Active Tuberculosis case finding using portable radiography reveals high undetected burden in rural Himachal Pradesh

Active Tuberculosis case finding using portable radiography reveals high undetected burden in rural Himachal Pradesh | 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 Active Tuberculosis case finding using portable radiography reveals high undetected burden in rural Himachal Pradesh Sunil Kumar Raina, Devendra Singh Dadhwal, Raman Chauhan, Hansraj Chaudhary, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8957491/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 16 You are reading this latest preprint version Abstract Background Active Case Finding (ACF) in tuberculosis (TB), a provider-initiated, systematic, community-based screening process targets high-risk populations and is used across India including Himachal Pradesh as a strategy to eliminate TB. Himachal Pradesh reports a disproportionately high proportion of extra-pulmonary TB (EP-TB) compared with the national average. An implementation study deploying portable digital X-ray devices in Kangra district as part of ACF identified structural barriers to case detection. Methods 146 villages of Block Dadasibha (population 1,15,766 ) in Kangra district were covered through preliminary screening of 20,207 high-risk individuals by Accredited Social Health Activist (ASHA) followed by screening camps using hand held x-rays and sputum smear examination of suspects held on weekdays using fixed facility and outreach approach. All X-rays were read by a single pulmonologist. NIKSHAY portal data for Kangra District (Year 2024, N = 2,952 cases) were used to benchmark expected prevalence and disease-type distribution. Results 7,969 people (770 chest symptomatic and 6,694 high-risk individuals) reported for TB screening at camps. Of these, 7,409 underwent X-ray screening using handheld devices, with 704 cases flagged as X-ray suggestive. Sputum testing was completed for 650 individuals, with only 2 confirmed as TB positives. The benchmark district NIKSHAY data revealed a TB notification rate of 193 per 100,000; 36.6% of these as EP-TB; 60.6% of cases were in the 20–59-year age group and only 37 (1.3%) were detected through ACF. Discussion The low ACF yield of our implementation study like the benchmark is explainable by four quantifiable structural barriers: (1) high EP-TB proportion (2) weekday-only camp timing (3) mobility barriers that reduced attendance among the elderly (4) residual factors. Together these barriers account for the entire observed gap between expected (14.3) and detected (2) case. Conclusion Effective TB screening in this setting requires a multilayered, context-adapted strategy each layer calibrated to the local epidemiological and geographic context. Tuberculosis Active case finding Portable X-ray Extra pulmonary tuberculosis Multilayered screening context-adapted strategies Introduction Tuberculosis (TB) remains the leading infectious-disease killer globally, and India accounts for the largest share of the world's TB burden. The National TB Elimination Programme (NTEP) targets elimination, defined as less than one case per million by 2025. [1] Active case finding (ACF) is one of the main strategies for reaching out to the large pool of undiagnosed or under-diagnosed cases, particularly in geographically remote and resource-limited settings [1]. Portable, hand-held digital X-ray devices have emerged as promising field tools for point-of-care TB screening in hard-to-reach communities as they overcome the traditional barriers of transporting patients to imaging centers. Several pilot programmes across India have demonstrated their technical feasibility. [2] However, the operational yield of X-ray–driven ACF, that is, the number of confirmed TB cases per number of individuals screened will not only depend on the sensitivity of the imaging device but also on the epidemiological profile of TB in the target population as well by the logistics of the screening programme itself. Himachal Pradesh is a northern hill state in India with unique TB epidemiology. NIKSHAY surveillance data (table 1) shows that the proportion of extra-pulmonary TB (EP-TB) in Himachal Pradesh is 25–40%, roughly double the national average of 15–20% [4]. EP-TB, which includes lymph-node, pleural, abdominal, spinal, and other forms is fundamentally undetectable by chest-symptom screening and sputum testing. In addition, the demographic and socioeconomic characteristics of hill populations introduce specific barriers to ACF; daily-wage employment among working-age adults, mobility constraints among the elderly, and the logistical challenges imposed by mountainous terrain. A study under the AccEEnd TB project, funded by the Indian Council of Medical Research (ICMR) was conducted in Block Dadasibha of Kangra district. The findings from a secondary analysis using district-level NIKSHAY data to benchmark the observed yield against the expected TB burden and to quantify the contribution of each structural barrier to the gap between the two is being presented here. Crucially, the analysis was designed not merely to catalogue what went wrong but to determine whether the barriers collectively require a fundamentally different, multilayered and context-adapted model of TB screening. Methods Block Dadasibha, Kangra district, Himachal Pradesh, comprising of 146 villages with an estimated population of 115,766 was selected because of it being a low-performing unit in prior TB notification analyses. ASHAs created a line-list of high-risk individuals in every village during the first month of the project, using a standardized (NTEP) set of risk variables (age, diabetes, smoking, previous TB, known contacts of TB patients, and chronic respiratory symptoms etc). Three to four days before each screening camp, ASHAs mobilized both the line-listed high-risk individuals and any currently chest-symptomatic persons to attend the camp at the designated camp site. Camps were conducted on weekdays. At the camp, every attendee underwent a portable digital chest X-ray after obtaining informed consent. A single pulmonologist (DSD; Doctorate of Medicine in Pulmonary Medicine) read all films and classified each as normal, abnormal but not suspicious, or suspicious of TB. Every individual identified as a chest symptomatic (cough of two or more weeks' duration or any other respiratory symptom meeting pre-specified criteria) and every individual whose X-ray was read as suspicious had sputum samples collected as per standard NTEP guidelines. Benchmarking with NIKSHAY Data and Analysis Framework: To place the observed yield in epidemiological context, all TB cases notified in Kangra district during the calendar year 2024 were extracted from the NIKSHAY portal (n = 2,921). These data were used to derive the district TB notification rate, the pulmonary-to-extra-pulmonary ratio, the age and sex distribution of cases, the proportion detected through ACF versus passive case finding (PCF), and the distribution of EP-TB sites. Expected TB cases in the study population were then calculated by applying the district notification rate to the screened population size. Four structural barriers were hypothesized to explain the gap between expected and detected cases; (i) EP-TB invisible to sputum screening; (ii) weekday camp timing excluding working-age adults; (iii) mobility barriers reducing elderly attendance; and (iv) residual factors (stigma, migration). Each barrier's contribution was quantified using the NIKSHAY age, sex, and site distributions as the denominator and further contextualization to the output obtained through screening. Ethical approval was obtained from the Institutional Ethics Committee vide letter no. HFW- H DRPGMC/Ethics /2023/146 date: 30.12.2023. The overarching analytical goal was to determine whether each barrier maps onto a distinct screening layer, and whether together they define a multilayered strategy that the current single-modality design cannot replicate. Results ASHAs identified 20,207 high-risk individuals across 146 villages. Of these, 7,464 people (770 chest symptomatic and 6,694 high-risk individuals) reported for TB screening at camps. 7,409 (99%) of 7464 underwent X-ray screening using handheld devices, with 704 cases flagged as X-ray suggestive. Sputum testing was completed for 650 individuals, with only 2 confirmed as TB positives. A total of 2,921 TB cases were notified in Kangra district during 2024, yielding a notification rate of 193 per 100,000 (as per 2011 census database for population). Pulmonary TB accounted for 1,798 cases (60.9%), while extra-pulmonary TB accounted for 1,059 cases (36.6%). The remaining had unclassified or mixed site data. Of all notified cases, only 37 (1.3%) were detected through ACF; the remainder (2,884; 98.7%) were detected through passive case finding (Table 1 ). Table 1 Kangra district TB notification profile, 2024 (NIKSHAY) Parameter Number % of Total Total TB cases notified (2024) 2,921 100.0 Pulmonary TB 1,798 60.9 Extra-pulmonary TB 1,079 36.6 — Pleural 364 33.7 (of EP) — Other / unspecified 331 30.7 (of EP) — Lymph node 155 14.4 (of EP) — Abdominal 95 8.8 (of EP) — Spinal 72 6.7 (of EP) — TBM 42 3.9 (of EP) ACF-detected 37 1.3 PCF-detected 2,884 98.7 EP = Extra pulmonary. Percentages in parentheses refer to EP-TB subtypes as a proportion of all EP-TB. Males constituted 66.7% of all district TB cases (1948/2921). The working-age group (20–59 years) accounted for 1,777 cases (60.6%), the elderly (60 years and older) for 996 cases (34), and children younger than 10 years for only 19 cases (0.6%) (Table 2 ). Table 2 Age distribution of TB cases, Kangra district 2024 Age Group Cases % of Total 0–9 years 19 0.6 10–19 years 129 4.4 20–29 years 463 15.8 30–39 years 375 12.8 40–49 years 476 16.2 50–59 years 463 15.8 60–69 years 504 17.2 70 + years 492 16.8 Working age (20–59) 1,777 60.6 Elderly (≥ 60) 996 34 Source: NIKSHAY portal. Quantification of Structural Barriers Applying the Kangra district notification rate (193 per 100,000) to the 7,409 X-ray screened individuals, screened yields an expected number of 14.3 TB cases. Detecting only 2 cases therefore represents a 12.3 case deficit. The following analysis attributes this deficit to four measurable structural barriers, each grounded in the NIKSHAY district data (Table 3 ) and further contextualized to screening conducted during the study. Table 3 Four-barrier decomposition of the expected-vs-detected case deficit Barrier Cases Missed % of Deficit Evidence Source Extra-pulmonary TB 5.3 37 NIKSHAY EP proportion (36.6%) Weekday Camp Timing 4.3 29 NIKSHAY working-age share (60.2%) Elderly/ Mobility 2.4 22 NIKSHAY ≥ 60 share (33.7%) Residual Selection Bias 0.3 2% Detected 2.0 — Study outcome Total deficit 12.3 — All expected-case calculations use the Kangra 2024 notification rate (197 per 100,000) applied to 7,409 X-ray screened individuals Barrier 1 — Extra-pulmonary TB (5.3 cases; 37%) NIKSHAY data show that 36.6% of all TB notified in Kangra is extra-pulmonary. Applying this proportion to the 14.3 expected cases yields 5.3 EP-TB cases among the screened population. These cases predominantly pleural, lymph-node, abdominal, and spinal produce no sputum and, unless accompanied by a chest-X-ray abnormality, are entirely invisible to a respiratory-symptom-and-sputum screening strategy. They represent the single largest contributor to the observed deficit. Barrier 2 — Weekday Camp Timing (4.3 cases; 30%) 60.8% of Kangra's TB cases occur in the 20–59-year working-age group. Daily-wage employment, loss of income, and the need for leave approval make weekday attendance at screening camps prohibitive for this group. We estimate that approximately half of working-age TB cases in the screened population did not attend weekday camps — a conservative lower-bound estimate, at or below the observed 56% overall non-attendance rate among all high-risk individuals. This accounts for approximately 4.3 undetected cases Barrier 3 — Mobility Constraints in the Elderly (2.4 cases; 17%) Persons aged 60 years and older constitute 34% of district TB notifications. In hilly terrain, travel even two or three kilometers to an HWC is difficult for individuals with mobility-limiting conditions; many require an accompanying caregiver who, in turn, may be unable to leave work. We estimate that approximately half of the elderly cases in the screened population did not attend weekday camps — a conservative lower-bound estimate, at or below the observed 56% overall non-attendance rate among all high-risk individuals. This accounts for approximately 2.4 undetected cases Barrier 4 —Residual Factors (0.3 cases; 2%) Beyond the three primary barriers, a small residual gap remains attributable to some other factors like acute illness preventing travel on screening days, temporary migration for seasonal work, and reluctance due to TB-associated stigma. While these factors undoubtedly affect attendance, their aggregate contribution to the case-finding deficit appears modest when extrapolated to three structural barriers identified above. Discussion ACF yield of 0.3% in our study though appears low in isolation; however, district-level Nikshay data show that only 1.3% of all TB cases in Kangra are detected through ACF; the vast majority is identified through passive case finding at routine health facilities. When the observed yield is benchmarked against the district notification rate rather than against textbook prevalence figures for high-risk populations the expected number of cases in the screened cohort falls to 14.3. Two cases identified therefore represent a deficit of 12.3, not the 50-plus that would be implied by higher prevalence assumptions. This precision matters for policy, because it focuses the improvement effort on barriers that are operationally addressable. The most striking feature of the Kangra NIKSHAY profile is the 37% EP-TB proportion — more than double the national average [ 5 ]. A screening programme that relies entirely on chest symptoms and sputum is therefore structurally unable to detect more than one-third of the TB burden in this district. This finding is consistent with prior reports from Himachal Pradesh. [ 6 ] Proportionately lower frequency of PTB at higher altitudes, combined with its inverse relationship with altitude, helps explain the high proportion of EPTB cases. [ 7 ] The working-age group (20–59 years) harbors 60.6% of the district's TB cases, yet this is precisely the population least able to attend a camp held during working hours on a weekday. Daily-wage laborers in hill agriculture and construction cannot afford to forego a day's income; salaried workers require formal leave; and TB stigma discourages public screening. Guidelines suggest conducting ACF on first Saturday or first Sunday of every month in order to facilitate the attendance by working persons and school going children. [ 8 ] This single modification is estimated to recover 4.3 out of 12.3, a 30% improvement in yield. Persons aged 60 and older account for 34% of district notifications, yet mountainous terrain and mobility limitations make camp attendance difficult for many of them. Himachal Pradesh has recorded a significant rise in its elderly population between 2011 and 2024, outpacing the national average. The hill state saw an increase of 2.9% from 10.2% to 13.1% of the total population, placing it fourth in the country. [ 9 ] Furthermore, elderly TB frequently presents with constitutional rather than respiratory symptoms, so that a cough-based screening criterion will miss a substantial proportion. Door-to-door screening with portable X-ray for the elderly are logical extensions of the current programme. The findings have implications beyond Kangra. Hill states across northern India share similar epidemiological features. A single-modality ACF protocol cannot, by design, reach the populations it should be reaching. What is needed is not a tweaked version of the existing protocol but a multilayered, context-adapted screening strategy: one in which the timing, reach, diagnostic toolkit, and clinical scope of screening are each calibrated to the epidemiological and geographic features of the target population. The present study provides the barrier-level evidence base on which such a strategy can be designed. The four-barrier analysis identifies four screening layers that together constitute a multilayered, context-adapted strategy. Each layer directly addresses one or more of the structural barriers quantified above; none can substitute for another. First, weekend and evening camps should replace or supplement weekday-only schedules so as to include the working-age population. Second, systematic lymph-node examination should be performed at every camp, with fine-needle aspiration cytology (FNAC) available on-site or via rapid referral; this alone is expected to recover the majority of the lymph-node and pleural TB cases that are currently missed. Third, door-to-door portable X-ray screening should target elderly individuals and persons with limited mobility who are identified by ASHAs but do not attend camps. Fourth, molecular testing (GeneXpert or equivalent) should replace or supplement sputum smear microscopy for all chest symptomatics to close the residual sensitivity gap. If all four modifications are implemented simultaneously, modelling against the district notification data suggests a potential yield of 12 cases from the same screened population — an improvement of six-fold over the observed yield. The implication is clear: the technology is not the constraint. The constraint is the architecture of the screening strategy. A multilayered design — temporal, spatial, clinical, and diagnostic layers operating in concert — is both necessary and, at this cost differential, strongly cost-effective. Taken together, the four barriers are not independent failures but structurally distinct layers of the TB burden in this district. Extra-pulmonary disease demands a clinical and histopathological screening layer; the working-age deficit demands a temporal layer (when camps are held); the elderly deficit demands a spatial layer (where screening reaches); selection bias demands a social and logistical layer. No single screening modality or camp format can address more than one of these layers simultaneously. The data therefore establish that an effective strategy for this context must be multilayered by design, with each layer calibrated to the local epidemiology and geography. Conclusion This study demonstrates that portable digital X-ray technology is operationally feasible in remote hill districts of Himachal Pradesh. However, when it is embedded in a screening strategy limited to weekday camps its yield is constrained by four structural barriers — of which extra-pulmonary disease and working-age exclusion are the two largest. Each barrier is quantifiable, each maps onto a discrete screening layer, and the additional cost of addressing all of them simultaneously is modest. The lesson from Kangra is not that portable X-ray failed, but that TB screening in remote hill districts is inherently a multilayered challenge. Meeting that challenge requires a context-adapted strategy calibrated to local epidemiology, geography, and socioeconomics and should be a priority for the national programme across hill states. Declarations Ethical Approval: Ethical approval was obtained from the Institutional Ethics Committee vide letter no. HFW- H DRPGMC/Ethics /2023/146 date: 30.12.2023. Statement on Methods We confirm that all methods were conducted in accordance with the relevant guidelines and regulations. Consent to participate: The participants were included only after obtaining a freely-given, informed consent from participants. Consent to publish: “Not applicable”. Funding and Conflicts of Interest This data used for this study has been obtained from the work that was funded by the Indian Council of Medical Research (ICMR) under the AccEEnd TB project. The authors declare no conflicts of interest. Author Contribution SKR—Wrote the manuscriptOthers—Reviewed and improved the manuscript Acknowledgements The authors thank the ASHAs of Block Dadasibha for their sustained field work, the staff of the District TB Unit, Kangra, and ICMR for project funding. Data Availability The data generated has been submitted as a year-end report to the funding agency and can be made available to the publishers if required. References NTEP, India TB, Report. 2024. New Delhi: Ministry of Health & Family Welfare; 2024. Datta B, Prakash A, Ford D. et.al. Trehan N. Implementing upfront mobile digital chest x-ray for tuberculosis diagnosis in India-feasibility and benefits. Trans R Soc Trop Med Hyg. 2020; 1;114(7):499–505. Raina SK, Chauhan N, Supehia S. A protocol on Improving tuberculosis detection and accelerating elimination through digital hand-held X-ray units for pre-diagnosis screening in rural communities: An implementation research in a health block of District Kangra, Himachal Pradesh, India. Amrita J Med 0;0:0 (ahead of print). Thakur A, Tomar S, Raina S, et al. Diminishing returns of risk-based tuberculosis control in Kangra district and the case for comprehensive strategies for elimination. Discov Public Health. 2026;23:164. Sharma SK, Ryan H, Khaparde S, et al. Index-TB guidelines: Guidelines on extrapulmonary tuberculosis for India. Indian J Med Res. 2017;145(4):448–63. Thakur A, Tomar S, Raina S, et al. Diminishing returns of risk-based tuberculosis control in Kangra district and the case for comprehensive strategies for elimination. Discov Public Health. 2026;23:164. Pérez-Guzmán C, Vargas MH, Arellano-Macías Mdel R, Hernández-Cobos S, García-Ituarte AZ, Serna-Vela FJ. Clinical and epidemiological features of extrapulmonary tuberculosis in a high incidence region. Salud Publica Mex. 2014;56(2):189–96. Guidelines for screening. camps to be held in the districts. Available online at: https://nhmodisha.gov.in Times of India, Punjab. Himachal Pradesh’s elderly population rise beats national average in 13 years: report. Available online at: Https://timesofindia.indiatimes.com Additional Declarations No competing interests reported. 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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-8957491","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":608579981,"identity":"02e424a9-e78d-488e-b42c-306f925ac526","order_by":0,"name":"Sunil Kumar Raina","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCklEQVRIiWNgGAWjYFCCAyBCAsJOKLCBsxnYiNNikEaMFhRgcBihBRfgZzz8+HNFjUUef/8ZswcPDM4n9s9uPviAocaGgU+6AasWyYZjZpJnjkkUS9zIMTdIMLidOOPOsWQDhmNpDGwyB7C75MABM8YGNonEhhs8ZhIgLQ03cswkGBsOM7BJJODQcvzzx4Z/Eonzz58BaTmXOJ+wljMGko1tEokbDuSAtBxI3EBIi2TDmTLJxj6JxI030sqAWpKNgYxkg4RjaTy4tPBLHN/8seFbXeK884e3Sf6osJOddyP54IMPNTZy8jOwa2GQQAsWxwYQCVTMg109yJoGVL49TpWjYBSMglEwYgEA3vxg23vbUiIAAAAASUVORK5CYII=","orcid":"","institution":"Dr. Rajendra Prasad Government Medical College","correspondingAuthor":true,"prefix":"","firstName":"Sunil","middleName":"Kumar","lastName":"Raina","suffix":""},{"id":608579982,"identity":"384af318-1f23-4ee4-89c3-1cf7129921b5","order_by":1,"name":"Devendra Singh Dadhwal","email":"","orcid":"","institution":"Dr. Rajendra Prasad Government Medical College","correspondingAuthor":false,"prefix":"","firstName":"Devendra","middleName":"Singh","lastName":"Dadhwal","suffix":""},{"id":608579983,"identity":"154a110f-4eef-48c2-9b1c-8ccf46e543af","order_by":2,"name":"Raman Chauhan","email":"","orcid":"","institution":"Dr. Rajendra Prasad Government Medical College","correspondingAuthor":false,"prefix":"","firstName":"Raman","middleName":"","lastName":"Chauhan","suffix":""},{"id":608579984,"identity":"f9e176f3-c661-424d-93fc-9ec34b30e5e9","order_by":3,"name":"Hansraj Chaudhary","email":"","orcid":"","institution":"Indian Council of Medical Research","correspondingAuthor":false,"prefix":"","firstName":"Hansraj","middleName":"","lastName":"Chaudhary","suffix":""},{"id":608579985,"identity":"a8aad119-cf57-4cd0-897e-6395dcbdde3a","order_by":4,"name":"Balaji Ramraj","email":"","orcid":"","institution":"Indian Council of Medical Research","correspondingAuthor":false,"prefix":"","firstName":"Balaji","middleName":"","lastName":"Ramraj","suffix":""},{"id":608579986,"identity":"381226f3-ad8b-4a92-b233-03c662e4d267","order_by":5,"name":"Debjani Ram PuraKayastha","email":"","orcid":"","institution":"Indian Council of Medical Research","correspondingAuthor":false,"prefix":"","firstName":"Debjani","middleName":"Ram","lastName":"PuraKayastha","suffix":""},{"id":608579987,"identity":"12e34b0f-0904-4e2a-90ec-d6b6029f83e1","order_by":6,"name":"Rajesh Sood","email":"","orcid":"","institution":"District Tuberculosis Office","correspondingAuthor":false,"prefix":"","firstName":"Rajesh","middleName":"","lastName":"Sood","suffix":""}],"badges":[],"createdAt":"2026-02-24 12:38:47","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8957491/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8957491/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105054297,"identity":"aa6a562e-8f1b-4b2e-822b-81699f627472","added_by":"auto","created_at":"2026-03-20 11:11:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":620672,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8957491/v1/9d956989-82b5-470c-8732-f6e549ae0d65.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Active Tuberculosis case finding using portable radiography reveals high undetected burden in rural Himachal Pradesh","fulltext":[{"header":"Introduction","content":"\u003cp\u003eTuberculosis (TB) remains the leading infectious-disease killer globally, and India accounts for the largest share of the world\u0026apos;s TB burden. The National TB Elimination Programme (NTEP) targets elimination, defined as less than one case per million by 2025. [1] Active case finding (ACF) is one of the main strategies for reaching out to the large pool of undiagnosed or under-diagnosed cases, particularly in geographically remote and resource-limited settings [1].\u003c/p\u003e\n\u003cp\u003ePortable, hand-held digital X-ray devices have emerged as promising field tools for point-of-care TB screening in hard-to-reach communities as they overcome the traditional barriers of transporting patients to imaging centers. Several pilot programmes across India have demonstrated their technical feasibility. [2] However, the operational yield of X-ray\u0026ndash;driven ACF, that is, the number of confirmed TB cases per number of individuals screened will not only depend on the sensitivity of the imaging device but also on the epidemiological profile of TB in the target population as well by the logistics of the screening programme itself. Himachal Pradesh is a northern hill state in India with unique TB epidemiology. NIKSHAY surveillance data (table 1) shows that the proportion of extra-pulmonary TB (EP-TB) in Himachal Pradesh is 25\u0026ndash;40%, roughly double the national average of 15\u0026ndash;20% [4]. \u0026nbsp;EP-TB, which includes lymph-node, pleural, abdominal, spinal, and other forms is fundamentally undetectable by chest-symptom screening and sputum testing. In addition, the demographic and socioeconomic characteristics of hill populations introduce specific barriers to ACF; daily-wage employment among working-age adults, mobility constraints among the elderly, and the logistical challenges imposed by mountainous terrain.\u003c/p\u003e\n\u003cp\u003eA study under the AccEEnd TB project, funded by the Indian Council of Medical Research (ICMR) was conducted in Block Dadasibha of Kangra district. The findings from a secondary analysis using district-level NIKSHAY data to benchmark the observed yield against the expected TB burden and to quantify the contribution of each structural barrier to the gap between the two is being presented here. Crucially, the analysis was designed not merely to catalogue what went wrong but to determine whether the barriers collectively require a fundamentally different, multilayered and context-adapted model of TB screening.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eBlock Dadasibha, Kangra district, Himachal Pradesh, comprising of 146 villages with an estimated population of 115,766 was selected because of it being a low-performing unit in prior TB notification analyses. ASHAs created a line-list of high-risk individuals in every village during the first month of the project, using a standardized (NTEP) set of risk variables (age, diabetes, smoking, previous TB, known contacts of TB patients, and chronic respiratory symptoms etc). Three to four days before each screening camp, ASHAs mobilized both the line-listed high-risk individuals and any currently chest-symptomatic persons to attend the camp at the designated camp site. Camps were conducted on weekdays. At the camp, every attendee underwent a portable digital chest X-ray after obtaining informed consent. A single pulmonologist (DSD; Doctorate of Medicine in Pulmonary Medicine) read all films and classified each as normal, abnormal but not suspicious, or suspicious of TB.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEvery individual identified as a chest symptomatic (cough of two or more weeks\u0026apos; duration or any other respiratory symptom meeting pre-specified criteria) and every individual whose X-ray was read as suspicious had sputum samples collected as per standard NTEP guidelines.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBenchmarking with NIKSHAY Data and Analysis Framework:\u0026nbsp;\u003c/strong\u003eTo place the observed yield in epidemiological context, all TB cases notified in Kangra district during the calendar year 2024 were extracted from the NIKSHAY portal (n = 2,921). These data were used to derive the district TB notification rate, the pulmonary-to-extra-pulmonary ratio, the age and sex distribution of cases, the proportion detected through ACF versus passive case finding (PCF), and the distribution of EP-TB sites. Expected TB cases in the study population were then calculated by applying the district notification rate to the screened population size.\u003c/p\u003e\n\u003cp\u003eFour structural barriers were hypothesized to explain the gap between expected and detected cases; (i) EP-TB invisible to sputum screening; (ii) weekday camp timing excluding working-age adults; (iii) mobility barriers reducing elderly attendance; and (iv) residual factors (stigma, migration). Each barrier\u0026apos;s contribution was quantified using the NIKSHAY age, sex, and site distributions as the denominator and further contextualization to the output obtained through screening. Ethical approval was obtained from the Institutional Ethics Committee vide letter no. HFW- H DRPGMC/Ethics /2023/146 date: 30.12.2023.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe overarching analytical goal was to determine whether each barrier maps onto a distinct screening layer, and whether together they define a multilayered strategy that the current single-modality design cannot replicate.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eASHAs identified 20,207 high-risk individuals across 146 villages. Of these, 7,464 people (770 chest symptomatic and 6,694 high-risk individuals) reported for TB screening at camps. 7,409 (99%) of 7464 underwent X-ray screening using handheld devices, with 704 cases flagged as X-ray suggestive. Sputum testing was completed for 650 individuals, with only 2 confirmed as TB positives. A total of 2,921 TB cases were notified in Kangra district during 2024, yielding a notification rate of 193 per 100,000 (as per 2011 census database for population). Pulmonary TB accounted for 1,798 cases (60.9%), while extra-pulmonary TB accounted for 1,059 cases (36.6%). The remaining had unclassified or mixed site data. Of all notified cases, only 37 (1.3%) were detected through ACF; the remainder (2,884; 98.7%) were detected through passive case finding (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eKangra district TB notification profile, 2024 (NIKSHAY)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e Parameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e% of Total\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal TB cases notified (2024)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,921\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePulmonary TB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,798\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExtra-pulmonary TB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026mdash; Pleural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33.7 (of EP)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026mdash; Other / unspecified\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30.7 (of EP)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026mdash; Lymph node\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.4 (of EP)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026mdash; Abdominal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.8 (of EP)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026mdash; Spinal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.7 (of EP)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026mdash; TBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.9 (of EP)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACF-detected\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePCF-detected\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,884\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e98.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003cem\u003eEP\u0026thinsp;=\u0026thinsp;Extra pulmonary. Percentages in parentheses refer to EP-TB subtypes as a proportion of all EP-TB.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eMales constituted 66.7% of all district TB cases (1948/2921). The working-age group (20\u0026ndash;59 years) accounted for 1,777 cases (60.6%), the elderly (60 years and older) for 996 cases (34), and children younger than 10 years for only 19 cases (0.6%) (Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAge distribution of TB cases, Kangra district 2024\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge Group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCases\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e% of Total\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u0026ndash;9 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u0026ndash;19 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u0026ndash;29 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e463\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u0026ndash;39 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40\u0026ndash;49 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e476\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50\u0026ndash;59 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e463\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e60\u0026ndash;69 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e504\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e70\u0026thinsp;+\u0026thinsp;years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e492\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWorking age (20\u0026ndash;59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,777\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElderly (\u0026ge;\u0026thinsp;60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003cem\u003eSource: NIKSHAY portal.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eQuantification of Structural Barriers\u003c/strong\u003e \u003cp\u003eApplying the Kangra district notification rate (193 per 100,000) to the 7,409 X-ray screened individuals, screened yields an expected number of 14.3 TB cases. Detecting only 2 cases therefore represents a 12.3 case deficit. The following analysis attributes this deficit to four measurable structural barriers, each grounded in the NIKSHAY district data (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) and further contextualized to screening conducted during the study.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFour-barrier decomposition of the expected-vs-detected case deficit\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBarrier\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCases Missed\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e% of Deficit\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEvidence Source\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExtra-pulmonary TB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNIKSHAY EP proportion (36.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeekday Camp Timing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNIKSHAY working-age share (60.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElderly/ Mobility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNIKSHAY\u0026thinsp;\u0026ge;\u0026thinsp;60 share (33.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidual Selection Bias\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDetected\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStudy outcome\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal deficit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cem\u003eAll expected-case calculations use the Kangra 2024 notification rate (197 per 100,000) applied to 7,409 X-ray screened individuals\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eBarrier 1 \u0026mdash; Extra-pulmonary TB (5.3 cases; 37%)\u003c/strong\u003e \u003cp\u003eNIKSHAY data show that 36.6% of all TB notified in Kangra is extra-pulmonary. Applying this proportion to the 14.3 expected cases yields 5.3 EP-TB cases among the screened population. These cases predominantly pleural, lymph-node, abdominal, and spinal produce no sputum and, unless accompanied by a chest-X-ray abnormality, are entirely invisible to a respiratory-symptom-and-sputum screening strategy. They represent the single largest contributor to the observed deficit.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eBarrier 2 \u0026mdash; Weekday Camp Timing (4.3 cases; 30%)\u003c/strong\u003e \u003cp\u003e60.8% of Kangra's TB cases occur in the 20\u0026ndash;59-year working-age group.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eDaily-wage employment, loss of income, and the need for leave approval make weekday attendance at screening camps prohibitive for this group.\u003c/p\u003e \u003cp\u003eWe estimate that approximately half of working-age TB cases in the screened population did not attend weekday camps \u0026mdash; a conservative lower-bound estimate, at or below the observed 56% overall non-attendance rate among all high-risk individuals. This accounts for approximately 4.3 undetected cases\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eBarrier 3 \u0026mdash; Mobility Constraints in the Elderly (2.4 cases; 17%)\u003c/strong\u003e \u003cp\u003ePersons aged 60 years and older constitute 34% of district TB notifications. In hilly terrain, travel even two or three kilometers to an HWC is difficult for individuals with mobility-limiting conditions; many require an accompanying caregiver who, in turn, may be unable to leave work. We estimate that approximately half of the elderly cases in the screened population did not attend weekday camps \u0026mdash; a conservative lower-bound estimate, at or below the observed 56% overall non-attendance rate among all high-risk individuals. This accounts for approximately 2.4 undetected cases\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eBarrier 4 \u0026mdash;Residual Factors (0.3 cases; 2%)\u003c/strong\u003e \u003cp\u003eBeyond the three primary barriers, a small residual gap remains attributable to some other factors like acute illness preventing travel on screening days, temporary migration for seasonal work, and reluctance due to TB-associated stigma. While these factors undoubtedly affect attendance, their aggregate contribution to the case-finding deficit appears modest when extrapolated to three structural barriers identified above.\u003c/p\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eACF yield of 0.3% in our study though appears low in isolation; however, district-level Nikshay data show that only 1.3% of all TB cases in Kangra are detected through ACF; the vast majority is identified through passive case finding at routine health facilities. When the observed yield is benchmarked against the district notification rate rather than against textbook prevalence figures for high-risk populations the expected number of cases in the screened cohort falls to 14.3. Two cases identified therefore represent a deficit of 12.3, not the 50-plus that would be implied by higher prevalence assumptions. This precision matters for policy, because it focuses the improvement effort on barriers that are operationally addressable.\u003c/p\u003e \u003cp\u003eThe most striking feature of the Kangra NIKSHAY profile is the 37% EP-TB proportion \u0026mdash; more than double the national average [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA screening programme that relies entirely on chest symptoms and sputum is therefore structurally unable to detect more than one-third of the TB burden in this district. This finding is consistent with prior reports from Himachal Pradesh. [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] Proportionately lower frequency of PTB at higher altitudes, combined with its inverse relationship with altitude, helps explain the high proportion of EPTB cases. [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] The working-age group (20\u0026ndash;59 years) harbors 60.6% of the district's TB cases, yet this is precisely the population least able to attend a camp held during working hours on a weekday. Daily-wage laborers in hill agriculture and construction cannot afford to forego a day's income; salaried workers require formal leave; and TB stigma discourages public screening. Guidelines suggest conducting ACF on first Saturday or first Sunday of every month in order to facilitate the attendance by working persons and school going children. [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] This single modification is estimated to recover 4.3 out of 12.3, a 30% improvement in yield.\u003c/p\u003e \u003cp\u003ePersons aged 60 and older account for 34% of district notifications, yet mountainous terrain and mobility limitations make camp attendance difficult for many of them. Himachal Pradesh has recorded a significant rise in its elderly population between 2011 and 2024, outpacing the national average. The hill state saw an increase of 2.9% from 10.2% to 13.1% of the total population, placing it fourth in the country. [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] Furthermore, elderly TB frequently presents with constitutional rather than respiratory symptoms, so that a cough-based screening criterion will miss a substantial proportion. Door-to-door screening with portable X-ray for the elderly are logical extensions of the current programme.\u003c/p\u003e \u003cp\u003eThe findings have implications beyond Kangra. Hill states across northern India share similar epidemiological features. A single-modality ACF protocol cannot, by design, reach the populations it should be reaching. What is needed is not a tweaked version of the existing protocol but a multilayered, context-adapted screening strategy: one in which the timing, reach, diagnostic toolkit, and clinical scope of screening are each calibrated to the epidemiological and geographic features of the target population. The present study provides the barrier-level evidence base on which such a strategy can be designed.\u003c/p\u003e \u003cp\u003eThe four-barrier analysis identifies four screening layers that together constitute a multilayered, context-adapted strategy. Each layer directly addresses one or more of the structural barriers quantified above; none can substitute for another. First, weekend and evening camps should replace or supplement weekday-only schedules so as to include the working-age population. Second, systematic lymph-node examination should be performed at every camp, with fine-needle aspiration cytology (FNAC) available on-site or via rapid referral; this alone is expected to recover the majority of the lymph-node and pleural TB cases that are currently missed. Third, door-to-door portable X-ray screening should target elderly individuals and persons with limited mobility who are identified by ASHAs but do not attend camps. Fourth, molecular testing (GeneXpert or equivalent) should replace or supplement sputum smear microscopy for all chest symptomatics to close the residual sensitivity gap.\u003c/p\u003e \u003cp\u003eIf all four modifications are implemented simultaneously, modelling against the district notification data suggests a potential yield of 12 cases from the same screened population \u0026mdash; an improvement of six-fold over the observed yield. The implication is clear: the technology is not the constraint. The constraint is the architecture of the screening strategy. A multilayered design \u0026mdash; temporal, spatial, clinical, and diagnostic layers operating in concert \u0026mdash; is both necessary and, at this cost differential, strongly cost-effective. Taken together, the four barriers are not independent failures but structurally distinct layers of the TB burden in this district. Extra-pulmonary disease demands a clinical and histopathological screening layer; the working-age deficit demands a temporal layer (when camps are held); the elderly deficit demands a spatial layer (where screening reaches); selection bias demands a social and logistical layer. No single screening modality or camp format can address more than one of these layers simultaneously. The data therefore establish that an effective strategy for this context must be multilayered by design, with each layer calibrated to the local epidemiology and geography.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study demonstrates that portable digital X-ray technology is operationally feasible in remote hill districts of Himachal Pradesh. However, when it is embedded in a screening strategy limited to weekday camps its yield is constrained by four structural barriers \u0026mdash; of which extra-pulmonary disease and working-age exclusion are the two largest. Each barrier is quantifiable, each maps onto a discrete screening layer, and the additional cost of addressing all of them simultaneously is modest.\u003c/p\u003e \u003cp\u003eThe lesson from Kangra is not that portable X-ray failed, but that TB screening in remote hill districts is inherently a multilayered challenge. Meeting that challenge requires a context-adapted strategy calibrated to local epidemiology, geography, and socioeconomics and should be a priority for the national programme across hill states.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthical Approval:\u003c/h2\u003e \u003cp\u003eEthical approval was obtained from the Institutional Ethics Committee vide letter no. HFW- H DRPGMC/Ethics /2023/146 date: 30.12.2023.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eStatement on Methods\u003c/strong\u003e \u003cp\u003e We confirm that all methods were conducted in accordance with the relevant guidelines and regulations.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent to participate:\u003c/strong\u003e \u003cp\u003eThe participants were included only after obtaining a freely-given, informed consent from participants.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent to publish:\u003c/strong\u003e \u003cp\u003e\u0026ldquo;Not applicable\u0026rdquo;.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eand Conflicts of Interest\u003c/p\u003e \u003cp\u003eThis data used for this study has been obtained from the work that was funded by the Indian Council of Medical Research (ICMR) under the AccEEnd TB project. The authors declare no conflicts of interest.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eSKR\u0026mdash;Wrote the manuscriptOthers\u0026mdash;Reviewed and improved the manuscript\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eThe authors thank the ASHAs of Block Dadasibha for their sustained field work, the staff of the District TB Unit, Kangra, and ICMR for project funding.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data generated has been submitted as a year-end report to the funding agency and can be made available to the publishers if required.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eNTEP, India TB, Report. 2024. New Delhi: Ministry of Health \u0026amp; Family Welfare; 2024.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDatta B, Prakash A, Ford D. et.al. Trehan N. Implementing upfront mobile digital chest x-ray for tuberculosis diagnosis in India-feasibility and benefits. Trans R Soc Trop Med Hyg. 2020; 1;114(7):499\u0026ndash;505.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRaina SK, Chauhan N, Supehia S. A protocol on Improving tuberculosis detection and accelerating elimination through digital hand-held X-ray units for pre-diagnosis screening in rural communities: An implementation research in a health block of District Kangra, Himachal Pradesh, India. Amrita J Med 0;0:0 (ahead of print).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThakur A, Tomar S, Raina S, et al. Diminishing returns of risk-based tuberculosis control in Kangra district and the case for comprehensive strategies for elimination. Discov Public Health. 2026;23:164.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSharma SK, Ryan H, Khaparde S, et al. Index-TB guidelines: Guidelines on extrapulmonary tuberculosis for India. Indian J Med Res. 2017;145(4):448\u0026ndash;63.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThakur A, Tomar S, Raina S, et al. Diminishing returns of risk-based tuberculosis control in Kangra district and the case for comprehensive strategies for elimination. Discov Public Health. 2026;23:164.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eP\u0026eacute;rez-Guzm\u0026aacute;n C, Vargas MH, Arellano-Mac\u0026iacute;as Mdel R, Hern\u0026aacute;ndez-Cobos S, Garc\u0026iacute;a-Ituarte AZ, Serna-Vela FJ. Clinical and epidemiological features of extrapulmonary tuberculosis in a high incidence region. Salud Publica Mex. 2014;56(2):189\u0026ndash;96.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuidelines for screening. camps to be held in the districts. Available online at: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://nhmodisha.gov.in\u003c/span\u003e\u003cspan address=\"https://nhmodisha.gov.in\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTimes of India, Punjab. Himachal Pradesh\u0026rsquo;s elderly population rise beats national average in 13 years: report. Available online at:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003eHttps://timesofindia.indiatimes.com\u003c/span\u003e\u003cspan address=\"http://Https://timesofindia.indiatimes.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"discover-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Public Health](https://link.springer.com/journal/12982)","snPcode":"12982","submissionUrl":"https://submission.springernature.com/new-submission/12982/3","title":"Discover Public Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Tuberculosis, Active case finding, Portable X-ray, Extra pulmonary tuberculosis, Multilayered screening, context-adapted strategies","lastPublishedDoi":"10.21203/rs.3.rs-8957491/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8957491/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eActive Case Finding (ACF) in tuberculosis (TB), a provider-initiated, systematic, community-based screening process targets high-risk populations and is used across India including Himachal Pradesh as a strategy to eliminate TB. Himachal Pradesh reports a disproportionately high proportion of extra-pulmonary TB (EP-TB) compared with the national average. An implementation study deploying portable digital X-ray devices in Kangra district as part of ACF identified structural barriers to case detection.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003e146 villages of Block Dadasibha (population 1,15,766\u003cb\u003e)\u003c/b\u003e in Kangra district were covered through preliminary screening of 20,207 high-risk individuals by Accredited Social Health Activist (ASHA) followed by screening camps using hand held x-rays and sputum smear examination of suspects held on weekdays using fixed facility and outreach approach. All X-rays were read by a single pulmonologist. NIKSHAY portal data for Kangra District (Year 2024, N\u0026thinsp;=\u0026thinsp;2,952 cases) were used to benchmark expected prevalence and disease-type distribution.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003e7,969 people (770 chest symptomatic and 6,694 high-risk individuals) reported for TB screening at camps. Of these, 7,409 underwent X-ray screening using handheld devices, with 704 cases flagged as X-ray suggestive. Sputum testing was completed for 650 individuals, with only 2 confirmed as TB positives. The benchmark district NIKSHAY data revealed a TB notification rate of 193 per 100,000; 36.6% of these as EP-TB; 60.6% of cases were in the 20\u0026ndash;59-year age group and only 37 (1.3%) were detected through ACF.\u003c/p\u003e\u003ch2\u003eDiscussion\u003c/h2\u003e \u003cp\u003eThe low ACF yield of our implementation study like the benchmark is explainable by four quantifiable structural barriers: (1) high EP-TB proportion (2) weekday-only camp timing (3) mobility barriers that reduced attendance among the elderly (4) residual factors. Together these barriers account for the entire observed gap between expected (14.3) and detected (2) case.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eEffective TB screening in this setting requires a multilayered, context-adapted strategy each layer calibrated to the local epidemiological and geographic context.\u003c/p\u003e","manuscriptTitle":"Active Tuberculosis case finding using portable radiography reveals high undetected burden in rural Himachal Pradesh","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-20 11:10:42","doi":"10.21203/rs.3.rs-8957491/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-01T19:31:35+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-31T10:35:47+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-29T10:29:31+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-28T16:54:42+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-24T03:13:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"289367817713961843690050677114944196854","date":"2026-03-20T08:50:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"148388760166621356333320846988428915496","date":"2026-03-20T06:47:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"54624698003465870110244840135615170011","date":"2026-03-19T03:01:27+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-18T09:31:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"195355662868390362573400818177357171925","date":"2026-03-18T09:24:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"321397168039647342723057174534673125103","date":"2026-03-18T08:07:50+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-18T06:13:02+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-17T17:06:40+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-17T00:18:49+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-13T16:41:28+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Public Health","date":"2026-03-13T05:18:29+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Public Health](https://link.springer.com/journal/12982)","snPcode":"12982","submissionUrl":"https://submission.springernature.com/new-submission/12982/3","title":"Discover Public Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"98046c56-d1c0-4c30-b862-d4a35113bfaf","owner":[],"postedDate":"March 20th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-13T14:08:53+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-20 11:10:42","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8957491","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8957491","identity":"rs-8957491","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}