Spatial distribution and clustering of multidrug-resistant tuberculosis in Uganda: a nationwide ecological study (2014-2023)

Spatial distribution and clustering of multidrug-resistant tuberculosis in Uganda: a nationwide ecological study (2014-2023) | Research Square 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 Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Spatial distribution and clustering of multidrug-resistant tuberculosis in Uganda: a nationwide ecological study (2014-2023) Sarah Rachael Akello, Jimmy Patrick Alunyo, Joseph KB Matovu, and 14 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8841417/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Multidrug-resistant and rifampicin-resistant tuberculosis (MDR/RR-TB) poses a major challenge to tuberculosis control, particularly in high-burden countries. While national surveillance data are routinely used to monitor MDR/RR-TB, reliance on national or regional aggregates may obscure important subnational heterogeneity. Evidence on the geographic distribution of MDR/RR-TB in Uganda remains limited. This study aimed to describe the national and subnational spatial distribution of MDR/RR-TB in Uganda over a ten-year period. Methods We conducted a national ecological geospatial analysis using routinely collected MDR/RR-TB surveillance data from Uganda’s District Health Information System 2 for the period 2014–2023. District- and regional-level notification data were mapped and analysed descriptively to characterise spatial patterns and changes over time. Results MDR/RR-TB notifications were highly heterogeneous across Uganda. The Central region consistently accounted for the highest burden, with persistent concentration in the Kampala metropolitan area. Selected districts in the Eastern and Northern regions showed increasing notifications in later years, while many districts reported persistently low or zero notifications throughout the study period. Conclusions MDR/RR-TB in Uganda is geographically patterned and dynamic over time. District-level geospatial analysis provides critical insights that are masked by regional summaries and can inform targeted MDR/RR-TB surveillance and control strategies. MDR-TB rifampicin-resistant TB spatial distribution geospatial analysis Uganda Figures Figure 1 Figure 2 Figure 3 Background Tuberculosis (TB) remains one of the leading causes of morbidity and mortality from infectious diseases worldwide. In 2023, an estimated 10·8 million people developed TB and approximately 1·25 million deaths were attributed to the disease, despite sustained global control efforts under the WHO End TB Strategy [ 1 ], [ 2 ]. While modest declines in overall TB incidence have been reported since 2015, progress remains uneven and insufficient to meet global elimination targets, particularly in low- and middle-income countries (LMICs) where health system constraints persist [ 3 ]. Multidrug-resistant and rifampicin-resistant tuberculosis (MDR/RR-TB) represents a critical threat to TB control and elimination. MDR/RR-TB, defined as TB resistant to rifampicin with or without resistance to isoniazid, is associated with prolonged treatment duration, higher costs, frequent adverse events, and substantially lower treatment success compared with drug-susceptible TB [ 4 ], [ 5 ]. Globally, approximately 400 000 people developed MDR/RR-TB in 2023, yet fewer than half were diagnosed and initiated on appropriate treatment, highlighting persistent diagnostic and surveillance gaps [ 2 ]. Increasing evidence indicates that MDR/RR-TB is not only a consequence of inadequate treatment but is also increasingly driven by ongoing community transmission, underscoring the need for more precise epidemiological intelligence to guide control strategies [ 6 ], [ 7 ]. Sub-Saharan Africa bears a disproportionate share of the MDR/RR-TB burden, driven by high HIV prevalence, population mobility, socio-economic inequalities, and uneven access to diagnostic and treatment services [ 8 ], [ 9 ]. However, the regional epidemiology of MDR/RR-TB remains poorly characterised, with limited application of advanced spatial analytical approaches to identify geographic heterogeneity, transmission hotspots, and priority areas for intervention. Studies from South Africa, China, and Ethiopia have demonstrated the utility of geospatial methods, such as spatial autocorrelation and hotspot analysis, in revealing clustered transmission patterns that are obscured in national aggregate data [ 10 ]–[ 12 ]. In contrast, many African countries continue to rely on descriptive summaries that lack spatial resolution and statistical validation. Uganda is classified among the 30 high-burden TB and TB/HIV countries globally and reports approximately 2,000 new MDR/RR-TB cases annually [ 13 ]. National estimates indicate MDR/RR-TB prevalence of about 4·4% among newly diagnosed TB cases and 17% among previously treated cases, reflecting both transmission and acquired resistance [ 13 ], [ 14 ]. The scale-up of Xpert MTB/RIF testing since 2012 has improved detection of rifampicin resistance, which is used programmatically as a proxy for MDR-TB in Uganda, in line with WHO recommendations [ 5 ], [ 15 ]. Nevertheless, diagnostic access and reporting completeness vary widely across districts, contributing to potential under-detection in rural and hard-to-reach areas and over-representation of cases in urban settings with stronger health infrastructure [ 16 ], [ 17 ]. Despite these challenges, national TB surveillance outputs in Uganda largely present MDR/RR-TB data in aggregated tabular formats, offering limited insight into subnational spatial patterns, clustering, or temporal shifts in disease burden. Previous studies have highlighted higher MDR/RR-TB notifications in Kampala and surrounding districts following diagnostic expansion, as well as high MDR/RR-TB mortality in referral hospitals, but these analyses have been geographically restricted or facility-based and have not systematically examined national spatial heterogeneity [ 18 ]–[ 20 ]. As a result, critical questions remain unanswered regarding where MDR/RR-TB is concentrated, whether apparent low-burden districts reflect true absence or surveillance gaps, and how spatial patterns have evolved over time. Understanding the geospatial distribution of MDR/RR-TB at national and district levels is essential for precision public health planning, efficient allocation of limited resources, and targeted implementation of intensified case-finding and treatment interventions. Integrating spatial epidemiology into routine TB surveillance can reveal heterogeneity in disease burden, distinguish true transmission hotspots from areas of under-detection, and support geographically tailored control strategies. This study aimed to characterise the spatial distribution and clustering of multidrug-resistant and rifampicin-resistant tuberculosis in Uganda from 2014–2023 using national surveillance data, applying robust geospatial analytical methods to identify priority districts and regions for targeted MDR/RR-TB control. Methods Study design We conducted a national ecological geospatial study to describe the spatial distribution of multidrug-resistant and rifampicin-resistant tuberculosis (MDR/RR-TB) in Uganda. The study relied on routinely collected secondary surveillance data and was designed to characterise geographic variation and changes in distribution over time rather than to assess causal relationships at the individual level. The ecological approach was appropriate given the use of aggregated population-level data and the study’s objective of informing subnational public health planning. Data source Data were obtained from Uganda’s District Health Information System 2 (DHIS2), the national electronic platform used by the Ministry of Health and the National Tuberculosis and Leprosy Programme to capture routine tuberculosis notifications. DHIS2 aggregates monthly TB data reported by public and private health facilities across all districts in the country and serves as the primary source of national TB surveillance data. In accordance with national TB programme reporting practice, rifampicin-resistant TB detected using Xpert MTB/RIF was used as a programmatic proxy for MDR-TB. Although confirmatory drug susceptibility testing is performed where available, rifampicin resistance forms the basis of MDR-TB notification for surveillance and planning purposes in Uganda. All data accessed for this study were aggregated and anonymised prior to analysis. Study area and spatial units The study covered the entire country of Uganda and included all districts reporting MDR/RR-TB notifications during the study period. Districts, corresponding to administrative level 2, were used as the primary spatial unit of analysis because they represent the operational level at which TB surveillance, programme implementation, and resource allocation are conducted. Using districts as the unit of analysis enabled detailed examination of subnational heterogeneity in MDR/RR-TB notifications. For descriptive and interpretive purposes, district-level findings were also summarised at the regional level, encompassing the Central, Eastern, Northern, and Western regions. This allowed broader geographic patterns to be contextualised while preserving the finer spatial resolution required for district-level planning. District boundary shapefiles were obtained from official national administrative sources, and district names and identifiers in the surveillance dataset were cross-checked and harmonised with spatial boundary files to ensure consistency across the study period, including accounting for administrative changes. Study period The analysis included MDR/RR-TB notifications reported between January 2014 and December 2023. This ten-year period was selected to allow examination of the geographic distribution of MDR/RR-TB over time and to capture changes occurring before and after major expansions in TB diagnostic capacity and surveillance strengthening in Uganda. Study variables The primary variable of interest was the number of MDR/RR-TB notifications reported at district and regional levels. Notifications were stratified by district, region, and year to allow assessment of geographic distribution and temporal shifts in burden. Population denominators were used where necessary to contextualise district-level distributions; however, the primary focus of this paper was on descriptive spatial patterns rather than formal rate-based comparisons or inferential spatial statistics. Data management and quality assurance DHIS2 data extracts were obtained in spreadsheet format and underwent systematic cleaning prior to analysis. Data management procedures included reviewing records for completeness across years, resolving inconsistencies in district names and administrative identifiers, and removing duplicate or incomplete records. Monthly notification data were aggregated into annual totals for spatial presentations and cleaned surveillance datasets were aligned with spatial boundary files to ensure accurate geographic representation. All cleaned datasets were securely stored and used exclusively for analytical purposes. Geospatial mapping and visualisation Geospatial analyses were conducted to visually describe the distribution of MDR/RR-TB across Uganda at both district and regional levels. Mapping focused on identifying areas with consistently high or low notification volumes and describing how the geographic distribution evolved over time. District-level maps were produced by joining MDR/RR-TB notifications to administrative boundary shapefiles using harmonised district identifiers. Regional maps were generated by aggregating district-level notifications to regional totals. Maps were produced for selected years as well as for cumulative periods to illustrate both annual variation and longer-term geographic persistence of MDR/RR-TB notifications. Spatial patterns were interpreted descriptively, recognising that observed distributions may reflect differences in diagnostic access, reporting completeness, and health system capacity, in addition to underlying disease burden. Software Data cleaning, aggregation, and descriptive summaries were conducted using R statistical software. Geospatial mapping and visualisation were performed using geographic information system software, enabling integration of surveillance data with administrative boundary files and the production of high-quality spatial maps suitable for publication. Results Geographic distribution of MDR/RR-TB at national and regional levels Between 2014 and 2023, multidrug-resistant and rifampicin-resistant tuberculosis (MDR/RR-TB) notifications in Uganda demonstrated marked geographic heterogeneity across regions. The Central region consistently reported the highest number of MDR/RR-TB notifications throughout the study period (Fig. 1 ). In contrast, the Eastern and Northern regions contributed fewer notifications in the early years but showed increasing numbers in the latter part of the study period, while the Western region maintained comparatively lower and more stable notification volumes over time (Fig. 1 ). Regional-level mapping revealed that the contribution of MDR/RR-TB notifications to the national burden was persistently skewed towards Central Uganda, with no sustained period in which any other region exceeded it. However, the relative contribution of the Eastern and Northern regions increased over time, indicating a shift in the geographic distribution of reported MDR/RR-TB cases. District-level spatial distribution of MDR/RR-TB At the district level, MDR/RR-TB notifications were highly unevenly distributed across the country (Fig. 2 ). Districts within and surrounding the Kampala metropolitan area—including Kampala, Wakiso, and Mukono, consistently reported the highest notification counts across multiple years. These districts formed a persistent high-notification corridor within Central Uganda. Beyond Central Uganda, elevated MDR/RR-TB notifications were observed in selected districts in the Eastern region, particularly within the Teso sub-region, as well as in parts of Northern Uganda (Fig. 2 ). In contrast, many districts, especially those that are remote or sparsely populated, reported persistently low or zero MDR/RR-TB notifications throughout the study period. Overall, a small number of districts accounted for a disproportionate share of reported MDR/RR-TB notifications nationally. Temporal evolution of spatial patterns Comparison of annual and cumulative district-level maps showed that the spatial distribution of MDR/RR-TB notifications evolved over time (Fig. 3 ). While the Kampala metropolitan corridor remained a consistent area of high notification across the entire study period, additional districts in the Eastern and Northern regions demonstrated increasing notification levels in later years. These increases were localised rather than uniform, with neighbouring districts often exhibiting contrasting notification patterns. The observed changes indicate that the geographic footprint of reported MDR/RR-TB expanded beyond its initial concentration in Central Uganda, although this expansion occurred in a heterogeneous and focal manner. Discussions This study aimed to describe the national and subnational geographic distribution of multidrug-resistant and rifampicin-resistant tuberculosis (MDR/RR-TB) in Uganda over a ten-year period using routine surveillance data. We found that MDR/RR-TB notifications were characterised by pronounced and persistent geographic heterogeneity, with sustained concentration in Central Uganda, particularly within the Kampala metropolitan area, and emerging increases in selected districts in the Eastern and Northern regions. At the district level, MDR/RR-TB notifications were unevenly distributed, with a small number of districts accounting for a disproportionate share of reported cases, while many districts consistently reported low or zero notifications. These findings indicate that MDR/RR-TB burden in Uganda is spatially patterned rather than uniformly distributed and that this pattern has evolved over time. The sustained predominance of MDR/RR-TB notifications in Central Uganda is consistent with evidence from other high-burden settings, where urban and peri-urban areas frequently report higher levels of drug-resistant TB [ 10 ], [ 12 ]. High population density, increased social mixing, and greater population mobility in urban centres are recognised drivers of TB transmission, including transmission of drug-resistant strains [ 7 ]. In addition, Central Uganda hosts the highest concentration of diagnostic infrastructure, referral facilities, and specialised TB treatment centres in the country, which likely enhances case detection and reporting. As a result, the observed concentration of MDR/RR-TB notifications in this region plausibly reflects a combination of increased transmission potential and higher diagnostic sensitivity. Beyond Central Uganda, the gradual emergence of higher MDR/RR-TB notification volumes in selected districts in Eastern and Northern Uganda suggests a spatial redistribution of reported burden over time. Similar geographic expansion has been observed in other sub-Saharan African countries following the scale-up of molecular diagnostics, where improved access to Xpert MTB/RIF testing revealed previously under-detected drug-resistant TB outside traditional urban centres [ 11 ], [ 21 ]. In Uganda, the observed increases after 2018 coincide with intensified diagnostic deployment and programmatic efforts to decentralise MDR/RR-TB services. Although it is not possible to distinguish increased transmission from improved detection using surveillance data alone, the localisation of increases to specific districts, rather than uniform changes across entire regions, suggests focal rather than widespread expansion. At the district level, MDR/RR-TB notifications displayed striking variability, with several districts reporting persistently high notification volumes across multiple years, while others consistently reported low or zero cases. This pattern mirrors findings from spatial studies in Ethiopia and South Africa, where drug-resistant TB notifications were concentrated in districts with stronger diagnostic capacity, while rural and peripheral districts appeared to have artificially low burdens due to under-detection [ 10 ], [ 11 ]. In the Ugandan context, districts with low notification volumes should therefore not be interpreted as free of MDR/RR-TB but may instead reflect limited diagnostic access, delayed care-seeking, or reporting gaps. The observed geographic heterogeneity also has important implications for MDR/RR-TB control strategies. Concentration of notifications along urban corridors and in selected border districts highlights the potential role of population mobility, labour migration, and referral patterns in shaping notification data [ 8 ]. Urban referral facilities may serve as aggregation points for patients from surrounding districts, which can amplify apparent spatial concentration at the place of diagnosis rather than the place of residence. This underscores the importance of interpreting surveillance-based spatial patterns within the broader context of health system organisation and patient movement. Our findings reinforce the growing recognition that MDR/RR-TB epidemiology is shaped not only by biological resistance mechanisms but also by structural and programmatic determinants, including diagnostic availability, health system capacity, and surveillance sensitivity [ 2 ], [ 17 ]. By describing where MDR/RR-TB is detected and how this distribution has evolved over time, this study provides a descriptive spatial foundation that can inform geographically targeted surveillance strengthening and guide more detailed investigations of clustering and temporal dynamics. Strengths and limitations A major strength of this study is the use of national surveillance data spanning a decade, encompassing all districts in Uganda and enabling comprehensive assessment of subnational spatial variation. The district-level resolution allowed identification of fine-scale heterogeneity that would be obscured in national or regional summaries. In addition, the use of routine programme data ensures direct relevance to national TB control planning and policy formulation. However, several limitations should be acknowledged. First, the analysis relied on routine notification data, which are influenced by diagnostic availability, reporting completeness, and health system performance. As such, the spatial patterns observed reflect MDR/RR-TB notifications rather than true incidence or transmission intensity. Second, the ecological design precludes inference at the individual level and does not allow assessment of patient-level risk factors, treatment history, or transmission pathways. Third, changes in diagnostic coverage and administrative boundaries over time may have affected notification patterns, although efforts were made to harmonise data and minimise inconsistencies. Conclusion MDR/RR-TB notifications in Uganda between 2014 and 2023 exhibited marked geographic heterogeneity, with persistent concentration in Central Uganda and emerging increases in selected districts in the Eastern and Northern regions. District-level analysis revealed substantial variation that is not captured by regional or national summaries. These findings underscore the importance of incorporating geospatial perspectives into routine MDR/RR-TB surveillance and highlight the need for geographically tailored approaches to TB control. Recommendations Based on these findings, national TB control efforts would benefit from continued integration of district-level geospatial analysis into routine surveillance to support targeted planning and resource allocation. Districts with persistently high notification volumes may require sustained focus on diagnostic capacity, treatment services, and patient support, while districts with consistently low notifications should be prioritised for assessment of diagnostic access and reporting completeness. Future studies integrating spatial analysis with individual-level, facility-level, and mobility data could further elucidate the drivers of observed geographic patterns and support more precise MDR/RR-TB control strategies. Declarations Ethics approval and consent to participate Ethical approval was obtained from the Busitema University Higher Degrees Committee and the relevant Research Ethics Committee, REC Approval No. MRRH-2025-605, with administrative clearance secured from the Ministry of Health at the national level. All research procedures adhered to established ethical standards for studies involving human participants. A waiver of informed consent was granted, as the study involved secondary analysis of anonymised, routinely collected surveillance data obtained from the national District Health Information System 2 (DHIS2) platform. Data confidentiality and participant privacy were strictly maintained throughout all stages of the research process. Consent for publication Not applicable 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. Author Contribution 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, 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. Data Availability The datasets used and/or analysed during the current study are available from the corresponding author upon reasonable request. References WHO, Tuberculosis (TB). 2023, [Online]. Available at: https://www.afro.who.int/health-topics/tuberculosis-tb#:~:text=In 2022%2C 2.5 million people,occur in the African Region. WHO. 2024 Global tuberculosis report . 2024. Ayenew et al. WHO’s end of TB targets: unachievable by 2035 without addressing under nutrition, forced displacement, and homelessness: trend analysis from 2015 to 2022, 2024. Kendall EA 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, 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). Loiseau C, et al. The relative transmission fitness of multidrug-resistant Mycobacterium tuberculosis in a drug resistance hotspot. Nat Commun. 2023;14(1). 10.1038/s41467-023-37719-y . Nikolayevskyy V et al. Role and value of whole genome sequencing in studying tuberculosis transmission, Clin. Microbiol. 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Factors influencing diagnosis and treatment initiation for multidrug-resistant/rifampicin-resistant tuberculosis in six sub-Saharan African countries: A mixed-methods systematic review, BMJ Glob. Heal. , vol 5, no 7, bll 1–25, 2020, 10.1136/bmjgh-2019-002280 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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