Skip to main content

Epidemiological profile of patients with rifampicin-resistant tuberculosis: an analysis of the Uganda National Tuberculosis Reference Laboratory Surveillance Data, 2014–2018



Drug-resistant tuberculosis (DR-TB), including rifampicin-resistant tuberculosis (RR-TB) and multidrug-resistant tuberculosis (MDR-TB, or RR-TB with additional isoniazid resistance), presents challenges to TB control. In Uganda, the GeneXpert test provides point-of-care testing for TB and rifampicin resistance. Patients identified with RR-TB receive culture-based drug susceptibility testing (DST) to identify additional resistance, if any. There are few data on the epidemiological profiles of current DR-TB patients in Uganda. We described patients with RR-TB in Uganda and assessed the trends of RR-TB to inform TB control interventions.


We identified patients with RR-TB whose samples were referred for culture and DST during 2014–2018 from routinely-generated laboratory surveillance data at the Uganda National Tuberculosis Reference Laboratory. Data on patient demographics and drug sensitivity profile of Mycobacterium tuberculosis isolates were abstracted. Population data were obtained from the Uganda Bureau of Statistics to calculate incidence. Descriptive epidemiology was performed, and logistic regression used to assess trends.


We identified 1474 patients whose mean age was 36 ± 17 years. Overall incidence was 3.8/100,000 population. Males were more affected by RR-TB than females (4.9 vs. 2.7/100,000, p ≤ 0.01). Geographically, Northern Uganda was the most affected region (IR = 6.9/100,000) followed by the Central region (IR = 5.01/100,000). The overall population incidence of RR-TB increased by 20% over the evaluation period (OR = 1.2; 95% CI 1.15–1.23); RR-TB in new TB cases increased by 35% (OR = 1.35; 95% CI 1.3–1.4) and by 7% in previously-treated cases (OR = 1.07; 95% CI 1.0–1.1). Of the 1474 patients with RR-TB, 923 (63%) were culture-positive of whom 670 (72%) had full DST available. Based on the DST results, 522/670 (78%) had MDR-TB.


Between 2014 and 2018, the incidence of RR-TB increased especially among newly-diagnosed TB patients. We recommend intensified efforts and screening for early diagnosis especially among previously treated patients. Mechanisms should be in put to ensure that all patients with RR-TB obtain DST.


Drug-resistant tuberculosis (DR-TB) undermines the global efforts to combat TB. Rifampicin-resistant-TB (RR-TB) defined as a Mycobacterium tuberculosis isolate with resistance to rifampicin detected using genotypic or phenotypic methods, with or without resistance to other first-line anti-TB drugs [1]. When TB is resistant to both rifampicin and isoniazid (the two traditional first-line drugs), it is termed multidrug-resistant TB (MDR-TB). Drug-resistant TB can be present in a primary TB infection or can develop as a complication during the course of patient treatment [2]. In 2018, 3.4% of newly-diagnosed and 18% previously-treated TB patients were estimated to have MDR-TB worldwide [3]. In Uganda, MDR-TB prevalence was estimated at 1% and 12% among new TB cases and previously treated patients respectively in 2018 [4]. Infection with MDR-TB has been associated with poorer treatment outcomes, longer treatment times, and higher treatment costs as compared to infection with drug-sensitive TB [5]. The impact is exacerbated in resource-limited settings due to the presence of other infectious diseases and limited access to well-equipped healthcare facilities [6, 7].

Tuberculosis control depends on the rapid detection and successful treatment of infectious patients, both to save lives and prevent onward transmission, especially for MDR-TB. To improve targeted diagnosis and treatment, in 2010, WHO recommended the GeneXpert MTB/RIF assay, which identifies Mycobacterium tuberculosis mutations associated with rifampicin with high sensitivity [8]. This test has a shorter turn-around time (about 2 h) than the gold standard method of culture (2–6 weeks) [9, 10]. In 2019, 236 of the 1500 TB diagnosis and treatment units in Uganda had GeneXpert machines [11]. At these GeneXpert sites, patients whose samples are identified as rifampicin-resistant are referred to a TB drug-resistance treatment unit.

Because nearly all RR-TB isolates also are resistant to isoniazid [12], RR-TB cases diagnosed by GeneXpert are treated using a standard regimen for MDR-TB [13]. However, RR-TB isolates may also be resistant to other drugs. To further guide management of patients with RR-TB, culture and drug susceptibility testing (DST) is recommended to identify any other anti-TB drug resistance. Patients diagnosed with RR-TB at the GeneXpert sites are referred to one of the 17 TB drug-resistance treatment units in Uganda [14], where the practice is to take a second sputum sample for culture and DST at the National Tuberculosis References Laboratory (NTRL). The DST results are then used to guide patient management [15].

Active surveillance involving active case finding through culture and DST at all levels of the health system is the most effective way to monitor the spread of resistant TB strains. However, it is resource-intensive, and many countries affected by TB are unable to implement this approach. This is why in Uganda, surveillance for drug-resistant TB is largely passive, relying on individuals presenting themselves to health care facilities [16]. For TB patients identified, contact tracing is done to identify and treat those who would have been exposed. In addition, culture and DST capacity for Mycobacterium tuberculosis is limited to a centralized NTRL facility. All specimens requiring these specialized tests must be referred from the GeneXpert health facilities located in rural and urban settings throughout the country to the NTRL [17]. This centralized testing requires rapid and safe transport of specimens from health facilities or lower-level laboratories to the higher-level laboratory, as well as expedient reporting of results back to clinicians [18]. However, there are challenges in specimen referral and result reporting systems which contribute to diagnostic delays in routine practice [19, 20]. Such delays have a potential to translate into under-reporting of drug resistance among TB isolates. In such situations, routinely generated surveillance data are important to monitor the effectiveness of TB control programs [21]. We described the epidemiology of rifampicin resistant TB patients and their drug resistance profiles in Uganda based on routinely generated laboratory surveillance data at the NTRL during 2014–2018.


Study site and design

This was a retrospective analysis of laboratory data collected at the National TB Reference Laboratory (NTRL) of Uganda from 2014 to 2018. All samples from the entire country are submitted to the NTRL for both culture and DST for Mycobacterium tuberculosis isolates. The NTRL is a one of the WHO approved Supra National Reference Laboratories for Africa and it is accredited by the South African National Accreditation System.

Study population

All records of patients with RR-TB whose samples were submitted to the NTRL for culture and DST during the period 2014–2018 were included in this study. The data were retrieved from the from the Laboratory Information System (LIS) where they are entered at sample submission and testing conclusion. The LIS of the NTRL is used to track patient samples from entry into the laboratory to the point when results are dispatched to the requesting health facilities. At the drug resistance treatment sites, data are entered in the Drug-Resistant Management Information System (DRMIS) for patient management.

Study variables and data abstraction

The primary outcome was the presence of any rifampicin resistance, in the form of mono-resistance, poly-resistance, MDR, or extensive drug resistance (XDR) in a Mycobacterium tuberculosis isolate. For each case-patient with RR-TB, we abstracted data on age, sex, district of residence of the patient, date of enrolment on treatment, culture results, patient categorization a new or previously treated, and the drug susceptibility profile from the LIS. Classification of drug resistance was based on the DST profiles of clinical isolates, as described by the WHO definitions and reporting framework [1, 22, 23] (Table 1). Using unique identifiers, we matched laboratory data in the LIS with information in the DRMIS to obtain the case-patient’s HIV status and history of any TB treatment. Records of patients with incomplete DST, patients without DST results, and those who were susceptible to all drugs were excluded in the classification of drug resistance type. Data on GeneXpert installations across the country over the study period were collected to compare with the numbers diagnosed with RR-TB.

Table 1 Definitions used for the classification of drug susceptibility profiles of patients with rifampicin-resistant tuberculosis, Uganda, 2014–2018

Data management and analysis

Data were entered into EpiInfo 7.2 for analysis. Descriptive statistics of the sample by person, place, and time were calculated. Line graphs were drawn to demonstrate trends whose significance was tested using logistic regression. Quantum Geographic Information System (QGIS) software was used to show the spatial trends of RR-TB in Uganda. DR-TB incidence rates were calculated using RR-TB cases per district and individual district populations. These were displayed on choropleth maps. District population estimates were calculated based on data from the 2014 National Population and Housing Census (cite), with a national growth rate of 3% to estimate the yearly populations [24]. Data on GeneXpert machine site distribution were superimposed on the incidence rate maps to analyze the relationship between incidence and GeneXpert machine availability.


Characteristics of patients with RR-TB

During the period under assessment, 1474 RR-TB patients, with a median age of 36 (IQR 17) years, were identified. Of these, 943 (64%) were male; 568 (38.5%) were new and 848 (57.5%) were previously-treated patients. In addition, 58 (3.9%) had an unknown / unrecorded history of treatment. Further, approximately half (687; 47%) were HIV-positive and 923 (63%) had positive cultures (Table 2).

Table 2 Socio-demographic and baseline clinical characteristics of patients with rifampicin resistant tuberculosis, Uganda, 2014–2018

Incidence rate of rifampicin resistant tuberculosis, Uganda, 2014–2018

The overall incidence of RR-TB was 3.8/100,000 population. Males were more affected than females (4.9 vs 2.7/100,000, p ≤ 0.01). Persons aged 35–44 years were most affected (IR = 12/100,000) followed by those between 25 and 34 (IR = 8.5/100,000) while those aged 0–14 years were the least affected (IR = 0.23/100,000).

Spatial distribution of rifampicin resistant tuberculosis patients, Uganda, 2014–2018

District coverage with GeneXpert machines in 1500 TB diagnostic units was 4.8% in 2014, 7.4% (2015), 7.5% (2016), 9% (2017), and 17% (2018). Similarly, there were increases in RR-TB diagnosis over time (Fig. 1). The northern region of Uganda had the highest rates of rifampicin-resistant TB patients (IR = 6.9/100,000), followed by the central region (IR = 5/100,000), western region (IR = 2.7/100,000), and eastern region (IR = 2.4/100,000) (Fig. 1). The districts with the highest incidence rates in the years 2014 to 2018 are shown in Additional file 1: Table S1.

Fig. 1
figure 1

Rifampicin-resistant tuberculosis incidence per 100,000 population, Uganda, 2014–2018

Drug-resistance profiles of patients with RR-TB, Uganda, 2014–2018

Of the 923 culture-positive patients, 707 (77%) had their DST done. Among those with DST results, 37 (5%) had incomplete DST (first-line susceptibility testing only) and 670 (95%) had complete DST (both first and second-line). Twenty-eight (3%) of the 670 patient isolates with complete DST were susceptible to all drugs.

Of the 642 isolates resistant to any TB drug, multidrug resistance was the most common (522; 81%) followed by monoresistance (65; 10%), poly-resistance (39; 6%), pre-XDR (11; 1.7%), and XDR (5; 0.8%) (Fig. 2).

Fig. 2
figure 2

Anti-tuberculosis drug resistance profile of patients with rifampicin resistant tuberculosis, N = 642, Uganda 2014–2018. INH, Isoniazid; RMP, Rifampicin; SM, Streptomycin; EMB, Ethambutol; PZ, Pyrazinamide; KAN, Kanamycin; LEV, Levofloxacin; MOX, Moxifloxacin; OFL, Ofloxacin; CAP, Capreomycin; AMK, Amikacin

Trends in incidence of rifampicin resistant tuberculosis, Uganda, 2014–2018

The incidence rate of rifampicin-resistant TB increased over time from 2014 to 2018, with a 20% increase across the study period (OR = 1.2; 95% CI 1.1–1.2). The incidence rates per 100,000 population were 0.49 in 2014, 0.63 in 2015, 0.91 in 2016, and 1.05 in 2018. GeneXpert machine installation around the country also increased over time from 33 in 2014 to 249 machines in 2018.

Trends in incidence of rifampicin-resistant tuberculosis among new and previously-treated patients

The incidence rates of RR-TB among both new and previously-treated cases increased from 2014 to 2018. Patients with previous TB treatment showed a greater incidence compared to the new cases over the entire study period. There was a bigger increase in the incidence among new TB cases (OR = 1.35; 95% CI 1.3–1.4) compared to previously-treated TB cases (OR = 1.07; 95% CI 1.03–1.13) (Fig. 3).

Fig. 3
figure 3

Trends of rifampicin resistant tuberculosis rates among new and previously treated cases, Uganda, 2014–2018

Trends in incidence of different drug resistance types among patients with rifampicin-resistant tuberculosis

The incidence of multidrug resistance was persistently higher than the other types of drug resistance, with an overall incidence of 1.3 per 100,000 population over the 5 years (Additional file 1: Figure S1). Changes in incidence over time were not significant for any of the types of drug resistance over the study period except pre-XDR TB (OR = 1.36; 95% CI 1.09–1.7). Among patients with MDR-TB, there was an overall increase in incidence of 5% while among patients with mono-resistance, there was a 4% increase. In addition, there was an 8% decrease among patients with poly-resistance, and a 1% increase among patients with XDR. The pre-XDR TB resistance patients had the largest increase, at 36% (Additional file 1: Table S2).


The study shows an increase in incidence of rifampicin-resistant TB in Uganda during 2014–2018. Although the majority of patients with RR-TB had been previously treated, the increase in incidence was greater among new cases than among previously treated cases. The coverage of GeneXpert machines greatly improved during the study period, and this might have contributed to the improved detection for rifampicin resistance in most parts of Uganda.

This study found an overall incidence of RR-TB of 3.8 per 100,000 population in Uganda. This is fairly comparable with the 2018 WHO estimate of RR-TB incidence for the country of 3.5 per 100,000 [4]. The largest proportion of patients with RR-TB had MDR-TB, with resistance to streptomycin, isoniazid, rifampicin, and ethambutol. The TB National guidelines have evolved from resistance testing largely targeting retreatment cases only in 2011 to resistance testing for all TB patients with RR-TB regardless of prior TB history in 2016 [25, 26]. Our observation that previously-treated TB patients were more likely than new cases to have MDR-TB has been made in multiple other studies [27,28,29]. Such findings call for attention to treatment completion rates for new cases and extra vigilance for drug resistance monitoring among those with history of previous treatment for TB. In addition, these findings are a good basis for strict implementation of infection control practices to prevent transmission and emergence of new cases, including drug-resistant cases.

Although the incidence of XDR-TB was low and no significant change was observed during the study period, pre-XDR TB showed a significant increase in incidence. This level of second-line resistance is a cause for concern, especially in resource-limited countries such as Uganda. This has the potential of introducing case management repercussions such as adjustment of treatment regimens, need for new therapeutic agents and introduction of new rapid diagnostic tools. Data from previous studies suggest that a similar increase is being observed worldwide [30,31,32,33,34]. Such trends point to the need to expand laboratory capacity for culture and DST for both first and second line anti-TB drugs in TB endemic countries like Uganda. Currently, this capacity is limited to two public health laboratories; decentralization of laboratory services with improved specimen collection and transport for culture and DST TB is needed in the country. Early identification of pre-XDR TB patients would enable closer monitoring to prevent the progression to XDR-TB. The distribution of GeneXpert machines in Uganda has greatly improved which has contributed to the TB case detection. However, even with specimen referral system a17% GeneXpert coverage is still below the desired target for the population in Uganda.

Males and persons aged 25–44 had the highest incidence rates of RR-TB. Male predominance in active TB is widely-reported globally and has been previously reported in Uganda [35, 36]. Persons aged 25–54 have also been identified as the most-affected age group in other countries [37]. Males in this age group have been reported to undertake occupations that are associated with an elevated risk for TB [36, 38]. However, our findings contradict other studies elsewhere which have found more females than males with MDR-TB [39]. Despite such contradictions, we think that targeted screenings for persons aged 25–54 may help reduce the overall burden of MDR-TB, as well as the burden in this age group.

Northern Uganda, with widespread poverty [40] and poor living conditions in overcrowded communities, has been reported previously to have a high prevalence of TB [14, 26]. The northern region, especially the Karamoja region, has been reported to have TB treatment success rates below 72%, mostly attributed to the loss to follow up (LTFU) [14]. Studies have shown that patients who are LTFU are at higher risk of developing MDR-TB, compared to those who are not LTFU [41]. Therefore, programs that can enhance treatment follow-up are suitable for such regions.

The HIV prevalence in the north-central region is also among the highest in the country, at 7.2% compared to the national prevalence of 6.2%, while in the northeast it is 5.3% [42]. HIV co-infection is known to increase the risk for developing drug-resistant TB as mechanisms linking drug-resistant TB to HIV infection have been suggested. Drug malabsorption in HIV-infected patients, especially for rifampin and ethambutol, can also lead to drug resistance as the patient receives a subclinical dose [43,44,45,46]. Similarly, Kampala, with an HIV prevalence of 6.9%, had higher incidence rates of drug-resistant TB compared to other districts [42]. Multiple studies have shown that there is an association between drug-resistant TB and HIV [43], although at least one study in Uganda found the opposite [47]. Extra efforts are needed to control tuberculosis transmission in the different parts of the country after having understood each area’s specific risk factors.

Study limitations

Our findings are based on routinely-generated laboratory surveillance data. This has the potential of underestimating the true burden of RR-TB because some patients do not access the healthcare system. Besides, RR-TB is only detected by GeneXpert which has a coverage of about 17% of all the TB diagnostic units in the country. This may have limited our ability to accurately determine the true incidence, and therefore the findings should be interpreted in this context. However, these findings provide a good reflection of the general trends in RR-TB incidence in the country over the study period.


Rifampicin-resistant TB incidence rates have consistently risen throughout the last 5 years in Uganda, but the burden is regionally diverse. There has been a significant increase in new patients diagnosed with RR-TB, with most being among those previously treated. Males and persons in the age-group 24–54 years were more affected by drug-resistant TB than females and the other age-groups. Strengthening prevention and control programs, especially among the most affected sub-populations, is crucial to the goal of minimizing the burden of resistant tuberculosis especially in resource limited settings.


It is important that all TB samples from patients with RR detected by the GeneXpert are referred for culture and DST before treatment initiation and contact tracing is initiated. It may also be efficient to adopt rapid diagnostic tests such as Cepheid Xpert MTB/XDR once they are approved in order to support the decentralization of DST testing. The National TB program should also consider having more machines installed in districts to enhance accessibility as well as instituting mechanisms to improve sample referral in areas without GeneXpert. Lastly, there is need to conduct a national TB drug resistance survey to determine the actual burden and risk factors associated with TB drug resistance in Uganda, and similar settings.

Availability of data and materials

For confidentiality reasons the datasets are not publicly available. However, the data sets can be availed upon reasonable request from the corresponding author and with permission from the Uganda Ministry of Health.



Incidence Rate


Rifampicin Resistant TB


Multidrug Resistance


Extensively Drug Resistance




Quantum Geographic Information System software


Drug sensitivity testing


Human immune deficiency Virus




National tuberculosis reference laboratory






















World Health Organization


  1. World Health Organization. WHO|TB drug resistance types. WHO.

  2. Ragonnet R, et al. High rates of multidrug-resistant and rifampicin-resistant tuberculosis among re-treatment cases: where do they come from? BMC Infect Dis. 2017;17(1):36.

    Article  PubMed  PubMed Central  Google Scholar 

  3. World Health Organization. Global tuberculosis report, in World Health Organisation. 2018.

  4. World Health Organization, WHO_HQ_Reports-G2-PROD-EXT-TBCountryProfile 2018. 2018.

  5. World Health Organization. Drug-resistant tuberculosis now at record levels. Geneva: World Health Organization; 2010.

    Google Scholar 

  6. Coll-Black S, et al. Reaching the poor: challenges for the TB programmes in the Western Pacific Region. 2004.

  7. Jassal MS, Bishai WR. Epidemiology and challenges to the elimination of global tuberculosis. Clin Infect Dis. 2010;50(Supplement_3):S156–64.

    Article  PubMed  PubMed Central  Google Scholar 

  8. World Health Organization. Rapid implementation of the Xpert MTB/RIF diagnostic test: technical and operational “How-to”; practical considerations. Geneva: World Health Organization; 2011.

    Google Scholar 

  9. Saeed M, et al. GeneXpert technology: a breakthrough for the diagnosis of tuberculous pericarditis and pleuritis in less than 2 hours. Saudi Med J. 2017;38(7):699–705.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Helb D, et al. Rapid detection of Mycobacterium tuberculosis and rifampin resistance by use of on-demand, near-patient technology. J Clin Microbiol. 2010;48(1):229–37.

    Article  CAS  PubMed  Google Scholar 

  11. National Tuberculosis and Leprosy Program, M.o.H.U., National tuberculosis and leprosy program quarterly bulletin. Kampala: Ministry of Health, Republic of Uganda; 2018.

  12. Hofmann-Thiel S, et al. Mechanisms of heteroresistance to isoniazid and rifampin of Mycobacterium tuberculosis in Tashkent. Uzbekistan Eur Respir J. 2009;33(2):368–74.

    Article  CAS  PubMed  Google Scholar 

  13. NTLP M. Algorithm for TB screening, diagnosis and Management_final 2017.

  14. Ministry of Health and National Tuberculosis and Leprosy Programme. NTLP June 2017–July 2018 Report. 2017–2018.

  15. National Tuberculosis and Leprosy Programme M. Algorithm for TB screening, diagnosis and Management_final 7th August 2017. 2017.

  16. Parsons LM, et al. Laboratory diagnosis of tuberculosis in resource-poor countries: challenges and opportunities. Clin Microbiol Rev. 2011;24(2):314–50.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Ministry of Health, National health facility master facility list 2018. 2018, Kampala: MOH.

  18. Joloba M, et al. Strengthening the tuberculosis specimen referral network in Uganda: the role of public-private partnerships. J Infect Dis. 2016;213(Suppl 2):S41–6.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Harries AD, et al. Using a bus service for transporting sputum specimens to the Central Reference Laboratory: effect on the routine TB culture service in Malawi. Int J Tuberc Lung Dis. 2004;8(2):204–10.

    CAS  PubMed  Google Scholar 

  20. Yagui M, et al. Timely diagnosis of MDR-TB under program conditions: is rapid drug susceptibility testing sufficient? Int J Tuberc Lung Dis. 2006;10(8):838–43.

    CAS  PubMed  Google Scholar 

  21. Diriba G, et al. Surveillance of drug resistance tuberculosis based on reference laboratory data in Ethiopia. Infect Dis Poverty. 2019;8(1):54.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Banerjee R, et al. Extensively drug-resistant tuberculosis in California, 1993–2006. Clin Infect Dis. 2008;47(4):450–7.

    Article  PubMed  Google Scholar 

  23. World Health Organization. Definitions and reporting framework for tuberculosis–2013 revision. 2013, World Health Organization.

  24. Uganda Bureau of Statistics, The National Population and Housing Census 2014–Main Report, Kampala. 2016.

  25. Ministry of Health, The National Tuberculosis and Leprosy Programme. Uganda national guidelines for the programmatic management of drug resistant tuberculosis. 2011.

  26. Ministry of Health Uganda, National Tuberculosis and Leprosy Control Programme. Revised National Strategic Plan 2015/16–2019/20, Kampala, Uganda. Uganda: Ministry of Health; 2017.

  27. Onyedum CC, Alobu I, Ukwaja KN. Prevalence of drug-resistant tuberculosis in Nigeria: a systematic review and meta-analysis. PLoS ONE. 2017;12(7):e0180996.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Nasiri MJ, et al. Prevalence of drug-resistant tuberculosis in Iran: systematic review and meta-analysis. Am J Infect Control. 2014;42(11):1212–8.

    Article  PubMed  Google Scholar 

  29. Berhan A, Berhan Y, Yizengaw D. A meta-analysis of drug resistant tuberculosis in Sub-Saharan Africa: how strongly associated with previous treatment and HIV co-infection? Ethiop J Health Sci. 2013;23(3):271–82.

    PubMed  PubMed Central  Google Scholar 

  30. Acosta C, et al. Drug-resistant tuberculosis in Eastern Europe: challenges and ways forward. Public Health Action. 2015;4:S3–12.

    Article  Google Scholar 

  31. Goyal V, et al. Prevalence of drug-resistant pulmonary tuberculosis in India: systematic review and meta-analysis. BMC Public Health. 2017;17(1):817.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Tasnim T, et al. Pre-extensively drug resistant tuberculosis (Pre-XDR-TB) among pulmonary multidrug resistant tuberculosis (MDR-TB) patients in Bangladesh. J Tuberc Res. 2018;6(3):199–206.

    Article  CAS  Google Scholar 

  33. Daniel O, et al. Pre-extensive drug resistant tuberculosis (pre-XDR-TB) among MDR-TB patents in Nigeria. Glob Adv Res J Microbiol. 2013;2:22–5.

    Google Scholar 

  34. Adwani S, Desai UD, Joshi JM. Prevalence of pre-extensively drug-resistant tuberculosis (Pre XDR-TB) and extensively drug-resistant tuberculosis (XDR-TB) among pulmonary multidrug resistant tuberculosis (MDR-TB) at a tertiary care center in Mumbai. JKIMSU. 2016;5:13–9.

    Google Scholar 

  35. Boum Y, et al. Male Gender is independently associated with pulmonary tuberculosis among sputum and non-sputum producers people with presumptive tuberculosis in Southwestern Uganda. BMC Infect Dis. 2014;14(1):638.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Guerra-Silveira F, Abad-Franch F. Sex bias in infectious disease epidemiology: patterns and processes. PLoS ONE. 2013;8(4):e62390.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Zignol M, et al. Multidrug-resistant tuberculosis in children: evidence from global surveillance. Eur Respir J. 2013;42(3):701–7.

    Article  PubMed  Google Scholar 

  38. Oni T, et al. Smoking, BCG and employment and the risk of tuberculosis infection in HIV-infected persons in South Africa. PLoS ONE. 2012;7(10):e47072.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Repossi A, et al. Gender and other risk factors for multidrug-resistant (MDR) tuberculosis (TB) among migrants to Milan, Italy. Eur Respir J. 2011;38(Suppl 55):3299.

    Google Scholar 

  40. Uganda Bureau of Statistics (UBOS), Uganda National Household Survey 2016/2017. UBOS: Kampala, Uganda; 2018.

  41. Caminero J. Multidrug-resistant tuberculosis: epidemiology, risk factors and case finding [State of the art series. Drug-resistant tuberculosis. Edited by CY. Chiang. Number 4 in the series]. Int J Tuberc Lung Dis. 2010;14(4):382–90.

    CAS  PubMed  Google Scholar 

  42. Ministry of Health Uganda. Uganda population-based HIV impact assessment (UPHIA): final report, 2016–2017. Kampala: Ministry of Health; 2019.

  43. Mesfin YM, et al. Association between HIV/AIDS and multi-drug resistance tuberculosis: a systematic review and meta-analysis. PLoS ONE. 2014;9(1):e82235.

    Article  PubMed  PubMed Central  Google Scholar 

  44. Dean AS, et al. HIV and multidrug-resistant tuberculosis: overlapping epidemics. Eur Respir J. 2014;44(1):251–4.

    Article  PubMed  Google Scholar 

  45. Patel KB, Belmonte R, Crowe HM. Drug malabsorption and resistant tuberculosis in HIV-infected patients. N Engl J Med. 1995;332(5):336–7.

    Article  CAS  PubMed  Google Scholar 

  46. Dye C, et al. Erasing the world’s slow stain: strategies to beat multidrug-resistant tuberculosis. Science. 2002;295(5562):2042–6.

    Article  CAS  PubMed  Google Scholar 

  47. Lukoye D, et al. Rates of anti-tuberculosis drug resistance in Kampala-Uganda are low and not associated with HIV infection. PLoS ONE. 2011;6(1):e16130–e16130.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references


We would like to thank the Ministry of Health for giving us the opportunity to access the National Tuberculosis and Leprosy and the National Tuberculosis Reference Laboratory databases. We are highly indebted to Dr. Julie. R. Harris, US Centers for Disease Control and Prevention, for technical guidance during the data analysis, critical review, and revision of the final manuscript. We thank the US-CDC for supporting the Uganda Public Health Fellowship Program activities, the Uganda National Tuberculosis and Leprosy Program for the support rendered by the NTRL data manager Diana Nadunga during data abstraction, and the laboratory team.


This project was supported by the President’s Emergency Plan for AIDS Relief (PEPFAR) through the US Centers for Disease Control and Prevention Cooperative Agreement Number GH001353-01 through Makerere University School of Public Health to the Uganda Public Health Fellowship Program, MoH. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the US Centers for Disease Control and Prevention, the Department of Health and Human Services, Makerere University School of Public Health, or the MoH. The staff of the funding body provided technical guidance in the design of the study, ethical clearance and collection, analysis, and interpretation of data and in writing the manuscript.

Author information

Authors and Affiliations



GB conceptualized and took lead in writing the manuscript. GB and RKM participated in study designing, manuscript drafting, and data analysis. RKM, SK, NA, BK, LB, ARA, MLJ, and ST participated in manuscript writing and reviewed the manuscript for intellectual content and scientific integrity. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Gloria Bahizi.

Ethics declarations

Ethics approval and consent to participate

We sought permission to use the routinely generated program data from the Uganda Ministry of Health, the National Tuberculosis and Leprosy Program and the National Tuberculosis Reference Laboratory. In addition, we received a project determination as non-research and clearance from the Centers for Disease Control and Prevention (CDC) to undertake this analysis.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1

: Table S1. Districts with highest RR TB incidence rates per 100,000 population, Uganda, 2014–2018. Figure S1. Trends of anti-tuberculosis drug resistance types among patients with rifampicin resistant tuberculosis, Uganda 2014–2018. Table S2. Odds ratios for changes in incidence by drug resistance types among RR-TB patients in Uganda 2014–2018.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bahizi, G., Majwala, R.K., Kisaka, S. et al. Epidemiological profile of patients with rifampicin-resistant tuberculosis: an analysis of the Uganda National Tuberculosis Reference Laboratory Surveillance Data, 2014–2018. Antimicrob Resist Infect Control 10, 76 (2021).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: