Post-TPT Risk of Tuberculosis Among Household Contacts: A Multistate Observational Cohort Study from India

Post-TPT Risk of Tuberculosis Among Household Contacts: A Multistate Observational Cohort Study from India | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Post-TPT Risk of Tuberculosis Among Household Contacts: A Multistate Observational Cohort Study from India Ridhima Sodhi, Shamim Mannan, Pranati Das, Sruthi Susan Abraham, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8176310/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background : India carries the highest TB burden globally, with 31% of the population estimated to be infected with Mycobacterium tuberculosis, albeit in a dormant form . Household contacts (HHCs) represent a particularly vulnerable group due to intense exposure, with TB prevalence several-fold higher than the general population. To mitigate progression from TB infection to active TB disease, the government expanded TB preventive therapy eligibility to all HHCs in 2021. While evidence exists on TB prevalence among HHCs, limited evidence exists about post-TPT outcomes or risk factors for disease progression. This study evaluates symptoms, care-seeking behaviours, and TB occurrence among HHCs who completed TPT under programmatic conditions across nine Indian states. Methods : A total of 4,012 household contacts of pulmonary drug-sensitive TB patients who successfully completed TB preventive therapy between October 2022 and March 2023, were sampled from nine Indian states, using a stratified sampling approach. Of these, 78% received 6H and 22% received 3HP. Contacts were followed twice post-TPT completion, within 12 months and again within 18 months, to capture symptomatic incidence, care-seeking behaviour, diagnostic pathways, and TB outcomes. Descriptive statistics, logistic regression, and survival analysis were used to identify predictors of symptom development, care-seeking, and TB diagnosis, and to evaluate symptom-based algorithms for triage in resource-limited settings. Results : Marked state-level variation was observed in CXR screening uptake, TPT initiation timeliness, and access to diagnostic services, influencing symptomatic prevalence and TB detection rates. Symptom burden was the strongest predictor of TB diagnosis, with specific combinations, particularly recurrent cough with weight loss, showing high diagnostic accuracy. All TB diagnoses (n=22) occurred among symptomatic individuals, and majority were detected within six months of TPT completion. However, only 17% of symptomatic contacts had gone for a medical consultation by the time of follow ups, and only 18% were assessed with NAAT. Suggestive pre-TPT CXR findings and delays in TPT initiation were associated with higher post-TPT symptom incidence. Individuals on 3HP reported fewer post-TPT symptoms than those on 6H. Conclusion: Health-system disparities across states shape TPT outcomes and the timely identification of post-TPT TB. Strengthening routine CXR screening, reducing delays in TPT initiation, improving adherence support, especially for longer 6H regimens, and implementing earlier, risk-stratified follow-up could enhance early TB detection among high-risk HHCs. Integrating symptom-based triage tools and ensuring equitable access to diagnostic evaluation are critical for optimizing the preventive impact of TPT in resource-constrained settings. Figures Figure 1 Figure 2 Figure 3 Figure 4 Background TB infection (TBI), or latent TB (LTBI) is the existence of Mycobacterium tuberculosis bacteria in an individual, albeit in a dormant state. In India, the crude TBI prevalence among adults (>=15 years) is 31.3%, and individuals in this state neither exhibit symptoms nor transmit the infection [1]. Progression from TBI to active TB disease varies, depending on factors such as age, comorbidities that compromise immunity, nutritional status, and environmental factors due to housing and occupation [2]. The primary public health intervention to reduce this risk is TB preventive therapy (TPT) [2, 3]. While national guidelines in India have historically guided people living with HIV (PLHIV) and under-6 child contacts for TPT [4], programmatic guidance was expanded in July 2021 to include all household contacts (HHCs), including adults [4]. HHCs represent a particularly high-risk group; a 2012 meta-analysis found that HHCs in low- and middle-income countries had a TB prevalence above 3% [5]. A recent (2024) study from Mumbai in India found 4% of HHCs (>=5 years) to have active TB [6], which is more than 25 times the adult TB (>=15 years) prevalence in the underlying state of Maharashtra (0.16%), and more than 13 times the general adult TB prevalence of 0.3% in India [1]. TPT uptake among adults, however, however, continues to be low [7, 8], with the risk further compounded by the fact that we miss at least two TB positive individuals for every one notified (TB prevalence to notification ratio of 2.84) [1]. Furthermore, even as targeted interventions such as Joint Effort for Elimination of Tuberculosis (JEET) 2.0 [9] have been employed to increase TPT among adult HHCs, risk for breakdown to TB persists [4]. A two-year follow up schedule following the completion of TPT, is hence recommended by the national TB elimination program, at the 6 th ,12 th ,18 th and 24 th month mark, to monitor for signs of TB disease [4]. While there exist studies on TB prevalence among HHCs, there is a lack of studies examining TB among those who have completed TPT. Our study attempts to bridge this gap, by analyzing follow-up data on individuals sampled from across nine states, who completed TPT under the JEET program. The analysis provides insights into risk factors associated with TB progression, the occurrence of TB among this cohort, and patterns of health-seeking behaviour shaped by symptomatic and contextual factors. Methods Study Setting The study was based within the programmatic operations of JEET 2.0, implemented between October 2021 and March 2024, in 207 districts, across 23 states and union territories of India. The program was funded by the Global Fund, and implemented by three agencies, namely, Union (7 states, 108 districts), FIND (5 states, 28 districts), and the William J. Clinton Foundation (WJCF) (11 states, 71 districts). This study was done within nine of the eleven WJCF managed states: Bihar, Delhi, Gujarat, Haryana, Kashmir, Rajasthan, Tamil Nadu, Uttarakhand, and Uttar Pradesh. JEET program activities can be segregated between those done prior to TPT initiation, and those done during TPT. Pre-TPT activities included contact tracing of household contacts (HHCs) of notified TB patients, followed by symptom screening (cough, fever, weight loss, night sweats) and chest X-ray evaluation. In select districts, tuberculin skin testing or interferon-gamma release assays were used to assess TB infection. During TPT, program staff provided counselling and logistical support to ensure drug availability and adherence. In addition to standard program activities, two post-TPT follow-ups were conducted among a sampled subset of HHCs to assess TB-related symptoms and understand TB risk after TPT completion. Study Design This was an observational, quantitative cohort study involving HHCs who had completed TPT. A representative sample was selected to assess TB-related symptoms at two time points approximately four months apart, with the first follow-up conducted 9 - 12 months after TPT completion. Questionnaire Design The follow-up assessments included questions on demographic characteristics (age and gender), medical history, lifestyle factors (tobacco and alcohol use), occupational exposures, and housing conditions. TB-related evaluation covered the WHO-recommended four-symptom (4S) screen [10], diagnostic tests undertaken since TPT completion, and any confirmed TB diagnoses. Data Source Three data sources were used for this study. The first dataset was obtained from the project management information system (MIS) and included information on index patients and household contacts, covering demographics (age, gender, diagnosing district), TPT screening details (screening date, symptom profile, TB history), and TPT regimen and outcomes. The second dataset comprised interview responses collected during the two follow-up visits conducted after TPT completion. Data from Nikshay, the national TB surveillance portal, were used to cross-verify programmatic information. Data Selection (Inclusion & Exclusion criteria) Eligible HHCs included those who had completed their TPT, particularly during the four-month period between October 2022, and January 2023. This was done in alignment with the first follow up period (Oct 2023-Nov 2023), ensuring that all those interviewed had completed their TPT 9-12 months prior. Exclusions included a) HHCs from Ladakh due to the smaller number of HHCs and extreme weather conditions from October to March, which would have impeded follow-up visits, b) HHCs from Kerala because of inability to reach HHCs as they were being managed by state officials, and c) HHCs who were on drug regimens with fewer than 50 people, which would have impacted our ability to confidently assess correlation with collated covariates. Figure 1 details the selection process for the final cohort of 78,647 individuals, accounting for inclusion and exclusion criteria. Eligible Population The final eligible population included 78,647 household contacts (HHCs) who completed TPT between 1 October 2022 and 31 January 2023, across nine JEET intervention states: Bihar, Delhi, Gujarat, Haryana, Kashmir, Rajasthan, Tamil Nadu, Uttarakhand, and Uttar Pradesh. Among them, 52% (40,893) were female, and 17 individuals identified as transgender. Detailed demographic characteristics are presented in Table S1. Sampling Method A stratified random sampling strategy was used across nine states, using TPT completions as the sampling frame. The study was powered to achieve 95% confidence with a 5% margin of error for multiple strata: (a) the overall cohort, (b) pre-screening type (IGRA, TST, or CXR only), (c) individual states, (d) TPT regimen (3HP and 6H), and (e) state-by-regimen combinations. Detailed sample size calculations and final achieved sample numbers are provided in Tables S2–S4. Required sample size across all powered strata was 3,803, and the final sampled cohort consisted of 4,190 individuals, meeting or exceeding the required thresholds for representativeness and precision. Survey Responses Of the 4,190 individuals listed post-sampling, responses were collected for 4,012 HHCs. Among them, 11 deaths were reported during the first follow-up by relatives or respondents, with 3 additional deaths recorded by the second follow-up. Outcomes of interest The primary objectives of this study are to a) investigate the rate of TB occurrence among HHCs following TPT completion, b) evaluating factors determinant of higher TB risk, and c) provide recommendations for TPT scale up. Analysis Our study employed a multifaceted analytical approach, integrating descriptive statistics, logistic regression, survival analysis, and pragmatic symptom-based algorithms, supported by visualizations. While descriptive analysis established a foundational understanding of the trends across different covariates collated in the study, logistic regression identified the most significant predictors of TB diagnosis, independently estimating their impact. Algorithmic models using symptom combinations were evaluated through accuracy, sensitivity, specificity, and precision. Survival analysis added a temporal dimension, estimating the probability of remaining TB-free over time and highlighting periods of elevated risk following TPT completion. Robustness Checks A series of robustness checks were conducted, including systematic inclusion/exclusion of different cohorts, and comparing descriptive results with different logistic model specifications, and are provided in the supplementary text. Software Analyses were conducted in R (2024.04.2) using dplyr, tidyr, ggplot2, and gtsummary for data processing and summaries; sandwich for robust standard errors; and survival, survminer, lmtest, and caret for survival and econometric analyses. Data Consolidation and Confidentiality Data were collected using secure digital tools (ODK/KOBO) and linked to JEET 2.0 records through unique patient and contact identifiers. All records were encrypted and anonymised using Universally Unique Identifiers (UUIDs), before being used for research. Results Demographic Summary Our cohort was nearly equally split by gender, with a median age of 23 years (Table 1). Approximately 5% of HHCs reported comorbidities, most commonly diabetes and hypertension. Prior to TPT initiation, contact screening involved symptom assessment and CXR evaluation: 3% reported symptoms and 84% underwent CXR screening. Among those CXR screened, 1.7% (n = 57) had findings suggestive of TB but were initiated on TPT following medical consultation. The median time from index patient diagnosis to TPT initiation was 59 days. TPT completion aligned with expected durations for each regimen: 12 weeks for 3HP (rifapentine + isoniazid) and six months for 6H (isoniazid). Lifestyle-related risks included tobacco use (12%) and alcohol consumption (2.5%). Additional risks assessed included being belonging to a tribal group (2.8%), migrant status (4.7%), and residence in urban slums (10%). A small share of HHCs reported working in construction (1.9%) or being a healthcare worker (0.4%). Housing conditions varied: 54% lived in pakka or concrete structures, 4.7% in kutcha housing, and 41% in mixed-type dwellings. Only 49% reported always using clean cooking fuel, while 41% used it intermittently and 10% relied exclusively on unclean fuel. A total of 14 HHCs were reported to have died at the time of the first or second follow up, and median age for this cohort was 59 (S5 Table). Regional variations are detailed in the supplementary text (S6 Table). Table 1 Summary Statistics (N = 4,012) Characteristic N = 4,012 1 Symptomatic + TB status died 14 (0.3%) non-symptomatic 3,658 (91%) symptomatic 318 (7.9%) symptomatic & tb positive 22 (0.5%) Gender female 2,057 (51%) male 1,954 (49%) transgender 1 (<0.1%) Age Median (Q1, Q3) 23 (11, 39) Mean 26 Age.Category 1. 15 2,630 (66%) State bihar 384 (9.6%) delhi 355 (8.8%) gujarat 770 (19%) haryana 515 (13%) jammu.&.kashmir 193 (4.8%) rajasthan 396 (9.9%) tamil.nadu 483 (12%) uttar.pradesh 445 (11%) uttarakhand 471 (12%) HHC is (Relationship with index TB patient) child 116 (2.9%) grandchild 104 (2.6%) grandparent 7 (0.2%) parent/parent-in-law 1,580 (39%) relative/others 674 (17%) sibling 445 (11%) son/daughter-in-law 36 (0.9%) spouse 514 (13%) unknown 536 (13%) Occupation agri & livestock 241 (6.0%) homemaker 1,072 (27%) labor 638 (16%) salaried 465 (12%) student 1,212 (30%) no information 384 (9.6%) Type House concrete 2,147 (54%) kaccha 228 (5.7%) mixed 1,630 (41%) unknown 7 (0.2%) Type Cooking Fuel both 1,639 (41%) clean 1,958 (49%) unclean 408 (10%) unknown 7 (0.2%) Pre-TPT Screening CxR Done for 3,387 (84%) CxR Test Results not suggestive 3,330 (98%) suggestive 57 (1.7%) Pre-TPT Asymptomatic 3878 (97%) (Pre-TPT) Sym Cough 94 (2.3%) (Pre-TPT) Sym Weight Loss 8 (0.2%) (Pre-TPT) Sym Night Sweat 6 (0.1%) (Pre-TPT) Sym Fever 50 (1.2%) TPT Details Drug Regimen 3HP (12-weeks-weekly rifapentine +isoniazid) 865 (22%) 6H (6-months-daily isoniazid) 3,147 (78%) Time to TPT initiation (from diag of index patient) Median (Q1, Q3) 59 (33, 104) Mean 77 TPT.completion.days - 3HP Median (Q1, Q3) 84 (84, 92) Mean 93 TPT.completion.days - 6H Median (Q1, Q3) 180 (179, 186) Mean 186 Post TPT Follow Ups First Follow up Done 3,858 (96%) First Follow up done after (days since TPT completion) Median (Q1, Q3) 332 (300, 367) Mean 333 Second Follow up Done 3,882 (97%) Second Follow up done after (days since TPT completion) Median (Q1, Q3) 448 (415, 482) Mean 449 Follow Up Status* died 14 (0.3%) no TB sym 3,658 (91%) started ATT 22 (0.5%) symptomatic, no med consult 191 (4.8%) symptomatic, pending med consult 92 (2.3%) symptomatic, TB not diagnosed 35 (0.9%) Risk Factors** None 3,239 (81%) Healthcare worker 18 (0.4%) Migrant 188 (4.7%) Construction 77 (1.9%) Miner 34 (0.8%) Tribal 112 (2.8%) Refugee 2 (<0.1%) Prisoner 4 (<0.1%) Urban Slum 421 (10%) Comorbidities** None 3,802 (95%) Diabetes 113 (2.8%) Hypertension 109 (2.7%) Kidney 4 (<0.1%) Liver 5 (0.1%) Cardio 9 (0.2%) Bronchial 13 (0.3%) Cancer 1 (<0.1%) Lifestyle Risks** Alcohol & Tobacco 64 (1.6%) Alcohol only 37 (0.9%) Tobacco only 401 (10%) Post-TPT Symptomatic Screening (Incidence) Number of Symptoms: Asymptomatic 3,658 (91%) Symptoms = 1 221 (5.5%) Symptoms = 2 93 (2.3%) Symptoms = 3 18 (0.5%) Symptoms = 4 8 (0.2%) Individual Symptoms: Sym Cough 212 (5.3%) Sym Weight Loss 49 (1.2%) Sym Night Sweat 16 (0.4%) Sym Fever 216 (5.4%) Medical Consultation, Diagnostic Assessment, and ATT Initiations Medical Consultation for TB (among symptomatic); N = 340 Not Planned 191 (4.8%) To be done 92 (2.3%) Consultation Done 57 (1.4%) Assessment Details for those who received consultation (N = 57) AFB /FNAC 10 (18%) Clinical/Chest X-Ray 32 (56%) NAAT (TrueNAT/CBNAAT) 10 (18%) Exact test not available 5 (8.8%) ATT initiated 22 (0.5%) Days between TPT Initiation & ATT initiation Median (Q1, Q3) 187 (153, 337) Mean 221 *Follow up status is derived from the two follow ups conducted; the exact follow ups received are detailed in supplementary text **An individual can have more than one risk factor, comorbidity or/and lifestyle risks. 1 n (%) Symptomatic Evaluation Approximately 8% of HHCs reported at least one symptom at follow-up (Table 1). Fever (5.4%) and recurrent cough (5.3%) were most common and frequently co-reported (Figure 2). Symptom prevalence varied by TPT regimen and region: HHCs on the 6H regimen reported more symptoms on average (S8 Table). Regional variations existed: those from Bihar (15%), Uttar Pradesh (14%), and Rajasthan (14%) had more symptomatic contacts (S6 Table). However, within symptomatic HHCs, mean symptoms were higher in Haryana (1.8), Gujarat (1.6), Delhi (1.5), and Rajasthan (1.5) than in Bihar (1.3) and Uttar Pradesh (1.4) (S9 Table). Sociodemographic variations explained some of the symptomatic variation: symptomatic HHCs were younger, more likely to live in kacchha housing (9.1% vs. 5.4%) and to use unclean fuel (61% vs. 50%, p < 0.048) (Table 2). The same were closely linked to regional variations: Majority of those living in kaccha housing were from Bihar (42%) and Uttar Pradesh (19%); and majority of those using unclean fuel being from Bihar (29%), Gujarat (26%), and Uttar Pradesh (21%) (S10-S11 Tables). Symptomatic HHCs also had a higher prevalence of prior illnesses (8% vs. 5%, p = 0.012), particularly liver-related conditions (0.9% vs. <0.1%, p < 0.001), bronchial illness (six-fold increase, p < 0.001), and hypertension (two-fold increase, p = 0.015). Alcohol and tobacco use were more common but not statistically significant. Differences existed prior to TPT initiation as well: Symptomatic HHCs were twice as likely to have reported symptoms at contact screening (6% vs. 3%, p = 0.007), were more likely to have a CXR suggestive of TB (3.1% vs. 1.6%, p < 0.001), and experienced longer delays in initiating TPT (median +14 days; mean +7 days). Table 2 Summary Statistics for surveyed and available (N = 3,998); segregated by symptomatic status Characteristic non-symptomatic symptomatic p-value 2 N = 3,658 1 N = 340 1 TB status <0.001*** tb positive 0 (0%) 22 (6.5%) Gender 0.3 female 1,864 (51%) 183 (54%) male 1,793 (49%) 157 (46%) transgender 1 (<0.1%) 0 (0%) Age 0.016* Median (Q1, Q3) 23 (11, 39) 19 (9, 38) Mean 26 24 Age.Category 0.004** 1. 15 2,419 (66%) 198 (58%) HHC is (Relationship with index TB patient) >0.9 child 113 (3.1%) 3 (0.9%) grandchild 95 (2.6%) 9 (2.6%) grandparent 7 (0.2%) 0 (0%) parent/parent-in-law 1,432 (39%) 140 (41%) relative/others 614 (17%) 57 (17%) sibling 402 (11%) 43 (13%) son/daughter-in-law 32 (0.9%) 4 (1.2%) spouse 465 (13%) 47 (14%) unknown 498 (14%) 37 (11%) Occupation 0.028* agri & livestock 226 (6.2%) 14 (4.1%) homemaker 982 (27%) 80 (24%) labor 584 (16%) 54 (16%) no information 347 (9.5%) 37 (11%) salaried 425 (12%) 39 (11%) student 1,094 (30%) 116 (34%) Type House 0.2 concrete 1,976 (54%) 167 (49%) kaccha 197 (5.4%) 31 (9.1%) mixed 1,482 (41%) 142 (42%) unknown 3 (<0.1%) 0 (0%) Type Cooking Fuel 0.048* both 1,472 (40%) 164 (48%) clean 1,818 (50%) 134 (39%) unclean 365 (10.0%) 42 (12%) unknown 3 (<0.1%) 0 (0%) Pre-TPT Screening CxR Done for 3,085 (84%) 289 (85%) 0.7 CxR Test Results 0.049* cxr not suggestive 3,037 (98%) 280 (97%) cxr suggestive 48 (1.6%) 9 (3.1%) (Pre-TPT) Asymptomatic 3,544 (97%) 320 (94%) 0.007** (Pre-TPT) Sym Cough 79 (2.2%) 15 (4.4%) 0.009** (Pre-TPT) Sym Weight Loss 6 (0.2%) 2 (0.6%) 0.094 (Pre-TPT) Sym Night Sweat 6 (0.2%) 0 (0%) 0.5 (Pre-TPT) Sym Fever 44 (1.2%) 6 (1.8%) 0.4 TPT Details Drug Regimen <0.001*** 3HP (12-weeks-weekly rifapentine +isoniazid) 810 (22%) 49 (14%) 6H (6-months-daily isoniazid) 2,848 (78%) 291 (86%) Time to TPT initiation (from diag of index patient) 0.002** Median (Q1, Q3) 58 (32, 102) 72 (38, 116) Mean 76 83 TPT.completion.days - 3HP 0.5 Median (Q1, Q3) 84 (84, 93) 84 (84, 91) Mean 93 90 TPT.completion.days - 6H 0.2 Median (Q1, Q3) 180 (179, 185) 180 (179, 188) Mean 185 189 Post TPT Follow Ups First Follow up Done 3,507 (96%) 338 (99%) 0.001** First Follow up after (days since TPT completion) 0.2 Median (Q1, Q3) 333 (300, 367) 331 (294, 363) Mean 333 330 Second Follow up Done 3,557 (97%) 322 (95%) 0.009** Second Follow up after (days since TPT completion) 0.2 Median (Q1, Q3) 449 (416, 482) 445 (412, 481) Mean 449 447 Follow Up Status* <0.001*** no TB sym 3,658 (100%) 0 (0%) started ATT 0 (0%) 22 (6.5%) symptomatic, no med consult 0 (0%) 191 (56%) symptomatic, pending med consult 0 (0%) 92 (27%) symptomatic, TB not diagnosed 0 (0%) 35 (10%) Risk Factors** No Risk Factors 2,956 (81%) 271 (80%) 0.5 Healthcare worker 15 (0.4%) 3 (0.9%) 0.2 Migrant 173 (4.7%) 15 (4.4%) 0.8 Construction 65 (1.8%) 12 (3.5%) 0.025* Miner 28 (0.8%) 6 (1.8%) 0.055 Tribal 102 (2.8%) 10 (2.9%) 0.9 Refugee 2 (<0.1%) 0 (0%) 0.7 Prisoner 2 (0.9 Hypertension 91 (2.5%) 16 (4.7%) 0.015* Kidney 3 (<0.1%) 1 (0.3%) 0.2 Liver 2 (<0.1%) 3 (0.9%) <0.001*** Cardio 7 (0.2%) 1 (0.3%) 0.7 Bronchial 7 (0.2%) 4 (1.2%) <0.001*** Cancer 1 (<0.1%) 0 (0%) 0.8 Lifestyle Risks** Alcohol 90 (2.5%) 11 (3.2%) 0.4 Tobacco 417 (11%) 47 (14%) 0.2 Post-TPT Symptomatic Screening (Incidence) Number of Symptoms: <0.001*** Asymptomatic 3,658 (100%) 0 (0%) Symptoms = 1 0 (0%) 221 (65%) Symptoms = 2 0 (0%) 93 (27%) Symptoms = 3 0 (0%) 18 (5.3%) Symptoms = 4 0 (0%) 8 (2.4%) Individual Symptoms: Sym Cough 0 (0%) 212 (62%) <0.001*** Sym Weight Loss 0 (0%) 49 (14%) <0.001*** Sym Night Sweat 0 (0%) 16 (4.7%) <0.001*** Sym Fever 0 (0%) 216 (64%) <0.001*** *Follow up status is derived from the two follow ups conducted; the exact follow ups received are detailed in supplementary text **An individual can have more than one risk factor, comorbidity or/and lifestyle risks. 1 n (%) 2 *p<0.05; **p<0.01; ***p<0.001 Medical consultations and TB Diagnosis Among symptomatic HHCs, the likelihood of seeking a medical consultation, as well as getting diagnosed, increased with symptom burden (Figure S1, S7 Table). Regional differences were evident: none of the symptomatic contacts from Bihar reported seeking care, while those from Delhi and Haryana were more likely to do so (S6 Table). Socioeconomic factors such as type of cooking fuel and housing, which are themselves also closely linked, influenced care seeking (S10–S12 Tables). Among symptomatic HHCs, assessment uptake was higher among those in concrete housing (21%) than those living in kaccha (13%) or mixed dwellings (13%), though the difference was not statistically significant (p = 0.13). Difference with respect to fuel usage showed high statistical significance (p<0.001), 26% of clean-fuel users sought TB assessment compared with 14% of unclean-fuel users. These differences affected TB positivity as well, those in concrete housing or/and using clean fuel had higher TB positivity rates. However, once analyses were restricted to individuals who actually sought consultation, these differences narrowed substantially (e.g., 43% vs 25% for concrete vs kaccha ; 43% vs 33% for clean vs unclean fuel). All 22 TB diagnoses occurred exclusively within the symptomatic cohort, with median time to ATT initiation being 187 days (Table 1). TB positivity varied by diagnostic testing, with those evaluated through clinical assessment and CXR having lower positivity rates (Table 3, Figure 3). Table 3 TB positivity by type of diagnostic test (N = 57) Characteristic AFB/FNAC Clinical/CXR NAAT Other* p-value 2 N = 10 1 N = 32 1 N = 10 1 N = 5 1 Symptoms (N) 0.037* Median (Q1, Q3) 2.00 (1.00, 2.00) 1.00 (1.00, 2.00) 3.00 (2.00, 3.00) 1.00 (1.00, 2.00) Mean 1.8 1.75 2.6 1.4 TB Positivity 7 (70%) 5 (16%) 8 (80%) 2 (40%) <0.001*** *Diagnostic test used for those within other category was not available 1 n (%) 2 *p<0.05; **p<0.01; ***p<0.001 Algorithmic Screening We evaluated if certain symptoms or combination of symptoms can guide meaningful diagnostic testing among targeted risk-groups, to optimize for TB detection in resource constrained environments. Because likelihood measures were calculated across all symptomatic HHCs, regardless of whether they underwent diagnostic testing, the observed TB positivity is likely underestimated, as untested individuals were implicitly classified as TB-negative. Among individual symptoms, weight loss stood out as a strong indicator, with 20% (n = 10) of those experiencing it being diagnosed with TB, despite its low prevalence (1.2%), leading to high accuracy (85%) and specificity (88%) (Tables 4, S13). TB diagnoses were rare without cough (0.03%, n = 1); leading to highest sensitivity (95%) but low precision (10%). Combining symptoms improved diagnostic performance (Tables 5, S10), with weight loss and recurrent cough giving the most optimum measure for screening: 91% accuracy, 94% specificity, and 34% precision, further confirmed by survival analysis (Figure 4). Logistic regression further supports these findings: symptom burden and specific symptom combinations (particularly recurrent cough and weight loss) were the strongest predictors of seeking a medical consultation, whereas socio-demographic factors contributed far less explanatory power (S14 Table). Table 4 Risk Ratios for each symptom, along with the probability / likelihood of each symptom & associated probability of having TB with those symptoms #Symptoms Risk Ratio (RR) RR - 95% C.I. Likelihood of having the symptom Likelihood of having TB with these symptoms Likelihood of having TB without these symptoms Recurrent Cough 394 53, 2916 5.1% (212) 10% (21/212) 0.03% (1/3978) Weight Loss 70 32, 155 1.2% (49) 20% (10/49) 0.29% (12/4141) Night Sweat 41 14, 126 0.4% (16) 19% (3/16) 0.46% (19/4174) Fever 83 28, 243 5.2% (216) 8% (18/216) 0.10% (4/3974) Interpretation: Weight loss was associated with the second-highest risk ratio for developing TB (70), following cough (394), though with a narrower 95% confidence interval. This indicates that individuals with weight loss are 70 times more likely to develop TB compared to those without this symptom. Additionally, weight loss had the highest proportion of individuals reporting TB among symptomatic cases, at 20%. Table 5 Performance of symptom-based screening algorithms under the assumption of complete testing; actual TB assessment numbers are also reported. Symptom Combination #Symptoms #HHCs TB Assessment Done TB Diagnosed Accuracy Precision Sensitivity Specificity Recurrent Cough & Weight Loss 29 13 (45%) 10 91% 35% 46% 94% (87.3%, 93.5%) (19.9%, 52.7%) (26.9%, 65.3%) (90.9%, 96.1%) Recurrent Cough & Weight Loss AND >=3 22 10 (45%) 8 92% 36% 36% 96% (88.4%, 94.2%) (19.7%, 57%) (19.7%, 57%) (92.7%, 97.4%) Recurrent Cough & Fever AND >=3 25 11 (44%) 9 92% 36% 41% 95% (88%, 94%) (20.2%, 55.5%) (23.3%, 61.3%) (92%, 96.9%) Weight Loss & Fever AND >=3 23 10 (43%) 8 92% 35% 36% 95% (88%, 94%) (18.8%, 55.1%) (19.7%, 57%) (92.4%, 97.1%) Weight Loss & Night Sweat AND >=3 9 3 (33%) 2 92% 22% 9% 98% (88.7%, 94.5%) (6.3%, 54.7%) (2.5%, 27.8%) (95.5%, 98.9%) Recurrent Cough & Weight Loss OR >=3 33 14 (42%) 11 90% 33% 50% 93% (86.7%, 93%) (19.8%, 50.4%) (30.7%, 69.3%) (89.7%, 95.4%) Note : Select algorithms are presented here, which allow for at least 33% sensitivity and precision. For the full list, refer to supplementary text (S13 Table) Discussion To the best of our knowledge, this is the first study to document symptom profiles, care-seeking behaviour, and subsequent TB diagnoses among household contacts who have completed TPT. Conducted within programmatic settings, the analysis provides critical insights on risk factors among HHCs who have completed TPT, which can be used for targeted interventions. Care Seeking: Gap between Symptom Onset & Medical Consultation Our analysis finds a substantial gap between symptom onset and care seeking among household contacts; only 17% HHCs actually went for a TB assessment, and 27% seemed to delay care-seeking. A lack of care seeking among those with TB-like symptoms, as well as HHCs, is well-documented [11, 12], and is often due to misattribution to mild or common illnesses [13, 14]. In our cohort, however, the lack of care seeking, may also reflect a (false) sense of security following the TPT completion. Delayed evaluation, particularly among individuals with active TB, can increase the likelihood of more severe or drug-resistant disease [15–19]. Lack of care seeking may also reflect systemic regional level challenges, reflective of differences in access, awareness, and socioeconomic constraints, and directly influencing identification of TB among the most vulnerable. Diagnostic gaps are known contributors to the “missing millions” in India’s TB care cascade [20], and disproportionately affect socio-economically disadvantaged groups [21]. In our cohort, when diagnostic assessment becomes more evenly distributed across socioeconomic groups, the observed differences in TB positivity narrow considerably, highlighting the need for more accessible and equitably delivered diagnostic testing, as well as stricter adherence to follow-up protocols to ensure timely evaluation of all symptomatic contacts. Algorithmic screening, a useful approach in resource constrained settings Consultative follow ups post TPT, hence become a critical measure [4], in supporting symptomatic triage for diagnostic screening among high-risk cohorts. Individual symptoms are found to be poor predictors – both in our cohort, and in historical literature [22]. Recent meta-analyses demonstrate that the presence of multiple symptoms, such as cough, weight loss, and fever, are significant markers for TB diagnosis [15, 23]. Our findings corroborate this, while also suggesting that certain symptom combinations can be used for prioritizing diagnostic testing in resource-constrained settings. Importantly, we identified a small high-risk subgroup; HHCs with weight loss and recurrent cough, or with ≥3 symptoms (0.8% of the cohort). Although fewer than half of these individuals sought care, a high proportion (11 of 14 evaluated) were diagnosed with TB. Applied programmatically, prioritising diagnostic testing for this group could yield substantial gains in early case detection. If extrapolated to the ~1 million individuals who received TPT in India in 2023 [24], targeted testing of just 0.8% (~7,876 people) could identify more than 2,600 TB cases. While a simplified extrapolation, the latte is also underestimated due to lack of diagnostic testing, further highlighting the probable cost effectiveness and feasibility of symptom-based triage. CXR Screening & TPT Initiation Individuals with a suggestive pre-TPT CXR were considerably more likely to develop symptoms later, underscoring the latter’s value for risk stratification. Additionally, while both NTEP and WHO outline detailed pathways for TPT initiation, including steps like ruling out active TB and adapting approaches for individuals with comorbidities, they stop short of specifying a timeframe, offering only a general recommendation to start TPT “ as soon as possible .” Our study suggests that delays in initiation are associated with an increased risk of developing TB-like symptoms post-TPT. Regional variations were significant in both CXR uptake and time taken to TPT initiation; Haryana had a 70% CXR uptake, and contacts in Bihar were initiated on TPT more than three months post the index patient diagnosis, indicating opportunities to strengthen health systems. Adherence & Drug Regimen Adherence during TPT is essential, as incomplete treatment can jeopardize the protective effect of TPT, and may contribute to the emergence of drug resistance [25]. Sub-optimal adherence [26], as well as low awareness of TB treatment , is commonly cited [27]. A perceived lack of need, coupled with long regimen, and associated side effects, have also been attributed as reasons precluding the initiation and completion of TPT [7, 26]. Recent studies, however, have found that those on shorter regimens are much more likely to complete the regimen [26, 28], with one study noting that those on 3HP regimens are seven times more likely to complete the regimen [27]. In our cohort, those on 3HP were less likely to develop symptoms, suggesting that prioritizing shorter regimens can help maximize TPT associated benefits. Regional variations State specific differences in TB prevalence are well documented in literature [1, 29, 30], and our findings further highlight patterns related to risk markers, health-seeking behaviours, and access to diagnostic services. For instance, the higher use of unclean fuels, kaccha housing, and lower medical consultation rates in Bihar and Uttar Pradesh may explain the higher proportion of symptomatic HHCs, yet a lower number of those who seek consultation. Broadly, the findings suggest that state specific strategies are essential to democratize TB detection and prevention across the country – as TB positivity is a feature of not only underlying risk factors, but also diagnostic capacities and health seeking behaviours. Recommendations for operational TPT guidelines Our study identifies key areas where current TPT guidelines can be strengthened to improve early TB detection, follow-up, and program efficiency. We strongly recommend mandating diagnostic testing for targeted subgroups of household contacts (HHCs) who, based on our analysis, are at higher risk of developing TB. These include HHCs with a) suggestive CXR findings prior to TPT, b) weight loss during or after TPT, and c) combinations of specific symptoms (e.g., weight loss and cough). While all symptomatic HHCs should ideally undergo TB testing, such decision systems can support diagnostic prioritization in resource-constrained settings. Symptom-based questionnaires offer a practical tool for such triage, balancing sensitivity and specificity (S13 Table). We also recommend making CxR screening a mandatory pre-initiation step - in order to support ruling out TB, and also to capture high-risk HHCs post TPT. In parallel, counselling should be strengthened at both TPT initiation and completion. Follow-ups by healthcare workers can also be aligned with TB treatment follow-up schedules to reduce logistical burden and improve efficiency. This is especially convenient for individuals on the 6H regimen, which often concludes alongside DS-TB treatment. Further, we recommend earlier post-TPT follow-ups, ideally within 60–90 days and again around 180 days. While current guidelines initiate follow-up at the 6-month mark, our data show that a significant share of TB cases are diagnosed earlier, thus warranting earlier follow ups. Finally, risk markers (e.g., poor housing, use of biomass fuel, occupational exposure, comorbidities, and tobacco use), if integrated within the Nikshay system, can help healthcare workers prioritize the most vulnerable. These augmentations can support a more pro-active and risk informed approach to TB prevention, contributing to more effective TB control. Conclusion Our study provides compelling evidence for region-specific, data-driven strategies to strengthen TB preventive treatment (TPT) follow-up in India. By analyzing symptomatic progression across a diverse cohort from nine states, the analysis highlights the importance of localized interventions that consider housing conditions, access to diagnostics, and health-seeking behaviours - all key predictors of TB outcomes. The insights also underscore the need to expand TPT access and introduce mandatory diagnostic testing for higher-risk household contacts, including universal pre-TPT chest X-ray screening. Collectively, the evidence supports pragmatic refinements to existing NTEP guidelines. Limitations This study was conducted within the programmatic framework of JEET 2.0, and certain limitations are be noted. First , only two follow-ups were conducted, approximately four months apart. These follow-ups were designed as an operational add-on to routine services and do not align with the recommended four-visit TPT follow-up schedule over two years (6, 12, 18, and 24 months). Earlier and later follow-ups, particularly at 6, 18, and 24 months, may have provided a more complete understanding of post-TPT TB breakdown. Second , although the sample size was sufficiently powered at multiple strata, it may not fully reflect the diversity of health-seeking behaviours, healthcare infrastructure, and TB incidence across India. Third , the study relies on self-reported symptoms and healthcare-seeking behaviours, which are subject to recall and social desirability bias. Fourth , certain objective measurements, such as the precise loss of weight from baseline, could have strengthened symptom assessment. Fifth , as an observational study, causal relationships cannot be inferred. Key associations may be influenced by unmeasured confounders, including socioeconomic status, education, and access to healthcare. Sixth, incomplete diagnostic information, particularly among individuals who did not seek medical care, limits our ability to ascertain true TB incidence and track symptom evolution. Because performance metrics for symptom-based algorithms were calculated under the assumption that all symptomatic HHCs underwent diagnostic testing, untested individuals were implicitly classified as TB-negative. Given that fewer than half sought medical consultation, true TB positivity is likely underestimated. Future studies incorporating routine or universal diagnostic testing at follow-up would enable more accurate estimation of TB risk. Finally , follow-up relied primarily on symptomatic screening, although TB is also noted among asymptomatic contacts, further limiting our ability to determine risk. Future research examining both symptomatic and asymptomatic TB transmission could inform more robust follow-up protocols and enhance the effectiveness of TPT scale-up strategies. List of abbreviations HHC – Household Contact AFB - Acid-Fast Bacillus FNAC - Fine Needle Aspiration Cytology ADR – Adverse Drug Reaction JEET – Joint Effort for Elimination of Tuberculosis OLS – Ordinary least squares CI – Confidence interval IQR – Interquartile range PPSA – Private Provider Support Agency TB – Tuberculosis NTEP – National Tuberculosis Elimination Program NSP – National Strategic Plan for Elimination of Tuberculosis Declarations Acknowledgements We would like to acknowledge Huzaifa Bilal, who worked on the sampling design and questionnaire used during follow ups, whilst also leading training of field workers and coordinating data collection. Ethics approval and consent to participate: A study protocol was approved by the Monk Prayogshala Institutional Review Board and Ethics Review Committee (Aug 26, 2025). A waiver of informed consent was granted as the analysis relied on anonymized and de-identified programmatic data. Consent for publication: Not applicable Availability of data and materials: The specific data utilized for this research has also been made available for public research use at the GitHub link: https://github.com/ridhimasodhi/post-TPT-progression/ Competing interests: None declared Funding: Data used in this study were collected under the JEET 2.0 program, which was funded by the Global Fund, and implemented across India (Grant Number: IND-T-WJCFP01-2020-2022). The William J. Clinton Foundation supported the preparation of this manuscript as part of its ongoing technical and programmatic support to TB prevention and care in India. Authors' contributions: MS and PD designed the study; SSA managed data collation for the study and conducted initial analyses. RS conducted the analysis, created the visualizations, and wrote the first draft; manuscript was revised across multiple iterations, which were contributed to by all authors; RS did the final editing; all authors read and approved the final version. References Ministry of Health & Family Welfare-Government of India. National TB Prevalence Survey of India (2019-21) Summary Report. World Health Organization, editor. Guidelines on the management of latent tuberculosis infection. Geneva: World health organization; 2015. Global Programme on Tuberculosis & Lung Health. WHO Global TB Report - 2025. 2025. National TB elimination program. Guidelines for Programmatic Management of Tuberculosis Preventive Treatment in India. 2021. Fox GJ, Barry SE, Britton WJ, Marks GB. Contact investigation for tuberculosis: a systematic review and meta-analysis. Eur Respir J. 2013;41:140–56. https://doi.org/10.1183/09031936.00070812. Shah D, Bhide S, Deshmukh R, Smith JP, Kaiplyawar S, Puri V, et al. 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Tuberculosis control interventions targeted to previously treated people in a high-incidence setting: a modelling study. Lancet Glob Health. 2018;6:e426–35. https://doi.org/10.1016/S2214-109X(18)30022-6. Tedla K, Medhin G, Berhe G, Mulugeta A, Berhe N. Delay in treatment initiation and its association with clinical severity and infectiousness among new adult pulmonary tuberculosis patients in Tigray, northern Ethiopia. BMC Infect Dis. 2020;20:456. https://doi.org/10.1186/s12879-020-05191-4. Akalu TY, Clements ACA, Gebreyohannes EA, Xu Z, Bai L, Alene KA. Risk factors for diagnosis and treatment delay among patients with multidrug-resistant tuberculosis in Hunan Province, China. BMC Infect Dis. 2024;24:159. https://doi.org/10.1186/s12879-024-09036-2. Subbaraman R, Jhaveri T, Nathavitharana RR. Closing gaps in the tuberculosis care cascade: an action-oriented research agenda. J Clin Tuberc Mycobact Dis. 2020;19:100144. https://doi.org/10.1016/j.jctube.2020.100144. Hargreaves JR, Boccia D, Evans CA, Adato M, Petticrew M, Porter JDH. The Social Determinants of Tuberculosis: From Evidence to Action. Am J Public Health. 2011;101:654–62. https://doi.org/10.2105/AJPH.2010.199505. Miller LG, Asch SM, Yu EI, Knowles L, Gelberg L, Davidson P. A Population-Based Survey of Tuberculosis Symptoms: How Atypical Are Atypical Presentations? Clin Infect Dis. 2000;30:293–9. https://doi.org/10.1086/313651. Yayan J, Franke K-J, Berger M, Windisch W, Rasche K. Early detection of tuberculosis: a systematic review. Pneumonia. 2024;16:11. https://doi.org/10.1186/s41479-024-00133-z. India TB Report - 2023. New Delhi, India: Central TB Division, Ministry of Health and Family Welfare, Government of India. Global Programme on Tuberculosis and Lung Health (GTB), Guidelines Review Committee. Latent tuberculosis infection: updated and consolidated guidelines for programmatic management. 2018. An Y, Khun KE. Factors associated with incomplete tuberculosis preventive treatment: a retrospective analysis of six-years programmatic data in Cambodia. Sci Rep. 2024;14:18458. https://doi.org/10.1038/s41598-024-67845-6. Sharma N, Basu S, Khanna A, Sharma P, Chandra S. The intention to receive tuberculosis preventive therapy in adult household contacts of pulmonary TB patients in Delhi, India. J Infect Dev Ctries. 2022;16:298–304. https://doi.org/10.3855/jidc.14910. Chen H, Zhang H, Cheng J, Sun D, Wang Q, Wu C, et al. Adherence to preventive treatment for latent tuberculosis infection in close contacts of pulmonary tuberculosis patients: A cluster-randomized controlled trial in China. Int J Infect Dis. 2024;147:107196. https://doi.org/10.1016/j.ijid.2024.107196. Golandaj JA, Naikar SK, Hallad JS. Trends and sub-national disparities in TB notifications in India: Insights from HMIS data. Indian J Tuberc. 2022;69:141–50. https://doi.org/10.1016/j.ijtb.2021.04.005. Chauhan A, Parmar M, Dash GC, Solanki H, Chauhan S, Sharma J, et al. The prevalence of tuberculosis infection in India: A systematic review and meta-analysis. Indian J Med Res. 2023;157:135–51. https://doi.org/10.4103/ijmr.ijmr_382_23. Additional Declarations No competing interests reported. Supplementary Files SuppTextS1.docx SuppTextS2.Questionnaires.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8176310","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":551748947,"identity":"2ff047e1-8611-4eba-9841-7d8ebbdb245d","order_by":0,"name":"Ridhima 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10:07:37","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":52879,"visible":true,"origin":"","legend":"\u003cp\u003eOverlap between symptomatic presence.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eNote\u003c/u\u003e: In figure, we see that 76 people had both cough and fever, signifying an overlap between the two symptoms. 105 people have only fever, and 103 only have cough.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8176310/v1/d9d2e0e76be37fae749093f0.jpg"},{"id":97130883,"identity":"fdf78537-f7ed-4738-89dd-44274621192b","added_by":"auto","created_at":"2025-12-01 08:40:52","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":88960,"visible":true,"origin":"","legend":"\u003cp\u003eDiagnostic Pathway for symptomatic HHCs; N = 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In India, the crude TBI prevalence among adults (\u0026gt;=15 years) is 31.3%, and individuals in this state neither exhibit symptoms nor transmit the infection [1]. Progression from TBI to active TB disease varies, depending on factors such as age, comorbidities that compromise immunity, nutritional status, and environmental factors due to housing and occupation [2]. The primary public health intervention to reduce this risk is TB preventive therapy (TPT) [2, 3]. While national guidelines in India have historically guided people living with HIV (PLHIV) and under-6 child contacts for TPT [4], programmatic guidance was expanded in July 2021 to include all household contacts (HHCs), including adults [4].\u003c/p\u003e\n\u003cp\u003eHHCs represent a particularly high-risk group; a 2012 meta-analysis found that HHCs in low- and middle-income countries had a TB prevalence above 3% [5]. A recent (2024) study from Mumbai in India found 4% of HHCs (\u0026gt;=5 years) to have active TB [6], which is more than 25 times the adult TB (\u0026gt;=15 years) prevalence in the underlying state of Maharashtra (0.16%), and more than 13 times the general adult TB prevalence of 0.3% in India [1]. TPT uptake among adults, however, however, continues to be low [7, 8], with the risk further compounded by the fact that we miss at least two TB positive individuals for every one notified (TB prevalence to notification ratio of 2.84) [1]. Furthermore, even as targeted interventions such as Joint Effort for Elimination of Tuberculosis (JEET) 2.0 [9] have been employed to increase TPT among adult HHCs, risk for breakdown to TB persists [4]. A two-year follow up schedule following the completion of TPT, is hence recommended by the national TB elimination program, at the 6\u003csup\u003eth\u003c/sup\u003e,12\u003csup\u003eth\u003c/sup\u003e,18\u003csup\u003eth\u003c/sup\u003e and 24\u003csup\u003eth\u003c/sup\u003e month mark, to monitor for signs of TB disease [4]. While there exist studies on TB prevalence among HHCs, there is a lack of studies examining TB among those who have completed TPT. Our study attempts to bridge this gap, by analyzing follow-up data on individuals sampled from across nine states, who completed TPT under the JEET program. The analysis provides insights into risk factors associated with TB progression, the occurrence of TB among this cohort, and patterns of health-seeking behaviour shaped by symptomatic and contextual factors.\u003c/p\u003e"},{"header":"Methods","content":"\u003ch2\u003eStudy Setting\u003c/h2\u003e\n\u003cp\u003eThe study was based within the programmatic operations of JEET 2.0, implemented between October 2021 and March 2024, in 207 districts, across 23 states and union territories of India. The program was funded by the Global Fund, and implemented by three agencies, namely, Union (7 states, 108 districts), FIND (5 states, 28 districts), and the William J. Clinton Foundation (WJCF) (11 states, 71 districts). This study was done within nine of the eleven WJCF managed states: Bihar, Delhi, Gujarat, Haryana, Kashmir, Rajasthan, Tamil Nadu, Uttarakhand, and Uttar Pradesh. JEET program activities can be segregated between those done prior to TPT initiation, and those done during TPT. Pre-TPT activities included contact tracing of household contacts (HHCs) of notified TB patients, followed by symptom screening (cough, fever, weight loss, night sweats) and chest X-ray evaluation. In select districts, tuberculin skin testing or interferon-gamma release assays were used to assess TB infection. During TPT, program staff provided counselling and logistical support to ensure drug availability and adherence. In addition to standard program activities, two post-TPT follow-ups were conducted among a sampled subset of HHCs to assess TB-related symptoms and understand TB risk after TPT completion.\u003c/p\u003e\n\u003ch2\u003eStudy Design\u003c/h2\u003e\n\u003cp\u003eThis was an observational, quantitative cohort study involving HHCs who had completed TPT. A representative sample was selected to assess TB-related symptoms at two time points approximately four months apart, with the first follow-up conducted 9 - 12 months after TPT completion.\u003c/p\u003e\n\u003ch2\u003eQuestionnaire Design\u003c/h2\u003e\n\u003cp\u003eThe follow-up assessments included questions on demographic characteristics (age and gender), medical history, lifestyle factors (tobacco and alcohol use), occupational exposures, and housing conditions. TB-related evaluation covered the WHO-recommended four-symptom (4S) screen [10], diagnostic tests undertaken since TPT completion, and any confirmed TB diagnoses.\u003c/p\u003e\n\u003ch2\u003eData Source\u003c/h2\u003e\n\u003cp\u003eThree data sources were used for this study. The first dataset was obtained from the project management information system (MIS) and included information on index patients and household contacts, covering demographics (age, gender, diagnosing district), TPT screening details (screening date, symptom profile, TB history), and TPT regimen and outcomes. The second dataset comprised interview responses collected during the two follow-up visits conducted after TPT completion. Data from Nikshay, the national TB surveillance portal, were used to cross-verify programmatic information.\u003c/p\u003e\n\u003ch2\u003eData Selection (Inclusion \u0026amp; Exclusion criteria)\u003c/h2\u003e\n\u003cp\u003eEligible HHCs included those who had completed their TPT, particularly during the four-month period between October 2022, and January 2023. This was done in alignment with the first follow up period (Oct 2023-Nov 2023), ensuring that all those interviewed had completed their TPT 9-12 months prior. Exclusions included a) HHCs from Ladakh due to the smaller number of HHCs and extreme weather conditions from October to March, which would have impeded follow-up visits, b) HHCs from Kerala because of inability to reach HHCs as they were being managed by state officials, and c) HHCs who were on drug regimens with fewer than 50 people, which would have impacted our ability to confidently assess correlation with collated covariates. Figure 1 details the selection process for the final cohort of 78,647 individuals, accounting for inclusion and exclusion criteria.\u003c/p\u003e\n\u003ch2\u003eEligible Population\u003c/h2\u003e\n\u003cp\u003eThe final eligible population included 78,647 household contacts (HHCs) who completed TPT between 1 October 2022 and 31 January 2023, across nine JEET intervention states: Bihar, Delhi, Gujarat, Haryana, Kashmir, Rajasthan, Tamil Nadu, Uttarakhand, and Uttar Pradesh. Among them, 52% (40,893) were female, and 17 individuals identified as transgender. Detailed demographic characteristics are presented in Table S1.\u003c/p\u003e\n\u003ch2\u003eSampling Method\u003c/h2\u003e\n\u003cp\u003eA stratified random sampling strategy was used across nine states, using TPT completions as the sampling frame. The study was powered to achieve 95% confidence with a 5% margin of error for multiple strata: (a) the overall cohort, (b) pre-screening type (IGRA, TST, or CXR only), (c) individual states, (d) TPT regimen (3HP and 6H), and (e) state-by-regimen combinations. Detailed sample size calculations and final achieved sample numbers are provided in Tables S2\u0026ndash;S4. Required sample size across all powered strata was 3,803, and the final sampled cohort consisted of 4,190 individuals, meeting or exceeding the required thresholds for representativeness and precision.\u003c/p\u003e\n\u003ch2\u003eSurvey Responses\u003c/h2\u003e\n\u003cp\u003eOf the 4,190 individuals listed post-sampling, responses were collected for 4,012 HHCs. Among them, 11 deaths were reported during the first follow-up by relatives or respondents, with 3 additional deaths recorded by the second follow-up.\u003c/p\u003e\n\u003ch2\u003eOutcomes of interest\u003c/h2\u003e\n\u003cp\u003eThe primary objectives of this study are to a) investigate the rate of TB occurrence among HHCs following TPT completion, b) evaluating factors determinant of higher TB risk, and c) provide recommendations for TPT scale up.\u003c/p\u003e\n\u003ch2\u003eAnalysis\u003c/h2\u003e\n\u003cp\u003eOur study employed a multifaceted analytical approach, integrating descriptive statistics, logistic regression, survival analysis, and pragmatic symptom-based algorithms, supported by visualizations. While descriptive analysis established a foundational understanding of the trends across different covariates collated in the study, logistic regression identified the most significant predictors of TB diagnosis, independently estimating their impact. Algorithmic models using symptom combinations were evaluated through accuracy, sensitivity, specificity, and precision. Survival analysis added a temporal dimension, estimating the probability of remaining TB-free over time and highlighting periods of elevated risk following TPT completion.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eRobustness Checks\u003c/h2\u003e\n\u003cp\u003eA series of robustness checks were conducted, including systematic inclusion/exclusion of different cohorts, and comparing descriptive results with different logistic model specifications, and are provided in the supplementary text.\u003c/p\u003e\n\u003ch2\u003eSoftware\u003c/h2\u003e\n\u003cp\u003eAnalyses were conducted in R (2024.04.2) using dplyr, tidyr, ggplot2, and gtsummary for data processing and summaries; sandwich for robust standard errors; and survival, survminer, lmtest, and caret for survival and econometric analyses.\u003c/p\u003e\n\u003ch2\u003eData Consolidation and Confidentiality\u003c/h2\u003e\n\u003cp\u003eData were collected using secure digital tools (ODK/KOBO) and linked to JEET 2.0 records through unique patient and contact identifiers. All records were encrypted and anonymised using Universally Unique Identifiers (UUIDs), before being used for research.\u003c/p\u003e"},{"header":"Results","content":"\u003ch2\u003eDemographic Summary\u003c/h2\u003e\n\u003cp\u003eOur cohort was nearly equally split by gender, with a median age of 23 years (Table 1). Approximately 5% of HHCs reported comorbidities, most commonly diabetes and hypertension. Prior to TPT initiation, contact screening involved symptom assessment and CXR evaluation: 3% reported symptoms and 84% underwent CXR screening. Among those CXR screened, 1.7% (n = 57) had findings suggestive of TB but were initiated on TPT following medical consultation. The median time from index patient diagnosis to TPT initiation was 59 days. TPT completion aligned with expected durations for each regimen: 12 weeks for 3HP (rifapentine + isoniazid) and six months for 6H (isoniazid).\u003c/p\u003e\n\u003cp\u003eLifestyle-related risks included tobacco use (12%) and alcohol consumption (2.5%). Additional risks assessed included being belonging to a tribal group (2.8%), migrant status (4.7%), and residence in urban slums (10%). A small share of HHCs reported working in construction (1.9%) or being a healthcare worker (0.4%). Housing conditions varied: 54% lived in \u003cem\u003epakka\u003c/em\u003e or concrete structures, 4.7% in \u003cem\u003ekutcha\u003c/em\u003e housing, and 41% in mixed-type dwellings. Only 49% reported always using clean cooking fuel, while 41% used it intermittently and 10% relied exclusively on unclean fuel. A total of 14 HHCs were reported to have died at the time of the first or second follow up, and median age for this cohort was 59 (S5 Table). Regional variations are detailed in the supplementary text (S6 Table).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Table 1 Summary Statistics (N = 4,012)\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eN = 4,012\u003cem\u003e\u003csup\u003e1\u003c/sup\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSymptomatic + TB status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; died\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e14 (0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; non-symptomatic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3,658 (91%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; symptomatic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e318 (7.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; symptomatic \u0026amp; tb positive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e22 (0.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2,057 (51%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1,954 (49%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; transgender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1 (\u0026lt;0.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Median (Q1, Q3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e23 (11, 39)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Mean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eAge.Category\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 1. \u0026lt;=5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e561 (14%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 2. 6-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e821 (20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 3. \u0026gt;15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2,630 (66%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eState\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; bihar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e384 (9.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; delhi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e355 (8.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; gujarat\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e770 (19%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; haryana\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e515 (13%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; jammu.\u0026amp;.kashmir\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e193 (4.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; rajasthan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e396 (9.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; tamil.nadu\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e483 (12%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; uttar.pradesh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e445 (11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; uttarakhand\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e471 (12%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eHHC is (Relationship with index TB patient)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; child\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e116 (2.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; grandchild\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e104 (2.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; grandparent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7 (0.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; parent/parent-in-law\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1,580 (39%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; relative/others\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e674 (17%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; sibling\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e445 (11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; son/daughter-in-law\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e36 (0.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; spouse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e514 (13%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; unknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e536 (13%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eOccupation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; agri \u0026amp; livestock\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e241 (6.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; homemaker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1,072 (27%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; labor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e638 (16%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; salaried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e465 (12%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; student\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1,212 (30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; no information\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e384 (9.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eType House\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; concrete\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2,147 (54%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; kaccha\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e228 (5.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; mixed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1,630 (41%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp; \u0026nbsp; unknown\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7 (0.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eType Cooking Fuel\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; both\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1,639 (41%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; clean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1,958 (49%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; unclean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e408 (10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp; \u0026nbsp; unknown\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7 (0.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ePre-TPT Screening\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eCxR Done for\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3,387 (84%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eCxR Test Results\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; not suggestive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3,330 (98%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; suggestive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e57 (1.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ePre-TPT Asymptomatic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3878 (97%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e(Pre-TPT) Sym Cough\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e94 (2.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e(Pre-TPT) Sym Weight Loss\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8 (0.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e(Pre-TPT) Sym Night Sweat\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6 (0.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e(Pre-TPT) Sym Fever\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e50 (1.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eTPT Details\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eDrug Regimen\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 3HP (12-weeks-weekly rifapentine +isoniazid)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e865 (22%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 6H (6-months-daily isoniazid)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3,147 (78%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eTime to TPT initiation (from diag of index patient)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Median (Q1, Q3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e59 (33, 104)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Mean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e77\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eTPT.completion.days - 3HP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Median (Q1, Q3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e84 (84, 92)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Mean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e93\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eTPT.completion.days - 6H\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Median (Q1, Q3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e180 (179, 186)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Mean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e186\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ePost TPT Follow Ups\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eFirst Follow up Done\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3,858 (96%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eFirst Follow up done after (days since TPT completion)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Median (Q1, Q3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e332 (300, 367)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Mean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e333\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSecond Follow up Done\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3,882 (97%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eSecond Follow up done after (days since TPT completion)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Median (Q1, Q3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e448 (415, 482)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Mean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e449\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eFollow Up Status*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; died\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e14 (0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; no TB sym\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3,658 (91%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; started ATT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e22 (0.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; symptomatic, no med consult\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e191 (4.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; symptomatic, pending med consult\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e92 (2.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; symptomatic, TB not diagnosed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e35 (0.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eRisk Factors**\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; None\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3,239 (81%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Healthcare worker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e18 (0.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Migrant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e188 (4.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Construction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e77 (1.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Miner\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e34 (0.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Tribal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e112 (2.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Refugee\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2 (\u0026lt;0.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Prisoner\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4 (\u0026lt;0.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Urban Slum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e421 (10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eComorbidities**\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; None\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3,802 (95%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Diabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e113 (2.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Hypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e109 (2.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Kidney\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4 (\u0026lt;0.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Liver\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5 (0.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Cardio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9 (0.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Bronchial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e13 (0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1 (\u0026lt;0.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eLifestyle Risks**\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Alcohol \u0026amp; Tobacco\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e64 (1.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Alcohol only\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e37 (0.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Tobacco only\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e401 (10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ePost-TPT Symptomatic Screening (Incidence)\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of Symptoms:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAsymptomatic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3,658 (91%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSymptoms = 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e221 (5.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSymptoms = 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e93 (2.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSymptoms = 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e18 (0.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSymptoms = 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8 (0.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eIndividual Symptoms:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSym Cough\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e212 (5.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSym Weight Loss\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e49 (1.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSym Night Sweat\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e16 (0.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSym Fever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e216 (5.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eMedical Consultation, Diagnostic Assessment, and ATT Initiations\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedical Consultation for TB (among symptomatic); N = 340\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNot Planned\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e191 (4.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTo be done\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e92 (2.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eConsultation Done\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e57 (1.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eAssessment Details for those who received consultation (N = 57)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; AFB /FNAC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10 (18%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Clinical/Chest X-Ray\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e32 (56%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; NAAT (TrueNAT/CBNAAT)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10 (18%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Exact test not available\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5 (8.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eATT initiated\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e22 (0.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eDays between TPT Initiation \u0026amp; ATT initiation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Median (Q1, Q3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e187 (153, 337)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Mean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e221\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003e*Follow up status is derived from the two follow ups conducted; the exact follow ups received are detailed in supplementary text\u003cbr\u003e\u0026nbsp;**An individual can have more than one risk factor, comorbidity or/and lifestyle risks.\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003e\u003csup\u003e1\u003c/sup\u003e\u003c/em\u003e\u003cem\u003e\u0026nbsp;n (%)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003eSymptomatic Evaluation\u003c/h2\u003e\n\u003cp\u003eApproximately 8% of HHCs reported at least one symptom at follow-up (Table 1). Fever (5.4%) and recurrent cough (5.3%) were most common and frequently co-reported (Figure 2). Symptom prevalence varied by TPT regimen and region: HHCs on the 6H regimen reported more symptoms on average (S8 Table). Regional variations existed: those from Bihar (15%), Uttar Pradesh (14%), and Rajasthan (14%) had more symptomatic contacts (S6 Table). However, within symptomatic HHCs, mean symptoms were higher in Haryana (1.8), Gujarat (1.6), Delhi (1.5), and Rajasthan (1.5) than in Bihar (1.3) and Uttar Pradesh (1.4) (S9 Table).\u003c/p\u003e\n\u003cp\u003eSociodemographic variations explained some of the symptomatic variation: symptomatic HHCs were younger, more likely to live in \u003cem\u003ekacchha\u003c/em\u003e housing (9.1% vs. 5.4%) and to use unclean fuel (61% vs. 50%, p \u0026lt; 0.048) (Table 2). The same were closely linked to regional variations: Majority of those living in kaccha housing were from Bihar (42%) and Uttar Pradesh (19%); and majority of those using unclean fuel being from Bihar (29%), Gujarat (26%), and Uttar Pradesh (21%) (S10-S11 Tables).\u003c/p\u003e\n\u003cp\u003eSymptomatic HHCs also had a higher prevalence of prior illnesses (8% vs. 5%, p = 0.012), particularly liver-related conditions (0.9% vs. \u0026lt;0.1%, p \u0026lt; 0.001), bronchial illness (six-fold increase, p \u0026lt; 0.001), and hypertension (two-fold increase, p = 0.015). Alcohol and tobacco use were more common but not statistically significant.\u003c/p\u003e\n\u003cp\u003eDifferences existed prior to TPT initiation as well: Symptomatic HHCs were twice as likely to have reported symptoms at contact screening (6% vs. 3%, p = 0.007), were more likely to have a CXR suggestive of TB (3.1% vs. 1.6%, p \u0026lt; 0.001), and experienced longer delays in initiating TPT (median +14 days; mean +7 days).\u003c/p\u003e\n\u003ch3\u003eTable 2 Summary Statistics for surveyed and available (N = 3,998); segregated by symptomatic status\u003c/h3\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003enon-symptomatic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003esymptomatic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003cem\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eN = 3,658\u003cem\u003e\u003csup\u003e1\u003c/sup\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eN = 340\u003cem\u003e\u003csup\u003e1\u003c/sup\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eTB status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; tb positive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e22 (6.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1,864 (51%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e183 (54%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1,793 (49%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e157 (46%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; transgender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1 (\u0026lt;0.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.016*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Median (Q1, Q3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e23 (11, 39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e19 (9, 38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Mean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eAge.Category\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.004**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 1. \u0026lt;=5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e503 (14%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e58 (17%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 2. 6-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e736 (20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e84 (25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 3. \u0026gt;15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2,419 (66%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e198 (58%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eHHC is (Relationship with index TB patient)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026gt;0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; child\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e113 (3.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3 (0.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; grandchild\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e95 (2.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9 (2.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; grandparent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7 (0.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; parent/parent-in-law\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1,432 (39%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e140 (41%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; relative/others\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e614 (17%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e57 (17%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; sibling\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e402 (11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e43 (13%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; son/daughter-in-law\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e32 (0.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4 (1.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; spouse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e465 (13%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e47 (14%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; unknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e498 (14%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e37 (11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eOccupation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.028*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; agri \u0026amp; livestock\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e226 (6.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e14 (4.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; homemaker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e982 (27%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e80 (24%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; labor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e584 (16%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e54 (16%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; no information\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e347 (9.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e37 (11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; salaried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e425 (12%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e39 (11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; student\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1,094 (30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e116 (34%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eType House\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; concrete\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1,976 (54%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e167 (49%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; kaccha\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e197 (5.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e31 (9.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; mixed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1,482 (41%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e142 (42%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; unknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3 (\u0026lt;0.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eType Cooking Fuel\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.048*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; both\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1,472 (40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e164 (48%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; clean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1,818 (50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e134 (39%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; unclean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e365 (10.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e42 (12%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; unknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3 (\u0026lt;0.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ePre-TPT Screening\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eCxR Done for\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3,085 (84%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e289 (85%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eCxR Test Results\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.049*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; cxr not suggestive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3,037 (98%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e280 (97%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; cxr suggestive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e48 (1.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9 (3.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e(Pre-TPT) Asymptomatic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3,544 (97%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e320 (94%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.007**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e(Pre-TPT) Sym Cough\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e79 (2.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e15 (4.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.009**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e(Pre-TPT) Sym Weight Loss\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6 (0.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2 (0.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.094\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e(Pre-TPT) Sym Night Sweat\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6 (0.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e(Pre-TPT) Sym Fever\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e44 (1.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6 (1.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eTPT Details\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eDrug Regimen\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 3HP (12-weeks-weekly rifapentine +isoniazid)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e810 (22%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e49 (14%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 6H (6-months-daily isoniazid)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2,848 (78%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e291 (86%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eTime to TPT initiation (from diag of index patient)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.002**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Median (Q1, Q3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e58 (32, 102)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e72 (38, 116)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Mean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eTPT.completion.days - 3HP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Median (Q1, Q3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e84 (84, 93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e84 (84, 91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Mean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eTPT.completion.days - 6H\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Median (Q1, Q3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e180 (179, 185)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e180 (179, 188)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Mean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e185\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e189\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ePost TPT Follow Ups\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eFirst Follow up Done\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3,507 (96%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e338 (99%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.001**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eFirst Follow up after (days since TPT completion)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Median (Q1, Q3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e333 (300, 367)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e331 (294, 363)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Mean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e330\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSecond Follow up Done\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3,557 (97%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e322 (95%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.009**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSecond Follow up after (days since TPT completion)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Median (Q1, Q3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e449 (416, 482)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e445 (412, 481)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Mean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e449\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e447\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eFollow Up Status*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; no TB sym\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3,658 (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; started ATT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e22 (6.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; symptomatic, no med consult\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e191 (56%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; symptomatic, pending med consult\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e92 (27%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; symptomatic, TB not diagnosed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e35 (10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eRisk Factors**\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNo Risk Factors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2,956 (81%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e271 (80%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHealthcare worker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e15 (0.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3 (0.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMigrant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e173 (4.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e15 (4.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eConstruction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e65 (1.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12 (3.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.025*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMiner\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e28 (0.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6 (1.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTribal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e102 (2.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10 (2.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eRefugee\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2 (\u0026lt;0.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePrisoner\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2 (\u0026lt;0.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2 (0.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.003**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eUrban Slum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e381 (10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e38 (11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eComorbidities**\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNo Comorbidities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3,481 (95%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e313 (92%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.012*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDiabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e101 (2.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9 (2.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026gt;0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e91 (2.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e16 (4.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.015*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eKidney\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3 (\u0026lt;0.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1 (0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLiver\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2 (\u0026lt;0.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3 (0.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCardio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7 (0.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1 (0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBronchial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7 (0.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4 (1.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1 (\u0026lt;0.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eLifestyle Risks**\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eAlcohol\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e90 (2.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11 (3.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eTobacco\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e417 (11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e47 (14%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ePost-TPT Symptomatic Screening (Incidence)\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eNumber of Symptoms:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAsymptomatic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3,658 (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSymptoms = 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e221 (65%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSymptoms = 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e93 (27%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSymptoms = 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e18 (5.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSymptoms = 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8 (2.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eIndividual Symptoms:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSym Cough\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e212 (62%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSym Weight Loss\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e49 (14%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSym Night Sweat\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e16 (4.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSym Fever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e216 (64%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003e\n \u003cp\u003e\u003cem\u003e*Follow up status is derived from the two follow ups conducted; the exact follow ups received are detailed in supplementary text\u003cbr\u003e\u0026nbsp;**An individual can have more than one risk factor, comorbidity or/and lifestyle risks.\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e\u003csup\u003e1\u003c/sup\u003e\u003c/em\u003e n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e *p\u0026lt;0.05; **p\u0026lt;0.01; ***p\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003eMedical consultations and TB Diagnosis\u003c/h2\u003e\n\u003cp\u003eAmong symptomatic HHCs, the likelihood of seeking a medical consultation, as well as getting diagnosed, increased with symptom burden (Figure S1, S7 Table). Regional differences were evident: none of the symptomatic contacts from Bihar reported seeking care, while those from Delhi and Haryana were more likely to do so (S6 Table). Socioeconomic factors such as type of cooking fuel and housing, which are themselves also closely linked, influenced care seeking (S10\u0026ndash;S12 Tables). Among symptomatic HHCs, assessment uptake was higher among those in concrete housing (21%) than those living in \u003cem\u003ekaccha\u003c/em\u003e (13%) or mixed dwellings (13%), though the difference was not statistically significant (p = 0.13). Difference with respect to fuel usage showed high statistical significance (p\u0026lt;0.001), 26% of clean-fuel users sought TB assessment compared with 14% of unclean-fuel users. These differences affected TB positivity as well, those in concrete housing or/and using clean fuel had higher TB positivity rates. However, once analyses were restricted to individuals who actually sought consultation, these differences narrowed substantially (e.g., 43% vs 25% for concrete vs \u003cem\u003ekaccha\u003c/em\u003e; 43% vs 33% for clean vs unclean fuel). All 22 TB diagnoses occurred exclusively within the symptomatic cohort, with median time to ATT initiation being 187 days (Table 1). TB positivity varied by diagnostic testing, with those evaluated through clinical assessment and CXR having lower positivity rates (Table 3, Figure 3).\u003c/p\u003e\n\u003ch3\u003eTable 3\u003c/h3\u003e\n\u003cp\u003eTB positivity by type of diagnostic test (N = 57)\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eAFB/FNAC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eClinical/CXR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eNAAT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eOther*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003cem\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eN = 10\u003cem\u003e\u003csup\u003e1\u003c/sup\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eN = 32\u003cem\u003e\u003csup\u003e1\u003c/sup\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eN = 10\u003cem\u003e\u003csup\u003e1\u003c/sup\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eN = 5\u003cem\u003e\u003csup\u003e1\u003c/sup\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSymptoms (N)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.037*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Median (Q1, Q3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.00 (1.00, 2.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (1.00, 2.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.00 (2.00, 3.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (1.00, 2.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Mean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eTB Positivity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7 (70%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5 (16%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8 (80%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2 (40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003e\n \u003cp\u003e\u003cem\u003e*Diagnostic test used for those within other category was not available\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003e\n \u003cp\u003e\u003cem\u003e\u003csup\u003e1\u003c/sup\u003e\u003c/em\u003e n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003e\n \u003cp\u003e\u003cem\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e *p\u0026lt;0.05; **p\u0026lt;0.01; ***p\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003eAlgorithmic Screening\u003c/h2\u003e\n\u003cp\u003eWe evaluated if certain symptoms or combination of symptoms can guide meaningful diagnostic testing among targeted risk-groups, to optimize for TB detection in resource constrained environments. \u003cem\u003eBecause likelihood measures were calculated across all symptomatic HHCs, regardless of whether they underwent diagnostic testing, the observed TB positivity is likely underestimated, as untested individuals were implicitly classified as TB-negative.\u003c/em\u003e Among individual symptoms, weight loss stood out as a strong indicator, with 20% (n = 10) of those experiencing it being diagnosed with TB, despite its low prevalence (1.2%), leading to high accuracy (85%) and specificity (88%) (Tables 4, S13). TB diagnoses were rare without cough (0.03%, n = 1); leading to highest sensitivity (95%) but low precision (10%). Combining symptoms improved diagnostic performance (Tables 5, S10), with weight loss and recurrent cough giving the most optimum measure for screening: 91% accuracy, 94% specificity, and 34% precision, further confirmed by survival analysis (Figure 4). Logistic regression further supports these findings: symptom burden and specific symptom combinations (particularly recurrent cough and weight loss) were the strongest predictors of seeking a medical consultation, whereas socio-demographic factors contributed far less explanatory power (S14 Table). \u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eTable 4\u003c/h3\u003e\n\u003cp\u003eRisk Ratios for each symptom, along with the probability / likelihood of each symptom \u0026amp; associated probability of having TB with those symptoms\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e#Symptoms\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eRisk Ratio (RR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eRR - 95% C.I.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eLikelihood of having the symptom\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eLikelihood of having TB with these symptoms\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eLikelihood of having TB without these symptoms\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eRecurrent Cough\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e394\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e53, 2916\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.1% (212)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10% (21/212)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.03% (1/3978)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eWeight Loss\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e32, 155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.2% (49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e20% (10/49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.29% (12/4141)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNight Sweat\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e14, 126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.4% (16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e19% (3/16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.46% (19/4174)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e28, 243\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.2% (216)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8% (18/216)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.10% (4/3974)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003e\n \u003cp\u003eInterpretation: Weight loss was associated with the second-highest risk ratio for developing TB (70), following cough (394), though with a narrower 95% confidence interval. This indicates that individuals with weight loss are 70 times more likely to develop TB compared to those without this symptom. Additionally, weight loss had the highest proportion of individuals reporting TB among symptomatic cases, at 20%.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch3\u003eTable 5\u003c/h3\u003e\n\u003cp\u003ePerformance of symptom-based screening algorithms under the assumption of complete testing; actual TB assessment numbers are also reported.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSymptom Combination\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e#Symptoms\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e#HHCs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eTB Assessment Done\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eTB Diagnosed\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eAccuracy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ePrecision\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSensitivity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSpecificity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eRecurrent Cough \u0026amp; Weight Loss\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e13 (45%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e91%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e35%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e46%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e94%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(87.3%, 93.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(19.9%, 52.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(26.9%, 65.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(90.9%, 96.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eRecurrent Cough \u0026amp; Weight Loss\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026gt;=3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10 (45%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e92%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e36%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e36%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e96%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(88.4%, 94.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(19.7%, 57%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(19.7%, 57%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(92.7%, 97.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eRecurrent Cough \u0026amp; Fever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026gt;=3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11 (44%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e92%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e36%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e41%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e95%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(88%, 94%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(20.2%, 55.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(23.3%, 61.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(92%, 96.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eWeight Loss \u0026amp; Fever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026gt;=3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10 (43%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e92%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e35%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e36%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e95%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(88%, 94%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(18.8%, 55.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(19.7%, 57%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(92.4%, 97.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eWeight Loss \u0026amp; Night Sweat\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026gt;=3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3 (33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e92%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e22%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e98%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(88.7%, 94.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(6.3%, 54.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(2.5%, 27.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(95.5%, 98.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eRecurrent Cough \u0026amp; Weight Loss\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026gt;=3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e14 (42%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e90%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e33%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e93%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(86.7%, 93%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(19.8%, 50.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(30.7%, 69.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(89.7%, 95.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"10\"\u003e\n \u003cp\u003e\u003cstrong\u003eNote\u003c/strong\u003e: Select algorithms are presented here, which allow for at least 33% sensitivity and precision. For the full list, refer to supplementary text (S13 Table)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Discussion","content":"\u003cp\u003eTo the best of our knowledge, this is the first study to document symptom profiles, care-seeking behaviour, and subsequent TB diagnoses among household contacts who have completed TPT. Conducted within programmatic settings, the analysis provides critical insights on risk factors among HHCs who have completed TPT, which can be used for targeted interventions.\u003c/p\u003e\n\u003ch2\u003eCare Seeking: Gap between Symptom Onset \u0026amp; Medical Consultation\u003c/h2\u003e\n\u003cp\u003eOur analysis finds a substantial gap between symptom onset and care seeking among household contacts; only 17% HHCs actually went for a TB assessment, and 27% seemed to delay care-seeking. A lack of care seeking among those with TB-like symptoms, as well as HHCs, is well-documented [11, 12], and is often due to misattribution to mild or common illnesses [13, 14]. In our cohort, however, the lack of care seeking, may also reflect a (false) sense of security following the TPT completion. Delayed evaluation, particularly among individuals with active TB, can increase the likelihood of more severe or drug-resistant disease [15\u0026ndash;19]. Lack of care seeking may also reflect systemic regional level challenges, reflective of differences in access, awareness, and socioeconomic constraints, and directly influencing identification of TB among the most vulnerable. Diagnostic gaps are known contributors to the \u0026ldquo;missing millions\u0026rdquo; in India\u0026rsquo;s TB care cascade [20], and disproportionately affect socio-economically disadvantaged groups [21]. In our cohort, when diagnostic assessment becomes more evenly distributed across socioeconomic groups, the observed differences in TB positivity narrow considerably, highlighting the need for more accessible and equitably delivered diagnostic testing, as well as stricter adherence to follow-up protocols to ensure timely evaluation of all symptomatic contacts.\u003c/p\u003e\n\u003ch2\u003eAlgorithmic screening, a useful approach in resource constrained settings\u003c/h2\u003e\n\u003cp\u003eConsultative follow ups post TPT, hence become a critical measure [4], in supporting symptomatic triage for diagnostic screening among high-risk cohorts. Individual symptoms are found to be poor predictors \u0026ndash; both in our cohort, and in historical literature [22]. Recent meta-analyses demonstrate that the presence of multiple symptoms, such as cough, weight loss, and fever, are significant markers for TB diagnosis [15, 23]. Our findings corroborate this, while also suggesting that certain symptom combinations can be used for prioritizing diagnostic testing in resource-constrained settings. Importantly, we identified a small high-risk subgroup; HHCs with weight loss and recurrent cough, or with \u0026ge;3 symptoms (0.8% of the cohort). Although fewer than half of these individuals sought care, a high proportion (11 of 14 evaluated) were diagnosed with TB. Applied programmatically, prioritising diagnostic testing for this group could yield substantial gains in early case detection. If extrapolated to the ~1 million individuals who received TPT\u0026nbsp;in India\u0026nbsp;in 2023 [24], targeted testing of just 0.8% (~7,876 people) could identify more than 2,600 TB cases. While a simplified extrapolation, the latte is also underestimated due to lack of diagnostic testing, further highlighting the probable cost effectiveness and feasibility of symptom-based triage.\u003c/p\u003e\n\u003ch2\u003eCXR Screening \u0026amp; TPT Initiation\u003c/h2\u003e\n\u003cp\u003eIndividuals with a suggestive pre-TPT CXR were considerably more likely to develop symptoms later, underscoring the latter\u0026rsquo;s value for risk stratification. Additionally, while both NTEP and WHO outline detailed pathways for TPT initiation, including steps like ruling out active TB and adapting approaches for individuals with comorbidities, they stop short of specifying a timeframe, offering only a general recommendation to start TPT \u0026ldquo;\u003cem\u003eas soon as possible\u003c/em\u003e.\u0026rdquo; Our study suggests that delays in initiation are associated with an increased risk of developing TB-like symptoms post-TPT. Regional variations were significant in both CXR uptake and time taken to TPT initiation; Haryana had a 70% CXR uptake, and contacts in Bihar were initiated on TPT more than three months post the index patient diagnosis, indicating opportunities to strengthen health systems.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eAdherence \u0026amp; Drug Regimen\u003c/h2\u003e\n\u003cp\u003eAdherence during TPT is essential, as incomplete treatment can jeopardize the protective effect of TPT, and may contribute to the emergence of drug resistance [25]. Sub-optimal adherence [26], as well as low awareness of TB treatment , is commonly cited\u0026nbsp;[27]. A perceived lack of need, coupled with long regimen, and associated side effects, have also been attributed as reasons precluding the initiation and completion of TPT [7, 26]. Recent studies, however, have found that those on shorter regimens are much more likely to complete the regimen [26, 28], with one study noting that those on 3HP regimens are seven times more likely to complete the regimen\u0026nbsp;[27]. In our cohort, those on 3HP were less likely to develop symptoms, suggesting that prioritizing shorter regimens can help maximize TPT associated benefits.\u003c/p\u003e\n\u003ch2\u003eRegional variations\u003c/h2\u003e\n\u003cp\u003eState specific differences in TB prevalence are well documented in literature [1, 29, 30], and our findings further highlight patterns related to risk markers, health-seeking behaviours, and access to diagnostic services. For instance, the higher use of unclean fuels, kaccha housing, and lower medical consultation rates in Bihar and Uttar Pradesh may explain the higher proportion of symptomatic HHCs, yet a lower number of those who seek consultation. Broadly, the findings suggest that state specific strategies are essential to democratize TB detection and prevention across the country \u0026ndash; as TB positivity is a feature of not only underlying risk factors, but also diagnostic capacities and health seeking behaviours.\u003c/p\u003e\n\u003ch2\u003eRecommendations for operational TPT guidelines\u003c/h2\u003e\n\u003cp\u003eOur study identifies key areas where current TPT guidelines can be strengthened to improve early TB detection, follow-up, and program efficiency. We strongly recommend mandating diagnostic testing for targeted subgroups of household contacts (HHCs) who, based on our analysis, are at higher risk of developing TB. These include HHCs with a) suggestive CXR findings prior to TPT, b) weight loss during or after TPT, and c) combinations of specific symptoms (e.g., weight loss and cough). While all symptomatic HHCs should ideally undergo TB testing, such decision systems can support diagnostic prioritization in resource-constrained settings. Symptom-based questionnaires offer a practical tool for such triage, balancing sensitivity and specificity (S13 Table). We also recommend making CxR screening a mandatory pre-initiation step - in order to support ruling out TB, and also to capture high-risk HHCs post TPT. In parallel, counselling should be strengthened at both TPT initiation and completion. Follow-ups by healthcare workers can also be aligned with TB treatment follow-up schedules to reduce logistical burden and improve efficiency. This is especially convenient for individuals on the 6H regimen, which often concludes alongside DS-TB treatment.\u003c/p\u003e\n\u003cp\u003eFurther, we recommend earlier post-TPT follow-ups, ideally within 60\u0026ndash;90 days and again around 180 days. While current guidelines initiate follow-up at the 6-month mark, our data show that a significant share of TB cases are diagnosed earlier, thus warranting earlier follow ups. Finally, risk markers (e.g., poor housing, use of biomass fuel, occupational exposure, comorbidities, and tobacco use), if integrated within the Nikshay system, can help healthcare workers prioritize the most vulnerable. These augmentations can support a more pro-active and risk informed approach to TB prevention, contributing to more effective TB control.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur study provides compelling evidence for region-specific, data-driven strategies to strengthen TB preventive treatment (TPT) follow-up in India. By analyzing symptomatic progression across a diverse cohort from nine states, the analysis highlights the importance of localized interventions that consider housing conditions, access to diagnostics, and health-seeking behaviours - all key predictors of TB outcomes. The insights also underscore the need to expand TPT access and introduce mandatory diagnostic testing for higher-risk household contacts, including universal pre-TPT chest X-ray screening. Collectively, the evidence supports pragmatic refinements to existing NTEP guidelines.\u003c/p\u003e\n\u003ch1\u003eLimitations\u003c/h1\u003e\n\u003cp\u003eThis study was conducted within the programmatic framework of JEET 2.0, and certain limitations are be noted. \u003cstrong\u003eFirst\u003c/strong\u003e, only two follow-ups were conducted, approximately four months apart. These follow-ups were designed as an operational add-on to routine services and do not align with the recommended four-visit TPT follow-up schedule over two years (6, 12, 18, and 24 months). Earlier and later follow-ups, particularly at 6, 18, and 24 months, may have provided a more complete understanding of post-TPT TB breakdown. \u003cstrong\u003eSecond\u003c/strong\u003e, although the sample size was sufficiently powered at multiple strata, it may not fully reflect the diversity of health-seeking behaviours, healthcare infrastructure, and TB incidence across India. \u003cstrong\u003eThird\u003c/strong\u003e, the study relies on self-reported symptoms and healthcare-seeking behaviours, which are subject to recall and social desirability bias. \u003cstrong\u003eFourth\u003c/strong\u003e, certain objective measurements, such as the precise loss of weight from baseline, could have strengthened symptom assessment. \u003cstrong\u003eFifth\u003c/strong\u003e, as an observational study, causal relationships cannot be inferred. Key associations may be influenced by unmeasured confounders, including socioeconomic status, education, and access to healthcare. \u003cstrong\u003eSixth,\u0026nbsp;\u003c/strong\u003eincomplete diagnostic information, particularly among individuals who did not seek medical care, limits our ability to ascertain true TB incidence and track symptom evolution. Because performance metrics for symptom-based algorithms were calculated under the assumption that all symptomatic HHCs underwent diagnostic testing, untested individuals were implicitly classified as TB-negative. Given that fewer than half sought medical consultation, true TB positivity is likely underestimated. Future studies incorporating routine or universal diagnostic testing at follow-up would enable more accurate estimation of TB risk. \u003cstrong\u003eFinally\u003c/strong\u003e, follow-up relied primarily on symptomatic screening, although TB is also noted among asymptomatic contacts, further limiting our ability to determine risk. Future research examining both symptomatic and asymptomatic TB transmission could inform more robust follow-up protocols and enhance the effectiveness of TPT scale-up strategies.\u003c/p\u003e"},{"header":"List of abbreviations","content":"\u003cp\u003eHHC \u0026ndash; Household Contact\u003c/p\u003e\n\u003cp\u003eAFB - Acid-Fast Bacillus\u003c/p\u003e\n\u003cp\u003eFNAC - Fine Needle Aspiration Cytology\u003c/p\u003e\n\u003cp\u003eADR \u0026ndash; Adverse Drug Reaction\u003c/p\u003e\n\u003cp\u003eJEET \u0026ndash; Joint Effort for Elimination of Tuberculosis\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOLS \u0026ndash; Ordinary least squares\u003c/p\u003e\n\u003cp\u003eCI \u0026ndash; Confidence interval\u003c/p\u003e\n\u003cp\u003eIQR \u0026ndash; Interquartile range\u003c/p\u003e\n\u003cp\u003ePPSA \u0026ndash; Private Provider Support Agency\u003c/p\u003e\n\u003cp\u003eTB \u0026ndash; Tuberculosis \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNTEP \u0026ndash; National Tuberculosis Elimination Program\u003c/p\u003e\n\u003cp\u003eNSP \u0026ndash; National Strategic Plan for Elimination of Tuberculosis\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eWe would like to acknowledge Huzaifa Bilal, who worked on the sampling design and questionnaire used during follow ups, whilst also leading training of field workers and coordinating data collection.\u003c/p\u003e\n\u003cul class=\"decimal_type\"\u003e\n \u003cli\u003eEthics approval and consent to participate: A study protocol was approved by the Monk Prayogshala Institutional Review Board and Ethics Review Committee (Aug 26, 2025). A waiver of informed consent was granted as the analysis relied on anonymized and de-identified programmatic data.\u003c/li\u003e\n \u003cli\u003eConsent for publication: Not applicable\u003c/li\u003e\n \u003cli\u003eAvailability of data and materials: The specific data utilized for this research has also been made available for public research use at the GitHub link: https://github.com/ridhimasodhi/post-TPT-progression/\u003c/li\u003e\n \u003cli\u003eCompeting interests: None declared\u003c/li\u003e\n \u003cli\u003eFunding: Data used in this study were collected under the JEET 2.0 program, which was funded by the Global Fund, and implemented across India (Grant Number: IND-T-WJCFP01-2020-2022). The William J. Clinton Foundation supported the preparation of this manuscript as part of its ongoing technical and programmatic support to TB prevention and care in India.\u003c/li\u003e\n \u003cli\u003eAuthors\u0026apos; contributions: MS and PD designed the study; SSA managed data collation for the study and conducted initial analyses. RS conducted the analysis, created the visualizations, and wrote the first draft; manuscript was revised across multiple iterations, which were contributed to by all authors; RS did the final editing; all authors read and approved the final version.\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMinistry of Health \u0026amp; Family Welfare-Government of India. National TB Prevalence Survey of India (2019-21) Summary Report.\u003c/li\u003e\n\u003cli\u003eWorld Health Organization, editor. Guidelines on the management of latent tuberculosis infection. 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Tuberculosis Diagnostics in 2015: Landscape, Priorities, Needs, and Prospects. J Infect Dis. 2015;211 suppl_2:S21\u0026ndash;8. https://doi.org/10.1093/infdis/jiu803.\u003c/li\u003e\n\u003cli\u003eWard H A, Marciniuk DD, Pahwa P, Hoeppner VH. Extent of pulmonary tuberculosis in patients diagnosed by active compared to passive case finding. 8,5 (2004): 593-7 The international journal of tuberculosis and lung disease : the official journal of the International Union against Tuberculosis and Lung Disease.\u003c/li\u003e\n\u003cli\u003eAcu\u0026ntilde;a-Villaordu\u0026ntilde;a C, Jones-L\u0026oacute;pez EC, Marques-Rodrigues P, Fregona G, Gaeddert M, Ribeiro-Rodrigues R, et al. Sustained effect of isoniazid preventive therapy among household contacts in Brazil. Int J Tuberc Lung Dis. 2022;26:406\u0026ndash;11. https://doi.org/10.5588/ijtld.21.0438.\u003c/li\u003e\n\u003cli\u003eMarx FM, Yaesoubi R, Menzies NA, Salomon JA, Bilinski A, Beyers N, et al. 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J Clin Tuberc Mycobact Dis. 2020;19:100144. https://doi.org/10.1016/j.jctube.2020.100144.\u003c/li\u003e\n\u003cli\u003eHargreaves JR, Boccia D, Evans CA, Adato M, Petticrew M, Porter JDH. The Social Determinants of Tuberculosis: From Evidence to Action. Am J Public Health. 2011;101:654\u0026ndash;62. https://doi.org/10.2105/AJPH.2010.199505.\u003c/li\u003e\n\u003cli\u003eMiller LG, Asch SM, Yu EI, Knowles L, Gelberg L, Davidson P. A Population-Based Survey of Tuberculosis Symptoms: How Atypical Are Atypical Presentations? Clin Infect Dis. 2000;30:293\u0026ndash;9. https://doi.org/10.1086/313651.\u003c/li\u003e\n\u003cli\u003eYayan J, Franke K-J, Berger M, Windisch W, Rasche K. Early detection of tuberculosis: a systematic review. Pneumonia. 2024;16:11. https://doi.org/10.1186/s41479-024-00133-z.\u003c/li\u003e\n\u003cli\u003eIndia TB Report - 2023. New Delhi, India: Central TB Division, Ministry of Health and Family Welfare, Government of India.\u003c/li\u003e\n\u003cli\u003eGlobal Programme on Tuberculosis and Lung Health (GTB), Guidelines Review Committee. Latent tuberculosis infection: updated and consolidated guidelines for programmatic management. 2018.\u003c/li\u003e\n\u003cli\u003eAn Y, Khun KE. Factors associated with incomplete tuberculosis preventive treatment: a retrospective analysis of six-years programmatic data in Cambodia. Sci Rep. 2024;14:18458. https://doi.org/10.1038/s41598-024-67845-6.\u003c/li\u003e\n\u003cli\u003eSharma N, Basu S, Khanna A, Sharma P, Chandra S. The intention to receive tuberculosis preventive therapy in adult household contacts of pulmonary TB patients in Delhi, India. J Infect Dev Ctries. 2022;16:298\u0026ndash;304. https://doi.org/10.3855/jidc.14910.\u003c/li\u003e\n\u003cli\u003eChen H, Zhang H, Cheng J, Sun D, Wang Q, Wu C, et al. Adherence to preventive treatment for latent tuberculosis infection in close contacts of pulmonary tuberculosis patients: A cluster-randomized controlled trial in China. Int J Infect Dis. 2024;147:107196. https://doi.org/10.1016/j.ijid.2024.107196.\u003c/li\u003e\n\u003cli\u003eGolandaj JA, Naikar SK, Hallad JS. Trends and sub-national disparities in TB notifications in India: Insights from HMIS data. Indian J Tuberc. 2022;69:141\u0026ndash;50. https://doi.org/10.1016/j.ijtb.2021.04.005.\u003c/li\u003e\n\u003cli\u003eChauhan A, Parmar M, Dash GC, Solanki H, Chauhan S, Sharma J, et al. The prevalence of tuberculosis infection in India: A systematic review and meta-analysis. Indian J Med Res. 2023;157:135\u0026ndash;51. https://doi.org/10.4103/ijmr.ijmr_382_23.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-8176310/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8176310/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: India carries the highest TB burden globally, with 31% of the population estimated to be infected with \u003cem\u003eMycobacterium tuberculosis,\u003c/em\u003e albeit in a dormant form\u003cem\u003e. \u003c/em\u003eHousehold contacts (HHCs) represent a particularly vulnerable group due to intense exposure, with TB prevalence several-fold higher than the general population. To mitigate progression from TB infection to active TB disease, the government expanded TB preventive therapy eligibility to all HHCs in 2021. While evidence exists on TB prevalence among HHCs, limited evidence exists about post-TPT outcomes or risk factors for disease progression. This study evaluates symptoms, care-seeking behaviours, and TB occurrence among HHCs who completed TPT under programmatic conditions across nine Indian states.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: A total of 4,012 household contacts of pulmonary drug-sensitive TB patients who successfully completed TB preventive therapy between October 2022 and March 2023, were sampled from nine Indian states, using a stratified sampling approach. Of these, 78% received 6H and 22% received 3HP. Contacts were followed twice post-TPT completion, within 12 months and again within 18 months, to capture symptomatic incidence, care-seeking behaviour, diagnostic pathways, and TB outcomes. Descriptive statistics, logistic regression, and survival analysis were used to identify predictors of symptom development, care-seeking, and TB diagnosis, and to evaluate symptom-based algorithms for triage in resource-limited settings.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: Marked state-level variation was observed in CXR screening uptake, TPT initiation timeliness, and access to diagnostic services, influencing symptomatic prevalence and TB detection rates. Symptom burden was the strongest predictor of TB diagnosis, with specific combinations, particularly recurrent cough with weight loss, showing high diagnostic accuracy. All TB diagnoses (n=22) occurred among symptomatic individuals, and majority were detected within six months of TPT completion. However, only 17% of symptomatic contacts had gone for a medical consultation by the time of follow ups, and only 18% were assessed with NAAT. Suggestive pre-TPT CXR findings and delays in TPT initiation were associated with higher post-TPT symptom incidence. Individuals on 3HP reported fewer post-TPT symptoms than those on 6H.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eHealth-system disparities across states shape TPT outcomes and the timely identification of post-TPT TB. Strengthening routine CXR screening, reducing delays in TPT initiation, improving adherence support, especially for longer 6H regimens, and implementing earlier, risk-stratified follow-up could enhance early TB detection among high-risk HHCs. Integrating symptom-based triage tools and ensuring equitable access to diagnostic evaluation are critical for optimizing the preventive impact of TPT in resource-constrained settings.\u003c/p\u003e","manuscriptTitle":"Post-TPT Risk of Tuberculosis Among Household Contacts: A Multistate Observational Cohort Study from India","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-01 08:40:47","doi":"10.21203/rs.3.rs-8176310/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"90bdcff5-724a-4b47-8b78-de4f2a0f46bc","owner":[],"postedDate":"December 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-03T07:56:18+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-01 08:40:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8176310","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8176310","identity":"rs-8176310","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}