Spatiotemporal trends in tuberculosis incidence in Thailand, 2012–2023: a nationwide, province-level analysis

Spatiotemporal trends in tuberculosis incidence in Thailand, 2012–2023: a nationwide, province-level analysis | 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 Spatiotemporal trends in tuberculosis incidence in Thailand, 2012–2023: a nationwide, province-level analysis Kittipong Sornlorm, Roshan Kumar Mahato, Sarayu Muntaphan, Kanit Hnuploy, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8460167/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract Background Tuberculosis (TB) remains a major public health challenge in Thailand, a high-burden country undergoing both epidemiological transition and pandemic-related disruption. This study examined temporal and spatial patterns of age-standardized TB incidence from 2012 to 2023 across Thailand’s 13 health regions and 77 provinces. Methods National tuberculosis surveillance data were used for all analyses. Age-standardized incidence rates (ASR) were calculated using the WHO World Standard Population (2000–2025). Temporal trends were assessed using Joinpoint regression to estimate Health region–specific annual percent change (APC) and average annual percent change (AAPC). Generalized Additive Models (GAM) were fitted to validate the temporal trajectories. Spatial clustering was evaluated using Global Moran’s I and Local Indicators of Spatial Association (LISA), applied to provincial AAPC values. Results National TB incidence declined substantially from 2012 to 2023, although marked regional heterogeneity persisted. Five regions demonstrated the strongest long-term reductions: region 3 (AAPC − 20.51, 95%CI: −33.00 to − 13.77), region 9 (− 19.42, 95%CI: −28.98 to − 8.57), region 4 (− 17.53, 95%CI: −23.64 to − 12.89), region 11 (− 11.79, 95%CI: −22.16 to − 0.04), and region 13 (− 11.19, 95%CI: −22.44 to − 3.74). Several regions exhibited biphasic trends, including region 6, which showed an early decline followed by stabilization, and region 12, which experienced a mid-period increase before a post-2019 reduction. Spatial analysis revealed limited global clustering, but LISA identified distinct local patterns. Three provinces (Phayao, Phetchabun, Yasothon) formed high-high clusters with increasing AAPC values, while four provinces (Buri Ram, Ang Thong, Nakhon Pathom, Bangkok) formed low-low clusters with sustained declines. High-low and low-high outliers highlighted further geographic heterogeneity. Conclusions Thailand has achieved substantial reductions in TB incidence over the past decade however, pronounced regional and provincial disparities persist. Localized hotspots and divergent temporal trajectories underscore the need for geographically targeted monitoring, equitable resource allocation, and pro-poor interventions to sustain progress toward TB elimination. Tuberculosis Thailand Spatial Analysis Spatiotemporal Analysis Disease Mapping Epidemiology Health Policy Joinpoint Regression Moran's I Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Tuberculosis (TB) remains one of the leading causes of morbidity and mortality worldwide, representing a persistent global health crisis. In 2022 alone, an estimated 10.6 million new cases and 1.3 million deaths were attributed to the disease [ 1 – 3 ]. Despite significant progress in healthcare infrastructure and disease control in recent decades, Thailand remains classified among the 30 high-burden countries for TB by the World Health Organization (WHO) [ 3 ]. To address this, the WHO End TB Strategy launched in 2015 established ambitious targets including a 90% reduction in TB incidence and a 95% reduction in TB deaths by 2035 relative to 2015 levels [ 4 ]. However, achieving these targets requires navigating complex epidemiological landscapes and unexpected global disruptions. The emergence of the COVID-19 pandemic profoundly disrupted global TB control programs, resulting in estimated declines of 25–40% in TB notifications during lockdown periods [ 5 , 6 ]. In Thailand, the pandemic placed immense strain on the healthcare system, necessitating a rapid reallocation of resources and creating disparities in healthcare accessibility. Previous research on the spatial distribution of COVID-19 health resources in Thailand highlighted significant inequalities in service coverage during the crisis, suggesting that the pandemic's impact was geographically uneven [ 7 ]. Reduced access to diagnostic services, healthcare system strain, and socioeconomic impacts created conditions that may have facilitated TB transmission while simultaneously lowering detection capacity. Understanding these pandemic-related disruptions through a spatial lens is critical for recovery planning and future preparedness. Spatial epidemiology provides powerful tools for understanding geographic heterogeneity in disease burden and identifying high-risk areas requiring intervention [ 8 , 9 ]. Moving beyond national averages is essential, as TB transmission commonly clusters in specific "hotspots" driven by local factors. Recent studies involving spatiotemporal analysis in other high-burden settings have demonstrated the usefulness of these methods. For instance, Kulldorff’s spatiotemporal scan statistics and discrete Poisson models have successfully identified persistent TB clusters and their geographic distribution, allowing for more precise resource prioritization [ 10 ]. TB disproportionately affects vulnerable and marginalized populations, particularly those living in poverty. In Thailand, geographic disparities in TB burden mirror broader patterns of socioeconomic inequality, with higher incidence concentrated in border regions, migrant communities, and areas with limited healthcare access. Understanding these spatial patterns is essential for designing pro-poor, equity-focused interventions. This study applied these advanced spatial methodologies to the Thai context. Spatial autocorrelation methods, including Global Moran's I and Local Indicators of Spatial Association (LISA), are employed to detect clustering patterns that inform resource allocation and targeted prevention strategies [ 11 , 12 ]. Complementing this, Joinpoint regression analysis is used to identify significant changes in epidemiological trends and to estimate annual percent change (APC) for distinct temporal segments [ 13 , 14 ]. This approach enables the detection of inflection points such as those associated with the implementation of the End TB Strategy or the onset of COVID-19 and provides insights into program effectiveness and external influences on TB epidemiology. This study aimed to: (1) characterize temporal trends in TB incidence across Thailand’s Health regions and 77 provinces from 2012 to 2023 using Joinpoint regression; (2) assess spatial autocorrelation and clustering patterns using Global Moran's I and LISA; (3) classify provinces into risk categories based on long-term trends; (4) quantify the COVID-19 pandemic's impact on provincial TB rates; and (5) identify priority areas for targeted intervention. Findings will inform evidence-based, geographically targeted strategies to accelerate progress toward TB elimination goals. Materials And Methods Study Design and Setting This study conducted a retrospective ecological study analyzing tuberculosis incidence across all 77 provinces of Thailand from 2012 to 2023. Thailand is administratively divided into 13 Health regions comprising 77 provinces. This study utilized population-level surveillance data to characterize temporal trends, spatial patterns, and pandemic-related disruptions in TB epidemiology. Data Sources TB Case Data: Monthly TB case notifications (2012–2023) were obtained from the National Disease Surveillance System (Report 506), Bureau of Epidemiology, Department of Disease Control, Ministry of Public Health, Thailand [ 15 ]. This system captures all TB cases diagnosed and reported throughout public and private healthcare facilities. Population Data: Annual mid-year population estimates by province, sex, and age group were obtained from the Official Statistics Registration Systems, Department of Provincial Administration, Ministry of Interior Thailand [ 16 ]. Age-specific population data were aggregated into 5-year age groups (0–4, 5–9, ..., 85+) for age standardization. WHO Standard Population: The WHO World Standard Population (2000–2025) [ 17 ] was used as the reference for age standardization to enable international comparability. Geographic Boundaries: Provincial administrative boundary shapefiles were obtained from the UN Office for the Coordination of Humanitarian Affairs (OCHA) Thailand database [ 18 ] for spatial analysis. Age-Standardized Rate (ASR) and Standard Error (SE) Calculation Age-standardized incidence rates (ASR) were calculated using the direct standardization method with the WHO World Standard Population (2000–2025) as reference. The ASR formula is: ASR = (Σ(a i × w i ) / Σw i ) × 100,000 where a i is the age-specific incidence rate in age group i, and w i is the proportion of the standard population in age group i. Age groups were categorized into 19 groups (0–4, 5–9, ..., 85–89, 90+). The standard error (SE) of ASR was calculated using [ 19 ]: SE(ASR) = √[Σ(w i ² × a i / n i )] / Σw i × 100,000 where n i is the population in age group i. For province-year observations with zero TB cases, this study applied the Exact Method with + 0.5 correction to enable rate calculation while acknowledging uncertainty. Zero-case observations were present in several provincial-year records, occurring predominantly in smaller provinces during low-incidence years. Joinpoint Regression Analysis and Generalized Additive Models: APC and AAPC Temporal trend analysis was performed using Joinpoint regression (Joinpoint Regression Program Version 5.0.2, National Cancer Institute) [ 14 ]. This method fits a series of joined straight lines on a log scale to identify significant changes in temporal trends. The annual percent change (APC) for each linear segment is calculated as: APC = [exp(β) − 1] × 100 where β is the slope of the log-linear regression for each segment. The average annual percent change (AAPC) summarizes the trend over the entire study period as a weighted average: AAPC = [exp(Σ(b i × w i ) / Σw i ) − 1] × 100 where b i is the slope coefficient for the ith segment and w i is the length of the ith segment. Model selection was performed using the Bayesian Information Criterion (BIC) to identify the optimal number of joinpoints. BIC provides a computationally efficient and theoretically grounded criterion by penalizing model complexity, thereby reducing the risk of overfitting in time-series with limited observation points. The joinpoint search allowed a maximum of two joinpoints per series, with a minimum of two observations between joinpoints and at least one observation at each end of the series to ensure model stability. Generalized Additive Models (GAM) with cubic regression splines were fitted to annual age-standardized TB incidence (ASR) for all 13 health service regions. The smoothing function was specified as s(Year, k = 5) to allow flexible but not overfitted temporal patterns. For each region, fitted smoothed values were extracted and numerical first derivatives were computed to approximate instantaneous annual percent change (APC). APC was calculated as: APC = (d/dt SmoothASR / SmoothASR) × 100 where d/dt is the first derivative with respect to time (year). Health region–specific Average Annual Percent Change (AAPC) was computed as the mean of all APC values across 2012–2023. GAM smoothing was applied solely to validate and cross-check joinpoint-based estimates, providing a model-free assessment of long-term trends. Percent Change Analysis for Classification Provincial classification utilized percent change calculations defined as: Percent Change = [(Rate₂ - Rate₁) / Rate₁] × 100 Long-term Classification (2012–2023): Provinces were classified using AAPC from Joinpoint regression. Safe Zone: AAPC < 0 (p < 0.05) OR p ≥ 0.05 (stable/non-significant trends). Alarming Zone: AAPC ≥ 0 (p < 0.05). COVID-19 Impact Assessment (2019–2023): Crude rate percent change was calculated comparing 2023 vs 2019 baseline. Categories: Decreased ( + 5%). Spatial Autocorrelation Analysis: Moran's I and LISA Spatial autocorrelation was assessed using Global Moran's I statistic [ 11 , 20 ]: Global Moran's I = (n / Σ i Σⱼ w i ⱼ) × [Σ i Σⱼ w i ⱼ(x i - x̄)(xⱼ - x̄) / Σ i (x i - x̄)²] where n is the number of provinces, x i and xⱼ are AAPC values for provinces i and j, x̄ is the mean AAPC, and w i ⱼ is the spatial weight between provinces i and j. Values range from − 1 (perfect dispersion) to + 1 (perfect clustering), with 0 indicating random spatial distribution. Local spatial clustering was identified using Local Indicators of Spatial Association (LISA) [ 11 ]: Local LISA: I i = (x i - x̄) × Σⱼ w i ⱼ(xⱼ - x̄) / [Σ i (x i - x̄)²/n] LISA identifies four types of spatial association: (1) High-High (HH): provinces with high AAPC surrounded by neighbors with high AAPC (hot spots); (2) Low-Low (LL): provinces with low AAPC surrounded by neighbors with low AAPC (cold spots); (3) High-Low (HL): high-AAPC provinces surrounded by low-AAPC neighbors (spatial outliers); (4) Low-High (LH): low-AAPC provinces surrounded by high-AAPC neighbors (spatial outliers). Spatial analysis was performed using GeoDa software version 1.20 [ 21 ]. Spatial weights matrices were constructed using queen contiguity (shared boundary or vertex) with row standardization. Statistical significance was assessed using 999 permutations (p < 0.05). Software and Statistical Analysis Statistical analyses were performed using: Joinpoint Regression Program 5.0.2 (National Cancer Institute); GeoDa 1.20 (spatial analysis); QGIS 3.34 [ 22 ] (cartography); R version 4.3.1 [ 23 ] (data management and visualization). Maps were created using QGIS with provincial boundary shapefiles from UN OCHA Thailand database. Statistical significance was set at α = 0.05 for all analyses. Results Overview of TB Burden and National Trends Generalized Additive Model (GAM) analysis with 95% confidence intervals revealed substantial geographical heterogeneity in age-standardized tuberculosis incidence rates (ASR per 100,000 population) across Thailand's 13 health regions during 2012–2023. The most dramatic declines were observed in health regions 3, 6, and 13, with observed ASR values decreasing from 41.87, 53.86, and 44.68 per 100,000 in 2012 to 2.83, 11.70, and 10.93 per 100,000 in 2023, respectively, representing reductions of 93.2%, 78.3%, and 75.5%. Health regions 4, 5, 9, and 11 demonstrated the strongest overall TB control performance, with observed ASR declining from 21.37, 25.19, 34.85, and 38.79 in 2012 to 2.11, 1.68, 2.16, and 2.94 per 100,000 in 2023, achieving reductions of 90.1%, 93.3%, 93.8%, and 92.4% respectively. Health regions 4, 9, 11, and 5 demonstrated the strongest overall TB control performance, with observed ASR declining from 21.37, 34.85, 38.79, and 25.19 in 2012 to 2.11, 2.16, 2.94, and 1.68 per 100,000 in 2023, achieving reductions of 90.1%, 93.8%, 92.4%, and 93.3% respectively. Health regions 1, 7, and 8 showed substantial declines from baseline rates of 29.35, 36.46, and 36.16 to 7.11, 16.20, and 10.66 per 100,000, representing reductions of 75.8%, 55.6%, and 70.5%. Health region 2 exhibited a more moderate decline from 28.97 to 13.06 per 100,000 (54.9% reduction), while Health region 10 showed the smallest reduction from 29.70 to 15.24 per 100,000 (48.7% decline), and thus maintaining relatively higher incidence levels throughout the study period. Health region 12 displayed a distinctive biphasic pattern, initially declining from 20.34 to 4.06 per 100,000 (80.0% reduction overall) (Supplementary Table S1 ). The GAM-smoothed curves (GAM Fit values) demonstrated strong concordance with observed data across all regions, with narrow 95% confidence intervals indicating robust model performance and reliable trend estimation, validating the heterogeneous patterns of TB control success across Thailand's diverse geographical and demographic contexts (Fig. 1 ). Regional Temporal Trends from Joinpoint Regression The Joinpoint regression analysis revealed heterogeneous trends in TB incidence across Thailand's 13 health regions during 2012–2023. Five health regions demonstrated statistically significant declining trends. Health region 3 exhibited the steepest decline with an average annual percent change (AAPC) of -20.51% (95%CI: -33.00 to -13.77, p < 0.001), followed by health region 9 with an AAPC of -19.42% (95%CI: -28.98 to -8.57, p = 0.003). Health region 4 showed a significant overall declining trend with an AAPC of -17.53% (95%CI: -23.64 to -12.89, p < 0.001), characterized by two distinct segments: an initial sharp decline during 2012–2016 (APC: -34.31%, 95%CI: -51.65 to -22.80, p < 0.001) followed by a more moderate, non-significant decline from 2016–2023 (APC: -6.09%, 95%CI: -18.36 to 45.13, p = 0.639). Additionally, a borderline significant decline was observed in health region 11 (AAPC = − 11.79%, 95% CI: −22.16 to − 0.04; p = 0.049), whereas health region 13 exhibited a statistically significant decline with an AAPC of − 11.19% (95% CI: −22.44 to − 3.74; p = 0.006). Eight health regions exhibited non-significant trends over the study period. Health region 6 showed a biphasic pattern with an initial sharp decline during 2012–2014 (APC: -46.12%, 95%CI: -66.28 to -4.06, p = 0.020) followed by a non-significant increase from 2014–2023 (APC: 6.21%, 95%CI: -17.73 to 61.83, p = 0.202), resulting in an overall non-significant AAPC of -6.12% (95%CI: -13.21 to 3.20, p = 0.170). Similarly, health region 12 demonstrated a significant increase during 2012–2019 (APC: 20.20%, 95%CI: 4.42 to 96.66, p = 0.024) followed by a significant decline from 2019–2023 (APC: -35.75%, 95%CI: -94.89 to -14.62, p = 0.020), yielding an overall non-significant AAPC of -4.28% (95%CI: -35.10 to 17.43, p = 0.468). Health regions 8 (AAPC: -6.37%, 95%CI: -15.49 to 3.73, p = 0.183), health regions 5 (AAPC: -6.05%, 95%CI: -14.82 to 2.10, p = 0.124), health regions 7 (AAPC: -3.14%, 95%CI: -13.32 to 13.66, p = 0.437), and health regions 1 (AAPC: -2.41%, 95%CI: -9.08 to 4.66, p = 0.420) showed non-significant declining trends. Health region 2 (AAPC: 1.28%, 95%CI: -8.92 to 12.63, p = 0.795) and health region 10 (AAPC: 4.91%, 95%CI: -9.81 to 32.56, p = 0.420) exhibited non-significant increasing trends, with health region 10 showing the largest magnitude of increase among all regions (Table 1 , Fig. 2). Table 1 Joinpoint Regression Analysis of Tuberculosis Incidence Trends by Health regions, Thailand, 2012–2023 Health region Segment Period APC (%) 95%CI p-value AAPC (%) 95%CI p-value 1 0 2012-2023 -2.41 (-9.08, 4.66) 0.420 -2.41 (-9.08, 4.66) 0.420 2 0 2012-2023 1.28 (-8.92, 12.63) 0.795 1.28 (-8.92, 12.63) 0.795 3 0 2012-2023 -20.51* (-33.00, -13.77) <0.001 -20.51* (-33.00, -13.77) <0.001 4 0 2012-2016 -34.31* (-51.65, -22.80) <0.001 -17.53* (-23.64, -12.89) <0.001 1 2016-2023 -6.09 (-18.36, 45.13) 0.639 5 0 2012-2023 -6.05 (-14.82, 2.10) 0.124 -6.05 (-14.82, 2.10) 0.124 6 0 2012-2014 -46.12* (-66.28, -4.06) 0.020 -6.12 (-13.21, 3.20) 0.170 1 2014-2023 6.21 (-17.73, 61.83) 0.202 7 0 2012-2023 -3.14 (-13.32, 13.66) 0.437 -3.14 (-13.32, 13.66) 0.437 8 0 2012-2023 -6.37 (-15.49, 3.73) 0.183 -6.37 (-15.49, 3.73) 0.183 9 0 2012-2023 -19.42* (-28.98, -8.57) 0.003 -19.42* (-28.98, -8.57) 0.003 10 0 2012-2023 4.91 (-9.81, 32.56) 0.420 4.91 (-9.81, 32.56) 0.420 11 0 2012-2023 -11.79* (-22.16, -0.04) 0.049 -11.79* (-22.16, -0.04) 0.049 12 0 2012-2019 20.20* (4.42, 96.66) 0.024 -4.28 (-35.10, 17.43) 0.468 1 2019-2023 -35.75* (-94.89, -14.62) 0.020 13 0 2012-2023 -11.19* (-22.44, -3.74) 0.006 -11.19* (-22.44, -3.74) 0.006 Note: APC = Annual Percent Change for each segment; AAPC = Average Annual Percent Change for entire period (2012-2023); CI = Confidence Interval; *Statistically significant at alpha = 0.05 level (two-tailed test). Spatial Autocorrelation Analysis Global Moran’s I of provincial AAPC values indicated a weak spatial autocorrelation (I = 0.102), suggesting minimal geographic structuring of TB trends. This pattern indicated that much of the variation is influenced by province-specific factors rather than strong spatial clustering (Fig. 3 C). Local Indicators of Spatial Association (LISA) identified 13 provinces (16.9%) with significant local spatial autocorrelation, categorized into four types (Fig. 3 B): High-High (HH) clusters – Hot spots (3 provinces): Phayao (AAPC = + 8.21%), Phetchabun (AAPC = + 5.37%), Yasothon (AAPC = + 4.92%). These provinces and their neighbors exhibited persistently high or increasing TB rates, indicating the need for priority intervention. Low-Low (LL) clusters - Cold spots (4 provinces): Buri Ram (AAPC = -25.70%), Ang Thong (AAPC = -30.33%), Nakhon Pathom (AAPC = -38.25%), Bangkok (AAPC = -20.05%). These provinces demonstrated sustained TB control success with declining rates, representing potential models for best practice dissemination. High-Low (HL) outliers (4 provinces): Suphan Buri (AAPC = + 9.97%), Sing Buri (AAPC = + 8.15%), Ratchaburi (AAPC = + 6.82%), Samut Prakan (AAPC = + 7.35%). These provinces showed increasing trends despite the surrounding provinces with declining trends. Low-High (LH) outliers (2 provinces): Uttaradit (AAPC = -16.47%), Chumphon (AAPC = -18.92%). Provincial Long-term Classification (2012–2023) Provincial-level joinpoint analysis yielded a long-term classification of 77 provinces based on AAPC values and statistical significance (Table 2 , Fig. 4 A): Safe Zone (69 provinces, 89.6%): Provinces with decreasing or stable TB trends. Subcategories include: (a) Significant decreasing trends: 36 provinces (46.8%) with AAPC < 0 and p < 0.05; (b) Stable or non-significant trends: 33 provinces (42.9%) with p ≥ 0.05. Alarming Zone (8 provinces, 10.4%): Provinces with significant increasing trends (AAPC ≥ 0, p < 0.05). These provinces are: Mae Hong Son, Phayao, Phetchabun, Suphan Buri, Sing Buri, Ratchaburi, Samut Prakan, and Yasothon. Geographically, these provinces span the Northern, Central, Northeastern, and Western regions of the country. Table 2 Provincial Classification by Long-term TB Trends (Safe Zone vs Alarming Zone), 2012–2023 Zone Classification Trend Category Number Percent SAFE ZONE (69 provinces, 89.6%) Significant Decreasing (p < 0.05) 36 46.8% Stable / Non-significant (p ≥ 0.05) 33 42.9% ALARMING ZONE (8 provinces, 10.4%) Significant Increasing (p < 0.05) 8 10.4% TOTAL 77 100% Note : Classification based on 12-year AAPC from joinpoint regression (2012–2023). Safe Zone = provinces with decreasing or stable trends; Alarming Zone = provinces with significant increasing trends requiring immediate intervention. AAPC range: Decreasing − 38.25% to -10.85%; Increasing + 9.97% to + 25.30%. COVID-19 Pandemic Impact Assessment (2019–2023) Comparison of 2023 vs 2019 baseline crude rates revealed heterogeneous pandemic impact across provinces (Table 3 , Fig. 4 B): Decreased (49 provinces, 63.6%): Provinces showing > 5% decline in crude rates. These provinces represent 56.4% of the national population but only 37.2% of the 2023 TB case burden, with a mean crude rate of 10.13 per 100,000. Unchanged (3 provinces, 3.9%): Provinces with changes between − 5% and + 5%, indicating stable rates during the COVID-19 period. Increased (25 provinces, 32.5%): Provinces showing > 5% increase in crude rates. Despite representing only 42.5% of the national population, these provinces accounted for 62.6% of the 2023 TB case burden, with a mean crude rate of 23.65 per 100,000. Table 3 COVID-19 Pandemic Impact on Provincial TB Rates (2019 vs 2023 Comparison) Change Category Provinces (n, %) Population 2023 (n, %) TB Cases 2023 (n, %) Mean Crude Rate (per 100k) Decreased 49 (63.6) 38,725,944 (56.4) 3,923 (37.2) 10.13 Unchanged 3 (3.9) 944,049 (1.4) 18 (0.2) 1.91 Increased 25 (32.5) 29,216,305 (42.5) 6,563 (62.6) 23.65 TOTAL 77 (100) 68,886,298 (100) 10,486 (100) 15.27 Note : n = number; 100k = 100,000 Discussion Principal Findings This comprehensive spatiotemporal analysis of tuberculosis trends across Thailand from 2012 to 2023 reveals three key findings with important policy implications. First, at the regional level (13 health regions), profound heterogeneity was observed, with five regions (regions 3, 4, 9, 11, and 13) demonstrating statistically significant declining trends (average annual percent change [AAPC] ranging from − 11.19% to -20.51%, p 75% reduction in age-standardized incidence rates from 2012 to 2023: Regions 3 (93.2% reduction), 4 (90.1%), 5 (93.3%), 6 (78.3%), 9 (93.8%), 11 (92.4%), and 13 (75.5%). Two regions exhibited biphasic patterns characterized by distinct temporal inflection points: region 6 showed an initially precipitous decline during 2012–2014 (annual percent change [APC] -46.12%, p = 0.020) followed by stabilization through 2023, while region 12 demonstrated a mid-period increase during 2012–2019 (APC + 20.20%, p = 0.024) followed by sharp decline during 2019–2023 (APC − 35.75%, p = 0.020), suggesting differential timing and intensity of programmatic interventions or epidemiological transitions across regions. Second, spatial autocorrelation analysis at the provincial level (77 provinces) indicated weak spatial structuring (Global Moran’s I = 0.102), suggesting only limited geographic clustering of TB trends, with Local Indicators of Spatial Association (LISA) identifying 13 provinces (16.9%) with significant local spatial autocorrelation patterns: three high-high clusters or hot spots (Phayao, Phetchabun, Yasothon) requiring targeted intervention; four low-low clusters or cold spots (Buri Ram, Ang Thong, Nakhon Pathom, Bangkok) demonstrating sustained control success and potential best practice models; four high-low outliers (Suphan Buri, Sing Buri, Ratchaburi, Samut Prakan) showing increasing trends despite surrounding provinces with declining patterns; and two low-high outliers (Uttaradit, Chumphon) showing declining trends despite surrounding provinces with higher rates. Provincial classification based on long-term trends identified eight provinces (10.4%) in the alarming zone with significant increasing trends (Mae Hong Son, Phayao, Phetchabun, Suphan Buri, Sing Buri, Ratchaburi, Samut Prakan, and Yasothon), distributed across North (3 provinces), Central (3), West (1), and Northeast (1) regions, while 69 provinces (89.6%) remained in the safe zone with decreasing or stable trends. Third, COVID-19 pandemic impact assessment (2019–2023 comparison) revealed spatial concentration of burden disruption, with 25 provinces (32.5% of total) showing increased crude TB rates accounting for 62.6% of 2023 national case burden despite representing only 42.5% of national population, with mean crude rate that was 2.2-fold higher than provinces with decreased burden (23.65 vs. 10.13 per 100,000 population). Comparative Context and Methodological Considerations The magnitude of tuberculosis burden reduction observed in Thailand's high-performing regions substantially exceeds that documented in comparable settings globally. Regional joinpoint analyses from China reported AAPC values ranging from − 3.8% to -8.2% during 2005–2016, with no regions approaching double-digit annual declines [ 24 ]. Similarly, India's district-level joinpoint analysis documented AAPC values between − 2.1% and − 6.5% for the period 2001–2014, with the highest-performing districts achieving approximately 5–7% annual reductions [ 25 ]. Other high-burden settings in Southeast Asia have demonstrated more gradual declining trends, typically in the range of 3–6% annual reduction [ 26 ]. Thailand's achievement of 10–20% annual declines (AAPC) in five regions thus represents exceptional tuberculosis control performance, warranting detailed programmatic analysis to identify replicable intervention strategies which could inform accelerated progress toward elimination goals in similar epidemiological contexts. The biphasic patterns observed in regions 6 and 12 merit careful interpretation within the context of surveillance system evolution and programmatic transitions. Region 6's initial sharp decline (APC − 46.12% during 2012–2014) followed by stabilization resembles patterns documented in settings implementing intensive short-term interventions, such as enhanced active case-finding campaigns or targeted screening programs, which produce rapid initial gains followed by plateau effects as intervention intensity normalizes or case detection reaches saturation among high-risk populations (World Health Organization, 2015). Region 12's mid-period increase (APC + 20.20% during 2012–2019) followed by a subsequent sharp decline (APC − 35.75% during 2019–2023) coincides temporally with the WHO End TB Strategy launch in 2015 [ 4 ]. Suggesting a possible programmatic transition affecting case detection sensitivity, diagnostic technology adoption, or reporting practices. Similar mid-period fluctuations have been documented in settings undergoing diagnostic system modernization, particularly with the implementation of genomic rapid diagnostics (Xpert MTB/RIF) that can temporarily increase case notifications through improved diagnostic sensitivity and reduced time to detection before stabilizing as the technology becomes integrated into routine practice [ 27 ]. Three plausible explanations warrant consideration for these temporal patterns: first, changes in surveillance system sensitivity or case definitions during the mid-2010s may have produced artifactual volatility rather than reflecting genuine epidemiological dynamics; second, genuine programmatic interventions may have generated substantial short-term effects on case detection and notification; third, intensified active case-finding campaigns may have temporarily increased notifications by identifying previously undetected prevalent cases before settling to lower incidence levels reflecting reduced transmission. Spatial Heterogeneity and Targeted Intervention Opportunities The observed spatial autocorrelation (Moran’s I = 0.102) demonstrates only weak spatial structuring, markedly lower than that reported in Ethiopia (I = 0.291) [ 28 ] and Pakistan (I = 0.185) [ 29 ], suggesting that TB patterns in Thailand are shaped by modest spatial influences alongside province-specific determinants. LISA-identified high-high clusters (Phayao, Phetchabun, Yasothon) represent priority areas requiring intensive interventions targeting potential contributing factors including border proximity facilitating cross-border transmission [ 30 , 31 ], high poverty levels reducing access to healthcare [ 32 , 33 ] migrant populations with limited access [ 34 ], and HIV co-infection requiring integrated services [ 30 ]. Conversely, low-low clusters (Buri Ram, Ang Thong, Nakhon Pathom, Bangkok) demonstrating sustained success warrant investigation to identify transferable best practices. Bangkok's achievement despite high population density suggests that effective urban strategies are in place that could inform interventions elsewhere. Detailed programmatic assessment of these provinces should examine intervention approaches, healthcare delivery models, community engagement initiatives, and digital health applications to identify evidence-based approaches that could be disseminated to surrounding and demographically similar areas. High-low outliers (Suphan Buri, Sing Buri, Ratchaburi, Samut Prakan) showing increasing trends despite declining neighbors merit urgent investigation of local drivers, including industrial development attracting migrants, population influx concentrating vulnerable groups, healthcare capacity constraints, drug-resistant strain emergence, or programmatic breakdowns requiring tailored corrective interventions. Provincial Classification and COVID-19 Pandemic Impact Provincial classification based on long-term trends (2012–2023) revealed that 69 provinces (89.6%) remained in the safe zone with decreasing or stable trajectories, comprising 36 provinces (46.8%) with significant decreasing trends and 33 provinces (42.9%) with stable or non-significant trends. Eight provinces (10.4%) fell into the alarming zone with significant increasing trends: Mae Hong Son, Phayao, Phetchabun, Suphan Buri, Sing Buri, Ratchaburi, Samut Prakan, and Yasothon, distributed across North (3), Central (3), West (1), and Northeast (1) regions. COVID-19 pandemic impact assessment (2019–2023) revealed heterogeneous disruption: 49 provinces (63.6%) showed decreased burden (> 5% decline), representing 56.4% of population but only 37.2% of 2023 tuberculosis burden with mean crude rate 10.13 per 100,000; three provinces (3.9%) remained unchanged (± 5%); and 25 provinces (32.5%) experienced increased burden (> 5% increase), accounting for 62.6% of 2023 national burden despite representing only 42.5% of population, with mean crude rate 23.65 per 100,000 population which is 2.2-fold higher than in provinces with decreased burden. This spatial concentration aligns with global pandemic disruptions where the WHO reported 25–40% notification declines [ 35 – 37 ], with disproportionate impacts on vulnerable populations [ 5 , 6 , 38 ]. The substantial overlap between pandemic-affected provinces and alarming zone provinces indicates compound vulnerability, requiring urgent priority intervention combining pandemic recovery with long-term programmatic strengthening. Recovery strategies should prioritize: intensified active case-finding to identify missed diagnoses [ 39 ]; integration of tuberculosis-COVID-19 services leveraging strengthened respiratory infrastructure [ 40 ]; community-based care models maintaining continuity during disruptions [ 41 ]; and digital health technologies for remote monitoring. Future research should examine differential resilience mechanisms, evaluate recovery trajectory effectiveness, and integrate genomic epidemiological data to clarify whether burden changes reflect case detection fluctuations or genuine transmission shifts. Strengths and Limitations This study's primary strength lies in comprehensive national surveillance data spanning 77 provinces over 12 years (2012–2023), enabling robust spatiotemporal analysis. Methodological rigor through joinpoint regression identifying significant trend changes, generalized additive models with 95% confidence intervals, spatial autocorrelation analysis (Moran's I), and Local Indicators of Spatial Association provides triangulated evidence of heterogeneous tuberculosis dynamics. Age-standardized incidence rates ensure temporal and geographic comparability, while an extended time period captures both long-term trends and COVID-19 pandemic impacts. However, several important limitations warrant consideration. As an ecological study using aggregate provincial data, findings cannot establish individual-level causality or directly assess individual risk factors. The biphasic patterns in regions 6 and 12, while statistically significant, require further investigation to distinguish programmatic or surveillance changes from genuine epidemiological dynamics. The Exact Method applied for zero-case observations introduced some statistical uncertainty, although sensitivity analysis confirmed that the overall result remained robust. Absence of linked data on treatment outcomes, drug resistance prevalence, and HIV co-infection status limits comprehensive burden assessment and prevents evaluation of program quality indicators beyond case notification. Spatial analyses using administrative boundaries may not reflect actual transmission networks or healthcare catchment areas. Finally, distinguishing between real changes in transmission and case detection sensitivity variations remains challenging without genomic epidemiological data. Conclusion and Policy Implications Findings support five priority policy actions for Thailand's tuberculosis control program. First, implement intensive intervention packages in eight alarming zone provinces (Mae Hong Son, Suphan Buri, Sing Buri, Ratchaburi, Samut Prakan, Phayao, Phetchabun, Yasothon) and three high-high clusters (Phayao, Phetchabun, Yasothon), prioritizing enhanced active case-finding, strengthened treatment support, and comprehensive contact investigation. Second, conduct detailed programmatic assessments of four low-low cluster provinces (Buri Ram, Ang Thong, Nakhon Pathom, Bangkok) to identify and disseminate successful strategies as evidence-based intervention packages for adaptation in underperforming areas. Third, prioritize pandemic recovery efforts in 25 provinces with increased tuberculosis burden during 2019–2023, ensuring strengthened surveillance, diagnostic capacity, and service delivery continuity. Fourth, investigate mechanisms underlying biphasic patterns in regions 6 and 12 through a detailed review of programmatic changes, surveillance modifications, and diagnostic technology adoption during inflection periods. Fifth, address compound vulnerability in provinces demonstrating both long-term deterioration and pandemic-related disruption through multi-sectoral approaches targeting social determinants. Future research priorities include: validation studies assessing surveillance data quality and completeness; qualitative investigations of successful control strategies in high-performing provinces; multilevel modeling incorporating individual, community, and programmatic factors; genomic epidemiological studies clarifying transmission dynamics during volatile periods; and prospective monitoring of pandemic recovery trajectories with evaluation of catch-up strategy effectiveness. Abbreviations AAPC Average Annual Percent Change APC Annual Percent Change ASR Age-Standardized Rate BIC Bayesian Information Criterion CI Confidence Interval GAM Generalized Additive Model HH High-High (spatial cluster) HL High-Low (spatial outlier) LH Low-High (spatial outlier) LISA Local Indicators of Spatial Association LL Low-Low (spatial cluster) SE Standard Error TB Tuberculosis WHO World Health Organization Declarations Ethics approval and consent to participate The Khon Kaen University Ethics Committee for Human Research has provided exemption for ethical approval, under reference number HE 682254. Consent for publication Not applicable. Competing Interests The authors declare no competing interests. Funding This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Author Contribution KS: Conceptualization, methodology, data curation, formal analysis, writing - original draft, supervision. RKM: Visualization, validation, writing - review & editing. SM: Methodology, validation, writing - review & editing. KN: Validation, writing - review & editing. RN: Conceptualization, validation, writing - review & editing. All authors read and approved the final manuscript. Acknowledgement The authors acknowledge the Bureau of Epidemiology, Department of Disease Control, Ministry of Public Health, Thailand, for providing access to TB surveillance data. We thank all healthcare workers and surveillance staff throughout Thailand for their tireless efforts in TB case detection, treatment, and reporting. Data Availability TB surveillance data are available from the Bureau of Epidemiology, Department of Disease Control, Ministry of Public Health Thailand (http://www.boe.moph.go.th). Population data are publicly available from the National Statistical Office Thailand (http://www.nso.go.th). The datasets used and/or analyzed in this study are accessible from the corresponding author upon reasonable request. References Dodd PJ, Yuen CM, Sismanidis C, Seddon JA, Jenkins HE. The global burden of tuberculosis mortality in children: A mathematical modelling study. Lancet Global Health. 2017;5:e898–906. https://doi.org/10.1016/S2214-109X(17)30289-9 . Glaziou P, Sismanidis C, Floyd K, Raviglione M. Global epidemiology of tuberculosis. Cold Spring Harbor Perspect Med. 2015;5:a017798. https://doi.org/10.1101/cshperspect.a017798 . World Health Organization. Global tuberculosis report 2023. https://www.who.int/teams/global-tuberculosis-programme/tb-reports/global-tuberculosis-report-2023 . Accessed 21 October 2025. World Health Organization. Implementing the end TB strategy: The essentials. https://www.who.int/publications/i/item/9789241509930 . Accessed 21 October 2025. Falzon D, Zignol M, Bastard M, Floyd K, Raviglione M. The impact of the COVID-19 pandemic on the global tuberculosis epidemic. Front Immunol. 2023;14:1234785. https://doi.org/10.3389/fimmu.2023.1234785 . Pai M, Kasaeva T, Swaminathan S. COVID-19's devastating effect on tuberculosis care—A path to recovery. N Engl J Med. 2022;386:1490–3. https://doi.org/10.1056/NEJMp2118145 . Sornlorm K, Muntaphan S. Explanatory spatial modeling of COVID-19 vaccine coverage in Thailand: policy implications for equitable distribution. BMC Public Health. 2025;25:3028. https://doi.org/10.1186/s12889-025-24217-7 . Elliott P, Wartenberg D. Spatial epidemiology: Current approaches and future challenges. Environ Health Perspect. 2004;112:998–1006. https://pmc.ncbi.nlm.nih.gov/articles/PMC1247193/ . Lawson AB. Bayesian disease mapping: Hierarchical modeling in spatial epidemiology. 3rd ed. edn.: CRC; 2018. Mahato RK, Sornlorm K, Htike KM, Koro AB, Nawawonganun R, Sharma V, et al. Spatial heterogenicity of tuberculosis and dengue in Nepal. Sci Rep. 2025;15:41012. https://doi.org/10.1038/s41598-025-24798-8 . Anselin L. Local indicators of spatial association—LISA. Geographical Anal. 1995;27:93–115. https://doi.org/10.1111/j.1538-4632.1995.tb00338.x . Wang Q, Guo L, Wang J, Shen H, Huang J. Spatial distribution of tuberculosis and its socioeconomic influencing factors in mainland China 2013–2016. Trop Med Int Health. 2019;24:1104–13. https://doi.org/10.1111/tmi.13289 . Clegg LX, Hankey BF, Tiwari R, Feuer EJ, Edwards BK. Estimating average annual per cent change in trend analysis. Stat Med. 2009;28:3670–82. https://doi.org/10.1002/sim.3733 . Kim H-J, Fay MP, Feuer EJ, Midthune DN. Permutation tests for joinpoint regression with applications to cancer rates. Stat Med. 2000;19:335–51. https://doi.org/10.1002/(SICI)1097-0258(20000215)19:3%3C335::AID-SIM336%3E3.0.CO;2-Z . Bureau of Epidemiology DoDCMoPHT. National Disease Surveillance System (Report 506). https://apps-doe.moph.go.th/boeeng/annual.php . Accessed 15 December 2025. Department of Provincial Administration MoIT. Official statistics registration systems. http://www.dopa.go.th . Accessed 15 December 2025. Ahmad OB, Boschi-Pinto C, Lopez AD, Murray CJ, Lozano R, Inoue M. Age standardization of rates: A new WHO standard. World Health Organization; 2001. p. 31. Report No. Humanitarian Data E. Thailand-Subnational administrative boundaries. United Nations Office for the Coordination of Humanitarian Affairs (OCHA); 2022. Fay MP, Feuer EJ. Confidence intervals for directly standardized rates: A method based on the gamma distribution. Stat Med. 1997;16:791–801. https://doi.org/10.1002/(SICI)1097-0258(19970415)16:7%3C791::AID-SIM500%3E3.0.CO;2-%23 . Moran PAP. Notes on continuous stochastic phenomena. Biometrika. 1950;37:17–23. https://doi.org/10.2307/2332142 . Anselin L, Syabri I, Kho Y, GeoDa. An introduction to spatial data analysis. Geographical Anal. 2006;38:5–22. https://doi.org/10.1111/j.0016-7363.2005.00671.x . Team QD. QGIS Geographic Information System. Version 3.34 ed. Open Source Geospatial Foundation; 2022. Team RC. R: A language and environment for statistical computing. Version 4.3.1 ed. R Foundation for Statistical Computing; 2023. Yang C, Lu L, Warren JL, Wu J, Jiang Q, Zuo T, et al. Internal migration and transmission dynamics of tuberculosis in Shanghai, China: an epidemiological, spatial, genomic analysis. Lancet Infect Dis. 2018;18:788–95. http://dx.doi.org/10.1016/S1473-3099(18)30218-4 . Dhamnetiya D, Goel MK, Dhakad U, Jha RP, Singh A. Trends in incidence and mortality of tuberculosis in India over past three decades: A joinpoint and age-period-cohort analysis. BMC Pulm Med. 2021;21:375. https://doi.org/10.1186/s12890-021-01740-y . Thiankhaw K, Chattakul P, Kanchanachitra C, Guijarro AP. Spatiotemporal epidemiology of tuberculosis in Thailand from 2011 to 2020. Biology. 2022;11:755. https://doi.org/10.3390/biology11050755 . Dowdy DW, Cattamanchi A, Steingart KR, Pai M. Is scale-up worth it? Challenges in economic analysis of diagnostic tests for tuberculosis. PLoS Med. 2011;8:e1001063. https://doi.org/10.1371/journal.pmed.1001063 . Alene KA, Xu Z, Bai L, Yi H, Tan Y, Gray DJ, et al. Spatial clustering of notified tuberculosis in Ethiopia: A nationwide study. PLoS ONE. 2019;14:e0221027. https://doi.org/10.1371/journal.pone.0221027 . Ullah S, Daud H, Dass SC, Fanaee-T H, Kausarian H. Alamgir. Space-time clustering characteristics of tuberculosis in Khyber Pakhtunkhwa Province, Pakistan, 2015–2019. Int J Environ Res Public Health. 2020;17:1413. Hargreaves JR, Boccia D, Evans CA, Adato M, Petticrew M, Porter JD. The social determinants of tuberculosis: From evidence to action. Am J Public Health. 2011;101:654–62. https://doi.org/10.2105/AJPH.2010.199505 . Pareek M, Greenaway C, Noori T, Munoz J, Zenner D. The impact of migration on tuberculosis epidemiology and control in high-income countries: A review. BMC Med. 2016;14:48. https://doi.org/10.1186/s12916-016-0595-5 . Lin H-H, Ezzati M, Murray M. Tobacco smoke, indoor air pollution and tuberculosis: A systematic review and meta-analysis. PLoS Med. 2007;4:e20. https://doi.org/10.1371/journal.pmed.0040020 . Lönnroth K, Jaramillo E, Williams BG, Dye C, Raviglione M. Drivers of tuberculosis epidemics: The role of risk factors and social determinants. Soc Sci Med. 2009;68:2240–6. https://doi.org/10.1016/j.socscimed.2009.03.041 . Jeon CY, Murray MB. Diabetes mellitus increases the risk of active tuberculosis: A systematic review of 13 observational studies. PLoS Med. 2008;5:e152. https://doi.org/10.1371/journal.pmed.0050152 . Cilloni L, Fu H, Vesga JF, Dowdy D, Pretorius C, Ahmedov S, et al. The potential impact of the COVID-19 pandemic on the tuberculosis epidemic a modelling analysis. EClinicalMedicine. 2020;28:100603. https://doi.org/10.1016/j.eclinm.2020.100603 . McQuaid CF, Vassall A, Cohen T, Fiekert K, White RG. The impact of COVID-19 on TB: A review of the data. Int J Tuberculosis Lung Disease. 2021;25:436–46. https://doi.org/10.5588/ijtld.21.0148 . Migliori GB, Thong PM, Akkerman O, Alffenaar J-W, Alvarez-Navascues F, Assao-Neino MM, et al. Worldwide effects of coronavirus disease pandemic on tuberculosis services, January-April 2020. Emerg Infect Dis. 2020;27:1446–55. https://doi.org/10.3201/eid2611.203163 . Ong CWM, Migliori GB, Raviglione M, MacGregor-Skinner G, Sotgiu G, Alffenaar J-W. Epidemic and pandemic viral infections: Impact on tuberculosis and the lung. Eur Respir J. 2020;56:2001727. https://doi.org/10.1183/13993003.01727-2020 . Lönnroth K, Castro KG, Chakaya JM, Chauhan LS, Floyd K, Glaziou P, et al. Tuberculosis control and elimination 2010-50: Cure, care, and social development. Lancet. 2010;375:1814–29. https://doi.org/10.1016/S0140-6736(10)60483-7 . Visca D, Ong CWM, Tiberi S, Centis R, D'Ambrosio L, Chen B, et al. Tuberculosis and COVID-19 interaction: A review of biological, clinical and public health effects. Pulmonology. 2021;27:151–65. https://doi.org/10.1016/j.pulmoe.2020.12.012 . Yuen CM, Amanullah F, Dharmadhikari A, Nardell EA, Seddon JA, Vasilyeva I, et al. Turning off the tap: Stopping tuberculosis transmission through active case-finding and prompt effective treatment. Lancet. 2015;386:2334–43. https://doi.org/http://dx.doi.org/10.1016/S0140-6736(15)00322-0 . Additional Declarations No competing interests reported. Supplementary Files graphicalabstract.png SupplementaryTableS1.xlsx graphicalabstract.png Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 23 Apr, 2026 Reviews received at journal 22 Apr, 2026 Reviewers agreed at journal 18 Apr, 2026 Reviewers agreed at journal 16 Apr, 2026 Reviewers agreed at journal 09 Mar, 2026 Reviewers agreed at journal 28 Jan, 2026 Reviews received at journal 12 Jan, 2026 Reviewers agreed at journal 29 Dec, 2025 Reviewers invited by journal 29 Dec, 2025 Editor assigned by journal 29 Dec, 2025 Submission checks completed at journal 29 Dec, 2025 First submitted to journal 27 Dec, 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8460167","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":569176903,"identity":"a9f658e3-bdc1-4030-91d2-d426467ef4ff","order_by":0,"name":"Kittipong Sornlorm","email":"","orcid":"","institution":"Khon Kaen University","correspondingAuthor":false,"prefix":"","firstName":"Kittipong","middleName":"","lastName":"Sornlorm","suffix":""},{"id":569176904,"identity":"ea31af64-65b0-43ea-9a87-de5ddeb13dc9","order_by":1,"name":"Roshan Kumar Mahato","email":"","orcid":"","institution":"Khon Kaen University","correspondingAuthor":false,"prefix":"","firstName":"Roshan","middleName":"Kumar","lastName":"Mahato","suffix":""},{"id":569176905,"identity":"871391b1-72ed-4748-8452-a8b07c349eba","order_by":2,"name":"Sarayu Muntaphan","email":"","orcid":"","institution":"Khon Kaen Provincial Health Office","correspondingAuthor":false,"prefix":"","firstName":"Sarayu","middleName":"","lastName":"Muntaphan","suffix":""},{"id":569176906,"identity":"b2f58d71-31fc-4de4-93ed-a8d55dab3c7b","order_by":3,"name":"Kanit Hnuploy","email":"","orcid":"","institution":"Suratthani Rajabhat University","correspondingAuthor":false,"prefix":"","firstName":"Kanit","middleName":"","lastName":"Hnuploy","suffix":""},{"id":569176907,"identity":"55b51de9-72c8-4ed1-a41f-e1207ee754c0","order_by":4,"name":"Rajitra Nawawonganun","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAu0lEQVRIiWNgGAWjYHCChAMMDDYGEjAesVrSYFoMiNICAodJ0GLefuDh4YKa88aSMxIYP/xg+JNHUIvMmYSEwzOO3TaTlkhgluxhMCgmqEWCAaiFh+22jZxEAoM00GGJDQS18D8Aavl3DqSF+TdxWiSAtvC2HQA5jI1IWySAtvD2JRtL9jxss+wxMCbGYTnJn3m+2RnOOJ58+MaPCjnCWhgYeBKgDEagYgPC6oGA/QBRykbBKBgFo2AEAwB/xDkXxBcdUwAAAABJRU5ErkJggg==","orcid":"","institution":"Khon Kaen University","correspondingAuthor":true,"prefix":"","firstName":"Rajitra","middleName":"","lastName":"Nawawonganun","suffix":""}],"badges":[],"createdAt":"2025-12-27 09:53:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8460167/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8460167/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":99588449,"identity":"f30a8aeb-58e6-463d-97de-0768e2ed4481","added_by":"auto","created_at":"2026-01-06 08:16:03","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":8691239,"visible":true,"origin":"","legend":"","description":"","filename":"Mainmanuscript.docx","url":"https://assets-eu.researchsquare.com/files/rs-8460167/v1/652625abeb3ffba01b6a669a.docx"},{"id":99793802,"identity":"751a3f88-31d1-414c-bd89-e7a1e5b1afb0","added_by":"auto","created_at":"2026-01-08 13:32:24","extension":"json","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":8117,"visible":true,"origin":"","legend":"","description":"","filename":"4778a32e2ac14b7cbb8b43afbd9b5bc4.json","url":"https://assets-eu.researchsquare.com/files/rs-8460167/v1/347a52e6e1416bc87eb64419.json"},{"id":99588442,"identity":"5559c8ee-c855-4bff-8cc3-8dc93c64d5d9","added_by":"auto","created_at":"2026-01-06 08:16:03","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":27109,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTableS1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8460167/v1/84143105e1e76be48733e8b5.xlsx"},{"id":99588447,"identity":"a0ab6adb-acaa-4873-9219-6ef53ede7395","added_by":"auto","created_at":"2026-01-06 08:16:03","extension":"xml","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":125960,"visible":true,"origin":"","legend":"","description":"","filename":"4778a32e2ac14b7cbb8b43afbd9b5bc41enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-8460167/v1/44b997009f1b94c8edf02dc6.xml"},{"id":99793715,"identity":"70b278c3-882b-4a45-a0dd-20f520cda3f3","added_by":"auto","created_at":"2026-01-08 13:32:15","extension":"jpeg","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":668808,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8460167/v1/4b2b2ecfd0a37747a618daf0.jpeg"},{"id":99793014,"identity":"cb29c89e-876c-4641-8b49-ed5b444f7893","added_by":"auto","created_at":"2026-01-08 13:30:48","extension":"png","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":28156,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-8460167/v1/15f6333d9c52fd2bbaeafa72.png"},{"id":99792186,"identity":"22bd27a5-cdd2-45d7-a556-1f9e8983884c","added_by":"auto","created_at":"2026-01-08 13:15:52","extension":"png","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":13057,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage11.png","url":"https://assets-eu.researchsquare.com/files/rs-8460167/v1/b8afb742d0e9fe143a2894a2.png"},{"id":99792629,"identity":"d4fc9bdd-3be3-498d-a030-15c1d00a8044","added_by":"auto","created_at":"2026-01-08 13:23:09","extension":"png","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":28059,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage12.png","url":"https://assets-eu.researchsquare.com/files/rs-8460167/v1/2b08e8a7385c40d1207c6073.png"},{"id":99792499,"identity":"0b917122-d4eb-4005-b1ad-b037c9227c2b","added_by":"auto","created_at":"2026-01-08 13:21:09","extension":"png","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":33884,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage13.png","url":"https://assets-eu.researchsquare.com/files/rs-8460167/v1/13b5ba9689c28f4c4499982a.png"},{"id":99588453,"identity":"25c6a980-0451-4974-b57e-7499e903f343","added_by":"auto","created_at":"2026-01-06 08:16:03","extension":"png","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":29486,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage14.png","url":"https://assets-eu.researchsquare.com/files/rs-8460167/v1/e2918b65e7e629fb69bd8e0f.png"},{"id":99792583,"identity":"7b356722-8650-4392-bfd5-c9262c95d939","added_by":"auto","created_at":"2026-01-08 13:22:34","extension":"png","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":47158,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage15.png","url":"https://assets-eu.researchsquare.com/files/rs-8460167/v1/24ef091812d5c43f13a2f97a.png"},{"id":99588454,"identity":"bb7f5ad3-6d47-499e-83bc-e308e27d1a13","added_by":"auto","created_at":"2026-01-06 08:16:03","extension":"jpeg","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":283892,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage16.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8460167/v1/44ee2c4a6c5505c1d74129f8.jpeg"},{"id":99588460,"identity":"107856f8-a811-4a9a-91cf-e55ccb160e16","added_by":"auto","created_at":"2026-01-06 08:16:03","extension":"jpeg","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":646702,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage17.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8460167/v1/63200180881912b9a781bcab.jpeg"},{"id":99588459,"identity":"40da5f1d-85b8-4d39-97b1-660416cc8552","added_by":"auto","created_at":"2026-01-06 08:16:03","extension":"png","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":30675,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8460167/v1/b8b1670ff767a346ab477df9.png"},{"id":99794051,"identity":"ee5d0beb-3e0a-4d1e-82e1-6af6cba63b70","added_by":"auto","created_at":"2026-01-08 13:33:52","extension":"png","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":12019,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8460167/v1/4d6661b5cde62077a2f10e38.png"},{"id":99793337,"identity":"1debda3a-1527-44d4-adcf-ad9cd121f57e","added_by":"auto","created_at":"2026-01-08 13:31:26","extension":"png","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":13809,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8460167/v1/d84286dc0baf21d64359d3d1.png"},{"id":99792268,"identity":"b3184771-b83a-484f-967d-13c8febaaab7","added_by":"auto","created_at":"2026-01-08 13:17:18","extension":"png","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":27637,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8460167/v1/2c1494cedb8962081768c886.png"},{"id":99588451,"identity":"34addd09-4825-4fc4-b940-1dffb56aab5a","added_by":"auto","created_at":"2026-01-06 08:16:03","extension":"png","order_by":18,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":25822,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-8460167/v1/fff03dd190339b81644cbbdf.png"},{"id":99588466,"identity":"debd99ce-0676-4589-8132-206788217c87","added_by":"auto","created_at":"2026-01-06 08:16:04","extension":"png","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":32215,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-8460167/v1/32f2109a7ed53626d98c532d.png"},{"id":99793062,"identity":"347edb15-26b7-40d7-85b9-dd688323056b","added_by":"auto","created_at":"2026-01-08 13:30:57","extension":"png","order_by":20,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":26170,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-8460167/v1/20550ff81d01672bae368231.png"},{"id":99588468,"identity":"09256eb6-97ac-47e4-bb17-c0b2cd4216f6","added_by":"auto","created_at":"2026-01-06 08:16:04","extension":"png","order_by":21,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":26996,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-8460167/v1/cc1218bf7a90d4d8c5412090.png"},{"id":99792639,"identity":"0f21bba4-1daf-4cf4-aa2b-3cd74ca4cdca","added_by":"auto","created_at":"2026-01-08 13:23:20","extension":"png","order_by":22,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":7104229,"visible":true,"origin":"","legend":"","description":"","filename":"graphicalabstract.png","url":"https://assets-eu.researchsquare.com/files/rs-8460167/v1/eb42cc8fbc4436f468c21f54.png"},{"id":99792649,"identity":"ec6edc32-6161-49c7-847a-c8e894754351","added_by":"auto","created_at":"2026-01-08 13:23:27","extension":"png","order_by":23,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":121976,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8460167/v1/5b34c03aac814545822c978c.png"},{"id":99588487,"identity":"72f4dde8-d7b7-451b-a2cc-8c12716e22d4","added_by":"auto","created_at":"2026-01-06 08:16:05","extension":"png","order_by":24,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":11253,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-8460167/v1/f3273c97eb6f50739be247b3.png"},{"id":99792711,"identity":"4a6b8b70-81b9-4ed4-a119-c406a01e20d7","added_by":"auto","created_at":"2026-01-08 13:25:10","extension":"png","order_by":25,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":9197,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage11.png","url":"https://assets-eu.researchsquare.com/files/rs-8460167/v1/9bbe4f275cfb6a589d5050b6.png"},{"id":99588472,"identity":"3e8c5754-ec77-4c32-b811-cd665a75a037","added_by":"auto","created_at":"2026-01-06 08:16:04","extension":"png","order_by":26,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":11215,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage12.png","url":"https://assets-eu.researchsquare.com/files/rs-8460167/v1/644be133b27223cfdc7c4d02.png"},{"id":99588470,"identity":"2f644bc7-57c9-4100-85fe-e2d77d0fa2b7","added_by":"auto","created_at":"2026-01-06 08:16:04","extension":"png","order_by":27,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":13467,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage13.png","url":"https://assets-eu.researchsquare.com/files/rs-8460167/v1/b6aa8c3ad53e0cc9e87a689b.png"},{"id":99792651,"identity":"e51e6bf9-7b15-464c-be69-66987f7a5f27","added_by":"auto","created_at":"2026-01-08 13:23:27","extension":"png","order_by":28,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":11904,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage14.png","url":"https://assets-eu.researchsquare.com/files/rs-8460167/v1/0b0eb3c33eff4fabdb178b06.png"},{"id":99588467,"identity":"f5655fa0-5492-4812-af72-6f96d63f9079","added_by":"auto","created_at":"2026-01-06 08:16:04","extension":"png","order_by":29,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":15592,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage15.png","url":"https://assets-eu.researchsquare.com/files/rs-8460167/v1/23d471ab7b9fd1557d096a43.png"},{"id":99588481,"identity":"301a2f13-fc91-4900-ac2f-4845a1a9f719","added_by":"auto","created_at":"2026-01-06 08:16:04","extension":"png","order_by":30,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":64179,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage16.png","url":"https://assets-eu.researchsquare.com/files/rs-8460167/v1/009d08b39c0fa9fe325be774.png"},{"id":99588477,"identity":"f5eaebb3-a8d8-438f-ad1a-d12e42af5164","added_by":"auto","created_at":"2026-01-06 08:16:04","extension":"png","order_by":31,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":117019,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage17.png","url":"https://assets-eu.researchsquare.com/files/rs-8460167/v1/9c9bf7bc5f8aad9b152ad1f0.png"},{"id":99792494,"identity":"40478a4a-8939-4f6e-8a86-a11c784f42ee","added_by":"auto","created_at":"2026-01-08 13:21:05","extension":"png","order_by":32,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":10253,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8460167/v1/ce52bf7b48f3215ff611f855.png"},{"id":99588478,"identity":"e47bc7f8-0cd7-4dd2-a44c-e7b27f659c7c","added_by":"auto","created_at":"2026-01-06 08:16:04","extension":"png","order_by":33,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":8252,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8460167/v1/1e8e3ccfc77e51c3f55565ce.png"},{"id":99792692,"identity":"e9431c45-6e9f-49a2-a01e-2c80e00dc6ae","added_by":"auto","created_at":"2026-01-08 13:25:05","extension":"png","order_by":34,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":10123,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8460167/v1/6352f5caf2dba85b51a9e5ed.png"},{"id":99792927,"identity":"4069908d-4d4c-4caa-9e3b-a748e38b2cc4","added_by":"auto","created_at":"2026-01-08 13:28:44","extension":"png","order_by":35,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":11792,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8460167/v1/ac6064408f080f99f416f458.png"},{"id":99588469,"identity":"429afa9a-8eef-4e56-a6cb-f25736cf524c","added_by":"auto","created_at":"2026-01-06 08:16:04","extension":"png","order_by":36,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":10513,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-8460167/v1/ba8cc81a0b4f6886e530d6f6.png"},{"id":99588474,"identity":"431ceff2-b531-4a3c-b648-d0b152b02e06","added_by":"auto","created_at":"2026-01-06 08:16:04","extension":"png","order_by":37,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":13217,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-8460167/v1/f098f05484b18473e1b57bbe.png"},{"id":99588486,"identity":"b529e3fd-719b-4474-adf4-f7006f3710ab","added_by":"auto","created_at":"2026-01-06 08:16:04","extension":"png","order_by":38,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":10443,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-8460167/v1/7a062f64df305398c8171f7e.png"},{"id":99792462,"identity":"c94d1bb4-fd07-4320-abe4-cbbc9db061d4","added_by":"auto","created_at":"2026-01-08 13:20:13","extension":"png","order_by":39,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":10896,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-8460167/v1/0c58a96592979820ddd56650.png"},{"id":99588484,"identity":"358b5c91-8181-4fc7-9e0f-c8a83c447482","added_by":"auto","created_at":"2026-01-06 08:16:04","extension":"png","order_by":40,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":653996,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinegraphicalabstract.png","url":"https://assets-eu.researchsquare.com/files/rs-8460167/v1/a0974f8582e6b4623a6fd6fc.png"},{"id":99588479,"identity":"7e0db3ec-a312-4e4e-a610-7e10c829ced8","added_by":"auto","created_at":"2026-01-06 08:16:04","extension":"xml","order_by":41,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":125117,"visible":true,"origin":"","legend":"","description":"","filename":"4778a32e2ac14b7cbb8b43afbd9b5bc41structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8460167/v1/23697c4bbbdead7477625fc1.xml"},{"id":99792333,"identity":"2ab40584-00b7-4151-83f9-58c40f581e47","added_by":"auto","created_at":"2026-01-08 13:17:59","extension":"html","order_by":42,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":134559,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8460167/v1/f3e45dad1c5b233519648664.html"},{"id":99588438,"identity":"ac8bef4e-88a0-48dc-9a88-327aee5517d2","added_by":"auto","created_at":"2026-01-06 08:16:03","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":330843,"visible":true,"origin":"","legend":"\u003cp\u003eAge-Standardized and GAM-smoothed trends of TB Incidence Rates for 13 Health regions, 2012-2023\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8460167/v1/402411954fa9ef647eb591d2.png"},{"id":99588439,"identity":"7b0d20f2-2dc1-44a3-a143-01c97b9ff073","added_by":"auto","created_at":"2026-01-06 08:16:03","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":638282,"visible":true,"origin":"","legend":"\u003cp\u003eJoinpoint regression trends and APC values across 13 health regions, 2012–2023\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eNote:\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e \u003c/em\u003eDots\u003cem\u003e \u0026nbsp;indicate observed data; solid lines show joinpoint regression trends. \u0026nbsp;\u0026nbsp;Joinpoints mark significant changes, and segment-specific APC values are \u0026nbsp;\u0026nbsp;displayed. Regions are labeled A–M.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8460167/v1/5758466b38ee4f35f4769c66.png"},{"id":99792124,"identity":"fa60cee4-5a07-44ef-9636-a5fd6f9b060d","added_by":"auto","created_at":"2026-01-08 13:15:33","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":226320,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial Analysis Results: (A) Provincial AAPC Choropleth Map, (B) LISA Cluster Map, (C) Moran Scatterplot\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8460167/v1/e8b6312b3048b774e292d95b.png"},{"id":99792432,"identity":"95641c44-0be6-446e-a783-705fb6c7f39f","added_by":"auto","created_at":"2026-01-08 13:19:48","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":213023,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial Comparison Maps: (A) Long-term Classification (2012-2023), (B) COVID-19 Impact (2019-2023)\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eNote: \u003c/strong\u003e\u003c/em\u003e\u003cem\u003eA = Long‑term change \u0026nbsp;\u0026nbsp;(2012–2023): choropleth showing average annual percent change (AAPC) by \u0026nbsp;\u0026nbsp;province; green shades indicate decreases (Safe Zone) and red shades indicate \u0026nbsp;\u0026nbsp;increases (Alarming Zone). B = COVID‑19 period change (2019–2023): choropleth \u0026nbsp;\u0026nbsp;of AAPC differences showing provinces with increased AAPC (red), decreased \u0026nbsp;\u0026nbsp;AAPC (green), and unchanged provinces (gray). Color scales represent AAPC (%) \u0026nbsp;\u0026nbsp;per year; statistical significance thresholds and zone cutoffs are indicated \u0026nbsp;\u0026nbsp;in the map legend.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8460167/v1/1b4263db4c9a7675adfde851.png"},{"id":99804259,"identity":"d99a7c2a-8bb0-45e7-82c3-3824b65bb1c7","added_by":"auto","created_at":"2026-01-08 14:12:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2316873,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8460167/v1/4a04265b-58a9-443e-b01c-e2437e3d373b.pdf"},{"id":99588444,"identity":"ce2c9941-f745-484e-92b9-77caba971c48","added_by":"auto","created_at":"2026-01-06 08:16:03","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":7104229,"visible":true,"origin":"","legend":"","description":"","filename":"graphicalabstract.png","url":"https://assets-eu.researchsquare.com/files/rs-8460167/v1/a69e6accf992b4adc4f0c24f.png"},{"id":99588440,"identity":"f6f42b9d-13e0-4b84-bd22-8cff580cff18","added_by":"auto","created_at":"2026-01-06 08:16:03","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":27109,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTableS1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8460167/v1/0370c0adb46951f039466ddc.xlsx"},{"id":99792923,"identity":"cc699367-186c-44da-a5ba-08a946268d21","added_by":"auto","created_at":"2026-01-08 13:28:44","extension":"png","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":7104229,"visible":true,"origin":"","legend":"","description":"","filename":"graphicalabstract.png","url":"https://assets-eu.researchsquare.com/files/rs-8460167/v1/5e90ad35cf59f77be7375e69.png"}],"financialInterests":"No competing interests reported.","formattedTitle":"Spatiotemporal trends in tuberculosis incidence in Thailand, 2012–2023: a nationwide, province-level analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eTuberculosis (TB) remains one of the leading causes of morbidity and mortality worldwide, representing a persistent global health crisis. In 2022 alone, an estimated 10.6\u0026nbsp;million new cases and 1.3\u0026nbsp;million deaths were attributed to the disease [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Despite significant progress in healthcare infrastructure and disease control in recent decades, Thailand remains classified among the 30 high-burden countries for TB by the World Health Organization (WHO) [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. To address this, the WHO End TB Strategy launched in 2015 established ambitious targets including a 90% reduction in TB incidence and a 95% reduction in TB deaths by 2035 relative to 2015 levels [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. However, achieving these targets requires navigating complex epidemiological landscapes and unexpected global disruptions.\u003c/p\u003e \u003cp\u003eThe emergence of the COVID-19 pandemic profoundly disrupted global TB control programs, resulting in estimated declines of 25\u0026ndash;40% in TB notifications during lockdown periods [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. In Thailand, the pandemic placed immense strain on the healthcare system, necessitating a rapid reallocation of resources and creating disparities in healthcare accessibility. Previous research on the spatial distribution of COVID-19 health resources in Thailand highlighted significant inequalities in service coverage during the crisis, suggesting that the pandemic's impact was geographically uneven [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Reduced access to diagnostic services, healthcare system strain, and socioeconomic impacts created conditions that may have facilitated TB transmission while simultaneously lowering detection capacity. Understanding these pandemic-related disruptions through a spatial lens is critical for recovery planning and future preparedness.\u003c/p\u003e \u003cp\u003eSpatial epidemiology provides powerful tools for understanding geographic heterogeneity in disease burden and identifying high-risk areas requiring intervention [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Moving beyond national averages is essential, as TB transmission commonly clusters in specific \"hotspots\" driven by local factors. Recent studies involving spatiotemporal analysis in other high-burden settings have demonstrated the usefulness of these methods. For instance, Kulldorff\u0026rsquo;s spatiotemporal scan statistics and discrete Poisson models have successfully identified persistent TB clusters and their geographic distribution, allowing for more precise resource prioritization [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. TB disproportionately affects vulnerable and marginalized populations, particularly those living in poverty. In Thailand, geographic disparities in TB burden mirror broader patterns of socioeconomic inequality, with higher incidence concentrated in border regions, migrant communities, and areas with limited healthcare access. Understanding these spatial patterns is essential for designing pro-poor, equity-focused interventions.\u003c/p\u003e \u003cp\u003eThis study applied these advanced spatial methodologies to the Thai context. Spatial autocorrelation methods, including Global Moran's I and Local Indicators of Spatial Association (LISA), are employed to detect clustering patterns that inform resource allocation and targeted prevention strategies [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Complementing this, Joinpoint regression analysis is used to identify significant changes in epidemiological trends and to estimate annual percent change (APC) for distinct temporal segments [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. This approach enables the detection of inflection points such as those associated with the implementation of the End TB Strategy or the onset of COVID-19 and provides insights into program effectiveness and external influences on TB epidemiology.\u003c/p\u003e \u003cp\u003eThis study aimed to: (1) characterize temporal trends in TB incidence across Thailand\u0026rsquo;s Health regions and 77 provinces from 2012 to 2023 using Joinpoint regression; (2) assess spatial autocorrelation and clustering patterns using Global Moran's I and LISA; (3) classify provinces into risk categories based on long-term trends; (4) quantify the COVID-19 pandemic's impact on provincial TB rates; and (5) identify priority areas for targeted intervention. Findings will inform evidence-based, geographically targeted strategies to accelerate progress toward TB elimination goals.\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design and Setting\u003c/h2\u003e \u003cp\u003eThis study conducted a retrospective ecological study analyzing tuberculosis incidence across all 77 provinces of Thailand from 2012 to 2023. Thailand is administratively divided into 13 Health regions comprising 77 provinces. This study utilized population-level surveillance data to characterize temporal trends, spatial patterns, and pandemic-related disruptions in TB epidemiology.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData Sources\u003c/h3\u003e\n\u003cp\u003eTB Case Data: Monthly TB case notifications (2012\u0026ndash;2023) were obtained from the National Disease Surveillance System (Report 506), Bureau of Epidemiology, Department of Disease Control, Ministry of Public Health, Thailand [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. This system captures all TB cases diagnosed and reported throughout public and private healthcare facilities.\u003c/p\u003e \u003cp\u003ePopulation Data: Annual mid-year population estimates by province, sex, and age group were obtained from the Official Statistics Registration Systems, Department of Provincial Administration, Ministry of Interior Thailand [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Age-specific population data were aggregated into 5-year age groups (0\u0026ndash;4, 5\u0026ndash;9, ..., 85+) for age standardization.\u003c/p\u003e \u003cp\u003eWHO Standard Population: The WHO World Standard Population (2000\u0026ndash;2025) [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] was used as the reference for age standardization to enable international comparability.\u003c/p\u003e \u003cp\u003eGeographic Boundaries: Provincial administrative boundary shapefiles were obtained from the UN Office for the Coordination of Humanitarian Affairs (OCHA) Thailand database [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] for spatial analysis.\u003c/p\u003e\n\u003ch3\u003eAge-Standardized Rate (ASR) and Standard Error (SE) Calculation\u003c/h3\u003e\n\u003cp\u003eAge-standardized incidence rates (ASR) were calculated using the direct standardization method with the WHO World Standard Population (2000\u0026ndash;2025) as reference. The ASR formula is:\u003c/p\u003e \u003cp\u003eASR = (Σ(a\u003csub\u003ei\u003c/sub\u003e \u0026times; w\u003csub\u003ei\u003c/sub\u003e) / Σw\u003csub\u003ei\u003c/sub\u003e) \u0026times; 100,000\u003c/p\u003e \u003cp\u003ewhere a\u003csub\u003ei\u003c/sub\u003e is the age-specific incidence rate in age group i, and w\u003csub\u003ei\u003c/sub\u003e is the proportion of the standard population in age group i. Age groups were categorized into 19 groups (0\u0026ndash;4, 5\u0026ndash;9, ..., 85\u0026ndash;89, 90+). The standard error (SE) of ASR was calculated using [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]:\u003c/p\u003e \u003cp\u003eSE(ASR) = \u0026radic;[Σ(w\u003csub\u003ei\u003c/sub\u003e\u0026sup2; \u0026times; a\u003csub\u003ei\u003c/sub\u003e / n\u003csub\u003ei\u003c/sub\u003e)] / Σw\u003csub\u003ei\u003c/sub\u003e \u0026times; 100,000\u003c/p\u003e \u003cp\u003ewhere n\u003csub\u003ei\u003c/sub\u003e is the population in age group i. For province-year observations with zero TB cases, this study applied the Exact Method with +\u0026thinsp;0.5 correction to enable rate calculation while acknowledging uncertainty. Zero-case observations were present in several provincial-year records, occurring predominantly in smaller provinces during low-incidence years.\u003c/p\u003e\n\u003ch3\u003eJoinpoint Regression Analysis and Generalized Additive Models: APC and AAPC\u003c/h3\u003e\n\u003cp\u003eTemporal trend analysis was performed using Joinpoint regression (Joinpoint Regression Program Version 5.0.2, National Cancer Institute) [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. This method fits a series of joined straight lines on a log scale to identify significant changes in temporal trends. The annual percent change (APC) for each linear segment is calculated as:\u003c/p\u003e \u003cp\u003eAPC = [exp(β) \u0026minus;\u0026thinsp;1] \u0026times; 100\u003c/p\u003e \u003cp\u003ewhere β is the slope of the log-linear regression for each segment. The average annual percent change (AAPC) summarizes the trend over the entire study period as a weighted average:\u003c/p\u003e \u003cp\u003eAAPC = [exp(Σ(b\u003csub\u003ei\u003c/sub\u003e \u0026times; w\u003csub\u003ei\u003c/sub\u003e) / Σw\u003csub\u003ei\u003c/sub\u003e) \u0026minus;\u0026thinsp;1] \u0026times; 100\u003c/p\u003e \u003cp\u003ewhere b\u003csub\u003ei\u003c/sub\u003e is the slope coefficient for the ith segment and w\u003csub\u003ei\u003c/sub\u003e is the length of the ith segment. Model selection was performed using the Bayesian Information Criterion (BIC) to identify the optimal number of joinpoints. BIC provides a computationally efficient and theoretically grounded criterion by penalizing model complexity, thereby reducing the risk of overfitting in time-series with limited observation points. The joinpoint search allowed a maximum of two joinpoints per series, with a minimum of two observations between joinpoints and at least one observation at each end of the series to ensure model stability.\u003c/p\u003e \u003cp\u003eGeneralized Additive Models (GAM) with cubic regression splines were fitted to annual age-standardized TB incidence (ASR) for all 13 health service regions. The smoothing function was specified as \u003cem\u003es(Year, k\u0026thinsp;=\u0026thinsp;5)\u003c/em\u003e to allow flexible but not overfitted temporal patterns. For each region, fitted smoothed values were extracted and numerical first derivatives were computed to approximate instantaneous annual percent change (APC). APC was calculated as:\u003c/p\u003e \u003cp\u003eAPC = (d/dt SmoothASR / SmoothASR) \u0026times; 100\u003c/p\u003e \u003cp\u003ewhere d/dt is the first derivative with respect to time (year). Health region\u0026ndash;specific Average Annual Percent Change (AAPC) was computed as the mean of all APC values across 2012\u0026ndash;2023. GAM smoothing was applied solely to validate and cross-check joinpoint-based estimates, providing a model-free assessment of long-term trends.\u003c/p\u003e\n\u003ch3\u003ePercent Change Analysis for Classification\u003c/h3\u003e\n\u003cp\u003eProvincial classification utilized percent change calculations defined as:\u003c/p\u003e \u003cp\u003ePercent Change = [(Rate₂ - Rate₁) / Rate₁] \u0026times; 100\u003c/p\u003e \u003cp\u003eLong-term Classification (2012\u0026ndash;2023): Provinces were classified using AAPC from Joinpoint regression. Safe Zone: AAPC\u0026thinsp;\u0026lt;\u0026thinsp;0 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) OR p\u0026thinsp;\u0026ge;\u0026thinsp;0.05 (stable/non-significant trends). Alarming Zone: AAPC\u0026thinsp;\u0026ge;\u0026thinsp;0 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eCOVID-19 Impact Assessment (2019\u0026ndash;2023): Crude rate percent change was calculated comparing 2023 vs 2019 baseline. Categories: Decreased (\u0026lt; -5%), Unchanged (-5% to +\u0026thinsp;5%), Increased (\u0026thinsp;\u0026gt;\u0026thinsp;+\u0026thinsp;5%).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eSpatial Autocorrelation Analysis: Moran's I and LISA\u003c/h2\u003e \u003cp\u003eSpatial autocorrelation was assessed using Global Moran's I statistic [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]:\u003c/p\u003e \u003cp\u003eGlobal Moran's I = (n / Σ\u003csub\u003ei\u003c/sub\u003e Σⱼ w\u003csub\u003ei\u003c/sub\u003eⱼ) \u0026times; [Σ\u003csub\u003ei\u003c/sub\u003e Σⱼ w\u003csub\u003ei\u003c/sub\u003eⱼ(x\u003csub\u003ei\u003c/sub\u003e - x̄)(xⱼ - x̄) / Σ\u003csub\u003ei\u003c/sub\u003e(x\u003csub\u003ei\u003c/sub\u003e - x̄)\u0026sup2;]\u003c/p\u003e \u003cp\u003ewhere n is the number of provinces, x\u003csub\u003ei\u003c/sub\u003e and xⱼ are AAPC values for provinces i and j, x̄ is the mean AAPC, and w\u003csub\u003ei\u003c/sub\u003eⱼ is the spatial weight between provinces i and j. Values range from \u0026minus;\u0026thinsp;1 (perfect dispersion) to +\u0026thinsp;1 (perfect clustering), with 0 indicating random spatial distribution.\u003c/p\u003e \u003cp\u003eLocal spatial clustering was identified using Local Indicators of Spatial Association (LISA) [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]:\u003c/p\u003e \u003cp\u003eLocal LISA: I\u003csub\u003ei\u003c/sub\u003e = (x\u003csub\u003ei\u003c/sub\u003e - x̄) \u0026times; Σⱼ w\u003csub\u003ei\u003c/sub\u003eⱼ(xⱼ - x̄) / [Σ\u003csub\u003ei\u003c/sub\u003e(x\u003csub\u003ei\u003c/sub\u003e - x̄)\u0026sup2;/n]\u003c/p\u003e \u003cp\u003eLISA identifies four types of spatial association: (1) High-High (HH): provinces with high AAPC surrounded by neighbors with high AAPC (hot spots); (2) Low-Low (LL): provinces with low AAPC surrounded by neighbors with low AAPC (cold spots); (3) High-Low (HL): high-AAPC provinces surrounded by low-AAPC neighbors (spatial outliers); (4) Low-High (LH): low-AAPC provinces surrounded by high-AAPC neighbors (spatial outliers).\u003c/p\u003e \u003cp\u003eSpatial analysis was performed using GeoDa software version 1.20 [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Spatial weights matrices were constructed using queen contiguity (shared boundary or vertex) with row standardization. Statistical significance was assessed using 999 permutations (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSoftware and Statistical Analysis\u003c/h3\u003e\n\u003cp\u003eStatistical analyses were performed using: Joinpoint Regression Program 5.0.2 (National Cancer Institute); GeoDa 1.20 (spatial analysis); QGIS 3.34 [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] (cartography); R version 4.3.1 [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] (data management and visualization). Maps were created using QGIS with provincial boundary shapefiles from UN OCHA Thailand database. Statistical significance was set at α\u0026thinsp;=\u0026thinsp;0.05 for all analyses.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eOverview of TB Burden and National Trends\u003c/h2\u003e\n \u003cp\u003eGeneralized Additive Model (GAM) analysis with 95% confidence intervals revealed substantial geographical heterogeneity in age-standardized tuberculosis incidence rates (ASR per 100,000 population) across Thailand\u0026apos;s 13 health regions during 2012\u0026ndash;2023. The most dramatic declines were observed in health regions 3, 6, and 13, with observed ASR values decreasing from 41.87, 53.86, and 44.68 per 100,000 in 2012 to 2.83, 11.70, and 10.93 per 100,000 in 2023, respectively, representing reductions of 93.2%, 78.3%, and 75.5%. Health regions 4, 5, 9, and 11 demonstrated the strongest overall TB control performance, with observed ASR declining from 21.37, 25.19, 34.85, and 38.79 in 2012 to 2.11, 1.68, 2.16, and 2.94 per 100,000 in 2023, achieving reductions of 90.1%, 93.3%, 93.8%, and 92.4% respectively.\u003c/p\u003e\n \u003cp\u003eHealth regions 4, 9, 11, and 5 demonstrated the strongest overall TB control performance, with observed ASR declining from 21.37, 34.85, 38.79, and 25.19 in 2012 to 2.11, 2.16, 2.94, and 1.68 per 100,000 in 2023, achieving reductions of 90.1%, 93.8%, 92.4%, and 93.3% respectively. Health regions 1, 7, and 8 showed substantial declines from baseline rates of 29.35, 36.46, and 36.16 to 7.11, 16.20, and 10.66 per 100,000, representing reductions of 75.8%, 55.6%, and 70.5%. Health region 2 exhibited a more moderate decline from 28.97 to 13.06 per 100,000 (54.9% reduction), while Health region 10 showed the smallest reduction from 29.70 to 15.24 per 100,000 (48.7% decline), and thus maintaining relatively higher incidence levels throughout the study period. Health region 12 displayed a distinctive biphasic pattern, initially declining from 20.34 to 4.06 per 100,000 (80.0% reduction overall) (Supplementary Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e). The GAM-smoothed curves (GAM Fit values) demonstrated strong concordance with observed data across all regions, with narrow 95% confidence intervals indicating robust model performance and reliable trend estimation, validating the heterogeneous patterns of TB control success across Thailand\u0026apos;s diverse geographical and demographic contexts (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eRegional Temporal Trends from Joinpoint Regression\u003c/h2\u003e\n \u003cp\u003eThe Joinpoint regression analysis revealed heterogeneous trends in TB incidence across Thailand\u0026apos;s 13 health regions during 2012\u0026ndash;2023. Five health regions demonstrated statistically significant declining trends. Health region 3 exhibited the steepest decline with an average annual percent change (AAPC) of -20.51% (95%CI: -33.00 to -13.77, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), followed by health region 9 with an AAPC of -19.42% (95%CI: -28.98 to -8.57, p\u0026thinsp;=\u0026thinsp;0.003). Health region 4 showed a significant overall declining trend with an AAPC of -17.53% (95%CI: -23.64 to -12.89, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), characterized by two distinct segments: an initial sharp decline during 2012\u0026ndash;2016 (APC: -34.31%, 95%CI: -51.65 to -22.80, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) followed by a more moderate, non-significant decline from 2016\u0026ndash;2023 (APC: -6.09%, 95%CI: -18.36 to 45.13, p\u0026thinsp;=\u0026thinsp;0.639). Additionally, a borderline significant decline was observed in health region 11 (AAPC\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;11.79%, 95% CI: \u0026minus;22.16 to \u0026minus;\u0026thinsp;0.04; p\u0026thinsp;=\u0026thinsp;0.049), whereas health region 13 exhibited a statistically significant decline with an AAPC of \u0026minus;\u0026thinsp;11.19% (95% CI: \u0026minus;22.44 to \u0026minus;\u0026thinsp;3.74; p\u0026thinsp;=\u0026thinsp;0.006).\u003c/p\u003e\n \u003cp\u003eEight health regions exhibited non-significant trends over the study period. Health region 6 showed a biphasic pattern with an initial sharp decline during 2012\u0026ndash;2014 (APC: -46.12%, 95%CI: -66.28 to -4.06, p\u0026thinsp;=\u0026thinsp;0.020) followed by a non-significant increase from 2014\u0026ndash;2023 (APC: 6.21%, 95%CI: -17.73 to 61.83, p\u0026thinsp;=\u0026thinsp;0.202), resulting in an overall non-significant AAPC of -6.12% (95%CI: -13.21 to 3.20, p\u0026thinsp;=\u0026thinsp;0.170). Similarly, health region 12 demonstrated a significant increase during 2012\u0026ndash;2019 (APC: 20.20%, 95%CI: 4.42 to 96.66, p\u0026thinsp;=\u0026thinsp;0.024) followed by a significant decline from 2019\u0026ndash;2023 (APC: -35.75%, 95%CI: -94.89 to -14.62, p\u0026thinsp;=\u0026thinsp;0.020), yielding an overall non-significant AAPC of -4.28% (95%CI: -35.10 to 17.43, p\u0026thinsp;=\u0026thinsp;0.468). Health regions 8 (AAPC: -6.37%, 95%CI: -15.49 to 3.73, p\u0026thinsp;=\u0026thinsp;0.183), health regions 5 (AAPC: -6.05%, 95%CI: -14.82 to 2.10, p\u0026thinsp;=\u0026thinsp;0.124), health regions 7 (AAPC: -3.14%, 95%CI: -13.32 to 13.66, p\u0026thinsp;=\u0026thinsp;0.437), and health regions 1 (AAPC: -2.41%, 95%CI: -9.08 to 4.66, p\u0026thinsp;=\u0026thinsp;0.420) showed non-significant declining trends. Health region 2 (AAPC: 1.28%, 95%CI: -8.92 to 12.63, p\u0026thinsp;=\u0026thinsp;0.795) and health region 10 (AAPC: 4.91%, 95%CI: -9.81 to 32.56, p\u0026thinsp;=\u0026thinsp;0.420) exhibited non-significant increasing trends, with health region 10 showing the largest magnitude of increase among all regions (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig.\u0026nbsp;2).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eJoinpoint Regression Analysis of Tuberculosis Incidence Trends by Health regions, Thailand, 2012\u0026ndash;2023\u0026nbsp;\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eHealth region\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 8.8699%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSegment\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003ePeriod\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eAPC (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e95%CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eAAPC (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e95%CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7.7143%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"52\" style=\"width: 1.1428%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd height=\"52\" style=\"width: 1.1428%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.8699%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2012-2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-2.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e(-9.08, 4.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.420\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-2.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(-9.08, 4.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.7143%;\"\u003e\n \u003cp\u003e0.420\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"33\" style=\"width: 1.1428%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.8699%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2012-2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e(-8.92, 12.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.795\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(-8.92, 12.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.7143%;\"\u003e\n \u003cp\u003e0.795\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"33\" style=\"width: 1.1428%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.8699%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2012-2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-20.51*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e(-33.00, -13.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-20.51*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(-33.00, -13.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.7143%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"33\" style=\"width: 1.1428%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.8699%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2012-2016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-34.31*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e(-51.65, -22.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e-17.53*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e(-23.64, -12.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7.7143%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"33\" style=\"width: 1.1428%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 8.8699%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2016-2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-6.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e(-18.36, 45.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.639\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"33\" style=\"width: 1.1428%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.8699%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2012-2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-6.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e(-14.82, 2.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-6.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(-14.82, 2.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.7143%;\"\u003e\n \u003cp\u003e0.124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"33\" style=\"width: 1.1428%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.8699%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2012-2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-46.12*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e(-66.28, -4.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e-6.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e(-13.21, 3.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7.7143%;\"\u003e\n \u003cp\u003e0.170\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"33\" style=\"width: 1.1428%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 8.8699%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2014-2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e6.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e(-17.73, 61.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.202\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"33\" style=\"width: 1.1428%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.8699%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2012-2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-3.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e(-13.32, 13.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.437\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-3.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(-13.32, 13.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.7143%;\"\u003e\n \u003cp\u003e0.437\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"33\" style=\"width: 1.1428%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.8699%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2012-2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-6.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e(-15.49, 3.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-6.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(-15.49, 3.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.7143%;\"\u003e\n \u003cp\u003e0.183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"33\" style=\"width: 1.1428%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.8699%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2012-2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-19.42*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e(-28.98, -8.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-19.42*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(-28.98, -8.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.7143%;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"33\" style=\"width: 1.1428%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.8699%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2012-2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e4.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e(-9.81, 32.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.420\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(-9.81, 32.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.7143%;\"\u003e\n \u003cp\u003e0.420\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"33\" style=\"width: 1.1428%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.8699%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2012-2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-11.79*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e(-22.16, -0.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-11.79*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(-22.16, -0.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.7143%;\"\u003e\n \u003cp\u003e0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"33\" style=\"width: 1.1428%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.8699%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2012-2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e20.20*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e(4.42, 96.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e-4.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e(-35.10, 17.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7.7143%;\"\u003e\n \u003cp\u003e0.468\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"33\" style=\"width: 1.1428%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 8.8699%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2019-2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-35.75*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e(-94.89, -14.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"33\" style=\"width: 1.1428%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.8699%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2012-2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-11.19*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e(-22.44, -3.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-11.19*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(-22.44, -3.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.7143%;\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"33\" style=\"width: 1.1428%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eNote:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cem\u003eAPC = Annual Percent Change for each segment; AAPC = Average Annual Percent Change for entire period (2012-2023); CI = Confidence Interval; *Statistically significant at alpha = 0.05 level (two-tailed test).\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003eSpatial Autocorrelation Analysis\u003c/h2\u003e\n \u003cp\u003eGlobal Moran\u0026rsquo;s I of provincial AAPC values indicated a weak spatial autocorrelation (I\u0026thinsp;=\u0026thinsp;0.102), suggesting minimal geographic structuring of TB trends. This pattern indicated that much of the variation is influenced by province-specific factors rather than strong spatial clustering (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eC).\u003c/p\u003e\n \u003cp\u003eLocal Indicators of Spatial Association (LISA) identified 13 provinces (16.9%) with significant local spatial autocorrelation, categorized into four types (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eB):\u003c/p\u003e\n \u003cp\u003eHigh-High (HH) clusters \u0026ndash; Hot spots (3 provinces): Phayao (AAPC\u0026thinsp;=\u0026thinsp;+\u0026thinsp;8.21%), Phetchabun (AAPC\u0026thinsp;=\u0026thinsp;+\u0026thinsp;5.37%), Yasothon (AAPC\u0026thinsp;=\u0026thinsp;+\u0026thinsp;4.92%). These provinces and their neighbors exhibited persistently high or increasing TB rates, indicating the need for priority intervention.\u003c/p\u003e\n \u003cp\u003eLow-Low (LL) clusters - Cold spots (4 provinces): Buri Ram (AAPC = -25.70%), Ang Thong (AAPC = -30.33%), Nakhon Pathom (AAPC = -38.25%), Bangkok (AAPC = -20.05%). These provinces demonstrated sustained TB control success with declining rates, representing potential models for best practice dissemination.\u003c/p\u003e\n \u003cp\u003eHigh-Low (HL) outliers (4 provinces): Suphan Buri (AAPC\u0026thinsp;=\u0026thinsp;+\u0026thinsp;9.97%), Sing Buri (AAPC\u0026thinsp;=\u0026thinsp;+\u0026thinsp;8.15%), Ratchaburi (AAPC\u0026thinsp;=\u0026thinsp;+\u0026thinsp;6.82%), Samut Prakan (AAPC\u0026thinsp;=\u0026thinsp;+\u0026thinsp;7.35%). These provinces showed increasing trends despite the surrounding provinces with declining trends.\u003c/p\u003e\n \u003cp\u003eLow-High (LH) outliers (2 provinces): Uttaradit (AAPC = -16.47%), Chumphon (AAPC = -18.92%).\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003eProvincial Long-term Classification (2012\u0026ndash;2023)\u003c/h2\u003e\n \u003cp\u003eProvincial-level joinpoint analysis yielded a long-term classification of 77 provinces based on AAPC values and statistical significance (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eA):\u003c/p\u003e\n \u003cp\u003eSafe Zone (69 provinces, 89.6%): Provinces with decreasing or stable TB trends. Subcategories include: (a) Significant decreasing trends: 36 provinces (46.8%) with AAPC\u0026thinsp;\u0026lt;\u0026thinsp;0 and p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; (b) Stable or non-significant trends: 33 provinces (42.9%) with p\u0026thinsp;\u0026ge;\u0026thinsp;0.05.\u003c/p\u003e\n \u003cp\u003eAlarming Zone (8 provinces, 10.4%): Provinces with significant increasing trends (AAPC\u0026thinsp;\u0026ge;\u0026thinsp;0, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). These provinces are: Mae Hong Son, Phayao, Phetchabun, Suphan Buri, Sing Buri, Ratchaburi, Samut Prakan, and Yasothon. Geographically, these provinces span the Northern, Central, Northeastern, and Western regions of the country.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eProvincial Classification by Long-term TB Trends (Safe Zone vs Alarming Zone), 2012\u0026ndash;2023\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eZone Classification\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTrend Category\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNumber\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePercent\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"1\" align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eSAFE ZONE (69 provinces, 89.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSignificant Decreasing (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"1\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStable / Non-significant (p\u0026thinsp;\u0026ge;\u0026thinsp;0.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"1\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eALARMING ZONE (8 provinces, 10.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSignificant Increasing (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"1\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTOTAL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"1\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003e\u003cstrong\u003eNote\u003c/strong\u003e: \u003cem\u003eClassification based on 12-year AAPC from joinpoint regression (2012\u0026ndash;2023). Safe Zone\u0026thinsp;=\u0026thinsp;provinces with decreasing or stable trends; Alarming Zone\u0026thinsp;=\u0026thinsp;provinces with significant increasing trends requiring immediate intervention. AAPC range: Decreasing \u0026minus;\u0026thinsp;38.25% to -10.85%; Increasing\u0026thinsp;+\u0026thinsp;9.97% to +\u0026thinsp;25.30%.\u003c/em\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003eCOVID-19 Pandemic Impact Assessment (2019\u0026ndash;2023)\u003c/h2\u003e\n \u003cp\u003eComparison of 2023 vs 2019 baseline crude rates revealed heterogeneous pandemic impact across provinces (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eB):\u003c/p\u003e\n \u003cp\u003eDecreased (49 provinces, 63.6%): Provinces showing\u0026thinsp;\u0026gt;\u0026thinsp;5% decline in crude rates. These provinces represent 56.4% of the national population but only 37.2% of the 2023 TB case burden, with a mean crude rate of 10.13 per 100,000.\u003c/p\u003e\n \u003cp\u003eUnchanged (3 provinces, 3.9%): Provinces with changes between \u0026minus;\u0026thinsp;5% and +\u0026thinsp;5%, indicating stable rates during the COVID-19 period.\u003c/p\u003e\n \u003cp\u003eIncreased (25 provinces, 32.5%): Provinces showing\u0026thinsp;\u0026gt;\u0026thinsp;5% increase in crude rates. Despite representing only 42.5% of the national population, these provinces accounted for 62.6% of the 2023 TB case burden, with a mean crude rate of 23.65 per 100,000.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCOVID-19 Pandemic Impact on Provincial TB Rates (2019 vs 2023 Comparison)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eChange Category\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eProvinces\u003c/p\u003e\n \u003cp\u003e(n, %)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePopulation 2023\u003c/p\u003e\n \u003cp\u003e(n, %)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTB Cases 2023\u003c/p\u003e\n \u003cp\u003e(n, %)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean Crude Rate\u003c/p\u003e\n \u003cp\u003e(per 100k)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDecreased\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49 (63.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38,725,944 (56.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,923 (37.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnchanged\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 (3.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e944,049 (1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18 (0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.91\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIncreased\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25 (32.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29,216,305 (42.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6,563 (62.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.65\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTOTAL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e77 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68,886,298 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10,486 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003e\u003cstrong\u003eNote\u003c/strong\u003e: \u003cem\u003en\u0026thinsp;=\u0026thinsp;number; 100k\u0026thinsp;=\u0026thinsp;100,000\u003c/em\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003ePrincipal Findings\u003c/h2\u003e \u003cp\u003eThis comprehensive spatiotemporal analysis of tuberculosis trends across Thailand from 2012 to 2023 reveals three key findings with important policy implications. First, at the regional level (13 health regions), profound heterogeneity was observed, with five regions (regions 3, 4, 9, 11, and 13) demonstrating statistically significant declining trends (average annual percent change [AAPC] ranging from \u0026minus;\u0026thinsp;11.19% to -20.51%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while seven regions achieved\u0026thinsp;\u0026gt;\u0026thinsp;75% reduction in age-standardized incidence rates from 2012 to 2023: Regions 3 (93.2% reduction), 4 (90.1%), 5 (93.3%), 6 (78.3%), 9 (93.8%), 11 (92.4%), and 13 (75.5%). Two regions exhibited biphasic patterns characterized by distinct temporal inflection points: region 6 showed an initially precipitous decline during 2012\u0026ndash;2014 (annual percent change [APC] -46.12%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.020) followed by stabilization through 2023, while region 12 demonstrated a mid-period increase during 2012\u0026ndash;2019 (APC\u0026thinsp;+\u0026thinsp;20.20%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.024) followed by sharp decline during 2019\u0026ndash;2023 (APC \u0026minus;\u0026thinsp;35.75%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.020), suggesting differential timing and intensity of programmatic interventions or epidemiological transitions across regions. Second, spatial autocorrelation analysis at the provincial level (77 provinces) indicated weak spatial structuring (Global Moran\u0026rsquo;s I\u0026thinsp;=\u0026thinsp;0.102), suggesting only limited geographic clustering of TB trends, with Local Indicators of Spatial Association (LISA) identifying 13 provinces (16.9%) with significant local spatial autocorrelation patterns: three high-high clusters or hot spots (Phayao, Phetchabun, Yasothon) requiring targeted intervention; four low-low clusters or cold spots (Buri Ram, Ang Thong, Nakhon Pathom, Bangkok) demonstrating sustained control success and potential best practice models; four high-low outliers (Suphan Buri, Sing Buri, Ratchaburi, Samut Prakan) showing increasing trends despite surrounding provinces with declining patterns; and two low-high outliers (Uttaradit, Chumphon) showing declining trends despite surrounding provinces with higher rates. Provincial classification based on long-term trends identified eight provinces (10.4%) in the alarming zone with significant increasing trends (Mae Hong Son, Phayao, Phetchabun, Suphan Buri, Sing Buri, Ratchaburi, Samut Prakan, and Yasothon), distributed across North (3 provinces), Central (3), West (1), and Northeast (1) regions, while 69 provinces (89.6%) remained in the safe zone with decreasing or stable trends. Third, COVID-19 pandemic impact assessment (2019\u0026ndash;2023 comparison) revealed spatial concentration of burden disruption, with 25 provinces (32.5% of total) showing increased crude TB rates accounting for 62.6% of 2023 national case burden despite representing only 42.5% of national population, with mean crude rate that was 2.2-fold higher than provinces with decreased burden (23.65 vs. 10.13 per 100,000 population).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eComparative Context and Methodological Considerations\u003c/h2\u003e \u003cp\u003eThe magnitude of tuberculosis burden reduction observed in Thailand's high-performing regions substantially exceeds that documented in comparable settings globally. Regional joinpoint analyses from China reported AAPC values ranging from \u0026minus;\u0026thinsp;3.8% to -8.2% during 2005\u0026ndash;2016, with no regions approaching double-digit annual declines [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Similarly, India's district-level joinpoint analysis documented AAPC values between \u0026minus;\u0026thinsp;2.1% and \u0026minus;\u0026thinsp;6.5% for the period 2001\u0026ndash;2014, with the highest-performing districts achieving approximately 5\u0026ndash;7% annual reductions [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Other high-burden settings in Southeast Asia have demonstrated more gradual declining trends, typically in the range of 3\u0026ndash;6% annual reduction [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Thailand's achievement of 10\u0026ndash;20% annual declines (AAPC) in five regions thus represents exceptional tuberculosis control performance, warranting detailed programmatic analysis to identify replicable intervention strategies which could inform accelerated progress toward elimination goals in similar epidemiological contexts.\u003c/p\u003e \u003cp\u003eThe biphasic patterns observed in regions 6 and 12 merit careful interpretation within the context of surveillance system evolution and programmatic transitions. Region 6's initial sharp decline (APC \u0026minus;\u0026thinsp;46.12% during 2012\u0026ndash;2014) followed by stabilization resembles patterns documented in settings implementing intensive short-term interventions, such as enhanced active case-finding campaigns or targeted screening programs, which produce rapid initial gains followed by plateau effects as intervention intensity normalizes or case detection reaches saturation among high-risk populations (World Health Organization, 2015). Region 12's mid-period increase (APC\u0026thinsp;+\u0026thinsp;20.20% during 2012\u0026ndash;2019) followed by a subsequent sharp decline (APC \u0026minus;\u0026thinsp;35.75% during 2019\u0026ndash;2023) coincides temporally with the WHO End TB Strategy launch in 2015 [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Suggesting a possible programmatic transition affecting case detection sensitivity, diagnostic technology adoption, or reporting practices. Similar mid-period fluctuations have been documented in settings undergoing diagnostic system modernization, particularly with the implementation of genomic rapid diagnostics (Xpert MTB/RIF) that can temporarily increase case notifications through improved diagnostic sensitivity and reduced time to detection before stabilizing as the technology becomes integrated into routine practice [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Three plausible explanations warrant consideration for these temporal patterns: first, changes in surveillance system sensitivity or case definitions during the mid-2010s may have produced artifactual volatility rather than reflecting genuine epidemiological dynamics; second, genuine programmatic interventions may have generated substantial short-term effects on case detection and notification; third, intensified active case-finding campaigns may have temporarily increased notifications by identifying previously undetected prevalent cases before settling to lower incidence levels reflecting reduced transmission.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eSpatial Heterogeneity and Targeted Intervention Opportunities\u003c/h2\u003e \u003cp\u003eThe observed spatial autocorrelation (Moran\u0026rsquo;s I\u0026thinsp;=\u0026thinsp;0.102) demonstrates only weak spatial structuring, markedly lower than that reported in Ethiopia (I\u0026thinsp;=\u0026thinsp;0.291) [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] and Pakistan (I\u0026thinsp;=\u0026thinsp;0.185) [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], suggesting that TB patterns in Thailand are shaped by modest spatial influences alongside province-specific determinants. LISA-identified high-high clusters (Phayao, Phetchabun, Yasothon) represent priority areas requiring intensive interventions targeting potential contributing factors including border proximity facilitating cross-border transmission [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], high poverty levels reducing access to healthcare [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] migrant populations with limited access [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], and HIV co-infection requiring integrated services [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Conversely, low-low clusters (Buri Ram, Ang Thong, Nakhon Pathom, Bangkok) demonstrating sustained success warrant investigation to identify transferable best practices. Bangkok's achievement despite high population density suggests that effective urban strategies are in place that could inform interventions elsewhere. Detailed programmatic assessment of these provinces should examine intervention approaches, healthcare delivery models, community engagement initiatives, and digital health applications to identify evidence-based approaches that could be disseminated to surrounding and demographically similar areas. High-low outliers (Suphan Buri, Sing Buri, Ratchaburi, Samut Prakan) showing increasing trends despite declining neighbors merit urgent investigation of local drivers, including industrial development attracting migrants, population influx concentrating vulnerable groups, healthcare capacity constraints, drug-resistant strain emergence, or programmatic breakdowns requiring tailored corrective interventions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eProvincial Classification and COVID-19 Pandemic Impact\u003c/h2\u003e \u003cp\u003eProvincial classification based on long-term trends (2012\u0026ndash;2023) revealed that 69 provinces (89.6%) remained in the safe zone with decreasing or stable trajectories, comprising 36 provinces (46.8%) with significant decreasing trends and 33 provinces (42.9%) with stable or non-significant trends. Eight provinces (10.4%) fell into the alarming zone with significant increasing trends: Mae Hong Son, Phayao, Phetchabun, Suphan Buri, Sing Buri, Ratchaburi, Samut Prakan, and Yasothon, distributed across North (3), Central (3), West (1), and Northeast (1) regions. COVID-19 pandemic impact assessment (2019\u0026ndash;2023) revealed heterogeneous disruption: 49 provinces (63.6%) showed decreased burden (\u0026gt;\u0026thinsp;5% decline), representing 56.4% of population but only 37.2% of 2023 tuberculosis burden with mean crude rate 10.13 per 100,000; three provinces (3.9%) remained unchanged (\u0026plusmn;\u0026thinsp;5%); and 25 provinces (32.5%) experienced increased burden (\u0026gt;\u0026thinsp;5% increase), accounting for 62.6% of 2023 national burden despite representing only 42.5% of population, with mean crude rate 23.65 per 100,000 population which is 2.2-fold higher than in provinces with decreased burden. This spatial concentration aligns with global pandemic disruptions where the WHO reported 25\u0026ndash;40% notification declines [\u003cspan additionalcitationids=\"CR36\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], with disproportionate impacts on vulnerable populations [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. The substantial overlap between pandemic-affected provinces and alarming zone provinces indicates compound vulnerability, requiring urgent priority intervention combining pandemic recovery with long-term programmatic strengthening. Recovery strategies should prioritize: intensified active case-finding to identify missed diagnoses [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]; integration of tuberculosis-COVID-19 services leveraging strengthened respiratory infrastructure [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]; community-based care models maintaining continuity during disruptions [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]; and digital health technologies for remote monitoring. Future research should examine differential resilience mechanisms, evaluate recovery trajectory effectiveness, and integrate genomic epidemiological data to clarify whether burden changes reflect case detection fluctuations or genuine transmission shifts.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and Limitations\u003c/h2\u003e \u003cp\u003eThis study's primary strength lies in comprehensive national surveillance data spanning 77 provinces over 12 years (2012\u0026ndash;2023), enabling robust spatiotemporal analysis. Methodological rigor through joinpoint regression identifying significant trend changes, generalized additive models with 95% confidence intervals, spatial autocorrelation analysis (Moran's I), and Local Indicators of Spatial Association provides triangulated evidence of heterogeneous tuberculosis dynamics. Age-standardized incidence rates ensure temporal and geographic comparability, while an extended time period captures both long-term trends and COVID-19 pandemic impacts. However, several important limitations warrant consideration. As an ecological study using aggregate provincial data, findings cannot establish individual-level causality or directly assess individual risk factors. The biphasic patterns in regions 6 and 12, while statistically significant, require further investigation to distinguish programmatic or surveillance changes from genuine epidemiological dynamics. The Exact Method applied for zero-case observations introduced some statistical uncertainty, although sensitivity analysis confirmed that the overall result remained robust. Absence of linked data on treatment outcomes, drug resistance prevalence, and HIV co-infection status limits comprehensive burden assessment and prevents evaluation of program quality indicators beyond case notification. Spatial analyses using administrative boundaries may not reflect actual transmission networks or healthcare catchment areas. Finally, distinguishing between real changes in transmission and case detection sensitivity variations remains challenging without genomic epidemiological data.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eConclusion and Policy Implications\u003c/h2\u003e \u003cp\u003eFindings support five priority policy actions for Thailand's tuberculosis control program. First, implement intensive intervention packages in eight alarming zone provinces (Mae Hong Son, Suphan Buri, Sing Buri, Ratchaburi, Samut Prakan, Phayao, Phetchabun, Yasothon) and three high-high clusters (Phayao, Phetchabun, Yasothon), prioritizing enhanced active case-finding, strengthened treatment support, and comprehensive contact investigation. Second, conduct detailed programmatic assessments of four low-low cluster provinces (Buri Ram, Ang Thong, Nakhon Pathom, Bangkok) to identify and disseminate successful strategies as evidence-based intervention packages for adaptation in underperforming areas. Third, prioritize pandemic recovery efforts in 25 provinces with increased tuberculosis burden during 2019\u0026ndash;2023, ensuring strengthened surveillance, diagnostic capacity, and service delivery continuity. Fourth, investigate mechanisms underlying biphasic patterns in regions 6 and 12 through a detailed review of programmatic changes, surveillance modifications, and diagnostic technology adoption during inflection periods. Fifth, address compound vulnerability in provinces demonstrating both long-term deterioration and pandemic-related disruption through multi-sectoral approaches targeting social determinants. Future research priorities include: validation studies assessing surveillance data quality and completeness; qualitative investigations of successful control strategies in high-performing provinces; multilevel modeling incorporating individual, community, and programmatic factors; genomic epidemiological studies clarifying transmission dynamics during volatile periods; and prospective monitoring of pandemic recovery trajectories with evaluation of catch-up strategy effectiveness.\u003c/p\u003e \u003c/div\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAAPC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAverage Annual Percent Change\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAPC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAnnual Percent Change\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eASR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAge-Standardized Rate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBIC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBayesian Information Criterion\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConfidence Interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGAM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGeneralized Additive Model\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHH\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHigh-High (spatial cluster)\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHigh-Low (spatial outlier)\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLH\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLow-High (spatial outlier)\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLISA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLocal Indicators of Spatial Association\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLow-Low (spatial cluster)\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStandard Error\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTB\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTuberculosis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWHO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWorld Health Organization\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003e\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Khon Kaen University Ethics Committee for Human Research has provided exemption for ethical approval, under reference number HE 682254.\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\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eKS: Conceptualization, methodology, data curation, formal analysis, writing - original draft, supervision. RKM: Visualization, validation, writing - review \u0026amp; editing. SM: Methodology, validation, writing - review \u0026amp; editing. KN: Validation, writing - review \u0026amp; editing. RN: Conceptualization, validation, writing - review \u0026amp; editing. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgement\u003c/h2\u003e\n\u003cp\u003eThe authors acknowledge the Bureau of Epidemiology, Department of Disease Control, Ministry of Public Health, Thailand, for providing access to TB surveillance data. We thank all healthcare workers and surveillance staff throughout Thailand for their tireless efforts in TB case detection, treatment, and reporting.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eTB surveillance data are available from the Bureau of Epidemiology, Department of Disease Control, Ministry of Public Health Thailand (http://www.boe.moph.go.th). Population data are publicly available from the National Statistical Office Thailand (http://www.nso.go.th). The datasets used and/or analyzed in this study are accessible from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eDodd PJ, Yuen CM, Sismanidis C, Seddon JA, Jenkins HE. The global burden of tuberculosis mortality in children: A mathematical modelling study. Lancet Global Health. 2017;5:e898\u0026ndash;906. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S2214-109X(17)30289-9\u003c/span\u003e\u003cspan address=\"10.1016/S2214-109X(17)30289-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGlaziou P, Sismanidis C, Floyd K, Raviglione M. Global epidemiology of tuberculosis. Cold Spring Harbor Perspect Med. 2015;5:a017798. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1101/cshperspect.a017798\u003c/span\u003e\u003cspan address=\"10.1101/cshperspect.a017798\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Health Organization. Global tuberculosis report 2023. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.who.int/teams/global-tuberculosis-programme/tb-reports/global-tuberculosis-report-2023\u003c/span\u003e\u003cspan address=\"https://www.who.int/teams/global-tuberculosis-programme/tb-reports/global-tuberculosis-report-2023\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 21 October 2025.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Health Organization. Implementing the end TB strategy: The essentials. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.who.int/publications/i/item/9789241509930\u003c/span\u003e\u003cspan address=\"https://www.who.int/publications/i/item/9789241509930\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 21 October 2025.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFalzon D, Zignol M, Bastard M, Floyd K, Raviglione M. The impact of the COVID-19 pandemic on the global tuberculosis epidemic. Front Immunol. 2023;14:1234785. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fimmu.2023.1234785\u003c/span\u003e\u003cspan address=\"10.3389/fimmu.2023.1234785\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePai M, Kasaeva T, Swaminathan S. COVID-19's devastating effect on tuberculosis care\u0026mdash;A path to recovery. N Engl J Med. 2022;386:1490\u0026ndash;3. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1056/NEJMp2118145\u003c/span\u003e\u003cspan address=\"10.1056/NEJMp2118145\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSornlorm K, Muntaphan S. Explanatory spatial modeling of COVID-19 vaccine coverage in Thailand: policy implications for equitable distribution. BMC Public Health. 2025;25:3028. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12889-025-24217-7\u003c/span\u003e\u003cspan address=\"10.1186/s12889-025-24217-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eElliott P, Wartenberg D. Spatial epidemiology: Current approaches and future challenges. Environ Health Perspect. 2004;112:998\u0026ndash;1006. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pmc.ncbi.nlm.nih.gov/articles/PMC1247193/\u003c/span\u003e\u003cspan address=\"https://pmc.ncbi.nlm.nih.gov/articles/PMC1247193/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLawson AB. Bayesian disease mapping: Hierarchical modeling in spatial epidemiology. 3rd ed. edn.: CRC; 2018.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMahato RK, Sornlorm K, Htike KM, Koro AB, Nawawonganun R, Sharma V, et al. Spatial heterogenicity of tuberculosis and dengue in Nepal. Sci Rep. 2025;15:41012. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41598-025-24798-8\u003c/span\u003e\u003cspan address=\"10.1038/s41598-025-24798-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAnselin L. Local indicators of spatial association\u0026mdash;LISA. Geographical Anal. 1995;27:93\u0026ndash;115. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1538-4632.1995.tb00338.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1538-4632.1995.tb00338.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang Q, Guo L, Wang J, Shen H, Huang J. Spatial distribution of tuberculosis and its socioeconomic influencing factors in mainland China 2013\u0026ndash;2016. Trop Med Int Health. 2019;24:1104\u0026ndash;13. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/tmi.13289\u003c/span\u003e\u003cspan address=\"10.1111/tmi.13289\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eClegg LX, Hankey BF, Tiwari R, Feuer EJ, Edwards BK. Estimating average annual per cent change in trend analysis. Stat Med. 2009;28:3670\u0026ndash;82. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/sim.3733\u003c/span\u003e\u003cspan address=\"10.1002/sim.3733\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim H-J, Fay MP, Feuer EJ, Midthune DN. Permutation tests for joinpoint regression with applications to cancer rates. Stat Med. 2000;19:335\u0026ndash;51. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/(SICI)1097-0258(20000215)19:3%3C335::AID-SIM336%3E3.0.CO;2-Z\u003c/span\u003e\u003cspan address=\"10.1002/(SICI)1097-0258(20000215)19:3%3C335::AID-SIM336%3E3.0.CO;2-Z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBureau of Epidemiology DoDCMoPHT. National Disease Surveillance System (Report 506). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://apps-doe.moph.go.th/boeeng/annual.php\u003c/span\u003e\u003cspan address=\"https://apps-doe.moph.go.th/boeeng/annual.php\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 15 December 2025.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDepartment of Provincial Administration MoIT. Official statistics registration systems. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.dopa.go.th\u003c/span\u003e\u003cspan address=\"http://www.dopa.go.th\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 15 December 2025.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAhmad OB, Boschi-Pinto C, Lopez AD, Murray CJ, Lozano R, Inoue M. Age standardization of rates: A new WHO standard. World Health Organization; 2001. p. 31. Report No.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHumanitarian Data E. Thailand-Subnational administrative boundaries. United Nations Office for the Coordination of Humanitarian Affairs (OCHA); 2022.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFay MP, Feuer EJ. Confidence intervals for directly standardized rates: A method based on the gamma distribution. Stat Med. 1997;16:791\u0026ndash;801. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/(SICI)1097-0258(19970415)16:7%3C791::AID-SIM500%3E3.0.CO;2-%23\u003c/span\u003e\u003cspan address=\"10.1002/(SICI)1097-0258(19970415)16:7%3C791::AID-SIM500%3E3.0.CO;2-%23\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoran PAP. Notes on continuous stochastic phenomena. Biometrika. 1950;37:17\u0026ndash;23. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2307/2332142\u003c/span\u003e\u003cspan address=\"10.2307/2332142\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAnselin L, Syabri I, Kho Y, GeoDa. An introduction to spatial data analysis. Geographical Anal. 2006;38:5\u0026ndash;22. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.0016-7363.2005.00671.x\u003c/span\u003e\u003cspan address=\"10.1111/j.0016-7363.2005.00671.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTeam QD. QGIS Geographic Information System. Version 3.34 ed. Open Source Geospatial Foundation; 2022.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTeam RC. R: A language and environment for statistical computing. Version 4.3.1 ed. R Foundation for Statistical Computing; 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang C, Lu L, Warren JL, Wu J, Jiang Q, Zuo T, et al. Internal migration and transmission dynamics of tuberculosis in Shanghai, China: an epidemiological, spatial, genomic analysis. Lancet Infect Dis. 2018;18:788\u0026ndash;95. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.1016/S1473-3099(18)30218-4\u003c/span\u003e\u003cspan address=\"10.1016/S1473-3099(18)30218-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDhamnetiya D, Goel MK, Dhakad U, Jha RP, Singh A. Trends in incidence and mortality of tuberculosis in India over past three decades: A joinpoint and age-period-cohort analysis. BMC Pulm Med. 2021;21:375. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12890-021-01740-y\u003c/span\u003e\u003cspan address=\"10.1186/s12890-021-01740-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThiankhaw K, Chattakul P, Kanchanachitra C, Guijarro AP. Spatiotemporal epidemiology of tuberculosis in Thailand from 2011 to 2020. Biology. 2022;11:755. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/biology11050755\u003c/span\u003e\u003cspan address=\"10.3390/biology11050755\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDowdy DW, Cattamanchi A, Steingart KR, Pai M. Is scale-up worth it? Challenges in economic analysis of diagnostic tests for tuberculosis. PLoS Med. 2011;8:e1001063. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pmed.1001063\u003c/span\u003e\u003cspan address=\"10.1371/journal.pmed.1001063\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlene KA, Xu Z, Bai L, Yi H, Tan Y, Gray DJ, et al. Spatial clustering of notified tuberculosis in Ethiopia: A nationwide study. PLoS ONE. 2019;14:e0221027. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0221027\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0221027\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUllah S, Daud H, Dass SC, Fanaee-T H, Kausarian H. Alamgir. Space-time clustering characteristics of tuberculosis in Khyber Pakhtunkhwa Province, Pakistan, 2015\u0026ndash;2019. Int J Environ Res Public Health. 2020;17:1413.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHargreaves JR, Boccia D, Evans CA, Adato M, Petticrew M, Porter JD. The social determinants of tuberculosis: From evidence to action. Am J Public Health. 2011;101:654\u0026ndash;62. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2105/AJPH.2010.199505\u003c/span\u003e\u003cspan address=\"10.2105/AJPH.2010.199505\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePareek M, Greenaway C, Noori T, Munoz J, Zenner D. The impact of migration on tuberculosis epidemiology and control in high-income countries: A review. BMC Med. 2016;14:48. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12916-016-0595-5\u003c/span\u003e\u003cspan address=\"10.1186/s12916-016-0595-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLin H-H, Ezzati M, Murray M. Tobacco smoke, indoor air pollution and tuberculosis: A systematic review and meta-analysis. PLoS Med. 2007;4:e20. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pmed.0040020\u003c/span\u003e\u003cspan address=\"10.1371/journal.pmed.0040020\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eL\u0026ouml;nnroth K, Jaramillo E, Williams BG, Dye C, Raviglione M. Drivers of tuberculosis epidemics: The role of risk factors and social determinants. Soc Sci Med. 2009;68:2240\u0026ndash;6. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.socscimed.2009.03.041\u003c/span\u003e\u003cspan address=\"10.1016/j.socscimed.2009.03.041\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJeon CY, Murray MB. Diabetes mellitus increases the risk of active tuberculosis: A systematic review of 13 observational studies. PLoS Med. 2008;5:e152. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pmed.0050152\u003c/span\u003e\u003cspan address=\"10.1371/journal.pmed.0050152\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCilloni L, Fu H, Vesga JF, Dowdy D, Pretorius C, Ahmedov S, et al. The potential impact of the COVID-19 pandemic on the tuberculosis epidemic a modelling analysis. EClinicalMedicine. 2020;28:100603. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.eclinm.2020.100603\u003c/span\u003e\u003cspan address=\"10.1016/j.eclinm.2020.100603\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcQuaid CF, Vassall A, Cohen T, Fiekert K, White RG. The impact of COVID-19 on TB: A review of the data. Int J Tuberculosis Lung Disease. 2021;25:436\u0026ndash;46. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5588/ijtld.21.0148\u003c/span\u003e\u003cspan address=\"10.5588/ijtld.21.0148\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMigliori GB, Thong PM, Akkerman O, Alffenaar J-W, Alvarez-Navascues F, Assao-Neino MM, et al. Worldwide effects of coronavirus disease pandemic on tuberculosis services, January-April 2020. Emerg Infect Dis. 2020;27:1446\u0026ndash;55. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3201/eid2611.203163\u003c/span\u003e\u003cspan address=\"10.3201/eid2611.203163\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOng CWM, Migliori GB, Raviglione M, MacGregor-Skinner G, Sotgiu G, Alffenaar J-W. Epidemic and pandemic viral infections: Impact on tuberculosis and the lung. Eur Respir J. 2020;56:2001727. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1183/13993003.01727-2020\u003c/span\u003e\u003cspan address=\"10.1183/13993003.01727-2020\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eL\u0026ouml;nnroth K, Castro KG, Chakaya JM, Chauhan LS, Floyd K, Glaziou P, et al. Tuberculosis control and elimination 2010-50: Cure, care, and social development. Lancet. 2010;375:1814\u0026ndash;29. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0140-6736(10)60483-7\u003c/span\u003e\u003cspan address=\"10.1016/S0140-6736(10)60483-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVisca D, Ong CWM, Tiberi S, Centis R, D'Ambrosio L, Chen B, et al. Tuberculosis and COVID-19 interaction: A review of biological, clinical and public health effects. Pulmonology. 2021;27:151\u0026ndash;65. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.pulmoe.2020.12.012\u003c/span\u003e\u003cspan address=\"10.1016/j.pulmoe.2020.12.012\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYuen CM, Amanullah F, Dharmadhikari A, Nardell EA, Seddon JA, Vasilyeva I, et al. Turning off the tap: Stopping tuberculosis transmission through active case-finding and prompt effective treatment. Lancet. 2015;386:2334\u0026ndash;43. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/http://dx.doi.org/10.1016/S0140-6736(15)00322-0\u003c/span\u003e\u003cspan address=\"10.1016/S0140-6736(15)00322-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"infectious-diseases-of-poverty","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"idop","sideBox":"Learn more about [Infectious Diseases of Poverty](http://idpjournal.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/idop/default.aspx","title":"Infectious Diseases of Poverty","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Tuberculosis, Thailand, Spatial Analysis, Spatiotemporal Analysis, Disease Mapping, Epidemiology, Health Policy, Joinpoint Regression, Moran's I","lastPublishedDoi":"10.21203/rs.3.rs-8460167/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8460167/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eTuberculosis (TB) remains a major public health challenge in Thailand, a high-burden country undergoing both epidemiological transition and pandemic-related disruption. This study examined temporal and spatial patterns of age-standardized TB incidence from 2012 to 2023 across Thailand\u0026rsquo;s 13 health regions and 77 provinces.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eNational tuberculosis surveillance data were used for all analyses. Age-standardized incidence rates (ASR) were calculated using the WHO World Standard Population (2000\u0026ndash;2025). Temporal trends were assessed using Joinpoint regression to estimate Health region\u0026ndash;specific annual percent change (APC) and average annual percent change (AAPC). Generalized Additive Models (GAM) were fitted to validate the temporal trajectories. Spatial clustering was evaluated using Global Moran\u0026rsquo;s I and Local Indicators of Spatial Association (LISA), applied to provincial AAPC values.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eNational TB incidence declined substantially from 2012 to 2023, although marked regional heterogeneity persisted. Five regions demonstrated the strongest long-term reductions: region 3 (AAPC \u0026minus;\u0026thinsp;20.51, 95%CI: \u0026minus;33.00 to \u0026minus;\u0026thinsp;13.77), region 9 (\u0026minus;\u0026thinsp;19.42, 95%CI: \u0026minus;28.98 to \u0026minus;\u0026thinsp;8.57), region 4 (\u0026minus;\u0026thinsp;17.53, 95%CI: \u0026minus;23.64 to \u0026minus;\u0026thinsp;12.89), region 11 (\u0026minus;\u0026thinsp;11.79, 95%CI: \u0026minus;22.16 to \u0026minus;\u0026thinsp;0.04), and region 13 (\u0026minus;\u0026thinsp;11.19, 95%CI: \u0026minus;22.44 to \u0026minus;\u0026thinsp;3.74). Several regions exhibited biphasic trends, including region 6, which showed an early decline followed by stabilization, and region 12, which experienced a mid-period increase before a post-2019 reduction. Spatial analysis revealed limited global clustering, but LISA identified distinct local patterns. Three provinces (Phayao, Phetchabun, Yasothon) formed high-high clusters with increasing AAPC values, while four provinces (Buri Ram, Ang Thong, Nakhon Pathom, Bangkok) formed low-low clusters with sustained declines. High-low and low-high outliers highlighted further geographic heterogeneity.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThailand has achieved substantial reductions in TB incidence over the past decade however, pronounced regional and provincial disparities persist. Localized hotspots and divergent temporal trajectories underscore the need for geographically targeted monitoring, equitable resource allocation, and pro-poor interventions to sustain progress toward TB elimination.\u003c/p\u003e","manuscriptTitle":"Spatiotemporal trends in tuberculosis incidence in Thailand, 2012–2023: a nationwide, province-level analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-06 08:15:57","doi":"10.21203/rs.3.rs-8460167/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-23T09:55:30+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-22T13:22:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"80918921010436551540098496197767103726","date":"2026-04-18T09:48:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"68293912335943226395191390538434252607","date":"2026-04-16T08:17:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"245631465750752046392838204082038335868","date":"2026-03-09T06:47:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"301358764422254742773955129800895545312","date":"2026-01-28T07:48:22+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-12T05:45:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"284810598083028920866884065436261030782","date":"2025-12-30T04:37:51+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-30T03:03:00+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-29T14:06:13+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-29T14:05:06+00:00","index":"","fulltext":""},{"type":"submitted","content":"Infectious Diseases of Poverty","date":"2025-12-27T09:36:16+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"infectious-diseases-of-poverty","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"idop","sideBox":"Learn more about [Infectious Diseases of Poverty](http://idpjournal.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/idop/default.aspx","title":"Infectious Diseases of Poverty","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4c03c6c8-b6de-452f-a057-e286df596e97","owner":[],"postedDate":"January 6th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-01T12:38:15+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-06 08:15:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8460167","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8460167","identity":"rs-8460167","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}