Survival and Determinants of Mortality among Pulmonary Tuberculosis Patients in Sakon Nakhon Province, Northeastern Thailand

Survival and Determinants of Mortality among Pulmonary Tuberculosis Patients in Sakon Nakhon Province, Northeastern Thailand | 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 Survival and Determinants of Mortality among Pulmonary Tuberculosis Patients in Sakon Nakhon Province, Northeastern Thailand Sopon Usaprom¹, Warinmad Kedthongma2, Wuttiphong Phakdeekul² This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8082141/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Background In 2022, there were 10.6 million new cases of tuberculosis (TB) and 1.3 million deaths from TB globally; it remains one of the most common causes of death from infectious diseases. Even though there have been considerable improvements in controlling TB, not enough is known about predicting long-term survival for TB patients, especially in rural community of the northeastern, Thailand. Methods A retrospective cohort study was conducted data (2014–2023) on 9,289 pulmonary and extrapulmonary tuberculosis patients in Sakon Nakhon province, Thailand. Data were retrieved from the National TB Information System (NTIP), which was refined to a cohort (n = 8,895) subsequent to the exclusion of transfers and modifications of diagnoses. Analysis was based on the Kaplan-Meier survival curve, Log rank test and a Cox proportional hazard model, with statistical significance set at p < 0.05. Results Among the 9,289 TB patients (mean 52.6 ± 16.7 years): 63.6% were male (male-to-female ratio, 1.74:1); 21.6% had HIV coinfection; and drug resistance was 0.4%. The overall treatment success rate was 85.6—46.8% with 38.8% completing the course. The overall death rate during treatment was 7.4%, while the early death rate (within 2 months) was 3.8%. The median survival was around 6.0 months (mean 6.6 ± 3.0 months). The success rate declined from 92.1% to 82.6% over the decade of analysis, with a modest dip observed during the 2021–2023 period. Mortality was independently associated with age ≥ 65 years (aHR 2.73; 95% CI 2.17–3.42), and HIV coinfection (aHR 1.53; 95% CI 1.26–1.84). Conclusions The overall median survival time of the TB patients was 6.0 months. The factors affecting patient survival were age ≥ 65 years and HIV coinfection. There was an upward trends in the mortality rate. Tuberculosis HIV coinfection Mortality Survival analysis community Health Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction TB, caused by the TB mycobacterium, is one of the deadliest infectious diseases caused by a single organism. The global decreased trend from 2015 to 2020 was reversed in 2023, with 10.8 million people with tuberculosis (95% CI: 10.1–11.7 million), resulting in 1.25 million fatalities [ 1 , 2 ]. The enormous difficulty of eliminating tuberculosis is demonstrated by the meager reduction for 180 to 134 cases per 100,000 individuals from 2000 to the present day [ 3 ]. The Southeast Asian countries of Thailand, India, Indonesia, and Myanmar account for over one-half of the world's tuberculosis cases [ 1 , 4 ]. Tuberculosis impedes both the World Health Organization's end TB strategy and Thailand’s Sustainable Development Goals. Multidrug-resistant TB is estimated at 11.6% (95% CI, 9.1–14.5%) and has been increasing [ 5 ], such that in 2022–2023 there were almost 410,000 new MDR/RR-TB cases [ 6 ]. Despite better treatment options, the cure rate for MDR/RR-TB is just 68%, well below the WHO's 90% objective [ 1 , 7 ]. Emerging digital monitoring tools, such as video-supervised treatment (VOT), text message alerts, and intelligent medication dispensers, have potential for improving patient compliance while reducing expenses [ 8 – 12 ]. However, the sustained effects of such tools on mortality and recovery outcomes have been inconsistent. In addition to adherence, studies indicate that undernutrition nearly doubles the risk of adverse outcomes [ 13 ], and HIV coinfection continues to contribute to elevated mortality, raising the risk three-fold to four-fold. Overall, these data confirm that social issues and physical health problems persist as threats to tuberculosis patients' lives [ 14 , 15 ]. Thailand has a major, ongoing TB burden. Counts of TB notifications were 111,000 and 103,000 in 2022 [ 16 ] and 2021 [ 17 ], respectively (155 and 143 per 100 000, respectively). During 2021–2022, treatment coverage was approximately 70% (71,488 patients), resulting in over 31,500 cases that were either unreported or not enrolled in treatment [ 18 ]. The treatment outcomes from 2014 to 2021 had success rates of 81.5–86.3%, which missed the WHO's 90% target every year. Patient deaths during treatment were 7.8–9.3% overall but increased markedly to 16.7–17.9% for people aged 65 years and older [ 18 ]. Despite progress, weaknesses in Thailand’s TB control persist. Most cases have been reported in the Northeast, with Sakon Nakhon being among the provinces with the greatest burden [ 19 ]. When we examined location and time patterns from 2011–2020, we saw that TB cases continued to increase, grouping together the Northeast and areas close to international borders, revealing the complex ways that income differences and people moving between countries interact with each other [ 20 ]. From 2019 to 2023, the TB rates in Sakon Nakhon Province were in the range 77.5–90.9 cases per 100,000 people. However, Akat Amnuai district experienced considerably higher rates of 120.5 per 100,000 exceeding Thailand's national standard [ 21 ]. Despite the high burden, there are limited community studies that track patients and identify factors that influence survival. Multiple interconnected barriers in rural settings social, financial, and service-related compound treatment challenges. Numbers from 2023 indicated that 8% of people with TB also had HIV, with these patients being more likely to die [ 22 , 23 ]. MDR/RR-TB intensifies operational and clinical difficulties. Based on recent global data for 2022–2023, there were more than 410,000 new cases of MDR/RR-TB; however, cure rates remained unchanged at just 68% [ 6 ]. Central Asian research published in 2025 documented 22% patient mortality and an average diagnostic interval of 8 weeks [ 24 ]. Furthermore, based on global data compiled in 2024, that many developing nations detected fewer than 60% of MDR-TB cases, and nearly one-half of all XDR-TB patients did not survive their illness [ 25 ]. Economic hardship and social barriers undermine survival. Thailand’s first nationwide patient-cost survey, completed in 2024, found catastrophic costs in 61.1% of TB-affected households with confirmed drug resistance [ 26 ]. Rapid diagnostics (Xpert, LAMP) are highly cost-effective, and wider use can lower long-term health-system spending [ 27 , 30 ]. Evidence from international studies places catastrophic costs at about 27–82% of TB-affected households [ 28 , 29 ]. Per-case spending reported in the WHO Global TB Report 2024 was the range USD 76–3,700 across settings [ 6 ]. These findings revealed economic burdens on both families and services, as well as social disadvantage, financial constraints, and care-pathway barriers together degrading outcomes. About 45% of the world’s TB burden lies in the WHO South-East Asia Region, with Thailand representing a substantial fraction of that total [ 1 , 4 ]. Recent efforts including a 10-year cohort of HIV–TB coinfection and a registry study on isoniazid-associated mortality, both reported in 2025, add valuable evidence but do not close the rural data gap [ 30 , 31 ]. Studies from multiple settings document prolonged time to diagnosis in rural TB, alongside poorer socioeconomic indices and elevated mortality relative to urban groups [ 32 – 35 ]. There is still a lack of detailed long-term information regarding rural areas. Recent studies in rural China [ 36 ], Southern Africa [ 37 ], and Kenya [ 34 ] offer valuable data, though they do not fully examine survival rates. A contemporary systematic review emphasized this knowledge deficit, noting the absence of survival studies within community-based research groups [ 29 ]. Bridging these information gaps represents a key component of Thailand's National TB Strategy for 2023–2027 [ 18 ], which focuses on reducing deaths, enhancing disease monitoring, and promoting healthcare equality, in accordance with WHO's end TB framework [ 38 ]. Thailand's strategic objectives align with broader regional initiatives supported by the Asian Development Bank [ 39 ], with international policy recommendations emphasizing coordinated approaches across low- and middle-income nations, including Thailand [ 40 , 41 ]. A contemporary systematic review highlighted this knowledge deficit, noting the scarcity of survival studies among community-based TB patient groups [ 34 , 42 , 43 ]. Resolving this research limitation represents a key milestone toward developing better evidence-supported TB interventions. The current study examined the data from 10 years of pulmonary TB patients in Sakon Nakhon Province in Northeast Thailand. In particular, the study considered how well treatments had worked and how they changed over time. In addition, characteristics were identified as independently associated with mortality, survival trends among various patient demographics, and disparities in clinical outcomes across geographies. Methodology Study design and setting A retrospective cohort study approach was used. The observation window covered 10 fiscal years (FYs; October–September) of Thailand’s TB surveillance system (FY2014–FY2023), following the Ministry of Public Health’s National TB Information System (NTIP) reporting cycles. The target area was Sakon Nakhon Province, Northeast Thailand (approximate population 1.1 million), which comprises 18 districts with a provincial referral hospital and multiple district hospitals integrated within the national TB control program. The source population included all registered TB patients (pulmonary and extrapulmonary, all ages) in the Sakon Nakhon provincial NTIP registry during FY2014–FY2023. Bacteriologically confirmed and clinically diagnosed cases were eligible. For descriptive summaries and crude outcomes, we reported the full cohort (n = 9,289). For time-to-event analyses (survival), we used an evaluable cohort after censoring records with a transfer out or change of diagnosis at the date of last contact (n = 8,895). Data sources and variables Individual-level records were extracted from the NTIP and cross-checked against provincial TB program listings for completeness and internal consistency. Sociodemographic variables: patient age (continuous data; grouped into age categories by year of 0–14, 15–44, 45–64, and ≥ 65 years), gender, citizenship, work type, and medical insurance coverage (Universal Coverage (UC), Civil Servant Medical Benefit Scheme, Social Security Scheme, other plans). Clinical variables: HIV status, TB site (pulmonary/extrapulmonary), sputum smear, chest radiography, drug resistance (isoniazid or rifampicin resistance, MDR-TB), and treatment regimen. Calendar variables: year of diagnosis/registration (FY2014–FY2023) to assess temporal trends. Success rates and mortality analysis In this 10-year provincial cohort, the success rate was 85.6% overall (46.8% cured; 38.8% completed). Mortality during treatment was 6.8%. This was defined as death from any cause occurring at any time point between treatment commencement and study censoring, in accordance with established WHO reporting standards. Secondary outcomes were: (i) early mortality (< 2 months from treatment initiation); (ii) survival time (days/months); and (iii) success (cure or completion). Handling of censoring, follow-up, and missing data Survival time was measured from the treatment start date to the earliest date of death, treatment completion, transfer out, change of diagnosis, loss to follow-up (LTFU), or administrative end of follow-up (September 30, 2023). Transfers and changes of diagnosis were censored at last contact and excluded from the survival risk set thereafter; LTFU was censored at the last recorded visit. Outcome completeness in the registry was high; analyses were performed with available data. Where covariate values were missing, models used complete-case analysis for those covariates. Inclusion of both facility-level and temporal summaries We summarized mortality and success rates at the facility level (district and provincial hospitals). Ranking tables and graphics excluded facilities with very small denominators (n < 30) from the main displays to enhance comparability; provincial totals included all facilities, with n < 30 facilities being documented in the figure and table captions. Annual trend analyses across FY2014–FY2023 were evaluated using the chi-square (χ²) test. Statistical analysis Descriptive statistics were used to describe the general characteristics of the sample (number, percentage, mean, standard deviation (SD), median). Event-free survival was analyzed using the Kaplan-Meier survival curve. Differences between groups were compared using the log-rank test, with significance set at p < 0.05 level. Inference statistics based on univariable analysis were used to analyze the relationships between factors affecting survival, without considering other risk factors; the Cox proportional hazard model was used, presented as a crude hazard ratio (HR) and the 95% CI range, with significance set at the p < 0.05 level. Results Participants’ characteristics For all 9,289 tuberculosis patients, the cohort mean age was 52.6 years (SD 16.7), with a median of 53 (IQR 41–65 years). In addition, gender breakdown was 63.6% males and 36.4% females, HIV co-infection was 21.0%, and documented resistance (MDR/RR-TB) was 0.4%. The mortality rate for patients aged ≥ 65 years was 12.3% (95% CI [11.0–13.7%]). Patients who were HIV-positive had a mortality rate of 9.4% (95% CI [8.1–10.6%]). The lack of overlap between these CIs confirmed that HIV co-infection was a major factor. There was a notably higher mortality rate for patients undergoing retreatment for TB of 20.8% (95% CI [11.8–29.8%]). This may have been due to factors such as drug resistance or a more severe, persistent disease. Non-Thai patients had a significantly higher mortality rate of 16.2% (95% CI [10.6–21.8%]) compared to Thai patients (7.3% [6.8–7.8%]). The non-overlapping CIs suggested that factors, such as barriers to care, delayed diagnosis, or different social determinants of health, may have contributed to the worse outcomes in this group. Patients with a smear-positive test result had a mortality rate of only 1.6% (95% CI [0.0–3.4%]), This seemingly counterintuitive finding may indicate that a positive smear, which provides a definitive and rapid diagnosis, leads to a more timely and aggressive initiation of appropriate treatment, thereby improving survival. Smear-negative TB can be more difficult to diagnose, potentially leading to treatment delays. In addition, the mortality rate for prisoners was 3.0% (95% CI [0.4–5.6%]), while the rate for non-prisoners was 7.5% (95% CI [7.0–8.0%]), suggesting that the structured environment of a prison, with directly observed therapy (DOT) and managed care, may lead to better treatment adherence and, consequently, better outcomes (Table 1 ). Table 1 Survival and mortality rates classified by population characteristics of pulmonary tuberculosis patients in Sakon Nakhon Province, 2014–2023 (n = 9,289) Characteristic Total n (%) Survived n (%) Died n (%) Mortality Rate (95% CI) Sex Male 5,905 (63.6) 5,453 (63.4) 452 (65.6) 7.7% (7.1% − 8.3%) Female 3,384 (36.4) 3,147 (36.6) 237 (34.4) 7.0% (6.1% − 7.9%) Age group (years) 0–14 75 (0.8) 70 (0.8) 1 (0.1) 1.3% (0.0% − 3.9%) 15–44 2,832 (30.5) 2,684 (31.2) 148 (21.5) 5.2% (4.4% − 6.0%) 45–64 3,976 (42.8) 3,733 (43.4) 243 (35.3) 6.1% (5.4% − 6.8%) ≥ 65 2,406 (25.9) 2,109 (24.5) 297 (43.1) 12.3% (11.0% − 13.7%) Nationality Thai 9,122 (98.2) 8,460 (98.2) 662 (96.1) 7.3% (6.8% − 7.8%) Non-Thai 167 (1.8) 140 (1.8) 27 (3.9) 16.2% (10.6% − 21.8%) HIV status Negative 7,279 (78.3) 6,778 (78.8) 501 (72.7) 6.9% (6.3% − 7.5%) Positive 2,010 (21.6) 1,822 (21.2) 188 (27.3) 9.4% (8.1% − 10.6%) Treatment category New treatment 9,212 (99.2) 8,539 (99.2) 673 (97.7) 7.3% (6.8% − 7.8%) Retreatment 77 (0.8) 61 (0.8) 16 (2.3) 20.8% (11.8% − 29.8%) Smear result Negative 9,104 (98.0) 8,418 (97.8) 686 (99.6) 7.5% (7.0% − 8.1%) Positive 185 (2.0) 182 (2.2) 3 (0.4) 1.6% (0.0% − 3.4%) Prisoner status No 9,120 (98.2) 8,436 (98.1) 684 (99.3) 7.5% (7.0% − 8.0%) Yes 169 (1.8) 164 (1.9) 5 (0.7) 3.0% (0.4% − 5.6%) Treatment outcomes (combined view) We analyzed 9,289 patients. Of these, 85.6% were cured or completed care. Overall, 7.4% died, with 3.8% dying in the first 2 months and 3.6% dying > 2 months after the treatment had started. For time-to-event analyses using the evaluable cohort after censoring transfers and changes of diagnosis (n = 8,895), the success rate was 89.4% with overall mortality being 7.7% and early and late deaths being 3.9% and 3.8%, respectively (Table 2 ). Table 2 Treatment outcomes (registry and survival analysis), Sakon Nakhon Province, 2014–2023 Outcome Total (n = 9,289) % of Total Evaluable (n = 8,895) % of Evaluable Success (cure or completion) 7,948 85.6 7,948 89.4 Cured 4,346 46.8 4,346 48.9 Completed treatment 3,602 38.8 3,602 40.5 Unsuccessful 947 10.2 947 10.6 Died 689 7.4 689 7.7 Early mortality (< 2 months) 350 3.8 350 3.9 Late mortality (≥ 2 months) 339 3.6 339 3.8 Lost to follow-up 202 2.2 202 2.3 Treatment failure 56 0.6 56 0.6 Transfer out - censored 95 1.0 0 0.0 Change of diagnosis- censored 299 3.2 0 0.0 Temporal trends. Annual TB caseloads were broadly stable (642–1,068 cases/year). The death rate rose from 3.5% in 2014 to 9.1% in 2023 (χ² trend test, p < 0.001). Conversely, the success rate declined from 92.1% to 82.6% over the decade, with a modest dip observed during the 2021–2023 period. MDR/RR-TB remained rare (< 1% annually), with a small uptick in 2023. These patterns are summarized in Fig. 1 . The Kaplan-Meier survival analysis of tuberculosis (TB) patients in Sakon Nakhon (2014–2023). That clearly reveals significant structural health inequities that persist beyond standard treatment completion. Elderly patients (aged ≥ 65 years) exhibit the lowest survival rates, underscoring an urgent need for integrated care programs. That effectively manage high comorbidity burdens alongside TB treatment. Furthermore, HIV-positive status remains a critical predictor of lower long-term survival, necessitating stricter, sustained adherence to collaborative TB/HIV management protocols. Disparities observed across insurance schemes suggest that socioeconomic determinants, as reflected in entitlement to benefits, still heavily influence access to quality of care. The overall conclusion was that TB control must evolve to include robust, equity-focused, long-term care strategies targeting. These vulnerable groups to achieve sustainable reductions in mortality. The Kaplan–Meier survival curves indicate that survival differed significantly by age group, HIV status, and insurance coverage, with the steepest decline during the first two months of treatment, as shown in Fig. 2 . Predictors of TB-related mortality In the multivariable Cox models of the evaluable cohort (n = 8,895), older age and HIV co-infection were the dominant predictors. Compared with patients < 45 years, those aged 45–64 years had a modest but not significantly higher hazard (aHR 1.26, 95% CI 1.00–1.59; p = 0.046), and those aged ≥ 65 years had nearly a three-fold higher hazard (aHR 2.73, 95% CI 2.17–3.42; p < 0.001). People who had HIV were 53% more likely to die (aHR 1.53, 95% CI 1.26–1.84; p < 0.001). Associations for sex, smear positivity, imprisonment, drug resistance, and insurance type were not statistically significant after adjustment, with insurance effects attenuating in multivariable analysis (Table 3 ). Table 3 Predictors of TB-related mortality from multivariable Cox regression analysis, Sakon Nakhon Province, 2014–2023 (n = 8,895) Predictor (reference) Univariable HR (95% CI) p-Value Multivariable aHR (95% CI) p-Value Sex (Female vs Male) 0.94 (0.79–1.12) 0.50 0.89 (0.75–1.06) 0.18 Age 45–64 years (vs < 45) 0.73 (0.61–0.86) < 0.001 1.26 (1.00–1.59) 0.046 Age ≥ 65 years (vs < 45) 2.29 (1.94–2.71) < 0.001 2.73 (2.17–3.42) < 0.001 HIV Positive (vs Negative) 1.31 (1.09–1.58) 0.005 1.53 (1.26–1.84) < 0.001 Drug resistance (Yes vs No) 2.30 (0.74–7.16) 0.15 2.28 (0.73–7.11) 0.16 Smear positive (vs Negative) 1.01 (0.55–1.83) 0.99 1.02 (0.56–1.85) 0.95 Prisoner (Yes vs No) 0.50 (0.21–1.21) 0.12 0.67 (0.28–1.64) 0.38 Insurance : Social Security vs UC 1.03 (0.86–1.24) 0.74 1.05 (0.87–1.26) 0.61 Insurance : Civil Servant vs UC 0.57 (0.35–0.93) 0.03 0.76 (0.46–1.27) 0.30 Insurance : Other vs UC 0.97 (0.40–2.35) 0.95 1.11 (0.46–2.68) 0.82 Notes : We started by building separate models for each predictor. Any variables with p-values under 0.10 was allocated to the combined model. We checked that proportional hazards assumptions were acceptable using Schoenfeld residuals. Treatment start was our time zero for all analyses, and we considered results significant at the p < 0.05 level. Based on the results of the multivariable Cox analysis, being aged 65 years or older and having HIV infection were major independent factors that predicted death, with adjusted hazard ratios of 2.73 (95% CI 2.17–3.42, p < 0.001) and 1.53 (95% CI 1.26–1.84, p < 0.001), respectively. Compared to the age < 45 years group, the group aged 45–64 years had an adjusted hazard ratio of 1.26 for death (95% CI 1.00–1.59; p = 0.046). Insurance effects observed in crude models were attenuated after adjustment, while drug resistance and prisoner status showed elevated hazard ratios but were not statistically significant (Fig. 3 ). When we controlled for sex, age group, HIV status, smear result, drug resistance, prisoner status, and health insurance type (using Universal Coverage (UC) as our reference), Cox regression analysis showed two main factors that increased death risk: being aged 65 years or older and having HIV infection. The other variables we looked at had a weaker significant connection or were not statistically significant for survival. The vertical dashed line shows HR = 1. Territorial disparities ( Table 4 , Panel A; Fig. 4 A-B ). Marked facility-level heterogeneity was observed across Sakon Nakhon Province. Mortality ranged from 0.6% in Charoen Sin to 11.6% in Phra Ajarn Baen Thanakaro, followed by 10.9% in Song Dao and 10.7% in Nikhom Nam Un. Success rates exceeded 90% in Kut Bak, Waritchaphum, Phang Khon, and Charoen Sin, whereas Khok Si Suphan (77.9%) and Phra Ajarn Baen Thanakaro (74.6%) had the lowest success, indicating local programmatic challenges. Larger facilities—Mueang Sakon Nakhon (Provincial Hospital, 7.5% mortality) and Sawang Daen Din (Somdet Phra Yupparaj Hospital, 4.3% mortality) had relatively stable performance. For comparisons with very small facilities, see Table 4 and Fig. 4 A–B. Table 4 Panel A Geographical and temporal disparities in TB treatment outcomes, Sakon Nakhon Province, 2014–2023 Facility-level (district/provincial) hospital outcomes (ranked by mortality rate) (n = 9,289) Hospital Total (n) Mortality (n, %) Successful treatment (n, %) Phra Ajarn Baen Thanakaro 189 22 (11.6%) 141 (74.6%) Song Dao 174 19 (10.9%) 148 (85.1%) Nikhom Nam Un 112 12 (10.7%) 97 (86.6%) Phra Ajarn Fan Acharo 636 64 (10.1%) 506 (79.6%) Akat Amnuai 474 46 (9.7%) 408 (86.1%) Wanon Niwat 769 74 (9.6%) 646 (84.0%) Phon Na Kaeo 862 81 (9.4%) 698 (81.0%) Phra Ajarn Mun Phurithatto 558 52 (9.3%) 468 (83.9%) Tao Ngoi 194 18 (9.3%) 169 (87.1%) Kusuman 336 29 (8.6%) 292 (86.9%) Mueang Sakon Nakhon (Provincial) 2468 185 (7.5%) 2,129 (86.3%) Waritchaphum 254 16 (6.3%) 230 (90.6%) Phang Khon 440 27 (6.1%) 396 (90.0%) Kham Ta Kla 319 19 (6.0%) 279 (87.5%) Kut Bak 342 20 (5.8%) 310 (90.6%) Sawang Daen Din (Somdet Phra Yupparaj) 1268 55 (4.3%) 1,099 (86.7%) Khok Si Suphan 349 13 (3.7%) 272 (77.9%) Charoen Sin 179 1 (0.6%) 164 (91.6%) Private Hospital (Raks Sakhon) 1 0 (0.0%) 1 (100.0%) Udom Clinic 1 0 (0.0%) 1 (100.0%) Temporal trends Program performance gradually worsened each year throughout the 10-year period. Mortality increased from 3.5% in 2014 to 9.1% in 2023, while the success rate decreased from 92.1% to 82.6% (χ² test for trend, p -value < 0.001), as summarized in Table 4 (Panel B) and visualized in Fig. 4 C–D. Based on These changes, there was an increase in TB-related deaths and weakening program effectiveness, coinciding with persistent operational constraints and a small uptick in drug resistance in recent years. Table 4 (Panel B) Annual outcomes (2014–2023) Year Total (n) Deaths (n, %) Treatment success (n, %) 2014 719 25 (3.5%) 662 (92.1%) 2015 642 31 (4.8%) 567 (88.3%) 2016 1,016 72 (7.1%) 875 (86.1%) 2017 1,016 67 (6.6%) 878 (86.4%) 2018 936 79 (8.4%) 792 (84.6%) 2019 1,068 75 (7.0%) 927 (86.8%) 2020 959 86 (9.0%) 820 (85.5%) 2021 845 72 (8.5%) 708 (83.8%) 2022 1,025 85 (8.3%) 841 (82.0%) 2023 1,063 97 (9.1%) 878 (82.6%) Discussion Geographic distribution and temporal trends Facility-level outcomes varied widely. Mortality was highest in Phra Ajarn Baen (11.6%) and Song Dao (10.9%), while several facilities had mortality rates < 5% (such as Charoen Sin 0.6%, Sawang Daen Din 4.3%). Success rates fell below the WHO 90% target in Khok Si Suphan (77.9%) and Phra Ajarn Baen (74.6%), whereas ≥ 90% was achieved in Waritchaphum and Kut Bak (both 90.6%), Phang Khon (90.0%), and Charoen Sin (91.6%). Over time, mortality rose from 3.5% in 2014 to 9.1% in 2023, Treatment success rates decreased significantly from 92.1% to 82.6% (χ² test for trend, p < 0.001). This trend revealed continuing program deficiencies and highlighted that certain facilities were facing disproportionate challenges (Fig. 4 A–D). Panel A Facility-level (district/provincial) mortality varied markedly, with the highest rates in Phra Ajarn Baen (11.6%), Song Dao (10.9%), and Nikhom Nam Un (10.7%), while several facilities maintained mortality below 5% (such as Charoen Sin 0.6%, Sawang Daen Din 4.3%). Panel B Success rates fell below the WHO 90% target in Khok Si Suphan (77.9%) and Phra Ajarn Baen (74.6%), whereas there was ≥ 90% success in Waritchaphum (90.6%), Kut Bak (90.6%), Phang Khon (90.0%), and Charoen Sin (91.6%). Panel C Mortality rose annually (3.5% in 2014 to 9.1% in 2023), with a significant upward trend (p < 0.001, chi-square test). Panel D Success rates for treatment decreased from 92.1% to 82.6% in the same period (p-value < 0.001). Analysis of the results for Sakon Nakhon Province indicates there is a critical situation. The decade-long decline in treatment success from 92.1% to 82.6% signifies a significant regression from the WHO’s 90% target [ 1 ]. This trend, coupled with rising mortality, suggests systemic failures that mirror Thailand's national challenges in curbing rising TB cases [ 19 ]. The extreme variation in outcomes between facilities, such as mortality rates from 0.6% to 11.6%, strongly indicates that localized factors, not just patient characteristics, are at play. These disparities may be driven by unexamined socioeconomic burdens on households or health system inequities [ 26 ]. Treatment outcomes There was an 85.6% success rate for the 10-year provincial cohort, comparable to national figures (81–86%) [ 21 ] but below the WHO end TB target of ≥ 90% [ 6 , 41 ]. Mortality occurred in 7.4% of patients, with nearly one-half of these deaths occurring in the first 2 months of treatment (≈ 51% of all deaths), underscoring the burden of early mortality. The same signals were reported in Asian and African cohorts, with early outcome deficits tied to delayed diagnosis, advanced-stage presentation, and coexisting HIV infection [ 22 , 23 , 43 ]. Temporal trends Across the decade, deaths increased from 3.5% to 9.1%, whereas the share completing treatment decreased from 92.1% to 82.6%, as summarized in Table 4 (Panel B) and visualized in Fig. 4 C–D. These outcomes coincided with service disruptions during the COVID-19 period and a small uptick in drug-resistant TB in 2023, while the burden of HIV co-infection remained substantial [ 24 , 40 , 41 ]. These patterns mirror country-level findings that show program outcomes remaining flat despite reduced TB occurrence [ 20 , 21 ]. Survival and predictors The Kaplan-Meier analysis indicated a median survival of 6 months, with excess mortality in the early intensive phase. Analyses were conducted in the evaluable cohort (n = 8,895; transfers, with censoring of changes in diagnosis). Based on the results from the multivariable Cox regression, age remained independently associated with mortality; adults aged 45–64 years (vs < 45 years) had higher hazards (aHR 1.26, 95% CI 1.00–1.59; p = 0.046), and those ≥ 65 years had the greatest hazards (aHR 2.73, 95% CI 2.17–3.42; p < 0.001). In addition, patients with HIV had a higher independent death risk (aHR 1.53, 95% CI 1.26–1.84; p < 0.001), consistent with studies from around the world showing that older age and HIV were important risk factors [ 13 , 22 , 23 , 43 ]. The predictors of TB-related mortality, primarily advanced age and HIV co-infection [ 13 ], reveal that immunological vulnerability is the dominant force escalating mortality risk. This finding resonates across the lifespan, paralleling the critical importance of nutritional well-being in infants [ 44 ] and the necessity of basic prophylactic measures, such as rinsing, during pandemics [ 45 ]. Ultimately, these interconnected findings emphasize that investing in and sustaining fundamental biological resilience throughout life from basic nutrition to continuous immunological support. It is the most effective public health strategy for reducing preventable deaths from chronic infectious diseases like TB among the most fragile populations. Furthermore, the strongly validates the original premise [ 46 ], effective TB control hinges on addressing vulnerability. It broadens the definition, showing that vulnerability in modern urban epidemiology is often driven by socio-economic factors that dismantle fundamental biological resilience, particularly in mobile populations. Public health strategy must adapt to target these systemic vulnerabilities alongside traditional clinical risk factors. The TB-related mortality risk is a complex outcome stemming from the erosion of fundamental biological resilience, extending beyond purely clinical factors. The Sakon Nakhon analysis confirms that immunological vulnerability (due to advanced age and HIV co-infection [ 47 ] is the dominant predictor of reduced survival rates. This core principle aligns with a broader socioeconomic dimension, as research demonstrates [ 46 ]. The precarious lifestyles of internal migrants severely undermine biological resilience, leading to higher TB incidence. Furthermore, studies reinforce the role of multimorbidity and behavioral risks as accelerants that continuously erode the body's fortitude, making vulnerable populations highly susceptible to fatal TB outcomes [ 48 , 49 , and 50 ]. Implications Overall, the findings indicated that biological factors (being older, having HIV) and structural problems (money issues, location differences) were the main reasons for mortality from TB. To address these gaps, we need targeted approaches focused on fairness, such as better fast testing, connecting TB and HIV care, and helping older people and those struggling financially. Our recommendations include focusing on high-risk healthcare facilities (defined as > 10% mortality rates or < 80% success rate) through the implementation of targeted early mortality reviews, immediate molecular diagnostic testing with same-day treatment start, and customized adherence interventions. Conclusions The analysis of this 10-year cohort study demonstrated that TB treatment outcomes in Sakon Nakhon Province remain below global targets, with disproportionate early mortality among the elderly and HIV-positive patients. Advanced age (≥ 65 years) and HIV coinfection were the most important factors that predicted death, while differences in income and location made patients' outcomes even worse. Our results highlighted the urgent need to implement wide-ranging approaches that include TB/HIV integration, better rapid testing, and fair interventions. These steps are very important to assist people who are at risk and to accelerate progress toward reaching Thailand's national goals and the WHO's end TB goals. Declarations This study was authorized by the Sakon Nakhon Provincial Public Health Office's Research Ethics Committee (Approval No. COA 234/2024). Patient data were analyzed retrospectively after ensuring the anonymity of each patient. This approach eliminated privacy concerns and the need for individual informed consent [28]. All procedures adhered to the Declaration of Helsinki and pertinent national regulations. Acknowledgments The Sakon Nakhon Provincial Health Office authorized the use of the de-identified NTIP records and the TB Clinic at Akat Amnuai Hospital compiled and validated the datasets. Staff in the Faculty of Public Health, Kasetsart University Sakon Nakhon Campus, Thailand provided advice and direction on study design. Analyses were performed jointly by the authors who accept full responsibility for any mistakes in their interpretations. Authors’ contributions Sopon Usaprom (SU): Led study conceptualization and methodology development; performed data analysis; prepared the original manuscript. Wuttiphong Phakdeekul (WP): Organized and validated the dataset, and provided critical review and manuscript editing. Warinmas Kasethongma (WK) : Supervised and provided critical revision of the manuscript, and final approval. All authors approved the final manuscript. Funding The study used internal institutional resources with no external funding. Availability of data and materials Patient privacy policies prohibit public data distribution. Researchers seeking data access should contact the corresponding author with justifieda requests. Ethics approval and consent to participate The study used de-identified NTIP records and received ethical approval from the Research Ethics Committee of the Sakon Nakhon Provincial Public Health Office (COA 234/2024). Given the retrospective nature and absence of direct identifiers, that Committee issued a consent waiver under the Declaration of Helsinki and national requirements. Consent for Publication Not applicable. To address these problems, we need a strategic approach that focuses on fairness. This includes more rapid diagnostics, combining TB and HIV services, and better support for older people and people who are poor or have low income. Consent for publication Not applicable. Competing Interests The authors declare no competing interests. References World Health Organization. Global Tuberculosis Report 2024–2.3 TB treatment coverage and outcomes. Geneva: World Health Organization; 2024 [cited 2025 Sep 17]. 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Relationship between tobacco use, alcohol consumption and non-communicable diseases among women in India: evidence from National Family Health Survey-2015-16. BMC Public Health. 2022 Apr 11;22(1). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 09 Jan, 2026 Reviewers agreed at journal 07 Jan, 2026 Reviewers invited by journal 25 Nov, 2025 Editor invited by journal 16 Nov, 2025 Editor assigned by journal 14 Nov, 2025 Submission checks completed at journal 14 Nov, 2025 First submitted to journal 10 Nov, 2025 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. 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08:32:27","extension":"html","order_by":29,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":206313,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8082141/v1/1025eb3f57fbff418c8a3cf3.html"},{"id":97127728,"identity":"16551c29-3dad-41f8-91cf-f785a58e8a65","added_by":"auto","created_at":"2025-12-01 08:32:26","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":104833,"visible":true,"origin":"","legend":"\u003cp\u003eTemporal trends in tuberculosis treatment outcomes, Sakon Nakhon Province, 2014–2023 (n = 9,289).\u003c/p\u003e","description":"","filename":"Fig1..jpg","url":"https://assets-eu.researchsquare.com/files/rs-8082141/v1/fe0154a0f973db4995a0df1b.jpg"},{"id":97141487,"identity":"47af1937-7016-474e-bc22-896d626c7204","added_by":"auto","created_at":"2025-12-01 10:06:44","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":166605,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier survival curves of tuberculosis patients (all forms), Sakon Nakhon Province, 2014–2023; evaluable cohort (n = 8,895).\u003c/p\u003e","description":"","filename":"Fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8082141/v1/d2f01bf567345ef9542e1301.jpg"},{"id":97127732,"identity":"4c442b3f-2314-40c1-8027-231edefbacd1","added_by":"auto","created_at":"2025-12-01 08:32:26","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":124384,"visible":true,"origin":"","legend":"\u003cp\u003eForest plots of predictors of TB-related mortality showing adjusted hazard ratios (aHR) with 95% confidence intervals, Sakon Nakhon Province, 2014–2023; \u003cstrong\u003eevaluable cohort (n =8,895).\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Fig3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8082141/v1/41e53ebfa108de4fb5ba574a.jpg"},{"id":97142017,"identity":"0f6143c9-521b-4d54-a397-32247dfc3d61","added_by":"auto","created_at":"2025-12-01 10:07:16","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1215967,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA–D Geographic distribution and temporal trends in TB treatment outcomes, Sakon Nakhon \u003c/strong\u003eProvince\u003cstrong\u003e, 2014–2023 (n = 9,289).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePanel A \u003c/strong\u003eFacility-level (district/provincial) mortality varied markedly, with the highest rates in Phra Ajarn Baen (11.6%), Song Dao (10.9%), and Nikhom Nam Un (10.7%), while several facilities maintained mortality below 5% (such as Charoen Sin 0.6%, Sawang Daen Din 4.3%).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePanel B Success rates\u003c/strong\u003e fell below the WHO 90% target in Khok Si Suphan (77.9%) and Phra Ajarn Baen (74.6%), whereas there was ≥90% success in Waritchaphum (90.6%), Kut Bak (90.6%), Phang Khon (90.0%), and Charoen Sin (91.6%).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePanel C \u003c/strong\u003eMortality rose annually (3.5% in 2014 to 9.1% in 2023), with a significant upward trend (p \u0026lt; 0.001, chi-square test).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePanel D \u003c/strong\u003eSuccess rates for treatment decreased from \u003cstrong\u003e92.1%\u003c/strong\u003eto \u003cstrong\u003e82.6%\u003c/strong\u003e in the same period (p-value \u0026lt; 0.001).\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8082141/v1/95eb3577e9396d36fe45b900.png"},{"id":97145369,"identity":"b5245b85-d111-498a-98da-562734eb1360","added_by":"auto","created_at":"2025-12-01 10:13:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3352311,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8082141/v1/e925f7a0-fd25-4a6e-ad0d-b9431661aa44.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Survival and Determinants of Mortality among Pulmonary Tuberculosis Patients in Sakon Nakhon Province, Northeastern Thailand","fulltext":[{"header":"Introduction","content":"\u003cp\u003eTB, caused by the TB mycobacterium, is one of the deadliest infectious diseases caused by a single organism. The global decreased trend from 2015 to 2020 was reversed in 2023, with 10.8\u0026nbsp;million people with tuberculosis (95% CI: 10.1\u0026ndash;11.7\u0026nbsp;million), resulting in 1.25\u0026nbsp;million fatalities [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The enormous difficulty of eliminating tuberculosis is demonstrated by the meager reduction for 180 to 134 cases per 100,000 individuals from 2000 to the present day [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The Southeast Asian countries of Thailand, India, Indonesia, and Myanmar account for over one-half of the world's tuberculosis cases [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Tuberculosis impedes both the World Health Organization's end TB strategy and Thailand\u0026rsquo;s Sustainable Development Goals.\u003c/p\u003e\u003cp\u003eMultidrug-resistant TB is estimated at 11.6% (95% CI, 9.1\u0026ndash;14.5%) and has been increasing [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], such that in 2022\u0026ndash;2023 there were almost 410,000 new MDR/RR-TB cases [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Despite better treatment options, the cure rate for MDR/RR-TB is just 68%, well below the WHO's 90% objective [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Emerging digital monitoring tools, such as video-supervised treatment (VOT), text message alerts, and intelligent medication dispensers, have potential for improving patient compliance while reducing expenses [\u003cspan additionalcitationids=\"CR9 CR10 CR11\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. However, the sustained effects of such tools on mortality and recovery outcomes have been inconsistent. In addition to adherence, studies indicate that undernutrition nearly doubles the risk of adverse outcomes [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], and HIV coinfection continues to contribute to elevated mortality, raising the risk three-fold to four-fold. Overall, these data confirm that social issues and physical health problems persist as threats to tuberculosis patients' lives [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThailand has a major, ongoing TB burden. Counts of TB notifications were 111,000 and 103,000 in 2022 [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] and 2021 [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], respectively (155 and 143 per 100 000, respectively). During 2021\u0026ndash;2022, treatment coverage was approximately 70% (71,488 patients), resulting in over 31,500 cases that were either unreported or not enrolled in treatment [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The treatment outcomes from 2014 to 2021 had success rates of 81.5\u0026ndash;86.3%, which missed the WHO's 90% target every year. Patient deaths during treatment were 7.8\u0026ndash;9.3% overall but increased markedly to 16.7\u0026ndash;17.9% for people aged 65 years and older [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Despite progress, weaknesses in Thailand\u0026rsquo;s TB control persist. Most cases have been reported in the Northeast, with Sakon Nakhon being among the provinces with the greatest burden [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eWhen we examined location and time patterns from 2011\u0026ndash;2020, we saw that TB cases continued to increase, grouping together the Northeast and areas close to international borders, revealing the complex ways that income differences and people moving between countries interact with each other [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. From 2019 to 2023, the TB rates in Sakon Nakhon Province were in the range 77.5\u0026ndash;90.9 cases per 100,000 people. However, Akat Amnuai district experienced considerably higher rates of 120.5 per 100,000 exceeding Thailand's national standard [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eDespite the high burden, there are limited community studies that track patients and identify factors that influence survival. Multiple interconnected barriers in rural settings social, financial, and service-related compound treatment challenges. Numbers from 2023 indicated that 8% of people with TB also had HIV, with these patients being more likely to die [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. MDR/RR-TB intensifies operational and clinical difficulties. Based on recent global data for 2022\u0026ndash;2023, there were more than 410,000 new cases of MDR/RR-TB; however, cure rates remained unchanged at just 68% [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Central Asian research published in 2025 documented 22% patient mortality and an average diagnostic interval of 8 weeks [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Furthermore, based on global data compiled in 2024, that many developing nations detected fewer than 60% of MDR-TB cases, and nearly one-half of all XDR-TB patients did not survive their illness [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eEconomic hardship and social barriers undermine survival. Thailand\u0026rsquo;s first nationwide patient-cost survey, completed in 2024, found catastrophic costs in 61.1% of TB-affected households with confirmed drug resistance [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Rapid diagnostics (Xpert, LAMP) are highly cost-effective, and wider use can lower long-term health-system spending [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Evidence from international studies places catastrophic costs at about 27\u0026ndash;82% of TB-affected households [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Per-case spending reported in the WHO Global TB Report 2024 was the range USD 76\u0026ndash;3,700 across settings [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThese findings revealed economic burdens on both families and services, as well as social disadvantage, financial constraints, and care-pathway barriers together degrading outcomes. About 45% of the world\u0026rsquo;s TB burden lies in the WHO South-East Asia Region, with Thailand representing a substantial fraction of that total [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Recent efforts including a 10-year cohort of HIV\u0026ndash;TB coinfection and a registry study on isoniazid-associated mortality, both reported in 2025, add valuable evidence but do not close the rural data gap [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Studies from multiple settings document prolonged time to diagnosis in rural TB, alongside poorer socioeconomic indices and elevated mortality relative to urban groups [\u003cspan additionalcitationids=\"CR33 CR34\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. There is still a lack of detailed long-term information regarding rural areas. Recent studies in rural China [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], Southern Africa [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], and Kenya [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] offer valuable data, though they do not fully examine survival rates. A contemporary systematic review emphasized this knowledge deficit, noting the absence of survival studies within community-based research groups [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eBridging these information gaps represents a key component of Thailand's National TB Strategy for 2023\u0026ndash;2027 [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], which focuses on reducing deaths, enhancing disease monitoring, and promoting healthcare equality, in accordance with WHO's end TB framework [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Thailand's strategic objectives align with broader regional initiatives supported by the Asian Development Bank [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], with international policy recommendations emphasizing coordinated approaches across low- and middle-income nations, including Thailand [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. A contemporary systematic review highlighted this knowledge deficit, noting the scarcity of survival studies among community-based TB patient groups [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Resolving this research limitation represents a key milestone toward developing better evidence-supported TB interventions. The current study examined the data from 10 years of pulmonary TB patients in Sakon Nakhon Province in Northeast Thailand. In particular, the study considered how well treatments had worked and how they changed over time. In addition, characteristics were identified as independently associated with mortality, survival trends among various patient demographics, and disparities in clinical outcomes across geographies.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy design and setting\u003c/h2\u003e\u003cp\u003eA retrospective cohort study approach was used. The observation window covered 10 fiscal years (FYs; October\u0026ndash;September) of Thailand\u0026rsquo;s TB surveillance system (FY2014\u0026ndash;FY2023), following the Ministry of Public Health\u0026rsquo;s National TB Information System (NTIP) reporting cycles. The target area was Sakon Nakhon Province, Northeast Thailand (approximate population 1.1\u0026nbsp;million), which comprises 18 districts with a provincial referral hospital and multiple district hospitals integrated within the national TB control program.\u003c/p\u003e\u003cp\u003eThe source population included all registered TB patients (pulmonary and extrapulmonary, all ages) in the Sakon Nakhon provincial NTIP registry during FY2014\u0026ndash;FY2023. Bacteriologically confirmed and clinically diagnosed cases were eligible. For descriptive summaries and crude outcomes, we reported the full cohort (n\u0026thinsp;=\u0026thinsp;9,289). For time-to-event analyses (survival), we used an evaluable cohort after censoring records with a transfer out or change of diagnosis at the date of last contact (n\u0026thinsp;=\u0026thinsp;8,895).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eData sources and variables\u003c/h3\u003e\n\u003cp\u003eIndividual-level records were extracted from the NTIP and cross-checked against provincial TB program listings for completeness and internal consistency.\u003c/p\u003e\u003cp\u003eSociodemographic variables: patient age (continuous data; grouped into age categories by year of 0\u0026ndash;14, 15\u0026ndash;44, 45\u0026ndash;64, and \u0026ge;\u0026thinsp;65 years), gender, citizenship, work type, and medical insurance coverage (Universal Coverage (UC), Civil Servant Medical Benefit Scheme, Social Security Scheme, other plans).\u003c/p\u003e\u003cp\u003eClinical variables: HIV status, TB site (pulmonary/extrapulmonary), sputum smear, chest radiography, drug resistance (isoniazid or rifampicin resistance, MDR-TB), and treatment regimen.\u003c/p\u003e\u003cp\u003eCalendar variables: year of diagnosis/registration (FY2014\u0026ndash;FY2023) to assess temporal trends.\u003c/p\u003e\n\u003ch3\u003eSuccess rates and mortality analysis\u003c/h3\u003e\n\u003cp\u003eIn this 10-year provincial cohort, the success rate was 85.6% overall (46.8% cured; 38.8% completed). Mortality during treatment was 6.8%. This was defined as death from any cause occurring at any time point between treatment commencement and study censoring, in accordance with established WHO reporting standards. Secondary outcomes were: (i) early mortality (\u0026lt;\u0026thinsp;2 months from treatment initiation); (ii) survival time (days/months); and (iii) success (cure or completion).\u003c/p\u003e\n\u003ch3\u003eHandling of censoring, follow-up, and missing data\u003c/h3\u003e\n\u003cp\u003eSurvival time was measured from the treatment start date to the earliest date of death, treatment completion, transfer out, change of diagnosis, loss to follow-up (LTFU), or administrative end of follow-up (September 30, 2023). Transfers and changes of diagnosis were censored at last contact and excluded from the survival risk set thereafter; LTFU was censored at the last recorded visit. Outcome completeness in the registry was high; analyses were performed with available data. Where covariate values were missing, models used complete-case analysis for those covariates.\u003c/p\u003e\n\u003ch3\u003eInclusion of both facility-level and temporal summaries\u003c/h3\u003e\n\u003cp\u003eWe summarized mortality and success rates at the facility level (district and provincial hospitals). Ranking tables and graphics excluded facilities with very small denominators (n\u0026thinsp;\u0026lt;\u0026thinsp;30) from the main displays to enhance comparability; provincial totals included all facilities, with n\u0026thinsp;\u0026lt;\u0026thinsp;30 facilities being documented in the figure and table captions. Annual trend analyses across FY2014\u0026ndash;FY2023 were evaluated using the chi-square (χ\u0026sup2;) test.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eDescriptive statistics were used to describe the general characteristics of the sample (number, percentage, mean, standard deviation (SD), median). Event-free survival was analyzed using the Kaplan-Meier survival curve. Differences between groups were compared using the log-rank test, with significance set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 level. Inference statistics based on univariable analysis were used to analyze the relationships between factors affecting survival, without considering other risk factors; the Cox proportional hazard model was used, presented as a crude hazard ratio (HR) and the 95% CI range, with significance set at the p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 level.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003eParticipants\u0026rsquo; characteristics\u003c/h2\u003e\u003cp\u003eFor all 9,289 tuberculosis patients, the cohort mean age was 52.6 years (SD 16.7), with a median of 53 (IQR 41\u0026ndash;65 years). In addition, gender breakdown was 63.6% males and 36.4% females, HIV co-infection was 21.0%, and documented resistance (MDR/RR-TB) was 0.4%.\u003c/p\u003e\u003cp\u003eThe mortality rate for patients aged\u0026thinsp;\u0026ge;\u0026thinsp;65 years was 12.3% (95% CI [11.0\u0026ndash;13.7%]). Patients who were HIV-positive had a mortality rate of 9.4% (95% CI [8.1\u0026ndash;10.6%]). The lack of overlap between these CIs confirmed that HIV co-infection was a major factor. There was a notably higher mortality rate for patients undergoing retreatment for TB of 20.8% (95% CI [11.8\u0026ndash;29.8%]). This may have been due to factors such as drug resistance or a more severe, persistent disease. Non-Thai patients had a significantly higher mortality rate of 16.2% (95% CI [10.6\u0026ndash;21.8%]) compared to Thai patients (7.3% [6.8\u0026ndash;7.8%]). The non-overlapping CIs suggested that factors, such as barriers to care, delayed diagnosis, or different social determinants of health, may have contributed to the worse outcomes in this group. Patients with a smear-positive test result had a mortality rate of only 1.6% (95% CI [0.0\u0026ndash;3.4%]), This seemingly counterintuitive finding may indicate that a positive smear, which provides a definitive and rapid diagnosis, leads to a more timely and aggressive initiation of appropriate treatment, thereby improving survival. Smear-negative TB can be more difficult to diagnose, potentially leading to treatment delays. In addition, the mortality rate for prisoners was 3.0% (95% CI [0.4\u0026ndash;5.6%]), while the rate for non-prisoners was 7.5% (95% CI [7.0\u0026ndash;8.0%]), suggesting that the structured environment of a prison, with directly observed therapy (DOT) and managed care, may lead to better treatment adherence and, consequently, better outcomes (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\u003eSurvival and mortality rates classified by population characteristics of pulmonary tuberculosis patients in Sakon Nakhon Province, 2014\u0026ndash;2023 (n\u0026thinsp;=\u0026thinsp;9,289)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal n (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSurvived n (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDied n (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMortality Rate (95% CI)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5,905 (63.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5,453 (63.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e452 (65.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e\u003cp\u003e7.7% (7.1% \u0026minus;\u0026thinsp;8.3%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3,384 (36.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3,147 (36.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e237 (34.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e\u003cp\u003e7.0% (6.1% \u0026minus;\u0026thinsp;7.9%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge group (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0\u0026ndash;14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e75 (0.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e70 (0.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1 (0.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e\u003cp\u003e1.3% (0.0% \u0026minus;\u0026thinsp;3.9%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e15\u0026ndash;44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2,832 (30.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2,684 (31.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e148 (21.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e\u003cp\u003e5.2% (4.4% \u0026minus;\u0026thinsp;6.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e45\u0026ndash;64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3,976 (42.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3,733 (43.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e243 (35.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e\u003cp\u003e6.1% (5.4% \u0026minus;\u0026thinsp;6.8%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2,406 (25.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2,109 (24.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e297 (43.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e\u003cp\u003e12.3% (11.0% \u0026minus;\u0026thinsp;13.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNationality\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eThai\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9,122 (98.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8,460 (98.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e662 (96.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e\u003cp\u003e7.3% (6.8% \u0026minus;\u0026thinsp;7.8%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-Thai\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e167 (1.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e140 (1.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e27 (3.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e\u003cp\u003e16.2% (10.6% \u0026minus;\u0026thinsp;21.8%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHIV status\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNegative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7,279 (78.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6,778 (78.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e501 (72.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e\u003cp\u003e6.9% (6.3% \u0026minus;\u0026thinsp;7.5%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePositive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2,010 (21.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1,822 (21.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e188 (27.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e\u003cp\u003e9.4% (8.1% \u0026minus;\u0026thinsp;10.6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTreatment category\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNew treatment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9,212 (99.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8,539 (99.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e673 (97.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e\u003cp\u003e7.3% (6.8% \u0026minus;\u0026thinsp;7.8%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRetreatment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e77 (0.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e61 (0.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e16 (2.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e\u003cp\u003e20.8% (11.8% \u0026minus;\u0026thinsp;29.8%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSmear result\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNegative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9,104 (98.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8,418 (97.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e686 (99.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e\u003cp\u003e7.5% (7.0% \u0026minus;\u0026thinsp;8.1%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePositive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e185 (2.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e182 (2.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3 (0.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e\u003cp\u003e1.6% (0.0% \u0026minus;\u0026thinsp;3.4%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePrisoner status\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9,120 (98.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8,436 (98.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e684 (99.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e\u003cp\u003e7.5% (7.0% \u0026minus;\u0026thinsp;8.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e169 (1.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e164 (1.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5 (0.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e\u003cp\u003e3.0% (0.4% \u0026minus;\u0026thinsp;5.6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eTreatment outcomes (combined view)\u003c/h2\u003e\u003cp\u003eWe analyzed 9,289 patients. Of these, 85.6% were cured or completed care. Overall, 7.4% died, with 3.8% dying in the first 2 months and 3.6% dying\u0026thinsp;\u0026gt;\u0026thinsp;2 months after the treatment had started. For time-to-event analyses using the evaluable cohort after censoring transfers and changes of diagnosis (n\u0026thinsp;=\u0026thinsp;8,895), the success rate was 89.4% with overall mortality being 7.7% and early and late deaths being 3.9% and 3.8%, respectively (Table\u0026nbsp;\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\u003eTreatment outcomes (registry and survival analysis), Sakon Nakhon Province, 2014\u0026ndash;2023\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOutcome\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;9,289)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e% of Total\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eEvaluable (n\u0026thinsp;=\u0026thinsp;8,895)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e% of Evaluable\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSuccess (cure or completion)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7,948\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e85.6\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7,948\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e89.4\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCured\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4,346\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e46.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4,346\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e48.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCompleted treatment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3,602\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e38.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3,602\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e40.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eUnsuccessful\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e947\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e10.2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e947\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e10.6\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDied\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e689\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e689\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e7.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEarly mortality (\u0026lt;\u0026thinsp;2 months)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e350\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e350\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLate mortality (\u0026ge;\u0026thinsp;2 months)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e339\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e339\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLost to follow-up\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e202\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e202\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTreatment failure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTransfer out - \u003cb\u003ecensored\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChange of diagnosis- \u003cb\u003ecensored\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e299\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTemporal trends. Annual TB caseloads were broadly stable (642\u0026ndash;1,068 cases/year). The death rate rose from 3.5% in 2014 to 9.1% in 2023 (χ\u0026sup2; trend test, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Conversely, the success rate declined from 92.1% to 82.6% over the decade, with a modest dip observed during the 2021\u0026ndash;2023 period. MDR/RR-TB remained rare (\u0026lt;\u0026thinsp;1% annually), with a small uptick in 2023. These patterns are summarized in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eThe Kaplan-Meier survival analysis of tuberculosis (TB) patients in Sakon Nakhon (2014\u0026ndash;2023).\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThat clearly reveals significant structural health inequities that persist beyond standard treatment completion. Elderly patients (aged\u0026thinsp;\u0026ge;\u0026thinsp;65 years) exhibit the lowest survival rates, underscoring an urgent need for integrated care programs. That effectively manage high comorbidity burdens alongside TB treatment. Furthermore, HIV-positive status remains a critical predictor of lower long-term survival, necessitating stricter, sustained adherence to collaborative TB/HIV management protocols. Disparities observed across insurance schemes suggest that socioeconomic determinants, as reflected in entitlement to benefits, still heavily influence access to quality of care. The overall conclusion was that TB control must evolve to include robust, equity-focused, long-term care strategies targeting. These vulnerable groups to achieve sustainable reductions in mortality. The Kaplan\u0026ndash;Meier survival curves indicate that survival differed significantly by age group, HIV status, and insurance coverage, with the steepest decline during the first two months of treatment, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003ePredictors of TB-related mortality\u003c/h2\u003e\u003cp\u003eIn the multivariable Cox models of the evaluable cohort (n\u0026thinsp;=\u0026thinsp;8,895), older age and HIV co-infection were the dominant predictors. Compared with patients\u0026thinsp;\u0026lt;\u0026thinsp;45 years, those aged 45\u0026ndash;64 years had a modest but not significantly higher hazard (aHR 1.26, 95% CI 1.00\u0026ndash;1.59; p\u0026thinsp;=\u0026thinsp;0.046), and those aged\u0026thinsp;\u0026ge;\u0026thinsp;65 years had nearly a three-fold higher hazard (aHR 2.73, 95% CI 2.17\u0026ndash;3.42; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). People who had HIV were 53% more likely to die (aHR 1.53, 95% CI 1.26\u0026ndash;1.84; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Associations for sex, smear positivity, imprisonment, drug resistance, and insurance type were not statistically significant after adjustment, with insurance effects attenuating in multivariable analysis (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\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\u003ePredictors of TB-related mortality from multivariable Cox regression analysis, Sakon Nakhon Province, 2014\u0026ndash;2023 (n\u0026thinsp;=\u0026thinsp;8,895)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePredictor (reference)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUnivariable HR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ep-Value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMultivariable aHR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep-Value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSex\u003c/b\u003e (Female vs Male)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.94 (0.79\u0026ndash;1.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.89 (0.75\u0026ndash;1.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge 45\u0026ndash;64 years\u003c/b\u003e (vs\u0026thinsp;\u0026lt;\u0026thinsp;45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.73 (0.61\u0026ndash;0.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.26 (1.00\u0026ndash;1.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.046\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge\u0026thinsp;\u0026ge;\u0026thinsp;65 years\u003c/b\u003e (vs\u0026thinsp;\u0026lt;\u0026thinsp;45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.29 (1.94\u0026ndash;2.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.73 (2.17\u0026ndash;3.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHIV Positive\u003c/b\u003e (vs Negative)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.31 (1.09\u0026ndash;1.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.005\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.53 (1.26\u0026ndash;1.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDrug resistance\u003c/b\u003e (Yes vs No)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.30 (0.74\u0026ndash;7.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.28 (0.73\u0026ndash;7.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.16\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSmear positive\u003c/b\u003e (vs Negative)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.01 (0.55\u0026ndash;1.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.02 (0.56\u0026ndash;1.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.95\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePrisoner\u003c/b\u003e (Yes vs No)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.50 (0.21\u0026ndash;1.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.67 (0.28\u0026ndash;1.64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.38\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eInsurance\u003c/b\u003e:\u003c/p\u003e\u003cp\u003eSocial Security vs UC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.03 (0.86\u0026ndash;1.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.05 (0.87\u0026ndash;1.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.61\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eInsurance\u003c/b\u003e:\u003c/p\u003e\u003cp\u003eCivil Servant vs UC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.57 (0.35\u0026ndash;0.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.76 (0.46\u0026ndash;1.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.30\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eInsurance\u003c/b\u003e:\u003c/p\u003e\u003cp\u003eOther vs UC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.97 (0.40\u0026ndash;2.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.11 (0.46\u0026ndash;2.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.82\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cb\u003eNotes\u003c/b\u003e: We started by building separate models for each predictor. Any variables with p-values under 0.10 was allocated to the combined model. We checked that proportional hazards assumptions were acceptable using Schoenfeld residuals. Treatment start was our time zero for all analyses, and we considered results significant at the p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 level.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eBased on the results of the multivariable Cox analysis, being aged 65 years or older and having HIV infection were major independent factors that predicted death, with adjusted hazard ratios of 2.73 (95% CI 2.17\u0026ndash;3.42, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and 1.53 (95% CI 1.26\u0026ndash;1.84, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), respectively. Compared to the age\u0026thinsp;\u0026lt;\u0026thinsp;45 years group, the group aged 45\u0026ndash;64 years had an adjusted hazard ratio of 1.26 for death (95% CI 1.00\u0026ndash;1.59; p\u0026thinsp;=\u0026thinsp;0.046). Insurance effects observed in crude models were attenuated after adjustment, while drug resistance and prisoner status showed elevated hazard ratios but were not statistically significant (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eWhen we controlled for sex, age group, HIV status, smear result, drug resistance, prisoner status, and health insurance type (using Universal Coverage (UC) as our reference), Cox regression analysis showed two main factors that increased death risk: being aged 65 years or older and having HIV infection. The other variables we looked at had a weaker significant connection or were not statistically significant for survival. The vertical dashed line shows HR\u0026thinsp;=\u0026thinsp;1.\u003c/p\u003e\u003cp\u003e\u003cb\u003eTerritorial disparities (\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e4\u003c/span\u003e, \u003cb\u003ePanel A;\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA-B\u003cb\u003e).\u003c/b\u003e\u003c/p\u003e\u003cp\u003eMarked facility-level heterogeneity was observed across Sakon Nakhon Province. Mortality ranged from 0.6% in Charoen Sin to 11.6% in Phra Ajarn Baen Thanakaro, followed by 10.9% in Song Dao and 10.7% in Nikhom Nam Un. Success rates exceeded 90% in Kut Bak, Waritchaphum, Phang Khon, and Charoen Sin, whereas Khok Si Suphan (77.9%) and Phra Ajarn Baen Thanakaro (74.6%) had the lowest success, indicating local programmatic challenges. Larger facilities\u0026mdash;Mueang Sakon Nakhon (Provincial Hospital, 7.5% mortality) and Sawang Daen Din (Somdet Phra Yupparaj Hospital, 4.3% mortality) had relatively stable performance. \u003cem\u003eFor comparisons with very small facilities, see\u003c/em\u003e Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA\u0026ndash;B.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cb\u003ePanel A\u003c/b\u003e Geographical and temporal disparities in TB treatment outcomes, Sakon Nakhon Province, 2014\u0026ndash;2023 Facility-level (district/provincial) hospital outcomes (ranked by mortality rate) (n\u0026thinsp;=\u0026thinsp;9,289)\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHospital\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003cp\u003e(n)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMortality (n, %)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSuccessful treatment\u003c/p\u003e\u003cp\u003e(n, %)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePhra Ajarn Baen Thanakaro\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e189\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e22 (11.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e141 (74.6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSong Dao\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e174\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e19 (10.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e148 (85.1%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNikhom Nam Un\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e112\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12 (10.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e97 (86.6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePhra Ajarn Fan Acharo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e636\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e64 (10.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e506 (79.6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAkat Amnuai\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e474\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e46 (9.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e408 (86.1%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWanon Niwat\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e769\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e74 (9.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e646 (84.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePhon Na Kaeo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e862\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e81 (9.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e698 (81.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePhra Ajarn Mun Phurithatto\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e558\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e52 (9.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e468 (83.9%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTao Ngoi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e194\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e18 (9.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e169 (87.1%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKusuman\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e336\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e29 (8.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e292 (86.9%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMueang Sakon Nakhon (Provincial)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2468\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e185 (7.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2,129 (86.3%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWaritchaphum\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e254\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e16 (6.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e230 (90.6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePhang Khon\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e440\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e27 (6.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e396 (90.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKham Ta Kla\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e319\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e19 (6.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e279 (87.5%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKut Bak\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e342\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e20 (5.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e310 (90.6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSawang Daen Din (Somdet Phra Yupparaj)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1268\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e55 (4.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1,099 (86.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKhok Si Suphan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e349\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13 (3.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e272 (77.9%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharoen Sin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e179\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1 (0.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e164 (91.6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrivate Hospital (Raks Sakhon)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0 (0.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1 (100.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUdom Clinic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0 (0.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1 (100.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eTemporal trends\u003c/h2\u003e\u003cp\u003eProgram performance gradually worsened each year throughout the 10-year period. Mortality increased from 3.5% in 2014 to 9.1% in 2023, while the success rate decreased from 92.1% to 82.6% \u003cb\u003e(χ\u0026sup2;\u003c/b\u003e test for trend, \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001), as summarized in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e4\u003c/span\u003e (Panel B) and visualized in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC\u0026ndash;D. Based on These changes, there was an increase in TB-related deaths and weakening program effectiveness, coinciding with persistent operational constraints and a small uptick in drug resistance in recent years.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cb\u003e(Panel B)\u003c/b\u003e Annual outcomes (2014\u0026ndash;2023)\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYear\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal (n)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDeaths (n, %)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTreatment success (n, %)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e719\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e25 (3.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e662 (92.1%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e642\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e31 (4.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e567 (88.3%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1,016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e72 (7.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e875 (86.1%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1,016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e67 (6.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e878 (86.4%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e936\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e79 (8.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e792 (84.6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1,068\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e75 (7.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e927 (86.8%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e959\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e86 (9.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e820 (85.5%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e845\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e72 (8.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e708 (83.8%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1,025\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e85 (8.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e841 (82.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1,063\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e97 (9.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e878 (82.6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eGeographic distribution and temporal trends\u003c/h2\u003e\u003cp\u003eFacility-level outcomes varied widely. Mortality was highest in Phra Ajarn Baen (11.6%) and Song Dao (10.9%), while several facilities had mortality rates\u0026thinsp;\u0026lt;\u0026thinsp;5% (such as Charoen Sin 0.6%, Sawang Daen Din 4.3%). Success rates fell below the WHO 90% target in Khok Si Suphan (77.9%) and Phra Ajarn Baen (74.6%), whereas \u0026ge;\u0026thinsp;90% was achieved in Waritchaphum and Kut Bak (both 90.6%), Phang Khon (90.0%), and Charoen Sin (91.6%). Over time, mortality rose from 3.5% in 2014 to 9.1% in 2023, Treatment success rates decreased significantly from 92.1% to 82.6% (χ\u0026sup2; test for trend, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This trend revealed continuing program deficiencies and highlighted that certain facilities were facing disproportionate challenges (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA\u0026ndash;D).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003ePanel A\u003c/b\u003e Facility-level (district/provincial) mortality varied markedly, with the highest rates in Phra Ajarn Baen (11.6%), Song Dao (10.9%), and Nikhom Nam Un (10.7%), while several facilities maintained mortality below 5% (such as Charoen Sin 0.6%, Sawang Daen Din 4.3%).\u003c/p\u003e\u003cp\u003e\u003cb\u003ePanel B Success rates\u003c/b\u003e fell below the WHO 90% target in Khok Si Suphan (77.9%) and Phra Ajarn Baen (74.6%), whereas there was \u0026ge;\u0026thinsp;90% success in Waritchaphum (90.6%), Kut Bak (90.6%), Phang Khon (90.0%), and Charoen Sin (91.6%).\u003c/p\u003e\u003cp\u003e\u003cb\u003ePanel C\u003c/b\u003e Mortality rose annually (3.5% in 2014 to 9.1% in 2023), with a significant upward trend (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, chi-square test).\u003c/p\u003e\u003cp\u003e\u003cb\u003ePanel D\u003c/b\u003e Success rates for treatment decreased from 92.1% to 82.6% in the same period (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003eAnalysis of the results for Sakon Nakhon Province indicates there is a critical situation. The decade-long decline in treatment success from 92.1% to 82.6% signifies a significant regression from the WHO\u0026rsquo;s 90% target [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. This trend, coupled with rising mortality, suggests systemic failures that mirror Thailand's national challenges in curbing rising TB cases [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The extreme variation in outcomes between facilities, such as mortality rates from 0.6% to 11.6%, strongly indicates that localized factors, not just patient characteristics, are at play. These disparities may be driven by unexamined socioeconomic burdens on households or health system inequities [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eTreatment outcomes\u003c/h2\u003e\u003cp\u003eThere was an 85.6% success rate for the 10-year provincial cohort, comparable to national figures (81\u0026ndash;86%) [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] but below the WHO end TB target of \u0026ge;\u0026thinsp;90% [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Mortality occurred in 7.4% of patients, with nearly one-half of these deaths occurring in the first 2 months of treatment (\u0026asymp;\u0026thinsp;51% of all deaths), underscoring the burden of early mortality. The same signals were reported in Asian and African cohorts, with early outcome deficits tied to delayed diagnosis, advanced-stage presentation, and coexisting HIV infection [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eTemporal trends\u003c/h2\u003e\u003cp\u003eAcross the decade, deaths increased from 3.5% to 9.1%, whereas the share completing treatment decreased from 92.1% to 82.6%, as summarized in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e4\u003c/span\u003e (Panel B) and visualized in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC\u0026ndash;D. These outcomes coincided with service disruptions during the COVID-19 period and a small uptick in drug-resistant TB in 2023, while the burden of HIV co-infection remained substantial [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. These patterns mirror country-level findings that show program outcomes remaining flat despite reduced TB occurrence [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eSurvival and predictors\u003c/h2\u003e\u003cp\u003eThe Kaplan-Meier analysis indicated a median survival of 6 months, with excess mortality in the early intensive phase. Analyses were conducted in the evaluable cohort (n\u0026thinsp;=\u0026thinsp;8,895; transfers, with censoring of changes in diagnosis). Based on the results from the multivariable Cox regression, age remained independently associated with mortality; adults aged 45\u0026ndash;64 years (vs\u0026thinsp;\u0026lt;\u0026thinsp;45 years) had higher hazards (aHR 1.26, 95% CI 1.00\u0026ndash;1.59; p\u0026thinsp;=\u0026thinsp;0.046), and those\u0026thinsp;\u0026ge;\u0026thinsp;65 years had the greatest hazards (aHR 2.73, 95% CI 2.17\u0026ndash;3.42; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In addition, patients with HIV had a higher independent death risk (aHR 1.53, 95% CI 1.26\u0026ndash;1.84; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), consistent with studies from around the world showing that older age and HIV were important risk factors [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe predictors of TB-related mortality, primarily advanced age and HIV co-infection [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], reveal that immunological vulnerability is the dominant force escalating mortality risk. This finding resonates across the lifespan, paralleling the critical importance of nutritional well-being in infants [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e] and the necessity of basic prophylactic measures, such as rinsing, during pandemics [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Ultimately, these interconnected findings emphasize that investing in and sustaining fundamental biological resilience throughout life from basic nutrition to continuous immunological support. It is the most effective public health strategy for reducing preventable deaths from chronic infectious diseases like TB among the most fragile populations. Furthermore, the strongly validates the original premise [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], effective TB control hinges on addressing vulnerability. It broadens the definition, showing that vulnerability in modern urban epidemiology is often driven by socio-economic factors that dismantle fundamental biological resilience, particularly in mobile populations. Public health strategy must adapt to target these systemic vulnerabilities alongside traditional clinical risk factors.\u003c/p\u003e\u003cp\u003eThe TB-related mortality risk is a complex outcome stemming from the erosion of fundamental biological resilience, extending beyond purely clinical factors. The Sakon Nakhon analysis confirms that immunological vulnerability (due to advanced age and HIV co-infection [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e] is the dominant predictor of reduced survival rates.\u003c/p\u003e\u003cp\u003eThis core principle aligns with a broader socioeconomic dimension, as research demonstrates [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. The precarious lifestyles of internal migrants severely undermine biological resilience, leading to higher TB incidence. Furthermore, studies reinforce the role of multimorbidity and behavioral risks as accelerants that continuously erode the body's fortitude, making vulnerable populations highly susceptible to fatal TB outcomes [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, and \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003eImplications\u003c/h2\u003e\u003cp\u003eOverall, the findings indicated that biological factors (being older, having HIV) and structural problems (money issues, location differences) were the main reasons for mortality from TB. To address these gaps, we need targeted approaches focused on fairness, such as better fast testing, connecting TB and HIV care, and helping older people and those struggling financially. Our recommendations include focusing on high-risk healthcare facilities (defined as \u0026gt;\u0026thinsp;10% mortality rates or \u0026lt;\u0026thinsp;80% success rate) through the implementation of targeted early mortality reviews, immediate molecular diagnostic testing with same-day treatment start, and customized adherence interventions.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe analysis of this 10-year cohort study demonstrated that TB treatment outcomes in Sakon Nakhon Province remain below global targets, with disproportionate early mortality among the elderly and HIV-positive patients. Advanced age (\u0026ge;\u0026thinsp;65 years) and HIV coinfection were the most important factors that predicted death, while differences in income and location made patients' outcomes even worse. Our results highlighted the urgent need to implement wide-ranging approaches that include TB/HIV integration, better rapid testing, and fair interventions. These steps are very important to assist people who are at risk and to accelerate progress toward reaching Thailand's national goals and the WHO's end TB goals.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eThis study was authorized by the Sakon Nakhon Provincial Public Health Office\u0026apos;s Research Ethics Committee (Approval No. COA 234/2024). Patient data were analyzed retrospectively after ensuring the anonymity of each patient. This approach eliminated privacy concerns and the need for individual informed consent [28].\u0026nbsp;All procedures adhered to the Declaration of Helsinki and pertinent national regulations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003cbr\u003eThe Sakon Nakhon Provincial Health Office authorized the use of the de-identified NTIP records and the TB Clinic at Akat Amnuai Hospital compiled and validated the datasets. Staff in the Faculty of Public Health, Kasetsart University Sakon Nakhon Campus, Thailand provided advice and direction on study design.\u0026nbsp;Analyses were performed jointly by the authors who accept full responsibility for any mistakes in their interpretations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003eSopon Usaprom (SU):\u0026nbsp;\u003c/strong\u003eLed study conceptualization and methodology development; performed data analysis; prepared the original manuscript.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eWuttiphong Phakdeekul (WP):\u003c/strong\u003e Organized and validated the dataset, and provided critical review and manuscript editing.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eWarinmas Kasethongma (WK)\u003c/strong\u003e: Supervised and provided critical revision of the manuscript, and final approval.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eAll authors approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study used internal institutional resources with no external funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePatient privacy policies prohibit public data distribution. Researchers seeking data access should contact the corresponding author with justifieda requests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study used de-identified NTIP records and received ethical approval from the Research Ethics Committee of the Sakon Nakhon Provincial Public Health Office (COA 234/2024). Given the retrospective nature and absence of direct identifiers, that Committee issued a consent waiver under the Declaration of Helsinki and national requirements.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication Not applicable.\u0026nbsp;\u003c/strong\u003eTo address these problems, we need a strategic approach that focuses on fairness. This includes more rapid diagnostics, combining TB and HIV services, and better support for older people and people who are poor or have low income.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWorld Health Organization. Global Tuberculosis Report 2024\u0026ndash;2.3 TB treatment coverage and outcomes. Geneva: World Health Organization; 2024 [cited 2025 Sep 17]. Available from: https://www.who.int/teams/global-programme-on-tuberculosis-and-lung-health/tb-reports/global-tuberculosis-report-2024/tb-diagnosis-and-treatment/2-3-tb-treatment--coverage-and-outcomes \u003c/li\u003e\n\u003cli\u003eWorld Health Organization, Regional Office for South-East Asia. 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BMC Public Health. 2022 Apr 11;22(1).\u003c/li\u003e\n\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":"bmc-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Tuberculosis, HIV coinfection, Mortality, Survival analysis, community Health","lastPublishedDoi":"10.21203/rs.3.rs-8082141/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8082141/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eIn 2022, there were 10.6\u0026nbsp;million new cases of tuberculosis (TB) and 1.3\u0026nbsp;million deaths from TB globally; it remains one of the most common causes of death from infectious diseases. Even though there have been considerable improvements in controlling TB, not enough is known about predicting long-term survival for TB patients, especially in rural community of the northeastern, Thailand.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eA retrospective cohort study was conducted data (2014\u0026ndash;2023) on 9,289 pulmonary and extrapulmonary tuberculosis patients in Sakon Nakhon province, Thailand. Data were retrieved from the National TB Information System (NTIP), which was refined to a cohort (n\u0026thinsp;=\u0026thinsp;8,895) subsequent to the exclusion of transfers and modifications of diagnoses. Analysis was based on the Kaplan-Meier survival curve, Log rank test and a Cox proportional hazard model, with statistical significance set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eAmong the 9,289 TB patients (mean 52.6\u0026thinsp;\u0026plusmn;\u0026thinsp;16.7 years): 63.6% were male (male-to-female ratio, 1.74:1); 21.6% had HIV coinfection; and drug resistance was 0.4%. The overall treatment success rate was 85.6\u0026mdash;46.8% with 38.8% completing the course. The overall death rate during treatment was 7.4%, while the early death rate (within 2 months) was 3.8%. The median survival was around 6.0 months (mean 6.6\u0026thinsp;\u0026plusmn;\u0026thinsp;3.0 months). The success rate declined from 92.1% to 82.6% over the decade of analysis, with a modest dip observed during the 2021\u0026ndash;2023 period. Mortality was independently associated with age\u0026thinsp;\u0026ge;\u0026thinsp;65 years (aHR 2.73; 95% CI 2.17\u0026ndash;3.42), and HIV coinfection (aHR 1.53; 95% CI 1.26\u0026ndash;1.84).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eThe overall median survival time of the TB patients was 6.0 months. The factors affecting patient survival were age\u0026thinsp;\u0026ge;\u0026thinsp;65 years and HIV coinfection. There was an upward trends in the mortality rate.\u003c/p\u003e","manuscriptTitle":"Survival and Determinants of Mortality among Pulmonary Tuberculosis Patients in Sakon Nakhon Province, Northeastern Thailand","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-01 08:32:21","doi":"10.21203/rs.3.rs-8082141/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-01-09T14:47:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"315824833190758528049867879718200457157","date":"2026-01-07T15:28:03+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-25T07:28:25+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-11-17T04:18:36+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-14T11:37:17+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-14T11:33:26+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Infectious Diseases","date":"2025-11-11T03:40:36+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"980aafbc-da58-412f-a5cc-3ea1cd6da729","owner":[],"postedDate":"December 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-12-01T08:32:22+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-01 08:32:21","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8082141","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8082141","identity":"rs-8082141","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}