{"paper_id":"98ef1e62-e767-4e24-971b-d58f358bdf0a","body_text":"Spatial autocorrelation and hotspot dynamics of multidrug-resistant tuberculosis in Uganda: a district-level LISA analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Spatial autocorrelation and hotspot dynamics of multidrug-resistant tuberculosis in Uganda: a district-level LISA analysis Sarah Rachael Akello, Jimmy Patrick Alunyo, Joseph KB Matovu, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8841683/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 17 You are reading this latest preprint version Abstract Background Spatial clustering of MDR/RR-TB may indicate focal transmission or inequities in diagnostic access, but such patterns are often obscured by analyses conducted at aggregated geographic scales. Limited evidence exists on the spatial autocorrelation of MDR/RR-TB in Uganda. This study aimed to assess spatial dependence and identify geographic clustering of MDR/RR-TB notifications. Methods We conducted a national ecological spatial analysis using MDR/RR-TB surveillance data from Uganda (2014–2023). Global and local spatial autocorrelation statistics were applied at regional and district levels to assess spatial dependence and identify clusters. Results Limited and inconsistent spatial autocorrelation was observed at the regional level. In contrast, district-level analyses demonstrated statistically significant spatial clustering in several years. Persistent high-notification clusters were identified in urban and peri-urban districts, particularly within the Kampala metropolitan area, while several districts consistently appeared as low-notification areas. Conclusions MDR/RR-TB in Uganda exhibits spatial clustering at fine geographic scales that is not apparent at broader administrative levels. District-level spatial analysis is critical for identifying priority areas for targeted surveillance and intervention. MDR-TB spatial clustering Moran’s I LISA Uganda Figures Figure 1 Figure 2 Figure 3 Background Tuberculosis (TB) transmission is inherently spatial, shaped by population density, mobility, shared environments, and health system access. Consequently, TB cases do not occur randomly across space but tend to cluster in specific geographic areas where social, structural, and programmatic factors converge [1]. Identifying such spatial dependence is particularly important for multidrug-resistant and rifampicin-resistant tuberculosis (MDR/RR-TB), where ongoing transmission can undermine control efforts and signal failures in timely diagnosis and treatment. MDR/RR-TB presents distinct epidemiological challenges compared with drug-susceptible TB. Treatment is prolonged, costly, and associated with poorer outcomes, while delayed detection increases the risk of sustained community transmission [2], [3]. Although MDR/RR-TB was historically viewed as largely acquired resistance resulting from inadequate treatment, accumulating evidence indicates that primary transmission of resistant strains now contributes substantially to the burden in many settings [4], [5]. This shift makes understanding spatial clustering and transmission dynamics increasingly critical for effective public health response. Traditional surveillance summaries often rely on aggregate counts or rates reported at national or regional levels. While useful for monitoring overall trends, such summaries can obscure important subnational heterogeneity and fail to distinguish random variation from statistically meaningful clustering. Spatial autocorrelation analysis offers a rigorous framework for addressing this limitation by quantifying the degree to which disease occurrence in one area is related to that in neighbouring areas [6]. Measures such as Global Moran’s I assess overall spatial dependence, while Local Indicators of Spatial Association (LISA) identify specific hotspots, cold spots, and spatial outliers, providing actionable insights for targeted intervention. In high-burden settings, spatial autocorrelation methods have been used to reveal persistent MDR/RR-TB clusters in urban and peri-urban corridors, border regions, and areas with high HIV prevalence or diagnostic concentration [1], [7], [8]. These analyses have demonstrated that apparent geographic patterns are often driven by localised transmission or systematic differences in surveillance performance, rather than uniform national risk. Importantly, clustering has frequently been shown to occur at finer administrative scales, such as districts or municipalities, and may disappear when data are aggregated to broader regions. In Uganda, national TB surveillance is conducted through the District Health Information System (DHIS2), which captures MDR/RR-TB notifications aggregated at district and regional levels. While this system provides comprehensive national coverage, spatial analyses of MDR/RR-TB have been limited. Existing reports primarily describe case counts by region and year, without formal assessment of spatial dependence or identification of statistically significant clusters [9]. As a result, it remains unclear whether observed geographic variations in MDR/RR-TB reflect true clustering and transmission hotspots, random fluctuation, or artefacts of diagnostic access and reporting completeness. Understanding whether MDR/RR-TB exhibits spatial autocorrelation, and at what administrative level such clustering occurs, is essential for precision public health. If clustering is present at district level but not at regional level, interventions must be geographically focused and locally tailored. Conversely, absence of spatial dependence would suggest that broad national strategies may be sufficient. Without this evidence, resource allocation risks being inefficient, with intensified interventions deployed too broadly or failing to reach high-risk areas. This study therefore aimed to assess the presence and scale of spatial autocorrelation in MDR/RR-TB prevalence in Uganda from 2014 to 2023, using global and local spatial statistical methods to identify significant clustering patterns and inform geographically targeted MDR/RR-TB control strategies. Methods Study design and data source We conducted a national ecological spatial analysis of multidrug-resistant and rifampicin-resistant tuberculosis (MDR/RR-TB) in Uganda using routinely collected surveillance data. The study utilised secondary MDR/RR-TB notification data obtained from Uganda’s District Health Information System 2 (DHIS2), the national health information platform managed by the Ministry of Health and the National Tuberculosis and Leprosy Programme (NTLP). In accordance with national reporting practice and WHO recommendations, rifampicin-resistant TB detected using Xpert MTB/RIF was used as a programmatic proxy for MDR-TB, with phenotypic drug susceptibility testing performed where available. All analyses were conducted on aggregated, anonymised data. Study area and spatial units The study covered all 146 districts of Uganda , spanning the four major administrative regions (Central, Eastern, Northern, and Western). Districts constituted the primary spatial unit of analysis, allowing for detection of fine-scale spatial dependence and clustering that may be obscured at higher administrative levels. Regional-level analyses were conducted for comparison to assess whether spatial autocorrelation persisted after aggregation. Geographic boundary files (administrative level 2) were obtained from official national sources and harmonised to reflect district configurations during the study period. Study period Spatial analyses were conducted using MDR/RR-TB notification data reported between January 2014 and December 2023, providing a ten-year window sufficient to assess the stability, persistence, and evolution of spatial clustering patterns over time. Study variables The primary outcome was the district-level MDR/RR-TB notification rate, calculated using reported MDR/RR-TB case counts and corresponding population estimates. Independent spatial dimensions included district location, regional affiliation, and year of notification. No individual-level variables were included due to the ecological nature of the data. Data management and preparation DHIS2 notification data were extracted in Microsoft Excel format and cleaned prior to analysis. Data preparation included verification of district identifiers, resolution of naming inconsistencies, and alignment with official administrative boundaries. Annual MDR/RR-TB case counts were aggregated by district and merged with population denominators to generate rates suitable for spatial analysis. All spatial datasets were projected using a consistent coordinate reference system to ensure accurate neighbourhood definition and mapping. Spatial statistical analysis Global spatial autocorrelation Global spatial autocorrelation of MDR/RR-TB notification rates was assessed using Global Moran’s I statistic. This measure evaluated whether MDR/RR-TB rates across districts were spatially clustered, dispersed, or randomly distributed. Statistical significance was assessed using Monte Carlo permutation tests. Global Moran’s I analyses were conducted annually to examine temporal variation in spatial dependence and to compare district-level and regional-level patterns. Local spatial autocorrelation (LISA) To identify the location and nature of local clustering, Local Indicators of Spatial Association (LISA) were computed using the Local Moran’s I statistic. LISA analyses classified districts into high–high (hotspots), low-low (cold spots), high-low, and low-high spatial associations, enabling identification of statistically significant clusters and spatial outliers. Neighbourhood structure was defined using queen contiguity, whereby districts sharing either a boundary or a vertex were considered neighbours. Statistical significance was determined using Monte Carlo simulations with 9,999 permutations, and a conservative significance threshold was applied to minimise false-positive cluster detection. Rate stabilisation Because MDR/RR-TB represents a relatively rare event in some districts, Empirical Bayes (EB) smoothing was applied to stabilise notification rates and reduce variance driven by small population sizes. Smoothed rates were used for spatial autocorrelation analyses to improve robustness and interpretability. Software and implementation Spatial analyses were conducted using GeoDa (version 1.20.0.8) and R statistical software (version 4.3). R packages including sf , spdep , tmap , and rgeoda were used for spatial data handling, mapping, and statistical testing. All analyses followed established spatial epidemiology best practices. Interpretation considerations Spatial clustering was interpreted cautiously, recognising that observed patterns may reflect a combination of true transmission dynamics, diagnostic access, reporting completeness, and health system performance. Absence of clustering in specific districts was not assumed to indicate absence of disease but may reflect under-detection or limited diagnostic capacity. Results Spatial autocorrelation of MDR/RR-TB at the regional level Analysis of spatial autocorrelation at the regional level showed limited and inconsistent spatial dependence of multidrug-resistant and rifampicin-resistant tuberculosis (MDR/RR-TB) notifications across the study period. Global Moran’s I statistics calculated annually from 2014 to 2023 indicated that statistically significant spatial autocorrelation was rare and short-lived, involving at most two regions in any given year and never persisting beyond two consecutive years (Fig. 1 ). By 2023, no region demonstrated statistically significant spatial autocorrelation, indicating minimal and unstable clustering at the regional level. Spatial autocorrelation of MDR/RR-TB at the district level In contrast to regional findings, district-level analysis demonstrated statistically significant spatial autocorrelation of MDR/RR-TB notifications. Global Moran’s I values indicated positive spatial dependence in selected years, confirming that MDR/RR-TB notifications were geographically clustered at finer spatial scales (Fig. 2 ). The presence of statistically significant clustering at the district level but not at the regional level indicates scale-dependent spatial structure. Local clustering and spatial outliers Local Indicators of Spatial Association (LISA) analysis identified distinct district-level clustering patterns (Fig. 3 ). Persistent high-high clusters were observed in Central Uganda, particularly in districts within and surrounding the Kampala metropolitan area, including Kampala, Wakiso, and Mukono. Additional emerging high-high clusters were identified in the Teso sub-region and selected districts in Northern Uganda during later years of the study period. Low–low clusters were observed in several districts characterised by consistently low MDR/RR-TB notification rates. In addition, spatial outliers, including high-low and low-high districts, were detected intermittently, although these did not persist across multiple years. Discussion This study aimed to assess whether MDR/RR-TB notifications in Uganda exhibit spatial autocorrelation and to identify the geographic scale at which clustering occurs. We found limited and inconsistent evidence of spatial autocorrelation at the regional level but clear and persistent clustering at the district level. Localised hotspots were consistently identified within and around the Kampala metropolitan area, while several districts in other regions exhibited persistently low notification rates. The presence of district-level clustering is consistent with spatial epidemiological studies from other high-burden settings, where MDR/RR-TB has been shown to cluster in urban and peri-urban areas with high population density, increased mobility, and concentrated diagnostic services [1], [7]. Studies from China and South Africa have similarly reported that drug-resistant TB clustering is often detectable only at fine geographic scales and may disappear when data are aggregated to broader administrative units [8], [10]. Our findings align with this evidence and highlight the importance of scale in spatial analysis. The persistent hotspots identified in the Kampala metropolitan area likely reflect a combination of epidemiological and programmatic factors. Urban environments facilitate TB transmission through crowding and social mixing, while referral hospitals and specialised TB treatment centres increase the likelihood of case detection and reporting. Similar urban-centric clustering has been documented in other African cities and has been attributed to both ongoing transmission and enhanced surveillance sensitivity [5], [11]. Conversely, the identification of low-low clusters in several districts may reflect under-detection rather than true absence of MDR/RR-TB. Studies in Ethiopia and other sub-Saharan African countries have shown that districts with limited diagnostic infrastructure often appear as cold spots in spatial analyses, despite evidence of ongoing transmission [1]. The reliance on routine surveillance data means that spatial clustering patterns may be shaped as much by health system capacity as by underlying epidemiology. The lack of consistent regional-level clustering observed in this study further illustrates the limitations of using aggregated administrative units for spatial inference. Regional aggregation smooths local variability and may obscure meaningful clusters that are relevant for intervention planning. This finding reinforces recommendations from spatial epidemiology literature advocating district- or community-level analyses when examining heterogeneous diseases such as MDR/RR-TB [6], [10]. Our findings suggest that MDR/RR-TB in Uganda exhibits spatial dependence at local scales, with persistent hotspots that warrant targeted attention. However, the interpretation of clustering must be cautious, as notification-based analyses cannot disentangle transmission dynamics from surveillance effects. Spatial autocorrelation analysis should therefore be viewed as a tool for prioritisation and hypothesis generation rather than definitive evidence of transmission intensity. Strengths and limitations A major strength of this study is the application of robust spatial statistical methods to national surveillance data over an extended period, enabling systematic assessment of spatial dependence at multiple geographic scales. The use of both global and local measures of spatial autocorrelation allowed identification of persistent hotspots and cold spots while accounting for spatial neighbourhood structure. Application of empirical Bayes smoothing further enhanced the stability of rate estimates in districts with small populations. However, the study has limitations. The analysis relied on routine notification data, which are influenced by diagnostic availability, reporting practices, and health system performance. As a result, spatial clusters may reflect detection patterns rather than true transmission dynamics. The ecological design precludes inference at the individual level and does not allow assessment of patient movement or place of infection. In addition, changes in administrative boundaries and diagnostic coverage over time may have affected spatial patterns, although efforts were made to harmonise data and minimise bias. Conclusion MDR/RR-TB notifications in Uganda exhibit clear spatial clustering at the district level but limited spatial dependence at the regional level. Persistent hotspots were identified in urban and peri-urban districts, while several districts consistently appeared as low-notification areas. These findings highlight the importance of fine-scale spatial analysis for understanding MDR/RR-TB distribution and underscore the limitations of relying on aggregated regional data for programmatic decision-making. Recommendations National TB control programmes should incorporate district-level spatial autocorrelation analysis into routine surveillance to identify and monitor MDR/RR-TB hotspots. Districts identified as persistent hotspots may benefit from intensified case-finding, diagnostic strengthening, and patient support, while districts appearing as cold spots should be assessed for potential under-detection and surveillance gaps. Future research integrating spatial analysis with individual-level, mobility, and facility-level data would further enhance understanding of MDR/RR-TB transmission and inform more precise control strategies. Declarations Ethics approval and consent to participate Ethical approval for this study was obtained from the Busitema University Higher Degrees Committee and the Mbale Regional Referral Hospital Research and Ethics Committee (MRRH-REC) (REC Approval No. MRRH-2025-605). Administrative clearance to access national surveillance data was granted by the Uganda Ministry of Health, National Tuberculosis and Leprosy Programme (NTLP). The study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki (World Medical Association, 2013 revision) and complied with national guidelines for research involving human participants in Uganda. Because this study involved secondary analysis of anonymised, aggregated routine surveillance data obtained from the national District Health Information System 2 (DHIS2) platform, the requirement for informed consent was formally waived by the Mbale Regional Referral Hospital Research and Ethics Committee (MRRH-REC). The waiver was granted on the grounds that the study posed minimal risk to participants, involved no direct contact with individuals, and used de-identified data in accordance with Uganda National Council for Science and Technology (UNCST) regulations for secondary data research. All data were handled confidentially. No individual-level identifiers were accessed or analysed, and all analyses were conducted using aggregated district-level data to ensure participant privacy and data protection. Consent for publication Not applicable Availability of data and materials The datasets used and/or analysed during the current study are available from the corresponding author upon reasonable request. Competing interests The authors declare that they have no competing interests. Funding This publication was produced as part of the EDCTP2 programme supported by the European Union. P-OO, JPA, SRA, JO, GNS, MNB, PB, MM, BCO, WO, SN, GP, and DA are supported through the EDCTP2 programme under the IDEA Fellowship (grant number CSA2020E-3126) funded by the National Institute for Health Research (NIHR) to support global health research. The views and opinions of authors expressed herein do not necessarily state or reflect those of EDCTP, the NIHR, or the UK Department of Health and Social Care. Authors' contributions SRA conceived and led the study, coordinated data collection, performed the initial data analysis, and wrote the first draft of the manuscript. JPA and AO contributed to study design, supported data analysis, and co-led manuscript writing and interpretation of findings. JK-BM provided technical and public health oversight and contributed to manuscript review. JPA, JO, GNS, MNB, PB, MM, BCO, WO, SN, EM, and DA participated in field implementation, data acquisition, and drafting of sections of the manuscript. DM and P-OO supervised the study, provided scientific oversight, and made major contributions to the writing and critical revision of the manuscript. JPA, AO, and GP supported statistical analysis and data validation. All authors reviewed and approved the final version of the manuscript. Acknowledgements We thank the Ministry of Health, Division of Health Information, for housing the DHIS2 dataset and made it available for the data abstraction process. References K. A. Alene et al. , “Spatial clustering of drug-resistant tuberculosis in Hunan province, China: An ecological study”, BMJ Open , vol 11, no 4, bll 1–8, 2021, doi: 10.1136/bmjopen-2020-043685. E. A. Kendall et al. , “The Spectrum of Tuberculosis Disease in an Urban Ugandan Community and Its Health Facilities”, Clin. Infect. Dis. , vol 72, no 12, bll E1035–E1043, 2021, doi: 10.1093/cid/ciaa1824. WHO, “1.3 Drug-resistant TB”, 2024. [Online]. Available at: https://www.who.int/teams/global-tuberculosis-programme/tb-reports/global-tuberculosis-report-2024/tb-disease-burden/1-3-drug-resistant-tb#:~:text=Globally%2C the estimated annual number,360 000–440 000). C. Loiseau et al. , “The relative transmission fitness of multidrug-resistant Mycobacterium tuberculosis in a drug resistance hotspot”, Nat. Commun. , vol 14, no 1, 2023, doi: 10.1038/s41467-023-37719-y. V. Nikolayevskyy et al. , “Role and value of whole genome sequencing in studying tuberculosis transmission”, Clin. Microbiol. Infect. , vol 25, no 11, bll 1377–1382, 2019, doi: 10.1016/j.cmi.2019.03.022. L. Anselin, “Local indicators of spatial association—LISA”, Geogr. Anal. , vol 27, no 2, bll 93–115, 1995. M. A. Mashamba, F. Tanser, S. Afagbedzi, en A. Beke, “Multi-drug-resistant tuberculosis clusters in Mpumalanga province, South Africa, 2013–2016: A spatial analysis”, Trop. Med. Int. Heal. , vol 27, no 2, bll 185–191, 2022, doi: 10.1111/tmi.13708. N. Nazia, Z. A. Butt, M. L. Bedard, W. C. Tang, H. Sehar, en J. Law, “Methods Used in the Spatial and Spatiotemporal Analysis of COVID-19 Epidemiology: A Systematic Review”, Int. J. Environ. Res. Public Health , vol 19, no 14, bll 1–18, 2022, doi: 10.3390/ijerph19148267. U. NTLP, “NTLP BULLETIN-National Quarterly Bulletin.”, 2023. Z. J. Ou et al. , “Trends in burden of multidrug-resistant tuberculosis in countries, regions, and worldwide from 1990 to 2017: results from the Global Burden of Disease study”, Infect. Dis. Poverty , vol 10, no 1, bll 1–10, 2021, doi: 10.1186/s40249-021-00803-w. J. A. M. Stadler, “Updated WHO definitions for tuberculosis outcomes: Simplified, unified and future-proofed”, vol 28, no 2, bll 48–49, 2022. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 02 Mar, 2026 Reviews received at journal 26 Feb, 2026 Reviews received at journal 24 Feb, 2026 Reviews received at journal 22 Feb, 2026 Reviews received at journal 20 Feb, 2026 Reviewers agreed at journal 20 Feb, 2026 Reviews received at journal 19 Feb, 2026 Reviewers agreed at journal 19 Feb, 2026 Reviewers agreed at journal 19 Feb, 2026 Reviewers agreed at journal 16 Feb, 2026 Reviewers agreed at journal 15 Feb, 2026 Reviewers agreed at journal 13 Feb, 2026 Reviewers invited by journal 13 Feb, 2026 Editor assigned by journal 13 Feb, 2026 Editor invited by journal 13 Feb, 2026 Submission checks completed at journal 12 Feb, 2026 First submitted to journal 12 Feb, 2026 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. 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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-8841683\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":592314361,\"identity\":\"78dcf6a9-20cc-4029-a716-320a370e884d\",\"order_by\":0,\"name\":\"Sarah Rachael 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1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":102154,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eSpatial autocorrelation of MDR/RR-TB prevalence by region\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8841683/v1/59c42db473d9ebe07b7d77f0.png\"},{\"id\":103049720,\"identity\":\"cdbb27d7-a27a-48c6-876a-429389761305\",\"added_by\":\"auto\",\"created_at\":\"2026-02-20 07:45:11\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":265879,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eShowing geospatial distribution and clustering of MDR/RR-TB in Uganda: 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Consequently, TB cases do not occur randomly across space but tend to cluster in specific geographic areas where social, structural, and programmatic factors converge [1]. Identifying such spatial dependence is particularly important for multidrug-resistant and rifampicin-resistant tuberculosis (MDR/RR-TB), where ongoing transmission can undermine control efforts and signal failures in timely diagnosis and treatment.\\u003c/p\\u003e \\u003cp\\u003eMDR/RR-TB presents distinct epidemiological challenges compared with drug-susceptible TB. Treatment is prolonged, costly, and associated with poorer outcomes, while delayed detection increases the risk of sustained community transmission [2], [3]. Although MDR/RR-TB was historically viewed as largely acquired resistance resulting from inadequate treatment, accumulating evidence indicates that primary transmission of resistant strains now contributes substantially to the burden in many settings [4], [5]. This shift makes understanding spatial clustering and transmission dynamics increasingly critical for effective public health response.\\u003c/p\\u003e \\u003cp\\u003eTraditional surveillance summaries often rely on aggregate counts or rates reported at national or regional levels. While useful for monitoring overall trends, such summaries can obscure important subnational heterogeneity and fail to distinguish random variation from statistically meaningful clustering. Spatial autocorrelation analysis offers a rigorous framework for addressing this limitation by quantifying the degree to which disease occurrence in one area is related to that in neighbouring areas [6]. Measures such as Global Moran\\u0026rsquo;s I assess overall spatial dependence, while Local Indicators of Spatial Association (LISA) identify specific hotspots, cold spots, and spatial outliers, providing actionable insights for targeted intervention.\\u003c/p\\u003e \\u003cp\\u003eIn high-burden settings, spatial autocorrelation methods have been used to reveal persistent MDR/RR-TB clusters in urban and peri-urban corridors, border regions, and areas with high HIV prevalence or diagnostic concentration [1], [7], [8]. These analyses have demonstrated that apparent geographic patterns are often driven by localised transmission or systematic differences in surveillance performance, rather than uniform national risk. Importantly, clustering has frequently been shown to occur at finer administrative scales, such as districts or municipalities, and may disappear when data are aggregated to broader regions.\\u003c/p\\u003e \\u003cp\\u003eIn Uganda, national TB surveillance is conducted through the District Health Information System (DHIS2), which captures MDR/RR-TB notifications aggregated at district and regional levels. While this system provides comprehensive national coverage, spatial analyses of MDR/RR-TB have been limited. Existing reports primarily describe case counts by region and year, without formal assessment of spatial dependence or identification of statistically significant clusters [9]. As a result, it remains unclear whether observed geographic variations in MDR/RR-TB reflect true clustering and transmission hotspots, random fluctuation, or artefacts of diagnostic access and reporting completeness.\\u003c/p\\u003e \\u003cp\\u003eUnderstanding whether MDR/RR-TB exhibits spatial autocorrelation, and at what administrative level such clustering occurs, is essential for precision public health. If clustering is present at district level but not at regional level, interventions must be geographically focused and locally tailored. Conversely, absence of spatial dependence would suggest that broad national strategies may be sufficient. Without this evidence, resource allocation risks being inefficient, with intensified interventions deployed too broadly or failing to reach high-risk areas.\\u003c/p\\u003e \\u003cp\\u003eThis study therefore aimed to assess the presence and scale of spatial autocorrelation in MDR/RR-TB prevalence in Uganda from 2014 to 2023, using global and local spatial statistical methods to identify significant clustering patterns and inform geographically targeted MDR/RR-TB control strategies.\\u003c/p\\u003e\"},{\"header\":\"Methods\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eStudy design and data source\\u003c/h2\\u003e \\u003cp\\u003eWe conducted a national ecological spatial analysis of multidrug-resistant and rifampicin-resistant tuberculosis (MDR/RR-TB) in Uganda using routinely collected surveillance data. The study utilised secondary MDR/RR-TB notification data obtained from Uganda\\u0026rsquo;s District Health Information System 2 (DHIS2), the national health information platform managed by the Ministry of Health and the National Tuberculosis and Leprosy Programme (NTLP).\\u003c/p\\u003e \\u003cp\\u003eIn accordance with national reporting practice and WHO recommendations, rifampicin-resistant TB detected using Xpert MTB/RIF was used as a programmatic proxy for MDR-TB, with phenotypic drug susceptibility testing performed where available. All analyses were conducted on aggregated, anonymised data.\\u003c/p\\u003e \\u003c/div\\u003e\\n\\u003ch3\\u003eStudy area and spatial units\\u003c/h3\\u003e\\n\\u003cp\\u003eThe study covered \\u003cb\\u003eall 146 districts of Uganda\\u003c/b\\u003e, spanning the four major administrative regions (Central, Eastern, Northern, and Western). Districts constituted the primary spatial unit of analysis, allowing for detection of fine-scale spatial dependence and clustering that may be obscured at higher administrative levels. Regional-level analyses were conducted for comparison to assess whether spatial autocorrelation persisted after aggregation.\\u003c/p\\u003e \\u003cp\\u003eGeographic boundary files (administrative level 2) were obtained from official national sources and harmonised to reflect district configurations during the study period.\\u003c/p\\u003e\\n\\u003ch3\\u003eStudy period\\u003c/h3\\u003e\\n\\u003cp\\u003eSpatial analyses were conducted using MDR/RR-TB notification data reported between January 2014 and December 2023, providing a ten-year window sufficient to assess the stability, persistence, and evolution of spatial clustering patterns over time.\\u003c/p\\u003e\\n\\u003ch3\\u003eStudy variables\\u003c/h3\\u003e\\n\\u003cp\\u003eThe primary outcome was the district-level MDR/RR-TB notification rate, calculated using reported MDR/RR-TB case counts and corresponding population estimates. Independent spatial dimensions included district location, regional affiliation, and year of notification. No individual-level variables were included due to the ecological nature of the data.\\u003c/p\\u003e\\n\\u003ch3\\u003eData management and preparation\\u003c/h3\\u003e\\n\\u003cp\\u003eDHIS2 notification data were extracted in Microsoft Excel format and cleaned prior to analysis. Data preparation included verification of district identifiers, resolution of naming inconsistencies, and alignment with official administrative boundaries. Annual MDR/RR-TB case counts were aggregated by district and merged with population denominators to generate rates suitable for spatial analysis.\\u003c/p\\u003e \\u003cp\\u003eAll spatial datasets were projected using a consistent coordinate reference system to ensure accurate neighbourhood definition and mapping.\\u003c/p\\u003e \\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eSpatial statistical analysis\\u003c/h2\\u003e \\u003cdiv id=\\\"Sec9\\\" class=\\\"Section3\\\"\\u003e \\u003ch2\\u003eGlobal spatial autocorrelation\\u003c/h2\\u003e \\u003cp\\u003eGlobal spatial autocorrelation of MDR/RR-TB notification rates was assessed using Global Moran\\u0026rsquo;s I statistic. This measure evaluated whether MDR/RR-TB rates across districts were spatially clustered, dispersed, or randomly distributed. Statistical significance was assessed using Monte Carlo permutation tests.\\u003c/p\\u003e \\u003cp\\u003eGlobal Moran\\u0026rsquo;s I analyses were conducted annually to examine temporal variation in spatial dependence and to compare district-level and regional-level patterns.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e\\n\\u003ch3\\u003eLocal spatial autocorrelation (LISA)\\u003c/h3\\u003e\\n\\u003cp\\u003eTo identify the location and nature of local clustering, Local Indicators of Spatial Association (LISA) were computed using the Local Moran\\u0026rsquo;s I statistic. LISA analyses classified districts into high\\u0026ndash;high (hotspots), low-low (cold spots), high-low, and low-high spatial associations, enabling identification of statistically significant clusters and spatial outliers.\\u003c/p\\u003e \\u003cp\\u003eNeighbourhood structure was defined using queen contiguity, whereby districts sharing either a boundary or a vertex were considered neighbours. Statistical significance was determined using Monte Carlo simulations with 9,999 permutations, and a conservative significance threshold was applied to minimise false-positive cluster detection.\\u003c/p\\u003e \\u003cdiv id=\\\"Sec11\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eRate stabilisation\\u003c/h2\\u003e \\u003cp\\u003eBecause MDR/RR-TB represents a relatively rare event in some districts, Empirical Bayes (EB) smoothing was applied to stabilise notification rates and reduce variance driven by small population sizes. Smoothed rates were used for spatial autocorrelation analyses to improve robustness and interpretability.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec12\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eSoftware and implementation\\u003c/h2\\u003e \\u003cp\\u003eSpatial analyses were conducted using GeoDa (version 1.20.0.8) and R statistical software (version 4.3). R packages including \\u003cem\\u003esf\\u003c/em\\u003e, \\u003cem\\u003espdep\\u003c/em\\u003e, \\u003cem\\u003etmap\\u003c/em\\u003e, and \\u003cem\\u003ergeoda\\u003c/em\\u003e were used for spatial data handling, mapping, and statistical testing. All analyses followed established spatial epidemiology best practices.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec13\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eInterpretation considerations\\u003c/h2\\u003e \\u003cp\\u003eSpatial clustering was interpreted cautiously, recognising that observed patterns may reflect a combination of true transmission dynamics, diagnostic access, reporting completeness, and health system performance. Absence of clustering in specific districts was not assumed to indicate absence of disease but may reflect under-detection or limited diagnostic capacity.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cdiv id=\\\"Sec15\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eSpatial autocorrelation of MDR/RR-TB at the regional level\\u003c/h2\\u003e \\u003cp\\u003eAnalysis of spatial autocorrelation at the regional level showed limited and inconsistent spatial dependence of multidrug-resistant and rifampicin-resistant tuberculosis (MDR/RR-TB) notifications across the study period. Global Moran\\u0026rsquo;s I statistics calculated annually from 2014 to 2023 indicated that statistically significant spatial autocorrelation was rare and short-lived, involving at most two regions in any given year and never persisting beyond two consecutive years (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). By 2023, no region demonstrated statistically significant spatial autocorrelation, indicating minimal and unstable clustering at the regional level.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec16\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eSpatial autocorrelation of MDR/RR-TB at the district level\\u003c/h2\\u003e \\u003cp\\u003eIn contrast to regional findings, district-level analysis demonstrated statistically significant spatial autocorrelation of MDR/RR-TB notifications. Global Moran\\u0026rsquo;s I values indicated positive spatial dependence in selected years, confirming that MDR/RR-TB notifications were geographically clustered at finer spatial scales (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e). The presence of statistically significant clustering at the district level but not at the regional level indicates scale-dependent spatial structure.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec17\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eLocal clustering and spatial outliers\\u003c/h2\\u003e \\u003cp\\u003eLocal Indicators of Spatial Association (LISA) analysis identified distinct district-level clustering patterns (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e). Persistent high-high clusters were observed in Central Uganda, particularly in districts within and surrounding the Kampala metropolitan area, including Kampala, Wakiso, and Mukono. Additional emerging high-high clusters were identified in the Teso sub-region and selected districts in Northern Uganda during later years of the study period.\\u003c/p\\u003e \\u003cp\\u003eLow\\u0026ndash;low clusters were observed in several districts characterised by consistently low MDR/RR-TB notification rates. In addition, spatial outliers, including high-low and low-high districts, were detected intermittently, although these did not persist across multiple years.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003eThis study aimed to assess whether MDR/RR-TB notifications in Uganda exhibit spatial autocorrelation and to identify the geographic scale at which clustering occurs. We found limited and inconsistent evidence of spatial autocorrelation at the regional level but clear and persistent clustering at the district level. Localised hotspots were consistently identified within and around the Kampala metropolitan area, while several districts in other regions exhibited persistently low notification rates.\\u003c/p\\u003e \\u003cp\\u003eThe presence of district-level clustering is consistent with spatial epidemiological studies from other high-burden settings, where MDR/RR-TB has been shown to cluster in urban and peri-urban areas with high population density, increased mobility, and concentrated diagnostic services [1], [7]. Studies from China and South Africa have similarly reported that drug-resistant TB clustering is often detectable only at fine geographic scales and may disappear when data are aggregated to broader administrative units [8], [10]. Our findings align with this evidence and highlight the importance of scale in spatial analysis.\\u003c/p\\u003e \\u003cp\\u003eThe persistent hotspots identified in the Kampala metropolitan area likely reflect a combination of epidemiological and programmatic factors. Urban environments facilitate TB transmission through crowding and social mixing, while referral hospitals and specialised TB treatment centres increase the likelihood of case detection and reporting. Similar urban-centric clustering has been documented in other African cities and has been attributed to both ongoing transmission and enhanced surveillance sensitivity [5], [11].\\u003c/p\\u003e \\u003cp\\u003eConversely, the identification of low-low clusters in several districts may reflect under-detection rather than true absence of MDR/RR-TB. Studies in Ethiopia and other sub-Saharan African countries have shown that districts with limited diagnostic infrastructure often appear as cold spots in spatial analyses, despite evidence of ongoing transmission [1]. The reliance on routine surveillance data means that spatial clustering patterns may be shaped as much by health system capacity as by underlying epidemiology.\\u003c/p\\u003e \\u003cp\\u003eThe lack of consistent regional-level clustering observed in this study further illustrates the limitations of using aggregated administrative units for spatial inference. Regional aggregation smooths local variability and may obscure meaningful clusters that are relevant for intervention planning. This finding reinforces recommendations from spatial epidemiology literature advocating district- or community-level analyses when examining heterogeneous diseases such as MDR/RR-TB [6], [10].\\u003c/p\\u003e \\u003cp\\u003eOur findings suggest that MDR/RR-TB in Uganda exhibits spatial dependence at local scales, with persistent hotspots that warrant targeted attention. However, the interpretation of clustering must be cautious, as notification-based analyses cannot disentangle transmission dynamics from surveillance effects. Spatial autocorrelation analysis should therefore be viewed as a tool for prioritisation and hypothesis generation rather than definitive evidence of transmission intensity.\\u003c/p\\u003e \\u003cdiv id=\\\"Sec19\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eStrengths and limitations\\u003c/h2\\u003e \\u003cp\\u003eA major strength of this study is the application of robust spatial statistical methods to national surveillance data over an extended period, enabling systematic assessment of spatial dependence at multiple geographic scales. The use of both global and local measures of spatial autocorrelation allowed identification of persistent hotspots and cold spots while accounting for spatial neighbourhood structure. Application of empirical Bayes smoothing further enhanced the stability of rate estimates in districts with small populations.\\u003c/p\\u003e \\u003cp\\u003eHowever, the study has limitations. The analysis relied on routine notification data, which are influenced by diagnostic availability, reporting practices, and health system performance. As a result, spatial clusters may reflect detection patterns rather than true transmission dynamics. The ecological design precludes inference at the individual level and does not allow assessment of patient movement or place of infection. In addition, changes in administrative boundaries and diagnostic coverage over time may have affected spatial patterns, although efforts were made to harmonise data and minimise bias.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Conclusion\",\"content\":\"\\u003cp\\u003eMDR/RR-TB notifications in Uganda exhibit clear spatial clustering at the district level but limited spatial dependence at the regional level. Persistent hotspots were identified in urban and peri-urban districts, while several districts consistently appeared as low-notification areas. These findings highlight the importance of fine-scale spatial analysis for understanding MDR/RR-TB distribution and underscore the limitations of relying on aggregated regional data for programmatic decision-making.\\u003c/p\\u003e \\u003cdiv id=\\\"Sec21\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eRecommendations\\u003c/h2\\u003e \\u003cp\\u003eNational TB control programmes should incorporate district-level spatial autocorrelation analysis into routine surveillance to identify and monitor MDR/RR-TB hotspots. Districts identified as persistent hotspots may benefit from intensified case-finding, diagnostic strengthening, and patient support, while districts appearing as cold spots should be assessed for potential under-detection and surveillance gaps. Future research integrating spatial analysis with individual-level, mobility, and facility-level data would further enhance understanding of MDR/RR-TB transmission and inform more precise control strategies.\\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\\u003eEthical approval for this study was obtained from the Busitema University Higher Degrees Committee and the Mbale Regional Referral Hospital Research and Ethics Committee (MRRH-REC) (REC Approval No. MRRH-2025-605). Administrative clearance to access national surveillance data was granted by the Uganda Ministry of Health, National Tuberculosis and Leprosy Programme (NTLP).\\u003c/p\\u003e\\n\\u003cp\\u003eThe study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki (World Medical Association, 2013 revision) and complied with national guidelines for research involving human participants in Uganda.\\u003c/p\\u003e\\n\\u003cp\\u003eBecause this study involved secondary analysis of anonymised, aggregated routine surveillance data obtained from the national District Health Information System 2 (DHIS2) platform, the requirement for informed consent was formally waived by the Mbale Regional Referral Hospital Research and Ethics Committee (MRRH-REC). The waiver was granted on the grounds that the study posed minimal risk to participants, involved no direct contact with individuals, and used de-identified data in accordance with Uganda National Council for Science and Technology (UNCST) regulations for secondary data research.\\u003c/p\\u003e\\n\\u003cp\\u003eAll data were handled confidentially. No individual-level identifiers were accessed or analysed, and all analyses were conducted using aggregated district-level data to ensure participant privacy and data protection.\\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\\u003eThe datasets used and/or analysed during the current study are available from the corresponding author upon reasonable request.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCompeting interests\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe 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 publication was produced as part of the EDCTP2 programme supported by the European Union. P-OO, JPA, SRA, JO, GNS, MNB, PB, MM, BCO, WO, SN, GP, and DA are supported through the EDCTP2 programme under the IDEA Fellowship (grant number CSA2020E-3126) funded by the National Institute for Health Research (NIHR) to support global health research. The views and opinions of authors expressed herein do not necessarily state or reflect those of EDCTP, the NIHR, or the UK Department of Health and Social Care.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAuthors\\u0026apos; contributions\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eSRA conceived and led the study, coordinated data collection, performed the initial data analysis, and wrote the first draft of the manuscript. JPA and AO contributed to study design, supported data analysis, and co-led manuscript writing and interpretation of findings. JK-BM provided technical and public health oversight and contributed to manuscript review. JPA, JO, GNS, MNB, PB, MM, BCO, WO, SN, EM, and DA participated in field implementation, data acquisition, and drafting of sections of the manuscript. \\u0026nbsp;DM and P-OO supervised the study, provided scientific oversight, and made major contributions to the writing and critical revision of the manuscript. JPA, AO, and GP supported statistical analysis and data validation. All authors reviewed and approved the final version of the manuscript.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAcknowledgements\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eWe thank the Ministry of Health, Division of Health Information, for housing the DHIS2 dataset and made it available for the data abstraction process.\\u0026nbsp;\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n\\u003cli\\u003eK. A. Alene \\u003cem\\u003eet al.\\u003c/em\\u003e, \\u0026ldquo;Spatial clustering of drug-resistant tuberculosis in Hunan province, China: An ecological study\\u0026rdquo;, \\u003cem\\u003eBMJ Open\\u003c/em\\u003e, vol 11, no 4, bll 1\\u0026ndash;8, 2021, doi: 10.1136/bmjopen-2020-043685.\\u003c/li\\u003e\\n\\u003cli\\u003eE. A. Kendall \\u003cem\\u003eet al.\\u003c/em\\u003e, \\u0026ldquo;The Spectrum of Tuberculosis Disease in an Urban Ugandan Community and Its Health Facilities\\u0026rdquo;, \\u003cem\\u003eClin. \\u003c/em\\u003e\\u003cem\\u003eInfect. Dis.\\u003c/em\\u003e, vol 72, no 12, bll E1035\\u0026ndash;E1043, 2021, doi: 10.1093/cid/ciaa1824.\\u003c/li\\u003e\\n\\u003cli\\u003eWHO, \\u0026ldquo;1.3 Drug-resistant TB\\u0026rdquo;, 2024. [Online]. Available at: https://www.who.int/teams/global-tuberculosis-programme/tb-reports/global-tuberculosis-report-2024/tb-disease-burden/1-3-drug-resistant-tb#:~:text=Globally%2C the estimated annual number,360 000\\u0026ndash;440 000).\\u003c/li\\u003e\\n\\u003cli\\u003eC. Loiseau \\u003cem\\u003eet al.\\u003c/em\\u003e, \\u0026ldquo;The relative transmission fitness of multidrug-resistant Mycobacterium tuberculosis in a drug resistance hotspot\\u0026rdquo;, \\u003cem\\u003eNat. Commun.\\u003c/em\\u003e, vol 14, no 1, 2023, doi: 10.1038/s41467-023-37719-y.\\u003c/li\\u003e\\n\\u003cli\\u003eV. Nikolayevskyy \\u003cem\\u003eet al.\\u003c/em\\u003e, \\u0026ldquo;Role and value of whole genome sequencing in studying tuberculosis transmission\\u0026rdquo;, \\u003cem\\u003eClin. Microbiol. Infect.\\u003c/em\\u003e, vol 25, no 11, bll 1377\\u0026ndash;1382, 2019, doi: 10.1016/j.cmi.2019.03.022.\\u003c/li\\u003e\\n\\u003cli\\u003eL. Anselin, \\u0026ldquo;Local indicators of spatial association\\u0026mdash;LISA\\u0026rdquo;, \\u003cem\\u003eGeogr. Anal.\\u003c/em\\u003e, vol 27, no 2, bll 93\\u0026ndash;115, 1995.\\u003c/li\\u003e\\n\\u003cli\\u003eM. A. Mashamba, F. Tanser, S. Afagbedzi, en A. Beke, \\u0026ldquo;Multi-drug-resistant tuberculosis clusters in Mpumalanga province, South Africa, 2013\\u0026ndash;2016: A spatial analysis\\u0026rdquo;, \\u003cem\\u003eTrop. Med. Int. Heal.\\u003c/em\\u003e, vol 27, no 2, bll 185\\u0026ndash;191, 2022, doi: 10.1111/tmi.13708.\\u003c/li\\u003e\\n\\u003cli\\u003eN. Nazia, Z. A. Butt, M. L. Bedard, W. C. Tang, H. Sehar, en J. Law, \\u0026ldquo;Methods Used in the Spatial and Spatiotemporal Analysis of COVID-19 Epidemiology: A Systematic Review\\u0026rdquo;, \\u003cem\\u003eInt. J. Environ. Res. Public Health\\u003c/em\\u003e, vol 19, no 14, bll 1\\u0026ndash;18, 2022, doi: 10.3390/ijerph19148267.\\u003c/li\\u003e\\n\\u003cli\\u003eU. NTLP, \\u0026ldquo;NTLP BULLETIN-National Quarterly Bulletin.\\u0026rdquo;, 2023.\\u003c/li\\u003e\\n\\u003cli\\u003eZ. J. Ou \\u003cem\\u003eet al.\\u003c/em\\u003e, \\u0026ldquo;Trends in burden of multidrug-resistant tuberculosis in countries, regions, and worldwide from 1990 to 2017: results from the Global Burden of Disease study\\u0026rdquo;, \\u003cem\\u003eInfect. Dis. Poverty\\u003c/em\\u003e, vol 10, no 1, bll 1\\u0026ndash;10, 2021, doi: 10.1186/s40249-021-00803-w.\\u003c/li\\u003e\\n\\u003cli\\u003eJ. A. M. Stadler, \\u0026ldquo;Updated WHO definitions for tuberculosis outcomes: Simplified, unified and future-proofed\\u0026rdquo;, vol 28, no 2, bll 48\\u0026ndash;49, 2022.\\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\":\"info@researchsquare.com\",\"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\":\"MDR-TB, spatial clustering, Moran’s I, LISA, Uganda\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-8841683/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-8841683/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003ch2\\u003eBackground\\u003c/h2\\u003e \\u003cp\\u003eSpatial clustering of MDR/RR-TB may indicate focal transmission or inequities in diagnostic access, but such patterns are often obscured by analyses conducted at aggregated geographic scales. Limited evidence exists on the spatial autocorrelation of MDR/RR-TB in Uganda. This study aimed to assess spatial dependence and identify geographic clustering of MDR/RR-TB notifications.\\u003c/p\\u003e\\u003ch2\\u003eMethods\\u003c/h2\\u003e \\u003cp\\u003eWe conducted a national ecological spatial analysis using MDR/RR-TB surveillance data from Uganda (2014\\u0026ndash;2023). Global and local spatial autocorrelation statistics were applied at regional and district levels to assess spatial dependence and identify clusters.\\u003c/p\\u003e\\u003ch2\\u003eResults\\u003c/h2\\u003e \\u003cp\\u003eLimited and inconsistent spatial autocorrelation was observed at the regional level. In contrast, district-level analyses demonstrated statistically significant spatial clustering in several years. Persistent high-notification clusters were identified in urban and peri-urban districts, particularly within the Kampala metropolitan area, while several districts consistently appeared as low-notification areas.\\u003c/p\\u003e\\u003ch2\\u003eConclusions\\u003c/h2\\u003e \\u003cp\\u003eMDR/RR-TB in Uganda exhibits spatial clustering at fine geographic scales that is not apparent at broader administrative levels. District-level spatial analysis is critical for identifying priority areas for targeted surveillance and intervention.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Spatial autocorrelation and hotspot dynamics of multidrug-resistant tuberculosis in Uganda: a district-level LISA analysis\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2026-02-18 14:32:15\",\"doi\":\"10.21203/rs.3.rs-8841683/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0},{\"type\":\"decision\",\"content\":\"Revision requested\",\"date\":\"2026-03-02T12:57:55+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2026-02-26T09:47:39+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2026-02-24T15:24:02+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2026-02-22T23:03:45+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2026-02-20T07:49:12+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"115324501724402946557066564984212667901\",\"date\":\"2026-02-20T07:12:35+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2026-02-20T03:31:35+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"271776839451855001944105402003636779949\",\"date\":\"2026-02-20T02:23:27+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"256404135847398056879001924715761157618\",\"date\":\"2026-02-19T07:57:25+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"130323014156417479792216170581099178205\",\"date\":\"2026-02-16T17:19:23+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"94021422449424190898438134853721392199\",\"date\":\"2026-02-15T21:16:17+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"77532475190067582814307109814307349715\",\"date\":\"2026-02-13T15:28:34+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewersInvited\",\"content\":\"\",\"date\":\"2026-02-13T10:04:34+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorAssigned\",\"content\":\"\",\"date\":\"2026-02-13T09:24:08+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvited\",\"content\":\"\",\"date\":\"2026-02-13T09:19:04+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"checksComplete\",\"content\":\"\",\"date\":\"2026-02-12T15:03:54+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"submitted\",\"content\":\"BMC Public Health\",\"date\":\"2026-02-12T15:00:05+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"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\":\"7c4b0e83-1cf4-42f0-ac53-d90955d49242\",\"owner\":[],\"postedDate\":\"February 18th, 2026\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"in-revision\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2026-03-02T13:10:37+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2026-02-18 14:32:15\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-8841683\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-8841683\",\"identity\":\"rs-8841683\",\"version\":[\"v1\"]},\"buildId\":\"XKTyCvWXoU3ODBz1xrDgd\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}