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The Indian Journal of Medical Research logoLink to The Indian Journal of Medical Research
. 2023 May 3;157(2-3):135–151. doi: 10.4103/ijmr.ijmr_382_23

The prevalence of tuberculosis infection in India: A systematic review and meta-analysis

Arohi Chauhan 1, Malik Parmar 2, Girish Chandra Dash 5, Hardik Solanki 3, Sandeep Chauhan 3, Jessica Sharma 5, Krushna Chandra Sahoo 5, Pranab Mahapatra 6, Raghuram Rao 4, Ravinder Kumar 4, Kirankumar Rade 2, Sanghamitra Pati 5,✉
PMCID: PMC10319385  PMID: 37202933

Abstract

Background & objectives:

The National Prevalence Survey of India (2019-2021) estimated 31 per cent tuberculosis infection (TBI) burden among individuals above 15 years of age. However, so far little is known about the TBI burden among the different risk groups in India. Thus, this systematic review and meta-analysis, aimed to estimate the prevalence of TBI in India based on geographies, sociodemographic profile, and risk groups.

Methods:

To identify the prevalence of TBI in India, data sources such as MEDLINE, EMBASE, CINAHL, and Scopus were searched for articles reporting data between 2013-2022, irrespective of the language and study setting. TBI data were extracted from 77 publications and pooled prevalence was estimated from the 15 community-based cohort studies. Articles were reviewed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines and were sourced using a predefined search strategy from different databases.

Results:

Out of 10,521 records, 77 studies (46 cross-sectional and 31 cohort studies) were included. The pooled TBI prevalence for India based on the community-based cohort studies was estimated as 41 per cent [95% confidence interval (CI) 29.5-52.6%] irrespective of the risk of acquiring it, while the estimation was 36 per cent (95% CI 28-45%) prevalence observed among the general population excluding high-risk groups. Regions with high active TB burden were found to have a high TBI prevalence such as Delhi and Tamil Nadu. An increasing trend of TBI was observed with increasing age in India.

Interpretation & conclusions:

This review demonstrated a high prevalence of TBI in India. The burden of TBI was commensurate with active TB prevalence suggesting possible conversion of TBI to active TB. A high burden was recorded among people residing in the northern and southern regions of the country. Such local epidemiologic variation need to be considered to reprioritize and implement-tailored strategies for managing TBI in India.

Keywords: Epidemiology, prevalence, systematic review, tuberculosis infection

Tuberculosis (TB) caused by Mycobacterium tuberculosis (MTB) is a well known and major public health challenge globally, and is the leading cause of death from a single infectious agent1. According to the Global TB Report 20221, an estimated 10.6 million incident cases of TB were reported in 2021. The World Health Organization (WHO) END TB strategy aims to reduce 95 per cent of TB deaths and 90 per cent of new incident cases of TB by 20352. The global TB targets for reductions in disease burden of TB can only be achieved if diagnostic, prevention and treatment services of TB are strengthened3,4. A state of persistent immune response due to stimulation by MTB antigens without any evidence of clinically manifested active TB is defined as TB infection (TBI). TBI can serve as the precursor for the development of full-blown TB disease, particularly among the immunocompromised5. This constitutes a large reservoir of individuals with TBI, and thus, the management of TBI is crucial for global efforts to curb TB burden, particulaly in high TB burden countries such as India.

Geographically, out of the six high-burden countries from south-east Asia region, India accounts for 28 per cent of the global TB burden6. It also has the highest TBI burden globally6. According to the National TB prevalence survey, 2021, the crude prevalence of TBI among individuals >15 yr was 31.3 per cent [95% confidence interval (CI); 30.8-31.9]7. Around 5-10 per cent of those with TBI reportedly develop clinically active TB disease8. A single active case of TB may infect several other individuals before receiving any anti-tubercular treatment due to delayed diagnosis, hence creating a perpetual reservoir of TB-infected individuals9. Since TB-infected individuals constitute a perennial source of risk for progression towards active disease, prevention of active TB disease by treating TB-infected individuals and interrupting the chain of transmission is the chief component of the WHO End TB strategy2,8.

The prevention of TB disease by the treatment of TBI is largely undervalued but remains as an important component of the National Strategic Plan 2017-25 for Ending TB in India by 2025, five year ahead of the sustainable development goals10. The lancet commission on TB mentions that the diagnosis and treatment strategies to end TB would be ineffective unless TB preventive treatment (TPT) is included in the comprehensive strategy11. There is a need to improve the implementation of proven interventions such as effective new regimens for TPT and ensure their efficient and rapid scale-up12. This applies to finding high-risk groups and initiating TPT. Thus, it is essential to estimate the burden of TBI among various states and groups in India, which has programmatic implications. Against this background, this review aimed to estimate the burden of TBI in the Indian population stratified according to geographies and sociodemographic profiles.

Material & Methods

Protocol and inclusion criteria: A systematic review of various studies examining the TBI among Indians was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. All primary studies conducted on Indian residents reporting TBI irrespective of the tests used to measure were included. We considered the use of any available test for identifying TBI including interferon gamma release assay or tuberculin skin test (TST) or T SPOT, TB or C-TB or Cy-TB among the participants. Reviews, case reports, editorials, opinion pieces, study protocols, conference abstracts, posters, thesis, reports or any unpublished material were excluded. TBI for the present study was defined as the immune response to MTB antigen without clinical evidence of active TB.

Databases, study selection and data extraction: We conducted electronic searches of four databases namely: MEDLINE, EMBASE, CINHAL and Scopus limiting the search from January 1, 2013 to December 31, 2022. A comprehensive search was done using the keywords and Medical Subject Headings (MeSH) terminology for Tuberculosis including, TB, Latent Tuberculosis Infection, sub-clinical Tuberculosis, inactive Tuberculosis, Tuberculosis Infection, LTBI, Pulmonary Tuberculosis, Koch’s Tuberculosis, Extra-pulmonary Tuberculosis, Latent TB, Tuberculin Skin test, Mantoux test, TST, Purified protein derivative, Interferon gamma release assay, skin test, IGRA, Enzyme-linked immunospot assay, Quantiferon, Quantiferon-TB Gold, T.SPOT TB, C-TB and Cy-TB. The complete search strategy per database consulted is presented as Appendix I in the Supplementary material.

Two authors independently searched from all four databases mentioned. Reference checking, hand searching of citations and reference lists were done to identify potentially missing literature.

All citations retrieved from the electronic searches were first imported to EndNote where duplicate entries were removed, the resulting entries were then uploaded to the Rayyan software, 201613. Two independent researchers then screened the titles and abstracts of the retrieved studies to identify all the articles that could be eligible for inclusion. Uncertainty or disagreement was settled by a third reviewer’s consensus decision. Two researchers conducted the full-text screening of the previously identified articles, and disagreement was settled by a third reviewer.

The information on study characteristics and the results of the included studies were extracted using a standardized data extraction form. Information on authors, publication, country, study design, study area, sample size, publication year, study year, sociodemographic factors, proportion of TBI and the test used was extracted. In addition, information for assessing the risk of bias was extracted. When the data were insufficient or missing or full text was not available, corresponding authors of the original articles were contacted via e-mail to provide relevant information.

Assessment quality and the risk of bias: Methodological quality and the risk of bias among the included studies were assessed by two independent researchers using the Joanna Briggs Institute (JBI) Critical Appraisal tools designed for use in systematic reviews – set of questionnaires used to asses cross-sectional, cohort and randomized controlled trials13,14. Studies were then graded, and according to the score obtained, these were classified as ‘low’, ‘moderate’ or ‘high’ risk of bias.

Data synthesis and analysis: The studies were categorized according to the study design, and then, the study characteristics using percentages and frequencies for categorical variables and standard deviations or median for continuous variables were summarized. The pooled prevalence of TBI was extracted using STATA at 95 per cent confidence interval (CI) to account for small variability between studies. The prevalence of TBI based on geographical location and age group was estimated. Studies were weighted using the random effect model. As two tests (TST and IGRA) were commonly used for estimating TBI prevalence, pooled prevalence was estimated for studies using TST as well as IGRA separately using the random effect model. Sub-group meta-analysis was carried out based on the demography and prevalence of active TBI in India. Effect sizes were expressed as odds ratio for dichotomous data and as weighted mean difference for the continuous data. All effect estimates were expressed using 95 per cent CI. We pooled data using the random effects model (DerSimonian and Laird) in the MetaXL software.

Results

Study characteristics: Initially, 10,521 studies were retrieved. Of these 519 duplicates were excluded and 9996 studies were eligible for screening. Of these, 9836 studies were further excluded after reviewing the title and abstract leaving 160 studies for full text review. Of these, the full text of 55 studies was unavailable. Additionally, 10 studies were included from cross references. So, in all 115 studies met the inclusion criteria for a full-text review. Following the full-text review, 77 studies were finally included for investigation. The PRISMA flow diagram is presented in Figure 1.

Fig. 1.

Fig. 1

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PRISMA flow diagram. PRISMA, preferred Reporting Items for Systematic Reviews and Meta-Analysis

Out of the 77 articles, 31 were cohort and 46 were cross-sectional studies. According to the zonal divisions of India15, most of the research was conducted in the southern part (15,813 i.e. 40.8% of patients) followed by the northern region (12,527 i.e.; 32.3% of patients), western region (3424 i.e. 8.8% of patients), central region (2296 i.e. 5.9% of patients) and eastern (174 i.e. 0.4% of patients) region of India. TST was used for diagnosing TBI in majority of the studies (70/77), IGRA was used in 10 per cent (7/77) studies and both TST & IGRA were used in 61 per cent (47/77) studies. A total of 59 studies considered TST positivity when reading recorded more than 10 mm induration and 11 studies considered it as more than 5 mm. A total of 38,767 individuals were studied across the included articles ranging from 33 to 2351 individuals. The Table provides an overview of the main characteristics of the included studies. As IGRA is a more specific diagnostic test for TBI compared to TST5, the pooled prevalence of TBI as depicted below was estimated using IGRA data as available. In the absence of the availability of IGRA data, TST results were considered for estimation. Furthermore, the pooled prevalence of TBI in India was estimated based on the community-based cohort studies only since hospital-based and cross-sectional studies generally, have inherent selection biases.

Table.

Study characteristics of the included articles

Author, year State Region Study period Study design Study setting Sample size Male (%) Risk group Test TST size (mm) Individuals with TBI
Agarwal et al16, 2014 UP Central 2011-2013 Cross-sectional Hospital 250 15 Inflammatory bowel disease TST ≥10 51
Agarwal et al17, 2015 Delhi North 2007-2010 Cross-sectional Hospital 185 70 Dialysis IGRA and TST ≥10 66
Arya et al18, 2018 UP Central 2018 Cohort Hospital 43 81 Healthcare workers TST ≥10 24
Aravindhan et al19, 2022 Tamil Nadu South 2016-2019 Cross-sectional Community 170 62 Diabetes IGRA NA 50
Bajgai et al20, 2016 Haryana North 2015 Cohort Hospital 100 77 Retinal detachment TST ≥10 16
Bapat et al21, 2015 Maharashtra West 2009-2011 Cohort Community 74 54 General population IGRA and TST ≥10 38
Bekken et al22, 2020 Andhra Pradesh South 2010-2012 Cross-sectional Community 476 41 Contacts IGRA and TST ≥10 266
Benachinmardi et al23, 2019 Karnataka South 2018-2019 Cross-sectional Community 77 6 Contacts IGRA and TST >5 31
Benachinmardi et al24, 2021 Karnataka South 2018-2019 Cross-sectional Community 77 39 Contacts IGRA and TST >5 32
Boddu et al25, 2019 Tamil Nadu South 2019 Cohort Community 80 57 Contacts IGRA and TST ≥10 23
Chandrasekaran et al26, 2018 Maharashtra and TN South-West 2014 Cohort Community 869 55 Contacts IGRA and TST >5 478
Chauhan et al27, 2013 UP Central 2005-2006 Cross-sectional Community 200 NA Contacts TST ≥10 78
Christopher et al28, 2014 Tamil Nadu South 2007-2009 Cohort Hospital 755 6 Healthcare workers TST ≥10 339
Dayal et al29, 2018 UP Central 2015-2016 Cross-sectional Hospital 271 49 Contacts TST ≥10 55
Dabhi et al30, 2020 Tamil Nadu South 2014-2015 Cross-sectional Hospital 200 65 Diabetes IGRA and TST ≥10 96
Dinkar et al31, 2022 UP Central 2020-2021 Cross-sectional Hospital 561 65 Healthcare workers TST ≥10 380
Dolla et al32, 2019 Maharashtra and TN South-West 2014 Cohort Community 1020 46 Contacts IGRA and TST >5 257
Dorjee et al33, 2019 Himachal Pradesh North 2017-2018 Cross-sectional Community 6177 49 General population TST ≥10 1220
Faujdar et al34, 2022 Himachal Pradesh North 2018-2020 Cross-sectional Community 789 NA General population TST ≥10 198
Girish et al35, 2021 Maharashtra West 2016-2017 Cohort Community 200 NA Healthcare workers IGRA and TST ≥10 45
Gupta et al36, 2020 Maharashtra West 2015-2016 Cross-sectional Community 205 NA Contacts IGRA and TST >5 173
Gupta et al37, 2021 Delhi North 2020 Cohort Hospital 60 100 COVID TST ≥10 23
Jain et al38, 2013 Maharashtra West 2010-2012 Cross-sectional Hospital 215 54 General population TST >5 27
James et al39, 2014 Karnataka South 2011 Cross-sectional Hospital 100 48 HIV IGRA and TST ≥10 33
Janagond et al40, 2017 Tamil Nadu South 2014-2015 Cohort Hospital 206 4 Healthcare workers TST ≥10 76
Jenum et al41, 2014 Andhra Pradesh South 2007-2010 Cross-sectional Hospital 702 53 Contacts IGRA and TST ≥10 69
Mukherjee et al42, 2014 Delhi North 2014 Cross-sectional Hospital 362 45 General population IGRA and TST ≥10 297
Syed Ahamed Kabeer et al43, 2012 Tamil Nadu South 2008-2009 Cohort Community 572 27 General population IGRA and TST ≥10 174
Kashyap et al44, 2014 Maharashtra West 2014 Cohort Community 162 32 Contacts IGRA and TST ≥10 44
Kashyap et al45, 2016 Maharashtra West 2009-2012 Cohort Community 398 53 General population IGRA and TST ≥10 96
Kaul et al46, 2022 Delhi North 2022 Cohort Community 80 53 Contacts IGRA NA 43
Kinikar et al47, 2019 Maharashtra West 2016-2017 Cohort Hospital 200 44 Healthcare workers IGRA and TST ≥10 45
Krishnamoorthy et al48, 2021 Tamil Nadu South 2014-2019 Cross-sectional Community 1523 40 Contacts TST >5 801
Vijay Kumar and Gopalakrishnan49, 2014 Karnataka South 2014 Cohort Hospital 125 NA Healthcare workers TST ≥10 14
Kumar et al50, 2019 Karnataka South 2016-2017 Cohort Hospital 598 24 Healthcare workers TST ≥10 120
Kumar et al51, 2022 Delhi North 2005-2012 Cohort Hospital 171 60 Healthcare workers TST ≥10 48
Madan et al52, 2021 Delhi North 2020 Cohort Hospital 60 88 COVID TST ≥10 15
Madan et al53, 2022 Delhi North 2018-2019 Cohort Hospital 327 51 Sarcoidosis TST ≥10 33
Malaviya et al54, 2018 Delhi North 2018 Cross-sectional Hospital 730 57 Rheumatoid arthritis IGRA and TST ≥10 36
Malaviya et al55, 2018 Delhi North 2019 Cross-sectional Hospital 44 NA Rheumatoid arthritis IGRA and TST ≥10 6
Mantri et al56, 2021 Delhi North 2021 Cohort Hospital 257 NA Inflammatory bowel disease IGRA and TST ≥10 48
Mathad et al57, 2014 Maharashtra West 2011-2012 Cross-sectional Hospital 401 NA Pregnancy IGRA and TST ≥10 150
Mathad et al58, 2016 Maharashtra West 2011-2014 Cross-sectional Hospital 252 NA Pregnancy IGRA and TST >5 71
Mave et al59, 2019 Maharashtra and TN South-West 2014-2017 Cross-sectional Community 780 45 Contacts IGRA and TST >5 460
Mishra et al60, 2017 UP Central 2016 Cross-sectional Community 200 50 Contacts TST ≥10 96
Munisankar et al61, 2022 Tamil Nadu South 2013-2020 Cross-sectional Community 2351 45 General population IGRA NA 1226
Murthy et al62, 2013 Andhra Pradesh South 2007-2010 Cross-sectional Community 5351 52 General population TST ≥10 794
NArasimhan et al63, 2017 Tamil Nadu South 2004-2005 Cross-sectional Community 663 46 Contacts IGRA and TST ≥10 292
Neema et al64, 2021 Maharashtra West 2021 Cohort Hospital 105 55 Psoriasis TST ≥10 33
Neema et al65, 2022 Maharashtra West 2022 Cross-sectional Hospital 75 64 Psoriasis IGRA and TST ≥10 16
Pattnaik et al66, 2022 Delhi North 2014-2020 Cohort Hospital 15 47 Sarcoidosis TST ≥10 4
Pandey and Warade67, 2014 UP Central 2008-2010 Cross-sectional Community 150 NA General population IGRA and TST ≥10 105
Paradkar et al68, 2020 Maharashtra and TN South-West 2014-2017 Cohort Community 997 44 Contacts IGRA and TST >5 484
Prabhavathi et al69, 2015 Tamil Nadu South 2015 Cohort Community 144 65 General population IGRA and TST ≥10 57
Prabhavati et al70, 2016 Tamil Nadu South 2013-2015 Cross-sectional Community 53 55 HIV IGRA NA 25
Patil et al71, 2014 Maharashtra West 2012-2013 Cross-sectional Hospital 100 48 HIV TST ≥10 44
Rajamanickam et al72, 2021 Tamil Nadu South 2021 Cross-sectional Community 133 56 COVID IGRA NA 61
Ramaraj et al73, 2017 Karnataka South 2015 Cross-sectional Community 70 66 General population TST ≥10 7
Rajalakshmi and Viknesh Prabu74, 2017 Tamil Nadu South 2017 Cross-sectional Community 196 48 Diabetes IGRA and TST ≥10 47
Reddy et al75, 2021 Tamil Nadu South 2020 Cohort Community 1189 41 Contacts TST ≥10 661
Sawhney et al76, 2015 Haryana North 2011-2013 Cross-sectional Hospital 200 43 Healthcare workers TST ≥10 29
Shah et al77, 2021 Maharashtra West 2012-2014 Cross-sectional Hospital 371 53 General population TST ≥10 227
Shah et al78, 2020 Maharashtra West 2011-2012 Cross-sectional Hospital 33 55 Contacts IGRA and TST ≥10 14
Sharma et al79, 2017 Delhi North 2008-2012 Cohort Community 1511 52 Contacts IGRA and TST ≥10 917
Shivakumar et al80, 2018 Maharashtra and TN South-West 2014-2017 Cross-sectional Community 639 44 Diabetes IGRA and TST >5 354
Shobha et al81, 2019 Karnataka South 2016 Cross-sectional Community 178 50 Rheumatoid arthritis IGRA and TST ≥10 18
Srivastava et al82, 2020 UP Central 2020 Cross-sectional Community 152 51 Contacts TST ≥10 62
Siddiqui et al83, 2022 Sikkim North-East 2019 Cross-sectional Community 174 95 General population IGRA NA 77
Singh et al84, 2013 Delhi North 2007-2009 Cohort Community 1389 54 Contacts TST ≥10 1172
Singh et al85, 2021 UP Central 2015-2017 Cross-sectional Community 469 62 HIV IGRA and TST ≥10 136
Surve et al86, 2021 Maharashtra West 2019-2021 Cohort Community 299 50 Contacts IGRA and TST ≥10 35
Thamke et al87, 2018 Maharashtra West 2015-2016 Cohort Hospital 80 54 Contacts IGRA and TST ≥10 34
Thomas et al88, 2022 Tamil Nadu South 2018-2021 Cohort Hospital 168 57 General population IGRA and TST ≥10 27
Uppada et al89, 2014 Andhra Pradesh South 2007-2010 Cross-sectional Community 6608 52 General population TST ≥10 794
Vyas et al90, 2015 Tamil Nadu South 2015 Cross-sectional Hospital 62 55 Sarcoidosis IGRA and TST ≥10 16
Zwerling et al91, 2014 Maharashtra West 2007-2009 Cross-sectional Hospital 226 39 Healthcare workers IGRA NA 64
Abdullah et al92, 2021 Delhi North 2021 Cross-sectional Hospital 100 NA Rheumatoid arthritis TST >5 36
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NA, not available; TST, tuberculin skin test; IGRA, interferon gamma release assay; TBI, tuberculosis infection

Prevalence of tuberculosis infection based on community-based cohort studies: As per the random-effects model, the pooled community-based cohort study-based prevalence of TBI was found to be 40.8 per cent (95% CI: 29.5-52.6%, Q=1648.9, P<0.0001, I2=99%) (Fig 2). After excluding the risk groups, the pooled prevalence of TBI for community-based general population group was found to be 36 per cent (95% CI: 28-45%, Q=17.38, P<0.0001, I2=83%). Among the general population, TBI prevalence among adults (≥15 yr) was 35 per cent (95% CI: 24-46%, Q=13.93, P<0.0001, I2=86%). Due to the remarkable heterogeneity (P<0.00001, I2=99%), a sensitivity analysis was performed. After excluding the studies causing the heterogeneity, the pooled TBI prevalence was found to be 38 per cent (95% CI: 29-46%, Q=10.72, P=0.02, I2=72%).

Fig. 2.

Fig. 2

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Community-based cohort studies TBI prevalence in India. TBI, tuberculosis infection

Prevalence of tuberculosis infection based on community-based cohort study according to the geography, age, gender and year-wise distribution: According to the available data, the prevalence of TBI based on community-based cohort studies in Delhi was 68 per cent (95% CI: 46-87%), in Tamil Nadu 42 per cent (95% CI: 24-61%) and in Maharashtra 26 per cent (95% CI: 16-36%). The pooled prevalence of TBI in urban areas was 37 per cent (95% CI: 16-60%), in rural areas 27 per cent (95%CI: 11-48%) and in tribal areas 33 per cent (95% CI: 20-47%). Among the paediatric population, the pooled prevalence of TBI was estimated as 33 per cent (95% CI: 24-42%) in under 5-yr-old children and in those aged 6-14 yr, it was 40 per cent (95%CI: 30-51%), respectively. In adult population, ranging from 15-45 yr of age, the TBI prevalence was 52 per cent (95% CI: 39-69%) and among older adults (above 45 yr), it was estimated as 62 per cent (95% CI: 50-74%). The estimated pooled prevalence of TBI was 41 per cent (95% CI: 19-65%) among the male population and in female population, it was 31 per cent (95% CI: 09-59%). A steady trend of TBI over the years was observed according to the data reported by various studies during 2013-2022.

Quality and risk of bias: Majority of the studies (76) were considered having a low risk of bias according to the JBI critical assessment score (>70%). Only one study was considered to have a moderate risk of bias (score 50-69%); Appendix I, Supplementary Table I and II. No articles were excluded based on the quality assessment.

Supplementary Table I.

Quality assessment of cross-sectional studies

Author name and year Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8
Agarwal et al16, 2014 Y Y Y Y Y Y Y Y
Agarwal et al17, 2015 Y Y Y Y Y N Y Y
Aravindhan et al19, 2022 Y Y Y Y Y Y Y Y
Bekken et al22, 2020 Y Y Y Y Y Y Y Y
Benachinmardi et al23, 2019 Y Y Y Y Y Y Y Y
Benachinmardi et al24, 2021 Y Y Y Y Y N Y Y
Chauhan et al27, 2013 Y Y Y Y Y Y Y Y
Dabhi et al30, 2022 Y Y Y Y Y Y Y Y
Dayal et al29, 2018 Y Y Y Y Y Y Y Y
Dinkar et al31, 2022 Y Y Y Y Y Y Y Y
Faujdar et al34, 2022 Y Y Y Y Y Y Y Y
Gupta et al36, 2020 Y Y Y Y Y Y Y Y
Jain et al38, 2013 Y Y Y Y Y Y Y Y
James et al39, 2014 Y Y Y Y Y Y Y Y
Jenum et al41, 2014 Y Y Y Y Y U Y Y
Krishnamoorthy et al, 2021 Y Y Y Y Y U Y Y
Malviya et al54, 2018 Y Y Y Y Y Y Y Y
Malviya et al55, 2019 Y Y Y Y Y Y Y Y
Mathad et al57, 2014 Y Y Y Y Y Y Y Y
Mathad et al58, 2016 Y Y Y Y Y Y Y Y
Mave et al59, 2019 Y Y Y Y Y U Y Y
Mishra et al60, 2017 Y Y Y Y Y Y Y Y
Mukherjee et al, 2014 Y Y Y Y Y Y Y Y
Munisankar et al61, 2022 Y Y Y Y N N Y Y
Murthy et al62, 2013 Y Y Y Y Y Y Y Y
Narasimhan et al63, 2017 Y Y Y Y Y Y Y Y
Neema et al65, 2019 Y Y Y Y Y U Y Y
Pandey and Warade67, 2014 Y Y Y Y Y U Y Y
Patil et al71, 2014 Y Y Y Y Y Y Y Y
Prabhavathi et al69, 2015 Y Y Y Y Y Y Y Y
Rajalakshmi and Viknesh Prabu74, 2017 Y Y Y Y Y U Y Y
Rajamanickam et al72, 2020 Y Y Y Y Y U Y Y
Ramaraj et al73, 2017 Y Y Y Y Y Y Y Y
Sawhney et al76, 2015 Y Y Y Y Y Y Y Y
Shah et al77, 2021 Y Y Y Y Y Y Y Y
Shah et al78, 2019 Y Y Y Y Y Y Y Y
Sharma et al79, 2017 Y Y Y Y Y Y Y Y
Shivakumar et al80, 2018 Y Y Y Y Y U Y Y
Shobha et al81, 2018 Y Y Y Y Y N Y Y
Srivastava et al82, 2020 Y Y Y Y Y Y Y Y
Siddiqui et al83, 2022 Y Y Y Y Y Y Y Y
Singh et al85, 2021 Y Y Y Y Y U Y Y
Uppada et al89, 2014 Y Y Y Y Y Y Y Y
Vyas et al90, 2015 Y Y Y Y Y Y Y Y
Zwerling et al91, 2013 Y Y Y Y Y Y Y Y
Abdullah et al92, 2021 Y Y Y Y Y U Y Y
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Supplementary Table II.

Quality assessment of cohort studies

Author name and year Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Q10 Q11
Arya et al18, 2018 Y Y Y Y Y N Y Y Y Y Y
Bapat et al21, 2015 Y Y Y Y Y Y Y Y U U Y
Boddu et al25, 2019 Y Y Y Y Y Y Y Y Y U Y
Bajgai et al20, 2015 Y Y Y Y Y Y Y Y Y Y Y
Gupta et al37, 2021 Y Y Y Y Y Y Y Y Y Y Y
Vijay Kumar and Gopalakrishnan49, 2014 Y Y Y Y N Y Y Y Y U Y
Girish et al35, 2021 Y Y Y Y Y Y Y Y Y Y Y
Madan et al52, 2021 Y Y Y Y Y Y Y Y Y Y Y
Janagond et al40, 2017 Y Y Y Y Y Y Y Y Y Y Y
Kashyap et al44, 2014 Y Y Y Y Y Y Y Y Y Y Y
Kashyap et al45, 2016 Y Y Y Y Y Y Y Y Y Y Y
Kabeer et al43, 2018 Y Y Y Y Y Y Y Y N N Y
Kumar et al50, 2019 Y Y Y Y Y Y Y Y Y Y Y
Kumar et al51, 2022 Y Y Y Y Y Y Y Y Y Y Y
Chandrasekharan et al26, 2018 Y Y Y Y Y Y Y Y Y Y Y
Mantri et al56, 2021 Y Y Y Y Y Y Y Y Y Y Y
Paradkar et al68, 2020 Y Y Y Y Y Y Y Y Y Y Y
Prabhavathi et al69, 2015 Y Y Y Y Y N Y Y N N Y
Reddy et al75, 2021 Y Y Y Y Y Y Y Y Y Y Y
Singh et al84, 2013 Y Y Y Y Y Y Y Y Y Y Y
Thomas et al88, 2022 Y Y Y Y Y Y Y Y U U Y
Madan et al53, 2022 Y Y Y Y Y N Y Y Y Y Y
Neema et al64, 2019 Y Y Y Y Y Y Y Y Y Y Y
Pattnaik et al, 2021 Y Y Y Y N N Y Y Y Y Y
Kaul et al46, 2022 Y Y Y Y Y Y Y Y Y U Y
Kinikar et al47, 2019 Y Y Y Y Y Y Y Y Y Y Y
Christopher et al28, 2014 Y Y Y Y Y Y Y Y N U Y
Dolla et al32, 2019 Y Y Y Y Y Y Y Y Y Y Y
Sharma et al79, 2017 Y Y Y Y Y Y Y Y Y Y Y
Thamke et al87, 2018 Y Y Y Y Y Y Y Y Y Y Y
Surve et al86, 2021 Y Y Y Y Y Y Y Y Y Y Y
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  • Q1. Were the two groups similar and recruited from the same population?

  • Q2. Were the exposures measured similarly to assign people to both exposed and unexposed groups?

  • Q3. Was the exposure measured in a valid and reliable way?

  • Q4. Were the confounding factors identified?

  • Q5. Were strategies to deal with confounding factors stated?

  • Q6. Were the groups/participants free of the outcome at the start of the study (or at the moment of exposure)?

  • Q7. Were the outcomes measured in a valid and reliable way?

  • Q8. Was the follow up time reported and sufficient to be long enough for outcomes to occur?

  • Q9. Was follow up complete, and if not, were the reasons to loss to follow up described and explored?

  • Q10. Were strategies to address incomplete follow up utilized?

  • Q11. Was appropriate statistical analysis used?

Publication bias: Supplementary Figure (50.1KB, tif) presents the Doi plot, which is used to give readers an indication of any possible publication bias in the study. The Doi plot indicates that the included studies had minor asymmetry (LFK index=2.00).

Discussion

The present review appraised available evidence on the burden of TBI among people residing in India based on the results of IGRA as well as TST, which are the tests being used to diagnose and target individuals eligible for TPT. Data were gathered from over 38,767 IGRA and TST results covering the different regions of India. Based on the community-based cohort studies, it was observed that TBI was prevalent in more than one-third of India’s population and it increased with advancing age.

Various researchers previously reported a similar prevalence of TBI in India, using Bayesian model. Woodruff et al15 and Collins et al93 estimated 31.9 per cent and 33.9 per cent prevalence among Indians, respectively whereas Houben and Dodd94 by mathematical modeling presented a 31 per cent estimate of TBI in the south-east Asian region94. The national TB prevalence survey in 2019-2021 also recorded a crude TBI prevalence of 31 per cent7. Considered together, all these evidences indicate that India has a huge reservoir of TBI, a considerable proportion of whom may progress to TB disease. This review noted a rising TBI prevalence with age, especially among adults and older adults. High prevalence among such groups is an impediment to the national TB elimination efforts. Chong et al95 in their mathematical model, pertaining to countries with intermediate TB prevalence, suggested that screening and providing TPT to 20-40 per cent of the elderly could result in 50 per cent overall decline of TB incidence.

We noted a steady trend of TBI maintained over the last decade suggesting the persistence of TB incidence. TBI treatment rates among the high-risk groups in India was sub-optimal (12%)6. The World Health Organization (WHO) recommends the prevention and treatment as key interventions to achieve, the End TB targets. End TB targets2 India’s National Strategic plan (2017-25) rests on prevent, detect and treat as the core pillars for the elimination of TB10. The treatment of active TB reduces the prevalence of infectious TB, eventually cutting down the transmission and lowering the incidence4, whereas treatment of TBI prevents the progression of latent infection to disease and directly reduces the incidence3. Evidence suggests the treatment of TBI and active TB act synergistically to reduce TB incidence96,97. Hence, when the treatment levels of active TB are high (95%)6, a similar payoff could accrue by stepping up the treatment of TBI. Thus, scaling up of the TPT will hasten the lowering of TB incidence and to achieve the EndTB targets in India98.

The current review recorded remarkable regional differences in TBI prevalence. A high burden was found in those areas with prevalent active TB (Delhi, Tamil Nadu, etc.) implying that TBI probably has a high conversion rate to active TB disease7. TBI case-finding has high implementation cost99. However, in the absence of TBI case-finding, the number of infectious individuals will grow demanding increased need for active case-finding and case-holding efforts99. At present, India’s National TB Elimination Programme (NTEP) recommends active case-finding and case-holding efforts for areas with high TB prevalence leading to a longer implementation period100. Considering, both active as well as TBI case-finding having similar cost-sensitivities, TBI case-finding has a shorter implementation period. TBI case-finding relies on the use of diagnosis and treatment of TBI99. Evidence suggests that there is no ideal way of testing for and diagnosing TBI101-103. The two main tests in use are TST and IGRA, both of which measure the immunological response to MTB antigens101. TST, although inexpensive, is logistically challenging, produces variable results depending on the type of purified protein derivative (PPD) used and shortage of quality-assured tuberculin posing an impediment globally104,105. On the other hand, IGRA has high specificity, demands single facility visit; however, it is costly and there are issues related to the reproducibility of the results102. Recently, validity of both the tests has been questioned with evidence, suggesting only 10 per cent patients who harbour viable MTB organisms capable of causing disease showing immunoreactivity to these tests12,106. Further, issues related to testing hesitancy challenges the implementation of widespread screening107,108. Growing experience from clinical studies and implementation in the field suggests newer regimens such as three-month weekly rifapentine and isoniazid (3HP) regimen and one-month daily rifapentine and isoniazid (1HP) regimens, both of these appear to have a similar efficacy as the daily isoniazid dosing-based preventive therapy but with a better safety profile, higher acceptance and completion rate3. Thus, faced with operational issues related to the TBI diagnostics and considering the consequences of missing positive cases leading to increase in number of infectious individuals, increased efforts for active case-finding and high case-holding efforts in high prevalence areas and with recent 3HP implementation in the programme7,100, ‘no test, treat only approach’ after ruling out active TB needs to be contemplated109,110.

The present review is a comprehensive review that reports the prevalence of TBI in India, wherein an extensive systematic literature search with hand-searching of references was performed with a multidisciplinary team. The pooled prevalence was estimated only for community-based cohort studies. This increased the validity and offered a deeper and comprehensive understanding of the burden of TBI among different groups as well as the general population. However, the evidence reflected all the parts of India except the eastern region thus limiting the comprehensive depiction of the findings across India. There were also some limitations. For TST, studies were not separated based on the strength of PPD or use of standardization of PPD used in the studies. Furthermore, there was a lack of uniformity in the diagnosis of TBI, especially while using TST method as some studies reported more than 5 mm as TBI positivity irrespective of the immunocompromised patient inclusion. Further, there is a possibility of poor detection of true heterogeneity, especially when small number of studies are analyzed using Cochran’s Q, Higgins and Thompson’s I2 test.

Implications and way-forward: Prioritization of initiation of TPT is required for the regions with a high prevalence of TBI. This is important as the chances of missing positive patients are high owing to the less predictive value of diagnostic tests and their high operational costs. A more robust approach is needed with consideration of ‘No test, treat only’ approach after ruling out active TB for specific high disease burden geographies in comparison to the current strategy of active TB case-finding and case-holding approach for such regions. Research is further needed to identify the prevalence of TBI among individuals with multimorbidity (presence of two or more chronic conditions in one person), e.g. diabetic with cardiovascular disease or rheumatoid arthritis. Further evidence needs to be generated related to TBI among migrants, prisoners and residents of mental health homes. Prioritization of States with high TB disease burden and TBI prevalence for community-based screening to rule out active TB and implement TPT policy at the population levels is needed. As the treatment of TBI is a pre-requisite for achieving TB elimination goals, the evidence presented in this review will pave way for a more strengthened programmatic management of TBI in India.

Overall, this review demonstrated a high prevalence of TBI, commensurate with active TB prevalence suggesting conversion of TBI to active TB-disease. A high burden among people residing in the northern and southern regions was observed, indicating the need for the TBI country-specific strategies targeting population-level interventions.

Supplementary Figure

Doi plot analysis and Luis Furuya-Kanamori (LFK) index for publication bias.

IJMR-157-135_Suppl1.tif (50.1KB, tif)

Supplementary Files

Appendix I

(‘tuberculosi’[All Fields] OR ‘tuberculosis’[MeSH Terms] OR ‘tuberculosis’[All Fields] OR ‘tuberculoses’[All Fields] OR ‘tuberculosis s’[All Fields] OR ((‘tuberculosis’[MeSH Terms] OR ‘tuberculosis’[All Fields] OR ‘tuberculous’[All Fields]) AND (‘infect’[All Fields] OR ‘infectability’[All Fields] OR ‘infectable’[All Fields] OR ‘infectant’[All Fields] OR ‘infectants’[All Fields] OR ‘infected’[All Fields] OR ‘infecteds’[All Fields] OR ‘infectibility’[All Fields] OR ‘infectible’[All Fields] OR ‘infecting’[All Fields] OR ‘infection s’[All Fields] OR ‘infections’[MeSH Terms] OR ‘infections’[All Fields] OR ‘infection’[All Fields] OR ‘infective’[All Fields] OR ‘infectiveness’[All Fields] OR ‘infectives’[All Fields] OR ‘infectivities’[All Fields] OR ‘infects’[All Fields] OR ‘pathogenicity’[MeSH Subheading] OR ‘pathogenicity’[All Fields] OR ‘infectivity’[All Fields])) OR (‘latent tuberculosis’[MeSH Terms] OR (‘latent’[All Fields] AND ‘tuberculosis’[All Fields]) OR ‘latent tuberculosis’[All Fields] OR (‘inactive’[All Fields] AND ‘tuberculosis’[All Fields]) OR ‘inactive tuberculosis’[All Fields]) OR ((‘subclinic’[All Fields] OR ‘subclinical’[All Fields] OR ‘subclinically’[All Fields] OR ‘subclinicals’[All Fields]) AND (‘tuberculosi’[All Fields] OR ‘tuberculosis’[MeSH Terms] OR ‘tuberculosis’[All Fields] OR ‘tuberculoses’[All Fields] OR ‘tuberculosis s’[All Fields])) OR ‘TB’[All Fields] OR (‘mycobacterium tuberculosis’[MeSH Terms] OR (‘mycobacterium’[All Fields] AND ‘tuberculosis’[All Fields]) OR ‘mycobacterium tuberculosis’[All Fields]) OR (‘tuberculosis, pulmonary’[MeSH Terms] OR (‘tuberculosis’[All Fields] AND ‘pulmonary’[All Fields]) OR ‘pulmonary tuberculosis’[All Fields] OR (‘pulmonary’[All Fields] AND ‘tuberculosis’[All Fields])) OR (‘tuberculosis, extrapulmonary’[MeSH Terms] OR (‘tuberculosis’[All Fields] AND ‘extrapulmonary’[All Fields]) OR ‘extrapulmonary tuberculosis’[All Fields] OR (‘extrapulmonary’[All Fields] AND ‘tuberculosis’[All Fields])) OR (‘Koch’[All Fields] AND (‘tuberculosi’[All Fields] OR ‘tuberculosis’[MeSH Terms] OR ‘tuberculosis’[All Fields] OR ‘tuberculoses’[All Fields] OR ‘tuberculosis s’[All Fields])) OR ((‘tuberculosis’[MeSH Terms] OR ‘tuberculosis’[All Fields] OR ‘tuberculous’[All Fields]) AND (‘infect’[All Fields] OR ‘infectability’[All Fields] OR ‘infectable’[All Fields] OR ‘infectant’[All Fields] OR ‘infectants’[All Fields] OR ‘infected’[All Fields] OR ‘infecteds’[All Fields] OR ‘infectibility’[All Fields] OR ‘infectible’[All Fields] OR ‘infecting’[All Fields] OR ‘infection s’[All Fields] OR ‘infections’[MeSH Terms] OR ‘infections’[All Fields] OR ‘infection’[All Fields] OR ‘infective’[All Fields] OR ‘infectiveness’[All Fields] OR ‘infectives’[All Fields] OR ‘infectivities’[All Fields] OR ‘infects’[All Fields] OR ‘pathogenicity’[MeSH Subheading] OR ‘pathogenicity’[All Fields] OR ‘infectivity’[All Fields])) OR ((‘latent’[All Fields] OR ‘latently’[All Fields] OR ‘latents’[All Fields]) AND ‘TB’[All Fields]) OR (‘latent tuberculosis’[MeSH Terms] OR (‘latent’[All Fields] AND ‘tuberculosis’[All Fields]) OR ‘latent tuberculosis’[All Fields] OR (‘latent’[All Fields] AND ‘tuberculosis’[All Fields] AND ‘infection’[All Fields]) OR ‘latent tuberculosis infection’[All Fields]) OR ‘LTBI’[All Fields] OR ((‘tuberculosi’[All Fields] OR ‘tuberculosis’[MeSH Terms] OR ‘tuberculosis’[All Fields] OR ‘tuberculoses’[All Fields] OR ‘tuberculosis s’[All Fields]) AND (‘contact’[All Fields] OR ‘contactable’[All Fields] OR ‘contacted’[All Fields] OR ‘contacting’[All Fields] OR ‘contacts’[All Fields]))) AND (‘tuberculin test’[MeSH Terms] OR (‘tuberculin’[All Fields] AND ‘test’[All Fields]) OR ‘tuberculin test’[All Fields] OR (‘tuberculin’[All Fields] AND ‘skin’[All Fields] AND ‘test’[All Fields]) OR ‘tuberculin skin test’[All Fields] OR ‘TST’[All Fields] OR (‘Mantoux’[All Fields] AND (‘research design’[MeSH Terms] OR (‘research’[All Fields] AND ‘design’[All Fields]) OR ‘research design’[All Fields] OR ‘test’[All Fields])) OR (‘tuberculin test’[MeSH Terms] OR (‘tuberculin’[All Fields] AND ‘test’[All Fields]) OR ‘tuberculin test’[All Fields]) OR (‘tuberculin’[MeSH Terms] OR ‘tuberculin’[All Fields] OR (‘purified’[All Fields] AND ‘protein’[All Fields] AND ‘derivative’[All Fields]) OR ‘purified protein derivative’[All Fields]) OR ‘PPD’[All Fields] OR ‘IGRA’[All Fields] OR (‘quantiferongold’[All Fields] AND ‘tube’[All Fields] AND (‘analysis’[MeSH Subheading] OR ‘analysis’[All Fields] OR ‘assay’[All Fields] OR ‘biological assay’[MeSH Terms] OR (‘biological’[All Fields] AND ‘assay’[All Fields]) OR ‘biological assay’[All Fields] OR ‘assay s’[All Fields] OR ‘assayed’[All Fields] OR ‘assaying’[All Fields] OR ‘assays’[All Fields])) OR (‘QuantiFERON-Gold’[All Fields] AND (‘plant tubers’[MeSH Terms] OR (‘plant’[All Fields] AND ‘tubers’[All Fields]) OR ‘plant tubers’[All Fields] OR ‘tuber’[All Fields] OR ‘tubers’[All Fields] OR ‘tuber s’[All Fields] OR ‘tuberization’[All Fields] OR ‘tuberize’[All Fields] OR ‘tuberized’[All Fields] OR ‘tuberizing’[All Fields] OR ‘tuberous’[All Fields]) AND (‘analysis’[MeSH Subheading] OR ‘analysis’[All Fields] OR ‘assay’[All Fields] OR ‘biological assay’[MeSH Terms] OR (‘biological’[All Fields] AND ‘assay’[All Fields]) OR ‘biological assay’[All Fields] OR ‘assay s’[All Fields] OR ‘assayed’[All Fields] OR ‘assaying’[All Fields] OR ‘assays’[All Fields])) OR ((‘interferon gamma’[MeSH Terms] OR ‘interferon gamma’[All Fields] OR (‘interferon’[All Fields] AND ‘gamma’[All Fields]) OR ‘interferon gamma’[All Fields]) AND (‘analysis’[MeSH Subheading] OR ‘analysis’[All Fields] OR ‘assay’[All Fields] OR ‘biological assay’[MeSH Terms] OR (‘biological’[All Fields] AND ‘assay’[All Fields]) OR ‘biological assay’[All Fields] OR ‘assay s’[All Fields] OR ‘assayed’[All Fields] OR ‘assaying’[All Fields] OR ‘assays’[All Fields])) OR (‘QuantiFERON-TB’[All Fields] AND (‘research design’[MeSH Terms] OR (‘research’[All Fields] AND ‘design’[All Fields]) OR ‘research design’[All Fields] OR ‘test’[All Fields])) OR (‘Quantiferon’[All Fields] AND (‘gold’[MeSH Terms] OR ‘gold’[All Fields])) OR (‘interferon gamma release tests’[MeSH Terms] OR (‘interferon gamma’[All Fields] AND ‘release’[All Fields] AND ‘tests’[All Fields]) OR ‘interferon gamma release tests’[All Fields] OR (‘interferon’[All Fields] AND ‘gamma’[All Fields] AND ‘release’[All Fields] AND ‘test’[All Fields]) OR ‘interferon gamma release test’[All Fields]) OR ‘QFT’[All Fields] OR (‘enzyme linked immunospot assay’[MeSH Terms] OR (‘enzyme linked’[All Fields] AND ‘immunospot’[All Fields] AND ‘assay’[All Fields]) OR ‘enzyme linked immunospot assay’[All Fields] OR (‘enzyme’[All Fields] AND ‘linked’[All Fields] AND ‘immunospot’[All Fields] AND ‘assay’[All Fields]) OR ‘enzyme linked immunospot assay’[All Fields])) AND (‘india’[MeSH Terms] OR ‘india’[All Fields] OR ‘india s’[All Fields] OR ‘indias’[All Fields]).

Supplementary File S2

  • Q1. Were the criteria for inclusion in the sample clearly defined?

  • Q2. Were the study subjects and the setting described in detail?

  • Q3. Was the exposure measured in a valid and reliable way?

  • Q4. Were objective, standard criteria used for measurement of the condition?

  • Q5. Were the confounding factors identified?

  • Q6. Were strategies to deal with confounding factors stated?

  • Q7. Were the outcomes measured in a valid and reliable way?

  • Q8. Was appropriate statistical analysis used?

Footnotes

Conflicts of Interest: Authors (MP, HS, SC and KR) are affiliated to the World Health Organization (WHO). The views expressed in this article are their own and not an official position of their respective institutions.

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Supplementary Figure

Doi plot analysis and Luis Furuya-Kanamori (LFK) index for publication bias.

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