Spatial and temporal distribution of drug-resistant tuberculosis and associated risk factors in the central region of Uganda (2019- 2023), a retrospective analysis

Spatial and temporal distribution of drug-resistant tuberculosis and associated risk factors in the central region of Uganda (2019- 2023), a retrospective 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 and temporal distribution of drug-resistant tuberculosis and associated risk factors in the central region of Uganda (2019- 2023), a retrospective analysis Ibrahim Sseddangira, Duncan Kabiito, Simon Kasasa, Noah Kiwanuka This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5055957/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Introduction Drug-resistant tuberculosis (DR-TB) poses a major public health threat in Uganda, especially in densely populated and urbanized Central Uganda. National estimates indicate that 1.6% of new cases and 12.1% of previously treated cases involve DR-TB, but specific data on its distribution and predictors in Central Uganda are limited. This study aimed to map the spatial and temporal distribution of DR-TB and identify associated risk factors from 2019 to 2023 to guide resource allocation. Methodology A cross-sectional retrospective study was conducted using data from DR-TB treatment centers in Central Uganda from January 2019 to December 2023. R (Version 4.4.0) statistical software and QGIS version 3.36.3 were used for spatial analysis, and significant clusters were identified using SaTscan’s Kulldorff statistic. Multivariable Poisson regression was employed to identify factors associated with DR-TB using STATA version 14. Results Of 803 DR-TB cases, 729 were eligible for analysis. The majority were male (69%), aged 25-34 (34.2%), married (51.9%), and HIV-positive (57.8%). Rifampicin resistance was found in 59% of cases. Kampala and Wakiso had the highest case numbers (30.0% and 24.3%, respectively), with fourteen hotspot clusters identified in these and other districts. Temporal analysis showed fluctuations, peaking in 2019 and 2022. Significant risk factors included age, sex, marital status, occupation, HIV status, and risk group. Conclusion DR-TB in Central Uganda clusters in urban districts, driven by urbanization, population density, and socioeconomic factors. Temporal fluctuations, influenced by disruptions like COVID-19, highlight the need for resilient healthcare systems. Key risk factors such as HIV co-infection, malnutrition, and demographics call for targeted interventions, integrated care, and enhanced surveillance. DR-TB Uganda Central region Spatial and temporal distribution Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Tuberculosis remains one of the top infectious disease burdens globally, with an estimated 10 million cases and 1.4 million deaths in 2022 [ 1 ]. DR-TB, defined as a disease caused by TB bacteria that are resistant to at least one of the most effective TB medicines, poses a major challenge to TB control efforts [ 2 ]. In 2022, the World Health Organization (WHO) reported a staggering 465,000 new cases of DR-TB worldwide [ 1 ]. This encompasses the formidable challenges posed by Rifampicin resistance (RR), multidrug-resistant (MDR-TB), and extensively drug-resistant TB. These strains demand complex, and costly treatment regimens, straining healthcare systems across the globe [ 3 ]. The prevalence of DR-TB is increasing in sub-Saharan Africa, including Uganda [ 4 – 6 ]. Though the region is grappling with an array of health challenges, DR-TB looms large on the public health stage. Roughly 4% of new TB cases and a substantial 21% of previously treated cases in Sub-Saharan Africa were estimated to harbor DR-TB [ 7 ]. The high prevalence of HIV co-infection, coupled with resource constraints within healthcare systems, compounds the challenges of combating DR-TB effectively in this region. Uganda is among the 30 highest burden countries, with a prevalence of 201 cases per 100,000 population annually [ 1 ]. However, Uganda faces the additional challenge of rising rates of DR-TB Recent national estimates suggest 5.2% of new TB cases and 14% of retreatment cases in Uganda demonstrating rifampicin resistance, indicating probable DR-TB [ 1 ]. However, subnational variations were apparent, with higher rates concentrated in urban settings. A tertiary hospital in Kampala recorded MDR-TB rates of 10.3% among culture-positive patients [ 8 ]. One local survey in Mbarara Municipality noted 8% MDR-TB prevalence, exceeding national estimates [ 9 ]. In Uganda, the treatment regimen for TB follows the WHO guidelines. For drug-susceptible TB, patients undergo a six-month regimen consisting of an intensive phase of two months with four drugs - isoniazid, rifampicin, pyrazinamide, and ethambutol - followed by a continuation phase of four months with isoniazid and rifampicin. For DR-TB, a more extended and complex regimen is required. Typically, this involves 18–20 months of treatment with second-line drugs, including fluoroquinolones, and second-line injectables. Throughout the treatment, patients are closely monitored for adherence and adverse drug reactions, especially drug toxicity. Regular sputum tests and culture tests are conducted to track progress and ensure the effectiveness of the treatment [ 10 ]. The central region of Uganda has been heavily affected by the HIV epidemic which fuels greater risk of TB co-infection and transmission of drug-resistant strain [ 11 ]. Weak health systems, inadequate patient adherence, and over-the-counter antibiotic access further propagate resistance [ 8 ]. Unfortunately, routine surveillance data on the DR-TB burden in central Uganda specifically has been very limited in recent years. Most epidemiological insights have been derived from small-scale studies at referral facilities. Furthermore, the region’s distinctive sociodemographic composition, varied economic activities, and healthcare infrastructure intricacies shape the complex dynamics of DR-TB within its borders [ 12 ]. Additionally, it receives referrals from other regions because of the existence of national referral hospitals. These patients are added to the central region’s data contributing to the high prevalence attributed to this region [ 13 ]. Despite its pivotal role in Uganda’s healthcare landscape, a conspicuous gap exists in localized data concerning it’s DR-TB. Hence there was an urgent need for updated distribution of DR-TB across central Uganda to guide appropriate public health responses. Spatial epidemiology and geospatial analytics have become pivotal tools for understanding infectious disease transmission dynamics and hotspots [ 14 ]. Techniques like geographic information systems (GIS), cluster and temporal analysis can delineate the geographic distribution of DR-TB. Such approaches have been implemented in varied settings. In China, integration of spatial statistics with electronic surveillance data enabled accurate prediction of MDR-TB probability across provinces [ 15 ]. This facilitated targeted interventions in high-risk areas. In conjunction with spatial and temporal distribution, determining local socio-demographic, clinical factors associated with DR-TB acquisition is essential to shape control strategies to the epidemiological context. But risk factors specifically in central Uganda had been understudied. Therefore, this study generates much-needed evidence on the spatial and temporal distribution, and factors associated with drug-resistant TB in central Uganda. This can chart the scale and distribution of the growing DR-TB threat. Methodology Study area This study was conducted in the central region of Uganda, which is one of the 5 major health regions in the country[16]. It covers an area of 61,403.2Km 2 with over 24 districts and houses over 9.5 million people. The study area included the following 24 districts (Butambala, Gomba, Mpigi, Bukomansibi, Kalangala, Kalungu,Lwengo, Lyantonde, Masaka, Rakai, Sembabule, Wakiso, Buikwe, Buvuma, Kayunga, Kiboga, Kyankwanzi, Luwero, Mityana, Mubende, Mukono, Nakaseke, Nakasongola, and Kampala). The region represents a mix of urban, peri-urban, and rural populations. Kampala, the capital city, located in this region, is the most urban district with a population of over 1.8 million people and a day time population of 2.5 million people [17]. Other large population centers include Mukono, Mpigi, and Wakiso districts neighboring Kampala. The region includes Lake Kyoga along the northern border and inhabits part of the Lake Victoria basin catchment. This area was selected as the study setting given the convergence of urban, agriculture, transport, and population centers which may influence TB transmission dynamics. Out of the 18 DR-TB treatment sites in the country, the region houses Mulago national referral hospital along with other regional referral hospitals: Masaka and Mubende. which serve as DR-TB initiation and treatment centers. These facilities served as sites for key data collection on DR-TB patients. Kampala, the capital city of Uganda is also located in this region. Study Design Across-sectional retrospective study was used to analyze secondary data collected from DR-TB treatment facilities in Central Uganda. This a common and effective approach in epidemiological studies for identifying disease distribution patterns [18] Study population DR-TB patients who visited Mulago National Referral hospital, Masaka and Mubende Regional Referral Hospitals between 2019 and 2023 and their information was present in the registers Inclusion criteria Patients with confirmed DR-TB and was a resident in the central region between January 2021 to December 2023 whose results from drug susceptibility testing (DST) of their sputum collected post screening indicated resistance to any anti- TB drug and were available in the register. Exclusion criteria DR-TB patients who had been referred from other regions. Sample size consideration All the DR-TB patient data collected between January 2019 and December 2023 was sampled and analyzed. This period was selected to provide insights into recent changes in disease patterns, factoring in events like the COVID-19 pandemic, which may have influenced healthcare access and disease incidence. Data collection Data were collected from 1 st April to 30 th April 2024. The study utilized a data extraction tool which was adapted from previous similar studies [19, 20] and modified for this study. All up to date shapefiles of administrative regions in Uganda were obtained from the Uganda Bureau of Statistics [21], these shapefiles included; district, sub-county, and villages. Data analysis Data cleaning was conducted in Microsoft Excel version 19 and SPSS version 25. This involved identifying and addressing inconsistencies, incompleteness, and missingness. Of the 729 DR-TB cases, 64 (8.8%) had missing geographic coordinates, and 102 (14.0%) had incomplete clinical data. Missing geographic coordinates were addressed through geocoding in QGIS version 3.36.3 Clinical data missingness was handled through multiple imputations in SPSS version 25. Data related to demographic characteristics of patients and clinical aspects was analyzed using STATA Version 14. Univariable analysis was conducted to obtain frequencies, percentages, and summary statistics for individual and clinical characteristics. At multivariable analysis, a poisson regression model was fitted, using the backward variable elimination criterion. Variables with high p-values (>0.2) were dropped one by one based on their importance determined by the Wald test. Variable dropping continued until no further improvement is observed in the model. Spatial data analysis involved mapping the geographic distribution of DR-TB cases across the study area using QGIS version 3.36.3 [22] , a powerful open-source GIS software. QGIS enabled the creation of choropleth maps, which visually represented the number of DR-TB cases per district. To assess spatial patterns, the study applied the Global Moran’s Index [23, 24] statistic in QGIS, which determined whether DR-TB cases were randomly distributed across the central region or followed a specific pattern. Moran’s Index helped identify positive spatial autocorrelation, where similar data values (high-high or low-low) clustered geographically, indicating areas of elevated risk. A statistically significant Moran’s Index value (p-value < 0.05, positive Z-score, and above zero) led to the rejection of the null hypothesis, confirming that DR-TB cases were not randomly distributed. To identify hotspots, I used both the Getis-Ord Gi statistic and SaTScan spatial analysis, each applying distinct criteria for hotspot detection. First the investigator calculated the Gi score for each location to assess the clustering of cases in relation to surrounding areas. Statistically significant positive scores indicated hotspots, where high concentrations of cases were spatially clustered compared to neighboring regions. Additionally , to identify significant clusters of DR-TB cases, the study also utilized SaTscan software [25] with the Kulldorff spatial scan statistic [26]. This method scanned spatial windows (initially set at 50% of the study area, later refined to 10%) to detect clusters with a higher-than-expected number of cases. By applying SaTscan, the study pinpointed high-incidence clusters in the region, further refining hotspot identification and providing a robust analysis of spatial clustering patterns. To determine the temporal patterns of DR-TB, R statistical software [27] and STATA (version 14) were utilized [28]. Trends in DR-TB cases were examined over time using R statistical software, which is well-suited for statistical computing and data visualization. Time series plots were generated to visualize the yearly trends of DR-TB cases, revealing patterns and changes over time. The use of R allowed for an effective analysis of the temporal data, with capabilities for advanced plotting and data manipulation. To model the trend and make forecasts, the Auto-Regressive Integrated Moving Average (ARIMA) model was applied using STATA version 14, which provides robust tools for econometric analysis and time series forecasting. The ARIMA model captures the relationship between past values to predict future trends, accounting for autoregression, differencing, and moving average components. The specific parameters of the ARIMA model were chosen based on the Akaike Information Criterion (AIC) to ensure the best fit. STATA’s ARIMA capabilities facilitated accurate trend modeling, enabling insights into future patterns of DR-TB incidence. To assess the significance of the observed trends, a Monte Carlo simulation in STATA was conducted. Monte Carlo simulation is a method of estimating the probability of outcomes by generating random samples based on observed data distribution. In this case, the investigator generated 1,000 random samples based on the mean and standard deviation of the DR-TB cases. By comparing the maximum observed case count with the simulated data, we calculated a p-value, which showed that the observed trend was statistically significant and unlikely due to random chance. Ethical considerations Ethical approval was sought from the School of Public Health Research and Ethics Committee (SPH-REC). Permission to conduct the study at DR-TB treatment centers was sought from hospital directors. Anonymity for extracted data was maintained. Results Demographics and clinical characteristics of study participants The study included a total of 729 DR-TB patients from the central region of Uganda, covering several districts. The age distribution showed that the majority were adults aged 25-34 years (34.2%), significantly more males (69.0%). Over half (51.9%) being married, were engaged in business or trade (32.6%). More than half of the participants (55.7%) had a normal nutritional status, were HIV positive 57.8% The classification of participants based on their TB treatment history revealed that 57.8% were new TB patients, and 57.2% having rifampicin-resistant TB (RR-TB) ( Table 1 and 2 ). Table 1 :Demographic characteristics of DR-TB patients Variable Response category Frequency Percent (%) Overall 729 100 Age ≤14 11 1.5 15-24 97 13.3 25-34 249 34.2 35-44 186 25.5 45-54 121 16.6 55-64 35 4.8 ≥65 30 4.1 Sex Male 503 69.0 Female 226 31.0 Marital Status Single 214 29.4 Married 378 51.9 Separated 98 13.4 Widowed 39 5.3 Occupation Farmer 188 25.8 Business/trade 238 32.6 Student 38 5.2 Others 197 27.0 Unemployed 68 9.3 Others: Boda Boda (Motorcycle taxi) riders , Taxi drivers, Saloon operators, Mechanics, Fisherfolks and Soldiers Table 2 :Clinical characteristics of DR-TB patients Variable (n=729) Response category Frequency Percent (%) Overall 729 100 Nutrition status Normal 406 55.7 Moderate acute malnutrition 273 37.4 Severe acute malnutrition 49 6.7 HIV status Negative 308 42.2 Positive 421 57.8 Patient type New 421 57.8 Relapse 133 18.2 Treatment after failure 129 17.7 Loss to follow up 46 6.3 Type of DRTB RR 417 57.2 MDR 312 42.8 Risk Group TB contact 430 59.0 Fisherfolk 96 13.2 Tobacco user 144 19.8 Prisoner 39 5.3 Armed personnel 20 2.7 Geographic Distribution by District of residence The geographic distribution of participants showed significant variation across districts. The highest number of cases resided in Kampala, with 219 participants (30.04%), followed by Wakiso with 177 participants (24.28%) Table 3 and Figure 1 . Table 3 : Distribution of DR-TB cases by district in the Central region District Frequency Percent (%) Buikwe 9 1.23 Bukomansimbi 2 0.27 Butambala 7 0.96 Buvuma 1 0.14 Gomba 2 0.27 Kalangala 8 1.1 Kalungu 19 2.61 Kampala 219 30.04 Kassanda 13 1.78 Kayunga 8 1.1 Kiboga 2 0.27 Kyotera 18 2.47 Luwero 25 3.43 Lwengo 12 1.65 Lyantonde 4 0.55 Masaka 21 2.88 Mityana 31 4.25 Mpigi 24 3.29 Mubende 35 4.8 Mukono 34 4.66 Nakaseke 18 2.47 Nakasongola 9 1.23 Rakai 20 2.74 Sembabule 11 1.51 Wakiso 177 24.28 Spatial autocorrelation The analysis of spatial autocorrelation indicated that the spatial distribution of DR-TB cases was nonrandom hence alternative hypotheses (The spatial distribution of DR-TB cases is nonrandom) is accepted in the central region of Uganda suggesting that DR-TB cases are significantly clustered in certain areas. The Global Moran’s I value of 0.33 and the Z-score of 3.81 (P-value < 0.0001) hinting on significant clustering of DR-TB in the study area suggesting that adjacent districts exhibit similar DR-TB incidence rates (Table 4 and Figure 2). Table 4:Moran’s I statistic Moran’s I statistic Expectation Variance Z-score P-Value 0.33 -0.042 0.01 3.81 P<0.0001 Hotspots of DR-TB cases by district in Central region of Uganda The hotspot analysis revealed distinct hotspots, particularly in and around Kampala. The map (Figure 3) indicates that Kampala, highlighted in deep red, is a significant hotspot with a high concentration of DR-TB cases. Clusters of DR-TB disaggregated by village in the central region of Uganda. A total of 24 significant clusters of DR-TB cases in the central region of Uganda. Among these, 14 were identified as hotspot clusters, indicating areas with a significantly higher incidence of DR-TB cases. The most prominent hotspot clusters seen in Kampala district (Kisenyi I, Kosovo, Munyonyo, and Gaba Tr.c D), Wakiso district ( Kabulengwa, Ochieng B, Kyengera Central A and Kitooro Central), Mukono (Kasenge A, Nama, Nanuyenje and Kirondo) and Mubende (Kasaano, Kaweri and Katwe). The remaining 10 clusters were identified as low clusters, shown by blue circles on the map. These clusters represent areas with fewer DR-TB cases than expected, predominantly found in rural areas (Figure 4). Annual trend of DR-TB cases in the central region of Uganda. The yearly trend analysis reveals significant fluctuations in the number of cases recorded each year. In 2019, the number of DR-TB cases was at its peak, with approximately 170 cases recorded. However, in 2020, there was a notable decline in the number of cases, dropping to around 140. The downward trend continued into 2021, reaching the lowest point in the five-year period with about 130 cases. However, in 2022, there was a significant rebound in the number of DR-TB cases, rising back to approximately 160. (Figure 5). Monthly distribution of DR-TB cases in the central region of Uganda between 2019-2023 Analysis revealed significant variations and disruptions in case numbers from January 2019 to early 2020. Starting from March 2020, the dotted line indicates the period when Uganda went into lockdown due to the COVID-19 pandemic whereas full line indicates end of lockdown During the lockdown period, there was a sharp decline in the number of DR-TB cases recorded, and this decline continued into mid-2021. Post-lockdown, from late 2021onwards, the trend shows a gradual recovery with fluctuations in case numbers (Figure 6). Factors associated with DR-TB in the central region of Uganda Individual factors associated with DR-TB in the central region of Uganda Age emerged as a significant factor, with participants aged 25-34 years showing a higher likelihood of DR-TB (aIRR:1.43 CI: 1.11 – 1.78) compared to those aged 14 years or younger. Females exhibited a lower incidence of DR-TB compared to males (aIRR: 0.66, 95% CI: 0.54 - 0.75). Marital status also influenced DR-TB incidence, with married individuals having a higher risk (aIRR: 1.77, 95% CI: 1.49 - 2.09) compared to single individuals. Patients involved in business or trade faced a higher risk (aIRR: 1.51, 95% CI: 1.16 - 1.79) compared to farmers. (Table 5) . Clinical factors associated with DR-TB in the central region of Uganda Patients with moderate acute malnutrition had a higher risk of DR-TB compared to those with normal nutrition, aIRR 1.21 (95% CI: 1.10–1.46). HIV-positive individuals showed a notably higher risk of DR-TB, with an aIRR of 1.36 (95% CI: 1.14–1.65) compared to those who are HIV-negative. Fisherfolk have an aIRR of 1.14 (95% CI: 1.10–1.51), and tobacco users have an aIRR of 1.23 (95% CI: 1.13–1.88) (Table 6) . Table 5 : Individual factors associated with DR-TB in the central region of Uganda. Factors Attribute cIRR (95%CI) aIRR (95%CI) Age ≤14 1.0 1.0 15-24 1.21 (0.95–1.52) 1.14 (0.88–1.50) 25-34 1.53 (1.20–1.85) 1.43 (1.11–1.78) 35-44 1.42 (1.35–1.60) 1.33 (1.25–1.68) 45-54 1.31 (1.25–1.77) 1.27 (1.15–1.52) 55-64 1.11 (0.95–1.51) 1.25 (0.90–1.65) ≥65 0.91 (0.80–1.45) 1.05 (0.75–1.40) Sex Male 1.0 1.0 Female 0.41 (0.36 - 0.48) 0.66 (0.54 - 0.75) Marital status Single 1.0 1.0 Married 1.62 (1.34 - 2.01) 1.77 (1.49 - 2.09) Separated 0.38 (0.26 - 0.45) 0.46 (0.36 - 0.58) Widowed 0.16(0.11 - 0.32) 0.18 (0.13 - 0.26) Occupation Farmer 1.0 1.0 Business/trade 1.27 (1.05 - 1.54) 1.51 (1.16 - 1.79) Student 0.20 (0.14 - 0.29) 1.34 (0.99 - 1.53) Others 1.05 (0.86 - 1.28) 1.22 (1.04 - 1.41) Unemployed 0.36(0.27 - 0.48) 0.43 (0.23 - 0.56) Table 6 :Clinical factors associated with DR-TB in the central region of Uganda Variable (n=729) Response category cIRR (95%CI) aIRR (95%CI) Nutrition status Normal 1 1 Moderate acute malnutrition 1.27 (1.05–1.48) 1.21 (1.10–1.46) Severe acute malnutrition 1.04 (0.90–1.26) 1.05 (1.02–1.37) HIV status Negative 1 1 Positive 1.42 (1.18–1.66) 1.36 (1.14–1.65) Patient type New 1 1 Relapse 1.33 (0.95–1.52) 1.15 (0.88–1.55) Treatment after failure 1.10 (1.00–1.68) 1.14 (0.95–1.65) Loss to follow up 1.05 (0.98–1.35) 1.32 (0.95–1.45) Risk Group TB contact 1 1 Fisherfolk 1.06 (1.01–1.45) 1.14 (1.10–1.51) Tobacco user 1.11 (1.85–1.42) 1.23 (1.13–1.88) Prisoner 1.03 (0.85–1.70) 1.12 (0.80–1.65) Armed personnel 1.05 (0.65–1.70) 1.00 (0.60–1.65) DISCUSSION This study investigated the spatial and temporal distribution of DR-TB in the central region of Uganda from 2019 to 2023, identifying key demographic and clinical factors associated with DR-TB incidence. Spatial analysis revealed significant clusters, particularly in urban areas, while temporal analysis showed fluctuations influenced by external events such as the COVID-19 pandemic as well as seasonality. Factors including age, sex, marital status, occupation, nutrition status, and HIV status were significantly associated with DR-TB incidence. The spatial analysis identified 24 significant clusters of DR-TB, with 14 high-incidence hotspots primarily in urban centers like Kampala, Wakiso, Mubende and Mukono. The concentration of DR-TB cases in these areas can be attributed to higher population densities, which facilitate the transmission of TB. Additionally, urban areas often have better diagnostic facilities, leading to higher detection rates. A study conducted in Kampala identified urban centers as hotspots for TB due to high population density and improved diagnostic capabilities. This is also consistent with findings from other studies, which also highlighted urbanization and population density as significant predictors of TB incidence [ 29 ]. Additionally, social determinants of health, including poverty and crowded living conditions, prevalent in urban settings, contribute to the higher incidence of DR-TB [ 30 ]. Moreover, urban centers like Kampala have a more transient population, which can lead to increased spread of TB as individuals move between districts. This transient nature, combined with high population density, creates an environment conducive to the rapid spread of DR-TB. Comparatively, rural areas, with lower population densities and less movement, showed fewer cases, aligning with the general understanding that TB transmission rates are lower in less densely populated areas as seen in a study conducted in Zimbabwe [ 31 ]. The temporal analysis revealed fluctuations in DR-TB cases, with notable impacts from the COVID-19 pandemic. The peaks observed during mid-year months, particularly between July and September, could be linked to seasonal factors such as changes in weather conditions, which affect the transmission dynamics of TB. The lockdown period in 2020 saw a sharp decline in the number of DR-TB cases, likely due to disruptions in healthcare services and reduced access to diagnostic facilities. This pattern aligns with global observations where TB case detection declined during the pandemic [ 32 ]. A similar study in Uganda reported a decrease in TB notifications during the COVID-19 lockdown, reflecting the broader impact of the pandemic on TB control programs [ 33 ]. The post-lockdown period showed a recovery in case numbers, indicating the resumption of TB services and increased case detection efforts. The fluctuation in DR-TB cases can also be attributed to changes in healthcare-seeking behavior during the pandemic. Fear of contracting COVID-19 and movement restrictions likely led to fewer individuals seeking medical care, resulting in underreporting of cases. Age was a significant factor, with older age groups showing a higher likelihood of acquiring DR-TB. This finding is consistent with a study conducted in Pakistan, which reported higher DR-TB rates among older populations due to cumulative exposure and longer disease duration [ 34 ]. The increased susceptibility in older age groups is attributed to weakened immune systems and a longer duration of exposure to TB bacteria. Females had a lower incidence of DR-TB compared to males, possibly due to differences in healthcare-seeking behavior. This gender disparity in TB incidence has been documented in other studies in Africa [ 35 , 36 ]. Men are often more likely to engage in behaviors that increase TB risk, such as smoking and working in environments with higher exposure to TB bacteria. Furthermore, the predominance of male participants (69%) aligns with global DR-TB data trends, which suggest higher TB exposure in men due to occupational risks and delayed health-seeking behaviors [ 37 ]. Additionally, women may face barriers to accessing healthcare, leading to underdiagnosis and underreporting of TB cases among females. Marital status influenced DR-TB incidence, with married individuals showing higher risks. This could be related to household transmission dynamics, where close contact with an infected spouse increases the risk of TB transmission. A study in Ethiopia found similar associations between marital status and TB incidence [ 38 ]. Occupation also played a role, with individuals in business or trade having higher risks, potentially due to increased social interactions and mobility. Conversely, unemployed individuals had a lower risk, possibly due to reduced social exposure. Similar findings have been documented in India [ 39 ]. The significant link between moderate acute malnutrition and increased DR-TB risk can be attributed to malnutrition’s weakening effect on immune function, particularly T-cell activity, which is vital for controlling TB infections. This immune suppression heightens susceptibility to TB and its drug-resistant forms. Similar findings have been observed in studies where malnutrition was correlated with higher TB incidence and worse treatment outcomes [ 40 , 41 ]. HIV status was another critical factor, with HIV-negative individuals having a lower risk of DR-TB. The co-infection of HIV and TB is a well-established challenge in Sub-Saharan Africa, exacerbating the TB epidemic due to the immunocompromised status of HIV patients [ 36 ]. HIV-positive individuals are more susceptible to developing TB due to their weakened immune systems, which struggle to control TB bacteria. The elevated DR-TB risk among fisherfolk and tobacco users is likely due to their occupational and lifestyle-related challenges, such as overcrowded living conditions and impaired lung health from smoking, which exacerbate TB exposure and complicate treatment. These observations align with other studies that have identified high-risk groups like fisherfolk and smokers as particularly vulnerable to TB due to environmental and behavioral factors [ 42 , 43 ]. Limitations The study was limited by incomplete and missing data, geographic coordinates were unavailable for 64 cases (8.8%), and clinical data were incomplete for 102 cases (14.0%). While geocoding and multiple imputation methods were applied to address these gaps, imputation may not fully replicate the true underlying data, potentially introducing biases. This missingness could affect the precision of spatial and clinical analyses, limiting the generalizability of findings to the entire DR-TB population in Central Uganda. Additionally, the retrospective analysis limits the ability to establish causal relationships between identified factors and DR-TB incidence, necessitating further longitudinal studies to confirm these associations. Moreover, only a subset of potential predictors of DR-TB identified in the literature were analyzed and not all contextual and environmental factors could be consistently retrieved from existing records. This limitation arose from the reliance on secondary data, which did not include several variables of interest, such as socio-economic factors, and lifestyle choices. The unavailability of these variables in the dataset constrained the analysis, potentially affecting the comprehensiveness of the findings. This study primarily focuses on public DR-TB treatment centers. It does not include private treatment centers like TASO, Baylor, and IDI, which may also manage DR-TB cases. This exclusion could lead to an underestimation of the total burden of DR-TB in the region. Conclusion This study demonstrates that DR-TB in Central Uganda is predominantly clustered in urban districts such as Kampala, Wakiso, Mukono, and Mubende, identifying them as hotspots with higher transmission potential. The spatial autocorrelation confirms non-random clustering, with densely populated urban areas contributing significantly to the spread of DR-TB. Temporal trends revealed a sharp decline in DR-TB cases in 2021, likely due to COVID-19-related disruptions in healthcare services, The findings also highlight a strong association between HIV co-infection and DR-TB. Demographic factors such as younger age, male gender, and marital status further influenced DR-TB risk. Therefore, expanding diagnostic and surveillance capacity in hotspot districts is imperative, with investments in laboratory infrastructure, training of healthcare personnel, and collaboration with local health authorities. Integrating TB and HIV care is critical to manage the high burden of co-infection, while incorporating nutritional support into DR-TB treatment protocols can mitigate the impact of malnutrition on patient outcomes. Strengthening the resilience of TB services is essential to ensure continuity during public health crises, as evidenced by the disruptions during the COVID-19 pandemic. Targeted public health campaigns should prioritize younger men in urban areas, promoting early healthcare-seeking behavior and TB prevention strategies. Furthermore, longitudinal surveillance systems are needed to monitor temporal variations in DR-TB and adapt response strategies accordingly. Research into the role of urbanization, environmental factors such as climate, and the contributions of private healthcare providers to DR-TB management will provide insights to inform more effective, data-driven interventions. Abbreviations AIDs: Acquired Immunodeficiency Syndrome DR -TB: Drug Resistant Tuberculosis GIS: Geographical Information System MDR-TB: Multi Drug Resistant Tuberculosis TB: Tuberculosis WHO: World Health Organization Declarations Ethics and Approval: Ethical approval was obtained from the School of Public Health Research and Ethics Committee (SPH-REC). Permission to conduct the study at DR-TB treatment centers was obtained from hospital directors. Anonymity for extracted data was maintained. Consent to Participate Declaration This study utilized secondary data collected from hospital registers. As such, no direct contact with human participants was made, and informed consent was not applicable. Permission to access and use the data was obtained from hospital directors through their research and ethics offices. Consent for Publication: Not applicable Availability of data: The datasets used during the analysis are available from the corresponding author on reasonable request. Competing interests: The authors declare they have no competing interests. Funding: The research was supported by funds from NIH as part of the D43 multi-disciplinary training program in digital mobile technologies to study tuberculosis through University of Georgia and Makerere University School of Public Health. Authors’ contribution: S.I conceptualized the manuscript idea and took lead in writing the manuscript and writing the methodology. D.K took the lead in data analysis, S.K and N.K reviewed the manuscript for intellectual content and scientific integrity. All authors read and approved the final manuscript. Acknowledgement: We extend our sincere gratitude to University of Georgia and Makerere University School of Public Health for offering us the opportunity and guidance during the conduct of this research. We would like to thank the staff of Mulago National Referral hospital and Masaka and Mubende Regional Referral hospitals for the support provided during data collection. References WHO, ORGANIZATION, W. H. Global Tuberculosis Report 2022. Geneva: World Health Organization. 27 October 2022.[Online] Accessed 27 October 2022. 2022. Batte, C., et al., Prevalence and factors associated with non-adherence to multi-drug resistant tuberculosis (MDR-TB) treatment at Mulago National Referral Hospital, Kampala, Uganda. 2021. 21 (1): p. 238-47. Rahman, M.A. and A. Sarkar, Extensively Drug-resistant Tuberculosis (XDR-TB): A daunting challenge to the current End TB Strategy and policy recommendations. Indian Journal of Tuberculosis, 2017. 64 (3): p. 153-160. Ismail, N., et al., Drug resistant tuberculosis in Africa: Current status, gaps and opportunities. African journal of laboratory medicine, 2018. 7 (2): p. 1-11. Musa, B.M., et al., Trends in prevalence of multi drug resistant tuberculosis in sub-Saharan Africa: a systematic review and meta-analysis. PLoS One, 2017. 12 (9): p. e0185105. Kidenya, B.R., et al., Epidemiology and genetic diversity of multidrug-resistant tuberculosis in East Africa. 2014. 94 (1): p. 1-7. Molla, K.A., M.A. Reta, and Y.Y. Ayene, Prevalence of multidrug-resistant tuberculosis in East Africa: a systematic review and meta-analysis. PloS one, 2022. 17 (6): p. e0270272. Bbosa, G.S., et al., Predictors of Multidrug-Resistant Tuberculosis among Patients with Pulmonary Tuberculosis at Mulago National Referral Hospital, Uganda. Tuberculosis Research and Treatment, 2019, Article ID 9601370, 7 pages. 2019. Muhumuza, J., et al., Prevalence and risk factors for multi-drug resistant tuberculosis among newly diagnosed tuberculosis patients in a metropolitan city with a high HIV/AIDS burden: a cross-sectional study in Kampala, Uganda. BMC Research Notes, 7, 560. 2014. MoH, Manual for Management and control of Tuberculosis and Leprosy. 2017. Nyamogoba, H.D., et al., HIV co-infection with tuberculous and non-tuberculous mycobacteria in western Kenya: challenges in the diagnosis and management. Afr Health Sci, 2012. 12 (3): p. 305-11. Stein, C.M., et al., Resistance and susceptibility to Mycobacterium tuberculosis infection and disease in tuberculosis households in Kampala, Uganda. American journal of epidemiology, 2018. 187 (7): p. 1477-1489. Bayowa, J.R., et al., Mortality rate and associated factors among patients co-infected with drug resistant tuberculosis/HIV at Mulago National Referral Hospital, Uganda, a retrospective cohort study. PLOS Global Public Health, 2023. 3 (7): p. e0001020. Weiss, K., et al., Geospatial modelling for the optimisation of TB case detection in HIV-infected populations. International Journal of Tuberculosis and Lung Disease, 18(7), 857–864. . 2014. Liu, Y., Li, X., , et al., Geospatial spread of multidrug-resistant tuberculosis (MDR TB) in a high-incidence setting: a retrospective spatial analysis in Shandong Province, China. BMC Infectious Diseases, 16(1), 409. 2016. UDHS, https://www.health.go.ug/cause/uganda-demographic-and-health-survey-udhs-2022-key-findings/ . 2022. UBOS, National population and housing census 2024. 2024. Talari, K. and M. Goyal, Retrospective studies–utility and caveats. Journal of the Royal College of Physicians of Edinburgh, 2020. 50 (4): p. 398-402. Nakafeero, B., Drug resistant tuberculosis in Karamoja region: prevalence, patterns and associated factors . 2018, Makerere University. Aturinde, A., et al., Spatial analysis of HIV-TB co-clustering in Uganda. BMC infectious diseases, 2019. 19 : p. 1-10. UBOS, Uganda Bureau of Statistics. Kampala, Uganda and Calverton, 2020. QGISDevelopmentTeam, QGIS Development Team (2024). QGIS Geographic Information System. Open Source Geospatial Foundation Project. Available at: https://qgis.org . 2024. Moran, P.A., Notes on continuous stochastic phenomena. Biometrika, 1950. 37 (1/2): p. 17-23. Chen, Y., New approaches for calculating Moran’s index of spatial autocorrelation. PloS one, 2013. 8 (7): p. e68336. SaTScan™, SaTScan™ software for the spatial, temporal, and space-time scan statistics. Available at: https://www.satscan.org/ . 2024. Kulldorff, M., Kulldorff, M. (1997). A spatial scan statistic. Communications in Statistics - Theory and Methods, 26(6), 1481–1496. 1997. RCoreTeam, A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Available at: https://www.R-project.org/ . 2023. StataCorp, Stata Statistical Software: Release 17. College Station, TX: StataCorp LLC. 2023. Kaur, K.K., et al., Urbanization and Tuberculosis in Peninsular, Malaysia. Malays J Med Sci, 2020. 16 : p. 63-69. Zille, A.I., et al., Social determinants of pulmonary tuberculosis in Brazil: an ecological study. BMC pulmonary medicine, 2019. 19 : p. 1-9. Chirenda, J., et al., Spatial distribution of Mycobacterium tuberculosis in metropolitan Harare, Zimbabwe. PloS one, 2020. 15 (4): p. e0231637. McQuaid, C.F., et al., The impact of COVID-19 on TB: a review of the data. The International Journal of Tuberculosis and Lung Disease, 2021. 25 (6): p. 436-446. Kendall, E.A., et al., Decline in prevalence of tuberculosis following an intensive case finding campaign and the COVID-19 pandemic in an urban Ugandan community. thorax, 2024. 79 (4): p. 325-331. Saifullah, A., et al., Evaluation of risk factors associated with the development of MDR-and XDR-TB in a tertiary care hospital: a retrospective cohort study. PeerJ, 2021. 9 : p. e10826. Oladimeji, O., et al., Gender and drug-resistant Tuberculosis in Nigeria. Tropical Medicine and Infectious Disease, 2023. 8 (2): p. 104. McNabb, K., A. Bergman, and J. Farley, Risk factors for poor engagement in drug-resistant TB care in South Africa: a systematic review. Public Health Action, 2021. 11 (3): p. 139-145. Chiposi, L., L.P. Cele, and M. Mokgatle, Prevalence of delay in seeking tuberculosis care and the health care seeking behaviour profile of tuberculous patients in a rural district of KwaZulu Natal, South Africa. Pan African Medical Journal, 2021. 39 (1). Badgeba, A., et al., Determinants of multidrug-resistant mycobacterium tuberculosis infection: a multicenter study from southern Ethiopia. Infection and Drug Resistance, 2022: p. 3523-3535. Das, U., EXPLORING FACTORS LEADING TO RISE OF TB AND MDR-TB CASES IN INDIA DESPITE THE REPORTED SUCCESS OF THE NATIONAL TB PROGRAMME. 2013. Bhargava, A., et al., Nutritional status of adult patients with pulmonary tuberculosis in rural central India and its association with mortality. PloS one, 2013. 8 (10): p. e77979. Téllez-Navarrete, N.A., et al., Malnutrition and tuberculosis: the gap between basic research and clinical trials. The Journal of Infection in Developing Countries, 2021. 15 (03): p. 310-319. Ragan, E., et al., The impact of alcohol use on tuberculosis treatment outcomes: a systematic review and meta-analysis. The International Journal of Tuberculosis and Lung Disease, 2020. 24 (1): p. 73-82. Silva, D.R., et al., Risk factors for tuberculosis: diabetes, smoking, alcohol use, and the use of other drugs. Jornal Brasileiro de Pneumologia, 2018. 44 : p. 145-152. Additional Declarations No competing interests reported. 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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-5055957","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":391624581,"identity":"8208d159-1a7e-43fd-9888-25b8b106fa47","order_by":0,"name":"Ibrahim 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2","display":"","copyAsset":false,"role":"figure","size":142007,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMap showing spatial relationship of districts where DR-TB cases were recorded in the study area\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5055957/v1/b2620db7883d20ab49584f0a.png"},{"id":72206098,"identity":"be6b5af2-2ee8-42c0-a630-ddc7d57baa0c","added_by":"auto","created_at":"2024-12-23 16:41:44","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":150136,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHotspots\u003c/strong\u003e \u003cstrong\u003eof\u003c/strong\u003e \u003cstrong\u003eDR-TB\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5055957/v1/420fdd713a2efb80589aa358.png"},{"id":72206102,"identity":"fdcafb39-f110-492d-ba82-3f6b9d048983","added_by":"auto","created_at":"2024-12-23 16:41:44","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":239051,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSpatial distribution of hotspot and cold spot clusters of DR-TB in the central region of Uganda\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5055957/v1/ccea9d3c5cfb092191d08e73.png"},{"id":72206095,"identity":"f3477b99-6038-48fd-9706-7f5ffe505e54","added_by":"auto","created_at":"2024-12-23 16:41:44","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":68581,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAnnual trend of DR-TB cases reported in the central region of Uganda between 2019 and 2023\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-5055957/v1/cfff249dfba947e63bc43ab8.png"},{"id":72206118,"identity":"2ec0baeb-a4ee-48de-8fda-cdb2910ec285","added_by":"auto","created_at":"2024-12-23 16:41:45","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":75339,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMonthly trend of DR-TB case from the central region of Uganda from January 2019 to December 2023\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-5055957/v1/6bc3ff657355832357f6d282.png"},{"id":72208777,"identity":"8985f850-3b03-4ee4-800a-b21aa09b9aa9","added_by":"auto","created_at":"2024-12-23 17:13:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1932633,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5055957/v1/85136cdf-f9e6-4d21-a3d8-e135ed02a889.pdf"},{"id":72206093,"identity":"647cb583-2df9-4c76-9cef-cc281a1e9e6c","added_by":"auto","created_at":"2024-12-23 16:41:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":22094,"visible":true,"origin":"","legend":"","description":"","filename":"DataExtractiontool.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5055957/v1/443dcfeb24a528fd3344a4f7.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Spatial and temporal distribution of drug-resistant tuberculosis and associated risk factors in the central region of Uganda (2019- 2023), a retrospective analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eTuberculosis remains one of the top infectious disease burdens globally, with an estimated 10\u0026nbsp;million cases and 1.4\u0026nbsp;million deaths in 2022 [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. DR-TB, defined as a disease caused by TB bacteria that are resistant to at least one of the most effective TB medicines, poses a major challenge to TB control efforts [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In 2022, the World Health Organization (WHO) reported a staggering 465,000 new cases of DR-TB worldwide [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. This encompasses the formidable challenges posed by Rifampicin resistance (RR), multidrug-resistant (MDR-TB), and extensively drug-resistant TB. These strains demand complex, and costly treatment regimens, straining healthcare systems across the globe [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe prevalence of DR-TB is increasing in sub-Saharan Africa, including Uganda [\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Though the region is grappling with an array of health challenges, DR-TB looms large on the public health stage. Roughly 4% of new TB cases and a substantial 21% of previously treated cases in Sub-Saharan Africa were estimated to harbor DR-TB [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The high prevalence of HIV co-infection, coupled with resource constraints within healthcare systems, compounds the challenges of combating DR-TB effectively in this region.\u003c/p\u003e \u003cp\u003eUganda is among the 30 highest burden countries, with a prevalence of 201 cases per 100,000 population annually [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. However, Uganda faces the additional challenge of rising rates of DR-TB\u003c/p\u003e \u003cp\u003eRecent national estimates suggest 5.2% of new TB cases and 14% of retreatment cases in Uganda demonstrating rifampicin resistance, indicating probable DR-TB [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. However, subnational variations were apparent, with higher rates concentrated in urban settings. A tertiary hospital in Kampala recorded MDR-TB rates of 10.3% among culture-positive patients [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. One local survey in Mbarara Municipality noted 8% MDR-TB prevalence, exceeding national estimates [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e In Uganda, the treatment regimen for TB follows the WHO guidelines. For drug-susceptible TB, patients undergo a six-month regimen consisting of an intensive phase of two months with four drugs - isoniazid, rifampicin, pyrazinamide, and ethambutol - followed by a continuation phase of four months with isoniazid and rifampicin. For DR-TB, a more extended and complex regimen is required. Typically, this involves 18\u0026ndash;20 months of treatment with second-line drugs, including fluoroquinolones, and second-line injectables. Throughout the treatment, patients are closely monitored for adherence and adverse drug reactions, especially drug toxicity. Regular sputum tests and culture tests are conducted to track progress and ensure the effectiveness of the treatment [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe central region of Uganda has been heavily affected by the HIV epidemic which fuels greater risk of TB co-infection and transmission of drug-resistant strain [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Weak health systems, inadequate patient adherence, and over-the-counter antibiotic access further propagate resistance [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Unfortunately, routine surveillance data on the DR-TB burden in central Uganda specifically has been very limited in recent years. Most epidemiological insights have been derived from small-scale studies at referral facilities.\u003c/p\u003e \u003cp\u003eFurthermore, the region\u0026rsquo;s distinctive sociodemographic composition, varied economic activities, and healthcare infrastructure intricacies shape the complex dynamics of DR-TB within its borders [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Additionally, it receives referrals from other regions because of the existence of national referral hospitals. These patients are added to the central region\u0026rsquo;s data contributing to the high prevalence attributed to this region [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Despite its pivotal role in Uganda\u0026rsquo;s healthcare landscape, a conspicuous gap exists in localized data concerning it\u0026rsquo;s DR-TB.\u003c/p\u003e \u003cp\u003eHence there was an urgent need for updated distribution of DR-TB across central Uganda to guide appropriate public health responses. Spatial epidemiology and geospatial analytics have become pivotal tools for understanding infectious disease transmission dynamics and hotspots [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Techniques like geographic information systems (GIS), cluster and temporal analysis can delineate the geographic distribution of DR-TB. Such approaches have been implemented in varied settings. In China, integration of spatial statistics with electronic surveillance data enabled accurate prediction of MDR-TB probability across provinces [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. This facilitated targeted interventions in high-risk areas.\u003c/p\u003e \u003cp\u003eIn conjunction with spatial and temporal distribution, determining local socio-demographic, clinical factors associated with DR-TB acquisition is essential to shape control strategies to the epidemiological context. But risk factors specifically in central Uganda had been understudied.\u003c/p\u003e \u003cp\u003eTherefore, this study generates much-needed evidence on the spatial and temporal distribution, and factors associated with drug-resistant TB in central Uganda. This can chart the scale and distribution of the growing DR-TB threat.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cp\u003e\u003cstrong\u003eStudy area\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in the central region of Uganda, which is one of the 5 major health regions in the country[16]. It covers an area of 61,403.2Km\u003csup\u003e2\u003c/sup\u003e with over 24 districts and houses over 9.5 million people. The study area included the following 24 districts (Butambala, Gomba, Mpigi, Bukomansibi, Kalangala, Kalungu,Lwengo, Lyantonde, Masaka, Rakai, Sembabule, Wakiso, Buikwe, Buvuma, Kayunga, Kiboga, Kyankwanzi, Luwero, Mityana, Mubende, Mukono, Nakaseke, Nakasongola, and Kampala). The region represents a mix of urban, peri-urban, and rural populations. Kampala, the capital city, located in this region, is the most urban district with a population of over 1.8 million people and a day time population of 2.5 million people\u0026nbsp;[17]. Other large population centers include Mukono, Mpigi, and Wakiso districts neighboring Kampala. The region includes Lake Kyoga along the northern border and inhabits part of the Lake Victoria basin catchment. This area was selected as the study setting given the convergence of urban, agriculture, transport, and population centers which may influence TB transmission dynamics.\u003c/p\u003e\n\u003cp\u003eOut of the 18 DR-TB treatment sites in the country, the region houses Mulago national referral hospital along with other regional referral hospitals: Masaka and Mubende. which serve as DR-TB initiation and treatment centers. These facilities served as sites for key data collection on DR-TB patients. Kampala, the capital city of Uganda is also located in this region. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy Design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAcross-sectional retrospective study was used to analyze secondary data collected from DR-TB treatment facilities in Central Uganda. This a common and effective approach in epidemiological studies for identifying disease distribution patterns [18]\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDR-TB patients who visited Mulago National Referral hospital, Masaka and Mubende Regional Referral Hospitals between 2019 and 2023 and their information was present in the registers\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInclusion criteria\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePatients with confirmed DR-TB and was a resident in the central region between January 2021 to December 2023 whose results from drug susceptibility testing (DST) of their sputum collected post screening indicated resistance to any anti- TB drug and were available in the register.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExclusion criteria\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDR-TB patients who had been referred from other regions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSample size consideration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the DR-TB patient data collected between January 2019 and December 2023 was sampled and analyzed. This period was selected to provide insights into recent changes in disease patterns, factoring in events like the COVID-19 pandemic, which may have influenced healthcare access and disease incidence.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData were collected from 1\u003csup\u003est\u003c/sup\u003e April to 30\u003csup\u003eth\u003c/sup\u003e April 2024. The study utilized a data extraction tool which was \u0026nbsp;adapted from previous similar studies \u0026nbsp;[19, 20] and modified for this study. All up to date \u0026nbsp;shapefiles of administrative regions in Uganda were obtained from the Uganda Bureau of Statistics [21], these shapefiles included; district, sub-county, and villages.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData cleaning was conducted in Microsoft Excel version 19 and SPSS version 25. This involved identifying and addressing inconsistencies, incompleteness, and missingness. Of the 729 DR-TB cases, 64 (8.8%) had missing geographic coordinates, and 102 (14.0%) had incomplete clinical data. Missing geographic coordinates were addressed through geocoding in QGIS version 3.36.3 Clinical data missingness was handled through multiple imputations in SPSS version 25.\u003c/p\u003e\n\u003cp\u003eData related to demographic characteristics of patients and clinical aspects was analyzed using STATA Version 14. Univariable analysis was conducted to obtain frequencies, percentages, and summary statistics for individual and clinical characteristics.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAt multivariable analysis, a poisson regression model was fitted, using the backward variable elimination criterion. Variables with high p-values (\u0026gt;0.2) were dropped one by one based on their importance determined by the Wald test. Variable dropping continued until no further improvement is observed in the model.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSpatial data analysis involved mapping the geographic distribution of DR-TB cases across the study area using QGIS version 3.36.3\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e[22]\u003c/strong\u003e, a powerful open-source GIS software. QGIS enabled the creation of choropleth maps, which visually represented the number of DR-TB cases per district.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo assess spatial patterns, the study applied the Global Moran\u0026rsquo;s Index [23, 24] statistic in QGIS, which determined whether DR-TB cases were randomly distributed across the central region or followed a specific pattern. Moran\u0026rsquo;s Index helped identify positive spatial autocorrelation, where similar data values (high-high or low-low) clustered geographically, indicating areas of elevated risk. A statistically significant Moran\u0026rsquo;s Index value (p-value \u0026lt; 0.05, positive Z-score, and above zero) led to the rejection of the null hypothesis, confirming that DR-TB cases were not randomly distributed.\u003c/p\u003e\n\u003cp\u003eTo identify hotspots, I used both the Getis-Ord Gi statistic and SaTScan spatial analysis, each applying distinct criteria for hotspot detection.\u003c/p\u003e\n\u003cp\u003eFirst the investigator calculated the Gi score for each location to assess the clustering of cases in relation to surrounding areas. Statistically significant positive scores indicated hotspots, where high concentrations of cases were spatially clustered compared to neighboring regions.\u003c/p\u003e\n\u003cp\u003eAdditionally\u003cstrong\u003e,\u0026nbsp;\u003c/strong\u003eto identify significant clusters of DR-TB cases, the study also utilized SaTscan software [25] with the Kulldorff spatial scan statistic [26]. This method scanned spatial windows (initially set at 50% of the study area, later refined to 10%) to detect clusters with a higher-than-expected number of cases. By applying SaTscan, the study pinpointed high-incidence clusters in the region, further refining hotspot identification and providing a robust analysis of spatial clustering patterns.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo determine the temporal patterns of DR-TB, R statistical software [27] and STATA (version 14) were utilized [28].\u0026nbsp;Trends in DR-TB cases were examined over time using R statistical software, which is well-suited for statistical computing and data visualization. Time series plots were generated to visualize the yearly trends of DR-TB cases, revealing patterns and changes over time. The use of R allowed for an effective analysis of the temporal data, with capabilities for advanced plotting and data manipulation.\u003c/p\u003e\n\u003cp\u003eTo model the trend and make forecasts, the Auto-Regressive Integrated Moving Average (ARIMA) model was applied using STATA version 14, which provides robust tools for econometric analysis and time series forecasting. The ARIMA model captures the relationship between past values to predict future trends, accounting for autoregression, differencing, and moving average components. The specific parameters of the ARIMA model were chosen based on the Akaike Information Criterion (AIC) to ensure the best fit. STATA\u0026rsquo;s ARIMA capabilities facilitated accurate trend modeling, enabling insights into future patterns of DR-TB incidence.\u003c/p\u003e\n\u003cp\u003eTo assess the significance of the observed trends, a Monte Carlo simulation in STATA was conducted. Monte Carlo simulation is a method of estimating the probability of outcomes by generating random samples based on observed data distribution. In this case, the investigator generated 1,000 random samples based on the mean and standard deviation of the DR-TB cases. By comparing the maximum observed case count with the simulated data, we calculated a p-value, which showed that the observed trend was statistically significant and unlikely due to random chance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical considerations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval was sought from the School of Public Health Research and Ethics Committee (SPH-REC). Permission to conduct the study at DR-TB treatment centers was sought from hospital directors. Anonymity for extracted data was maintained.\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eDemographics and clinical characteristics of study participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study included a total of 729 DR-TB patients from the central region of Uganda, covering several districts. The age distribution showed that the majority were adults aged 25-34 years (34.2%), significantly more males (69.0%). Over half (51.9%) being married, were engaged in business or trade (32.6%).\u003c/p\u003e\n\u003cp\u003eMore than half of the participants (55.7%) had a normal nutritional status, were HIV positive 57.8%\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe classification of participants based on their TB treatment history revealed that 57.8% were new TB patients, and 57.2% having rifampicin-resistant TB (RR-TB) (\u003cstrong\u003eTable 1 and 2\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003cstrong\u003e:Demographic characteristics of DR-TB patients\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"504\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eResponse category\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFrequency\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePercent (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverall\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e729\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"7\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u0026le;14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e15-24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e13.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e25-34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e249\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e34.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e35-44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e186\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e25.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e45-54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e16.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e55-64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e4.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u0026ge;65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e4.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e503\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e69.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e226\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e31.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital Status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eSingle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e214\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e29.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e378\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e51.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eSeparated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e13.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eWidowed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e5.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOccupation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eFarmer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e25.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eBusiness/trade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e32.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eStudent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e5.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e197\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e27.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eUnemployed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e9.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eOthers: Boda Boda (Motorcycle taxi) riders , Taxi drivers, Saloon operators, Mechanics, Fisherfolks and Soldiers \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003cstrong\u003e:Clinical characteristics of DR-TB patients\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"592\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 158px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable (n=729)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 158px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eResponse category\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 149px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFrequency\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePercent (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 158px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverall\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 158px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 149px;\"\u003e\n \u003cp\u003e729\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 158px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNutrition status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e406\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e55.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 158px;\"\u003e\n \u003cp\u003eModerate acute malnutrition\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e273\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e37.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 158px;\"\u003e\n \u003cp\u003eSevere acute malnutrition\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e6.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 158px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHIV status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 158px;\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e308\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e\n \u003cp\u003e42.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e421\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e\n \u003cp\u003e57.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 158px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePatient type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003eNew\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e421\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e57.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003eRelapse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e18.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003eTreatment after failure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e17.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003eLoss to follow up\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e6.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 158px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eType of DRTB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003eRR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e417\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e57.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003eMDR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e312\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e42.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" style=\"width: 158px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRisk Group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003eTB contact\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e430\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e59.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003eFisherfolk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e13.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003eTobacco user\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e19.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003ePrisoner\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e5.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003eArmed personnel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e2.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eGeographic Distribution by District of residence\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe geographic distribution of participants showed significant variation across districts. The highest number of cases resided in Kampala, with 219 participants (30.04%), followed by Wakiso with 177 participants (24.28%) \u003cstrong\u003eTable 3 and Figure 1\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003cstrong\u003e: Distribution of DR-TB cases by district in the Central region\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"618\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDistrict\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFrequency\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePercent (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 192px;\"\u003e\n \u003cp\u003eBuikwe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 174px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 252px;\"\u003e\n \u003cp\u003e1.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 192px;\"\u003e\n \u003cp\u003eBukomansimbi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 174px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 252px;\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 192px;\"\u003e\n \u003cp\u003eButambala\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 174px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 252px;\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 192px;\"\u003e\n \u003cp\u003eBuvuma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 174px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 252px;\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 192px;\"\u003e\n \u003cp\u003eGomba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 174px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 252px;\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 192px;\"\u003e\n \u003cp\u003eKalangala\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 174px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 252px;\"\u003e\n \u003cp\u003e1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 192px;\"\u003e\n \u003cp\u003eKalungu\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 174px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 252px;\"\u003e\n \u003cp\u003e2.61\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 192px;\"\u003e\n \u003cp\u003eKampala\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 174px;\"\u003e\n \u003cp\u003e219\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 252px;\"\u003e\n \u003cp\u003e30.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 192px;\"\u003e\n \u003cp\u003eKassanda\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 174px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 252px;\"\u003e\n \u003cp\u003e1.78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 192px;\"\u003e\n \u003cp\u003eKayunga\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 174px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 252px;\"\u003e\n \u003cp\u003e1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 192px;\"\u003e\n \u003cp\u003eKiboga\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 174px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 252px;\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 192px;\"\u003e\n \u003cp\u003eKyotera\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 174px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 252px;\"\u003e\n \u003cp\u003e2.47\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 192px;\"\u003e\n \u003cp\u003eLuwero\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 174px;\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 252px;\"\u003e\n \u003cp\u003e3.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 192px;\"\u003e\n \u003cp\u003eLwengo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 174px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 252px;\"\u003e\n \u003cp\u003e1.65\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 192px;\"\u003e\n \u003cp\u003eLyantonde\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 174px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 252px;\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 192px;\"\u003e\n \u003cp\u003eMasaka\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 174px;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 252px;\"\u003e\n \u003cp\u003e2.88\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 192px;\"\u003e\n \u003cp\u003eMityana\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 174px;\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 252px;\"\u003e\n \u003cp\u003e4.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 192px;\"\u003e\n \u003cp\u003eMpigi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 174px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 252px;\"\u003e\n \u003cp\u003e3.29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 192px;\"\u003e\n \u003cp\u003eMubende\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 174px;\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 252px;\"\u003e\n \u003cp\u003e4.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 192px;\"\u003e\n \u003cp\u003eMukono\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 174px;\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 252px;\"\u003e\n \u003cp\u003e4.66\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 192px;\"\u003e\n \u003cp\u003eNakaseke\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 174px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 252px;\"\u003e\n \u003cp\u003e2.47\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 192px;\"\u003e\n \u003cp\u003eNakasongola\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 174px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 252px;\"\u003e\n \u003cp\u003e1.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 192px;\"\u003e\n \u003cp\u003eRakai\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 174px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 252px;\"\u003e\n \u003cp\u003e2.74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 192px;\"\u003e\n \u003cp\u003eSembabule\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 174px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 252px;\"\u003e\n \u003cp\u003e1.51\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 192px;\"\u003e\n \u003cp\u003eWakiso\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 174px;\"\u003e\n \u003cp\u003e177\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 252px;\"\u003e\n \u003cp\u003e24.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eSpatial autocorrelation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe analysis of spatial autocorrelation indicated that the spatial distribution of DR-TB cases was nonrandom hence alternative hypotheses (The spatial distribution of DR-TB cases is nonrandom) is accepted in the central region of Uganda suggesting that DR-TB cases are significantly clustered in certain areas.\u0026nbsp;The Global Moran\u0026rsquo;s I value of 0.33 and the Z-score of 3.81 (P-value \u0026lt; 0.0001) hinting on significant clustering of DR-TB in the study area\u0026nbsp;suggesting that adjacent districts exhibit similar DR-TB incidence rates\u003cstrong\u003e\u0026nbsp;(Table 4\u003c/strong\u003e and \u003cstrong\u003eFigure 2).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4:Moran\u0026rsquo;s I statistic\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eMoran\u0026rsquo;s I statistic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eExpectation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eVariance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eZ-score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eP-Value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e-0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e3.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eP\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eHotspots of DR-TB cases by district in Central region of Uganda\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe hotspot analysis revealed distinct hotspots, particularly in and around Kampala. The map \u003cstrong\u003e(Figure 3)\u003c/strong\u003e indicates that Kampala, highlighted in deep red, is a significant hotspot with a high concentration of DR-TB cases.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClusters of DR-TB disaggregated by village in the central region of Uganda.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 24 significant clusters of DR-TB cases in the central region of Uganda. Among these, 14 were identified as hotspot clusters, indicating areas with a significantly higher incidence of DR-TB cases. The most prominent hotspot clusters seen in Kampala district (Kisenyi I, Kosovo, Munyonyo, and Gaba Tr.c D), Wakiso district ( Kabulengwa, Ochieng B, Kyengera Central A and Kitooro Central), Mukono (Kasenge A, Nama, Nanuyenje and Kirondo) and Mubende (Kasaano, Kaweri and Katwe). The remaining 10 clusters were identified as low clusters, shown by blue circles on the map. These clusters represent areas with fewer DR-TB cases than expected, predominantly found in rural areas \u003cstrong\u003e(Figure 4).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnnual trend of DR-TB cases in the central region of Uganda.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe yearly trend analysis reveals significant fluctuations in the number of cases recorded each year.\u003c/p\u003e\n\u003cp\u003eIn 2019, the number of DR-TB cases was at its peak, with approximately 170 cases recorded. However, in 2020, there was a notable decline in the number of cases, dropping to around 140.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe downward trend continued into 2021, reaching the lowest point in the five-year period with about 130 cases. However, in 2022, there was a significant rebound in the number of DR-TB cases, rising back to approximately 160. \u003cstrong\u003e(Figure 5).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMonthly distribution of DR-TB cases in the central region of Uganda between 2019-2023\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAnalysis revealed significant variations and disruptions in case numbers from January 2019 to early 2020. Starting from March 2020, the dotted line indicates the period when Uganda went into lockdown due to the COVID-19 pandemic whereas full line indicates end of lockdown\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDuring the lockdown period, there was a sharp decline in the number of DR-TB cases recorded, and this decline continued into mid-2021.\u003c/p\u003e\n\u003cp\u003ePost-lockdown, from late 2021onwards, the trend shows a gradual recovery with fluctuations in case numbers \u003cstrong\u003e(Figure 6).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFactors associated with DR-TB in the central region of Uganda\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIndividual factors associated with DR-TB in the central region of Uganda\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAge emerged as a significant factor, with participants aged 25-34 years showing a higher likelihood of DR-TB (aIRR:1.43 CI: 1.11 \u0026ndash; 1.78) compared to those aged 14 years or younger. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFemales exhibited a lower incidence of DR-TB compared to males (aIRR: 0.66, 95% CI: 0.54 - 0.75). Marital status also influenced DR-TB incidence, with married individuals having a higher risk (aIRR: 1.77, 95% CI: 1.49 - 2.09) compared to single individuals.\u003c/p\u003e\n\u003cp\u003ePatients involved in business or trade faced a higher risk (aIRR: 1.51, 95% CI: 1.16 - 1.79) compared to farmers. \u003cstrong\u003e(Table 5)\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical factors associated with DR-TB in the central region of Uganda\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePatients with moderate acute malnutrition had a higher risk of DR-TB compared to those with normal nutrition, aIRR 1.21 (95% CI: 1.10\u0026ndash;1.46).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHIV-positive individuals showed a notably higher risk of DR-TB, with an aIRR of 1.36 (95% CI: 1.14\u0026ndash;1.65) compared to those who are HIV-negative.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFisherfolk have an aIRR of 1.14 (95% CI: 1.10\u0026ndash;1.51), and tobacco users have an aIRR of 1.23 (95% CI: 1.13\u0026ndash;1.88)\u003cstrong\u003e(Table 6)\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003cstrong\u003e: Individual factors associated with DR-TB in the central region of Uganda.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"588\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFactors\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 129px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAttribute\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 169px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ecIRR (95%CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eaIRR (95%CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"7\" style=\"width: 107px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e\u0026le;14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 169px;\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e15-24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 169px;\"\u003e\n \u003cp\u003e1.21 (0.95\u0026ndash;1.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\n \u003cp\u003e1.14 (0.88\u0026ndash;1.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e25-34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 169px;\"\u003e\n \u003cp\u003e1.53 (1.20\u0026ndash;1.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\n \u003cp\u003e1.43 \u003cstrong\u003e(1.11\u0026ndash;1.78)\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e35-44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 169px;\"\u003e\n \u003cp\u003e1.42 (1.35\u0026ndash;1.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\n \u003cp\u003e1.33 \u003cstrong\u003e(1.25\u0026ndash;1.68)\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e45-54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 169px;\"\u003e\n \u003cp\u003e1.31 (1.25\u0026ndash;1.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\n \u003cp\u003e1.27 \u003cstrong\u003e(1.15\u0026ndash;1.52)\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e55-64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 169px;\"\u003e\n \u003cp\u003e1.11 (0.95\u0026ndash;1.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\n \u003cp\u003e1.25 (0.90\u0026ndash;1.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e\u0026ge;65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 169px;\"\u003e\n \u003cp\u003e0.91 (0.80\u0026ndash;1.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 182px;\"\u003e\n \u003cp\u003e1.05 (0.75\u0026ndash;1.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 169px;\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 169px;\"\u003e\n \u003cp\u003e0.41 (0.36 - 0.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\n \u003cp\u003e0.66 \u003cstrong\u003e(0.54 - 0.75)\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003eSingle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 169px;\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 169px;\"\u003e\n \u003cp\u003e1.62 (1.34 - 2.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\n \u003cp\u003e1.77 \u003cstrong\u003e(1.49 - 2.09)\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003eSeparated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 169px;\"\u003e\n \u003cp\u003e0.38 (0.26 - 0.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\n \u003cp\u003e0.46 \u003cstrong\u003e(0.36 - 0.58)\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003eWidowed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 169px;\"\u003e\n \u003cp\u003e0.16(0.11 - 0.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\n \u003cp\u003e0.18 \u003cstrong\u003e(0.13 - 0.26)\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOccupation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003eFarmer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 169px;\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003eBusiness/trade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 169px;\"\u003e\n \u003cp\u003e1.27 (1.05 - 1.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\n \u003cp\u003e1.51 \u003cstrong\u003e(1.16 - 1.79)\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003eStudent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 169px;\"\u003e\n \u003cp\u003e0.20 (0.14 - 0.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\n \u003cp\u003e1.34 (0.99 - 1.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 169px;\"\u003e\n \u003cp\u003e1.05 (0.86 - 1.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\n \u003cp\u003e1.22 \u003cstrong\u003e(1.04 - 1.41)\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003eUnemployed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 169px;\"\u003e\n \u003cp\u003e0.36(0.27 - 0.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\n \u003cp\u003e0.43 \u003cstrong\u003e(0.23 - 0.56)\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003cstrong\u003e:Clinical factors associated with DR-TB in the central region of Uganda\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"639\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 131px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable (n=729)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 204px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eResponse category\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ecIRR (95%CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 159px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eaIRR (95%CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 131px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNutrition status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 204px;\"\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 144px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 159px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 204px;\"\u003e\n \u003cp\u003eModerate acute malnutrition\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 144px;\"\u003e\n \u003cp\u003e1.27 (1.05\u0026ndash;1.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 159px;\"\u003e\n \u003cp\u003e1.21 \u003cstrong\u003e(1.10\u0026ndash;1.46)\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 204px;\"\u003e\n \u003cp\u003eSevere acute malnutrition\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 144px;\"\u003e\n \u003cp\u003e1.04 (0.90\u0026ndash;1.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 159px;\"\u003e\n \u003cp\u003e1.05 \u003cstrong\u003e(1.02\u0026ndash;1.37)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 131px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHIV status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 204px;\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 144px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 159px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 204px;\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 144px;\"\u003e\n \u003cp\u003e1.42 (1.18\u0026ndash;1.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 159px;\"\u003e\n \u003cp\u003e1.36 \u003cstrong\u003e(1.14\u0026ndash;1.65)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 131px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePatient type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 204px;\"\u003e\n \u003cp\u003eNew\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 144px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 159px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 204px;\"\u003e\n \u003cp\u003eRelapse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 144px;\"\u003e\n \u003cp\u003e1.33 (0.95\u0026ndash;1.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 159px;\"\u003e\n \u003cp\u003e1.15 (0.88\u0026ndash;1.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 204px;\"\u003e\n \u003cp\u003eTreatment after failure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 144px;\"\u003e\n \u003cp\u003e1.10 (1.00\u0026ndash;1.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 159px;\"\u003e\n \u003cp\u003e1.14 (0.95\u0026ndash;1.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 204px;\"\u003e\n \u003cp\u003eLoss to follow up\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 144px;\"\u003e\n \u003cp\u003e1.05 (0.98\u0026ndash;1.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 159px;\"\u003e\n \u003cp\u003e1.32 (0.95\u0026ndash;1.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" style=\"width: 131px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRisk Group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 204px;\"\u003e\n \u003cp\u003eTB contact\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 144px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 159px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 204px;\"\u003e\n \u003cp\u003eFisherfolk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 144px;\"\u003e\n \u003cp\u003e1.06 (1.01\u0026ndash;1.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 159px;\"\u003e\n \u003cp\u003e1.14 \u003cstrong\u003e(1.10\u0026ndash;1.51)\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 204px;\"\u003e\n \u003cp\u003eTobacco user\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 144px;\"\u003e\n \u003cp\u003e1.11 (1.85\u0026ndash;1.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 159px;\"\u003e\n \u003cp\u003e1.23 \u003cstrong\u003e(1.13\u0026ndash;1.88)\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 204px;\"\u003e\n \u003cp\u003ePrisoner\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 144px;\"\u003e\n \u003cp\u003e1.03 (0.85\u0026ndash;1.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 159px;\"\u003e\n \u003cp\u003e1.12 (0.80\u0026ndash;1.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 204px;\"\u003e\n \u003cp\u003eArmed personnel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 144px;\"\u003e\n \u003cp\u003e1.05 (0.65\u0026ndash;1.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 159px;\"\u003e\n \u003cp\u003e1.00 (0.60\u0026ndash;1.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis study investigated the spatial and temporal distribution of DR-TB in the central region of Uganda from 2019 to 2023, identifying key demographic and clinical factors associated with DR-TB incidence. Spatial analysis revealed significant clusters, particularly in urban areas, while temporal analysis showed fluctuations influenced by external events such as the COVID-19 pandemic as well as seasonality. Factors including age, sex, marital status, occupation, nutrition status, and HIV status were significantly associated with DR-TB incidence.\u003c/p\u003e \u003cp\u003eThe spatial analysis identified 24 significant clusters of DR-TB, with 14 high-incidence hotspots primarily in urban centers like Kampala, Wakiso, Mubende and Mukono. The concentration of DR-TB cases in these areas can be attributed to higher population densities, which facilitate the transmission of TB. Additionally, urban areas often have better diagnostic facilities, leading to higher detection rates. A study conducted in Kampala identified urban centers as hotspots for TB due to high population density and improved diagnostic capabilities. This is also consistent with findings from other studies, which also highlighted urbanization and population density as significant predictors of TB incidence [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Additionally, social determinants of health, including poverty and crowded living conditions, prevalent in urban settings, contribute to the higher incidence of DR-TB [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMoreover, urban centers like Kampala have a more transient population, which can lead to increased spread of TB as individuals move between districts. This transient nature, combined with high population density, creates an environment conducive to the rapid spread of DR-TB. Comparatively, rural areas, with lower population densities and less movement, showed fewer cases, aligning with the general understanding that TB transmission rates are lower in less densely populated areas as seen in a study conducted in Zimbabwe [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe temporal analysis revealed fluctuations in DR-TB cases, with notable impacts from the COVID-19 pandemic. The peaks observed during mid-year months, particularly between July and September, could be linked to seasonal factors such as changes in weather conditions, which affect the transmission dynamics of TB. The lockdown period in 2020 saw a sharp decline in the number of DR-TB cases, likely due to disruptions in healthcare services and reduced access to diagnostic facilities. This pattern aligns with global observations where TB case detection declined during the pandemic [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. A similar study in Uganda reported a decrease in TB notifications during the COVID-19 lockdown, reflecting the broader impact of the pandemic on TB control programs [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The post-lockdown period showed a recovery in case numbers, indicating the resumption of TB services and increased case detection efforts.\u003c/p\u003e \u003cp\u003eThe fluctuation in DR-TB cases can also be attributed to changes in healthcare-seeking behavior during the pandemic. Fear of contracting COVID-19 and movement restrictions likely led to fewer individuals seeking medical care, resulting in underreporting of cases.\u003c/p\u003e \u003cp\u003eAge was a significant factor, with older age groups showing a higher likelihood of acquiring DR-TB. This finding is consistent with a study conducted in Pakistan, which reported higher DR-TB rates among older populations due to cumulative exposure and longer disease duration [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. The increased susceptibility in older age groups is attributed to weakened immune systems and a longer duration of exposure to TB bacteria.\u003c/p\u003e \u003cp\u003eFemales had a lower incidence of DR-TB compared to males, possibly due to differences in healthcare-seeking behavior. This gender disparity in TB incidence has been documented in other studies in Africa [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Men are often more likely to engage in behaviors that increase TB risk, such as smoking and working in environments with higher exposure to TB bacteria. Furthermore, the predominance of male participants (69%) aligns with global DR-TB data trends, which suggest higher TB exposure in men due to occupational risks and delayed health-seeking behaviors [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Additionally, women may face barriers to accessing healthcare, leading to underdiagnosis and underreporting of TB cases among females.\u003c/p\u003e \u003cp\u003eMarital status influenced DR-TB incidence, with married individuals showing higher risks. This could be related to household transmission dynamics, where close contact with an infected spouse increases the risk of TB transmission. A study in Ethiopia found similar associations between marital status and TB incidence [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Occupation also played a role, with individuals in business or trade having higher risks, potentially due to increased social interactions and mobility. Conversely, unemployed individuals had a lower risk, possibly due to reduced social exposure. Similar findings have been documented in India [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe significant link between moderate acute malnutrition and increased DR-TB risk can be attributed to malnutrition\u0026rsquo;s weakening effect on immune function, particularly T-cell activity, which is vital for controlling TB infections. This immune suppression heightens susceptibility to TB and its drug-resistant forms. Similar findings have been observed in studies where malnutrition was correlated with higher TB incidence and worse treatment outcomes [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHIV status was another critical factor, with HIV-negative individuals having a lower risk of DR-TB. The co-infection of HIV and TB is a well-established challenge in Sub-Saharan Africa, exacerbating the TB epidemic due to the immunocompromised status of HIV patients [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. HIV-positive individuals are more susceptible to developing TB due to their weakened immune systems, which struggle to control TB bacteria.\u003c/p\u003e \u003cp\u003eThe elevated DR-TB risk among fisherfolk and tobacco users is likely due to their occupational and lifestyle-related challenges, such as overcrowded living conditions and impaired lung health from smoking, which exacerbate TB exposure and complicate treatment. These observations align with other studies that have identified high-risk groups like fisherfolk and smokers as particularly vulnerable to TB due to environmental and behavioral factors [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eThe study was limited by incomplete and missing data, geographic coordinates were unavailable for 64 cases (8.8%), and clinical data were incomplete for 102 cases (14.0%). While geocoding and multiple imputation methods were applied to address these gaps, imputation may not fully replicate the true underlying data, potentially introducing biases. This missingness could affect the precision of spatial and clinical analyses, limiting the generalizability of findings to the entire DR-TB population in Central Uganda. Additionally, the retrospective analysis limits the ability to establish causal relationships between identified factors and DR-TB incidence, necessitating further longitudinal studies to confirm these associations.\u003c/p\u003e \u003cp\u003eMoreover, only a subset of potential predictors of DR-TB identified in the literature were analyzed and not all contextual and environmental factors could be consistently retrieved from existing records. This limitation arose from the reliance on secondary data, which did not include several variables of interest, such as socio-economic factors, and lifestyle choices. The unavailability of these variables in the dataset constrained the analysis, potentially affecting the comprehensiveness of the findings.\u003c/p\u003e \u003cp\u003eThis study primarily focuses on public DR-TB treatment centers. It does not include private treatment centers like TASO, Baylor, and IDI, which may also manage DR-TB cases. This exclusion could lead to an underestimation of the total burden of DR-TB in the region.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study demonstrates that DR-TB in Central Uganda is predominantly clustered in urban districts such as Kampala, Wakiso, Mukono, and Mubende, identifying them as hotspots with higher transmission potential. The spatial autocorrelation confirms non-random clustering, with densely populated urban areas contributing significantly to the spread of DR-TB. Temporal trends revealed a sharp decline in DR-TB cases in 2021, likely due to COVID-19-related disruptions in healthcare services, The findings also highlight a strong association between HIV co-infection and DR-TB. Demographic factors such as younger age, male gender, and marital status further influenced DR-TB risk. Therefore, expanding diagnostic and surveillance capacity in hotspot districts is imperative, with investments in laboratory infrastructure, training of healthcare personnel, and collaboration with local health authorities. Integrating TB and HIV care is critical to manage the high burden of co-infection, while incorporating nutritional support into DR-TB treatment protocols can mitigate the impact of malnutrition on patient outcomes. Strengthening the resilience of TB services is essential to ensure continuity during public health crises, as evidenced by the disruptions during the COVID-19 pandemic. Targeted public health campaigns should prioritize younger men in urban areas, promoting early healthcare-seeking behavior and TB prevention strategies. Furthermore, longitudinal surveillance systems are needed to monitor temporal variations in DR-TB and adapt response strategies accordingly. Research into the role of urbanization, environmental factors such as climate, and the contributions of private healthcare providers to DR-TB management will provide insights to inform more effective, data-driven interventions.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAIDs:\u0026nbsp;Acquired Immunodeficiency Syndrome\u003c/p\u003e\n\u003cp\u003eDR -TB: Drug Resistant Tuberculosis\u003c/p\u003e\n\u003cp\u003eGIS: Geographical Information System\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMDR-TB: Multi Drug Resistant Tuberculosis\u003c/p\u003e\n\u003cp\u003eTB: Tuberculosis\u003c/p\u003e\n\u003cp\u003eWHO: World Health Organization\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics and Approval:\u0026nbsp;\u003c/strong\u003eEthical approval was obtained from the School of Public Health Research and Ethics Committee (SPH-REC). Permission to conduct the study at DR-TB treatment centers was obtained from hospital directors. Anonymity for extracted data was maintained.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate Declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study utilized secondary data collected from hospital registers. As such, no direct contact with human participants was made, and informed consent was not applicable. Permission to access and use the data was obtained from hospital directors through their research and ethics offices.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication:\u0026nbsp;\u003c/strong\u003eNot applicable\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data:\u0026nbsp;\u003c/strong\u003eThe datasets used during the analysis are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e\u0026nbsp; The authors declare they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThe research was supported by funds from NIH as part of the D43 multi-disciplinary training program in digital mobile technologies to study tuberculosis through University of Georgia and Makerere University School of Public Health.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contribution:\u0026nbsp;\u003c/strong\u003eS.I conceptualized the manuscript idea and took lead in writing the manuscript and writing the methodology. D.K took the lead in data analysis, \u0026nbsp;S.K and N.K reviewed the manuscript for intellectual content and scientific integrity. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement:\u0026nbsp;\u003c/strong\u003eWe extend our sincere gratitude to University of Georgia and Makerere University School of Public Health for offering us the opportunity and guidance during the conduct of this research.\u003c/p\u003e\n\u003cp\u003eWe would like to thank the staff of Mulago National Referral hospital and Masaka and Mubende Regional Referral hospitals for the support provided during data collection.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWHO, \u003cem\u003eORGANIZATION, W. H. Global Tuberculosis Report 2022. Geneva: World Health Organization. 27 October 2022.[Online] Accessed 27 October 2022.\u003c/em\u003e 2022.\u003c/li\u003e\n\u003cli\u003eBatte, C., et al., \u003cem\u003ePrevalence and factors associated with non-adherence to multi-drug resistant tuberculosis (MDR-TB) treatment at Mulago National Referral Hospital, Kampala, Uganda.\u003c/em\u003e 2021. \u003cstrong\u003e21\u003c/strong\u003e(1): p. 238-47.\u003c/li\u003e\n\u003cli\u003eRahman, M.A. and A. Sarkar, \u003cem\u003eExtensively Drug-resistant Tuberculosis (XDR-TB): A daunting challenge to the current End TB Strategy and policy recommendations.\u003c/em\u003e Indian Journal of Tuberculosis, 2017. \u003cstrong\u003e64\u003c/strong\u003e(3): p. 153-160.\u003c/li\u003e\n\u003cli\u003eIsmail, N., et al., \u003cem\u003eDrug resistant tuberculosis in Africa: Current status, gaps and opportunities.\u003c/em\u003e African journal of laboratory medicine, 2018. \u003cstrong\u003e7\u003c/strong\u003e(2): p. 1-11.\u003c/li\u003e\n\u003cli\u003eMusa, B.M., et al., \u003cem\u003eTrends in prevalence of multi drug resistant tuberculosis in sub-Saharan Africa: a systematic review and meta-analysis.\u003c/em\u003e PLoS One, 2017. \u003cstrong\u003e12\u003c/strong\u003e(9): p. e0185105.\u003c/li\u003e\n\u003cli\u003eKidenya, B.R., et al., \u003cem\u003eEpidemiology and genetic diversity of multidrug-resistant tuberculosis in East Africa.\u003c/em\u003e 2014. \u003cstrong\u003e94\u003c/strong\u003e(1): p. 1-7.\u003c/li\u003e\n\u003cli\u003eMolla, K.A., M.A. Reta, and Y.Y. Ayene, \u003cem\u003ePrevalence of multidrug-resistant tuberculosis in East Africa: a systematic review and meta-analysis.\u003c/em\u003e PloS one, 2022. \u003cstrong\u003e17\u003c/strong\u003e(6): p. e0270272.\u003c/li\u003e\n\u003cli\u003eBbosa, G.S., et al., \u003cem\u003ePredictors of Multidrug-Resistant Tuberculosis among Patients with Pulmonary Tuberculosis at Mulago National Referral Hospital, Uganda. Tuberculosis Research and Treatment, 2019, Article ID 9601370, 7 pages.\u003c/em\u003e 2019.\u003c/li\u003e\n\u003cli\u003eMuhumuza, J., et al., \u003cem\u003ePrevalence and risk factors for multi-drug resistant tuberculosis among newly diagnosed tuberculosis patients in a metropolitan city with a high HIV/AIDS burden: a cross-sectional study in Kampala, Uganda. BMC Research Notes, 7, 560.\u003c/em\u003e 2014.\u003c/li\u003e\n\u003cli\u003eMoH, \u003cem\u003eManual for Management and control of Tuberculosis and Leprosy.\u003c/em\u003e 2017.\u003c/li\u003e\n\u003cli\u003eNyamogoba, H.D., et al., \u003cem\u003eHIV co-infection with tuberculous and non-tuberculous mycobacteria in western Kenya: challenges in the diagnosis and management.\u003c/em\u003e Afr Health Sci, 2012. \u003cstrong\u003e12\u003c/strong\u003e(3): p. 305-11.\u003c/li\u003e\n\u003cli\u003eStein, C.M., et al., \u003cem\u003eResistance and susceptibility to Mycobacterium tuberculosis infection and disease in tuberculosis households in Kampala, Uganda.\u003c/em\u003e American journal of epidemiology, 2018. \u003cstrong\u003e187\u003c/strong\u003e(7): p. 1477-1489.\u003c/li\u003e\n\u003cli\u003eBayowa, J.R., et al., \u003cem\u003eMortality rate and associated factors among patients co-infected with drug resistant tuberculosis/HIV at Mulago National Referral Hospital, Uganda, a retrospective cohort study.\u003c/em\u003e PLOS Global Public Health, 2023. \u003cstrong\u003e3\u003c/strong\u003e(7): p. e0001020.\u003c/li\u003e\n\u003cli\u003eWeiss, K., et al., \u003cem\u003eGeospatial modelling for the optimisation of TB case detection in HIV-infected populations. International Journal of Tuberculosis and Lung Disease, 18(7), 857\u0026ndash;864. .\u003c/em\u003e 2014.\u003c/li\u003e\n\u003cli\u003eLiu, Y., Li, X., , et al., \u003cem\u003eGeospatial spread of multidrug-resistant tuberculosis (MDR TB) in a high-incidence setting: a retrospective spatial analysis in Shandong Province, China. BMC Infectious Diseases, 16(1), 409.\u003c/em\u003e 2016.\u003c/li\u003e\n\u003cli\u003eUDHS, \u003cem\u003ehttps://www.health.go.ug/cause/uganda-demographic-and-health-survey-udhs-2022-key-findings/\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e 2022.\u003c/li\u003e\n\u003cli\u003eUBOS, \u003cem\u003eNational population and housing census 2024.\u003c/em\u003e 2024.\u003c/li\u003e\n\u003cli\u003eTalari, K. and M. Goyal, \u003cem\u003eRetrospective studies\u0026ndash;utility and caveats.\u003c/em\u003e Journal of the Royal College of Physicians of Edinburgh, 2020. \u003cstrong\u003e50\u003c/strong\u003e(4): p. 398-402.\u003c/li\u003e\n\u003cli\u003eNakafeero, B., \u003cem\u003eDrug resistant tuberculosis in Karamoja region: prevalence, patterns and associated factors\u003c/em\u003e. 2018, Makerere University.\u003c/li\u003e\n\u003cli\u003eAturinde, A., et al., \u003cem\u003eSpatial analysis of HIV-TB co-clustering in Uganda.\u003c/em\u003e BMC infectious diseases, 2019. \u003cstrong\u003e19\u003c/strong\u003e: p. 1-10.\u003c/li\u003e\n\u003cli\u003eUBOS, \u003cem\u003eUganda Bureau of Statistics.\u003c/em\u003e Kampala, Uganda and Calverton, 2020.\u003c/li\u003e\n\u003cli\u003eQGISDevelopmentTeam, \u003cem\u003eQGIS Development Team (2024). QGIS Geographic Information System. Open Source Geospatial Foundation Project. Available at: \u003c/em\u003e\u003cem\u003ehttps://qgis.org\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e 2024.\u003c/li\u003e\n\u003cli\u003eMoran, P.A., \u003cem\u003eNotes on continuous stochastic phenomena.\u003c/em\u003e Biometrika, 1950. \u003cstrong\u003e37\u003c/strong\u003e(1/2): p. 17-23.\u003c/li\u003e\n\u003cli\u003eChen, Y., \u003cem\u003eNew approaches for calculating Moran\u0026rsquo;s index of spatial autocorrelation.\u003c/em\u003e PloS one, 2013. \u003cstrong\u003e8\u003c/strong\u003e(7): p. e68336.\u003c/li\u003e\n\u003cli\u003eSaTScan\u0026trade;, \u003cem\u003eSaTScan\u0026trade; software for the spatial, temporal, and space-time scan statistics. Available at: \u003c/em\u003e\u003cem\u003ehttps://www.satscan.org/\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e 2024.\u003c/li\u003e\n\u003cli\u003eKulldorff, M., \u003cem\u003eKulldorff, M. (1997). A spatial scan statistic. Communications in Statistics - Theory and Methods, 26(6), 1481\u0026ndash;1496.\u003c/em\u003e 1997.\u003c/li\u003e\n\u003cli\u003eRCoreTeam, \u003cem\u003eA language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Available at: \u003c/em\u003e\u003cem\u003ehttps://www.R-project.org/\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e 2023.\u003c/li\u003e\n\u003cli\u003eStataCorp, \u003cem\u003eStata Statistical Software: Release 17. College Station, TX: StataCorp LLC.\u003c/em\u003e 2023.\u003c/li\u003e\n\u003cli\u003eKaur, K.K., et al., \u003cem\u003eUrbanization and Tuberculosis in Peninsular, Malaysia.\u003c/em\u003e Malays J Med Sci, 2020. \u003cstrong\u003e16\u003c/strong\u003e: p. 63-69.\u003c/li\u003e\n\u003cli\u003eZille, A.I., et al., \u003cem\u003eSocial determinants of pulmonary tuberculosis in Brazil: an ecological study.\u003c/em\u003e BMC pulmonary medicine, 2019. \u003cstrong\u003e19\u003c/strong\u003e: p. 1-9.\u003c/li\u003e\n\u003cli\u003eChirenda, J., et al., \u003cem\u003eSpatial distribution of Mycobacterium tuberculosis in metropolitan Harare, Zimbabwe.\u003c/em\u003e PloS one, 2020. \u003cstrong\u003e15\u003c/strong\u003e(4): p. e0231637.\u003c/li\u003e\n\u003cli\u003eMcQuaid, C.F., et al., \u003cem\u003eThe impact of COVID-19 on TB: a review of the data.\u003c/em\u003e The International Journal of Tuberculosis and Lung Disease, 2021. \u003cstrong\u003e25\u003c/strong\u003e(6): p. 436-446.\u003c/li\u003e\n\u003cli\u003eKendall, E.A., et al., \u003cem\u003eDecline in prevalence of tuberculosis following an intensive case finding campaign and the COVID-19 pandemic in an urban Ugandan community.\u003c/em\u003e thorax, 2024. \u003cstrong\u003e79\u003c/strong\u003e(4): p. 325-331.\u003c/li\u003e\n\u003cli\u003eSaifullah, A., et al., \u003cem\u003eEvaluation of risk factors associated with the development of MDR-and XDR-TB in a tertiary care hospital: a retrospective cohort study.\u003c/em\u003e PeerJ, 2021. \u003cstrong\u003e9\u003c/strong\u003e: p. e10826.\u003c/li\u003e\n\u003cli\u003eOladimeji, O., et al., \u003cem\u003eGender and drug-resistant Tuberculosis in Nigeria.\u003c/em\u003e Tropical Medicine and Infectious Disease, 2023. \u003cstrong\u003e8\u003c/strong\u003e(2): p. 104.\u003c/li\u003e\n\u003cli\u003eMcNabb, K., A. Bergman, and J. Farley, \u003cem\u003eRisk factors for poor engagement in drug-resistant TB care in South Africa: a systematic review.\u003c/em\u003e Public Health Action, 2021. \u003cstrong\u003e11\u003c/strong\u003e(3): p. 139-145.\u003c/li\u003e\n\u003cli\u003eChiposi, L., L.P. Cele, and M. Mokgatle, \u003cem\u003ePrevalence of delay in seeking tuberculosis care and the health care seeking behaviour profile of tuberculous patients in a rural district of KwaZulu Natal, South Africa.\u003c/em\u003e Pan African Medical Journal, 2021. \u003cstrong\u003e39\u003c/strong\u003e(1).\u003c/li\u003e\n\u003cli\u003eBadgeba, A., et al., \u003cem\u003eDeterminants of multidrug-resistant mycobacterium tuberculosis infection: a multicenter study from southern Ethiopia.\u003c/em\u003e Infection and Drug Resistance, 2022: p. 3523-3535.\u003c/li\u003e\n\u003cli\u003eDas, U., \u003cem\u003eEXPLORING FACTORS LEADING TO RISE OF TB AND MDR-TB CASES IN INDIA DESPITE THE REPORTED SUCCESS OF THE NATIONAL TB PROGRAMME.\u003c/em\u003e 2013.\u003c/li\u003e\n\u003cli\u003eBhargava, A., et al., \u003cem\u003eNutritional status of adult patients with pulmonary tuberculosis in rural central India and its association with mortality.\u003c/em\u003e PloS one, 2013. \u003cstrong\u003e8\u003c/strong\u003e(10): p. e77979.\u003c/li\u003e\n\u003cli\u003eT\u0026eacute;llez-Navarrete, N.A., et al., \u003cem\u003eMalnutrition and tuberculosis: the gap between basic research and clinical trials.\u003c/em\u003e The Journal of Infection in Developing Countries, 2021. \u003cstrong\u003e15\u003c/strong\u003e(03): p. 310-319.\u003c/li\u003e\n\u003cli\u003eRagan, E., et al., \u003cem\u003eThe impact of alcohol use on tuberculosis treatment outcomes: a systematic review and meta-analysis.\u003c/em\u003e The International Journal of Tuberculosis and Lung Disease, 2020. \u003cstrong\u003e24\u003c/strong\u003e(1): p. 73-82.\u003c/li\u003e\n\u003cli\u003eSilva, D.R., et al., \u003cem\u003eRisk factors for tuberculosis: diabetes, smoking, alcohol use, and the use of other drugs.\u003c/em\u003e Jornal Brasileiro de Pneumologia, 2018. \u003cstrong\u003e44\u003c/strong\u003e: p. 145-152.\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":"DR-TB, Uganda, Central region, Spatial and temporal distribution","lastPublishedDoi":"10.21203/rs.3.rs-5055957/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5055957/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eIntroduction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDrug-resistant tuberculosis (DR-TB) poses a major public health threat in Uganda, especially in densely populated and urbanized Central Uganda. National estimates indicate that 1.6% of new cases and 12.1% of previously treated cases involve DR-TB, but specific data on its distribution and predictors in Central Uganda are limited. This study aimed to map the spatial and temporal distribution of DR-TB and identify associated risk factors from 2019 to 2023 to guide resource allocation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethodology\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA cross-sectional retrospective study was conducted using data from DR-TB treatment centers in Central Uganda from January 2019 to December 2023. R (Version 4.4.0) statistical software and QGIS version 3.36.3 were used for spatial analysis, and significant clusters were identified using SaTscan’s Kulldorff statistic. Multivariable Poisson regression was employed to identify factors associated with DR-TB using STATA version 14.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOf 803 DR-TB cases, 729 were eligible for analysis. The majority were male (69%), aged 25-34 (34.2%), married (51.9%), and HIV-positive (57.8%). Rifampicin resistance was found in 59% of cases. Kampala and Wakiso had the highest case numbers (30.0% and 24.3%, respectively), with fourteen hotspot clusters identified in these and other districts. Temporal analysis showed fluctuations, peaking in 2019 and 2022. Significant risk factors included age, sex, marital status, occupation, HIV status, and risk group.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDR-TB in Central Uganda clusters in urban districts, driven by urbanization, population density, and socioeconomic factors. Temporal fluctuations, influenced by disruptions like COVID-19, highlight the need for resilient healthcare systems. Key risk factors such as HIV co-infection, malnutrition, and demographics call for targeted interventions, integrated care, and enhanced surveillance.\u003c/p\u003e","manuscriptTitle":"Spatial and temporal distribution of drug-resistant tuberculosis and associated risk factors in the central region of Uganda (2019- 2023), a retrospective analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-23 16:41:39","doi":"10.21203/rs.3.rs-5055957/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-08-18T05:28:00+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-08T13:47:42+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-12-18T00:14:38+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2024-12-16T11:00:22+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":"f5ab3a2d-401b-4c31-96c1-e99d00edc9b0","owner":[],"postedDate":"December 23rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-09-10T03:53:19+00:00","versionOfRecord":[],"versionCreatedAt":"2024-12-23 16:41:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5055957","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5055957","identity":"rs-5055957","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}