Prevalence of Mycobacterium Tuberculosis and Rifampicin-Resistant Tuberculosis Among Tuberculosis Presumptive Adults in Northern Ethiopia, 2016-2019

Prevalence of Mycobacterium Tuberculosis and Rifampicin-Resistant Tuberculosis Among Tuberculosis Presumptive Adults in Northern Ethiopia, 2016-2019 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Prevalence of Mycobacterium Tuberculosis and Rifampicin-Resistant Tuberculosis Among Tuberculosis Presumptive Adults in Northern Ethiopia, 2016-2019 Tsehaye Asmelash Dejene, Genet Gebrehiwet Hailu, Araya Gebreyesus Wasihun This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-527048/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Tuberculosis (TB) is the second leading cause of mortality from an infectious disease worldwide. Multidrug-resistant tuberculosis (MDR-TB), where rifampicin-resistant TB is the most contributor, remains a global health threat. There is scant data on MTB and rifampicin resistance (RR-MTB) using Gene Xpert MTB/RIF assay in Ethiopia, particularly in the study area. This study aimed at determining the prevalence of MTB and RR-MTB among presumptive TB patients in Tigray, Northern Ethiopia. Methods: A multi-center retrospective cross-sectional study was conducted from October 2019 to December 2019 among presumptive MTB patients from four governmental hospitals in Tigray regional state. Records of sputum sample results of presumptive MTB patients with Gene Xpert MTB/RIF assay from January 2016 to December 2019 were investigated. Data-extraction tool was used to collect data from registration books and analyzed using SPSS ver.21 statistical software. Statistically significant was set at P-value ≤ 0.05. Results: Out of the total 17,329 presumptive adult MTB patients who had submitted sputum samples for TB diagnosis, 16,437 (94.9%) had complete data and were included in the study. More than half (60.2%) of them were males and the age of the patients ranged from 18-98 years, with a mean age of 44.2 (±16.4 SD) years. The majority, 15,047(91.5%) and 11,750 (71.5%) of the participants were new cases and with unknown HIV status, respectively. Prevalence of MTB was 9.7% (95% CI: 9.2-10.2%) of these, rifampicin resistant-MTB was 8.7% (95% CI: 7.32-10.09%). Age (being greater than 29 years) [p < 0.001] and new cases [AOR= 0.46; 95%CI = 0.39, 0.53, p < 0.001] were associated with low TB infection. Whereas age groups of 18-29 years were associated with higher RR-MTB [AOR= 3.08; 95% CI= 1.07, 8.72, p = 0.036]. Gender (being male) [AOR= 0.68; 95 % CI= 0.47, 0.96, p= 0.032] and having no history of previous treatment [AOR= 0.29; 95 % CI= 0.202, 0.44, p < 0.001] were associated with lower RR-MTB. Conclusion: Nearly one-tenth of the presumptive tuberculosis patients tested positive for MTB; out of those, 8.7% were positive rifampicin-resistant-MTB. The high prevalence of TB and RR-MTB at a young age and previously treated cases calls for a concerted effort to improve and monitor TB treatment to reduce the problem. Infectious Diseases Health Policy Rifampicin resistance MDR-TB Xpert MTB/RIF assay Tigray Ethiopia Figures Figure 1 Figure 2 Figure 3 Figure 4 Background Tuberculosis (TB) and multidrug-resistant (MDR-TB) is a major global health problem. According to the 2019 report of the WHO, globally, an estimated 10.0 million (range, 9.0 – 11.1 million) people fell ill with TB. Similarly, the WHO reported about 1.2 million (range, 1.1-1.3 million) TB deaths among HIV-negative people in 2018. There were about half a million new cases of rifampicin-resistant TB (of which 78% had multidrug-resistant TB) in 2018 [1]. Delay in early diagnosis and appropriate treatment initiation, and high prevalence of HIV in resource-limited settings made TB and MDR-TB associated morbidity and mortality to be quite high [2]. A key element in the management of TB and MDR-TB is early diagnosis and immediate initiation of appropriate treatment [3]. Conventionally, the diagnosis has relied upon culture and sensitivity testing, a process that needs a longer time, expensive laboratory infrastructure, extensive bio-safety precautions, and specialized laboratory personnel which are not feasible in resources limited settings [4]. To overcome this problem, the WHO endorsed Xpert MTB/RIF assay in 2010, a rapid and automated molecular system to detect the DNA of MTB and rifampicin resistance concurrently [5]. Rifampicin-resistance (RR) is a surrogate marker for MDR-TB in more than 90% of the cases [6]. Initially, this method was indicated for patients with TB/HIV co-infection, presumptive MDR-TB, and paediatrics TB patients [7]. Three years after its implementation, however, it was recommended for all patients suspected of TB infection [8]. In Ethiopia, Xpert MTB/RIF assay was implemented in all general and referral hospitals since 2014[9]. Ethiopia is among the high TB burden countries ranking10th among the high-TB-pandemic and 15th among the 27 high MDR-TB countries [10]. A systematic review and meta-analysis on the epidemiology of MDR-TB in Ethiopia showed 2.18% of newly diagnosed and 21.07 % of previously treated cases had MDR-TB [11]. The review was on articles published using culture and drug sensitivity test methods for the detection of TB and MDR -TB. Since the implementation of the Xpert assay, there is limited data on MTB and RR-MTB in Ethiopia. Few studies exist from Addis Ababa [12], Amhara Regional state [5], and southern Ethiopia [13] on the prevalence of RR-MTB using Xpert MTB/RIF assay. The studies in Amhara Regional state and South Ethiopia collected data from a single hospital each, they used one-year data. Similarly, the report conducted in Addis Ababa collected 12,414 samples from four health facilities, but it may not represent the national picture of the prevalence. Thus more data from regions with a representative sample from many health facilities will give a reasonable power to help policymakers and implementers to plan and design intervention strategies to prevent and control TB associated morbidities and mortalities. A study from Tigray Regional state reported a total of 9,594 TB cases in 2015 [14]. In the region, there are two studies on MDR-TB [15, 16]. These studies, however, were on MDR-TB suspected patients [failure, who have contact with MDR-TB patients and relapsed] which cannot show the magnitude of TB and MDR-TB among the presumptive TB patients in the region. Besides, the results were from culture and drug susceptibility testing methods on a small sample size. The aim of this study was, therefore, to determine the prevalence and associated factors of MTB and RR-MTB among presumptive adult TB patients in Tigray using Xpert MTB/RIF assay. Methods Study design and study population Study setting Tigray Regional State, one of the nine National Regional states of Ethiopia, is the northernmost of the Federal Democratic Republic of Ethiopia. According to the population and housing census of 2007, the region has a population size of 6,960,003 within an area of 54,572.6 km 2 . The capital city of the state of Tigray, Mekelle, is located 783 kms north of Addis Ababa, the capital of Ethiopia. The region is administratively divided into seven Zones and 52 districts (rural = 34, urban =18). One teaching and specialized hospital, 15 general hospitals, 20 primary hospitals, 204 health centers, 712 health posts [village clinic], and 500 private health facilities provide the health services in Tigray. A multi-center health facility-based retrospective cross-sectional study design was used to collect data from October 2019 to December 2019 from four public hospitals. The hospitals were: Adwa, St. Mary, Sihul, and Kahsay Abera hospitals located in the three zones of Tigray region (Central, Northwest, and Western zones) ( Fig 1). There were three primary hospitals in addition to the list of general hospitals in the three study zones but were not included in the study because their data record on Xpert assay was limited. Hence, we purposively selected the four general hospitals which started Gene Xpert for the diagnosis of TB since 2016 to assess the trend of MTB and RR-MTB. All health facilities use Directly Observed Treatment, Short-Course (DOTS) TB treatment protocol. The region has three MDR-TB treatment initiation centers and 52 treatment follow-up centers [17]. The source population was all patients (N=17,329) with clinical signs and symptoms suggestive of TB and visited the hospitals between January 2016 and December 2019, and gave sputum samples for Xpert MTB/RIF assay. Our study participants were all adult patients (≥18 years) (N=16, 437) having data on age, gender, Xpert MTB/RIF results, HIV status, and TB treatment history. Whereas, those children and with any missing information in age, gender, Xpert MTB/RIF results, invalid, indeterminate Xpert MTB/RIF results, HIV status, and TB treatment history were excluded from the study ( Figure 2 ). Variables Outcome Variable: Prevalence of MTB and RR-MTB among presumptive adult TB patients. Independent variables: Age, gender, HIV status and TB treatment history. Operational definitions Failure case: a TB patient whose sputum smear or culture is positive at month 5 or later during treatment Relapse case : a TB patient who has become (and remained) culture-negative while receiving therapy but after completion of therapy become culture positive again Lost to follow up: a TB patient who did not start treatment or whose treatment was interrupted for 2 consecutive months or more New cases: Patients have never been treated for TB before MDR-TB: Isolate of M. tuberculosis showed resistance to at least two of the most important first-line anti-TB drugs, rifampicin and isoniazid Rifampicin-resistant TB (RR-MTB): resistance to rifampicin detected using genotypic or phenotypic methods with or without resistance to other first-line anti-TB drugs Data collection Patients' socio-demographic characteristics (such as age and gender) and clinical-related data (such as Xpert MTB/RIF results, HIV status, and MTB treatment history) were collected using a structured data extraction sheet from Xpert MTB/RIF registration books in each health facility. Laboratory Processing During data extraction, Standard Operating Procedures (SOP) were checked for consistency and proper collection and testing of sputum specimens from patients in each health service facility included in this study. This was important to make sure that the results in the registry books were obtained following similar procedures in all study health facilities. All health facilities used the working protocols presented hereafter. A single sputum sample per patient was used for the diagnosis of MTB using Xpert MTB/RIF assay (Cepheid, Sunnyvale, CA, USA). Samples were collected before the start of anti-TB treatment and processed using Gene Xpert MTB/ RIF assay using the standard protocol. Briefly, after sputum was collected, it was mixed with a sample reagent buffer in 1:2 (sample: sample reagent buffer) volume ratio. Then, closing it tightly, vortexed for 15 seconds and allowed to stand at room temperature for 10 min. It was again vortexed after 10 min and allowed to stand for 5 min. Using the Pasteur pipette provided with the kit >2mL of the (just above 2 ml mark on pipette) processed sample was put into the Xpert MTB/RIF cartridge. Then the cartridge with the specimen was loaded to the Gene Xpert machine. Eventually, results were collected from the Gene Xpert computer after 2h [17]. HIV testing Testing for HIV was done according to the national algorithm recommended by the Federal Ministry of Health of Ethiopia. Briefly, two rapid HIV (1 + 2) test strip (KHB) and Stat-Pak were run sequentially. Samples were tested first with KHB. Positive samples were confirmed with Stat-Pak. In case of discordant results appear, they were resolved using a third confirmatory testing kit, HIV-1/2 Unigold recombinant assay. Quality control and Data analysis Gene Xpert MTB/RIF assay was done using the standard operating method. After data completeness was checked, it was entered and analyzed using SPSS Version 21. Frequency, mean, range, and standard deviation were computed. Besides, Chi-square and logistic regression analysis were computed to identify the associated factors with MTB and RR-MTB. Variables that showed significant association (p<0.05) with the outcome variables in the binary logistic regression were further analyzed using multiple logistic regressions to identify if they have a real association with MTB and RR-MTB at p-value less or equal to 0.05. Results Socio-demographic, Clinical characteristics and TB results of the participants Of the total 17,329 presumptive adult TB patients who submitted sputum samples for TB diagnosis, 16,437 (94.9%) had complete data and were, therefore, included in the study. Males (i.e., 9,894 or 60.2%) dominate females, and the age of the patients ranged from 18-98 years, with a mean age of 44.2 (±16.4 SD). Of the total participants of the study, the majority (15,047, i.e., 91.5%) were new cases, and 11,750 (71.5%) of them were with unknown HIV status. The overall prevalence of MTB was 9.7% among suspected patients, out of which the prevalence of RR-MTB was 8.7% [Table 1]. Table 1: Socio-demographic, clinical characteristics and MTB result of MTB presumptive adult patients in Central, Northwest and Western Tigray, Ethiopia, 2016-2019 (N= 16437) Variables Frequency % Gender Male 9894 60.2 Female 6543 39.8 Age 18-29 3499 21.3 30-39 3727 22.7 40-49 2971 18.1 50-59 2493 15.2 60-69 2242 13.6 70-98 1505 9.2 HIV Status Positive 1701 10.3 Negative 2986 18.2 Unknown 11750 71.5 TB Treatment History New cases 15047 91.5 Relapse 1297 7.9 Lost to follow up 30 0.2 Failure 63 0.4 MTB Result Positive 1587 9.7 Negative 14850 90.3 RR-MTB Result (N=1587) RR-MTB Positive 138 8.7 RR-MTB Negative 1449 91.3 Factors Associated with MTB infection Adjusting for age, HIV status, and previous TB treatment history, the odds of having TB showed a decreasing trend by age. Patients whose age was greater than 29 years were less likely to have TB compared to 18-29 years (p < 0.001). Likewise, new cases were 54% times [AOR= 0.46, 95%CI = 0.39, 0.53, p <0.001] less likely to have TB compared to the previously treated cases [ Table 2 ]. Table 2: Prevalence of MTB among adult TB patients by gender, age, treatment history, and HIV status in Central, Northwest and Western Tigray, 2016-2019 (N=16437) RR- MTB = rifampicin resistant MTB Variables MTB Pos. N (%) MTB Neg. N (%) COR (95% CI) P value AOR(95%CI) P -value Gender Male 983(9.9) 8911(90.1) Ref Ref Female 604(9.2) 5939(90.8) 0.92(0.83-1.03) 0.135 0.91 (0.82 - 1.02) 0.105 Age 18-29 468(13.4) 3031(86.6) Ref Ref 30-39 401(10.8) 3326(89.2) 0.78 (0.68-0.90) <0.001* 0.78 (0.67-0.88) < 0.001* 40-49 275(9.3) 2696(90.7) 0.66 (0.56-0.77) <0.001* 0.62 (0.53-0.73) < 0.001* 50-59 181(7.3) 2312(92.7) 0.51 (0.42-0.61) <0.001* 0.49 (0.41-0.59) < 0.001* 60-69 163(7.3) 2079(92.7) 0.51 (0.42-0.61) <0.001* 0.49 (0.41-0.59) < 0.001* 70-98 99(6.6) 1406(93.4) 0.46 (0.36 -0.57) <0.001* 0.44 (0.35-0.56) < 0.001* HIV status ( n= 4687) Positive 181( 10.6) 1520( 89.4) 1.08(0.89-1.31) 0.42 Negative 341( 11.4) 2645(88.6 ) Ref TB treatment history New cases 1332( 8.9) 13715(91.1 ) 0.46 (0.40-0.54) <0.001* O.46 (0.39-0.53) < 0.001* Previously treated cases 255 (18.3) 1135(81.7) Ref Ref *Statistically significant (p<0.05) Factors Associated with RR-MTB infections Of the total 1,587, TB confirmed patients, 138 (8.7%) were tested positive for RR-MTB. As shown in Table 3, adjusted for gender, age and TB treatment history, males were 32% [AOR= 0.68, 95% CI= 0.47, 0.96, p=0.032] less likely to be infected by RR-MTB compared to females. Similarly, TB presumptive patients who had no history of previous treatment were 71% less likely to be infected by RR-MTB [AOR, 0.29, 95% CI= 0.202, 0.44, p <0.001] compared to previously treated cases. Whereas, the age group of 18-29 years was 3.08 times [AOR=3.08, 95% CI=1.07, 8.72, p=0.036] more likely to be infected by RR-MTB compared to the age group of 70-98 years [Table 3]. Table 3: Prevalence of RR- MTB among adult TB patients by gender, age, treatment history, and HIV status in Central, Northwest and Western Tigray, 2016-2019 (N=1589) Variables RR-MTB N (%) Not RR-MTB N (%) COR (95% CI) P value AOR (95%CI) P- value Gender Male 74 (7.5) 911( 92.5) 0.69 (0.48-0.97) 0.035 0.68(0.47-0.96) 0.032 Female 64 ( 10.6) 540( 89.4) Ref Ref Age 18-29 57 (12.2 ) 412(87.8 ) 3.22(1.14-9.09) 0.027 3.08 (1.07-8.72) 0.036* 30-39 36 (9 ) 366(91 ) (2.290.80-6.59) 0.13 2.20(0.76-6.42) 0.15 40-49 14( 5.1) 261( 94.9) 1.25(0.4-3.89) 0.70 1.09 (0.35-3.47) 0.88 50-59 13( 7.1) 170( 92.9) 1.8(0.56-5.61) 0.33 1.47(0.46-6.77) 0.51 60-69 14( 8.6) 149(91.4 ) 2.2(0.70-6.84) 0.18 2.16 (0.68-0.97) 0.19 70-98 9(9.3) 93( 90.7) Ref Ref HIV Status (n= 524) Positive 18( 9.9 ) 163( 89.1) 1.11( 0.60-2.05) 0.74 Negative 31( 9) 312( 91) Ref TB Treatment History New cases 91(6.8) 1242(93.2) 0.33(0.22-0.48) <0.001 0.29 (0.202-0.44) <0.001* Previously treated cases 46(18.4) 209(81.6) Ref Ref *Statistically significant (p<0.05) Figure 3 compares the percentage prevalence of MTB and RR-MTB by study years. Accordingly, our study revealed that MTB prevalence significantly decreased from 16.9% in 2016 to 8.1%in 2019 (p<0.001, data not shown). Likewise, the trend of RR-MTB has shown a decline from 14.3% in 2016 to 5.8% in 2019 (p < 0.001) [Fig 3]. The number of MTB increases from 314 in 2019 to 531 in 2019, while RR-MTB decreased from 45 in 2016 to 31 in 2019 (Figure 4). This increasing number of MTB was owing to the increasing number of TB suspected patients from 2016 - 2019. Table 4: Comparison of RR-MTB prevalence with other studies Study area Authors RR-MTB prevalence (%) Addis Ababa, Ethiopia Balew et al. [12] Sinshaw et al. [18] 9.9 11 Amhara region, Ethiopia Mulu et al. [5] 10.3 South Ethiopia, Ethiopia Hordofa & Adela [13] 3.4 Oromia region, Ethiopia Mulisa et al. [22] Abebe et al. [2] 33 2.2 Tigray region, Ethiopia Tesfay et al. [15] 54.6 Nigeria Denue et al. [ 27] Ukwamedua [35] Ikuabe1 & Ebuuenyi [26] 6.1 7.3 14.7 India Ramandeep et al. [31] Reddy & Alvarez-uria [29] Ingole et al. [30] I9.9 9.2 9.43 Seoul Kim et al. [21] 8.9 Uganda Mboowa et al. [23] 3.8 Bangui, Farra et al. [24] 42.2 Togo Dagnra et al. [25] 24 Russia, Toungoussova et al. [36] 25.2 Bangladesh Rahman et al. [37] 35 Pakistan Ullah et al. [28] 29 China, Hai et al. [32] 15.3 Zambia Masenga et al. [38] 5.9 Tigray, Ethiopia This study 8.7 Discussion Availability of local epidemiological data on MTB and RR-MTB prevalence and identification of potentially modifiable predisposing factors are essential to design appropriate intervention strategies. An overall prevalence of MTB of 9.7% among suspected patients from which a prevalence of RR-MTB of 8.7% were found. The MTB prevalence (9.7%) in the present study was comparable with previous reports from Addis Ababa, 6.5% [18], the Amhara region, 8% [19],South Africa, 13% [20] and Korea, 13.8% [21]. The prevalence in this study is lower than those conducted in South Ethiopia,16.5% [13], Addis Ababa,15.11% [12], Eastern Ethiopia,19.4% [4], the Oromia region,60.4% [22], 23.2% [5], Uganda, 20.9% [23], Bangui,79.1% [24], Togo, 57%[25], Nigeria, 22.9%[26], 19.1% [27], Pakistan, 59% [28], India, 60% [29], 63.6% [30], 20.3%, [27], 81.1% [31], and China, 51.4% [32]. However, the prevalence in this study is higher than previous ones from Addis Ababa, 6% [18]. Possible reasons for the variations in MTB prevalence could be due to differences in methodological techniques, study participants, study period, geographical and TB control, and prevention policies. The high TB prevalence reported in other studies [18, 25, 27, 28, 30, 31] could as well be attributed to their study participants who were MDR presumptive patients (relapse, defaulter, lost to follow up and failure). By contrast, TB suspected patients were enrolled in this study. Another possible reason could be the small sample size that they used. In other words, small sampling could generate a higher prevalence rate. High prevalence of MTB in other reports [4, 26, 27, 30, 35] compared to the result of this study could be attributed to the difference in the study period (2011- 2014) during which GeneXpert was indicated only for patients with TB/HIV co-infection and presumptive MDR-TB patients. In this study, data was collected from 2016 to 2019, when GeneXpert was adopted for all presumptive TB patients. The ages of the study participants ranged from 18 to 98 years. Of these, patients aged 18-29 years were less infected by TB compared to the other age groups (p=0.037). Though there is no clear-cut for the age group, other studies reported that age groups of 16-30 years [13] are to be less likely to be infected by TB. On the other hand, no association was reported between age and TB infection elsewhere [2, 12, 22, 27]. A study by Mulu et al. (2017) from Amhara region has reported that males were more infected by TB than females [5] which contradicts this study where there was not any association between gender and TB infection. The high MTB prevalence among previously TB treated cases in this study could indicate that the presence of high TB transmission in the community. This again calls for coordinated action to combat the problem in the study region. As can be seen from Table 4, the prevalence of RR-MTB (8.7%) among the MTB confirmed cases was similar with previous reports from Addis Ababa, [12,18], Amhara region [5] Nigeria [27,35 ], India [29-31] and Seoul [21]. However, this prevalence was lower than those found in previous studies in Oromia region, [22], Tigray [15], Bangui [24], Togo [25], Nigeria [26], Russia [36], India [31], Bangladesh [37], Pakistan [28] and China [32]. Others have reported lower RR-MTB prevalence in south Ethiopia [13], Oromia region [2, 23] and Zambia [38] [ Table 4 ]. There are many possible reasons for the variation in RR-MTB reports. For example, differences in geographical, methodology (sample size, method of diagnosis, study participants), study setting, study period, and TB control practice could be among the reasons. The high RR-MTB prevalence reported by [22, 23, 25] could be due to the fact that their study participants were previously TB positive (i.e., relapse, defaulter, lost to follow up, or failure) and had a history of MDR contacts which put them at a higher risk to develop MDR-MTB whereas this study included presumptive TB patients . The high RR-MTB in studies conducted in Somali region [4], Pakistan [28], Bangladesh [37], India, [27], and Togo [25] compared to our results might be due to the temporal difference. Their studies were from 2011 to 2014, when Gene Xpert assay was used for patients with presumptive MDR-TB patients. By contrast, in this study, data was collected from records of patients who visited the hospitals from 2016 to 2019, and the method was used for all TB suspected patients. Age groups of 18-29 years were more likely to be infected by RR-MTB. Others reported 0-20 years and 61-80 years [35]. The high prevalence of RR-MTB among confirmed TB cases among the productive age group (18-29 years) may indicate the circulation of resistant strain of MTB in the community. Given this age has a tendency to travel from place to place and communicate with other people (like school communities), more attention to combat the problem sounds critical. Regarding the association between RR-MTB and gender, females were significantly infected by RR-MTB compared to males (p= 0.032). These results are consistent with other reports [12, 36]. Other studies reported more RR-MTB infections among males than females [15, 27, 35]. The higher prevalence of RR-MTB in females reported in this study could be due to the poor knowledge of females about TB transmission and control[40], poor health-seeking behavior, and hence delay in detection in females [3,39]. These may help the bacteria to disseminate to the household members (such as children) as mothers are more responsible for giving care for the children and have more close contacts. The other independent predictor for RR-MTB was found to be previous TB treatment, which was supported by many similar studies [12, 22, 27, 36]. The high prevalence of rifampicin resistance-MTB among previously treated patients highlights for more concerted effort of the regional government and stakeholders to improve the monitoring of TB treatment and thereby reduce the emergence of circulating drug-resistant TB strains in the community. The prevalence of TB and RR-MTB were compared by the study years. Accordingly, it was revealed that TB prevalence among TB presumptive patients significantly decreased from 16.9% in 2016 to 8.1 % in 2019. Similarly, the RR-MTB among the TB confirmed cases indicated a significant decrease in trend (from 14.3 % in 2016 to 5.8% in 2019).The overall declining trend of MTB and RR-MTB in the region might indicate that the implementation of the policies on TB diagnosis and treatment by the regional government and stakeholders are in the right direction. The significant increase in tests done per year could also be a factor in decreasing the prevalence. Despite this decreasing trend of TB among TB presumptive patients, the prevalence still calls for more efforts to be exerted to reduce the morbidities and mortalities associated with MTB. The strong association of rifampicin resistance-MTB with patients having a history of previous treatment (relapse, failure, and lost to follow up) implies the need for evaluating and monitoring the existing directly observed treatment, short-course TB treatment services of the health of the health facilities. This, in turn, helps to intervene and minimize the magnitude of further emergence of drug-resistant MTB strains in the community. The strength of this study was that it is a multicenter health facility-based study in the region with a large sample size that can complement and give latest data on the prevalence of TB and RR-MTB for the regional and national governments. However, the study was not devoid of limitations. First, as we examined in a single region in Ethiopia, the economic and regional disparities limited the generalizability of the results to a national level. Second, we were not able to do microbiological confirmation of tuberculosis, phenotypic rifampicin resistance, and resistance to other anti-TB drugs because of the retrospective nature of the study. Third, retrospective data provided little information on the contact history of MDR-TB and TB, education, and living conditions of patients. Fourth, given the data was only from four hospitals, results may not be generalizable to the region. Fifth, the higher number of patients with unknown HIV status did not allow us to see the association of MTB and RR-MTB with HIV. Conclusion The overall prevalence of MTB was 9.7% among TB suspected patients, and the prevalence of RR-MTB from among the MTB positive was 8.7%. Those aged greater than 29 years and who have no histories of previous treatment were associated with lower TB infection. While males and new cases were associated with lower RR-MTB, participants in the age groups of 18-29 years were associated with higher RR-MTB. Overall, the prevalence of TB and RR-MTB during the study period showed a decline. Even so, it showed the need for more work in order to minimize TB and RR-MTB associated morbidities and mortalities in the study area. Besides, as patients having a history of previous treatment were infected with rifampicin resistant-MTB, evaluation, and monitoring of the directly observed treatment, short TB treatment services in the region need more attention. Abbreviations HIV: Human immunodeficiency virus, MDR-TB: Multidrug-resistant tuberculosis, RR-MTB: Rifampicin resistant mycobacterium tuberculosis, TB: Tuberculosis, WHO: World Health Organization Declarations Ethical consideration Ethical clearance was obtained from Aksum University, College of Health Sciences Institutional Review Board (IRB). Besides, a letter of cooperation was written from the Tigray Regional Health Bureau (THRB) to each study hospital, and permission was obtained accordingly. As the study was a retrospective type, we did not get informed consent and assent from the study participants. Consent to publish Not applicable Availability of Data and Materials The data sets used and analyzed during the current study are available from the corresponding author on reasonable request. Conflict of interest statement We declare that we have no conflict of interest. Funding Not applicable Author Contributions TA, AGW, and GG designed the study. AGW worked on the analysis and interpretation of the data and prepared the draft manuscript. TA, AGW, and GG prepared the final manuscript for publication. All authors read and approved the final paper. Acknowledgments We would like to thank all the hospital directors and laboratory staff of the study hospitals for their cooperation in giving us access to the records to extract the data. References WHO. Global Tuberculosis Report: 2019. Abebe G, Abdissa K, Abdissa A, Apers L, Agonafir M, Bouke C, et al. 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Davidson, Paul Bassett, Robert Wall, Geoffrey Pasvol and KLF. Rapid molecular detection of rifampicin resistance facilitates early diagnosis and treatment of multi-drug resistant tuberculosis. PLoS One. 2008,3: 1–7. WHO. Rapid implementation of the Xpert MTB/RIF diagnostic test. Geneva: 2011. WHO. Xpert MTB/RIF assay for the diagnosis of TB Meeting report. 2016. Implementation Guideline for Gene Xpert MTB/RIF assay In Ethiopia: Adiss Ababa. 2014. WHO 2015. Ethiopia Tuberculosis Progress in 2014. 2015. Girum T, Muktar E, Lentiro K, Wondiye H, Shewangizaw M. Epidemiology of multidrug-resistant tuberculosis ( MDR-TB ) in Ethiopia : a systematic review and meta-analysis of the prevalence , determinants and treatment outcome. Tropical Diseases, Travel Medicine and Vaccines, 2018,4: 1–12. Arega B, Menbere F, Getachew Y. Prevalence of rifampicin resistant Mycobacterium tuberculosis among presumptive tuberculosis patients in selected governmental hospitals in Addis. BMC Infectious Diseases, 2019,19: 1–5. Hordofa MW, Adela TB. Prevalence of Refampcin Mono Resistant Mycobacterium Tuberculosis among Suspected Cases Attending at Yirgalem Hospital. Clin Med Res. 2015,4: 75–78. doi:10.11648/j.cmr.20150403.13 TRHB. Tigray Regional Health Bureau First Draft 2008 EFY Annual Profile. 2016. Tesfay K, Tesfay S, Nigus E, Gebreyesus A. More than half of presumptive multidrug-resistant cases referred to a tuberculosis referral laboratory in the Tigray region of Ethiopia are multidrug resistant. Asian-African Society for Mycobacteriology, 2016,5: 324–327. doi:10.1016/j.ijmyco.2016.07.007 Mehari K, Asmelash T, Hailekiros H, Wubayehu T, Godefay H, Araya T, et al. Prevalence and Factors Associated with Multidrug-Resistant Tuberculosis ( MDR-TB ) among Presumptive MDR-TB Patients in Tigray Region , Northern Ethiopia. Can J Infect Dis Med Microbiol. 2019,2019: 1–8. Cepheid. GeneXpert Dx System Users’ manual. 2012. pp. 2–13. Sinshaw W, Kebede A, Bitew A, Tesfaye E, Tadesse M, Mehamed Z, et al. Prevalence of tuberculosis , multidrug resistant tuberculosis and associated risk factors among smear negative presumptive pulmonary tuberculosis patients in Addis. BMC Infectious Diseases, 2019,19: 1–15. Biadglegne F, Rodloff AC, Sack U. A First Insight into High Prevalence of Undiagnosed Smear-Negative Pulmonary Tuberculosis in Northern Ethiopian Prisons : Implications for Greater Investment and Quality Control. PLoS One. 2014,9. doi:10.1371/journal.pone.0106869 Nicol MP, Workman L, Isaacs W, Munro J, Black F, Eley B, et al. Europe PMC Funders Group Accuracy of the Xpert MTB / RIF test for the diagnosis of pulmonary tuberculosis in children admitted to hospital in Cape Town , South Africa : a descriptive study. Lancet Infect Dis. 2014,11: 819–824. doi:10.1016/S1473-3099(11)70167-0.Accuracy Kim C, Hyun IG, Hwang YIL, Kim D, Lee CY, Lee MG, et al. Identification of Mycobacterium tuberculosis and Rifampin Resistance in Clinical Specimens Using the Xpert MTB / RIF Assay. Ann Clin Lab Sci vol. 2015,45: 32–38. Mulisa G, Workneh T, Hordofa N, Suaudi M, Abebe G. International Journal of Infectious Diseases Multidrug-resistant Mycobacterium tuberculosis and associated risk factors in Oromia Region of Ethiopia. Int J Infect Dis. International Society for Infectious Diseases, 2015,39: 57–61. doi:10.1016/j.ijid.2015.08.013 Mboowa G, Namaganda C, Ssengooba W. Rifampicin resistance mutations in the 81 bp RRDR of rpoB gene in Mycobacterium tuberculosis clinical isolates using Xpert ® MTB / RIF in Kampala , Uganda : a retrospective study. BMC Infect Dis. 2014,14: 1–5. Farra A, Manirakiza A, Yambiyo BM, Zandanga G, Lokoti B, Berlioz-arthaud A, et al. Surveillance of Rifampicin Resistance With GeneXpert MTB / RIF in the National Reference Laboratory for Tuberculosis at the Institut Pasteur in Bangui , 2015 – 2017. Open Forum Infect Dis. 2019,6: 2015–2017. doi:10.1093/ofid/ofz075 Dagnra AY, Mlaga KD, Adjoh K, Kadanga E, Disse K, Adekambi T. Prevalence of multidrug-resistant tuberculosis cases among HIV-positive and HIV-negative patients eligible for retreatment regimen in Togo using GeneXpert MTB / RIF. New Microbes New Infect. The Authors, 2015,8: 24–27. doi:10.1016/j.nmni.2015.09.001 Peter Ogie Ikuabe1,& Ikenna Desmond Ebuuenyi. assay in patients with pulmonary tuberculosis in Yenagoa , Nigeria. Pan African Med Journal. 2018,29: 1–4. doi:10.11604/pamj.2018.29.204.14579 Denue BA, Miyanacha WJ, Wudiri Z, Alkali MB, Goni BW, Akawu CB. rifampicin resistance among presumptive pulmonary tuberculosis cases in Borno state , North ‑ Eastern Nigeria. Port Harcourt Med J |. 2019,12: 64–69. doi:10.4103/phmj.phmj Ullah I, Shah AA, Basit A, Ali M, Ullah U, Ihtesham M, et al. Rifampicin resistance mutations in the 81 bp RRDR of rpo B gene in Mycobacterium tuberculosis clinical isolates using Xpert MTB / RIF in Khyber Pakhtunkhwa , Pakistan : a retrospective study. BMC Infectious Diseases, 2016, 4–9. doi:10.1186/s12879-016-1745-2 Reddy R, Alvarez-uria G. Molecular Epidemiology of Rifampicin Resistance in Mycobacterium tuberculosis Using the GeneXpert MTB / RIF Assay from a Rural Setting in India. Hindawi. 2017,2017: 1–5. Ingole K, Kamble SW, Mundhada S. Prevalence of Mycobacterium Tuberculosis and Multidrug Resistance Tuberculosis by Using GeneXpert MTB / RIF System at a Tertiary Care Center in Maharashtra. Int J Curr Res Rev. 2018,10: 1–5. Ramandeep Kaur, Neerja Jindal, Shilpa Arora and SK. Epidemiology of Rifampicin Resistant Tuberculosis and Common Mutations in rpoB Gene of Mycobacterium tuberculosis: A Retrospective Study from Six Districts of Punjab (India) Using Xpert MTB/RIF Assay. J Lab Physicians. 2016,8: 96–100. Hai Huang, Yanlin Zhang, Sheng Li, Jun Wang, Jun Chen, Zhiyun Pan and Hui Gan. Rifampicin Resistance and Multidrug-Resistant Detection Using Xpert MTB/RIF in Wuhan, China: A Retrospective Study. Microb DRUG Resist. 2018,24: 675–680. doi:10.1089/mdr.2017.0114 Telele NF, Kalu AW, Gebre-Selassie S, Fekade D, Abdurahman S, Marrone G, et al. Pretreatment drug resistance in a large countrywide Ethiopian HIV-1C cohort: A comparison of Sanger and high-throughput sequencing /631/326/2521 /631/337/151/1431 /38/23 /38/77 /38/90 /14/63 /38/43 /38/47 /42/40 article. Sci Rep. 2018,8: 1–10. doi:10.1038/s41598-018-25888-6 Ukwamedua H, Omote V, Etaghene J, Ejike M, Celia I, Agbroko H. Heliyon Rifampicin resistance among noti fi ed pulmonary tuberculosis ( PTB ) cases in South-Southern Nigeria. Heliyon. Elsevier Ltd, 2019,5: e02096. doi:10.1016/j.heliyon.2019.e02096 Toungoussova S1, Caugant DA, Sandven P, Mariandyshev AO BG. Drug resistance of Mycobacterium tuberculosis strains isolated from patients with pulmonary tuberculosis in Archangels, Russia. Int J Tuberc Lung Dis. 2002,6: 406–14. Rahman A, Sahrin M, Afrin S, Earley K, Ahmed S. Comparison of Xpert MTB / RIF Assay and GenoType MTBDR plus DNA Probes for Detection of Mutations Associated with Rifampicin Resistance in Mycobacterium tuberculosis. PLoS One. 2016,11: 1–11. doi:10.1371/journal.pone.0152694 Masenga SK, Mubila H, Hamooya BM. Rifampicin resistance in mycobacterium tuberculosis patients using GeneXpert at Livingstone Central Hospital for the year 2015 : a cross sectional explorative study.BMC Infectious Diseases, 2017,17: 1–4. doi:10.1186/s12879-017-2750-9 Abebe G, Deribew A, Apers L, Woldemichael K, Shiffa J, Abdissa A, et al. Knowledge , Health Seeking Behavior and Perceived Stigma towards Tuberculosis among Tuberculosis Suspects in a Rural Community in Southwest Ethiopia. PLoS One. 2010,5: 1–7. doi:10.1371/journal.pone.0013339 Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-527048","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research","associatedPublications":[],"authors":[{"id":32544791,"identity":"d9f506e9-d2ff-4a99-9360-57a7d2f20239","order_by":0,"name":"Tsehaye Asmelash Dejene","email":"","orcid":"","institution":"Mekelle University College of Health Sciences","correspondingAuthor":false,"prefix":"","firstName":"Tsehaye","middleName":"Asmelash","lastName":"Dejene","suffix":""},{"id":32544792,"identity":"39f86e60-63ef-4abd-ac65-b65b8dd0425f","order_by":1,"name":"Genet Gebrehiwet Hailu","email":"","orcid":"","institution":"Mekelle University College of Health Sciences","correspondingAuthor":false,"prefix":"","firstName":"Genet","middleName":"Gebrehiwet","lastName":"Hailu","suffix":""},{"id":32544793,"identity":"b35c7d17-624f-4a6b-96d9-b2f54bf51fe0","order_by":2,"name":"Araya Gebreyesus Wasihun","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA70lEQVRIiWNgGAWjYNACgxoGfvnDB4AsCRkitRQcY5CcwZYA0sJDpJYPzAwGN3gMQEzCWvhnH34m8cOATY7hds/nVzdqLHgY2A8f3YBPi8S5NDPJHgMZY8Y5Z7dZ5xwDOownLe0GXmvOMJhJ8BiwJTYz5G4zzmEDapHgMcOrRf4M+zfJPwbM9W0MOc+Mc/4RocXgDI+ZNI8BcwKPRA7z49w2IrQYnuEptpYxOGY4g+eYGXNunwQPGyG/yJ1h33jzzZ8aefvjzY8/53yrk+NnP3wMv/cZGFgkoAw2MIONgHIQYP6AzhgFo2AUjIJRgAIAbE1EGFff8YsAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0003-0225-2386","institution":"Mekelle University College of Health Sciences","correspondingAuthor":true,"prefix":"","firstName":"Araya","middleName":"Gebreyesus","lastName":"Wasihun","suffix":""}],"badges":[],"createdAt":"2021-05-15 08:57:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-527048/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-527048/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":10397484,"identity":"1beddbfc-080c-400c-a3f4-48f6a03e5fc1","added_by":"auto","created_at":"2021-06-15 16:15:24","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":223912,"visible":true,"origin":"","legend":"Map of the study area\nNote: The designations employed and the presentation of the material on this map do not imply the expression of any opinion whatsoever on the part of Research Square concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. This map has been provided by the authors.","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-527048/v1/dfd40590a34ae6e746a307ab.png"},{"id":10397374,"identity":"2d30495a-6ebe-4ab0-a5ae-b5a54938050c","added_by":"auto","created_at":"2021-06-15 16:12:24","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":138478,"visible":true,"origin":"","legend":"Flow charts for inclusion and exclusion criteria.","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-527048/v1/2ebd14c8dcbb677843ae584b.png"},{"id":10397652,"identity":"b3d5a65f-e68f-4a1e-9a5e-029f77360672","added_by":"auto","created_at":"2021-06-15 16:18:24","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":96871,"visible":true,"origin":"","legend":"Percentage of MTB and RR-MTB by study years (2016-2019)","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-527048/v1/a94d32ef266f07c9f9a6f7cb.png"},{"id":10397651,"identity":"f949eb76-68a5-42c6-9c31-3371c2ada693","added_by":"auto","created_at":"2021-06-15 16:18:24","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":82170,"visible":true,"origin":"","legend":"Trends in the number of MTB and RR-MTB by study years (2016-2019)","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-527048/v1/021a91985b5bdcbebcc2b02a.png"},{"id":15673352,"identity":"36c52a4e-c604-4ea3-ba34-8a3929ba309b","added_by":"auto","created_at":"2021-11-18 14:17:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":998496,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-527048/v1/f6f57a56-e424-49ca-8983-b8415eaaacc7.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003ePrevalence of \u003cem\u003eMycobacterium Tuberculosis\u003c/em\u003e and Rifampicin-Resistant Tuberculosis Among Tuberculosis Presumptive Adults in Northern Ethiopia, 2016-2019\u003c/p\u003e","fulltext":[{"header":"Background ","content":"\u003cp\u003eTuberculosis (TB) and multidrug-resistant (MDR-TB) is a major global health problem. According to the 2019 report of the WHO, globally, an estimated 10.0 million (range, 9.0 \u0026ndash; 11.1 million) people fell ill with TB. Similarly, the WHO reported about 1.2 million (range, 1.1-1.3 million) TB deaths among HIV-negative people in 2018. There were about half a million new cases of rifampicin-resistant TB (of which 78% had multidrug-resistant TB) in 2018 [1]. Delay in early diagnosis and appropriate treatment initiation, and high prevalence of HIV in resource-limited settings made TB and MDR-TB associated morbidity and mortality to be quite high [2]. \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA key element in the management of TB and MDR-TB is early diagnosis and immediate initiation of appropriate treatment [3]. Conventionally, the diagnosis has relied upon culture and sensitivity testing, a process that needs a longer time, expensive laboratory infrastructure, extensive bio-safety precautions, and specialized laboratory personnel which are not feasible in resources limited settings [4]. To overcome this problem, the WHO endorsed Xpert MTB/RIF assay in 2010, a rapid and automated molecular system to detect the DNA of MTB and rifampicin resistance concurrently [5]. Rifampicin-resistance (RR) is a surrogate marker for MDR-TB in more than 90% of the cases [6]. Initially, this method was indicated for patients with TB/HIV co-infection, presumptive MDR-TB, and paediatrics TB patients [7]. Three years after its implementation, however, it was recommended for all patients suspected of TB infection [8]. In Ethiopia, Xpert MTB/RIF assay was implemented in all general and referral hospitals since 2014[9].\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Ethiopia is among the high TB burden countries ranking10th among the high-TB-pandemic and 15th among the 27 high MDR-TB countries [10]. A systematic review and meta-analysis on the epidemiology of MDR-TB in Ethiopia showed 2.18% of newly diagnosed and 21.07 % of previously treated cases had MDR-TB [11]. The review was on articles published using culture and drug sensitivity test methods for the detection of TB and MDR -TB.\u003c/p\u003e\n\u003cp\u003eSince the implementation of the Xpert assay, there is limited data on MTB and RR-MTB in Ethiopia. Few studies exist from Addis Ababa [12], Amhara Regional state [5], and southern Ethiopia [13] on the prevalence of RR-MTB using Xpert MTB/RIF assay. The studies in Amhara Regional state and South Ethiopia collected data from a single hospital each, they used one-year data. Similarly, the report conducted in Addis Ababa collected 12,414 samples from four health facilities, but it may not represent the national picture of the prevalence. Thus more data from regions with a representative sample from many health facilities will give a reasonable power to help policymakers and implementers to plan and design intervention strategies to prevent and control TB associated morbidities and mortalities.\u003c/p\u003e\n\u003cp\u003eA study from Tigray Regional state reported a total of 9,594 TB cases in 2015 [14]. In the region, there are two studies on MDR-TB [15, 16]. These studies, however, were on MDR-TB suspected patients [failure, who have contact with MDR-TB patients and relapsed] which cannot show the magnitude of TB and MDR-TB among the presumptive TB patients in the region. Besides, the results were from culture and drug susceptibility testing methods on a small sample size. The aim of this study was, therefore, to determine the prevalence and associated factors of MTB and RR-MTB among presumptive adult TB patients in Tigray using Xpert MTB/RIF assay.\u003c/p\u003e"},{"header":"Methods ","content":"\u003cp\u003e\u003cstrong\u003eStudy design and study population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy setting\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTigray Regional State, one of the nine National Regional states of Ethiopia, is the northernmost of the Federal Democratic Republic of Ethiopia. According to the population and housing census of 2007, the region has a population size of 6,960,003 within an area of 54,572.6 km\u003csup\u003e2\u003c/sup\u003e. \u0026nbsp;The capital city of the state of Tigray, Mekelle, is located 783 kms north of Addis Ababa, the capital of Ethiopia. The region is administratively divided into seven Zones and 52 districts (rural = 34, urban =18). One teaching and specialized hospital, 15 general hospitals, 20 primary hospitals, 204 health centers, 712 health posts [village clinic], and 500 private health facilities provide the health services in Tigray. A multi-center health facility-based retrospective cross-sectional study design was used to collect data from October 2019 to December 2019 from four public hospitals. The hospitals were: Adwa, St. Mary, Sihul, and Kahsay Abera hospitals located in the three zones of Tigray region (Central, Northwest, and Western zones) (\u003cstrong\u003eFig 1).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere were three primary hospitals in addition to the list of general hospitals in the three study zones but were not included in the study because their data record on Xpert assay was limited. Hence, we purposively selected the four general hospitals which started Gene Xpert for the diagnosis of TB since 2016 to assess the trend of MTB and RR-MTB. All health facilities use Directly Observed Treatment, Short-Course (DOTS) TB treatment protocol. The region has three MDR-TB treatment initiation centers and 52 treatment follow-up centers [17].\u003c/p\u003e\n\u003cp\u003eThe source population was all patients (N=17,329) with clinical signs and symptoms suggestive of TB and visited the hospitals between January 2016 and December 2019, and gave sputum samples for Xpert MTB/RIF assay. Our study participants were all adult patients (\u0026ge;18 years) (N=16, 437) having data on age, gender, Xpert MTB/RIF results, HIV status, and TB treatment history. Whereas, those children and with any missing information in age, gender, Xpert MTB/RIF results, invalid, indeterminate Xpert MTB/RIF results, HIV status, and TB treatment history were excluded from the study (\u003cstrong\u003eFigure 2\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVariables\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOutcome Variable:\u003c/strong\u003e\u0026nbsp; Prevalence of MTB and RR-MTB among presumptive adult TB patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIndependent variables:\u003c/strong\u003e Age, gender, HIV status and TB treatment history.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOperational definitions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFailure case:\u003c/strong\u003e a TB patient whose sputum smear or culture is positive at month 5 or later during treatment\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRelapse case\u003c/strong\u003e: a TB patient who has become (and remained) culture-negative while receiving therapy but after completion of therapy become culture positive again\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLost to follow up:\u003c/strong\u003e a TB patient who did not start treatment or whose treatment was interrupted for 2 consecutive months or more\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNew cases:\u003c/strong\u003e Patients have never been treated for TB before\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMDR-TB:\u003c/strong\u003e Isolate of M. tuberculosis showed resistance to at least two of the most important first-line anti-TB drugs, rifampicin and isoniazid\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRifampicin-resistant TB (RR-MTB):\u003c/strong\u003e resistance to rifampicin detected using genotypic or phenotypic methods with or without resistance to other first-line anti-TB drugs\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData collection\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePatients\u0026apos; socio-demographic characteristics (such as age and gender) and clinical-related data (such as Xpert MTB/RIF results, HIV status, and MTB treatment history) were collected using a structured data extraction sheet from Xpert MTB/RIF registration books in each health facility.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLaboratory Processing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDuring data extraction, Standard Operating Procedures (SOP) were checked for consistency and proper collection and testing of sputum specimens from patients in each health service facility included in this study. This was important to make sure that the results in the registry books were obtained following similar procedures in all study health facilities. All health facilities used the working protocols presented hereafter.\u003c/p\u003e\n\u003cp\u003eA single sputum sample per patient was used for the diagnosis of MTB using Xpert MTB/RIF assay (Cepheid, Sunnyvale, CA, USA). Samples were collected before the start of anti-TB treatment and processed using Gene Xpert MTB/ RIF assay using the standard protocol. \u0026nbsp;Briefly, after sputum was collected, it was mixed with a sample reagent buffer in 1:2 (sample: sample reagent buffer) volume ratio. Then, closing it tightly, vortexed for 15 seconds and allowed to stand at room temperature for 10 min. It was again vortexed after 10 min and allowed to stand for 5 min. Using the Pasteur pipette provided with the kit \u0026gt;2mL of the (just above 2 ml mark on pipette) processed sample was put into the Xpert MTB/RIF cartridge. Then the cartridge with the specimen was loaded to the Gene Xpert machine. Eventually, results were collected from the Gene Xpert computer after 2h [17].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHIV testing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTesting for HIV was done according to the national algorithm recommended by the Federal Ministry of Health of Ethiopia. Briefly, two rapid HIV (1 + 2) test strip (KHB) and Stat-Pak were run sequentially. Samples were tested first with KHB. Positive samples were confirmed with Stat-Pak. In case of discordant results appear, they were resolved using a third confirmatory testing kit, HIV-1/2 Unigold recombinant assay.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQuality control and Data analysis\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGene Xpert MTB/RIF assay was done using the standard operating method. After data completeness was checked, it was entered and analyzed using SPSS Version 21. Frequency, mean, range, and standard deviation were computed. Besides, Chi-square and logistic regression analysis were computed to identify the associated factors with MTB and RR-MTB. Variables that showed significant association (p\u0026lt;0.05) with the outcome variables in the binary logistic regression were further analyzed using multiple logistic regressions to identify if they have a real association with MTB and RR-MTB at p-value less or equal to 0.05.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eSocio-demographic, Clinical characteristics and TB results of the participants\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOf the total 17,329 presumptive adult TB patients who submitted sputum samples for TB diagnosis, 16,437 (94.9%) had complete data and were, therefore, included in the study. Males (i.e., 9,894 or 60.2%) dominate females, and the age of the patients ranged from 18-98 years, with a mean age of 44.2 (\u0026plusmn;16.4 SD). Of the total participants of the study, the majority (15,047, i.e., 91.5%) were new cases, and 11,750 (71.5%) of them were with unknown HIV status. The overall prevalence of MTB was 9.7% among suspected patients, out of which the prevalence of RR-MTB was 8.7% \u003cstrong\u003e[Table 1].\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1: Socio-demographic, clinical characteristics and MTB result of MTB presumptive adult patients in Central, Northwest and Western Tigray, Ethiopia, 2016-2019 (N= 16437)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003eVariables\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003eFrequency \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003e9894\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003e60.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003e6543\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003e39.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;Age\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003e18-29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003e3499\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003e21.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003e30-39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003e3727\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003e22.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003e40-49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003e2971\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003e18.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003e50-59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003e2493\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003e15.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003e60-69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003e2242\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003e13.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003e70-98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003e1505\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003e9.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003e\u003cstrong\u003eHIV Status\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003ePositive\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003e1701\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003e10.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003e2986\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003e18.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003eUnknown\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003e11750\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003e71.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTB Treatment History\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003eNew cases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003e15047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003e91.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003eRelapse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003e1297\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003e7.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003eLost \u0026nbsp; \u0026nbsp; to follow up\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003e\u0026nbsp;30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003eFailure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMTB \u0026nbsp;Result\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003e1587\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003e9.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003e14850\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003e90.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"66.66666666666667%\"\u003e\n \u003cp\u003e\u003cstrong\u003eRR-MTB Result \u0026nbsp;(N=1587)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003eRR-MTB Positive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003e138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003e8.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003eRR-MTB Negative\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003e1449\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003e91.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eFactors Associated with MTB infection \u0026nbsp; \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAdjusting for age, HIV status, and previous TB treatment history, the odds of having TB showed a decreasing trend by age. Patients whose age was greater than 29 years were less likely to have TB compared to 18-29 years (p \u0026lt; 0.001). Likewise, new cases were 54% times [AOR= 0.46, 95%CI = 0.39, 0.53, p \u0026lt;0.001] less likely to have TB compared to the previously treated cases [\u003cstrong\u003eTable 2\u003c/strong\u003e].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2: Prevalence of MTB among adult TB patients by gender, age, treatment history, and HIV status in Central, Northwest and Western Tigray, 2016-2019 (N=16437)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRR- MTB = rifampicin resistant MTB\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"13.445378151260504%\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.22689075630252%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMTB Pos. N (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.546218487394958%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMTB Neg. N (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.647058823529413%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCOR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.243697478991596%\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.80672268907563%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAOR(95%CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.084033613445378%\"\u003e\n \u003cp\u003e\u003cstrong\u003eP -value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" width=\"46.21848739495798%\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"13.445378151260504%\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.22689075630252%\"\u003e\n \u003cp\u003e983(9.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.546218487394958%\"\u003e\n \u003cp\u003e8911(90.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.647058823529413%\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.243697478991596%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.80672268907563%\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.084033613445378%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"13.445378151260504%\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.22689075630252%\"\u003e\n \u003cp\u003e604(9.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.546218487394958%\"\u003e\n \u003cp\u003e5939(90.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.647058823529413%\"\u003e\n \u003cp\u003e0.92(0.83-1.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.243697478991596%\"\u003e\n \u003cp\u003e0.135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.80672268907563%\"\u003e\n \u003cp\u003e0.91 (0.82 - 1.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.084033613445378%\"\u003e\n \u003cp\u003e0.105\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" width=\"46.21848739495798%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"13.445378151260504%\"\u003e\n \u003cp\u003e18-29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.22689075630252%\"\u003e\n \u003cp\u003e468(13.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.546218487394958%\"\u003e\n \u003cp\u003e3031(86.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.647058823529413%\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.243697478991596%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.80672268907563%\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.084033613445378%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"13.445378151260504%\"\u003e\n \u003cp\u003e30-39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.22689075630252%\"\u003e\n \u003cp\u003e401(10.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.386554621848738%\"\u003e\n \u003cp\u003e3326(89.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.80672268907563%\"\u003e\n \u003cp\u003e0.78 (0.68-0.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.243697478991596%\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.80672268907563%\"\u003e\n \u003cp\u003e0.78 (0.67-0.88)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.084033613445378%\"\u003e\n \u003cp\u003e\u0026lt; 0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"13.445378151260504%\"\u003e\n \u003cp\u003e40-49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.22689075630252%\"\u003e\n \u003cp\u003e275(9.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.386554621848738%\"\u003e\n \u003cp\u003e2696(90.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.80672268907563%\"\u003e\n \u003cp\u003e0.66 (0.56-0.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.243697478991596%\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.80672268907563%\"\u003e\n \u003cp\u003e0.62 (0.53-0.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.084033613445378%\"\u003e\n \u003cp\u003e\u0026lt; 0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"13.445378151260504%\"\u003e\n \u003cp\u003e50-59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.22689075630252%\"\u003e\n \u003cp\u003e181(7.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.386554621848738%\"\u003e\n \u003cp\u003e2312(92.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.80672268907563%\"\u003e\n \u003cp\u003e0.51 (0.42-0.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.243697478991596%\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.80672268907563%\"\u003e\n \u003cp\u003e0.49 (0.41-0.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.084033613445378%\"\u003e\n \u003cp\u003e\u0026lt; 0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"13.445378151260504%\"\u003e\n \u003cp\u003e60-69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.22689075630252%\"\u003e\n \u003cp\u003e163(7.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.386554621848738%\"\u003e\n \u003cp\u003e2079(92.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.80672268907563%\"\u003e\n \u003cp\u003e0.51 (0.42-0.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.243697478991596%\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.80672268907563%\"\u003e\n \u003cp\u003e0.49 (0.41-0.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.084033613445378%\"\u003e\n \u003cp\u003e\u0026lt; 0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"13.445378151260504%\"\u003e\n \u003cp\u003e70-98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.22689075630252%\"\u003e\n \u003cp\u003e99(6.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.386554621848738%\"\u003e\n \u003cp\u003e1406(93.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.80672268907563%\"\u003e\n \u003cp\u003e0.46 (0.36 -0.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.243697478991596%\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.80672268907563%\"\u003e\n \u003cp\u003e0.44 (0.35-0.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.084033613445378%\"\u003e\n \u003cp\u003e\u0026lt; 0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" width=\"46.21848739495798%\"\u003e\n \u003cp\u003e\u003cstrong\u003eHIV \u0026nbsp; \u0026nbsp; status \u0026nbsp;( n= 4687)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"13.445378151260504%\"\u003e\n \u003cp\u003ePositive\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.22689075630252%\"\u003e\n \u003cp\u003e181( 10.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.386554621848738%\"\u003e\n \u003cp\u003e1520( 89.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.80672268907563%\"\u003e\n \u003cp\u003e\u0026nbsp;1.08(0.89-1.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.243697478991596%\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.80672268907563%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.084033613445378%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"13.445378151260504%\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.22689075630252%\"\u003e\n \u003cp\u003e341( 11.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.386554621848738%\"\u003e\n \u003cp\u003e2645(88.6 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.80672268907563%\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.243697478991596%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.80672268907563%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.084033613445378%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" width=\"46.21848739495798%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTB treatment history \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"13.445378151260504%\"\u003e\n \u003cp\u003eNew cases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.22689075630252%\"\u003e\n \u003cp\u003e1332( 8.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.546218487394958%\"\u003e\n \u003cp\u003e13715(91.1 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.647058823529413%\"\u003e\n \u003cp\u003e0.46 (0.40-0.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.243697478991596%\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.80672268907563%\"\u003e\n \u003cp\u003eO.46 (0.39-0.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.084033613445378%\"\u003e\n \u003cp\u003e\u0026lt; 0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"13.445378151260504%\"\u003e\n \u003cp\u003ePreviously treated cases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.22689075630252%\"\u003e\n \u003cp\u003e255 (18.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.546218487394958%\"\u003e\n \u003cp\u003e1135(81.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.647058823529413%\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.243697478991596%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.80672268907563%\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.084033613445378%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*Statistically significant (p\u0026lt;0.05)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFactors Associated with RR-MTB infections\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOf the total 1,587, TB confirmed patients, 138 (8.7%) were tested positive for RR-MTB. As shown in Table 3, adjusted for gender, age and TB treatment history, males were 32% [AOR= 0.68, 95% CI= 0.47, 0.96, p=0.032] less likely to be infected by RR-MTB compared to females. Similarly, TB presumptive patients who had no history of previous treatment were 71% less likely to be infected by RR-MTB [AOR, 0.29, 95% CI= 0.202, 0.44, p \u0026lt;0.001] compared to previously treated cases. \u0026nbsp;Whereas, the age group of 18-29 years was 3.08 times [AOR=3.08, 95% CI=1.07, 8.72, p=0.036] more likely to be infected by RR-MTB compared to the age group of 70-98 years \u003cstrong\u003e[Table 3].\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cstrong\u003eTable 3: Prevalence of RR- MTB among adult TB patients by gender, age, treatment history, and HIV status in Central, Northwest and Western Tigray, 2016-2019 (N=1589)\u003c/strong\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"114%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003e\u003cstrong\u003eRR-MTB N (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.587628865979383%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNot RR-MTB N (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.43298969072165%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCOR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.24742268041237%\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.49484536082474%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAOR (95%CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.309278350515465%\"\u003e\n \u003cp\u003e\u003cstrong\u003eP- value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" width=\"49.494949494949495%\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" width=\"50.505050505050505%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003e74 (7.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.587628865979383%\"\u003e\n \u003cp\u003e911( 92.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.43298969072165%\"\u003e\n \u003cp\u003e0.69 (0.48-0.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.24742268041237%\"\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.49484536082474%\"\u003e\n \u003cp\u003e0.68(0.47-0.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.309278350515465%\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003e64 ( 10.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.587628865979383%\"\u003e\n \u003cp\u003e540( 89.4)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.43298969072165%\"\u003e\n \u003cp\u003eRef\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.24742268041237%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.49484536082474%\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.309278350515465%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" width=\"49.494949494949495%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" width=\"50.505050505050505%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003e18-29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003e\u0026nbsp;57 (12.2 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.587628865979383%\"\u003e\n \u003cp\u003e412(87.8 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.43298969072165%\"\u003e\n \u003cp\u003e3.22(1.14-9.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.24742268041237%\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.49484536082474%\"\u003e\n \u003cp\u003e3.08 (1.07-8.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.309278350515465%\"\u003e\n \u003cp\u003e0.036*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003e30-39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003e36 (9 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.587628865979383%\"\u003e\n \u003cp\u003e366(91 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.43298969072165%\"\u003e\n \u003cp\u003e(2.290.80-6.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.24742268041237%\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.49484536082474%\"\u003e\n \u003cp\u003e2.20(0.76-6.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.309278350515465%\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003e40-49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003e14( 5.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.587628865979383%\"\u003e\n \u003cp\u003e261( 94.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.43298969072165%\"\u003e\n \u003cp\u003e1.25(0.4-3.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.24742268041237%\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.49484536082474%\"\u003e\n \u003cp\u003e1.09 (0.35-3.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.309278350515465%\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003e50-59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003e13( 7.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.587628865979383%\"\u003e\n \u003cp\u003e170( 92.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.43298969072165%\"\u003e\n \u003cp\u003e1.8(0.56-5.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.24742268041237%\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.49484536082474%\"\u003e\n \u003cp\u003e1.47(0.46-6.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.309278350515465%\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003e60-69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003e14( 8.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.587628865979383%\"\u003e\n \u003cp\u003e149(91.4 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.43298969072165%\"\u003e\n \u003cp\u003e2.2(0.70-6.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.24742268041237%\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.49484536082474%\"\u003e\n \u003cp\u003e2.16 (0.68-0.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.309278350515465%\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003e70-98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003e9(9.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.587628865979383%\"\u003e\n \u003cp\u003e93( 90.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.43298969072165%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Ref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.24742268041237%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.49484536082474%\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.309278350515465%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" width=\"49.494949494949495%\"\u003e\n \u003cp\u003e\u003cstrong\u003eHIV Status \u0026nbsp;(n= 524)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" width=\"50.505050505050505%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003ePositive\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003e\u0026nbsp;18( 9.9 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.587628865979383%\"\u003e\n \u003cp\u003e163( 89.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.43298969072165%\"\u003e\n \u003cp\u003e1.11( 0.60-2.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.24742268041237%\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.49484536082474%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.309278350515465%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003e31( 9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.587628865979383%\"\u003e\n \u003cp\u003e312( 91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.43298969072165%\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.24742268041237%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.49484536082474%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.309278350515465%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" width=\"49.494949494949495%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTB \u0026nbsp; \u0026nbsp; Treatment History\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" width=\"50.505050505050505%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003eNew cases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003e91(6.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.587628865979383%\"\u003e\n \u003cp\u003e1242(93.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.43298969072165%\"\u003e\n \u003cp\u003e\u0026nbsp;0.33(0.22-0.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.24742268041237%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.49484536082474%\"\u003e\n \u003cp\u003e0.29 (0.202-0.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.309278350515465%\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003ePreviously treated cases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003e46(18.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.587628865979383%\"\u003e\n \u003cp\u003e209(81.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.43298969072165%\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.24742268041237%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.49484536082474%\"\u003e\n \u003cp\u003eRef\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.309278350515465%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*Statistically significant (p\u0026lt;0.05)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 3\u003c/strong\u003e compares the percentage prevalence of MTB and RR-MTB by study years. Accordingly, our study revealed that MTB prevalence significantly decreased from 16.9% in 2016 to 8.1%in 2019 (p\u0026lt;0.001, data not shown). \u0026nbsp;Likewise, the trend of RR-MTB has shown a decline from 14.3% in 2016 to 5.8% in 2019 (p \u0026lt; 0.001) \u003cstrong\u003e[Fig 3].\u003c/strong\u003e The number of MTB increases from 314 in 2019 to 531 in 2019, while \u0026nbsp;RR-MTB decreased from 45 in 2016 to 31 in 2019 \u003cstrong\u003e(Figure 4).\u003c/strong\u003e This increasing number of MTB was owing to the increasing number of TB suspected patients from 2016 - 2019. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4: Comparison of RR-MTB \u0026nbsp; prevalence with other studies\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"101%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"32.6530612244898%\"\u003e\n \u003cp\u003e\u003cstrong\u003eStudy area\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.673469387755105%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAuthors\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.673469387755105%\"\u003e\n \u003cp\u003e\u003cstrong\u003eRR-MTB prevalence (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"32.6530612244898%\"\u003e\n \u003cp\u003eAddis Ababa, Ethiopia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.673469387755105%\"\u003e\n \u003cp\u003eBalew et al. [12]\u003c/p\u003e\n \u003cp\u003eSinshaw et al. [18]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.673469387755105%\"\u003e\n \u003cp\u003e9.9\u003c/p\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"32.6530612244898%\"\u003e\n \u003cp\u003eAmhara region, Ethiopia\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.673469387755105%\"\u003e\n \u003cp\u003eMulu et al. [5]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.673469387755105%\"\u003e\n \u003cp\u003e10.3\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"32.6530612244898%\"\u003e\n \u003cp\u003eSouth Ethiopia, Ethiopia\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.673469387755105%\"\u003e\n \u003cp\u003eHordofa \u0026nbsp;\u0026amp; \u0026nbsp; \u0026nbsp; Adela \u0026nbsp;[13]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.673469387755105%\"\u003e\n \u003cp\u003e3.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"32.6530612244898%\"\u003e\n \u003cp\u003eOromia region, Ethiopia\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.673469387755105%\"\u003e\n \u003cp\u003eMulisa et al. [22]\u003c/p\u003e\n \u003cp\u003eAbebe \u0026nbsp;et al. [2]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.673469387755105%\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003cp\u003e2.2 \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"32.6530612244898%\"\u003e\n \u003cp\u003eTigray \u0026nbsp;region, \u0026nbsp;Ethiopia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.673469387755105%\"\u003e\n \u003cp\u003eTesfay et al. [15]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.673469387755105%\"\u003e\n \u003cp\u003e54.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"32.6530612244898%\"\u003e\n \u003cp\u003eNigeria\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.673469387755105%\"\u003e\n \u003cp\u003eDenue et al. [ 27]\u003c/p\u003e\n \u003cp\u003eUkwamedua [35]\u003c/p\u003e\n \u003cp\u003eIkuabe1 \u0026amp; \u0026nbsp;Ebuuenyi [26]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.673469387755105%\"\u003e\n \u003cp\u003e6.1\u003c/p\u003e\n \u003cp\u003e7.3\u003c/p\u003e\n \u003cp\u003e14.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"32.6530612244898%\"\u003e\n \u003cp\u003eIndia\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.673469387755105%\"\u003e\n \u003cp\u003eRamandeep et al. [31]\u003c/p\u003e\n \u003cp\u003eReddy \u0026nbsp;\u0026amp; Alvarez-uria [29]\u003c/p\u003e\n \u003cp\u003eIngole et al. [30]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.673469387755105%\"\u003e\n \u003cp\u003eI9.9\u003c/p\u003e\n \u003cp\u003e9.2\u003c/p\u003e\n \u003cp\u003e9.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"32.6530612244898%\"\u003e\n \u003cp\u003eSeoul\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.673469387755105%\"\u003e\n \u003cp\u003eKim et al. [21]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.673469387755105%\"\u003e\n \u003cp\u003e8.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"32.6530612244898%\"\u003e\n \u003cp\u003eUganda\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.673469387755105%\"\u003e\n \u003cp\u003eMboowa et al. [23]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.673469387755105%\"\u003e\n \u003cp\u003e3.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"32.6530612244898%\"\u003e\n \u003cp\u003eBangui, \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.673469387755105%\"\u003e\n \u003cp\u003eFarra et al. \u0026nbsp;[24]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.673469387755105%\"\u003e\n \u003cp\u003e42.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"32.6530612244898%\"\u003e\n \u003cp\u003eTogo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.673469387755105%\"\u003e\n \u003cp\u003eDagnra et al. [25]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.673469387755105%\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"32.6530612244898%\"\u003e\n \u003cp\u003eRussia,\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.673469387755105%\"\u003e\n \u003cp\u003eToungoussova et al. [36]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.673469387755105%\"\u003e\n \u003cp\u003e25.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"32.6530612244898%\"\u003e\n \u003cp\u003eBangladesh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.673469387755105%\"\u003e\n \u003cp\u003eRahman et al. [37]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.673469387755105%\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"32.6530612244898%\"\u003e\n \u003cp\u003ePakistan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.673469387755105%\"\u003e\n \u003cp\u003eUllah et al. [28]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.673469387755105%\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"32.6530612244898%\"\u003e\n \u003cp\u003eChina,\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.673469387755105%\"\u003e\n \u003cp\u003eHai et al. [32]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.673469387755105%\"\u003e\n \u003cp\u003e15.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"32.6530612244898%\"\u003e\n \u003cp\u003eZambia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.673469387755105%\"\u003e\n \u003cp\u003eMasenga et al. [38]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.673469387755105%\"\u003e\n \u003cp\u003e5.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"32.6530612244898%\"\u003e\n \u003cp\u003eTigray, Ethiopia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.673469387755105%\"\u003e\n \u003cp\u003eThis study\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.673469387755105%\"\u003e\n \u003cp\u003e8.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Discussion ","content":"\u003cp\u003eAvailability of local epidemiological data on MTB and RR-MTB prevalence and identification of potentially modifiable predisposing factors are essential to design appropriate intervention strategies. An overall prevalence of MTB of 9.7% among suspected patients from which a prevalence of RR-MTB of 8.7% were found. The MTB prevalence (9.7%) in the present study was comparable with previous reports from Addis Ababa, 6.5% [18], the Amhara region, 8% [19],South Africa, 13% [20] and Korea, 13.8% [21]. The prevalence in this study is lower than those conducted in South Ethiopia,16.5% [13], Addis Ababa,15.11% [12], Eastern Ethiopia,19.4% [4], the Oromia region,60.4% [22], 23.2% [5], Uganda, 20.9% [23], Bangui,79.1% [24], Togo, 57%[25], Nigeria, 22.9%[26], 19.1% [27], Pakistan, 59% [28], India, 60% [29], 63.6% [30], 20.3%, [27], \u0026nbsp;81.1% [31], and China, 51.4% [32]. However, the prevalence in this study is higher than previous ones from Addis Ababa, 6% [18].\u003c/p\u003e\n\u003cp\u003ePossible reasons for the variations in MTB prevalence could be due to differences in methodological techniques, study participants, study period, geographical and TB control, and prevention policies. The high TB prevalence reported in other studies [18, 25, 27, 28, 30, 31] could as well be attributed to their study participants who were MDR presumptive patients (relapse, defaulter, lost to follow up and failure). By contrast, TB suspected patients were enrolled in this study. Another possible reason could be the small sample size that they used. In other words, small sampling could generate a higher prevalence rate.\u003c/p\u003e\n\u003cp\u003eHigh prevalence of MTB in other reports [4, 26, 27, 30, 35] compared to the result of this study could be attributed to the difference in the study period (2011- 2014) during which GeneXpert was indicated only for patients with TB/HIV co-infection and presumptive MDR-TB patients. In this study, data was collected from 2016 to 2019, when GeneXpert was adopted for all presumptive TB patients. The ages of the study participants ranged from 18 to 98 years. Of these, patients aged 18-29 years were less infected by TB compared to the other age groups (p=0.037). Though there is no clear-cut for the age group, other studies reported that age groups of 16-30 years [13] are to be less likely to be infected by TB. On the other hand, no association was reported between age and TB infection elsewhere [2, 12, 22, 27]. A study by Mulu et al. (2017) from Amhara region has reported that males were more infected by TB than females [5] which contradicts this study where there was not any association between gender and TB infection. The high MTB prevalence among previously TB treated cases in this study could indicate that the presence of high TB transmission in the community. This again calls for coordinated action to combat the problem in the study region.\u003c/p\u003e\n\u003cp\u003eAs can be seen from \u003cstrong\u003eTable 4,\u003c/strong\u003e the \u0026nbsp;prevalence of RR-MTB (8.7%) among the MTB \u0026nbsp;confirmed \u0026nbsp;cases \u0026nbsp; was similar with \u0026nbsp;previous reports from Addis Ababa, [12,18], Amhara region [5] Nigeria [27,35 ], India [29-31] and \u0026nbsp;Seoul [21]. However, this prevalence was lower than those found in previous studies in Oromia region, [22], Tigray [15], Bangui [24], Togo [25], Nigeria [26], Russia [36], India [31], Bangladesh [37], Pakistan [28] and China [32]. Others have reported lower RR-MTB prevalence in south Ethiopia [13], Oromia region [2, 23] and Zambia \u0026nbsp; [38] [\u003cstrong\u003eTable 4\u003c/strong\u003e].\u003c/p\u003e\n\u003cp\u003eThere are many possible reasons for the variation in RR-MTB reports. For example, differences in geographical, methodology (sample size, method of diagnosis, study participants), study setting, study period, and TB control practice could be among the reasons. The high RR-MTB prevalence reported by [22, 23, 25] could be due to the fact that their study participants were previously TB positive (i.e., relapse, defaulter, lost to follow up, or failure) and had a history of MDR contacts which put them at a higher risk to develop MDR-MTB whereas this study included \u0026nbsp;presumptive TB \u0026nbsp;patients .\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;The high RR-MTB in studies conducted in Somali region [4], Pakistan [28], Bangladesh [37], India, [27], and Togo [25] compared to our results might be due to the temporal difference. Their studies were from 2011 to 2014, when Gene Xpert assay was used for patients with presumptive MDR-TB patients. By contrast, in this study, data was collected from records of patients who visited the hospitals from 2016 to 2019, and the method was used for all TB suspected patients.\u003c/p\u003e\n\u003cp\u003eAge groups of 18-29 years were more likely to be infected by RR-MTB. Others reported 0-20 years and 61-80 years [35]. The high prevalence of RR-MTB among confirmed TB cases among the productive age group (18-29 years) may indicate the circulation of resistant strain of MTB in the community. Given this age has a tendency to travel from place to place and communicate with other people (like school communities), more attention to combat the problem sounds critical. Regarding the association between RR-MTB and gender, females were significantly infected by RR-MTB compared to males (p= 0.032).\u0026nbsp;These results are\u0026nbsp;consistent with other reports [12, 36].\u0026nbsp;Other studies reported more RR-MTB infections among males than females [15, 27, 35]. The higher prevalence of RR-MTB in females reported in this study could be due to the poor knowledge of females about TB transmission and control[40], poor health-seeking behavior, and hence delay in detection in females [3,39]. These may help the bacteria to disseminate to the household members (such as children) as mothers are more responsible for giving care for the children and have more close contacts.\u003c/p\u003e\n\u003cp\u003eThe other independent predictor for RR-MTB was found to be previous TB treatment, which was supported by many similar studies [12, 22, 27, 36]. The high prevalence of rifampicin resistance-MTB among previously treated patients highlights for more concerted effort of the regional government and stakeholders to improve the monitoring of TB treatment and thereby reduce the emergence of circulating drug-resistant TB strains in the community.\u003c/p\u003e\n\u003cp\u003eThe prevalence of TB and RR-MTB were compared by the study years. Accordingly, it was revealed that TB prevalence among TB presumptive patients significantly decreased from 16.9% in 2016 to 8.1 % in 2019.\u0026nbsp;Similarly, the RR-MTB among the TB confirmed cases indicated a significant decrease in trend (from 14.3 % in 2016 to 5.8% in 2019).The overall declining trend of MTB and RR-MTB in the region might indicate that the implementation of the policies on TB diagnosis and treatment by the regional government and stakeholders are in the right direction. The significant increase in tests done per year could also be a factor in decreasing the prevalence.\u003c/p\u003e\n\u003cp\u003eDespite this decreasing trend of TB among TB presumptive patients, the prevalence still calls for more efforts to be exerted to reduce the morbidities and mortalities associated with MTB. The strong association of rifampicin resistance-MTB with patients having a history of previous treatment (relapse, failure, and lost to follow up) implies the need for evaluating and monitoring the existing directly observed treatment, short-course TB treatment services of the health of the health facilities. This, in turn, helps to intervene and minimize the magnitude of further emergence of drug-resistant MTB strains in the community.\u003c/p\u003e\n\u003cp\u003eThe strength of this study was that it is a multicenter health facility-based study in the region with a large sample size that can complement and give latest data on the prevalence of TB and RR-MTB for the regional and national governments. However, the study was not devoid of limitations. First, as we examined in a single region in Ethiopia, the economic and regional disparities limited the generalizability of the results to a national level. Second, we were not able to do microbiological confirmation of tuberculosis, phenotypic rifampicin resistance, and resistance to other anti-TB drugs because of the retrospective nature of the study. Third, retrospective data provided little information on the contact history of MDR-TB and TB, education, and living conditions of patients. Fourth, given the data was only from four hospitals, results may not be generalizable to the region. Fifth, the higher number of patients with unknown HIV status did not allow us to see the association of MTB and RR-MTB with HIV.\u003c/p\u003e"},{"header":"Conclusion ","content":"\u003cp\u003eThe overall prevalence of MTB was 9.7% among TB suspected patients, and the prevalence of RR-MTB from among the MTB positive was 8.7%. \u0026nbsp;Those aged greater than 29 years and who have no histories of previous treatment were associated with lower TB infection. While males and new cases were associated with lower RR-MTB, participants in the age groups of 18-29 years were associated with higher RR-MTB. Overall, the prevalence of TB and RR-MTB during the study period showed a decline. Even so, it showed the need for more work in order to minimize TB and RR-MTB associated morbidities and mortalities in the study area. Besides, as patients having a history of previous treatment were infected with rifampicin resistant-MTB, evaluation, and monitoring of the directly observed treatment, short TB treatment services in the region need more attention.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eHIV: Human immunodeficiency virus, MDR-TB: Multidrug-resistant tuberculosis, RR-MTB: Rifampicin resistant mycobacterium tuberculosis, TB: Tuberculosis, WHO: World Health Organization\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical consideration \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical clearance was obtained from Aksum University, College of Health Sciences Institutional Review Board (IRB). Besides, a letter of cooperation was written from the Tigray Regional Health Bureau (THRB) to each study hospital, and permission was obtained accordingly. As the study was a retrospective type, we did not get informed consent and assent from the study participants. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of Data and Materials\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data sets used and analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe declare that we have no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTA, AGW, and GG designed the study.\u003c/p\u003e\n\u003cp\u003eAGW worked on the analysis and interpretation of the data and prepared the draft manuscript.\u003c/p\u003e\n\u003cp\u003eTA, AGW, and GG prepared the final manuscript for publication. All authors read and approved the final paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank all the hospital directors and laboratory staff of the study hospitals for their cooperation in giving us access to the records to extract the data.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eWHO. Global Tuberculosis Report: 2019. \u003c/li\u003e\n \u003cli\u003eAbebe G, Abdissa K, Abdissa A, Apers L, Agonafir M, Bouke C, et al. Relatively low primary drug resistant tuberculosis in southwestern Ethiopia. BMC Research Notes, 2012,5: 1. doi:10.1186/1756-0500-5-225\u003c/li\u003e\n \u003cli\u003eStorla DG, Yimer S, Bjune GA. A systematic review of delay in the diagnosis and treatment of tuberculosis. BMC Public Health. 2008,9: 1\u0026ndash;9. doi:10.1186/1471-2458-8-15\u003c/li\u003e\n \u003cli\u003eGeleta DA, Megerssa YC, Gudeta AN, Akalu GT. 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Sci Rep. 2018,8: 1\u0026ndash;10. doi:10.1038/s41598-018-25888-6\u003c/li\u003e\n \u003cli\u003eUkwamedua H, Omote V, Etaghene J, Ejike M, Celia I, Agbroko H. Heliyon Rifampicin resistance among noti fi ed pulmonary tuberculosis ( PTB ) cases in South-Southern Nigeria. Heliyon. Elsevier Ltd, 2019,5: e02096. doi:10.1016/j.heliyon.2019.e02096\u003c/li\u003e\n \u003cli\u003eToungoussova S1, Caugant DA, Sandven P, Mariandyshev AO BG. Drug resistance of Mycobacterium tuberculosis strains isolated from patients with pulmonary tuberculosis in Archangels, Russia. Int J Tuberc Lung Dis. 2002,6: 406\u0026ndash;14. \u003c/li\u003e\n \u003cli\u003eRahman A, Sahrin M, Afrin S, Earley K, Ahmed S. Comparison of Xpert MTB / RIF Assay and GenoType MTBDR plus DNA Probes for Detection of Mutations Associated with Rifampicin Resistance in Mycobacterium tuberculosis. PLoS One. 2016,11: 1\u0026ndash;11. doi:10.1371/journal.pone.0152694\u003c/li\u003e\n \u003cli\u003eMasenga SK, Mubila H, Hamooya BM. Rifampicin resistance in mycobacterium tuberculosis patients using GeneXpert at Livingstone Central Hospital for the year 2015 : a cross sectional explorative study.BMC Infectious Diseases, 2017,17: 1\u0026ndash;4. doi:10.1186/s12879-017-2750-9\u003c/li\u003e\n \u003cli\u003eAbebe G, Deribew A, Apers L, Woldemichael K, Shiffa J, Abdissa A, et al. Knowledge , Health Seeking Behavior and Perceived Stigma towards Tuberculosis among Tuberculosis Suspects in a Rural Community in Southwest Ethiopia. PLoS One. 2010,5: 1\u0026ndash;7. doi:10.1371/journal.pone.0013339\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Rifampicin resistance, MDR-TB, Xpert MTB/RIF assay, Tigray, Ethiopia","lastPublishedDoi":"10.21203/rs.3.rs-527048/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-527048/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Tuberculosis (TB) is the second leading cause of mortality from an infectious disease worldwide. Multidrug-resistant tuberculosis (MDR-TB), where rifampicin-resistant TB is the most contributor, remains a global health threat. There is scant data on MTB and rifampicin resistance (RR-MTB) using Gene Xpert MTB/RIF assay in Ethiopia, particularly in the study area. This study aimed at determining the prevalence of MTB and RR-MTB among presumptive TB patients in Tigray, Northern Ethiopia.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e A multi-center retrospective cross-sectional study was conducted from October 2019 to December 2019 among presumptive MTB patients from four governmental hospitals in Tigray regional state. Records of sputum sample results of presumptive MTB patients with Gene Xpert MTB/RIF assay from January 2016 to December 2019 were investigated. Data-extraction tool was used to collect data from registration books and analyzed using SPSS ver.21 statistical software. Statistically significant was set at P-value ≤ 0.05. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Out of the total 17,329 presumptive adult MTB patients who had submitted sputum samples for TB diagnosis, 16,437 (94.9%) had complete data and were included in the study.\u0026nbsp;More than half (60.2%) of them were males and the age of the patients ranged from 18-98 years, with a mean age of 44.2 (±16.4 SD) years. The majority, 15,047(91.5%) and 11,750 (71.5%) of the participants were new cases and with unknown HIV status, respectively.\u0026nbsp;Prevalence of MTB was 9.7% (95% CI: 9.2-10.2%) of these, rifampicin resistant-MTB was 8.7% (95% CI: 7.32-10.09%). Age (being greater than 29 years) [p \u0026lt; 0.001] and new cases [AOR= 0.46; 95%CI = 0.39, 0.53, p \u0026lt; 0.001] were associated with low TB infection. Whereas age groups of 18-29 years were associated with higher RR-MTB [AOR= 3.08; 95% CI= 1.07, 8.72, p = 0.036]. Gender (being male) [AOR= 0.68; 95 % CI= 0.47, 0.96, p= 0.032] and having no history of previous treatment [AOR= 0.29; 95 % CI= 0.202, 0.44, p \u0026lt; 0.001] were associated with lower RR-MTB. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eNearly one-tenth of the presumptive tuberculosis patients tested positive for MTB; out of those, 8.7% were positive rifampicin-resistant-MTB. The high prevalence of TB and RR-MTB at a young age and previously treated cases calls for a concerted effort to improve and monitor TB treatment to reduce the problem.\u003c/p\u003e","manuscriptTitle":"Prevalence of Mycobacterium Tuberculosis and Rifampicin-Resistant Tuberculosis Among Tuberculosis Presumptive Adults in Northern Ethiopia, 2016-2019","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-06-15 16:12:22","doi":"10.21203/rs.3.rs-527048/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e8eacfd6-d8f6-4515-9262-f4670f61eb74","owner":[],"postedDate":"June 15th, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":4997186,"name":"Infectious Diseases"},{"id":4997187,"name":"Health Policy"}],"tags":[],"updatedAt":"2021-06-15T16:12:23+00:00","versionOfRecord":[],"versionCreatedAt":"2021-06-15 16:12:22","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-527048","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-527048","identity":"rs-527048","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}