Spatiotemporal Heterogeneity of Multidrug-Resistant Pulmonary Tuberculosis Among Local Residents and Internal Migrants in Hangzhou, China, 2014-2024

Spatiotemporal Heterogeneity of Multidrug-Resistant Pulmonary Tuberculosis Among Local Residents and Internal Migrants in Hangzhou, China, 2014-2024 | 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 Heterogeneity of Multidrug-Resistant Pulmonary Tuberculosis Among Local Residents and Internal Migrants in Hangzhou, China, 2014-2024 Qingchun Li, Xuexin Bai, Zike Cheng, Qinglin Cheng, Yifei Wu, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7266883/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Background Multidrug-resistant pulmonary tuberculosis (MDR-PTB) poses a severe public health threat in urbanizing China, with internal migrants (IM) facing elevated risks due to socioeconomic disparities and fragmented healthcare. This study examines the spatiotemporal heterogeneity of MDR-PTB among local residents (LR) and IM in Hangzhou (2014–2024) to inform precision interventions. Methods A retrospective analysis of 753 laboratory-confirmed MDR-PTB cases was conducted using data from China’s Tuberculosis Information Management System. Temporal trends were assessed via seasonal-trend decomposition (STL) and Prais-Winsten regression, while spatial clustering was analyzed using Global Moran’s I and Getis-Ord Gi* statistics at the subdistrict level. Results IM constituted 28.3% (213/753) of MDR-PTB cases, exhibiting younger age (mean 35.5 vs. 49.9 years, P <0.001) and higher retreatment rates (51.6% vs. 42.8%, P =0.034) than LR. The proportion of MDR-PTB among all TB cases declined significantly (monthly percent change [MPC]=-0.645%, P <0.001), with sharper reductions in IM post-2019 (case MPC=-0.823%, P =0.002). Spatial analysis revealed hotspots in central urban districts for both populations and migrant-dense suburbs for IM (Moran’s I=0.15-0.17, P <0.001). Conclusions MDR-PTB burden diverges spatiotemporally between IM and LR, driven by migration patterns, healthcare access barriers, and localized transmission. Targeted screening in industrial zones, mobile clinics for migrants, and policy reforms for cross-provincial insurance equity are critical to reducing MDR-PTB in urban China. Multidrug-resistant tuberculosis Internal migration Spatiotemporal heterogeneity Health disparities Urbanization Figures Figure 1 Figure 2 Figure 3 Background Multidrug-resistant pulmonary tuberculosis (MDR-TB), defined as resistance to at least rifampicin and isoniazid, remains a critical global health threat. According to WHO’ latest report[ 1 ], an estimated 362,700 incident MDR-TB cases occurred worldwide in 2023, with China bearing the second-highest burden after India. Global disease burden of MDR-TB increased from 1990 to 2021 and is predicted to grow till 2050[ 2 ]. In China, MDR-TB accounts for 5.7% of new TB cases and 26% of previously treated cases, reflecting significant challenges in TB control programs[ 1 ]. The high mortality and prolonged treatment regimens associated with MDR-TB underscore the urgent need for targeted interventions to curb transmission and reduce disease burden[ 3 ]. The temporal dynamics of MDR-TB exhibit complex patterns that are critical for optimizing control strategies[ 4 , 5 ]. Studies in China have demonstrated significant temporal fluctuations in MDR-TB incidence, with distinct peaks occurring every 2–3 years and seasonal variations observed in notification rates[ 6 , 7 ]. Understanding these temporal patterns is essential for: (1) identifying periods of elevated transmission risk, (2) optimizing resource allocation for case detection and treatment, and (3) evaluating the long-term impact of control interventions. Geographic precision in TB control is increasingly recognized as pivotal for optimizing resource allocation. Evidence indicates that spatially targeted interventions can enhance case detection rates by approximately twofold compared to untargeted approaches, significantly reducing community transmission[ 8 ]. Understanding spatiotemporal heterogeneity-particularly the clustering of MDR-TB in specific populations, time periods, and locations-enables health systems to deploy rapid diagnostics, contact tracing, and treatment access where most impactful[ 9 – 11 ]. While spatial analyses of drug-susceptible TB are well-documented[ 12 – 14 ], data on the spatiotemporal dynamics of MDR-TB remain scarce, especially in high-migration urban settings[ 15 , 16 ]. This gap impedes the design of precision public health strategies for MDR-TB control. Hangzhou, the capital of Zhejiang Province, exemplifies the urbanization-driven epidemiological shifts in TB across China. Between 2012 and 2023, its population surged from 7.0 million to 12.37 million, driven predominantly by internal migration (IM)[ 17 , 18 ]. IM often concentrated in low-wage occupations and overcrowded living conditions, face heightened MDR-PTB risks due to limited access to quality healthcare-a disparity exacerbated by poverty and unequal resource distribution[ 19 , 20 ]. Studies indicate that migrants contribute disproportionately to TB transmission and exhibit higher MDR-TB rates compared to local residents[ 21 , 22 ]. In Hangzhou, despite achieving a relatively high MDR-TB treatment success rate and expanded diagnostic coverage[ 3 ], the absence of an effective MDR-TB vaccine [ 23 ]and limited access to novel regimens[ 24 ] and drugs[ 25 ] underline the importance of preventing imported MDR-TB. The convergence of rapid urbanization, high migrant influx, and sustained MDR-TB burden necessitates granular insights into the spatiotemporal distribution of cases among local and migrant populations. Such data are vital to inform spatially adaptive interventions, such as hotspot-targeted screening, mobile clinics for migrants, and dynamic resource redistribution during seasonal or spatial case surges[ 13 ]. This study aims to address this gap, leveraging spatiotemporal epidemiology to inform precision public health interventions. Methods Study Design and Data Sources Hangzhou (30°16'N, 120°12'E), the capital of Zhejiang Province in eastern China, comprises 11 urban districts, one county-level city, and two rural counties. According to 2023 municipal census data, its resident population exceeds 12.5 million. Tuberculosis care is delivered through 13 designated facilities, including two tertiary centers specializing in drug-resistant TB management. This retrospective cohort analysis utilized de-identified surveillance records from China’s Tuberculosis Information Management System (TBIMS)[ 26 ], a nationally standardized electronic platform operated by the Chinese Center for Disease Control and Prevention. Under mandatory infectious disease reporting protocols, all laboratory-confirmed MDR-TB cases are uniformly documented in TBIMS, which archives demographic profiles, clinical diagnostics, therapeutic interventions, and epidemiological monitoring data. We extracted records of MDR-PTB cases diagnosed between January 1, 2014, and December 31, 2024, across all designated TB facilities in Hangzhou. Eligible cases met three criteria: 1) confirmed diagnosis of MDR-TB; 2) current residential address within Hangzhou; 3) age ≥ 16 years. Cases were excluded if they resided outside Hangzhou or had incomplete clinical data. Definition of Research Subjects Internal migrant (IM) TB cases were categorized as individuals whose household registration ( hukou ) resided outside Hangzhou, while their current residential address was within the city. Corresponding MDR-TB cases were designated IMMDR . Local resident (LR) TB cases were defined as patients with both hukou registration and current residence within Hangzhou. Their MDR-TB cases were labeled LRMDR . Local residence (LR) TB cases (LRMDR) were defined as patients diagnosed with TB whose both current residential address and household registration (hukou) were registered within Hangzhou. MDR-TB cases within these patients are denoted as LRMDR. Statistical analysis Baseline data analysis Frequencies and proportions were calculated for categorical variables, while Means and Standard deviation were calculated for continuous variables. Chi-square tests were employed to compare the differences in various categorical variables between IMMDR and LRMDR, while t-tests were employed for continuous variables. All analyses were performed using R software, version 4.3.3. A P -value < 0.05 was considered statistically significant. Time series analysis. To ascertain the temporal variation and trend of MDR throughout the study period, seasonal-trend decomposition by loess (STL), a method rooted in locally weighted regression, was employed. By decomposing a time series, the STL procedure filters out stochastic noise and removes seasonal effects, thereby isolating the data's underlying long-term trend. Monthly proportions and case counts were summarized and presented alongside the temporal trend identified by STL. Prais-Winsten autoregression was subsequently employed to categorize the temporal trend of MDR-TB as either upward, downward, or stationary during the study period. Monthly percent change (MPC) and its respective 95%CI were calculated when the temporal trend was categorized as upward or downward, stratified by IM and LR populations. Time series analysis and visualization were conducted using R software, version 4.3.3. Spatial association analysis To elucidate the geographical distribution and prevalence of MDR-TB cases in Hangzhou, we stratified the number and proportion of MDR-TB cases among all TB patients by county. Thematic maps were visually generated using ArcGIS software, version 10.8, with data delineated by county to illustrate the spatial variation. Global Moran's I analysis was employed to assess global autocorrelation of the study area. Subsequently, the Getis-Ord Gi* technique was employed to identify the spatial association of MDR-TB, which discerned statistically significant spatial clusters of high-value "hot spots" and low-value "cold spots" based on the values of individual area and its neighbors. This analysis was performed using ArcGIS version 10.8, with each identified cluster mapped according to 99%, 95%, or 90% confidence intervals (CI). Spatial analysis was conducted in all TB, IMMDR, and LRMDR patients, respectively. Results Demographic and Clinical Characteristics of MDR-TB cases From 2014 to 2024, a total of 753 MDR-TB cases were included, comprising 540 (71.7%) LRMDR and 213 (28.3%) IMMDR. LRMDR were older (mean age: 49.90 ± 18.81 years) than IMMDR (35.50 ± 13.86 years, P < 0.001). IMMDR showed a higher proportion of retreatment cases (51.6%) compared to LRMDR (42.8%, P = 0.034). Ethnically, 95.3% of IMMDR were Han Chinese, versus 99.8% of LRMDR ( P < 0.001). The proportion of IMMDR among all MDR-TB cases decreased from 69.0% in 2014–2019 to 31.0% in 2020–2024 ( P = 0.004, Table 1 ). Table 1 Demographic and Clinical Characteristics of MDR-TB cases Variable Total (n = 753) LRMDR (n = 540) IMMDR(n = 213) P Sex 0.742 Male 529 (70.3) 377 (69.8) 152 (71.4) Female 224 (29.7) 163 (30.2) 61 (28.6) Age (mean ± sd) 45.83 ± 18.71 49.90 ± 18.81 35.50 ± 13.86 < 0.001 Ethic group < 0.001 Han 742 (98.5) 539 (99.8) 203 (95.3) non-Han 11 (1.5) 1 (0.2) 10 (4.7) Occupations 0.225 Labour worker and farmer 417 (55.4) 307 (56.9) 110 (51.6) Others 336 (44.6) 233 (43.1) 103 (48.4) TB history 0.034 New case 412 (54.7) 309 (57.2) 103 (48.4) Retreated case 341 (45.3) 231 (42.8) 110 (51.6) Year 0.004 2014–2019 456 (60.6) 309 (57.2) 147 (69.0) 2020–2024 297 (39.4) 231 (42.8) 66 (31.0) Temporal Trends of MDR-TB Temporal Trends of MDR-TB in Hangzhou The temporal distribution of MDR-TB cases in Hangzhou from 2014 to 2024 exhibited significant fluctuations, with an overall non-significant downward trend (Coefficient = -0.018, 95% CI: -0.038 to 0.002, P = 0.074) (Table 2 , Fig. 1 A). The moving average of monthly cases ranged from 0 to 20, with distinct peaks observed approximately every 2–3 years. In contrast, the proportion of MDR-TB among all TB cases showed a statistically significant downward trend (MPC = -0.645%, 95% CI: -0.934 to -0.357, P < 0.001) (Table 2 , Fig. 1 B). Table 2 Monthly percent changes of MDR-TB cases and proportions Category Coefficient (95%CI) P Trend MPC (95%CI) MDR-TB cases -0.018 (-0.038, 0.002) 0.074 Stationary - LRMDR -0.005 (-0.020, 0.010) 0.511 Stationary - IMMDR -0.013 (-0.022, -0.005) 0.002 Downward -0.823 (-1.337, -0.310) MDR-TB proportions (%) -0.020 (-0.028, -0.011) < 0.001 Downward -0.645 (-0.934, -0.357) LRMDR -0.014 (-0.022, -0.005) 0.003 Downward -0.525 (-0.870, -0.181) IMMDR -0.033 (-0.059, -0.007) 0.014 Downward -0.653 (-1.174, -0.132) Temporal Patterns in LRTB and IMTB Populations Among LRTB, LRMDR remained stable throughout the study period (Coefficient = -0.005, 95% CI: -0.020 to 0.010, P = 0.511) (Table 2 , Fig. 1 C). However, the proportion of LRMDR among LRTB decreased significantly (MPC = -0.525%, 95% CI: -0.870 to -0.181, P = 0.003) (Table 2 , Fig. 1 D). The temporal trend line for LRMDR cases showed less volatility compared to the overall population, suggesting more stable transmission dynamics among LR. For IMTB, both case numbers and proportions demonstrated statistically significant downward trends. IMTB cases among migrants decreased by 0.823% per month (95% CI: -1.337 to -0.310, P = 0.002) (Table 2 , Fig. 1 E), while the proportion decreased by 0.653% monthly (95% CI: -1.174 to -0.132, P = 0.014) (Table 2 , Fig. 1 F). The most pronounced decline was observed between 2019 and 2024. Spatial Heterogeneity of MDR-TB Distribution Global Spatial Autocorrelation Global Moran’s I analysis was conducted to assess spatial clustering across the entire study area (Table 3 ). The results indicated significant positive spatial autocorrelation for the absolute number of MDR-TB cases in the total population (Moran’s I = 0.15, P < 0.001), the LRMDR population (I = 0.08, P < 0.001), and the IMMDR population (I = 0.17, P < 0.001). This suggests that areas with high numbers of MDR-TB cases tend to be geographically clustered. Similarly, the proportion of MDR-TB showed significant positive spatial clustering for the total TB population (I = 0.19, P < 0.001) and the LRMDR population(I = 0.12, P < 0.001). However, the spatial clustering for the proportion of MDR-TB among the IMMDR population was not statistically significant (I = 0.03, P = 0.09). Table 3 Global Moran’s I index for cases and proportions of MDR-TB Category Z score P-value Global Moran I MDR-TB cases 5.91 < 0.001 0.15 LRMDR 3.21 < 0.001 0.08 IMMDR 7.10 < 0.001 0.17 MDR-TB proportions (%) 7.77 < 0.001 0.19 LRMDR 4.68 < 0.001 0.12 IMMDR 1.65 0.099 0.03 Local Spatial Cluster Analysis The subdistricts/Town -level spatial distributions of both the absolute case numbers (Fig. 2 A) and the proportions (Fig. 2 B) of MDR-TB throughout Hangzhou were delineated in Fig. 2 . Subgroup analysis revealed significant spatial heterogeneity in MDR-TB prevalence. High absolute case numbers (Fig. 2 A) were observed in Liangzhu Subdistrict (Yuhang District), Baiyang Subdistrict (Qiantang District), Fuchun Subdistrict (Fuyang District), and Qiandaohu Town (Chun'an County). High proportions (Fig. 2 B) were observed in Zuokou Township (Chun'an County), Hubin Subdistrict (Shangcheng District), and Wenhui Subdistrict (Gongshu District). For IMMDR, high case numbers (Fig. 2 C) were observed in Baiyang Subdistrict (Qiantang District), Liangzhu Subdistrict (Yuhang District), and Linping Subdistrict (Linping District). High proportions (Fig. 2 D) were observed in Dongqiao Town (Fuyang District), Datong Town (Jiande City), and Wanshi Town (Fuyang District). In contrast, LRMDR cases (Fig. 2 E) were observed in Qiandaohu Town (Chun'an County), Liangzhu Subdistrict (Yuhang District), and Weiping Town (Chun'an County). The spatial distribution of high proportions for LRMDR (Fig. 2 F) was comparatively more dispersed, concentrated primarily in Zuokou Township (Chun'an County), Hubin Subdistrict (Shangcheng District), the Economic and Technological Development Zone (Xiaoshan District), Lianhua Town (Jiande City), and Lijia Town (Jiande City). To illustrate local spatial clusters, a Getis-Ord Gi* analysis was performed to identify statistically significant hot spots (high-value clusters) and cold spots (low-value clusters), as depicted in Fig. 3 . Hot spots of MDR-TB cases were significantly clustered in the central metropolitan area (Gongshu, Shangcheng, Xihu, Binjiang, Yuhang, and Linping districts), no matter in IMMDR and LRMDR populations. Similarly, these regions also largely exhibited a pronounced clustering of hot spots with respect to MDR-TB proportions. In addition to the primary urban core, several statistically significant hotspots were identified in suburban and rural areas, particularly for specific subgroups. For the proportion of IMMDR among IMTB, hotspots were detected in subdistricts within Jiande, Fuyang, Lin’an, and Chun’an counties, such as Datong and Hangtou in Jiande, and Wanshi and Dongqiao in Fuyang. For the LRTB population, hotspots of case numbers were found in areas like Yongchang (Fuyang) and Lianhua(Chun’an). Hotspots for the proportion of LRMDR in LRTB were also identified in Chun’an county, specifically in Linqi and Jinfeng towns. Coldspots were less prevalent among the IMTB population compared to the LRTB population. Discussion This study delineates the spatiotemporal heterogeneity of MDR-TB among LR and IM in Hangzhou. Our findings reveal distinct demographic, temporal, and spatial patterns between the two populations, offering critical insights for targeted public health interventions. Demographically, IMMDR cases were younger and had a higher proportion of retreatment cases compared to LRMDR, aligning with observations from other cities[ 27 , 28 ]. This disparity likely stems from three interconnected factors: (1) The transient nature of migrant work disrupts treatment continuity, increasing the risk of treatment failure and retreatment-a phenomenon well-documented in studies of labor migrants in China’s Pearl River Delta[ 20 ]. (2) Barriers to cross-provincial medical insurance reimbursement, compounded by socioeconomic vulnerability, delay timely care-seeking among IM, exacerbating treatment irregularities[ 10 , 29 , 30 ]. (3) Many IM in Hangzhou originate from regions with suboptimal TB control infrastructure, where delayed diagnosis and inadequate treatment contribute to higher retreatment rates[ 31 ]. While genomic data would be needed to definitively disentangle reinfection from treatment failure, our findings underscore the need to address the unique challenges faced by migrant populations in sustaining TB care. Temporally, the overall MDR-TB case count in Hangzhou remained stable, but the proportion of MDR-TB among all TB cases declined significantly-a trend driven by improved detection and treatment efficacy. This reduction coincides with scaled-up interventions in Hangzhou, including expanded drug susceptibility testing (DST) eligibility, widespread adoption of GeneXpert MTB/RIF for rapid diagnosis, enhanced insurance coverage for MDR-TB treatment, and increased investment in specialized TB facilities[ 32 ]. These measures have likely reduced misdiagnosis of primary drug resistance and curbed transmission through earlier treatment initiation. Notably, Hangzhou’s MDR-TB treatment success rate has risen from 66.2% (2011) to 92.7% (2020)[ 3 , 33 ], mirroring global evidence that high treatment success directly reduces community transmission. Stratified analyses revealed divergent temporal trajectories for IM and LR. Among IM, both MDR-TB case numbers and their proportion relative to all migrant TB cases declined significantly, with an accelerated drop post-2019. This inflection point coincides with COVID-19-related mobility restrictions, which drastically reduced interprovincial migration-a pattern observed in other infectious disease dynamics during the pandemic[ 12 , 34 , 35 ]. Disentangling whether this decline reflects sustained epidemiological control or reduced migrant inflow requires long-term post-pandemic surveillance, as seen in Guinea’s post-restriction TB trend analyses[ 36 ]. For LR, MDR-TB case numbers remained stable, but their proportion relative to all local TB cases decreased, suggesting more effective detection of drug-susceptible TB among residents, possibly due to better healthcare access. These heterogeneous trends highlight the influence of population-specific factors, including age structure[ 37 ], healthcare utilization[ 7 ], and living conditions[ 38 ], economic status[ 39 ], external policies[ 36 ], and urbanization dynamics[ 11 ], on MDR-TB dynamics. Spatially, significant positive autocorrelation in MDR-TB distribution confirms that transmission is driven by localized hotspots rather than random spread-consistent with findings in urban settings globally, including Portugal[ 40 ] and South Africa[ 41 ]. Central urban districts emerged as shared hotspots for both LRMDR and IMMDR, reflecting high population density, social mixing, and occupational clustering-key drivers of TB transmission in urban cores[ 20 , 36 , 42 ]. Notably, subgroup analyses revealed distinct spatial patterns: IMMDR hotspots clustered in suburban areas with concentrated migrant labor, echoing spatial associations between occupational mobility and TB transmission in China’s manufacturing hubs[ 10 ] and South Africa’s mining regions[ 41 ]. In contrast, LRMDR hotspots were dispersed in rural areas, potentially linked to aging populations and healthcare access barriers, as observed in Sichuan’s rural TB studies[ 7 ]. The paucity of IMMDR in economically underdeveloped regions like Chun’an and Jiande further supports the role of employment opportunities in shaping migrant-driven transmission networks. These findings reinforce the value of spatially targeted interventions. Geographically focused screening in migrant-dense industrial zones and urban cores could mirror the 2-fold improvement in case detection seen in Uganda’s targeted campaigns, while tailored strategies for rural LR-such as mobile clinics and aging-population outreach-may address reactivation-driven clusters. Limitations This study has several limitations. First, the absence of whole-genome sequencing data impedes the differentiation between IMMDR and LRMDR and the quantification of transmission chains between IM and LR populations, a limitation underscored in global tuberculosis genomic studies[ 43 , 44 ]. Second, the post-2019 decline in IMMDR may reflect reduced migration during COVID-19 rather than intrinsic intervention efficacy, long-term surveillance post-pandemic is needed to clarify this[ 36 ]. Third, spatial analyses at the subdistrict level may mask finer-scale heterogeneities that are critical for hyper-localized interventions. Finally, unmeasured confounding factors-such as housing conditions or occupational exposure-could further explain observed spatiotemporal patterns[ 29 , 38 ]. Future research should integrate genomic and epidemiological data to trace transmission dynamics, and evaluate the sustainability of post-pandemic trends. Addressing these gaps will strengthen precision public health strategies for MDR-TB control in migrant-intensive urban settings. Declarations Ethics approval and consent to participate This study was approved by the Institutional Review Board of Hangzhou Center for Disease Control and Prevention (Hangzhou Health Supervision Institution) and was conducted in accordance with the Declaration of Helsinki and the Ethical Review Measures for Biomedical Research Involving Humans of China. Given the retrospective analysis of de-identified surveillance data from the national Tuberculosis Information Management System (TBIMS), the ethics committee specifically waived the requirement for obtaining individual informed consent to participate. Data access was granted by the Chinese Center for Disease Control and Prevention in accordance with national infectious disease reporting regulations. Consent for publication Not Applicable. Availability of data and materials According to Chinese law, the public health data are not publicly available, but are available on reasonable requests from the corresponding author. Competing interests All authors declare that they have no competing interests. Funding This study was supported by Zhejiang Science and Technology Plan for Disease Prevention and Control (Project No. 2025JK030), Zhejiang General Research Project on Medical Health and Science Technology Plan (No.2021KY951), Hangzhou Medical Health Science and The Public Welfare Technology Research Program in Zhejiang Province(No. LGF21H190002). Authors’ contributions Qingchun Li : Conceptualization, Methodology, Formal analysis, Writing-original draft and Funding acquisition. Xuexin Bai : Formal analysis. Zike Cheng : Methodology. Qinglin Cheng : Project administration. Yifei Wu : Funding acquisition. Liyun Ai : Data curation. Yinyan Huang : Data curation, Funding acquisition. Qingjun Jia : Data curation. Nan Jiang : Formal analysis. Zijian Fang : Data curation. Xu Song : Data curation. Ruoqi Dai : Visualization, Writing-review and editing, Supervision, Corresponding author. Hanyan Yuan : Writing-review and editing, Supervision, Corresponding author. 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Antimicrob Resist Infect Control. 2018;7:61. Li Q, Shi CX, Lu M, Wu L, Wu Y, Wang M, et al. Treatment outcomes of multidrug-resistant tuberculosis in Hangzhou, China, 2011 to 2015. Medicine (Baltimore). 2020;99(30):e21296. Caren GJ, Iskandar D, Pitaloka DAE, Abdulah R, Suwantika AA. COVID-19 Pandemic Disruption on the Management of Tuberculosis Treatment in Indonesia. Journal of Multidisciplinary Healthcare. 2022;Volume 15:175-83. Nalunjogi J, Mucching-Toscano S, Sibomana JP, Centis R, D'Ambrosio L, Alffenaar J-W, et al. Impact of COVID-19 on diagnosis of tuberculosis, multidrug-resistant tuberculosis, and on mortality in 11 countries in Europe, Northern America, and Australia. A Global Tuberculosis Network study. International Journal of Infectious Diseases. 2023;130:S25-S9. Nanque AR, Ramos ACV, Moura HSD, Berra TZ, Tavares RBV, Monroe AA, et al. Spatial and temporal analysis of tuberculosis incidence in Guinea-Bissau, 2018 to 2020. Revista Brasileira de Enfermagem. 2023;76(4). Wang L, Xu C, Hu M, Qiao J, Chen W, Li T, et al. Spatio-temporal variation in tuberculosis incidence and risk factors for the disease in a region of unbalanced socio-economic development. BMC Public Health. 2021;21(1). Lin H, Zhang R, Wu Z, Li M, Wu J, Shen X, et al. Assessing the spatial heterogeneity of tuberculosis in a population with internal migration in China: a retrospective population-based study. Frontiers in Public Health. 2023;11. Zhdanova E, Goncharova O, Davtyan H, Alaverdyan S, Sargsyan A, Harries AD, et al. 9-12 months short treatment for patients with MDR-TB increases treatment success in Kyrgyzstan. J Infect Dev Ctries. 2021;15(9.1):66S-74S. Areias C, Briz T, Nunes C. Pulmonary tuberculosis space-time clustering and spatial variation in temporal trends in Portugal, 2000-2010: an updated analysis. Epidemiology and infection. 2015;143(15):3211-9. Sy KTL, Leavitt SV, de Vos M, Dolby T, Bor J, Horsburgh CR, Jr., et al. Spatial heterogeneity of extensively drug resistant-tuberculosis in Western Cape Province, South Africa. Scientific reports. 2022;12(1):10844. Dangisso MH, Datiko DG, Lindtjorn B. Identifying geographical heterogeneity of pulmonary tuberculosis in southern Ethiopia: a method to identify clustering for targeted interventions. Glob Health Action. 2020;13(1):1785737. Li M, Zhang Y, Wu Z, Jiang Y, Sun R, Yang J, et al. Transmission of fluoroquinolones resistance among multidrug-resistant tuberculosis in Shanghai, China: a retrospective population-based genomic epidemiology study. Emerging microbes & infections. 2024;13(1). Li M, Quan Z, Xu P, Takiff H, Gao Q. Internal migrants as drivers of long-distance cross-regional transmission of tuberculosis in China. Clinical Microbiology and Infection. 2025;31(1):71-7. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 04 Jan, 2026 Reviews received at journal 16 Sep, 2025 Reviewers agreed at journal 10 Sep, 2025 Reviews received at journal 08 Sep, 2025 Reviewers agreed at journal 31 Aug, 2025 Reviewers invited by journal 29 Aug, 2025 Editor assigned by journal 09 Aug, 2025 Editor invited by journal 08 Aug, 2025 Submission checks completed at journal 06 Aug, 2025 First submitted to journal 06 Aug, 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7266883","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":508460469,"identity":"ce3c53e1-76e9-4e5c-b1e4-a5319f63ea84","order_by":0,"name":"Qingchun Li","email":"","orcid":"","institution":"Hangzhou Center for Disease Control and Prevention(Hangzhou Health Supervision Institution)","correspondingAuthor":false,"prefix":"","firstName":"Qingchun","middleName":"","lastName":"Li","suffix":""},{"id":508460470,"identity":"616583b2-3cef-48a9-b233-3577b97e3c42","order_by":1,"name":"Xuexin Bai","email":"","orcid":"","institution":"Hangzhou Center for Disease Control and Prevention(Hangzhou Health Supervision Institution)","correspondingAuthor":false,"prefix":"","firstName":"Xuexin","middleName":"","lastName":"Bai","suffix":""},{"id":508460471,"identity":"ac7989ab-4e8f-437c-81a0-92a585428ac3","order_by":2,"name":"Zike Cheng","email":"","orcid":"","institution":"Binzhou Center for Disease Control and Prevention","correspondingAuthor":false,"prefix":"","firstName":"Zike","middleName":"","lastName":"Cheng","suffix":""},{"id":508460472,"identity":"98e35186-d0a7-41d6-8d1b-56032cfed8f1","order_by":3,"name":"Qinglin Cheng","email":"","orcid":"","institution":"Hangzhou Center for Disease Control and Prevention(Hangzhou Health Supervision Institution)","correspondingAuthor":false,"prefix":"","firstName":"Qinglin","middleName":"","lastName":"Cheng","suffix":""},{"id":508460473,"identity":"9104453d-e3f6-4ebf-8e28-1583c7356032","order_by":4,"name":"Yifei Wu","email":"","orcid":"","institution":"Hangzhou Center for Disease Control and Prevention(Hangzhou Health Supervision Institution)","correspondingAuthor":false,"prefix":"","firstName":"Yifei","middleName":"","lastName":"Wu","suffix":""},{"id":508460474,"identity":"58a75a2d-9ef1-48ad-a77b-ce7d3e46621d","order_by":5,"name":"Liyun Ai","email":"","orcid":"","institution":"Hangzhou Center for Disease Control and Prevention(Hangzhou Health Supervision Institution)","correspondingAuthor":false,"prefix":"","firstName":"Liyun","middleName":"","lastName":"Ai","suffix":""},{"id":508460475,"identity":"aad20faf-adf0-41f8-82e4-221b4dfde89a","order_by":6,"name":"Yinyan Huang","email":"","orcid":"","institution":"Hangzhou Center for Disease Control and Prevention(Hangzhou Health Supervision Institution)","correspondingAuthor":false,"prefix":"","firstName":"Yinyan","middleName":"","lastName":"Huang","suffix":""},{"id":508460476,"identity":"f9bbedd4-a692-46e6-91aa-4e01a2887b73","order_by":7,"name":"Qingjun Jia","email":"","orcid":"","institution":"Hangzhou Center for Disease Control and Prevention(Hangzhou Health Supervision Institution)","correspondingAuthor":false,"prefix":"","firstName":"Qingjun","middleName":"","lastName":"Jia","suffix":""},{"id":508460477,"identity":"dafb8bdf-3d5c-4238-bc2b-7430675a82ae","order_by":8,"name":"Nan jiang","email":"","orcid":"","institution":"Hangzhou Center for Disease Control and Prevention(Hangzhou Health Supervision Institution)","correspondingAuthor":false,"prefix":"","firstName":"Nan","middleName":"","lastName":"jiang","suffix":""},{"id":508460478,"identity":"0d118315-f32a-4b33-a861-1508a2b8a4ea","order_by":9,"name":"Zijian Fang","email":"","orcid":"","institution":"Hangzhou Center for Disease Control and Prevention(Hangzhou Health Supervision Institution)","correspondingAuthor":false,"prefix":"","firstName":"Zijian","middleName":"","lastName":"Fang","suffix":""},{"id":508460479,"identity":"9c072b97-c1d8-4419-bc38-f2b15dcb119f","order_by":10,"name":"Xu Song","email":"","orcid":"","institution":"Hangzhou Center for Disease Control and Prevention(Hangzhou Health Supervision Institution)","correspondingAuthor":false,"prefix":"","firstName":"Xu","middleName":"","lastName":"Song","suffix":""},{"id":508460480,"identity":"2c824c4e-71dd-4e6c-80bb-71500d2fa16a","order_by":11,"name":"Ruoqi Dai","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuElEQVRIiWNgGAWjYHACA4aEigNAOoEkLWdI1sLYRooW8/bkjR8ezrtjz9+e/IDhR8U2Bv7ZDfi1yJx5ViyRuO0Zs8SZZwaMPWduM0jcOYBfi4REjgFQy2E2A4kcBmbGttsMBhIEXAjUYvwjcc5hHpK0mEkkNhyWIEELz7Myi4Rjhw1AfjkI9AuPxA1CWtiTN9/8UXMYFGIPH/youC3HP4OAFpToOADEPITUM5AU6aNgFIyCUTBCAQDnckLRDBmhkgAAAABJRU5ErkJggg==","orcid":"","institution":"Hangzhou Center for Disease Control and Prevention(Hangzhou Health Supervision Institution)","correspondingAuthor":true,"prefix":"","firstName":"Ruoqi","middleName":"","lastName":"Dai","suffix":""},{"id":508460481,"identity":"cfeb5eb4-ed58-413d-b1b1-de6486585dd1","order_by":12,"name":"Hanyan Yuan","email":"","orcid":"","institution":"Hangzhou Gongshu Center for Disease Control and Prevention(Hangzhou Gongshu Health Supervision Institution)","correspondingAuthor":false,"prefix":"","firstName":"Hanyan","middleName":"","lastName":"Yuan","suffix":""}],"badges":[],"createdAt":"2025-08-01 03:23:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7266883/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7266883/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90882184,"identity":"1618535c-7520-4cbe-9d08-b704c04e3985","added_by":"auto","created_at":"2025-09-09 09:52:17","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":190974,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTemporal trends of MDR-PTB cases and proportions in Hangzhou, 2014–2024\u003c/strong\u003e\u003cbr\u003e\n \u003cem\u003e(A)\u003c/em\u003e Monthly counts of total MDR-TB cases with STL decomposition trend line.\u003cbr\u003e\n \u003cem\u003e(B)\u003c/em\u003e Monthly proportion of MDR-TB among all TB cases with Prais-Winsten regression trend.\u003cbr\u003e\n \u003cem\u003e(C–F)\u003c/em\u003e Stratified trends for LRMDR and IMMDR: \u003cem\u003e(C)\u003c/em\u003e case counts in LR, \u003cem\u003e(D)\u003c/em\u003e proportion in LR, \u003cem\u003e(E)\u003c/em\u003e case counts in IM, \u003cem\u003e(F)\u003c/em\u003e proportion in IM. Significant downward trends identified for proportions (all \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05) and IMMDR cases (\u003cem\u003eP\u003c/em\u003e=0.002). STL: Seasonal-Trend decomposition using Loess.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7266883/v1/3a9abd85320af03ea12ef953.png"},{"id":90881489,"identity":"b06d594b-07fa-4730-8844-baae98b37f89","added_by":"auto","created_at":"2025-09-09 09:44:17","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":294352,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSubdistricts/Town -level spatial distribution of MDR-TB cases and proportions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e(A)\u003c/em\u003e Absolute case counts of total MDR-TB. \u003cem\u003e(B)\u003c/em\u003e Proportions (%) of MDR-TB among all TB cases. \u003cem\u003e(C)\u003c/em\u003e IMMDR case counts. \u003cem\u003e(D)\u003c/em\u003e Proportions of IMMDR among migrant TB cases. \u003cem\u003e(E)\u003c/em\u003e LRMDR case counts. \u003cem\u003e(F)\u003c/em\u003e Proportions of LRMDR among local TB cases. Data visualized using ArcGIS 10.8; administrative boundaries sourced from National Geomatics Center of China.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7266883/v1/f775cc8df30b44e458a7196e.png"},{"id":90879566,"identity":"bce55dc0-372d-490f-adf3-620da733c4d6","added_by":"auto","created_at":"2025-09-09 09:36:17","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":319707,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHot spots and cold spots of cases and proportions of MDR-TB\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Total MDR-PTB case clusters; (B) Total MDR-PTB proportion clusters; (C) IMMDR case clusters; (D) IMMDR proportion clusters (among IMTB); (E) LRMDR case clusters; (F) LRMDR proportion clusters (among LRTB); Getis-Ord Gi* statistics identified significant (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05); spatial clusters: \u003cstrong\u003eRed\u003c/strong\u003e: Hotspots (high-value clusters, Z-score \u0026gt;1.96); \u003cstrong\u003eBlue\u003c/strong\u003e: Coldspots (low-value clusters, Z-score \u0026lt;-1.96);Analysis performed at 95% confidence (ArcGIS 10.8).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7266883/v1/cd0206904bd400eadaaba920.png"},{"id":90884309,"identity":"209d6890-e33a-4070-b298-b7e5f3979510","added_by":"auto","created_at":"2025-09-09 10:00:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1661762,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7266883/v1/d943c21c-6564-403b-8b06-76ad64bb9e0e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Spatiotemporal Heterogeneity of Multidrug-Resistant Pulmonary Tuberculosis Among Local Residents and Internal Migrants in Hangzhou, China, 2014-2024","fulltext":[{"header":"Background","content":"\u003cp\u003eMultidrug-resistant pulmonary tuberculosis (MDR-TB), defined as resistance to at least rifampicin and isoniazid, remains a critical global health threat. According to WHO\u0026rsquo; latest report[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], an estimated 362,700 incident MDR-TB cases occurred worldwide in 2023, with China bearing the second-highest burden after India. Global disease burden of MDR-TB increased from 1990 to 2021 and is predicted to grow till 2050[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In China, MDR-TB accounts for 5.7% of new TB cases and 26% of previously treated cases, reflecting significant challenges in TB control programs[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The high mortality and prolonged treatment regimens associated with MDR-TB underscore the urgent need for targeted interventions to curb transmission and reduce disease burden[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe temporal dynamics of MDR-TB exhibit complex patterns that are critical for optimizing control strategies[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Studies in China have demonstrated significant temporal fluctuations in MDR-TB incidence, with distinct peaks occurring every 2\u0026ndash;3 years and seasonal variations observed in notification rates[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Understanding these temporal patterns is essential for: (1) identifying periods of elevated transmission risk, (2) optimizing resource allocation for case detection and treatment, and (3) evaluating the long-term impact of control interventions.\u003c/p\u003e\u003cp\u003eGeographic precision in TB control is increasingly recognized as pivotal for optimizing resource allocation. Evidence indicates that spatially targeted interventions can enhance case detection rates by approximately twofold compared to untargeted approaches, significantly reducing community transmission[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Understanding spatiotemporal heterogeneity-particularly the clustering of MDR-TB in specific populations, time periods, and locations-enables health systems to deploy rapid diagnostics, contact tracing, and treatment access where most impactful[\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. While spatial analyses of drug-susceptible TB are well-documented[\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], data on the spatiotemporal dynamics of MDR-TB remain scarce, especially in high-migration urban settings[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. This gap impedes the design of precision public health strategies for MDR-TB control.\u003c/p\u003e\u003cp\u003eHangzhou, the capital of Zhejiang Province, exemplifies the urbanization-driven epidemiological shifts in TB across China. Between 2012 and 2023, its population surged from 7.0\u0026nbsp;million to 12.37\u0026nbsp;million, driven predominantly by internal migration (IM)[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. IM often concentrated in low-wage occupations and overcrowded living conditions, face heightened MDR-PTB risks due to limited access to quality healthcare-a disparity exacerbated by poverty and unequal resource distribution[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Studies indicate that migrants contribute disproportionately to TB transmission and exhibit higher MDR-TB rates compared to local residents[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. In Hangzhou, despite achieving a relatively high MDR-TB treatment success rate and expanded diagnostic coverage[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], the absence of an effective MDR-TB vaccine [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]and limited access to novel regimens[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] and drugs[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] underline the importance of preventing imported MDR-TB. The convergence of rapid urbanization, high migrant influx, and sustained MDR-TB burden necessitates granular insights into the spatiotemporal distribution of cases among local and migrant populations. Such data are vital to inform spatially adaptive interventions, such as hotspot-targeted screening, mobile clinics for migrants, and dynamic resource redistribution during seasonal or spatial case surges[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. This study aims to address this gap, leveraging spatiotemporal epidemiology to inform precision public health interventions.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy Design and Data Sources\u003c/h2\u003e\u003cp\u003eHangzhou (30\u0026deg;16'N, 120\u0026deg;12'E), the capital of Zhejiang Province in eastern China, comprises 11 urban districts, one county-level city, and two rural counties. According to 2023 municipal census data, its resident population exceeds 12.5\u0026nbsp;million. Tuberculosis care is delivered through 13 designated facilities, including two tertiary centers specializing in drug-resistant TB management.\u003c/p\u003e\u003cp\u003eThis retrospective cohort analysis utilized de-identified surveillance records from China\u0026rsquo;s Tuberculosis Information Management System (TBIMS)[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], a nationally standardized electronic platform operated by the Chinese Center for Disease Control and Prevention. Under mandatory infectious disease reporting protocols, all laboratory-confirmed MDR-TB cases are uniformly documented in TBIMS, which archives demographic profiles, clinical diagnostics, therapeutic interventions, and epidemiological monitoring data. We extracted records of MDR-PTB cases diagnosed between January 1, 2014, and December 31, 2024, across all designated TB facilities in Hangzhou.\u003c/p\u003e\u003cp\u003eEligible cases met three criteria: 1) confirmed diagnosis of MDR-TB; 2) current residential address within Hangzhou; 3) age\u0026thinsp;\u0026ge;\u0026thinsp;16 years. Cases were excluded if they resided outside Hangzhou or had incomplete clinical data.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eDefinition of Research Subjects\u003c/h3\u003e\n\u003cp\u003eInternal migrant (IM) TB cases were categorized as individuals whose household registration (\u003cem\u003ehukou\u003c/em\u003e) resided outside Hangzhou, while their current residential address was within the city. Corresponding MDR-TB cases were designated \u003cb\u003eIMMDR\u003c/b\u003e.\u003c/p\u003e\u003cp\u003eLocal resident (LR) TB cases were defined as patients with both \u003cem\u003ehukou\u003c/em\u003e registration and current residence within Hangzhou. Their MDR-TB cases were labeled \u003cb\u003eLRMDR\u003c/b\u003e.\u003c/p\u003e\u003cp\u003eLocal residence (LR) TB cases (LRMDR) were defined as patients diagnosed with TB whose both current residential address and household registration (hukou) were registered within Hangzhou. MDR-TB cases within these patients are denoted as LRMDR.\u003c/p\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\u003ch2\u003eBaseline data analysis\u003c/h2\u003e\u003cp\u003eFrequencies and proportions were calculated for categorical variables, while Means and Standard deviation were calculated for continuous variables. Chi-square tests were employed to compare the differences in various categorical variables between IMMDR and LRMDR, while t-tests were employed for continuous variables. All analyses were performed using R software, version 4.3.3. A \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\u003cp\u003e\u003cb\u003eTime series analysis.\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo ascertain the temporal variation and trend of MDR throughout the study period, seasonal-trend decomposition by loess (STL), a method rooted in locally weighted regression, was employed. By decomposing a time series, the STL procedure filters out stochastic noise and removes seasonal effects, thereby isolating the data's underlying long-term trend. Monthly proportions and case counts were summarized and presented alongside the temporal trend identified by STL. Prais-Winsten autoregression was subsequently employed to categorize the temporal trend of MDR-TB as either upward, downward, or stationary during the study period. Monthly percent change (MPC) and its respective 95%CI were calculated when the temporal trend was categorized as upward or downward, stratified by IM and LR populations. Time series analysis and visualization were conducted using R software, version 4.3.3.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\n\u003ch3\u003eSpatial association analysis\u003c/h3\u003e\n\u003cp\u003eTo elucidate the geographical distribution and prevalence of MDR-TB cases in Hangzhou, we stratified the number and proportion of MDR-TB cases among all TB patients by county. Thematic maps were visually generated using ArcGIS software, version 10.8, with data delineated by county to illustrate the spatial variation. Global Moran's I analysis was employed to assess global autocorrelation of the study area. Subsequently, the Getis-Ord Gi* technique was employed to identify the spatial association of MDR-TB, which discerned statistically significant spatial clusters of high-value \"hot spots\" and low-value \"cold spots\" based on the values of individual area and its neighbors. This analysis was performed using ArcGIS version 10.8, with each identified cluster mapped according to 99%, 95%, or 90% confidence intervals (CI). Spatial analysis was conducted in all TB, IMMDR, and LRMDR patients, respectively.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003eDemographic and Clinical Characteristics of MDR-TB cases\u003c/h2\u003e\n \u003cp\u003eFrom 2014 to 2024, a total of 753 MDR-TB cases were included, comprising 540 (71.7%) LRMDR and 213 (28.3%) IMMDR. LRMDR were older (mean age: 49.90\u0026thinsp;\u0026plusmn;\u0026thinsp;18.81 years) than IMMDR (35.50\u0026thinsp;\u0026plusmn;\u0026thinsp;13.86 years, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). IMMDR showed a higher proportion of retreatment cases (51.6%) compared to LRMDR (42.8%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.034). Ethnically, 95.3% of IMMDR were Han Chinese, versus 99.8% of LRMDR (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The proportion of IMMDR among all MDR-TB cases decreased from 69.0% in 2014\u0026ndash;2019 to 31.0% in 2020\u0026ndash;2024 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004, Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDemographic and Clinical Characteristics of MDR-TB cases\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal (n\u0026thinsp;=\u0026thinsp;753)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLRMDR (n\u0026thinsp;=\u0026thinsp;540)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIMMDR(n\u0026thinsp;=\u0026thinsp;213)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\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\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.742\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e529 (70.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e377 (69.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e152 (71.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e224 (29.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e163 (30.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e61 (28.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;sd)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e45.83\u0026thinsp;\u0026plusmn;\u0026thinsp;18.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e49.90\u0026thinsp;\u0026plusmn;\u0026thinsp;18.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35.50\u0026thinsp;\u0026plusmn;\u0026thinsp;13.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEthic group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e742 (98.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e539 (99.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e203 (95.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003enon-Han\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11 (1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1 (0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10 (4.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOccupations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.225\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLabour worker and farmer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e417 (55.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e307 (56.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e110 (51.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e336 (44.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e233 (43.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e103 (48.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTB history\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNew case\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e412 (54.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e309 (57.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e103 (48.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRetreated case\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e341 (45.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e231 (42.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e110 (51.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2014\u0026ndash;2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e456 (60.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e309 (57.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e147 (69.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2020\u0026ndash;2024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e297 (39.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e231 (42.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e66 (31.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003ch3\u003eTemporal Trends of MDR-TB\u003c/h3\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eTemporal Trends of MDR-TB in Hangzhou\u003c/h2\u003e\n \u003cp\u003eThe temporal distribution of MDR-TB cases in Hangzhou from 2014 to 2024 exhibited significant fluctuations, with an overall non-significant downward trend (Coefficient = -0.018, 95% CI: -0.038 to 0.002, P\u0026thinsp;=\u0026thinsp;0.074) (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eA). The moving average of monthly cases ranged from 0 to 20, with distinct peaks observed approximately every 2\u0026ndash;3 years. In contrast, the proportion of MDR-TB among all TB cases showed a statistically significant downward trend (MPC = -0.645%, 95% CI: -0.934 to -0.357, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eB).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eMonthly percent changes of MDR-TB cases and proportions\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCategory\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCoefficient (95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTrend\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMPC (95%CI)\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\u003e\u003cstrong\u003eMDR-TB cases\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.018 (-0.038, 0.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStationary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLRMDR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.005 (-0.020, 0.010)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.511\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStationary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIMMDR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.013 (-0.022, -0.005)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDownward\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.823 (-1.337, -0.310)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMDR-TB proportions (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.020 (-0.028, -0.011)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDownward\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.645 (-0.934, -0.357)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLRMDR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.014 (-0.022, -0.005)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDownward\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.525 (-0.870, -0.181)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIMMDR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.033 (-0.059, -0.007)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDownward\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.653 (-1.174, -0.132)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eTemporal Patterns in LRTB and IMTB Populations\u003c/h2\u003e\n \u003cp\u003eAmong LRTB, LRMDR remained stable throughout the study period (Coefficient = -0.005, 95% CI: -0.020 to 0.010, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.511) (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eC). However, the proportion of LRMDR among LRTB decreased significantly (MPC = -0.525%, 95% CI: -0.870 to -0.181, P\u0026thinsp;=\u0026thinsp;0.003) (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eD). The temporal trend line for LRMDR cases showed less volatility compared to the overall population, suggesting more stable transmission dynamics among LR.\u003c/p\u003e\n \u003cp\u003eFor IMTB, both case numbers and proportions demonstrated statistically significant downward trends. IMTB cases among migrants decreased by 0.823% per month (95% CI: -1.337 to -0.310, P\u0026thinsp;=\u0026thinsp;0.002) (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eE), while the proportion decreased by 0.653% monthly (95% CI: -1.174 to -0.132, P\u0026thinsp;=\u0026thinsp;0.014) (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eF). The most pronounced decline was observed between 2019 and 2024.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003eSpatial Heterogeneity of MDR-TB Distribution\u003c/h2\u003e\n \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e\n \u003ch2\u003eGlobal Spatial Autocorrelation\u003c/h2\u003e\n \u003cp\u003eGlobal Moran\u0026rsquo;s I analysis was conducted to assess spatial clustering across the entire study area (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). The results indicated significant positive spatial autocorrelation for the absolute number of MDR-TB cases in the total population (Moran\u0026rsquo;s I\u0026thinsp;=\u0026thinsp;0.15, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), the LRMDR population (I\u0026thinsp;=\u0026thinsp;0.08, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and the IMMDR population (I\u0026thinsp;=\u0026thinsp;0.17, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This suggests that areas with high numbers of MDR-TB cases tend to be geographically clustered. Similarly, the proportion of MDR-TB showed significant positive spatial clustering for the total TB population (I\u0026thinsp;=\u0026thinsp;0.19, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and the LRMDR population(I\u0026thinsp;=\u0026thinsp;0.12, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). However, the spatial clustering for the proportion of MDR-TB among the IMMDR population was not statistically significant (I\u0026thinsp;=\u0026thinsp;0.03,\u0026nbsp;\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.09).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eGlobal Moran\u0026rsquo;s I index for cases and proportions of MDR-TB\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCategory\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eZ score\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGlobal Moran I\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\u003e\u003cstrong\u003eMDR-TB cases\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLRMDR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIMMDR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMDR-TB proportions (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLRMDR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIMMDR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.099\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003eLocal Spatial Cluster Analysis\u003c/h2\u003e\n \u003cp\u003eThe subdistricts/Town -level spatial distributions of both the absolute case numbers (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA) and the proportions (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eB) of MDR-TB throughout Hangzhou were delineated in Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. Subgroup analysis revealed significant spatial heterogeneity in MDR-TB prevalence. High absolute case numbers (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA) were observed in Liangzhu Subdistrict (Yuhang District), Baiyang Subdistrict (Qiantang District), Fuchun Subdistrict (Fuyang District), and Qiandaohu Town (Chun\u0026apos;an County). High proportions (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eB) were observed in Zuokou Township (Chun\u0026apos;an County), Hubin Subdistrict (Shangcheng District), and Wenhui Subdistrict (Gongshu District).\u003c/p\u003e\n \u003cp\u003eFor IMMDR, high case numbers (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eC) were observed in Baiyang Subdistrict (Qiantang District), Liangzhu Subdistrict (Yuhang District), and Linping Subdistrict (Linping District). High proportions (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eD) were observed in Dongqiao Town (Fuyang District), Datong Town (Jiande City), and Wanshi Town (Fuyang District).\u003c/p\u003e\n \u003cp\u003eIn contrast, LRMDR cases (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eE) were observed in Qiandaohu Town (Chun\u0026apos;an County), Liangzhu Subdistrict (Yuhang District), and Weiping Town (Chun\u0026apos;an County). The spatial distribution of high proportions for LRMDR (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eF) was comparatively more dispersed, concentrated primarily in Zuokou Township (Chun\u0026apos;an County), Hubin Subdistrict (Shangcheng District), the Economic and Technological Development Zone (Xiaoshan District), Lianhua Town (Jiande City), and Lijia Town (Jiande City).\u003c/p\u003e\n \u003cp\u003eTo illustrate local spatial clusters, a Getis-Ord Gi* analysis was performed to identify statistically significant hot spots (high-value clusters) and cold spots (low-value clusters), as depicted in Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e. Hot spots of MDR-TB cases were significantly clustered in the central metropolitan area (Gongshu, Shangcheng, Xihu, Binjiang, Yuhang, and Linping districts), no matter in IMMDR and LRMDR populations. Similarly, these regions also largely exhibited a pronounced clustering of hot spots with respect to MDR-TB proportions. In addition to the primary urban core, several statistically significant hotspots were identified in suburban and rural areas, particularly for specific subgroups. For the proportion of IMMDR among IMTB, hotspots were detected in subdistricts within Jiande, Fuyang, Lin\u0026rsquo;an, and Chun\u0026rsquo;an counties, such as Datong and Hangtou in Jiande, and Wanshi and Dongqiao in Fuyang. For the LRTB population, hotspots of case numbers were found in areas like Yongchang (Fuyang) and Lianhua(Chun\u0026rsquo;an). Hotspots for the proportion of LRMDR in LRTB were also identified in Chun\u0026rsquo;an county, specifically in Linqi and Jinfeng towns. Coldspots were less prevalent among the IMTB population compared to the LRTB population.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study delineates the spatiotemporal heterogeneity of MDR-TB among LR and IM in Hangzhou. Our findings reveal distinct demographic, temporal, and spatial patterns between the two populations, offering critical insights for targeted public health interventions.\u003c/p\u003e\u003cp\u003eDemographically, IMMDR cases were younger and had a higher proportion of retreatment cases compared to LRMDR, aligning with observations from other cities[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. This disparity likely stems from three interconnected factors: (1) The transient nature of migrant work disrupts treatment continuity, increasing the risk of treatment failure and retreatment-a phenomenon well-documented in studies of labor migrants in China\u0026rsquo;s Pearl River Delta[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. (2) Barriers to cross-provincial medical insurance reimbursement, compounded by socioeconomic vulnerability, delay timely care-seeking among IM, exacerbating treatment irregularities[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. (3) Many IM in Hangzhou originate from regions with suboptimal TB control infrastructure, where delayed diagnosis and inadequate treatment contribute to higher retreatment rates[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. While genomic data would be needed to definitively disentangle reinfection from treatment failure, our findings underscore the need to address the unique challenges faced by migrant populations in sustaining TB care.\u003c/p\u003e\u003cp\u003eTemporally, the overall MDR-TB case count in Hangzhou remained stable, but the proportion of MDR-TB among all TB cases declined significantly-a trend driven by improved detection and treatment efficacy. This reduction coincides with scaled-up interventions in Hangzhou, including expanded drug susceptibility testing (DST) eligibility, widespread adoption of GeneXpert MTB/RIF for rapid diagnosis, enhanced insurance coverage for MDR-TB treatment, and increased investment in specialized TB facilities[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. These measures have likely reduced misdiagnosis of primary drug resistance and curbed transmission through earlier treatment initiation. Notably, Hangzhou\u0026rsquo;s MDR-TB treatment success rate has risen from 66.2% (2011) to 92.7% (2020)[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], mirroring global evidence that high treatment success directly reduces community transmission.\u003c/p\u003e\u003cp\u003eStratified analyses revealed divergent temporal trajectories for IM and LR. Among IM, both MDR-TB case numbers and their proportion relative to all migrant TB cases declined significantly, with an accelerated drop post-2019. This inflection point coincides with COVID-19-related mobility restrictions, which drastically reduced interprovincial migration-a pattern observed in other infectious disease dynamics during the pandemic[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Disentangling whether this decline reflects sustained epidemiological control or reduced migrant inflow requires long-term post-pandemic surveillance, as seen in Guinea\u0026rsquo;s post-restriction TB trend analyses[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. For LR, MDR-TB case numbers remained stable, but their proportion relative to all local TB cases decreased, suggesting more effective detection of drug-susceptible TB among residents, possibly due to better healthcare access. These heterogeneous trends highlight the influence of population-specific factors, including age structure[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], healthcare utilization[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], and living conditions[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], economic status[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], external policies[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], and urbanization dynamics[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], on MDR-TB dynamics.\u003c/p\u003e\u003cp\u003eSpatially, significant positive autocorrelation in MDR-TB distribution confirms that transmission is driven by localized hotspots rather than random spread-consistent with findings in urban settings globally, including Portugal[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] and South Africa[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Central urban districts emerged as shared hotspots for both LRMDR and IMMDR, reflecting high population density, social mixing, and occupational clustering-key drivers of TB transmission in urban cores[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eNotably, subgroup analyses revealed distinct spatial patterns: IMMDR hotspots clustered in suburban areas with concentrated migrant labor, echoing spatial associations between occupational mobility and TB transmission in China\u0026rsquo;s manufacturing hubs[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] and South Africa\u0026rsquo;s mining regions[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. In contrast, LRMDR hotspots were dispersed in rural areas, potentially linked to aging populations and healthcare access barriers, as observed in Sichuan\u0026rsquo;s rural TB studies[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The paucity of IMMDR in economically underdeveloped regions like Chun\u0026rsquo;an and Jiande further supports the role of employment opportunities in shaping migrant-driven transmission networks.\u003c/p\u003e\u003cp\u003eThese findings reinforce the value of spatially targeted interventions. Geographically focused screening in migrant-dense industrial zones and urban cores could mirror the 2-fold improvement in case detection seen in Uganda\u0026rsquo;s targeted campaigns, while tailored strategies for rural LR-such as mobile clinics and aging-population outreach-may address reactivation-driven clusters.\u003c/p\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eLimitations\u003c/h2\u003e\u003cp\u003eThis study has several limitations. First, the absence of whole-genome sequencing data impedes the differentiation between IMMDR and LRMDR and the quantification of transmission chains between IM and LR populations, a limitation underscored in global tuberculosis genomic studies[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Second, the post-2019 decline in IMMDR may reflect reduced migration during COVID-19 rather than intrinsic intervention efficacy, long-term surveillance post-pandemic is needed to clarify this[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Third, spatial analyses at the subdistrict level may mask finer-scale heterogeneities that are critical for hyper-localized interventions. Finally, unmeasured confounding factors-such as housing conditions or occupational exposure-could further explain observed spatiotemporal patterns[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eFuture research should integrate genomic and epidemiological data to trace transmission dynamics, and evaluate the sustainability of post-pandemic trends. Addressing these gaps will strengthen precision public health strategies for MDR-TB control in migrant-intensive urban settings.\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Institutional Review Board of Hangzhou Center for Disease Control and Prevention (Hangzhou Health Supervision Institution) and was conducted in accordance with the Declaration of Helsinki and the Ethical Review Measures for Biomedical Research Involving Humans of China. Given the retrospective analysis of de-identified surveillance data from the national Tuberculosis Information Management System (TBIMS), the ethics committee specifically waived the requirement for obtaining individual informed consent to participate. Data access was granted by the Chinese Center for Disease Control and Prevention in accordance with national infectious disease reporting regulations.\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\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAccording to Chinese law, the public health data are not publicly available, but are available on reasonable requests from the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by Zhejiang Science and Technology Plan for Disease Prevention and Control (Project No. 2025JK030), Zhejiang General Research Project on Medical Health and Science Technology Plan (No.2021KY951), Hangzhou Medical Health Science and The\u0026nbsp;Public\u0026nbsp;Welfare\u0026nbsp;Technology\u0026nbsp;Research\u0026nbsp;Program\u0026nbsp;in\u0026nbsp;Zhejiang\u0026nbsp;Province(No. LGF21H190002).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQingchun Li\u003c/strong\u003e: Conceptualization, Methodology, Formal analysis, Writing-original draft and Funding acquisition. \u003cstrong\u003eXuexin Bai\u003c/strong\u003e: Formal analysis. \u003cstrong\u003eZike Cheng\u003c/strong\u003e: Methodology. \u003cstrong\u003eQinglin Cheng\u003c/strong\u003e: Project administration. \u003cstrong\u003eYifei Wu\u003c/strong\u003e: Funding acquisition. \u003cstrong\u003eLiyun Ai\u003c/strong\u003e: Data curation. \u003cstrong\u003eYinyan Huang\u003c/strong\u003e: Data curation, Funding acquisition. \u003cstrong\u003eQingjun Jia\u003c/strong\u003e: Data curation. \u003cstrong\u003eNan Jiang\u003c/strong\u003e: Formal analysis. \u003cstrong\u003eZijian Fang\u003c/strong\u003e: Data curation. \u003cstrong\u003eXu Song\u003c/strong\u003e: Data curation. \u003cstrong\u003eRuoqi Dai\u003c/strong\u003e: Visualization, Writing-review and editing, Supervision, Corresponding author. \u003cstrong\u003eHanyan Yuan\u003c/strong\u003e: Writing-review and editing, Supervision, Corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eWHO. 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Pulmonary tuberculosis space-time clustering and spatial variation in temporal trends in Portugal, 2000-2010: an updated analysis. Epidemiology and infection. 2015;143(15):3211-9.\u003c/li\u003e\n \u003cli\u003eSy KTL, Leavitt SV, de Vos M, Dolby T, Bor J, Horsburgh CR, Jr., et al. Spatial heterogeneity of extensively drug resistant-tuberculosis in Western Cape Province, South Africa. Scientific reports. 2022;12(1):10844.\u003c/li\u003e\n \u003cli\u003eDangisso MH, Datiko DG, Lindtjorn B. Identifying geographical heterogeneity of pulmonary tuberculosis in southern Ethiopia: a method to identify clustering for targeted interventions. Glob Health Action. 2020;13(1):1785737.\u003c/li\u003e\n \u003cli\u003eLi M, Zhang Y, Wu Z, Jiang Y, Sun R, Yang J, et al. Transmission of fluoroquinolones resistance among multidrug-resistant tuberculosis in Shanghai, China: a retrospective population-based genomic epidemiology study. Emerging microbes \u0026amp; infections. 2024;13(1).\u003c/li\u003e\n \u003cli\u003eLi M, Quan Z, Xu P, Takiff H, Gao Q. Internal migrants as drivers of long-distance cross-regional transmission of tuberculosis in China. Clinical Microbiology and Infection. 2025;31(1):71-7.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Multidrug-resistant tuberculosis, Internal migration, Spatiotemporal heterogeneity, Health disparities, Urbanization","lastPublishedDoi":"10.21203/rs.3.rs-7266883/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7266883/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground \u003c/strong\u003eMultidrug-resistant pulmonary tuberculosis (MDR-PTB) poses a severe public health threat in urbanizing China, with internal migrants (IM) facing elevated risks due to socioeconomic disparities and fragmented healthcare. This study examines the spatiotemporal heterogeneity of MDR-PTB among local residents (LR) and IM in Hangzhou (2014–2024) to inform precision interventions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods \u003c/strong\u003eA retrospective analysis of 753 laboratory-confirmed MDR-PTB cases was conducted using data from China’s Tuberculosis Information Management System. Temporal trends were assessed via seasonal-trend decomposition (STL) and Prais-Winsten regression, while spatial clustering was analyzed using Global Moran’s I and Getis-Ord Gi* statistics at the subdistrict level.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults \u003c/strong\u003eIM constituted 28.3% (213/753) of MDR-PTB cases, exhibiting younger age (mean 35.5 vs. 49.9 years, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001) and higher retreatment rates (51.6% vs. 42.8%, \u003cem\u003eP\u003c/em\u003e=0.034) than LR. The proportion of MDR-PTB among all TB cases declined significantly (monthly percent change [MPC]=-0.645%, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001), with sharper reductions in IM post-2019 (case MPC=-0.823%, \u003cem\u003eP\u003c/em\u003e=0.002). Spatial analysis revealed hotspots in central urban districts for both populations and migrant-dense suburbs \u0026nbsp;for IM (Moran’s I=0.15-0.17,\u003cem\u003eP\u003c/em\u003e\u0026lt;0.001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions \u003c/strong\u003e\u0026nbsp;MDR-PTB burden diverges spatiotemporally between IM and LR, driven by migration patterns, healthcare access barriers, and localized transmission. Targeted screening in industrial zones, mobile clinics for migrants, and policy reforms for cross-provincial insurance equity are critical to reducing MDR-PTB in urban China.\u003c/p\u003e","manuscriptTitle":"Spatiotemporal Heterogeneity of Multidrug-Resistant Pulmonary Tuberculosis Among Local Residents and Internal Migrants in Hangzhou, China, 2014-2024","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-09 09:36:12","doi":"10.21203/rs.3.rs-7266883/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-04T07:52:56+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-17T01:32:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"158632946784897203055214021280111345343","date":"2025-09-10T07:22:36+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-09T01:04:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"281585237670134830382427351327050774316","date":"2025-08-31T22:40:39+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-29T08:59:54+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-09T12:35:43+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-08-08T04:19:03+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-07T03:40:59+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2025-08-07T03:38:07+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ae71110a-88e0-412d-8fd4-f4e4600829ff","owner":[],"postedDate":"September 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-01-08T05:08:06+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-09 09:36:12","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7266883","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7266883","identity":"rs-7266883","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}