Abstract
Background
Drug-resistant tuberculosis (DR-TB) is a significant challenge to the national tuberculosis (TB) control program in Ethiopia. The Tigray region in northern Ethiopia has shown a surge in the incidence of DR-TB cases. However, the determinants of DR-TB in the region are not studied. This study is aimed at identifying the factors associated with the development of DR-TB in the Tigray region of northern Ethiopia.
Methods
The study used an unmatched case-control design to identify determinants of DR-TB in the Tigray region, northern Ethiopia, whereby 86 patients and 86 controls who registered for TB treatment follow-up in selected hospitals were recruited. Trained nurses collected both primary and secondary data, which were analyzed using descriptive statistics and binary logistic regression. The test statistics was conducted with a 95% confidence level, and a p-value of less than 0.05 was considered significant.
Results
The study included 86 patients with DR-TB (cases) and an equal number of patients with drug-susceptible (controls). The case and control groups had 38 (44.2%) and 47 (54.7%) males, respectively. The study revealed the study participants with male gender (adjusted odds ratio [AOR] = 4.9, 95% confidence interval [CI: 1.2–19.9), single marital status (AOR = 13.6, 95% CI: 2.3–81.2), history of TB treatment (AOR = 58.2, 95% CI: 11.2–302.1), experienced a delay of more than 60 days before TB diagnosis (AOR = 4.8, 95% CI: 1.2–19.3), interrupted treatment at least once (AOR = 4.9, 95% CI: 1.02–23.9), and unsuccessful treatment outcome at first treatment (AOR = 7.6, 95% CI: 1.8–35.9) had a higher risk of DR-TB.
Conclusions
The study highlights determinants of DR-TB in the region, including gender, marital status, delayed diagnosis (over 60 days), previous treatment history, interrupted treatment, and unsuccessful treatment outcomes during initial treatment. It is recommended that healthcare providers focus on targeted interventions, such as supporting males and unmarried individuals, ensuring early diagnosis and prompt initiation of treatment, improving treatment adherence, and providing tailored support for patients with histories of incomplete treatment and unsuccessful initial treatment outcomes.
Supplementary Information
The online version contains supplementary material available at 10.1186/s41043-025-01021-y.
Keywords: Drug-resistant tuberculosis, Determinants, Case-control, Tigray, Ethiopia
Background
Tuberculosis (TB), caused by Mycobacterium tuberculosis, is world’s leading infectious disease killer, overtook COVID-19 in 2023 [1]. According to 2024 World Health Organization (WHO) Global TB report, there were 10.8 million cases of active TB, resulting in 1.25 million deaths, with 98% of these cases and fatalities occurred in low-and middle-income countries [1]. The emergence and spread of drug-resistant TB (DR-TB) is threatening global efforts to combat the disease and placing additional strain on the healthcare systems of nations [1]. DR-TB is a form of TB that is resistant to one or more first-line anti-TB drugs [2], which are crucial for the treatment of drug-susceptible TB (DS-TB) [3, 4]. Globally, in 2023 alone, the estimated proportion of people with DR-TB was 3.2% among new cases and 16% among those previously treated [1].
In Ethiopia, DR-TB remains a major threat to the country’s TB control efforts. According to the 2024 Global TB Report, Ethiopia is one of the 30 high-burden countries that together account for approximately 87% of the global TB burden [1]. In 2023, the estimated annual TB incidence was 146 per 100,000 population [1], reflecting a 16% increase from 126 per 100,000 in 2022 population [5]. Approximately, 1.1% of these new TB cases and 12% of previously treated TB cases had DR-TB in 2022 [5]. Similarly, evidence from the 2019 Global Burden of Disease (GBD) report indicated a concerning rise in the incidence of DR-TB, including multidrug-resistant TB (MDR-TB) and extremely drug-resistant TB (XDR-TB), at both national and sub-national levels from 1990 to 2019 [6]. As a result, Ethiopia has made TB control one of its major public health programs and has demonstrated political commitment to ending the TB epidemic by 2035 [7]. To achieve this goal, Ethiopia has endorsed the WHO’s END TB strategy and revised its national TB Strategic Plan in line with global targets of reducing TB-related deaths by 95% and the incidence of TB cases by 90% between 2015 and 2035 [8]. One of the key activities to achieve this goal is addressing the underlying determinants that contribute to the emergence and spread of DR-TB [1].
Several studies have documented that the emergence and transmission of DR-TB can be influenced by a range of factors, such as socio-demographic, behavioral, environmental, and health-related factors. Socio-demographic determinants include age [9–12], gender [3, 12], place of residence [3], educational level [3], income level [9, 10, 13], marital status [14], and household size [15]. Others have identified environmental factors: crowded living conditions [16], poor ventilation [17], and behavioral and health-related factors: alcohol use disorder [9], smoking [3, 18], HIV/AIDS infection [10, 12], diabetes [19], acid-fast bacilli smear-positive [20, 21], pulmonary space [9, 21, 22], history of TB [21], previous history of treatment [3, 10, 12, 18, 20, 21], previous contact history with a TB patient [3, 20], and treatment failure [9] as the determinants of DR-TB. However, the relative importance of each of the factors may significantly vary among different populations, local settings [23], and even countries [24, 25].
According to previous studies, the Tigray region in northern Ethiopia has shown a rise in DR-TB cases, which ranges from 16.2% [18] to 18.6% [11]. This is higher than earlier studies conducted in other regions of Ethiopia [26, 27]. The current situation of DR-TB in Tigray is likely worsened by the consequences of the war, including mass displacement, disruption of healthcare services, and potential increases in malnutrition. Available limited cross-sectional studies may overlook the specific risk factors influencing DR-TB in the region. Given the increasing cases of DR-TB, this case-control study offers a deeper investigation into the specific risk factors contributing to its emergence in the region. By comparing individuals with DR-TB to those without, the study provides a clearer insights into the underlying determinants and risk associations. This will enable policymakers and healthcare providers to implement targeted strategies and effective interventions to improve treatment outcomes and reduce the incidence and spread of drug-resistant strains in the region. Additionally, the findings of the study will contribute to the existing limited body of knowledge on TB in Ethiopia and provide valuable insights for future research and interventions.
Methods
Study area
The study was conducted in the Tigray region, located in northern Ethiopia. The region shares international borders with Sudan to the west and Eritrea to the north, and national boundaries with the Afar and Amhara regions to the east and south, respectively. Tigray is divided into seven administrative zones and 52 districts. Before November 2020, the region’s healthcare services were provided by 40 hospitals, 226 health centers, and 741 health posts [28]. All hospitals and most health centers equipped with diagnostic technology are authorized to diagnose TB and provide directly-observed treatment strategy (DOTS) services. For this study, four hospitals were randomly selected: Mekelle Hospital, Alamata Hospital, Adigrat Hospital, and Lemlem Karl Hospital (Fig. 1). These Hospitals provide primary and referral services to their respective communities.
Fig. 1.
Map showing study hospitals: AGH: Adigrat hospital; MH: Mekelle hospital; LKH: Lemlem Karl hospital; AMH: Alamata hospital
Study design and period
A health facility-based unmatched case-control study design was used to identify the determinants of DR-TB by recruiting an equal number of cases (individuals with DR-TB) and controls (individuals with DS-TB) between September 2016 and June 2017 Gregorian Calendar (Meskerem 2009 to Sene 2010 Ethiopian Calendar) in Tigray region, northern Ethiopia.
Study population and study subjects
All bacteriologically confirmed TB patients registered before the study period and still on anti-TB treatment or follow-up were included (n = 593). The study subjects were randomly selected cases and controls registered for TB treatment follow-up in the above-mentioned hospitals in the region.
Eligibility criteria
Inclusion criteria
For the cases, individuals aged 18 years and older with confirmed DR-TB (diagnosed through the GeneXpert® system, culture, acid-fast bacilli (AFB) test, drug sensitivity test (DST), and/or clinical and radiological evidence) in the inpatient department (IPD) on intensive second-line anti-TB treatment, who were residents of the Tigray region during the study period and undergoing treatment for DR-TB during the study period, were included. For the controls, individuals aged 18 years and older at the outpatient department (OPD) with DS-TB (turned negative at six-months and registered as cured) and residents of the Tigray region during the study period, were included.
Exclusion criteria
Those patients whose medical records in the TB registration logbooks were incomplete, or who refused to participate in the study, were excluded from the cases. For the controls, individuals with a prior diagnosis of DR-TB, those whose medical records or exposure history could not be verified or were incomplete, and those who declined to participate in the study were excluded.
Sample size and sampling
The sample sizes for the cases and controls were calculated using Epi Info 6.04 software. A treatment history greater than or equal to two times was considered as exposure, with the proportion of controls exposed at 58.2%, the proportion of cases exposed at 79.1% among TB patients [9], a 5% significance level, power of 80%, a case to control ratio of 1:1, and an odds ratio of 2.72. A total of 172 participants (86 cases and 86 controls) were included in this study. Participants were randomly selected from among inpatients and outpatients. In addition, participants from the controls were interviewed by tracing their addresses.
Data collection method
We used a data-recording format to record data from the TB registry logbook. Secondary data were obtained from the national TB control program registry documented in hospitals, and an interviewer-administered structured questionnaire was to collect additional data from patients with TB (Supplementary file 1). The questionnaire was developed based on a review of relevant literature, national and WHO TB guidelines, and consultation with TB experts. The data collectors were trained nurses familiar with collecting health facility data. Additional outpatient data were collected through face-to-face interviews.
Data quality assurance
The medical registration logbook for DR-TB, developed by the national TB control program, was tailored to specific settings. The checklist and questionnaire were modified to align with the objectives of this study. Data collectors and supervisors underwent comprehensive training. The questionnaire was initially prepared in English and then translated into Tigrinya, an official language in the region. To ensure linguistic accuracy, it was back translated into English by a different translator. The content validity was reviewed by public health experts. The questionnaire was then pretested on a small sample of TB patients to assess clarity, cultural relevance, and comprehension. Based on the feedback, necessary modifications were made before the final version was administered. Double data entry was performed, followed by thorough data cleaning to ensure data quality.
Ethical consideration
Ethical clearance was obtained from the Ethical Review and Research Committee of College of Health Sciences at Mekelle University (ERC 0830/2016). Participants were informed of the objective of the study and were invited to participate voluntarily. They were also assured that their information would be kept confidential. Interviews were conducted privately with only those who provided verbal consent to participate.
Data analysis
Epi-info version 6.04 was used to clean and enter the data, and STATA software version 14 (Stata Corp., College Station, TX, USA) was used for analysis. Two people individually cross-checked every entry to guarantee the accuracy of the data. Frequencies and proportions were used for the descriptive analysis. To control the effect of data quality on the outcome, the investigators performed data cleaning processes, such as outlier detection, missing value analysis, detection of multicollinearity, and typographical errors. Assumptions about the normality of the data for the variables were also made. Outlier detection was performed by graphing the data using a box plot. Missing values were analyzed for all variables, and missing values were handled by replacing the modal values for the categorical variables and the mean values for the continuous variables of each variable. Statistical methods such as descriptive statistics and univariable and multivariable binary logistic regression were employed to predict the factors associated with DR-TB development. The logistic regression analysis results were presented using Odds Ratios (OR), 95% Confidence Intervals (CI), and p-values. Variables with a p-value less than 0.25 in the univariable binary logistic analyses were included in the multivariable model. Model fitness was checked using the Hosmer–Lemshow method [29].
Results
Sociodemographic characteristics of study participants
The study included 86 DR-TB patients and an equal number of DS-TB patients. The case and control groups had 38 (44.2%) and 47 (54.7%) males, respectively. About half of the cases, 43 (50%), and 32 (37.2%) of the controls were 26–45 years old. Urban residents had the highest proportion, with 106 (61.6%) study participants, of whom 46 (53.5%) and 60 (69.8%) were cases and controls, respectively. Most of the patients were single (90 [52.3%]). Among them, 53 (61.6%) and 37 (43%) were cases and controls, respectively. Of the study participants, 104 (60.5%) had a secondary or lower educational status, which included 54 (62.8%) cases and 50 (58.1%) controls. Of the 137 (79.9%) unemployed participants, 70 (81.4%) were cases, and 67 (77.9%) were controls. Monthly income between 501 and 1500 ETB had the highest proportion of 70 (40.7%), of which 26 (30.2%) and 44 (51.2%) were in cases and controls, respectively. A family size of three or more members per household had the highest number of 123 (68.3%), of which 61 (70.9%) and 62 (72.1%) were cases and controls, respectively (Table 1).
Table 1.
Sociodemographic characteristics of the cases and controls in Tigray region, Northern Ethiopia, 2016–2017
| Variable | Cases n (%) | Controls n (%) | Total N (%) | |
|---|---|---|---|---|
| Gender | Female | 48 (55.8) | 39 (45.3) | 87 (50.6) |
| Male | 38 (44.2) | 47 (54.7) | 85 (49.4) | |
| Age (years) | ≤ 25 yrs | 30 (34.9) | 33 (38.4) | 63 (36.6) |
| 26-45yrs | 43 (50.0) | 32 (37.2) | 75 (43.6) | |
| ≥ 46 yrs | 13 (15.1) | 21 (24.4) | 34 (19.8) | |
| Residence | Urban | 46 (53.5) | 60 (69.8) | 106 (61.6) |
| Rural | 40 (46.5) | 26 (30.2) | 66 (38.4) | |
| Marital Status | Single | 53 (61.6) | 37 (43.0) | 90 (52.3) |
| Married | 33(38.4) | 49 (57) | 82 (47.7) | |
| Educational Status | Illiterate | 22 (25.6) | 24 (27.9) | 46 (26.7) |
| Primary school | 54 (62.8) | 50 (58.1) | 104 (60.5) | |
| Secondary school and above | 10 (11.6) | 12 (14.0) | 22 (12.8) | |
| Occupational Status | Have Work | 16 (18.6) | 19 (22.1) | 35 (20.3) |
| Not Have Work | 70 (81.4) | 67 (77.9) | 137 (79.7) | |
| Monthly Income (ETB) | ≤ 500 | 39(45.3) | 25(29.1) | 64(37.2) |
| 501–1500 | 26(30.2) | 44(51.2) | 70(40.7) | |
| 1501–2000 | 10(11.6) | 8(9.3) | 18(10.5) | |
| ≥ 2001 | 11(12.8) | 9(10.5) | 20(11.6) | |
| Family Size | ≤ 2 | 25(29.1) | 24(27.9) | 49(28.5) |
| ≥ 3 | 61(70.9) | 62(72.1) | 123(71.5) | |
Clinical and behavioral characteristics of the study participants
The majority of participants (n = 110, 64.0%) had pulmonary TB (PTB), of whom 57 (66.3%) and 53 (61.6%) were cases and controls, respectively. Of the 36 (20.3%) HIV-seropositive participants, 17 (19.8%) were cases and 19 (20.9%) were controls. The histories of alcohol consumption and cigarette smoking during treatment were higher in cases: 15 (17.4%) and 9 (10.5%), respectively. A delay of above 60 days before the diagnosis of TB accounted for 49 (57%) cases and 22 (25.6%) controls. Among the study participants, 19 (22.1%) cases and 26 (30.2%) controls were lived alone. A history of anti-TB treatment was higher in the case group (n = 71, 82.6%) than the control group (n = 13, 15.1%). Irregular treatment was observed in 38 cases (44.2%) and 9 controls (10.5%). Of the 109 (64.1%) patients with successful first treatment outcomes, 31 (36.9%) were cases and 78 (90.7%) were controls (Table 2).
Table 2.
Clinical and behavioral characteristics of the cases and controls in Tigray region, Northern Ethiopia, 2016–2017
| Variable | Cases n (%) | Controls n (%) | Total N (%) | |
|---|---|---|---|---|
| Site of TB Infection | PTB | 57(66.3) | 53(61.6) | 110(64) |
| EPTB | 29(33.7) | 33(38.4) | 62(36) | |
| HIV-Serostatus | Negative | 69(80.2) | 68(79.1) | 137(79.7) |
| Positive | 17(19.8) | 18(20.9) | 35(20.3) | |
| Alcohol Intake | No | 71(82.6) | 75(87.2) | 146(84.9) |
| Yes | 15(17.4) | 11(12.8) | 26(15.1) | |
| Smoking | No | 77(89.5) | 83(96.5) | 160(93) |
| Yes | 9(10.5) | 3(3.5) | 12(7) | |
| Delay Before Diagnosis | ≤ 60 days | 37(43) | 64(74.4) | 101(58.7) |
| > 60 days | 49(57) | 22(25.6) | 71(41.3) | |
| Living Situation | With Family | 67(77.9) | 60(69.8) | 127(73.8) |
| Alone | 19(22.1) | 26(30.2) | 45(26.2) | |
| First Treatment Site | General Hospital | 21(24.4) | 5(5.8%) | 26(15.1) |
| Private Hospital | 65(75.6) | 81(94.2) | 146(84.9) | |
| History of Anti-TB Treatment | Yes | 71(82.6) | 13(15.1) | 84(48.8) |
| No | 15(17.4) | 73(84.9) | 88(51.2) | |
| History of Irregular Treatment | Yes | 38(44.2) | 9(10.5) | 47(27.3) |
| No | 48(55.8) | 77(89.5) | 125(72.7) | |
| First Treatment Outcome | Successful | 33(38.4) | 78(90.7) | 111(64.5) |
| Unsuccessful | 53(61.6) | 8(9.3) | 61(35.5) | |
EXTB: Extrapulmonary Tuberculosis; PTB: Pulmonary Tuberculosis
Factors associated with drug-resistant TB
The association of different explanatory variables with DR-TB was analyzed using univariate and multivariate binary logistic regression analyses (Table 3). Multivariable binary logistic regression analysis was performed on variables with a significance level of 0.25 as the cut-off point during the univariate binary logistic regression analysis. Accordingly, those male TB patients were had about five times more odds of developing DR-TB as compared to females (adjusted odds ratio [AOR] = 4.9, 95% CI: 1.2–19.9). Patients with a single marital status had nearly 14 times more chance of developing drug resistance to anti-TB drugs (AOR = 13.6, 95% CI: 2.3–81.2) than those who were married. Similarly, TB patients with a duration of delay before diagnosis above 60 days had almost five times higher odds of developing resistance to anti-TB drugs (AOR = 4.8, 95% CI: 1.2–19.3) than their counterparts. The TB patients with a previous history of treatment had more chance of developing DR-TB as compared to those who did not have a history of anti-TB treatment (AOR = 58.2, 95% CI: 11.2–302.1). TB patients who interrupted anti-TB treatment had five times higher odds of developing DR-TB (AOR = 4.9, 95% CI: 1.02–23.9). TB patients who had unsuccessful treatment outcomes at the first treatment had almost eight times higher chance of resistance to anti-TB drugs than those who had successful treatment outcomes at the first (AOR = 7.6, 95% CI: 1.8–35.9).
Table 3.
Univariate and multivariate binary logistic regression analysis for factors associated with DR-TB in Tigray region, Northern Ethiopia, 2016–2017
| Variable | Cases n(%) | Control n(%) | COR (95% CI) | P-value | AOR (95% CI) | P-value | |
|---|---|---|---|---|---|---|---|
| Gender | Male | 38(44.7) | 47(55.3) | 1.5(0.8–2.8) | 0.171 | 4.9(1.2–19.9) | 0.025 |
| Female | 48(52.8) | 39(44.8) | Reference | Reference | |||
| Age (years) | ≤ 25 | 30(47.6) | 33(52.4) | 0.7(0.3–1.6) | 0.376 | 2.8(0.4–19.6) | 0.298 |
| 26–45 | 43(57.3) | 32(42.7) | 0.7(0.2–1.1) | 0.067 | 1.1(0.2–5.4) | 0.942 | |
| ≥ 46 | 13(38.2) | 21(61.8) | Reference | Reference | |||
| Residence | Rural | 40(60.6) | 26(39.4) | 2(1.1–3.8) | 0.029 | 1.1(0.3–3.9) | 0.9 |
| Urban | 46(43.4) | 60(56.6) | Reference | Reference | |||
| Marital Status | Single | 53(58.9) | 37(41.1) | 2.1(1.2–3.9) | 0.015 | 13.6(2.3–81.2) | 0.004 |
| Married | 33(40.2) | 49 (59.8) | Reference | Reference | |||
| Monthly Income (ETB) | ≤ 500 | 39(60.9) | 25(39.1) | Reference | Reference | ||
| 501–1500 | 26(37.1) | 44(62.9) | 2.6(1.3–5.3) | 0.006 | 3.7(0.6–22.1) | 0.153 | |
| 1501–2000 | 10(55.6) | 8(44.4) | 1.2(0.4–3.5) | 0.68 | 4.1(0.7–23.3) | 0.107 | |
| ≥ 2001 | 11(55) | 9(45) | 1.3(0.5–3.5) | 0.63 | 0.2(0.02–2.6) | 0.231 | |
| Delay Before Diagnosis | ≤ 60 days | 37(36.6) | 64(63.4) | Reference | Reference | ||
| > 60 days | 49(69) | 22(31) | 3.8(2-7.3) | < 0.001 | 4.8(1.2–19.3) | 0.029 | |
| Living Situation | With Family | 67(52.8) | 60(47.2) | Reference | Reference | ||
| Alone | 19(42.2) | 26(57.8) | 0.7(0.3–1.3) | 0.226 | 0.7(0.2–2.9) | 0.648 | |
| History of Anti-TB Treatment | No | 15(17) | 73(83) | Reference | Reference | ||
| Yes | 71(84.5) | 13(15.5) | 26.6(11.8–59.8) | < 0.001 | 58.2(11.2-302.1) | < 0.001 | |
| First Treatment Site | General Hospital | 21(24.4) | 5(5.8%) | Reference | Reference | ||
| Private Hospital | 65(75.6) | 81(94.2) | 5.3(1.9–14.8) | 0.001 | 1.3(0.2–8.6) | 0.822 | |
| History of Irregular Treatment | No | 48(38.4) | 77(61.6) | Reference | Reference | ||
| Yes | 38(80.9) | 9(19.1) | 6.7(3-15.2) | < 0.001 | 4.9(1.02–23.9) | 0.047 | |
| First Treatment Outcome | Unsuccessful | 53(61.6) | 8(9.3) | 16.7(7.1–39.1) | < 0.001 | 7.6(1.8–35.9) | 0.006 |
| Successful | 33(38.4) | 78(90.7) | Reference | Reference | |||
Note: Seven variables ─ Educational Status, Occupational Status, Family Size, Site of TB Infection, HIV Serostatus, Alcohol Intake, and Smoking ─ were excluded from Table 3 because their p-values were above the cutoff value, and multivariate logistic regression was not done
Discussion
The emergence of DR-TB poses a significant threat to public health globally. It has the potential to reverse successful progress made to reduce TB-related morbidity and mortality over the past two decades [30]. In the Tigray region, the number of patients with DR-TB has been increasing every year since the diagnosis and treatment of DR-TB began in 2013 [11]. Therefore, understanding the determinants of DR-TB in this particular region is crucial for designing targeted and effective control measures. Therefore, the purpose of this case-control study was to identify the factors that contribute to DR-TB in the Tigray Region of northern Ethiopia.
DR-TB is a complex issue with several factors influencing its development and spread, including demographic factors. The study identified being male as a risk factor for the development of DR-TB. A similar study in another part of the world showed that males had a higher risk of DR-TB [3]. Moreover, this study identified that patients with a single marital status were found to be 13.6 times more likely to be resistant to anti-TB drugs than married patients. A similar finding was observed in a study conducted in the Amhara region, which stated that the proportion of DR-TB patients was higher among those with a single marital status [9]. This could be due to the reason that single patients may have less social support, which could lead to poor access to TB services and poor adherence to treatment regimens [31], may ultimately contribute to the development of DR-TB.
Rapid and proper diagnosis are essential step in TB care [32]. Without timely and accurate identification of TB cases, patients may not receive appropriate treatment, leading to the development of DR-TB. This study revealed that TB patients who had a history of delay of more than 60 days before diagnosis had an almost five times higher risk of developing resistance to anti-TB drugs than those with a history of fewer than 60 days before the diagnosis of TB. Similarly, a previous study found that patients who were diagnosed and treated 60 days after symptom onset were more likely to develop MDR-TB than those who received prompt treatment [33]. This could be because the diagnostic delay prolongs patients’ time to carry TB bacteria and allows the disease to progress without proper intervention. According to a systematic review of studies, the main reasons associated with diagnostic delay include: coexistence of chronic cough and/or other lung diseases, negative sputum smear, EXPTB, low access to health care providing services, low awareness of TB, self-treatment, stigma, and initial visitation of a governmental low-level health care facility, private practitioner, or traditional healer [34]. Additional reasons may include poor accessibility to TB dispensaries or the healthcare system, and providers or patients might not seek medical services due to financial burden.
According to the WHO, DR-TB is more commonly reported in previously treated TB patients than in newly diagnosed [35]. Similarly, in this study, TB patients who had previously received therapy were more likely to develop DR-TB than those who did not. Other case-control studies conducted in different areas of Ethiopia also reported comparable findings in which people who had a previous history of anti-TB treatment were more likely to develop MDR-TB than those who had been untreated [12, 20]. A systematic review of risk factors conducted in Europe also identified that the history of treatment was one of the strongest determinants of MDR-TB [36]. Different possible reasons can explain this. Firstly, if the initial treatment regimen is not effective in killing off all of the bacteria, the remaining bacteria may become resistant to the drugs. Secondly, suppose the previously treated patients have been exposed to inadequate or improper drug regimens. In that case, this can allow the TB bacteria to adapt and become resistant to the drugs. Thirdly, patients who have a history of TB treatment may have been exposed to other drug-resistant strains of TB, which can increase the likelihood of developing DR-TB [24].
A six-month treatment plan consisting of rifampicin and isoniazid, supplemented with pyrazinamide and either streptomycin or ethambutol for the first two months, can cure more than 95% of cases if the medication is taken properly [37]. However, poor medication adherence and irregular therapy are thought to be the most significant risk factors for MDR-TB [38, 39]. This study also revealed that the incidence of DR-TB was five times higher among patients who did not adhere to proper treatment during the treatment period. This may be because non-adherence to TB treatment could allow the TB bacteria to adapt and become resistant to the drugs, which would provide more opportunities for individual mutations in different independent genes to accumulate [40].
A number of studies have found that individuals with a history of treatment failure are more likely to develop MDR-TB than those who have finished the course of TB treatment [9, 41, 42]. Similarly, in this study, patients who had a history of unsuccessful outcomes at the first treatment were nearly eight times more at risk of developing DR-TB than those who had successful treatment outcomes at the first treatment. According to Eshetie and co-authors, patients with a history of treatment failure are more likely to develop MDR-TB than those who have finished their TB treatment [24].
Limitations of the study
The current study has limitations that should be considered. Firstly, the small sample size may result in wide confidence intervals for some variables, indicating a need for larger sample to achieve the precise estimates, requiring more resources. Secondly, data collection methods such as face-to-face interviews, may introduce recall bias, leading to inaccuracies in reporting risk factors due to discrepancies in participants’ memory and understanding of questions. Lastly, the presence of unmeasured variables (e.g., diabetes, nutritional factors, ventilation, etc.) could lead to residual confounding, affecting the validity of the conclusion of the study.
Conclusions
This case-control study provided valuable insights into the key factors contributing to the development of DR-TB in Tigray region. The findings emphasize the significance of gender, marital status, delayed diagnosis of above 60 days, interrupted treatment at least once, unsuccessful treatment outcome at first treatment, and a history of treatment as key predictors of DR-TB in the region. Accordingly, it is recommended that public health programs and healthcare systems focus on targeted interventions such as educating and supporting males and unmarried individuals, ensuring early diagnosis and prompt initiation of treatment, improving treatment adherence, and providing tailored support for patients with histories of incomplete treatment and unsuccessful initial treatment outcomes. Future research should explore the effectiveness of these recommendations and evaluate the impact of interventions implemented based on the findings of this study.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Acknowledgements
The authors would like to express their gratitude to the College of Health Sciences at Mekelle University for sponsoring this research project. We would also like to extend our sincere appreciation to the Tigray Regional Health Bureau and all health workers of the study hospitals who assisted us during data collection. Finally, we would like to thank the data collectors for their careful execution of the data collection process.
Abbreviations
- AFB
Acid Fast Bacilli
- AOR
Adjusted odds ratio
- COR
Crude odds ratio
- DOTS
Directly-observed treatment strategy
- DST
Drug susceptibility Testing
- DS-TB
Drug-susceptible Tuberculosis
- DR-TB
Drug resistant Tuberculosis
- EXTB
Extrapulmonary Tuberculosis
- GBD
Global Burden of Diseases
- HIV
Human Immunodeficiency Virus
- IPD
Inpatient department
- MDR-TB
Multidrug-resistant Tuberculosis
- OPD
Outpatient department
- PTB
Pulmonary Tuberculosis
- TB
Tuberculosis
- WHO
World Health Organization
- XDR-TB
Extremely drug-resistant Tuberculosisulosis
Author contributions
DM, GB, KT, MG, GG conceptualized, investigated, visualized, and supervised the study, developed methodology, administered the project, performed data curation and formal analysis, acquired funds, validated, and secured resources. DM drafted the manuscript. All the authors read and approved the final manuscript.
Funding
This research work was sponsored by College of Health Sciences of Mekelle University. We declare the funding body has no role in the design of the study and collection, analysis and interpretation of data and writing the manuscript.
Data availability
No datasets were generated or analysed during the current study.
Declarations
Ethics approval and consent to participate
Ethical clearance was obtained from the Ethical Review and Research Committee of College of Health Sciences at Mekelle University (ERC 0830/2016). Participants were informed of the study’s objective and were invited to participate voluntarily. They were also assured that their information would be kept confidential. Interviews were conducted privately with only those who provided verbal consent to participate.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
No datasets were generated or analysed during the current study.