Risk factors of multi-drug-resistant tuberculosis among tuberculosis patients in Bagmati Province, Nepal: a matched case-control study

Abstract

Background

Drug-resistant tuberculosis poses a significant threat to global TB control efforts, potentially reversing progress made in reducing TB-related morbidity and mortality. This study aims to identify risk factors for multidrug-resistant TB [MDR-TB] in Bagmati Province, Nepal.

Methodology

This study employed a matched case-control design to identify risk factors for multidrug-resistant tuberculosis (MDR-TB) in Bagmati Province, Nepal. Conducted in Kathmandu, Bhaktapur, and Chitwan, cases were MDR-TB patients from authorized treatment centers, while controls were patients with non-MDR-TB from Directly Observed Treatment, Short-course (DOTS) centers. A total of 154 participants were included, with 77 cases and 77 controls matched by gender. Data was collected through face-to-face interviews by a trained professional, with stringent COVID-19 safety measures in place. Statistical analysis was performed using SPSS, involving descriptive statistics and binary logistic regression to identify significant predictors of MDR-TB. Ethical approval was obtained from BP Koirala Institute of Health Sciences, and informed consent was secured from all participants.

Results

A total of 154 patients (77 cases,77 controls) were interviewed, and the study found significant associations between pulmonary TB and the case group (89.6%) compared to controls (57.1%) (p < 0.001; OR: 6.5; 95% CI: 2.7–15.2), indicating it as a substantial risk factor. Controls showed higher rates of Extra Pulmonary TB (42.9%) compared to cases (10.4%). Previous TB history was more prevalent among cases (63.6%) than controls (10.4%) (p < 0.001; OR: 15.09; 95% CI: 6.34–35.91), highlighting it as a significant risk factor. Cases were more likely to report close contact with TB patients (64.9%) compared to controls (32.5%) (p < 0.001; OR: 3.85; 95% CI: 1.97–7.51). Cases also had a significant association with close contact with drug-resistant TB patients (p = 0.001; OR: 78; 95% CI: 10.31-589.62), suggesting exposure as a risk factor.

Conclusion

The study findings revealed significant risk factors for MDR-TB, including Pulmonary Tuberculosis [PTB], previous TB treatment history, and close contact with DR-TB patients. The study highlights the importance of prevention measures to break transmission chains and infection control in health facilities.

Supplementary Information

The online version contains supplementary material available at https://doi.org/10.1186/s12879-026-13975-3.

Background

Tuberculosis (TB) remains a major global public health challenge and is one of the leading causes of death worldwide, ranking as the leading cause of mortality from a single infectious agent. In 2024, an estimated 10.7 million people developed TB globally, with approximately 1.23 million deaths, corresponding to an incidence rate of 131 cases per 100,000 population and a case fatality rate of 11.5% [1, 2].

In April 1993, the World Health Organization [WHO] declared tuberculosis a public health emergency [3]. After defining the nature and size of the global TB problem, WHO introduced the Directly Observed Treatment Short-course [DOTS] as a solution to an emergency [4]. The expanding HIV epidemic and the growth of drug-resistant tuberculosis [DR-TB] further subverted the DOTS strategy, which was hampered by imprecise diagnostic tools and passive case detection [5].

Antimicrobial resistance is an increasingly significant threat to public health, and drug-resistant tuberculosis is a major component of that threat [6, 7]. Multidrug-resistant tuberculosis (MDR-TB) refers to TB caused by Mycobacterium tuberculosis strains resistant to at least isoniazid and rifampicin [8]; such strains are harder and slower to treat than drug-susceptible TB [9], and globally only about half of MDR-TB patients are treated successfully [5]. Drug resistance arises from inadequate treatment—incorrect drug combinations, inadequate dose or duration, or irregular adherence—and is particularly prevalent in poorly regulated settings [10]. In 2024, an estimated 10.7 million people developed TB globally with 1.23 million deaths, and although case numbers declined for the first time since 2020, the MDR/RR-TB treatment gap remained concentrated in five countries (India, the Philippines, Indonesia, China, and Pakistan), which together account for about 60% of the global gap [1]. MDR-TB also imposes substantial financial hardship: 27–83% of households affected by any form of TB and 67–100% of those affected by drug-resistant TB experience catastrophic costs [11]. Tuberculosis remains a serious public health problem across the WHO Southeast Asia Region [12], and DR-TB could be responsible for an estimated 2.5 million of the projected 10 million annual AMR-related deaths by 2050 if action is not taken [13].

In Nepal, TB remains a major public health challenge, with an estimated 68,000 cases in 2023 (incidence: 229 per 100,000). HIV-associated TB was low (430 cases), while MDR/RR-TB affected around 3,000 people (10 per 100,000) [14].In 2022/23, 693 laboratory-confirmed MDR/RR-TB cases were notified, of which 546 were enrolled in treatment. Treatment coverage varied across provinces, with the highest in Lumbini (27.1%) and Madhesh (21.7%) and lowest in Karnali (1.8%) and Sudurpaschim (9.3%). Among cases, 48.5% received short-term MDR treatment, 34.6% long-term MDR, 15.4% Pre-XDR, and 1.5% XDR. Despite a 76% notification gap, treatment success reached 82%, with a 9% death rate [15]. There are major challenges for diagnosis of MDR TB in Nepal, such as inadequate training, frequent power failure, difficulty in maintaining an appropriate steady temperature, module failure, which is often not replaced in time, issues with calibration and timely availability of cartridges, as well as appropriate ways to store the new cartridges and safe disposal of the used cartridges [16]. From a public health perspective, the MDR-TB growing epidemics will not be controlled merely by the introduction of a few new antibiotics, as it is also linked to patient compliance and adequate case management supported by efficient TB programs [17]. Deaths from drug-resistant TB now account for about one-third of all antimicrobial resistance deaths worldwide. Treating DR-TB is costlier and may take three to four times as long, as not all people will survive [7]. Many people with MDR/RR-TB still struggle to complete treatment successfully, with success rates varying widely from 60% in the Americas to 77% in Southeast Asia, highlighting the urgent need to provide better care and support for all affected individuals [1]. Available resources have been judged insufficient to end the epidemic in the region [18]. As MDR-TB involves more extensive and longer treatment courses than drug-susceptible TB, households with multidrug resistance are at a particularly greater risk of incurring catastrophic costs. The study identified the factors associated with resistance that might assist concerned authorities in focusing efforts on those factors in the community. This might help prevent and control tuberculosis and MDR-TB by influencing Public Health Policy. Hence, ending TB will benefit the poorest ones [19]. This study aims to identify risk factors associated with Multi-Drug-Resistant Tuberculosis (MDR-TB) among tuberculosis patients in Bagmati Province, Nepal. MDR-TB poses a significant public health challenge, and understanding these factors is crucial for improving diagnosis, treatment, and prevention efforts. By identifying factors associated with MDR-TB in this region, the research will help inform targeted strategies to control the disease, reduce its socioeconomic impact, and support global efforts to end tuberculosis.

Methods

Study design and settings

This study utilized a 1:1 gender-matched case-control design (controls were initially recruited at a 1:2 case-to-control ratio via systematic random sampling and then matched 1:1 to cases by gender; see Participant Selection) to assess the risk factors for multidrug-resistant tuberculosis (MDR-TB) in Bagmati Province, a region with a high MDR-TB burden. Provincial data showed that Bagmati Province, Gandaki, and Lumbini Province collectively accounted for 66% of all TB cases, with Bagmati Province contributing 24% [8]. And three districts were selected based on the case load: Kathmandu (3183 all forms of TB cases), Chitwan(1052), and Bhaktapur (603) [8]. This study focused on government-authorized and registered MDR-TB treatment centres and sub-centres. These centres, including the Nepal Anti-TB Association [20] and Japan Nepal Tuberculosis Research Association (JANTRA) [21] in Kathmandu, the National Tuberculosis Control Centre in Bhaktapur [22], and the Nepal Anti-TB Association in Chitwan [20], were specifically selected for their adherence to national standards for tuberculosis care.

All government-authorized MDR-TB treatment centres in the selected districts were included based on their high caseloads and their management of all forms of drug-resistant TB during the study period. These centres were prioritized to ensure complete capture of MDR-TB cases and to avoid selection bias, as they handled the full volume of DR-TB patients at the time of data collection and provided standardized diagnosis and treatment in accordance with national tuberculosis guidelines.

Controls were selected from TB patients receiving anti-tuberculosis treatment at Directly Observed Treatment, Short-course (DOTS) [4] centres within the same districts, including Manmohan Memorial Medical College and Teaching Hospital in Kathmandu, Nagarik Community Hospital and NTCC in Bhaktapur, and various health offices and health posts in Chitwan. For comparison, DOTS centres within the same districts were selected as they managed a high number of drug-susceptible TB patients and adhered to national DOTS guidelines. Using similar criteria for both MDR-TB and DOTS centres helped ensure a fair comparison between study groups, and strengthened the representativeness of the findings. Information for the centres’ selection was obtained from the National Tuberculosis Control Centre [8] (Fig. 1).

Fig. 1.

Study site. This map was created by the authors for this study

Participants selection

A comprehensive list of eligible drug-resistant tuberculosis (DR-TB) cases was compiled from government-authorized multidrug-resistant TB (MDR-TB) treatment centres across Chitwan, Kathmandu, and Bhaktapur districts. Cases included patients diagnosed with MDR-TB through drug susceptibility testing per national TB guidelines. Participating centres comprised Nepal Anti-TB Association (NATA) and Japan Nepal Tuberculosis Research Association (JANTRA) in Kathmandu (n = 47), the National Tuberculosis Control Centre in Bhaktapur (n = 22), and NATA in Chitwan (n = 16), resulting in a total of 85 DR-TB patients. After follow-up and study information, 77 patients consented and enrolled as cases.

Controls with drug-susceptible pulmonary or extrapulmonary TB (diagnosed via sputum smear microscopy, culture, or chest X-ray) were recruited from high-caseload DOTS centres in the same districts at a 1:2 case-to-control ratio, using systematic random sampling. Manmohan Memorial Medical College/Teaching Hospital, Genetup, and JANTRA in Kathmandu (n ≈ 94); Nagarik Community Hospital and NTCC in Bhaktapur (n ≈ 44); and Bharatpur Health Office, Parbatipur Health Post, and NATA in Chitwan (n ≈ 32). Cases and controls were then matched 1:1 by gender in SPSS, resulting in 77 matched pairs. Chi-square testing confirmed successful matching (p > 0.05). Written informed consent was obtained from all participants aged ≥ 18 years. For participants < 18 years, assent was obtained in addition to parental/guardian consent and consent from a legally authorized representative, i.e., with DOTS centre staff for residential patients of NATA Kathmandu.

Data collection

Data collection for the study was conducted by a trained public health professional amid the challenges posed by the COVID-19 pandemic, particularly during the second wave in Nepal. A face-to-face interview technique was used to collect data for both MDR-TB cases and controls. A structured questionnaire was used as a data collection tool, prepared under the supervision of the supervisor mentioned in this study, and uploaded as a supplementary file. Data collected included sociodemographic characteristics (age, gender, education, occupation, residence, household income, and so on) and TB-related variables (diagnosis type, treatment history and duration, adherence, and so on). The participants’ clinical attributes were meticulously validated against their laboratory records to ensure accuracy. Patients were presented in collaboration with the participating institutions, including government-authorized MDR-TB treatment centres and DOTS centers. To mitigate pandemic-related risks, interviews were conducted in designated counselling rooms provided by the institutions. Appropriate personal protective equipment (PPE) was used, and strict adherence to social distancing measures was maintained throughout the data collection process. Recent episodes of tuberculosis history were recorded to minimize recall bias and ensure the relevance of the information provided. The researcher personally collected the data to reduce interviewer variations and maintain consistency.

Measures

Dependent variable

The dependent variable is the presence of Multidrug-Resistant Tuberculosis (MDR-TB).

Independent variables

We measured independent variables such as age, gender, and weight as fundamental demographic and health indicators. Marital status and family type were recorded to address social dynamics. We also measured residence (urban vs. rural) and means of transportation, both of which affect access to healthcare services. Education and occupation were captured to reflect socioeconomic factors. Monthly income and ethnicity were included to offer additional insights into socioeconomic status and potential cultural influences. Religion was assessed to understand its potential effect on health behaviors and treatment adherence. Co-morbid conditions, particularly HIV/AIDS and other diseases, were documented as they can compromise immune function. Stigma and social isolation were measured as psychosocial factors. We also measured subjective feelings of sadness to gauge mental health.

Associated factors measured include the type of TB (e.g., pulmonary or extrapulmonary), previous history of TB, interruption of TB treatment, and contact with TB patients (including those with drug-resistant TB). The response to the treatment phase was recorded, along with participation in counseling sessions related to TB.

Statistical analysis

Data entry was conducted using Microsoft Excel and subsequently transferred to SPSS version 16 for statistical analysis. Before entry, numerical coding was manually prepared based on the nature of the data, and regular cross-checking ensured accuracy, with verification after every ten entries. Descriptive statistics, Frequency, and percentages were employed to present case and control frequencies. The inferential analysis utilised chi-square tests for categorical nominal data association and binary regression analysis for variables potentially predictive of MDR-TB, with significance set at p < 0.05 and 95% confidence intervals. Factors exhibiting a p-value less than 0.020 in bivariate analysis underwent binary logistic regression for further analysis after adjusting for confounders. Although cases and controls were matched 1:1 by gender, an unconditional (binary) logistic regression model was used rather than a conditional model. Because matching was performed on a single, non-confounding demographic variable (gender) for which the distribution was identical between groups by design, and because individual pair identifiers were not retained for the analysis, unconditional logistic regression with gender included as a covariate was considered appropriate; this approach yields valid estimates of association under frequency (group-level) matching on a balanced factor.

Ethical consideration

The study was conducted from March 2021 to August 2021, following strict ethical guidelines. Ethical approval was obtained from the Institutional Review Committee (IRC) of BP Koirala Institute of Health Sciences (BPKIHS), Dharan, with Ref no: IRC/1981/020. An original copy of the ethical approval has been submitted as related files. Prior to the beginning of the study, informed consent was obtained from all participants. Their privacy, anonymity, and confidentiality were rigorously maintained throughout the study. Permission to conduct the research was also obtained from the National Tuberculosis Control Centre, Bhaktapur. The participants’ identities were anonymized in all study records, and data confidentiality was ensured in accordance with ethical standards.

Results

Table 1 illustrates demographic and socio-economic characteristics between cases and controls. While both groups have similar gender distributions, with males representing 55.8% and females 44.2%, the control group has a higher proportion of individuals under 35 years [64.9%] compared to the case group [58.4%]. Marital status and ethnicity are largely comparable, but there are differences in residence, education, and employment status. The control group has more residents in metropolitan cities [62.3% vs. 41.6% in cases] and fewer in rural areas [13.0% vs. 37.7% in cases]. Additionally, a higher percentage of cases are unemployed [44.2% vs. 33.8% in controls], and more are students [24.7% vs. 31.2% in controls]. Access to health facilities within 30 min is higher in the control group [96.1% vs. 66.2% in cases], and they utilize private health facilities more [85.7% vs. 58.8% in cases].

Table 1.

Demographic and socio-economic characteristics of respondents

Characteristics	Case (n = 77)	Control (n = 77)
Age
< 35	45 (58.4%)	50 (64.9%)
≥ 35	32 (41.6%)	27 (35.1%)
Gender
Male	43 (55.8%)	43 (55.8%)
Female	34 (44.2%)	34 (44.2%)
Marital Status
Unmarried	32 (41.6%)	35 (45.5%)
Married	43 (55.8%)	41 (53.2%)
Widow	1 (1.3%)	1 (1.3%)
Widower	1 (1.3%)	0 (0.0%)
Residence
Metropolitan city	32 (41.6%)	48 (62.3%)
Municipality	16 (20.8%)	19 (24.7%)
Rural Municipality	29 (37.7%)	10 (13.0%)
Education
Uneducated	5 (6.5%)	5 (6.5%)
Primary level	22 (28.6%)	13 (16.9%)
Secondary level	20 (26.0%)	28 (36.4%)
Higher level education	30 (39.0%)	31 (40.3%)
Occupation
Unemployed	34 (44.2%)	26 (33.8%)
Formal sector employee	10 (13.0%)	15 (19.5%)
Informal sector employee	14 (18.2%)	12 (15.6%)
Student	19 (24.7%)	24 (31.2%)
Monthly Income of Employed
< Rs 24,000	12 (50.0%)	15 (55.6%)
≥ Rs 24,000	12 (50.0%)	12 (44.4%)
Ethnicity
Dalit	2 (2.6%)	4 (5.2%)
Janajati	41 (53.2%)	41 (53.2%)
Madhesi	2 (2.6%)	1 (1.3%)
Muslim	3 (3.9%)	1 (1.3%)
Brahmin/Chhetri	29 (37.7%)	29 (37.7%)
Others	0 (0.0%)	1 (1.3%)
Religion
Hindu	57 (74.0%)	65 (84.4%)
Buddhist	17 (22.1%)	8 (10.4%)
Muslim	3 (3.9%)	1 (1.3%)
Christian	0 (0.0%)	3 (3.9%)
Family Type
Nuclear	39 (50.6%)	46 (59.7%)
Joint	36 (46.8%)	30 (39.0%)
Extended	2 (2.6%)	1 (1.3%)
Presence of Adequate Ventilation
No	20 (25.9%)	8 (10.3%)
Yes	57 (74.1%)	69 (89.7%)
Recent Migration
No	35 (45.5%)	50 (64.9%)
Yes	42 (54.5%)	27 (35.1%)
Types of Migration
Internal Migration	37 (88.1%)	23 (85.2%)
External Migration	5 (11.9%)	4 (14.8%)
Time to Reach Health Facilities
Less than 30 min	51 (66.2%)	74 (96.1%)
Greater than 30 min	26 (33.8%)	3 (3.9%)
Means of Transportation
Cycle	1 (1.3%)	2 (2.6%)
Bike/Scooter	11 (14.3%)	25 (32.5%)
Public Vehicle	64 (83.1%)	41 (53.2%)
By foot	1 (1.3%)	9 (11.7%)
Health-Seeking Behavior
Too late	57 (74.0%)	51 (66.2%)
In time	17 (22.1%)	25 (32.5%)
Early	3 (3.9%)	1 (1.3%)
Treatment Initiation Site
Public Health Facilities	43 (55.8%)	11 (14.3%)
Private Health Facilities	34 (44.2%)	66 (85.7%)

Table 2 shows that pulmonary TB cases were higher among the cases, at 89.6%, compared to 57.1% in the control group. Conversely, Extra Pulmonary TB cases were more prevalent in the Controls at 42.9% versus 10.4% in the Cases. Notably, a prior history of TB was significantly more common among Cases, with 63.6% compared to a mere 10.4% in Controls. Among those with a TB history, Cases experienced treatment interruptions at a higher rate of 51.0%, contrasting with just 12.5% in Controls. Reasons for such interruptions varied, with complications accounting for 8% among Cases and none among Controls, while loss to follow-up stood at 16% for Cases versus 100% for Controls. Additionally, 25% of Cases switched to MDR treatment compared to none in Controls. Close contact with TB patients was markedly more prevalent among Cases [64.9%] than Controls [32.5%], with similar trends observed for contact with DR TB patients. Notably, negative sputum conversion rates were higher among Cases [79.2%] versus Controls [66.2%]. Hospital stays were significantly more common among Cases [89.6%] than Controls [31.2%]. Treatment modalities also exhibited disparities, with 44.2% of Cases receiving treatment after failure compared to none in Controls and 48.1% of Cases undergoing the intensive treatment phase versus 74.0% of Controls. In terms of TB treatment categories, Cases predominantly fell into LR-1 (Longer Regimen I, 58.5%) and LR-2 (Longer Regimen II, 31.1%) categories, while Controls were exclusively categorized as CAT I [100%].

Table 2.

Clinical characteristics of respondents

Characteristics	Case (n = 77)	Control (n = 77)
Tuberculosis Types
Pulmonary TB	69 (89.6%)	44 (57.1%)
Extra Pulmonary TB	8 (10.4%)	33 (42.9%)
Previous history of TB
No	28 (36.4%)	69 (89.6%)
Yes	49 (63.6%)	8 (10.4%)
If yes, was there any interruption of TB treatment [n = 25 cases, 1 controls]
No	24 (49.0%)	7 (87.5%)
Yes	25 (51.0%)	1 (12.5%)
Reason for interruption of TB treatment [n = 25 cases, 1 controls]
Complication	2 (8%)	0 (0%)
Loss to follow up	4 (16%)	1 (100%)
Switch to MDR	5 (25%)	0 (0%)
Site Changed	1 (4%)	0 (0%)
TAF	12 (48%)	0 (0%)
Workload	1 (4%)	0 (0%)
Close contact with TB Patients
No	27 (35.1%)	52 (67.5%)
Yes	50 (64.9%)	25 (32.5%)
Close contact with DR TB patients
No	38 (49.4%)	76 (98.7%)
Yes	39 (50.6%)	1 (1.3%)
Response to TB treatment (Sputum Conversion)
Negative	61 (79.2%)	51 (66.2%)
Positive	16 (20.8%)	26 (33.8%)
Experience of hospital stays
No	8 (10.4%)	53 (68.8%)
Yes	69 (89.6%)	24 (31.2%)
Registration category of patients
New	23 (29.9%)	69 (89.6%)
Relapse	26 (33.8%)	8 (10.4%)
Treatment After Failure	34 (44.2%)	0 (0%)
Treatment after loss to follow-up	2 (2.6%)	0 (0%)
Others previously Treated	1 (1.3%)	0 (0%)
Previous Treatment History Unknown	1 (1.3%)	0 (0%)
Treatment Phase
Intensive	37 (48.1%)	57 (74.0%)
Continuous	40 (51.9%)	20 (26.0%)
TB Treatment Category
LR1	45 (58.5%)	0 (0%)
LR2	24 (31.1%)	0 (0%)
LR3	1 (1.3%)	0 (0%)
SSTR	4 (5.2%)	0 (0%)
Others	3 (3.9%)	0 (0%)
CAT I	0 (0%)	77 (100%)

Table 3 presents psychological and social tuberculosis-related variables [TB] among cases and controls. In the cases group, 54.5% attended TB counselling sessions, compared to 85.7% in the control group, indicating that non-attendance at TB counselling sessions was significantly more common among MDR-TB cases. Regarding HIV status, 93.5% of Cases were negative, compared to 100% of Controls. Additionally, 31.2% of Cases reported having other diseases, contrasting with 24.7% in the control group. Alcohol consumption was notably prevalent among Cases at 76.6%, whereas 68.8% of Controls reported the same. Smoking was more common among Cases [51.9%] than in Controls [18.2%], and similarly, illicit drug use was higher in Cases [18.2%] than in Controls [2.6%]. The incidence of imprisonment was minimal in both groups, with Cases slightly higher at 3.9% compared to Controls at 2.6%. Social isolation was more pronounced among Cases at 53.2% compared to Controls at 39.0%. Cases also reported higher proportions of perceived stigma associated with TB, with 74.0% fearing loss of friends and employment, 77.9% experiencing social stigma, and 55.8% preferring privacy regarding their TB status, whereas Controls reported lower rates in these categories. Furthermore, Cases exhibited higher nervousness levels [76.6%] and sadness [58.4%] than Controls. Troubles within the past 12 months were reported by 46.8% of Cases and 14.3% of Controls. Despite challenges, both groups expressed happiness, with 79.2% of Cases and 88.3% of Controls considering themselves happy despite their TB status.

Table 3.

Psychological and co-morbidity status of respondents

Variables	Case (n = 77)	Control (n = 77)
Attended any TB counselling sessions
No	35 (45.5%)	11 (14.3%)
Yes	42 (54.5%)	66 (85.7%)
HIV Status
Positive	5 (6.5%)	0 (0.0%)
Negative	72 (93.5%)	77 (100%)
Presence of Other Disease
No	53 (68.8%)	58 (75.3%)
Yes	24 (31.2%)	19 (24.7%)
Alcohol Consumption
No	18 (23.4%)	24 (31.2%)
Yes	59 (76.6%)	53 (68.8%)
Smoking
No	37 (48.1%)	63 (81.8%)
Yes	40 (51.9%)	14 (18.2%)
Illicit Drug Use
No	63 (81.8%)	75 (97.4%)
Yes	14 (18.2%)	2 (2.6%)
Imprisonment
No	74 (96.1%)	75 (97.4%)
Yes	3 (3.9%)	2 (2.6%)
Social Isolation
No	36 (46.8%)	30 (39.0%)
Yes	41 (53.2%)	47 (61.0%)
I feel that I will lose friends and employment because I have TB
No	20 (26.0%)	55 (71.4%)
Yes	57 (74.0%)	22 (28.6%)
People look at me differently because I have tuberculosis
No	17 (22.1%)	49 (63.6%)
Yes	60 (77.9%)	28 (36.4%)
I don’t like other people to know that I have TB
No	34 (44.2%)	24 (31.2%)
Yes	43 (55.8%)	53 (68.8%)
I consider myself nervous
No	18 (23.4%)	41 (53.2%)
Yes	59 (76.6%)	36 (46.8%)
Sometimes, I feel so sad that nothing can cheer me up
No	32 (41.6%)	68 (88.3%)
Yes	45 (58.4%)	9 (11.7%)
Did something trouble you during the past 12 months?
No	41 (53.2%)	66 (85.7%)
Yes	36 (46.8%)	11 (14.3%)
Despite the disease, I think I am happy
No	16 (20.8%)	9 (11.7%)
Yes	61 (79.2%)	68 (88.3%)

Table 4 shows several significant associations highlighting key environmental and socio-economic factors. The cases group exhibits a notably higher prevalence in rural areas, with 37.7% residing in rural municipalities compared to only 13.0% of controls [p = 0.02]. Recent migration emerges as a significant factor, with 54.5% of Cases reporting recent migration compared to 35.1% of controls [p = 0.02]. Additionally, inadequate ventilation is more prevalent among the case group, with 26.0% reporting inadequate ventilation compared to 10.4% of controls [p = 0.01]. Access to healthcare services is also impacted, as evidenced by 66.2% of cases taking more than 30 min to reach health facilities compared to only 3.9% of controls [p = 0.00]. These findings underscore the critical influence of environmental and socio-economic determinants on TB prevalence and access to healthcare, emphasizing the importance of targeted interventions to address these disparities for effective TB prevention and control strategies.

Table 4.

Key demographic and socio-economic factors associated with MDR-TB

Characteristics	Case (n = 77)	Control (n = 77)	P-value
Age
< 35	45 (58.4%)	50 (64.9%)	0.41
≥ 35	32 (41.6%)	27 (35.1%)
Marital Status			0.85
Unmarried	32 (41.6%)	35 (45.5%)
Married	43 (55.8%)	41 (53.2%)
Widow	1 (1.3%)	1 (1.3%)
Widower	1 (1.3%)	0 (0.0%)
Residence			< 0.01
Metropolitan City	32 (41.6%)	48 (62.3%)
Municipality	16 (20.8%)	19 (24.7%)
Rural Municipality	29 (37.7%)	10 (13.0%)
Education			0.10
Uneducated	5 (6.5%)	5 (6.5%)
Primary Level	22 (28.6%)	13 (16.9%)
Secondary Level	20 (26.0%)	28 (36.4%)
Higher Level Education	30 (39.0%)	31 (40.3%)
Occupation			0.36
Unemployed	34 (44.2%)	26 (33.8%)
Formal Sector Employee	10 (13.0%)	15 (19.5%)
Informal Sector Employee	14 (18.2%)	12 (15.6%)
Student	19 (24.7%)	24 (31.2%)
Monthly Income of Employed			0.74
< Rs 24,000	12 (50.0%)	15 (55.6%)
≥Rs 24,000	12 (50.0%)	12 (44.4%)
Ethnicity			0.65
Dalit	2 (2.6%)	4 (5.2%)
Janajati	41 (53.2%)	41 (53.2%)
Madhesi	2 (2.6%)	1 (1.3%)
Muslim	3 (3.9%)	1 (1.3%)
Brahmin/Chhetri	29 (37.7%)	29 (37.7%)
Others	0 (0.0%)	1 (1.3%)
Religion			0.04
Hindu	57 (74.0%)	65 (84.4%)
Buddhist	17 (22.1%)	8 (10.4%)
Muslim	3 (3.9%)	1 (1.3%)
Christian	0 (0.0%)	3 (3.9%)
Family Type			0.45
Nuclear	39 (50.6%)	46 (59.7%)
Joint	36 (46.8%)	30 (39.0%)
Extended	2 (2.6%)	1 (1.3%)
Presence of Adequate Ventilation			0.01
No	20 (25.9%)	8 (10.3%)
Yes	57 (74.1%)	69 (89.7%)
Recent Migration			0.02
No	35 (45.5%)	50 (64.9%)
Yes	42 (54.5%)	27 (35.1%)
Types of Migration			0.73
Internal Migration	37 (88.1%)	23 (85.2%)
External Migration	5 (11.9%)	4 (14.8%)
Time to Reach Health Facilities			< 0.01
Less than 30 min	51 (66.2%)	74 (96.1%)
Greater than 30 min	26 (33.8%)	3 (3.9%)
Means of Transportation			0.06
Cycle	1 (1.3%)	2 (2.6%)
Bike/Scooter	11 (14.3%)	25 (32.5%)
Public Vehicle	64 (83.1%)	41 (53.2%)
By foot	1 (1.3%)	9 (11.7%)
Health-Seeking Behavior			0.36
Too Late	57 (74.0%)	51 (66.2%)
In Time	17 (22.1%)	25 (32.5%)
Early	3 (3.9%)	1 (1.3%)

Table 5 depicts Pulmonary TB as notably more prevalent among cases [89.6%] compared to controls [57.1%], indicating it as a significant risk factor [p < 0.001; OR: 6.5; 95% CI: 2.7–15.2]. Conversely, controls exhibited a higher incidence of Extra Pulmonary TB [42.9%] compared to cases [10.4%]. Individuals with a previous TB history were notably more common among cases [63.6%] than controls [10.4%], indicating it as a substantial risk factor [p < 0.001; OR: 15.09; 95% CI: 6.34–35.91]. Similarly, cases were more likely to report close contact with TB patients [64.9%] compared to controls [32.5%], showing a significant association [p < 0.001; OR: 3.85; 95% CI: 1.97–7.51] likewise, cases were significantly associated with close contact with drug-resistant TB patients [p = 0.001; OR: 78; 95% CI: 10.31-589.62] suggesting the exposure as a risk factor. Cases reported higher levels of perceived stigma, with concerns about losing friends and employment due to TB [p < 0.001; OR: 7.12; 95% CI: 3.50-14.49] and feeling that people looked at them differently [p < 0.001; OR: 6.17; 95% CI: 3.02–12.57], indicating poor psychosocial health. Additionally, cases exhibited a higher prevalence of nervousness [p < 0.001; OR: 3.73; 95% CI: 1.86–7.45] and consistent sadness [p < 0.001; OR: 10.62; 95% CI: 4.63–24.36], signifying these as factors of concern. These findings highlight the importance of addressing these psychosocial risk factors alongside medical interventions in TB management.

Table 5.

Association of MDR-TB with clinical characteristics of the respondents

Categories	Case (n = 77)	Control (n = 77)	P-value	OR	95% CI [Lower-Upper]
Tuberculosis Types
Pulmonary TB	69 (89.6%)	44 (57.1%)	< 0.01	6.5	[2.7–15.2]
Extra Pulmonary TB	8 (10.4%)	33 (42.9%)
Previous History of TB
Yes	49 (63.6%)	8 (10.4%)	< 0.01	15.09	[6.34–35.91]
No	28 (36.4%)	69 (89.6%)
If Yes, Was There Any Interruption of TB Treatment			0.06	7.29	[0.83–63.70]
Yes	25 (51.0%)	1 (12.5%)
No	24 (49.0%)	7 (87.5%)
Close Contact with TB Patients
Yes	50 (64.9%)	25 (32.5%)	< 0.01	3.85	[1.97–7.51]
No	27 (35.1%)	52 (67.5%)
Close Contact with Drug Resistant TB Patients			0.00	78	[10.31-589.62]
Yes	39 (50.6%)	1 (1.3%)
No	38 (49.4%)	76 (98.7%)
Response to TB Treatment
Positive	16 (20.8%)	26 (33.8%)	0.07	0.51	[0.24–1.06]
Negative	61 (79.2%)	51 (66.2%)
Experience of Hospital Stays
Yes	69 (89.6%)	24 (31.2%)	< 0.01	19.04	[7.92–45.76]
No	8 (10.4%)	53 (68.8%)
Attended Any TB Counselling Sessions
Yes	42 (54.5%)	66 (85.7%)	< 0.01	5.00	[2.29–10.90]
No	35 (45.5%)	11 (14.3%)
HIV Status
Positive	5 (6.5%)	0 (0.0%)	0.02	-
Negative	72 (93.5%)	77 (100.0%)
Presence of Other Disease
Yes	24 (31.2%)	19 (24.7%)	0.36	1.38	[0.68–2.80]
No	53 (68.8%)	58 (75.3%)
Alcohol Consumption
Yes	59 (76.6%)	53 (68.8%)	0.28	1.48	[0.72–3.03]
No	18 (23.4%)	24 (31.2%)
Smoking
Yes	40 (51.9%)	14 (18.2%)	< 0.01	4.86	[2.34–10.11]
No	37 (48.1%)	63 (81.8%)
Illicit Drug Use
Yes	14 (18.2%)	2 (2.6%)	0.00	8.33	[1.82–38.06]
No	63 (81.8%)	75 (97.4%)
Imprisonment
Yes	3 (3.9%)	2 (2.6%)	1.00	1.52	[0.24–9.36]
No	74 (96.1%)	75 (97.4%)
Social Isolation
Yes	41 (53.2%)	47 (61.0%)	0.33	0.72	[0.38–1.37]
No	36 (46.8%)	30 (39.0%)
I Feel That I Will Lose Friends and Employment Because I Have TB			0.00	7.12	[3.50-14.49]
Yes	57 (74.0%)	22 (28.6%)
No	20 (26.0%)	55 (71.4%)
People Look at Me Differently Because I Have Tuberculosis			0.00	6.17	[3.02–12.57]
Yes	60 (77.9%)	28 (36.4%)
No	17 (22.1%)	49 (63.6%)
I Don’t Like Other People to Know That I Have TB			0.00	0.57	[0.29–1.10]
Yes	43 (55.8%)	53 (68.8%)
No	34 (44.2%)	24 (31.2%)
I Consider Myself Nervous
Yes	59 (76.6%)	36 (46.8%)	< 0.01	3.73	[1.86–7.45]
No	18 (23.4%)	41 (53.2%)
Sometimes, I Feel So Sad That Nothing Can Cheer Me Up			< 0.01	10.62	[4.63–24.36]
Yes	45 (58.4%)	9 (11.7%)
No	32 (41.6%)	68 (88.3%)
Did Something Trouble You During the Past 12 Months?			< 0.01	5.26	[2.41–11.48]
Yes	36 (46.8%)	11 (14.3%)
No	41 (53.2%)	66 (85.7%)
Despite the Disease, I Think I Am Happy			0.13	1.98	[0.81–4.81]
Yes	61 (79.2%)	68 (88.3%)
No	16 (20.8%)	9 (11.7%)

After adjusting for the confounding factors, the final regression model [Table 6] showed that subjects with pulmonary tuberculosis had higher odds of MDR-TB compared with respondents who had extrapulmonary tuberculosis (aOR = 14.74; 95% CI: 2.32–93.54). Respondents with a previous TB treatment history had higher odds of MDR-TB compared with those who had not been previously treated (aOR = 5.60; 95% CI: 1.03–24.88). Respondents whose treatment was initiated at a private health facility had lower odds of MDR-TB than those initiating treatment at a public facility (aOR = 0.10; 95% CI: 0.02–0.43). MDR-TB patients were also more likely to report a subjective feeling of sadness (aOR = 42.33; 95% CI: 1.36–1315.4); the very wide confidence interval indicates limited precision, and this association should be interpreted with caution. All effect sizes are reported as adjusted odds ratios with 95% confidence intervals; the case-control design supports statistical association but not causal inference.

Table 6.

Multivariate analysis of factors associated with MDR-TB

Variables	P-value	Odds Ratio	95% C.I. for OR
Tuberculosis Type (Ref: PTB)	0.00	14.7	[2.32–93.54]
Previous TB Treatment History (Ref: No)	0.04	5.60	[1.03–24.87]
Treatment Initiation Site (Ref: Public)	0.00	0.10	[0.02–0.43]
Sometimes, I Feel So Sad That Nothing Can Cheer Me Up (Ref: No)	0.03	42.33	[1.36–1315.40]

Ref: Reference category. N = 154

Model specification: logit(P[MDR-TB]) = β0 + β1·PTB + β2·Previous TB history + β3·Treatment initiation site + β4·Subjective sadness. Coefficient and odds-ratio estimates are reported in Table 6; no deterministic interpretation of coefficients is intended.

Discussion

The study has provided relevant information about factors associated with MDR-TB, which can support activities being implemented to decrease the burden of TB in Nepal for policymakers. This study showed that Pulmonary Tuberculosis type, previous TB treatment history, treatment initiation site, and subjective feelings of sadness were significantly associated with MDR-TB in the adjusted model, while close contact with TB and DR-TB patients was significantly associated with MDR-TB in the unadjusted analysis.

Among sociodemographic factors, we identified that people residing in rural municipalities are more likely to develop MDR-TB than those living in urban areas [p = 0.02], as supported by other studies [23, 24]. These data are alarming, given the geographical features of our country, such as Nepal. At the same time, the study conducted in Ukraine showed that the prevalence of RR/MDR-TB in the rural population was significantly lower than in the urban population at 20.9% and 29.0%, respectively [P = 0.000] [25]. This study also showed migration as associated with MDR-TB [P = 0.015, OR = 2.22, 95% CI 1.16–4.25]. The study conducted in China showed that MDR is more prevalent among migrants [odds ratios (OR) 1.32, 95% confidence interval (CI) 1.02–1.72] [26]; migration is considered an important contributor to missing cases and may lead to poor outcomes [27]. To mitigate the number of migrants, it’s essential to ensure uniform standards of care accessible nationwide, facilitated by robust communication channels such as telemedicine and referral systems. Additionally, implementing a unique identifier system would enable seamless patient connectivity across provinces and the entire nation.

This study showed that inadequate ventilation at home was associated with MDR-TB [p = 0.01]. Poor ventilation and overcrowding have been documented as risk factors for MDR-TB in several other studies in a variety of settings as well [28, 29]. Likewise, those subjects who lived in a house with no window or one window were almost two times more likely to develop airborne infection compared to people whose house had multiple windows [AOR = 1.81; 95% CI:1.06, 3.07] [30], which may be due to the availability of a favourable environment for MDR-TB transmission. Therefore, advocating for adequate ventilation is crucial to disrupt the transmission chain of MDR-TB [31].

Our study showed clinical features such as types of TB, previous TB treatment history, close contact with TB and DR-TB patients, and experience of hospital stays as factors associated with MDR-TB, with p = 0.001 for each factor in the unadjusted analysis. The majority of cases [89.6%]and controls [57.1%] had Pulmonary Tuberculosis, which is similar to the study conducted in Ethiopia, with the majority of participants having pulmonary TB in cases 141 [92.2%] and controls 137 [89.5%] [32]. We found that patients having Pulmonary Tuberculosis were more likely to develop MDR-TB than those of Extra pulmonary TB patients, which is statistically significant in the final binary logistic regression model with OR = 14.735, 95% CI [2.321–93.54], as confirmed by the different studies in different countries, India [33] and Thailand [34]. A study from Germany also showed that cases with pulmonary TB had a higher occurrence of MDR- TB with 2.3% in comparison to 1.3% of cases with extrapulmonary TB (p < 0.01) [35].

This study showed previous TB treatment history was strongly associated with MDR-TB in the final regression model, with OR = 5.601 [95% CI, 1.03-24.876]. A previous study also showed that the majority of MDR-TB cases (82.0%) were previously treated cases, and only the remaining 18.0% were new cases [36]. Moreover, the increased proportion of MDR-TB among previously treated cases has been indicated in various studies of various settings [33, 34, 37–40]; The rise in MDR-TB cases among previously treated individuals underscores the urgency for higher quality treatment to prevent the development of drug resistance.

Our study showed that 51.0% of MDR cases had interruption of TB treatment, which is similar to the study conducted by Sanju B et al. in 2017 [41]. We found patients with Interruption in TB treatment were more likely to develop MDR-TB than those without TB-Treatment interruption [p = 0.059, OR = 7.292, 95%CI 0.83–63.79], and another study also confirmed interruption of treatment as a risk factor of MDR-TB [33, 42–45]. Interruption of TB treatment promotes the risk of bacterial mutations that eventually culminate in relapses and MDR- TB.

Our study showed that 64.9% of cases had a history of contact with a TB patient. Those who had a history of close contact with TB patients were more likely to have MDR-TB than those without close contact [p < 0.001, OR = 3.85, 95% CI 1.97–7.51]. Several studies conducted in different settings also confirmed that close contact with TB patients is a risk factor for MDR-TB), which is in contrast to studies conducted in Ethiopia [46] and Ethiopia [47].

Similarly, close contact with DR-TB patients was associated with MDR-TB in the unadjusted analysis. The unadjusted estimate indicated markedly higher odds of MDR-TB among those in close contact with DR-TB patients [OR = 22.18, 95% CI 1.83-268.12]; however, the very wide confidence interval reflects limited precision and small subgroup size, so this estimate should be interpreted with caution, and this variable was not retained in the final adjusted model. Several studies documented close contact with DR-TB as the major risk factor of MDR-TB [44, 48], which is in contrast to the study conducted in Serbia [49]. Our study showed that experience of hospital stays was associated with MDR-TB [p = 0.001], i.e., patients who had a hospitalization history were more likely to have MDR-TB than those without a previous history of hospital stay [p < 0.001, OR = 19.05, 95% CI 7.92–45.76]; the wide confidence interval indicates limited precision, which is also documented in other studies [30, 36]. The study conducted by V. Crudu et al. documented that, in 75% of cases, the MDR-TB strain was genetically distinct from the non-MDR-TB strain at baseline, suggesting a high rate of nosocomial transmission of MDR-TB [47]. Proper infection control measures in health facilities are crucial to protect other patients and healthcare workers, whereas preventive measures to reduce transmission within the community are required.

This study showed that not attending TB counselling sessions [p < 0.001, OR = 5.00, 95% CI 2.29–10.90] was associated with MDR-TB. One of the studies concluded that the Provision of counselling and financial support may not only reduce their vulnerability but also increase cure rates of MDR-TB [50]. This study showed that smokers were more likely to have MDR-TB than non-smokers [p < 0.001, OR = 4.86, 95% CI 2.34–10.11], as confirmed by other studies conducted in a different setting, china [51, 52], Ethiopia [53], Lima Peru [54], Sudan [55], which is a contrast to the study conducted in Addis Ababa [43], Nepal [56]. Smoking had also affected the global TB treatment success rate, as shown in prior work. This study showed illicit drug use as associated with MDR-TB [P = 0.002, OR = 8.33, 95% CI 1.82–38.06], which is in contrast to the study conducted in Canada [57] and Ethiopia [47]. Engaging in illicit drug use and smoking can disrupt the balance of the immune system, potentially heightening susceptibility to infection.

This study examined whether stigma associated with TB was associated with MDR-TB [p = 0.34, OR = 0.72, 95% CI 0.38–1.37]; this association was not statistically significant, and because stigma was measured after diagnosis it should not be interpreted as causal. Associations between TB-related stigma and MDR-TB have also been reported in other studies conducted in Serbia [49], China [52], Nepal [50]. A study from Serbia reported an association between TB-related stigma and MDR-TB with OR = 2.97; 95% CI = 1.18–7.45 [49]. It’s imperative to safeguard and defend the rights of all individuals with tuberculosis, as well as those most vulnerable to contracting the disease. Implementing policies and practices aimed at shielding them from stigmatization and discrimination is essential [5].

There were significant differences in the mental health of respondents between the two groups. This study showed associations between MDR-TB and the persistence of a subjective feeling of nervousness [p < 0.001, OR = 3.73, 95% CI 1.86–7.45], the persistence of a subjective feeling of sadness [aOR = 42.33, 95% CI 1.36-1315.4] and the experience of difficult situations in the last 12 months [p < 0.001, OR = 5.26, 95% CI 2.42–11.49]. Because these psychosocial factors were measured after MDR-TB diagnosis, the direction of association is uncertain and reverse causality cannot be excluded; these associations should not be interpreted as causal. These findings are similar to the study conducted in Serbia [49]. The study conducted in China also showed experiencing more life pressure/stress [AOR, 10.8; 95% CI, 2.8–41.5] as a risk factor of MDR-TB [28]. A life rejuvenation program tailored for tuberculosis patients in high-risk areas is imperative. As the study covers a large number of DR-TB cases in Nepal during the study period, the findings can be generalized.

Strengths and limitations

Strengths of the study include collecting information on recent episodes of previous TB treatment aimed at minimising recall bias. However, a notable limitation is the absence of data on additional treatment episodes, particularly concerning patients with multidrug-resistant tuberculosis [MDR-TB] who often undergo multiple treatments. Additionally, while the study successfully achieved its objectives, it did not employ multiple data-gathering methods to explore sensitive topics such as stigma and mental health issues. Notably, this study represents one of the early studies to investigate the risk factors of multidrug-resistant tuberculosis in three districts of Bagmati Province, which harbours the highest burden of DR-TB cases in Nepal, favouring generalization of findings. Prior work has also examined MDR-TB risk factors in Nepal [58]. Several further limitations should be acknowledged. First, selection bias cannot be excluded: cases were recruited from MDR-TB treatment centres and controls from DOTS centres in the same districts, and although standardized eligibility criteria were applied, differences in care-seeking behaviour and access between the two patient populations may have influenced enrolment. Second, the case-control design and the timing of data collection mean that several exposures—particularly self-reported stigma, social isolation, and subjective feelings of sadness—were measured after MDR-TB diagnosis. The direction of association between these psychosocial factors and MDR-TB is therefore uncertain, and reverse causality (i.e., MDR-TB diagnosis and treatment contributing to psychological distress, rather than distress contributing to MDR-TB) cannot be ruled out. Third, several adjusted odds ratios were estimated with very wide 95% confidence intervals (e.g., subjective feeling of sadness aOR = 42.33, 95% CI: 1.36–1315.4; close contact with DR-TB patients aOR = 22.18, 95% CI: 1.83–268.12), reflecting small subgroup sizes and limited statistical precision; these point estimates should be interpreted with caution and confirmed in larger studies.

Study implications

The study’s findings have significant implications for public health policies and practices. Policymakers can use the identified risk factors—such as rural residency, previous TB treatment, poor home ventilation, and mental health issues—to tailor targeted interventions and improve treatment protocols. Healthcare planning should include enhanced infection control measures and optimized treatment regimens to prevent the development of MDR-TB. Addressing migration and mobility issues through uniform care standards and robust referral systems can help manage and track patients effectively. Integrating mental health support into TB care is crucial for improving patient outcomes, as mental health issues are strongly linked to MDR-TB. Public health campaigns should focus on reducing stigma, promoting smoking cessation, and tackling substance abuse. Finally, the study’s limitations highlight the need for future research using diverse methods to explore additional aspects of MDR-TB risk, which can further refine prevention and treatment strategies.

Conclusion

This case-control study delved into factors associated with MDR-TB occurrence, identifying Pulmonary Tuberculosis, prior TB treatment history, close contact with DR-TB patients, and persistent subjective feelings of sadness as factors significantly associated with MDR-TB. The findings underscore the necessity for quality treatment coupled with attentive monitoring to curb drug resistance evolution among previously treated patients. Preventive measures should emphasize breaking transmission chains within communities, strengthened by robust infection control measures in healthcare facilities to minimize acquired MDR-TB cases among visitors, patients, and health workers. Additionally, the study underscores the pressing need for life rejuvenation programs targeting tuberculosis patients to safeguard their mental well-being. Several of these associations were estimated with wide confidence intervals, reflecting limited precision and small subgroup sizes; the findings should therefore be interpreted as associations rather than established causes and require confirmation in larger, adequately powered studies. Given the case-control design and the timing of exposure measurement, particularly for psychosocial factors assessed after diagnosis, causal interpretations should be avoided.

Supplementary Information

Below is the link to the electronic supplementary material.

Abbreviations

AMR

Antimicrobial Resistance

AOR

Adjusted Odds Ratio

BPKIHS

B.P. Koirala Institute of Health Sciences

CAT-I

Category I

CI

Confidence Interval

COPD

Chronic Obstructive Pulmonary Diseases

DOTS

Directly Observed Treatment Short Course

DR-TB

Drug-Resistant Tuberculosis

DST

Drug Susceptibility Test

DS-TB

Drug-Susceptible Tuberculosis

EPTB

Extra Pulmonary Tuberculosis

HF

Health Facility

HIV/AIDS

Human Immunodeficiency Virus/Acquired Immunodeficiency Syndrome

HR

Isoniazid Rifampicin

HRZE

Isoniazid, Rifampicin, Pyrazinamide, Ethambutol

HRZELfx

Isoniazid, Rifampicin, Pyrazinamide, Ethambutol, Levofloxacin

JANTRA

Japan Nepal Health and Tuberculosis Research Association

LPA

Line Probe Assay

LR-1

Longer Regiment I

LR-2

Longer Regiment II

LR-3

Longer Regiment III

LTBI

Latent Tuberculosis Infection

M. Tuberculosis

Mycobacterium Tuberculosis

MDR-TB

Multi-Drug-Resistant Tuberculosis

MDR/RR-TB

Multi-Drug-Resistant Rifampicin-Resistant Tuberculosis

NATA

Nepal Anti Tuberculosis Association

NTC

National Tuberculosis Center

OR

Odds Ratio

PLS-PM

Partial Least Square Path Modelling

PTB

Pulmonary Tuberculosis

RR/MDR

Rifampicin-Resistant/Multi-Drug-Resistant

SDG

Sustainable Development Goals

SEAR

South East Asia Region

SPSS

Statistical Package for the Social Sciences

SSTR

Shorter Standardized Treatment Regimen

TB

Tuberculosis

TAF

Treatment After Failure

TPT

Tuberculosis Preventive Therapy

TSR

Treatment Success Rate

WHO

World Health Organization

XDRTB

Extremely Drug-Resistant Tuberculosis

Author contributions

Conceptualisation was carried out by P.A. and N.B. Data curation was performed by P.A., N.B., S.S., B.R.K., and B.K.Y. Formal analysis was conducted by P.A., N.B., R.B., and K.R.S. Methodology was developed by P.A. and N.B. Supervision was provided by V.K.K., B.K.Y., and K.R.S. Validation was undertaken by V.K.K., B.K.Y., K.R.S., and B.R.K. The original draft was written by P.A., and N.B. Review and editing of the manuscript were done by P.A., N.B., R.B., S.S., and B.R.K.

Funding

The authors receive no funding for this study.

Data availability

The manuscript mentions all the required information; The manuscript mentions all the required information; data sheets are available upon request to Puspa Acharya (puspacharyas@gmail.com), the corresponding author.

Declarations

Ethics approval and consent to participate

Ethical approval was obtained from the Institutional Review Committee (IRC) of BP Koirala Institute of Health Sciences (BPKIHS), Dharan, with Ref no: IRC/1981/020. An original copy of the ethical approval has been submitted as related files. Prior to the beginning of the study, informed consent was obtained from all participants. For those under 18, informed consent was also obtained from the DOTS center for individuals residing at the TB center and from parents or legal guardians for those visiting the DOTS center daily. For any participant under the age of 16, informed consent to participate was provided by their parent or legal guardian. Their privacy, anonymity, and confidentiality were rigorously maintained throughout the study. Permission to conduct the research was also obtained from the National Tuberculosis Control Centre, Bhaktapur. The participants’ identities were anonymised in all study records, and data confidentiality was ensured in accordance with ethical standards.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.