inpatients	Confirmed or highly probable disease	NS	Dec 25, 2019—Feb 7, 2020	Medical records	NS	179	NS	Proportion, mortality	6
Gupta N, 2020 [40]	New Delhi, India	Prospective	Inpatients	Confirmed disease	NS	Mar 20—May 8, 2020	Medical records	NS	200	NS	Proportion	5
Ibrahim OR, 2020 [41]	Katsina, Nigeria	Retrospective	Inpatients	Confirmed disease in adult patients (> = 18 years)	NS	Apr 10—Jun 10, 2020	Medical records	Confirmation during hospital stay	45	NS	Proportion, mortality	6
Lee SG, 2020 [42]	South Korea	Retrospective	General population	Confirmed disease in adult patients (> = 18 years)	NS	Mar 26—May 15, 2020	Health insurance database	Diagnostic codes	7,339	0.1%	Proportion, severity, mortality	7
Li G, 2020 [43]	59 countries in China, North America, and Europe	Retrospective	Inpatients	Confirmed disease	Patients receiving remdesivir or dexamethasone, lack of treatment records, data from countries with <5 records	Jan 1—Apr 30, 2020	Medical records	NS	598	NS	Proportion, mortality	7
Li X, 2020 [44]	Wuhan, China	Ambispective	Inpatients	Confirmed or highly probable disease	NS	Jan 26—Feb 5, 2020	Personal/telephonic interviews, medical records	ICD-10 diagnostic codes	548	NS	Proportion, severity	6
Liu J, 2020 [45]	Wuhan, China	Retrospective	Outpatients and inpatients	Confirmed disease in adult patients (> = 18 years)	NS	Dec 29, 2019—Feb 28, 2020	Medical records	Medical records	1,190	NS	Proportion, hospitalization, mortality	6
Liu SJ, 2020 [46]	Ezhou, China	Retrospective	Inpatients	NS	NS	Jan 23—Feb 12, 2020	Medical records	NS	342	NS	Proportion, severity	5
Ma Y, 2020 [47]	9 Chinese provinces	Retrospective	Inpatients	Confirmed disease in adult patients (> = 18 years)	NS	Jan 13—Apr 13, 2020	Medical records	Self-reported or diagnosed at admission	1,160	NS	Proportion, severity, mortality	6
Maciel EL, 2020 [48]	Espirito Santo, Brazil	Retrospective	Inpatients	Confirmed disease in patients with definite outcomes (discharge or death)	NS	Feb 26—May 14, 2020	Regional epidemiologic studies database	NS	440	1.0%	Proportion, mortality	5
Nachega JB, 2020 [49]	Kinshasa, DR Congo	Retrospective	Inpatients	Confirmed disease	Incomplete information	Mar 10—Jul 31, 2020	Medical records	NS	766	1.6%	Proportion, severity	6
Parker A, 2020 [50]	Cape Town, South Africa	Retrospective	Inpatients	Confirmed disease in adult patients (> = 18 years)	NS	Mar 24–11 May, 2020	Medical records	NS	113	21.2%	Proportion, hospitalization	5
Sun Y, 2020 [51]	Beijing, China	Retrospective	Inpatients	Confirmed disease	NS	NS	Medical records	NS	63	NS	Proportion	5
Sy KTL, 2020 [52]	Philippines	Retrospective	General population	Confirmed disease	NS	Up to May 17, 2020	National COVID-19 surveillance registry	History or current diagnosis of tuberculosis	12,513	NS	Proportion, hospitalization, mortality	8
Xiao KH, 2020 [53]	Chongqing, China	Retrospective	Inpatients	Confirmed disease	NS	Jan 23—Feb 8, 2021	Medical records	NS	143	NS	Proportion, severity	5
Yu HH, 2020 [54]	Wuhan, China	Retrospective	Inpatients	Confirmed disease	NS	Jan 27—Mar 5, 2020	Medical records	NS	1561	NS	Proportion, severity	7
Zeng JH, 2020 [55]	Shenzhen, China	Retrospective	Inpatients	Confirmed disease	NS	Jan 11—Apr 1, 2020	Medical records	NS	416	NS	Proportion, severity	5
Zhang JJ, 2020 [56]	Wuhan, China	Prospective	Inpatients	Confirmed disease	NS	Jan 16—Feb 3, 2020	Medical records	NS	140	NS	Proportion, severity	6
Zhang YT, 2020 [57]	Guangdong, China	Retrospective	Inpatients	Confirmed disease	NS	Jan 15—Mar 4, 2020	Disease surveillance database	NS	1,350	NS	Proportion, severity	5
Abraha HE, 2021 [58]	Mekelle, Ethiopia	Retrospective	Inpatients	Confirmed disease	NS	May 10—Oct 16, 2020	Medical records	NS	2,617	0.9%	Proportion, severity	7
Dave JA, 2021 [59]	Western Cape, South Africa	Retrospective	General population	Confirmed disease	NS	Mar 4—Jul 15, 2020	Regional health information database	Database records	64,476	12.3%	Proportion	6
du Bruyn, 2021 [60]	Cape Town, South Africa w	Retrospective	Inpatients	Confirmed disease	NS	Jun 11—Aug 28, 2020	Medical records	Microbiologically proven or clinically diagnosed	104	29.8%	Proportion, severity	6
Gajbhiye RK, 2021 [61]	Mumbai, India	Retrospective	Inpatients	Confirmed disease in pregnant/postpartum women	NS	Apr—Sep, 2020	Medical records	NS	879	NS	Proportion	4
Hesse R, 2021 [62]	South Africa	Retrospective	General population	Confirmed disease in adult patients (> = 18 years)	Indeterminate COVID-19 test results	Mar 1—Jul 7, 2020	Health records	GeneXpert positivity within six months before COVID diagnosis	98,335	6.3%	Proportion	6
Kapoor D, 2021 [63]	New Delhi, India	Retrospective	Inpatients	Confirmed disease in children <18 years age	NS	Mar 1—Dec 31, 2020	Medical records	NS	120	NS	Proportion, mortality	4
Lagrutta L, 2021 [64]	Buenos Aires, Argentina	Retrospective	Outpatients and inpatients	Confirmed disease	NS	Jul 5—Oct 17, 2020	Hospital registry	Bacteriological confirmation or recent clinical diagnosis	5,447	7.2%	Proportion	6
Li S, 2021 [65]	Wuhan, China	Retrospective	Inpatients	Confirmed disease in patients with definite outcomes (discharge or death)	Patients still hospitalized at study end date, death within 24 hours a fter admission, loss to follow up	Jan 18—Mar 29, 2020	Medical records	NS	2,924	NS	Proportion, mortality	4
Lu Y, 2021 [66]	Wuhan, China	Retrospective	Inpatients	Confirmed severe disease in adult patients (<65 years)	NS	Jan 25—Feb 15, 2020	Medical records	NS	77	NS	Proportion, mortality	5
Meng M, 2021 [67]	Wuhan, China	Retrospective	Inpatients	Confirmed severe disease	NS	Jan 2—Mar 28, 2020	Medical records	NS	415	None	Proportion, mortality	5
Mithal A, 2021 [68]	New Delhi, India	Retrospective	Inpatients	Confirmed disease in adult patients (> = 18 years)	NS	Jul 9—Aug 8, 2020	Medical records	NS	401	NS	Proportion	5
Moolla MS, 2021 [69]	Cape Town, South Africa	Prospective	Inpatients	Confirmed disease	NS	Mar 26—Aug 31, 2020	Medical records	NS	363	14.6%	Proportion	6
Song J, 2021 [70]	Wuhan, China	Retrospective	Inpatients	Confirmed disease in patients with definite outcomes (discharge or death)	NS	Feb 1—Mar 6, 2020	Medical records	Self-report on admission	961	NS	Proportion, severity	6
van der Zalm MM, 2021 [71]	Cape Town, South Africa	Retrospective	Inpatients	Confirmed disease in children (< = 13 years)	Infants born diagnosed in the neonatal service, multisystem inflammatory syndrome	Apr 17—Jul 24, 2020	Medical records	Medical records	159	1.3%	Proportion, mortality	5
Verma R, 2021 [72]	Firozabad, India	Retrospective	Inpatients	Confirmed disease among critically ill patients in ICU	Patients referred to other centres	Jul 1—Dec 31, 2020	Medical records	NS	120	NS	Proportion	4
Yan B, 2021 [73]	Jilin, China	Retrospective	Inpatients	Confirmed disease	NS	Jan 28—Mar 25, 2020	Medical records	NS	190	NS	Proportion, severity	5
Yang C, 2021 [74]	Taiyuan, China	Retrospective	Inpatients	Confirmed disease	NS	Jan 24—Apr 25, 2020	Medical records	NS	104	NS	Proportion, severity	6
Yitao Z, 2021 [75]	Guangzhou, China	Retrospective	Inpatients	Confirmed disease	NS	Jan 21—Mar 23, 2020	Medical records	NS	257	NS	Proportion, severity	5
Zhang W, 2021 [76]	Taiyuan, China	Retrospective	Inpatients	Confirmed disease	Patients with malignant tumors, hypertension, heart disease, diabetes, etc.	Jan 1—May 31, 2020	Medical records	NS	500	NS	Proportion	5
Zheng B, 2021 [77]	Honghu, China	Retrospective	Inpatients	Confirmed disease	Laboratory and radiology workup at other hospitals, no pulmonary lesion of chest CT scan	Jan 1—Mar 27, 2020	Medical records	NS	198	NS	Proportion, severity	5

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COVID-19 Coronavirus disease 2019, ICD International Classification of Diseases, NOS Newcastle-Ottawa Scale for study quality, NS Not specified, PLHIV People living with human immunodeficiency virus infection.

Proportion of patients with active tuberculosis

The proportion of patients having active pulmonary tuberculosis among COVID-19 patients could be computed from all 43 studies. It ranged from 0.18% to 14.42% (Fig 2). The highest occurrence was noted in a study conducted in a high HIV prevalence South African setting [60]. All other studies described figures below 6%. Almost all studies reported proportion estimates of comorbid pulmonary tuberculosis among COVID-19 patients that were higher than their corresponding WHO country estimates for annual tuberculosis incidence (Fig 2). The pooled proportion estimate from all 43 studies was 1.07% (95% CI 0.81%-1.36%).

Fig 2 — Individual proportion estimates are depicted by solid squares, and the corresponding country estimate of annual tuberculosis incidence by hollow circles.

There was substantial heterogeneity between the studies (I² 94.7%). Baujat’s plot suggested that three studies unduly influenced heterogeneity as well as pooled estimates (S1 Fig) [36, 59, 62]. Omitting these three studies from meta-analysis resulted in a slightly higher summary estimate of proportion (1.15%, 95% CI 0.84%-1.50%) with only a minor reduction in heterogeneity (I² 86.9%). On influence analysis, a single study with the highest reported proportion of active tuberculosis patients was associated with large values of Studentized residuals, Cook’s distance and DFFITS (S2 Fig), and was considered potentially influential [60]. After removing this study, the pooled proportion estimate from remaining 42 studies was lower at 1.00% (95% CI 0.75%-1.28%) with hardly any reduction in heterogeneity (I² 94.5%). On sensitivity analysis, omitting other studies one at a time also did not appreciably influence summary estimates or heterogeneity (S3 Fig). On subgroup analysis, studies conducted in low tuberculosis burden or multiple countries showed lesser heterogeneity (Table 2). Overall, the pooled estimates on proportion were much lower from studies conducted in countries not having high tuberculosis burden, as well as from population-based and high-quality studies (Table 2).

Table 2. Subgroup analysis for summary estimates for proportion of COVID-19 patients with active pulmonary tuberculosis.

Criteria and subgroups		No. of studies	Summary proportion, % (95% CI)	I², %
Overall		43	0.99 (0.74–1.27)	94.7
Continent:	Africa	10	1.31 (0.73–2.04)	98.4
	Asia	30	1.13 (0.81–1.49)	83.5
	Other/Multiple countries	3	0.37 (0.02–1.02)	-
Study design:	Prospective	4	1.49 (0.47–2.98)	68.1
	Not prospective	39	1.04 (0.78–1.34)	95.1
Study setting:	Hospital-based	37	1.34 (0.94–1.80)	84.5
	Population-based	6	0.71 (0.35–1.19)	99.1
Patient inclusion:	Confirmed cases only	40	1.03 (0.77–1.33)	94.9
	Probable cases also	3	1.80 (0.41–4.01)	-
Tuberculosis definition:	Criteria specified	15	0.95 (0.60–1.38)	97.9
	Criteria not specified	28	1.21 (0.82–1.65)	75.1
Tuberculosis burden:	High burden countries	39	1.24 (0.94–1.57)	94.5
	Other/multiple countries	4	0.30 (0.15–0.49)	72.6
Study quality:	NOS score > = 7	6	0.83 (0.43–1.35)	95.7
	NOS score <7	37	1.19 (0.87–1.56)	94.2

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95% CI 95% confidence interval, I² Higgins’ inconsistency index, NOS Newcastle-Ottawa Scale for study quality.

Severe COVID-19

Twenty studies with 24,371 COVID-19 patients, of whom 161 (0.7%) had tuberculosis, provided information on severe COVID-19 [35, 38, 42, 44, 46, 47, 49, 51, 53–58, 70, 73–77]. All, except four (20.0%), of these publications were from China [35, 42, 49, 58]. Severe COVID-19 was defined based on World Health Organization guidance in three (15.0%) studies [35, 49, 58], recommendations from international professional bodies in two (10.0%) studies [44, 70], national guidelines in 11 (55.0%) studies [38, 42, 46, 47, 51, 53, 56, 57, 73, 74, 76], and institutional policy in four (20.0%) studies [54, 55, 75, 77]. Only three (15.0%) studies were considered high quality [42, 54, 58]. All studies, except two, included patients with laboratory confirmed COVID-19 [44, 46]. Only one (12.5%) had a prospective study design [56]. Of the 3431 patients with severe disease in the included cohorts, 36 (1.0%) had underlying tuberculosis. Only four (20.0%) studies reported a RR for severe COVID-19 that significantly exceeded 1.0 (Fig 3) [42, 46, 56, 77]. COVID-19 patients who also had tuberculosis were 1.46 (95% CI 1.05–2.02) times more likely to develop severe COVID-19 as compared to COVID-19 patients without tuberculosis (Fig 3).

There was moderate heterogeneity between the studies (I² 42.9%). Baujat’s plot indicated that one Korean study unduly influenced heterogeneity as well as pooled estimates [42]. No additional influential study was identified on formal influence analysis. Omitting this single study from analysis resulted in a lower summary RR estimate (1.35, 95% CI 0.98–1.87) and lesser heterogeneity (I² 29.5%). On sensitivity analysis, omitting other studies one at a time did not significantly affect heterogeneity (S4 Fig). On subgroup analysis, studies conducted in Africa or in low tuberculosis burden or multiple countries, as well as population-based studies, showed negligible heterogeneity (Table 3). Overall, the pooled RR estimates were much higher from studies conducted in countries not having high tuberculosis burden, as well as from population-based studies (Table 3). There was no significant publication bias (S5 Fig).

Table 3. Subgroup analysis for summary relative risk of adverse outcomes among COVID-19 patients with active pulmonary tuberculosis.

Criteria and subgroups			COVID-19 severity			Mortality
		No. of studies	Summary relative risk (95% CI)	I², %	No. of studies	Summary relative risk (95% CI)	I², %
Overall		20	1.46 (1.05–2.02)	42.9	17	1.93 (1.56–2.39)	21.5
Continent:	Africa	2	0.87 (0.38–2.01)	0.0	5	2.10 (1.38–3.18)	24.7
	Asia	18	1.52 (1.08–2.15)	42.9	10	1.94 (1.47–2.56)	23.1
	Other/Multiple countries	-	-	-	2	1.14 (0.53–2.44)	0.0
Study design:	Prospective	1	2.05 (1.19–3.53)	-	1	0.44 (0.03–6.76)	-
	Not prospective	19	1.37 (0.95–1.98)	45.3	16	1.93 (1.57–2.41)	21.5
Study setting:	Hospital-based	18	1.32 (0.95–1.82)	30.9	14	1.66 (1.25–2.22)	14.4
	Population-based	2	3.17 (2.02–4.97)	0.0	3	2.37 (1.90–2.96)	0.0
Patient inclusion:	Confirmed cases only	18	1.44 (0.98–2.11)	43.1	16	1.93 (1.56–2.39)	21.5
	Probable cases also	2	1.39 (0.65–2.97)	65.0	1	0.44 (0.03–6.76)	0.0
Tuberculosis definition:	Criteria specified	5	1.46 (0.64–3.33)	72.3	6	2.58 (2.08–3.20)	0.0
	Criteria not specified	15	1.50 (1.06–2.11)	23.7	11	1.41 (1.08–1.84)	0.0
Tuberculosis burden:	High burden countries	18	1.32 (0.95–1.82)	30.9	15	1.94 (1.59–2.38)	11.4
	Other/multiple countries	2	3.17 (2.02–4.97)	0.0	2	2.38 (0.65–8.66)	77.7
Study quality:	NOS score > = 7	3	1.31 (0.29–5.97)	75.3	4	2.44 (1.75–3.41)	42.3
	NOS score <7	17	1.45 (1.05–1.99)	27.0	13	1.58 (1.22–2.04)	0.0

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95% CI 95% confidence interval, I² Higgins’ inconsistency index, NOS Newcastle-Ottawa Scale for study quality.

Need for hospitalization

Four publications with 28,438 COVID-19 patients, of whom 479 (1.7%) had tuberculosis, provided data on hospitalization due to COVID-19 [36, 45, 52, 71]. All these studies were from high tuberculosis burden countries (two from South Africa, and one each from China and Philippines), had a retrospective study design, and included patients with laboratory confirmed COVID-19. Two (50.0%) studies were of high quality [36, 52]. Overall, 20.6% of patients were hospitalized. Of the 5853 patients who required hospitalization in the included cohorts, 227 (3.9%) had underlying tuberculosis. Two studies reported a RR for hospitalization that statistically significantly exceeded 1.0 (Fig 3) [36, 71]. COVID-19 patients who also had tuberculosis were 1.86 (95% CI 0.91–3.81) times more likely require hospitalization as compared to COVID-19 patients without tuberculosis (Fig 3). This pointed to the absence of any statistically significant risk of hospitalization among COVID-19 patients with tuberculosis.

There was considerable heterogeneity between the studies (I² 97.5%). A subgroup analysis was not undertaken due to small number of studies. There was no significant publication bias (S5 Fig).

Mortality

Seventeen studies with 42,321 COVID-19 patients, of whom 632 (1.5%) had tuberculosis, reported on deaths due to COVID-19 [36, 39, 41–43, 45, 47–50, 52, 57, 60, 63, 65–67]. All studies were conducted in high tuberculosis burden countries, except one from South Korea and another that combined data from multiple nations [42, 43]. All studies, except one, included patients with laboratory confirmed COVID-19 [39]. Only one publication had a prospective study design [39]. Four (23.5%) studies were considered as high quality [36, 42, 43, 52]. Of the 2822 patients who died in the included cohorts, 97 (3.4%) had underlying tuberculosis. Only four (23.5%) studies reported RR for mortality that clearly exceeded 1.0 (Fig 3) [36, 42, 45, 52]. The confidence limits for all other studies were wide (Fig 3). COVID-19 patients who also had tuberculosis were 1.93 (95% CI 1.56–2.39) times more likely to die as compared to COVID-19 patients without tuberculosis (Fig 3).

There was only mild heterogeneity between the studies (I² 21.5%). Inspection of Baujat’s plot indicated that four studies unduly affected heterogeneity as well as pooled estimates [36, 42, 52, 67]. Two of these were also considered to be influential on formal influence analysis [52, 67]. Omitting these four studies from analysis lowered the summary RR estimate (1.65%, 95% CI 1.25–2.18) and resulted in negligible heterogeneity (I² 0.0%). On sensitivity analysis, heterogeneity could also be further decreased by individually omitting three of these studies one at a time (S4 Fig) [36, 42, 67]. Stratification by whether criteria for tuberculosis definition were specified in the studies resulted in homogeneity in either group (Table 3). On subgroup analysis, studies conducted outside Asia or Africa or in low tuberculosis burden or multiple countries, population-based studies, those including patients with a clinico-radiological diagnosis of COVID-19, and low-quality publications showed negligible heterogeneity (Table 3). There was no significant publication bias (S5 Fig).

Discussion

We found that 0.99% of the COVID-19 patients had active pulmonary tuberculosis. These patients showed higher risk for mortality, but not for severe disease or hospitalization, than COVID-19 patients without tuberculosis. Our data synthesis summarizes far greater number of studies than previous meta-analyses and provides information both on tuberculosis frequency and COVID-19 outcome estimates [12, 19, 20]. Unlike previous meta-analyses that reported summary odds ratios, we present summary RR estimates for adverse clinical outcomes, which are much easier to interpret and understand in a clinical setting.

The summary proportion of those with active pulmonary tuberculosis among COVID-19 patients appears higher than the recent WHO estimates for annual incidence of tuberculosis in some of the high tuberculosis burden countries where most of the studies were conducted (China 0.06%, India 0.19%, Nigeria 0.22%, Philippines 0.55%, and South Africa 0.61%) [31]. However, this proportion of active tuberculosis is lower than the generally reported proportion of other comorbid conditions, like diabetes or hypertension [5, 8]. Whether the lower tuberculosis proportion is due to under-reporting or under-recognition of active tuberculosis among COVID-19 patients, or to safeguarding strategies commonly employed by people with respiratory disorders, is not certain. However, when patients with active pulmonary tuberculosis do acquire COVID-19, there is a significantly greater risk (about two-fold higher) of COVID-19 mortality. Our summary estimate for relative risk of mortality in COVID-19 patients having tuberculosis is quite similar to the relative risk estimates of mortality for COVID-19 patients having other comorbid conditions (like diabetes, hypertension, or cardiovascular diseases) widely known to adversely affect prognosis in COVID-19 patients [1–3]. It is likely that superadded COVID-19 pneumonia in a lung that is already structurally damaged by tuberculosis may manifest as more severe disease. Importantly, local alterations in lung immunity resulting from active pulmonary tuberculosis can also adversely influence host response to SARS-CoV-2 virus. Recent in-vitro data from COVID-19 patients with active pulmonary tuberculosis has shown an attenuated interferon-gamma response after stimulation of whole blood with peptides derived from SARS-CoV-2 spike protein, in contrast to a normal response to Mycobacterium tuberculosis-specific antigens [78].

There are several similarities between COVID-19 and pulmonary tuberculosis. In several countries, COVID-19 too is a stigmatizing disorder, much like tuberculosis. Both diseases show airborne transmission when people are in close contact. Both present with similar symptoms like fever and cough. This can complicate decision-making, especially is nations with high tuberculosis burden. Although several countries have proposed bidirectional screening of both COVID-19 and pulmonary tuberculosis patients, such policy remains difficult to implement in resource-constrained settings. This might contribute to underdiagnosis of tuberculosis in COVID-19 patients. As it is, under-reporting of tuberculosis is a problem that is globally recognized. This is further compounded by reduced access to tuberculosis diagnosis and treatment as a result of COVID-19 related restrictions. It is therefore possible that our calculations regarding pulmonary tuberculosis among COVID-19 patients might be an underestimate.

Our systematic review has a few limitations. Due to the dynamic nature of the pandemic, and the lag between data collection and publication of results, most studies provide information from the initial months of 2020 and from regions that were severely afflicted earlier. Thus, the figures may not truly represent the patient data from all the geographic locations. Also, most of the included studies had a retrospective design, and collated data from medical records that were likely completed in an overwhelmed health system. This could have resulted in both underreporting as well as misclassification of comorbid health conditions. Several studies reported only on inpatients who have a higher probability of adverse outcomes compared to patients in the community. Only 15.6% of the included studies were of sufficiently high quality. There were differences in healthcare strategies regarding SARS-CoV-2 testing and admission/transfer criteria, variability in institutional practices in the timing of investigations and other evaluations, and the level and extent of medical intervention available to patients. Such heterogeneity can restrict the generalizability of our results. We cannot rule out an overestimation from lack of adjustment for potential confounders (like age, HIV status, other comorbid health conditions, or other patient characteristics) as we focused on univariate estimates. In particular, only one South African study reported on tuberculosis frequency data and outcome parameters stratified by HIV status, and there is need to gather more information on the impact of HIV on COVID-19 and tuberculosis associations.

Conclusion

In summary, the available evidence suggests that COVID-19 patients show relatively higher proportion of concurrent active pulmonary tuberculosis. Active pulmonary tuberculosis significantly increases the risk of severe COVID-19 and COVID-19-related mortality.

Supporting information

S1 Table. Details of Newcastle-Ottawa Scale scoring for study quality.

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Click here for additional data file.^{(83.7KB, pdf)}

S1 Fig. Baujat’s plot for studies reporting prevalence of tuberculosis among COVID-19 patients.

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Click here for additional data file.^{(110.2KB, pdf)}

S2 Fig. Influence statistics for studies reporting prevalence of tuberculosis among COVID-19 patients.

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Click here for additional data file.^{(74.7KB, pdf)}

S3 Fig. Sensitivity analysis for studies reporting prevalence of tuberculosis among COVID-19 patients.

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Click here for additional data file.^{(626.6KB, pdf)}

S4 Fig. Sensitivity analysis for studies reporting outcomes of patients COVID-19 patients with comorbid tuberculosis.

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Click here for additional data file.^{(555.1KB, pdf)}

S5 Fig. Contour-enhanced trim-and-fill funnel plots.

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Click here for additional data file.^{(182.1KB, pdf)}

Data Availability

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

Funding Statement

The authors received no specific funding for this work.

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PLoS One. doi: 10.1371/journal.pone.0259006.r001

Decision Letter 0

Girish Chandra Bhatt

25 Aug 2021

PONE-D-21-23415

Active pulmonary tuberculosis and coronavirus disease 2019: a systematic review and meta-analysis

PLOS ONE

Dear Dr. Aggarwal,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

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Academic Editor

PLOS ONE

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2. Please confirm that you have included all items recommended in the PRISMA checklist including:

- the full electronic search strategy used to identify studies with all search terms and limits for at least one database.

- a Supplemental file of the results of the individual components of the quality assessment, not just the overall score, for each study included.

- an assessment of publication bias using graphical methods (e.g. Funnel plot) and statistical methods (e.g. Egger’s test) as appropriate

- See https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1000100#pmed-1000100-t003 for guidance on reporting.

Additional Editor Comments:

The systematic review is performed with robust methodology. The authors should revise the manuscript as per the suggestions by reviewers. I have few more suggestions to further improve the manuscript:

1) As there is unexplained heterogeneity (for proportion of the patients with active TB) the authors should use Bajaut's curve to identify the outlier studies and perform an influential analysis. Also, leave one out is also recommended in case of high heterogeneity.

2) Wherever, the pooled results are given, authors should also give I2 stats.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Partly

Reviewer #2: Yes

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: No

Reviewer #2: Yes

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: 1. Please provide at least search strategy for Pubmed/Medline as free text search has a chance to miss out some of the studies that may have important bearing on the findings. Moreover, search strategy should be reproducible (it is like participants in a study, and any wrong selection method may lead to selection bias.

2. We explored the reasons for heterogeneity only if data from ten or more studies were summarized for any outcome: why 10 studies ?? any particular reason (if yes, may provide citation)

3. For searching the reasons for heterogeneity, we performed subgroup analyses: Is it sub-group or sensitivity analysis ?

4. The authors found that TB is increasing mortality but not severity of Covid 19..What is the explanation for this ?

5. The figures are of poor quality

Reviewer #2: This is the first real comprehensive and systematic analysis on how active tuberculosis (TB) infection impacts COVID-19 outcomes, including severity of disease, hospitalization, and mortality. This is a well written manuscript that covers an important area of research that has not been addressed in the literature in such a systematic way. That said, I still propose some minor ways to revise and strengthen the current manuscript.

Revisions

1. Line 76-80: The sentence begins by describing “TB patients with COVID-19” and ends with “COVID-19 patients without TB”. The language must be consistent and accurate, throughout the entire manuscript. In this case, you must be clear that you are referring to “COVID-19 patients with/without TB”, and not the other way around (the interpretation of the data would be completely different). Again, please check the language throughout the entire manuscript.

2. Line 96-97: COVID manifests in different ways for different people, many of whom are asymptomatic. I do not think it is accurate to say COVID-19 often manifests as severe pneumonia, especially without a supporting reference.

3. Line 181: Add a comma for long numbers to make it more legible.

4. Line: 202: Why were two studies that did not report patients with active TB included? In the PRISMA diagram, it shows that studies were excluded because there was no data on TB. All this to say, these 2 papers (Krati 2020, Gidado M, 2020) do not seem relevant to this meta-analysis and I do not think they should be included as they may bias the results.

5. Lie 208: Because you are talking about two diseases (TB and COVID), you must pay attention to which disease you are referring to. In this line, “low burden” is probably referring to TB, but this may not be clear to all readers. Please specific “low TB/COVID burden” when relevant throughout the paper.

6. Line 212-213: Might be helpful to include an appendix that have the Egger’s test graphs for the readers to evaluate the publication bias themselves.

7. Line 215: There needs to be some way of describing what is meant by ‘sever COVID-19’. Was this a definition that the studies used? Was it the same across studies? If not, how did you decide to combine studies with different definitions of COVID-19 severity? Not clear

8. Line 225-229: When talking about quantitative data in the text it’s usually good to include it in the text so the reader doesn’t have to go look for the data right away. This is regarding the sub-group heterogeneity and the pooled RRs mentioned in this section.

9. Line 250: 6.7% mortality rate for who? All COVID-19 patients? COVID-19 patients with TB? Not clear

10. Line 253-254: General comment about describing results. Similar to my first point, you must be very clear with the interpretation of the RR in this paper. For example, I would say: “people with COVID who also had TB were 1.93 (CI XX) times more likely to die compared to people with COVID who didn’t have TB”. Make sure this interpretation is applied to all RR results.

11. Line 263-269: Since only one study stratified by HIV status, it doesn’t seem relevant to repeat the results here. This just seems like you are re-iterating the results from another study instead of presenting a new analysis. Suggest removing this section entirely and discussing the importance of segregating by HIV status/acknowledge one on paper did this in the discussion.

12. Line 285-287: This is a very important point that I think needs further elaboration. I think there is evidence in the literature that TB cases have been severely underreported. I think there is also evidence that the overlapping symptoms between COVID and TB complicate clinical decision making. The lack of bi-directional screening further complicates this matter. I think this should be highlighted in the discussion further as it is very important.

13. Line 289: Which summary estimate are you referring to?

14. Line 291-293: are these alterations in lungs due to TB?

**********

6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

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Reviewer #1: No

Reviewer #2: No

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PLoS One. 2021 Oct 21;16(10):e0259006. doi: 10.1371/journal.pone.0259006.r002

Author response to Decision Letter 0

22 Sep 2021

PONE-D-21-23415

Active pulmonary tuberculosis and coronavirus disease 2019: a systematic review and meta-analysis

POINTWISE REPLIES TO COMMENTS

Journal Requirements:

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming.

This has been ensured.

2. Please confirm that you have included all items recommended in the PRISMA checklist including:

- the full electronic search strategy used to identify studies with all search terms and limits for at least one database.

- a Supplemental file of the results of the individual components of the quality assessment, not just the overall score, for each study included.

- an assessment of publication bias using graphical methods (e.g. Funnel plot) and statistical methods (e.g. Egger’s test) as appropriate

The full search strategy has been provided for PubMed database in the Methods section (lines 127-130). S1 Table (online supplement) details the individual NOS score components for each included study. Additional details on formal statistical and graphical evaluation of bias are now included in text, and funnel plots have been provided as a supplemental file (S5 Fig).

Additional Editor Comments:

1) As there is unexplained heterogeneity (for proportion of the patients with active TB) the authors should use Baujat's curve to identify the outlier studies and perform an influential analysis. Also, leave one out is also recommended in case of high heterogeneity.

All these additional statistical procedures have been incorporated into the manuscript text (lines 170-171, 176-180, 214-223, 243-248, 281-286), and graphical details provided as supplemental files (S1 Fig, S2 Fig, S3 Fig, and S4 Fig).

2) Wherever, the pooled results are given, authors should also give I2 stats.

I2 values are provided throughout in the text wherever pooled estimates are given.

Reviewer #1:

1. Please provide at least search strategy for Pubmed/Medline as free text search has a chance to miss out some of the studies that may have important bearing on the findings. Moreover, search strategy should be reproducible (it is like participants in a study, and any wrong selection method may lead to selection bias.

The actual search string used to query the PubMed database is now provided in the Methods section (lines 127-130).

2. We explored the reasons for heterogeneity only if data from ten or more studies were summarized for any outcome: why 10 studies ?? any particular reason (if yes, may provide citation)

There is no clear guidance on the minimum number of studies needed to explore heterogeneity. The Cochrane handbook suggests 10 studies in each group for performing subgroup analysis or meta-regression. Fu et al (J Clin Epidemiol 2011;64:1187-97) have proposed that each categorical subgroup should have a minimum of 4 studies. While Geissbuhler et al (BMC Med Res Methodol 2021;21:123) recently recommend at least five studies per group. We considered analysing multiple subgroups, each with a potentially variable number of studies, and arbitrarily decided beforehand on a minimum figure of ten studies (total) before undertaking subgroup analysis. We may also point out that we ultimately performed subgroup analysis for all summary estimates, except for hospitalization as outcome (four studies only), based on this arbitrary number. However, we understand that our choice was at best subjective, and have therefore removed this sentence from the manuscript. For the hospitalization outcome section in the Results, we have already stated that subgroup analysis was not performed due to extremely few studies.

3. For searching the reasons for heterogeneity, we performed subgroup analyses: Is it sub-group or sensitivity analysis?

We performed subgroup analyses that focused on differential effects between distinct subgroups, rather than on separate effects in different analyses. We now also present results on sensitivity analysis (leave-one-study-out analysis).

4. The authors found that TB is increasing mortality but not severity of Covid 19. What is the explanation for this ?

We apologize that due to a typographical error, we reported erroneous values for relative risk for severe COVID-19, both in the text and the Forest plot. The error has now been rectified (lines 240-242) and the correct figures suggest that COVID-19 patients with tuberculosis are at a significantly increased risk of severe COVID-19.

5. The figures are of poor quality.

We have submitted figures at 600 dpi resolution. We have also passed all three figures through the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool to ensure they meet PLOS requirements.

Reviewer #2:

We apologise for the inconsistency and understand that this may result in an inaccurate understanding of our results. The same has been checked and corrected throughout the manuscript (lines 76-77, 240-242, 262-263, 278-280).

We agree and have removed the phrase from Introduction.

3. Line 181: Add a comma for long numbers to make it more legible.

As suggested, a comma has been added as a thousand’s separator throughout the manuscript text, as well as in tables and figures.

As suggested, we have removed these two studies from our meta-analysis and have re-calculated our summary estimates accordingly.

5. Line 208: Because you are talking about two diseases (TB and COVID), you must pay attention to which disease you are referring to. In this line, “low burden” is probably referring to TB, but this may not be clear to all readers. Please specific “low TB/COVID burden” when relevant throughout the paper.

As suggested, the necessary corrections have been made wherever “low burden” meant “low TB burden” (lines 224, 289, 311).

6. Line 212-213: Might be helpful to include an appendix that have the Egger’s test graphs for the readers to evaluate the publication bias themselves.

We have included funnel plots, and presented data on Egger’s test, in a supplemental file (S5 Fig).

7. Line 215: There needs to be some way of describing what is meant by ‘severe COVID-19’. Was this a definition that the studies used? Was it the same across studies? If not, how did you decide to combine studies with different definitions of COVID-19 severity? Not clear

There is no standard definition for severe COVID-19. Stratification into severe or non-severe disease is based on a combination of several parameters that include respiratory rate, oxygenation, and mode of respiratory support, etc. We therefore defined severe COVID-19 based on use of institutional or national guidelines, or guidance from international professional bodies or the World Health Organization, as chosen by individual authors. Most such criteria have only minor differences in the way COVID-19 severity is categorized, and hence we pooled all such data under a single umbrella grouping of severe COVID-19. This is now mentioned in the Methods section (lines 146-147), and further elaborated in the severe COVID-19 section of the Results (lines 232-236).

We understand that it is simple for the reader to have the numbers in the text, rather than a separate table placed remotely in the publication, if only limited data is presented. In the current case, we would need to present more than half the table in the text, with some tables needing citation more than once. This would unnecessarily confuse the reader, while a table puts everything together at one place for easy reference and comparison. We therefore request the honorable reviewer to allow us to continue with the present scheme. We have already summarized the important observations from the tables (with references as appropriate).

9. Line 250: 6.7% mortality rate for who? All COVID-19 patients? COVID-19 patients with TB? Not clear.

This sentence has been deleted.

We agree, and have modified all such statements as suggested (lines 76-77, 240-242, 262-263, 278-280).

As suggested, we have removed the entire section on HIV status from our results and have simply mentioned this while describing characteristics of included studies in the first paragraph of Results section (lines 202-203). We have also added this as a limitation of our meta-analysis, and outlined the need for additional information, in the last paragraph of our Discussion (lines 353-355).

We have highlighted these points in Discussion as a new paragraph (lines 328-338).

13. Line 289: Which summary estimate are you referring to?

We meant summary estimate for relative risk of mortality in COVID-19 patients having tuberculosis. This has now been elaborated (lines 318-320).

14. Line 291-293: are these alterations in lungs due to TB?

Yes. This has now been more explicitly stated in second paragraph of Discussion (lines 322-324).

Attachment

Submitted filename: Reply to comments.docx

Click here for additional data file.^{(20.8KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0259006.r003

Decision Letter 1

Girish Chandra Bhatt

11 Oct 2021

Active pulmonary tuberculosis and coronavirus disease 2019: a systematic review and meta-analysis

PONE-D-21-23415R1

Dear Dr. Aggarwal,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Kind regards,

Girish Chandra Bhatt, MD, FASN

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: Yes

**********

6. Review Comments to the Author

Reviewer #1: Dear authors

Thank you for your response. I could find that all my concerns have been addressed satisfactorily. I dont have any further comments to make.

Thank you

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: Yes: Rashmi Ranjan Das

PLoS One. doi: 10.1371/journal.pone.0259006.r004

Acceptance letter

Girish Chandra Bhatt

13 Oct 2021

PONE-D-21-23415R1

Active pulmonary tuberculosis and coronavirus disease 2019: a systematic review and meta-analysis

Dear Dr. Aggarwal:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Girish Chandra Bhatt

Academic Editor

PLOS ONE

Associated Data

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

Supplementary Materials

S1 Table. Details of Newcastle-Ottawa Scale scoring for study quality.

(PDF)

Click here for additional data file.^{(83.7KB, pdf)}

S1 Fig. Baujat’s plot for studies reporting prevalence of tuberculosis among COVID-19 patients.

(PDF)

Click here for additional data file.^{(110.2KB, pdf)}

S2 Fig. Influence statistics for studies reporting prevalence of tuberculosis among COVID-19 patients.

(PDF)

Click here for additional data file.^{(74.7KB, pdf)}

S3 Fig. Sensitivity analysis for studies reporting prevalence of tuberculosis among COVID-19 patients.

(PDF)

Click here for additional data file.^{(626.6KB, pdf)}

S4 Fig. Sensitivity analysis for studies reporting outcomes of patients COVID-19 patients with comorbid tuberculosis.

(PDF)

Click here for additional data file.^{(555.1KB, pdf)}

S5 Fig. Contour-enhanced trim-and-fill funnel plots.

(PDF)

Click here for additional data file.^{(182.1KB, pdf)}

Attachment

Submitted filename: Reply to comments.docx

Click here for additional data file.^{(20.8KB, docx)}

Data Availability Statement

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

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PERMALINK

Active pulmonary tuberculosis and coronavirus disease 2019: A systematic review and meta-analysis

Ashutosh Nath Aggarwal

Ritesh Agarwal

Sahajal Dhooria

Kuruswamy Thurai Prasad

Inderpaul Singh Sehgal

Valliappan Muthu

Roles

Abstract

Objective

Methods

Results

Conclusion

Introduction

Methods

Search strategy

Selection of studies

Data extraction and study quality

Statistical analysis

Results

Fig 1. Flow chart for study selection.

Table 1. Characteristics of included studies.

Proportion of patients with active tuberculosis

Fig 2. Proportion of COVID-19 patients also having tuberculosis and corresponding 95% confidence intervals (CI).

Table 2. Subgroup analysis for summary estimates for proportion of COVID-19 patients with active pulmonary tuberculosis.

Severe COVID-19

Fig 3. Relative risk, and corresponding 95% confidence intervals (CI), of adverse clinical outcomes among COVID-19 patients having tuberculosis.

Table 3. Subgroup analysis for summary relative risk of adverse outcomes among COVID-19 patients with active pulmonary tuberculosis.

Need for hospitalization

Mortality

Discussion

Conclusion

Supporting information

Data Availability

Funding Statement

References

Decision Letter 0

Girish Chandra Bhatt

Roles

Author response to Decision Letter 0

Decision Letter 1

Girish Chandra Bhatt

Roles

Acceptance letter

Girish Chandra Bhatt

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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