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Abstract
Objectives We verified subnational (state/union territory (UT)/district) claims of achievements in reducing tuberculosis (TB) incidence in 2020 compared with 2015, in India.
Design A community-based survey, analysis of programme data and anti-TB drug sales and utilisation data.
Setting National TB Elimination Program and private TB treatment settings in 73 districts that had filed a claim to the Central TB Division of India for progress towards TB-free status.
Participants Each district was divided into survey units (SU) and one village/ward was randomly selected from each SU. All household members in the selected village were interviewed. Sputum from participants with a history of anti-TB therapy (ATT), those currently experiencing chest symptoms or on ATT were tested using Xpert/Rif/TrueNat. The survey continued until 30 Mycobacterium tuberculosis cases were identified in a district.
Outcome measures We calculated a direct estimate of TB incidence based on incident cases identified in the survey. We calculated an under-reporting factor by matching these cases within the TB notification system. The TB notification adjusted for this factor was the estimate by the indirect method. We also calculated TB incidence from drug sale data in the private sector and drug utilisation data in the public sector. We compared the three estimates of TB incidence in 2020 with TB incidence in 2015.
Results The estimated direct incidence ranged from 19 (Purba Medinipur, West Bengal) to 1457 (Jaintia Hills, Meghalaya) per 100 000 population. Indirect estimates of incidence ranged between 19 (Diu, Dadra and Nagar Haveli) and 788 (Dumka, Jharkhand) per 100 000 population. The incidence using drug sale data ranged from 19 per 100 000 population in Diu, Dadra and Nagar Haveli to 651 per 100 000 population in Centenary, Maharashtra.
Conclusion TB incidence in 1 state, 2 UTs and 35 districts had declined by at least 20% since 2015. Two districts in India were declared TB free in 2020.
- Public health
- Tuberculosis
- Epidemiology
Data availability statement
Data are available upon reasonable request.
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Strengths and limitations of this study
This was the first and largest exercise of its kind to verify subnational progress towards TB-free status in a country.
Incidence of TB was calculated by three methods—direct method from a community-based survey, indirect method by correcting the notification rate for under-reporting of cases in the notification system, and using drug sales and utilisation data
Global Positioning System coordinates captured at every surveyed household, in-the-field supervision, real-time data monitoring through state and national dashboards, random review of audio and written transcripts of nominal group technique and key informant interviews, ensured quality of the data.
Though we tested only one spot sputum sample and did not perform chest radiography, we are less likely to have missed patients with TB since we tested all participants currently on anti-TB therapy, with a history of TB or currently experiencing any TB symptoms.
Background
The Government of India (GoI) has set an ambitious goal for ending tuberculosis (TB) by reducing the incidence of new TB cases by 80% by 2025 compared with 2015.1 India has a high TB burden (2.69 million cases in 2019), with a notification rate of approximately 159 cases/100 000 population.2 There is a wide variation in TB burden across the country. The efforts toward ending TB also vary across states/union territories (UTs) and districts of India. It is, therefore, crucial to monitor the progress towards the elimination goal at the subnational level.
The Central TB Division (CTD) of the Ministry of Health and Family Welfare, GoI decided to incentivise states/UTs/districts for their progress towards TB-free status.3 4 Under this initiative, the CTD sought claims from states/UTs/districts for achievements in reducing TB incidence in 2020 compared with 2015. These achievements were considered under four categories: 20%–39% (bronze), 40%–59% (silver), 60%–79% (gold) and ≥80% (TB free).
The claims received by the CTD were to be independently verified by an external agency. A national task force comprising experts in the field of TB epidemiology drafted methodology for this verification. The objective of the verification process was to estimate the incidence of TB in 2020, calculate the decline in the incidence of TB between 2015 and 2020 and verify the claim made by the respective state/UT/district for progress towards TB-free status. We describe the methodology of the verification process and the challenges faced during its implementation.
Methods
The verification process included a community-based survey and review of programme data and anti-TB drug utilisation data in each district. Incidence of TB for each district was estimated by three methods: (1) through a community-based survey (direct method), (2) by correcting the notification rate for under-reporting of cases in the notification system (indirect method) and (3) using drug sales and utilisation data.
Study settings
The District Tuberculosis Centre functions as the nodal point for all TB control activities under the National Tuberculosis Elimination Program (NTEP) in the district through a tuberculosis unit (TU) at the subdistrict level. The first points of contact with the community where TB diagnosis and treatment initiation happens are peripheral health institutions manned by at least one medical officer and Health and wellness centres equipped to provide comprehensive Primary healthcare. TB is a notifiable disease in India since 2012. TB diagnosis and treatment are offered free of cost under the NTEP. Nikshay is the standard notification system that also captures patients diagnosed in the private sector.
Community-based household surveys
Sampling procedure
Sixty-seven districts, one state and two UTs had filed claims under different categories of achievement. For verifying the claim from the state, we selected four districts using probability proportionate to size (PPS) sampling. The probability of selection of the district was proportional to the size of the population of the district. The districts with a higher population had a higher probability of selection into the sample. Thus, we verified the claims in a total of 73 districts. We divided each district into survey units (SU) based on the number of TUs in it to represent the entire district in the household survey. We divided the districts with <5 TUs into 5 SU, districts with 6–10 TUs into 10 SU and districts with >10 TUs into 15 SU. We selected one village/ward from each SU by PPS sampling (probability of selection of cluster proportionate to its population size) from the 2011 census list of villages and wards.
Sample size
We used the inverse sampling method to detect a predetermined number of bacteriologically positive TB patients in each district. Inverse sampling is a sampling technique to estimate a proportion (P) for rare events.5 In this method, the survey is continued until a predetermined number of rare events (m) are detected in the surveyed population. The required sample size (n) is not known in advance, contrary to the conventional cluster sampling. We decided to detect 30 bacteriologically confirmed TB cases in a district, assuming an expected proportion of TB of 274 cases/100 000 population and a coefficient of variation of 18.5.
Survey procedure
In each selected cluster, the survey teams started from a randomly selected household and moved sequentially. Using an android application developed by the WHO India, the trained survey teams interviewed participants of any gender, and all ages, residing in the selected village/ward for at least 1 year. Those who reported any symptoms (persistent cough for ≥2 weeks, fever for ≥2 weeks, significant weight loss, presence of blood in sputum any time during last 6 months, chest pain in last 1 month), or were currently on anti-TB treatment (ATT) or had a history of ATT, were eligible for sputum collection. The teams obtained written informed consent from those eligible and collected one spot sputum sample, which was transported according to NTEP Guidelines. The nearest NTEP testing centre performed a rapid molecular test (CBNAAT or TrueNat) for Mycobacterium tuberculosis (MTB).6 7 The teams uploaded the results to the web page of the application within 24–48 hours of collection.
The survey continued until 30 cases of TB were detected from each district. If the desired number of bacteriologically positive TB patients were not achieved within the selected cluster, we selected the next geographically adjacent cluster and repeated the process. The survey was stopped once the target of 30 TB cases were detected. We considered additional stop rules for districts that could not detect 30 TB cases—either covering 10 000 households or 5% of the total population, whichever they met first.
Data analysis
We used the survey data to calculate the incidence of TB in 2020 by two methods—direct and indirect. The direct method used incident MTB cases detected during the survey (defined as those with a history of ATT between April 2020 and March 2021, those currently on ATT on the date of survey with a date of diagnosis between April 2020 and March 2021 and those newly testing positive during the survey) as the numerator and total population surveyed as the denominator. In the indirect method, we matched the incident cases detected among the surveyed population with those notified in Nikshay database. We calculated an under-reporting factor for each district using the number of TB cases that could not be matched with Nikshay database. We adjusted the Nikshay-based TB notification rate for 2020 using this factor.
Secondary data verification
We verified the programmatic data and TB drug utilisation data from the public and private sectors using rifampicin as the indicator drug.
TB drug utilisation data
We obtained data about rifampicin consumption in the public sector for 2015–2020 from NTEP drug consumption registers. We collected sales data of rifampicin-containing drugs in the private sector from Clearing and Forwarding (C&F) agencies, drug manufacturers, distributors, chemists, drug commissioners, drug inspectors and private practitioners. All formulations, paediatric and adult, fixed-dose combinations, and separate tablets were included. Besides the incomplete nature of recording and maintaining data in the private sector, certain issues challenged the validity of private sector drug sale data. In some districts where the private sector was non-existent, people were purchasing drugs from nearby districts. Some patients notified from the private sector were being treated under the public sector. Also, some rifampicin-containing drugs were used for conditions other than TB, and the number of drug formulations were many; each being prescribed in a multitude of dosing regimens. We obtained this information through qualitative research techniques. Trained members conducted 2–3 nominal group technique (NGT) sessions with chemists, private practitioners and drug manufacturers and 5 key informal interviews (KII) with state drug controller, drug inspectors, and C&F agency in the district. The data were collected in paper forms and uploaded to the web portal developed by Indian Council of Medical Research-National Institute of Epidemiology (ICMR-NIE).
Data quality assurance
We formed verification teams at the central, state and district levels and trained them to standardise data collection and analysis. The district verification teams trained and supervised the district survey teams. Central and state teams supervised the secondary data verification performed by district verification teams.
Data analysis: For a given drug (i), based on the quantity of drug consumed in the public sector, we calculated patient months treated for TB, using the following formula:
where Ti is the number of treatment months represented by each unit of that drug i.
We calculated Ti of a given formulation, the strength of rifampicin in it, and its dosing regimen, based on the inputs from qualitative interviews.
We adjusted private sector drug sale data for rifampicin usage for non-TB conditions and coverage of drug sales data.
Ti—the number of treatment months represented by each unit of sale of drug i.
Xi—the proportion of sale of drug i intended for tuberculosis.
Ci—the proportion of sales of drug i that has been reported.
We calculated the drug data-based TB incidence by dividing the total patient months (sum of patient months in the public and private sector) by the average duration of treatment for TB and the district’s population.
Comparison of TB incidence between 2015 and 2020
We estimated the incidence of TB for 2020 for each district based on direct, indirect and drug data-based methods.8 9 We compared these estimates with the baseline TB incidence for 2015. This baseline incidence was calculated based on TB notification under the programme, presumptive TB examination rate, presence of private sector facilities in the district, drug sales data (for state-level incidence) and local intelligence. For district-level TB incidence estimations, state-level estimations were divided into districts with district-level weights for public sector notification rates, the number of private health facilities, private sector notification rates and presumptive examination per bacteriologically positive case. The criteria for recommending a district for subnational claims for progress towards TB-free status are summarised in table 1.
Criteria for the recommendation of recognition for achievements in progress towards TB-free status in India, 2020
Patient and public involvement
Patients and members of the community were not part of the conception and design of the study. Volunteers from the community were involved in community mobilisation/sensitisation and survey data collection.
Results
TB incidence: direct method
Survey teams (n=645) visited 355 171 households across 73 districts and interviewed 1 184 106 (86.4%) individuals who consented to participate in the survey. Of the total participants, 52% (n=617 500) were females, 28% (n=333 569) were aged between 15 and 30 years and 79% (n=935 256) were Hindu by religion. Overall, 24 410 (2.06%) reported one or more symptoms, 6371 (0.5%) had a history of ATT and 1041 (0.1%) were currently on ATT. Of the total 24 422 samples tested, survey detected 998 MTB cases.
Among the 73 districts surveyed, the criteria of stop rule was achieved in 25 districts (30 active cases with TB detected in 11 districts, 10 000 households surveyed in 6 districts, 5% of the district population surveyed in 8 districts). The remaining 48 districts did not meet any of the three stop rules. The estimated incidence of TB using the direct method ranged from 19 (Purba Medinipur, West Bengal) to 1457 (Jaintia hills, Meghalaya) per 100 000 in different Indian districts (table 2, online supplemental table 1, figure 1).
Supplemental material
Decline in tuberculosis (TB) calculated using the direct, indirect and using drug data based methodsin 2020, compared to baseline TB incidence in 2015
Comparison of baseline tuberculosis (TB) incidence with direct, indirect and drug data based TB incidence for subnational verification of TB claims in India, 2020.
TB incidence: indirect method
Of the 1507 incident cases (currently on ATT and history of TB between April 2020 and March 2021), excluding those newly diagnosed during the survey, 358 (23.8%) cases could not be verified with Nikshay. In 25 of the 73 districts, all incident TB cases could be matched within Nikshay indicating that there was no under-reporting. The under-reporting was <20% in 18 districts, between 21% and 40% in 18 districts, 41% and 60% in 8 and 61% and 80% in 4 districts (table 3). Adjusting the TB notification rate in 2020 from Nikshay for the level of under-reporting observed, the estimated incidence of TB ranged between 19 (Diu, Dadra and Nagar Haveli) and 788 (Dumka, Jharkhand) per 100 000 population (table 2, online supplemental table 1, figure 1).
Under reporting of active tuberculosis (TB) cases in Nikshay portal calculated as part of verification of subnational claims for progress towards TB-free status in India, 2020
TB incidence: drug consumption/sale data-based method
Overall, 1329 individuals including 475 (35.7%) private providers, 679 (51.1%) chemists, and 175 (13.2%) drug inspectors, assistant drug controller, president of chemist association or Stockists participated in NGT/KII. In the private sector, the median (IQR) duration of treatment given to patients treated in the private sector in different districts was 6 (6, 6) months, a median (IQR) 100% (99%–100%) of the drug sale was intended for TB and the median (IQR) coverage of sales data was 95% (80%, 100%). The incidence of TB using drug sale data ranged from 19 per 100 000 population in Diu, Dadra and Nagar Haveli to 651 per 100 000 population in Centenary, Maharashtra (table 2, online supplemental table 1, figure 1).
The agreement between the three estimates is presented in figure 2. Based on the verification criteria enlisted in table 1, we recommended certifying Lakshadweep (UT) and Budgam district, Jammu & Kashmir as TB free (≥80% reduction in TB incidence). We recommended Diu district for award under the gold category (60%–79% reduction in TB incidence) and four districts under silver (40%–59% reduction in TB incidence). Twenty-eight districts, one UT and one state were recommended for the bronze category (20%–39% reduction in TB incidence).
Estimates of tuberculosis (TB) incidence per 100 000 population by three methods, India, 2020.
Discussion
We conducted a nationwide exercise in India in 2020 to verify the subnational claims submitted by different states/districts/UTs for various levels of progress towards TB-free status. In 2 of the 73 districts, the incidence was less than the national target for 2025 of 44 per 100 000 population. Compared with the incidence in 2015, the incidence in 2020 declined by 60%–80% in 1 district, 40%–60% in 4 districts and 20%–40% in 28 districts, 1 UT and 1 state.
Strengths
To the best of our knowledge, this is the first and largest exercise of its kind to verify subnational progress towards TB-free status in a country. The use of anti-TB drug sales and consumption data available in the programme or primary data collected from multiple sources to estimate incidence was used on such a large scale for the first time, including both public and private sectors.8 10 Three different methods of estimation of TB incidence were employed so that the strengths of one method shall overcome the limitations of another. We used the under-reporting factor calculated from the survey to adjust the incidence derived from the Nikshay notification portal to obtain a more realistic estimate. Similarly, the drug data from the private sector was adjusted using correction factors obtained from qualitative research techniques. Quality assurance of this process was multipronged and at multiple levels. We captured Global Positioning System coordinates at every household included in the survey process. Besides, we ensured in-the-field supervision, real-time monitoring of the data through state and national dashboards, review of audio and written transcripts of NGT and KIIs, and the quality of the data at every level. The web application developed by the WHO and ICMR-NIE as a one-time effort is now available for efficient and quality implementation of future rounds of this exercise.
Limitations
We tested eligible participants identified from the survey using only one spot sputum sample. Though we screened for ‘any TB symptom’, and tested all persons reporting symptoms, currently on ATT or reporting history of consumption of ATT, the survey might have missed patients with new onset extrapulmonary TB. We did not use chest radiography (CXR) to screen the survey participants. Though CXR has higher accuracy than symptom screening alone, screening for ‘any TB symptom’ had a high sensitivity of 90% on par with the 94% sensitivity of CXR.11 Further, we included participants currently on ATT and those with a history of TB who are likely to have been the people who would have been captured by CXR. Chadha et al have reported a significant additional yield (3.3%–21.3%) of cases when screening for a history of ATT.12 Hence, we might not have missed a substantial number of eligible participants to have influenced our incidence estimates.
Though we used the inverse sampling technique, since most districts did not attain the target number of cases, we proceeded with the calculation variance and 95% CI consistent with random sampling. Thus, the survey could have ended up studying a larger sample than would have been required had an appropriate sample size calculation been performed based on the incidence of TB in that region.13 In the light of this experience, for the future rounds of this exercise, we recommend recalculation of the target number of cases to be achieved by the districts based on their baseline incidence and population.
We could not adjust the private sector drug data-based incidence calculation for the proportion of private sector patients taking treatment from the public sector in some districts. Thus, we could have overestimated the incidence based on drug data calculation in those districts. But we do not anticipate this might have influenced our recommendations as the drug data-based incidence estimates were the lowest of the three estimates in most districts. Though drug sale and utilisation data-based incidence estimation posed challenges, we have established a baseline estimate through this exercise against which estimates from future rounds can be compared.
Anti-TB drug sale data for incidence estimation
A comprehensive estimation of the burden of TB, especially in a high Tb burden country, should account for all patients with TB irrespective of their place of diagnosis and compliance with treatment. Though an efficient national TB elimination programme is in place in India offering free diagnosis and treatment for TB, a sizeable proportion of TB patients are diagnosed and treated in the private sector.14 With the availability of many anti-TB drug formulations over the counter, compounded by the varying prescription practices for TB treatment and with an average duration of treatment of 2–6 months,8 15 counting the number of patients treated for TB in the private sector is challenging.16 17 This complex heterogeneity in the private sector TB diagnosis and care makes the usefulness of drug sale data in burden estimation and monitoring apparent. The data from the private TB drug market also have some limitations, such as its lack of organised and complete recording and reporting.15 18
It was challenging to enlist all drugs available in the market and calculate the number of treatment months represented by each unit of sale of drug (Ti) for each of them, given the various formulations and dosing regimens. Through multiple rounds of training, hands-on sessions, and field visits to interview chemists, programme staff and private practitioners, we standardised the list of the drugs and their Ti. We recommend using this list with modifications in the future rounds without repeating the exercise in its entirety. Our study captures the incomplete reporting of TB drug sales data in the private sector, and we have corrected the private sector drug sale volume for the issues mentioned above.
Individual district divisions under metropolitan cities had applied separately for TB-free status. It was challenging to obtain drug sales data at the disaggregated level as the data are usually available at the level of metro city only.
Agreement between incidence estimates
In 45 districts, all three estimates of TB incidence suggested a decline in the incidence of TB. An efficient Tb notification system with minimal or no under-reporting and diligent maintenance of drug sales and utilisation data ensured that all three estimates agreed. In 17 districts, the indirect and drug data-based estimates pointed toward a decline, while the direct estimate pointed an increase in TB incidence since 2015. National TB Elimination Programme misses 10% of estimated drug-sensitive TB and 50% of estimated drug-resistant TB cases. (India TB report 2020). Even after the adjustment for the under-reporting, indirect estimates were lower than the direct estimate in these districts, as the under-reporting factor might not have captured the full extent of under-reporting.19 This could have been due to the sampling error induced variations in the direct estimate and the under-reporting factor used to calculate the indirect estimate.
In 11 districts where 2 estimates pointed towards an increase in incidence, the only estimate pointing towards a decline in incidence was the drug data-based estimate. It is likely that the drug data from these districts could be incomplete, leading to a very low estimate of the incidence by this method. This might be because, in some districts, the private sector does not exist and people migrate to nearby districts to buy ATT. Though we tried to capture this proportion through NGT/KIIs, it is still likely to be an underestimate. Rifampicin is a Schedule H1 drug that cannot be sold in retail without a prescription from a registered medical practitioner and the details of the supply shall be recorded in a separate register, maintained for 3 years, and be available for inspection. Despite this fact, chemists’ reporting of rifampicin sales is incomplete, leading to missing drug sales data. In such cases, the drug data-based incidence might show a trend opposite the other two estimates. To avoid decisions based on an apparent steep decline in one incidence estimate, we had framed the verification criteria based on reduction suggested by at least two of the three estimates.
The possible impact of COVID-19
Given that we carried out the exercise during the pandemic, we would like to describe its possible impact on our estimates. The pandemic and the control measures instituted might have led to under notification of TB due to the constraints under which the general healthcare system of the country functioned during this period.19 In September 2020, the NTEP announced a Rapid Response Plan to mitigate the impact of the COVID-19 pandemic on TB epidemic and NTEP activities, which called for bidirectional TB–COVID screening.20 It is likely that TB notification might have increased after implementation of this plan.
Implications for policy and future research
Based on the experience of subnational verification of progress towards TB-free status in India in 2020, we make the following recommendations. First, yearly monitoring of progress towards TB-free status is to be carried out at subnational level by setting up a standardised mechanism for periodic aggregation and reporting of data at the TU and district level. Second, it is essential to ensure universal and complete reporting of Schedule H drug sale data providing details of the prescriber, patient, indication, name of drug and quantity sold. Third, there is a need to refine the methodology of drug data-based incidence estimation and ensure better quality of drug data required for incidence estimation. This can be done by sensitisation of all the stakeholders including the drug controllers, drug inspectors, C&F agencies and private practitioners regarding recording and reporting of drug sale and utilisation data.
Conclusion
Subnational verification of claims for progress towards TB-free status in India showed that the incidence of TB in 1 state, 2 UTs and 35 districts has declined by atleast 20% since 2015. Two districts in India were TB free in 2020.
Data availability statement
Data are available upon reasonable request.
Ethics statements
Patient consent for publication
Ethics approval
This study involves human participants and was approved by Institutional Human Ethics Committee, National Institute of Epidemiology, Chennai, ID: NIEIHEC/202012-12, dated on 17 December 2020. We obtained informed written consent from all participants in the survey. The secondary data analysed used no identifiers, and we present only aggregate measures. We preserved the confidentiality of the data by allowing access to data only for verification team members. We linked persons newly diagnosed with TB during the survey to the NTEP and their treatment was initiated without any delay. Participants gave informed consent to participate in the study before taking part.
Acknowledgments
We are grateful for the cooperation and contribution of the volunteers who collected data for the survey, the NTEP officials at the state and district levels. We also thank the members of the national taskforce for their constructive input to the methods of this verification exercise.
Supplementary materials
Supplementary Data
This web only file has been produced by the BMJ Publishing Group from an electronic file supplied by the author(s) and has not been edited for content.
Footnotes
KJ and JT contributed equally.
Collaborators Subnational TB claims verification group: Jayakrishna Kurada (Wadhwani Institute of Artificial Intelligence, India); Mayank Sharma (PATH India, India); Shamim Mannan (William J Clinton Foundation, India); Debajyoti Mohapatra (REACH, India); Yogesh Patel (World Health Partners India); Sapna Thekkepat (PATH India); Karthikeyan Kumaraswamy (Karnataka Health Promotion Trust, India); Mallika Tharakan (Karnataka Health Promotion Trust, India); Shramana Majumdar (Karnataka Health Promotion Trust, India); Vrinda Manocha (Karnataka Health Promotion Trust, India); Sukriti Chauhan (Karnataka Health Promotion Trust, India); Swati Mahajan (Johns Hopkins Program for International Education in Gynecology and Obstetrics, India); Shekhar Waikar (Johns Hopkins Program for International Education in Gynecology and Obstetrics, India); Puja Ambule (Johns Hopkins Program for International Education in Gynecology and Obstetrics, India); Krithika Murali (Johns Hopkins Program for International Education in Gynecology and Obstetrics, India); Devi Madhavi Bhimarasetty (Andhra Medical College, Visakhapatnam, Andhra Pradesh, India); Tulika Goswami (Assam Medical College, Assam, India); Darshan Mahyavanshi (NAMO Medical Education and Research Institute, Silvaasa, Dadra Nagar Haveli, India); Krutarth Bhrahmbhatt (GMERS Medical College Junagadh, India); Bhautik Modi (Dr N D Desai Faculty of Medical Science & Research, Nadiad, Gujarat, India); Anmol Gupta (Indira Gandhi Medical College, Shimla, Himachal Pradesh, India); Sunil Raina (Dr. R.P. Govt, Medical College, Tanda, Himachal Pradesh, India); Rajiv Kumar Gupta (GMC Jammu, India); Ratnesh Sinha (Manipal Tata Medical College, Jamshedpur, Jharkhand, India); Sairu Philip (Government TD Medical College, Alappuzha, Kerala, India); Aswathy Sreedevi (Amrita Institute of Medical Sciences, Kochi, India); Manju Toppo (Gandhi Medical College, Bhopal, MP, India); Lalit Sankhe (Grant Medical College, Mumbai, Maharastra India); Gajen Medhi (NEIGHRIMS Shillong, Meghalaya, India); Swati M Patki (Zoram Medical College, Mizoram, India); Bishnu Ram Das (Jorhat Medical College Assam, India); Lisa Sarangi (Hi Tech Medical College, Bhubaneswar, Orissa, India); Ganapathy Kalaiselvan (Sri Manakular Vinayagar Medical College & Hospital, Puducherry, India); Sarit Sharma (Dayanand Medical College, Ludhiana, Punjab, India); Afsal Hakim (Dr SN Medical College, Jodhpur, Rajasthan, India); Suresh Balan (Kanyakumari Govt Medical College, Achirepallam, Tamil Nadu, India); Baer Philip Ravikumar (Mamata Medical College, Khammam, Telangana, India); Subrata Baidya (Agartala Government Medical College, Tripura, India); Abhik Sinha (ICMR- National Institute of Cholera and Enteric Diseases, Kolkata, WB, India); Boddepalli Nagendra Naidu (Government Medical College (RIMS), Andhra Pradesh, India); Bikash Chandra Nayak (Maharaja Institute of Medical Sciences, Andhra Pradesh, India); Anoop Dev (Tomo Riba Institute of Health & Medical Sciences, Naharlagun, India); Pallavi Boro (Tomo Riba Institute of Health & Medical Sciences, Naharlagun, India); Debarshi Paul (Tomo Riba Institute of Health & Medical Sciences, Naharlagun, India); Amrita Sarkar (Tomo Riba Institute of Health & Medical Sciences, Naharlagun, India); Bhavesh Bariya (NAMO Medical Education and Research Institute, Silvaasa, India); Paras Mehta (NAMO Medical Education and Research Institute, Silvaasa, India); Jatin Chhaya (GMERS Medical College, Junagadh, Gujarat, India); Dixit Chauhan (Government Medical College, Bhavnagar, Gujarat, India); Mithun Sanghavi (M.P. Shah Medical College, Jamnagar, Gujarat, India); Nilesh Thakor (GMERS Medical College, Vadnagar, Gujarat, India); Rohit Ram (M.P. Shah Medical College, Jamnagar, Gujarat, India); Anupam Banerjee (PDU Medical College, Rajkot, Gujarat, India); Hinal Baria (GMERS Medical College, Valsad. Valsad, Gujarat, India); Abhilash Sood (Dr. Radhakrishnan Government Medical College Hamirpur, Himachal Pradesh, India); Anupam Parashar (Indira Gandhi Medical College Shimla, India); Devender Kumar (Dr. Radhakrishnan Government Medical College Hamirpur, Himachal Pradesh, India); Yangchen Dolma (Government Medical College, Kathua, India); Rakesh Bahl (Government Medical College, Jammu, India); Surinder Singh (Dumka Medical College, Jharkhand, India); Dilip Kumar (Palamu Medical College, Jharkhand, India); Mohammad Salim Khan (Government Medical College, Srinagar, India); A K Jayasree (Government Medical College Kannur); Zinia Nujum (Government Medical College, Kollam); Sheela P Haveri (MES Medical College, Perinthalmanna); Dinesh Bhatnagar (R.D. Gardi Medical College Ujjain, India); Subrata Kumar Palo (ICMR-Regional Medical Research Centre, Bhubaneswar); Priyesh Marskole (Government Medical College Khandwa, Madhya Pradesh, India); Prashant Verma (Netaji Subhash Chandra Bose Medical College Jabalpur, India); Dinesh Kumar Pal (Gandhi Medical College, Bhopal, India);cNeelam Toppo (Netaji Subhash Chandra Bose Medical College Jabalpur, India);cSanjay R Quraishi (Bharati Vidyapeeth Deemed to be University Medical College and Hospital, Sangli, India);cSunil Panigrahi (Dr. Vasantrao Pawar Medical College, Hospital & Research Center, Nashik, India);cRambadan Chauhan (BKL Walawalkar Rural Medical College, Sawarde, Ratnagiri, India); Jayashree Gothankar (Bharati Vidyapeeth Deemed to be University Medical College and Hospital, Pune, India); Swapnali S Kadam (Rajiv Gandhi Medical College, Thane, India); Dasi Rao (Lokmanya Tilak Medical College, Mumbai, India); Vijay Singh (Lokmanya Tilak Municipal General Hospital And Medical College, Mumbai, India); Sudhir Wanje (National Institute of Public Health Training and Research, Mumbai, India); Pallavi Uplap (Grant Medical College, Mumbai, India); Ningombam Joenna Devi (North Eastern Indira Gandhi Regional Institute of Health & Medical Sciences (NEIGRIHMS), Shillong Meghalaya, India); Rikrak Ch Marak (North Eastern Indira Gandhi Regional Institute of Health & Medical Sciences (NEIGRIHMS), Shillong Meghalaya, India); Markordor Lyngdoh (North Eastern Indira Gandhi Regional Institute of Health & Medical Sciences (NEIGRIHMS), Shillong Meghalaya, India); Manoj B Patki (Zoram Medical College, Mizoram, India); Satyajit Sundar Ray (Government Medical College, Balangir, India); Kavita Vasudevan (Indira Gandhi Medical College Hospital and Research Institute, Puducherry, India); Mahesh Satija (DMCH, Ludhiana, India); Jasleen Kaur (GMC Amritsar, India); Mahesh Choudhary (DMCH, Ludhiana, India); Mitin Parmar (Ananta institute of medical sciences, India); Venkatesh Govindasamy (Tiruvamnamalai MC, India); Karlapudi Nitesh Kumar (Mamata Medical College, Khammam, India); Goutham Thumari (Government medical college, Siddipet, India); Amar Tripura (Agartala Government Medical College, Tripura, India); Chanda Mog (Agartala Government Medical College, Tripura, India); Rituparna Das (Agartala Government Medical College, Tripura, India); Shampa Das (Agartala Government Medical College, Tripura, India); Ritesh Singh (AIIMS Kalyani, India); Chandra Shekar Maity (All Institute of Hygiene and Public Health, Kolkata, India); Mamta Gehlawat (Government medical college, Siddipet, India).
Contributors KJ conceptualised the study design, analysed data, prepared the first draft of the manuscript, provided critical inputs to revise the manuscript. MM and JVWT conceptualised the study design, analysed data and provided critical inputs to revise the manuscript. VS, KJ, JVWT and RSa analysed data. AkS, MV, DSR, RaS, SaM, RE, MN and BJ collected data, analysed data and provided critical inputs to revise the manuscript. KR, BM, AB, AJP, BV and RaR conceptualised the study design, analysed data and provided critical inputs to revise the manuscript. AM, AlS, AmS, AnS, AvC, AcS, AnC, DD, HS, KS, KK, MP, ND, PD, QTA, RR, RP, RoS, RB, SC, SB, SA, SBN, SC, SK, SM, SC, VS, VR and KS collected data, analysed data and provided critical inputs to revise the manuscript. The members of the “Subnational TB claims verification group” contributed to the data collection, data analysis. MM is the guarantor of the study. All authors read and approved the final manuscript.
Funding Central TB Division, Ministry of Health & Family Welfare, India (no award/grant number).
Competing interests None declared.
Patient and public involvement Patients and/or the public were involved in the design, or conduct, or reporting, or dissemination plans of this research. Refer to the Methods section for further details.
Provenance and peer review Not commissioned; externally peer reviewed.
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