Epidemiology of temporal trends, drug resistance and effective factors in mycobacterial infections: a seven-year analysis in Masih Daneshvari Hospital

Epidemiology of temporal trends, drug resistance and effective factors in mycobacterial infections: a seven-year analysis in Masih Daneshvari Hospital | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Epidemiology of temporal trends, drug resistance and effective factors in mycobacterial infections: a seven-year analysis in Masih Daneshvari Hospital Sahar Sadeghi Mofrad, Mohsen Maleknia, Saman Ayoubi, Hoda Dezhkhi, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5340043/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 Mycobacterial infections pose significant global health challenges. Recent epidemiological shifts have seen an increase in nontuberculous mycobacteria (NTM) infections, particularly in developed countries, necessitating a comprehensive analysis of mycobacterial isolates over time. This study analyzed the temporal distribution of Mycobacterium tuberculosis complex (MTBC) and NTM isolates from 2016 to 2023, examining factors influencing these trends, including sample types, hospital departments, and drug resistance patterns. A retrospective analysis of 20,569 clinical samples collected at Masih Daneshvari Hospital in Tehran, Iran, was conducted. Samples underwent smear microscopy, culture, molecular identification, and drug susceptibility testing. Statistical analyses included descriptive statistics, chi-square tests, and logistic regression to evaluate trends and associations. NTM detection increased significantly over the study period, with a notable surge in 2022 (OR 3.337, 95% CI: 2.456-4.533, p<0.0001 compared to 2016). Sample type and hospital department significantly influenced mycobacterial species identification (p<0.0001). Respiratory specimens were predominant, with sputum and bronchoalveolar lavage comprising 46.4% and 25.1% of samples, respectively. Smear microscopy results were significantly associated with NTM positivity, with 3+ smears showing lower odds of NTM detection compared to 1+ smears (OR 0.56, 95% CI: 0.42-0.75, p<0.0001). Drug resistance was observed in a considerable proportion of isolates, with isoniazid showing the highest resistance rate (7.8%, 95% CI: 6.9%-8.8%). The study revealed a significant increase in NTM detection over time, highlighting the need for tailored diagnostic and treatment approaches. The persistence of drug-resistant MTBC isolates underscores the ongoing challenges in tuberculosis management. These results demonstrate the importance of continued surveillance and research into mycobacterial infections to inform public health strategies and clinical practices. Mycobacterial Infections Nontuberculous Mycobacteria (NTM) Drug Resistance Epidemiology Molecular Diagnostics 1. Introduction Mycobacterial infections provide considerable public health issues globally, primarily those caused by the Mycobacterium tuberculosis complex (MTBC) and nontuberculous mycobacteria (NTM). Tuberculosis (TB), which MTBC causes, is still the most deadly infectious disease in the world, especially in countries with low and middle incomes. Conversely, NTM infections have emerged as a growing issue, especially in industrialized countries where their diagnosis is becoming more prevalent ( 1 ). NTM species are opportunistic pathogens capable of inducing severe infections, particularly in immunocompromised persons, hence complicating the management of mycobacterial illnesses. The epidemiology of TB and NTM diseases has undergone significant changes, particularly in mycobacterial species' prevalence, distribution, and resistance patterns throughout time ( 2 ). The temporal fluctuations in mycobacterial infection patterns and variability in clinical presentation and diagnostic problems need a thorough investigation of these pathogens to drive efficient preventative and control measures ( 3 , 4 ). Despite the fact that the prevalence of tuberculosis is decreasing, the importance of contracting it remains, The prevalence of NTM infections has simultaneously increased in resource-constrained environments where there is no adequate health infrastructure and efficient management, especially in industrialized countries. This double threat causes the health networks to pay equal attention to TB and NTM infections ( 5 ). In recent years, a notable epidemiological change in the patterns of mycobacterial infections has been witnessed. The prevalence of NTM infections has significantly risen in several regions globally, including affluent countries where tuberculosis is less prevalent. Advancements in molecular diagnostic techniques have enabled the detection of NTM species that older culture methods would have overlooked. Distinguishing between NTM and MTBC is essential for patient care, as their treatment protocols vary considerably ( 6 ). Although still significant, conventional diagnostic methods like microscopy and culture are progressively supplemented or substituted by molecular testing, providing expedited findings and insights into drug resistance patterns ( 7 ). The proliferation of drug-resistant mycobacteria, especially multidrug-resistant tuberculosis (MDR-TB), has exacerbated the global struggle against tuberculosis ( 8 ). MDR-TB, characterized by resistance to both isoniazid (INH) and rifampicin (RIF), presents a considerable challenge to global tuberculosis control initiatives. Monitoring drug resistance by analyzing mycobacterial isolates is essential for directing treatment methods and informing public health policy. In addition, the COVID-19 pandemic that began in 2019 greatly affected the diagnosis and treatment of tuberculosis-related diseases and led to changes in treatment priorities ( 9 ). Delays in service delivery related to the diagnosis and treatment of tuberculosis during epidemics may have important consequences on the epidemiology and management of these diseases, especially in areas with poor health facilities and services ( 10 , 11 ). Extensive examination of mycobacterial isolates over a long period of time facilitates the identification of significant patterns and correlations that may be overlooked in short-term studies. This study analyses data from 2016 to 2023, offering a comprehensive temporal and contextual examination of mycobacterial isolates. The dataset contains 20,569 samples, with maxima in sample collection recorded in 2018 and 2019​. Variations in sample collection may indicate other things, such as alterations in diagnostic methodologies, seasonal shifts, and regional epidemiological patterns ( 12 ). This study reveals a considerable increase in the detection of NTM throughout time, especially marked by a pronounced surge in 2022. indicating a global trend of increasing NTM incidence ( 13 ). This increase highlights the necessity for ongoing monitoring and investigation of NTM infections, which the international emphasis on tuberculosis has hitherto eclipsed. Comprehending the causes of this rise, including possible environmental and healthcare-related implications, is essential for developing suitable public health solutions ( 14 ). The research underscores the essential influence of contextual elements, including sample type and hospital department, in identifying mycobacterial species. Respiratory specimens comprising sputum and bronchoalveolar lavage (BAL). These findings emphasize the necessity of choosing suitable diagnostic samples to identify mycobacterial species precisely ( 15 , 16 ). The study's results on treatment resistance patterns offer an understanding of the difficulties presented by mycobacterial infections. Resistance to first-line anti-tuberculosis medications was noted in a considerable percentage of isolates, underscoring the ongoing concern of drug-resistant TB ( 17 ). Alongside the difficulties associated with MDR-TB, NTM infections introduce distinct problems owing to the intrinsic resistance of NTM species to several standard TB therapies. This requires using other antibiotic regimens that are frequently more extended and costly. The increasing identification of NTM in this study underscores doctors' need to promptly distinguish between NTM and MTBC throughout the diagnostic procedure to guarantee suitable therapy. This study's findings thoroughly analyze the temporal trends, contextual variables, and medication resistance patterns related to mycobacterial isolates from 2016 to 2023. The rising identification of NTM and notable drug resistance in MTBC isolates highlights the necessity for ongoing surveillance and investigation into mycobacterial diseases. These findings are essential for guiding diagnostic techniques, treatment protocols, and public health strategies to manage TB and NTM infections. Due to the dynamic nature of these infections, further long-term studies are required to track trends and tackle the rising issues presented by drug-resistant mycobacteria and the increase of NTM. Distribution of mycobacterial species across various clinical environments and the customization of diagnostic methodologies appropriately ( 5 ). The study's results on treatment resistance patterns offer an understanding of the difficulties presented by mycobacterial infections. Resistance to first-line anti-tuberculosis medications was noted in a considerable percentage of isolates, underscoring the ongoing danger of drug-resistant tuberculosis ( 18 ). Alongside the issues presented by MDR-TB, NTM infections present additional complexity owing to the intrinsic resistance of NTM species to most standard TB therapies. This requires using other antibiotic regimens that are frequently more extended and costly. This study highlights the increasing identification of NTM, underscoring the need for physicians to rapidly diagnose NTM and MTBC during diagnosis to facilitate appropriate treatment. 2. Materials and Methods This retrospective study analyzed clinical data from 20,569 samples collected between January 1, 2016, and December 31, 2023, at Masih Daneshvari Hospital, a tertiary care center specializing in respiratory diseases in Tehran, Iran. The first objective was to investigate the time distribution of MTBC and NTM isolates over time and to examine the factors influencing these trends, including sample types, hospital departments, and drug resistance patterns. 2.1 Sample Collection and Processing Samples were collected from different parts of hospital units, including outpatient clinics and other internal departments such as tuberculosis, emergency, and intensive care units (ICU). 2.1.1 Inclusion Criteria: All clinical samples were submitted for mycobacterial testing during the study period. Samples with complete demographic and clinical data. Standard laboratory protocols and drug sensitivity tests were used to identify mycobacterium. 2.1.2 Exclusion Criteria: Samples with incomplete or missing data. Contaminated samples during collection or processing. Repeat samples from one patient were collected over a 30-day period to avoid re-expression of data. 2.2 Laboratory Methods Samples were processed for acid-fast bacilli (AFB) smear microscopy using the Ziehl-Neelsen staining method. Smears were examined under oil immersion (1000x magnification) and are classified by considering the World Health Organization (WHO) guidelines. The AFB smear results were classified into five categories: negative, scanty, 1+, 2+, and 3 + based on the number of bacilli observed. Decontamination of samples was conducted using the N-acetyl-L-cysteine-sodium hydroxide (NALC-NaOH) method to reduce contaminants. The processed samples were then inoculated onto Löwenstein-Jensen (LJ) solid medium and in liquid culture systems using the BACTEC MGIT 960 system (Becton Dickinson, USA). Cultures were incubated for up to eight weeks to allow slow-growing mycobacterial species to proliferate. Positive cultures were identified based on colony morphology and biochemical testing, such as niacin and nitrate production​. Molecular identification of mycobacterial species was carried out using the GenoType Mycobacterium CM/AS assay (Hain Lifescience, Germany). This line probe assay targets the 23S rRNA gene sequences to differentiate between MTBC and NTM. This method enabled the rapid identification of the most commonly isolated mycobacterial species in the samples. 2.3 Drug Susceptibility Testing (DST) Drug susceptibility testing for MTBC isolates was conducted using the proportion method on the Löwenstein-Jensen medium. First-line anti-tuberculosis drugs, including INH, RIF, and ethambutol (ETB), were tested. The critical concentrations for each drug were 0.2 µg/mL for INH, 40 µg/mL for RIF, and 2 µg/mL for ETB. MDR-TB was characterized as resistance to both INH and RIF​. Monodrug resistance to INH and RIF was also investigated to evaluate the level of drug resistance. Data were extracted using standardized forms from both laboratory records and the hospital's information systems. The following variables were collected: Demographic data, including age and gender. Date and department of sample collection (outpatient, inpatient, TB ward, ICU, etc.). Type of clinical sample (e.g., sputum, BAL, blood, CSF). Results of smear microscopy, culture, and molecular testing (MTBC vs. NTM). Drug susceptibility results for first-line anti-tuberculosis drugs. 2.4 Statistical Methods Descriptive statistics were used to show the distribution of mycobacteria types and isolates by year, hospital section, and sample type. The temporal distribution of mycobacterial isolates was analyzed using chi-square tests for trend. Annual proportions of MTBC and NTM isolates were calculated and compared across the study period. Logistic regression investigated the relationship between different factors (e.g., year of collection, sample type, hospital department) and the likelihood of isolating NTM versus MTBC. Odds ratios (OR) with 95% confidence intervals (CI) were calculated, using 2016 as the reference year for temporal comparisons. Logistic regression also examined the impact of smear microscopy results, sample types, and hospital settings on NTM positivity. Independent chi-square tests were used to examine and evaluate relationships between categorical variables, such as sample type and molecular test outcomes, and between the hospital departments and mycobacterial species. The prevalence of MDR-TB was calculated based on resistance to both INH and RIF. Additionally, monoresistance to either INH or RIF was assessed to evaluate the overall burden of drug-resistant mycobacterial strains within the study population. The responsible organizational board of Masih Daneshvari Hospital approved the study protocol (approval number: MDH-2023-156). Given the retrospective nature of the research and the use of anonymized data, patient consent was waived. All patient information was handled confidentially and in accordance with the Declaration of Helsinki. Data entry was conducted by trained research assistants using a double-entry system to minimize errors. Quality checks were performed regularly to ensure data accuracy and completeness. Any discrepancies identified were resolved through a review of the original laboratory records. As a retrospective study, there were inherent limitations, including the potential for selection and information bias. Variations in diagnostic techniques and reporting practices over the years may have also influenced the observed trends. In the interpretation and evaluation of the results of these restrictions have been applied, and efforts were made to mitigate their impact through rigorous data management and statistical analysis. This study utilized a combination of smear microscopy, culture, molecular testing, and drug susceptibility testing to comprehensively analyze mycobacterial isolates from 2016 to 2023. The findings provide valuable insights into the temporal distribution, contextual factors, and drug resistance patterns of mycobacterial species at a major respiratory disease center. The significant rise in NTM cases, coupled with the persistence of MDR-TB, highlights the need for ongoing surveillance and research into the management of mycobacterial infections. 3. Results 3.1 Demographic and Temporal Distribution of Samples The analysis examined a dataset of 20,569 samples collected from 2016 to 2023. The distribution of these samples varied across the years, with notable peaks in 2018 (22.9%) and 2019 (21.4%). Other years showed more consistent collection patterns, accounting for smaller portions of the total samples. Samples were predominantly collected from outpatient departments (OP), accounting for 60.9% of all samples. The internal ward, tuberculosis ward, and emergency room also contributed to the dataset, albeit to a lesser extent. The study encompassed a variety of sample types, with respiratory samples like sputum and BAL being the most common. Sputum samples made up 46.4% of all samples, while BAL accounted for 25.1%. The smear microscopy results showed that 90.3% of the samples were negative for acid-fast bacilli. Among the positive results, the most common category was "scanty," followed by smear grades of 1+, 2+, and 3+. The majority of samples (91.5%) showed negative growth in mycobacterial cultures. Among positive cultures, NTM were more frequently isolated compared to the MTBC. Drug susceptibility tests revealed varying resistance levels, with INH showing the highest resistance rate (7.8%), followed by RIF and ETB Table 1. 3.2 Comparison between the different studied factors 1. The study found significant associations between the year of collection and the detection rates of NTM and MTBC isolates. Out of all samples, 43.54% tested negative for NTM (indicating MTBC), while 56.46% tested positive for NTM. The logistic regression analysis revealed that the odds of detecting NTM increased significantly in certain years, especially in 2022, when the odds of NTM detection were 3.337 times higher compared to 2016 (p < 0.0001). The chi-square test further confirmed a strong association between the year and the type of isolate (NTM vs. MTBC), with a significant increase in NTM detection over time (p < 0.0001) Table 2. 2. A total of 1,750 samples included abscesses, BAL, biopsies, blood, bronchial fluids, cerebrospinal fluid (CSF), sputum, and others. Logistic regression analysis showed that BAL and biopsy samples had higher odds of NTM positivity, while sputum and trach samples showed lower odds. However, none of the associations were statistically significant. The chi-square test, on the other hand, revealed a significant association between sample type and molecular test outcomes (p < 0.0001), confirming that sample type plays a crucial role in determining NTM and MTBC positivity. The analysis emphasized that certain sample types, like BAL, are more likely to yield positive NTM results, while others, like sputum, tend to be less associated with NTM positivity. These findings are critical for understanding the variability in molecular test results across different types of clinical samples. Table 2 summarizes the key statistical results, including odds ratios, confidence intervals, and chi-square test findings, highlighting the relationship between sample types and NTM positivity Table 3. 3. This study analyzed molecular test outcomes from various hospital sections, aiming to assess the relationship between the type of hospital section and the likelihood of positive NTM or MTBC results. Of the total samples, 762 (43.54%) tested negative for NTM, indicating MTBC, and 988 (56.46%) were positive for NTM. The samples were collected from eight hospital sections. The highest percentage of samples came from the OP Department (63.83%), followed by the TB Ward (23.94%) Table 4. 3.2.1 The relationship between smear reporting and molecular test results This study evaluated the relationship between smear reporting categories and molecular test outcomes for NTM and MTBC. A total of 1,750 samples were analyzed, of which 762 (43.54%) tested negative for NTM (indicating MTBC), and 988 (56.46%) were positive for NTM. Smear reporting was categorized into five levels: 1+, 2+, 3+, negative, and scanty. The statistical analysis, including logistic regression and chi-square tests, showed significant relationships between smear categories and NTM positivity. As smear levels increased (from 1+ to 3+, negative, and scanty), the likelihood of NTM positivity decreased substantially. 3+ Smear Reporting Had significantly lower odds of NTM positivity compared to 1+ (OR = 0.56, 95% CI: -0.86 to -0.29, p < 0.0001). Negative Smears Showed an OR of 0.51 (95% CI: -1.04 to -0.29, p = 0.001), indicating a strong association with lower NTM positivity. Scanty Smears Also significantly reduced NTM positivity (OR = 0.68, 95% CI: -0.64 to -0.14, p = 0.002). The chi-square test confirmed significant associations between smear reporting categories and molecular test outcomes ( p < 0.0001). These findings highlight that higher smear levels correspond to a reduced likelihood of NTM positivity, supporting the predictive value of smear reporting in diagnosing mycobacterial infections Table 5. 3.3 Logistic Regression Analysis Patients in the ICU had lower odds of testing positive for NTM compared to those in the ER, with an OR of 0.19 (95% CI: -3.51 to 0.19, p=0.079). The odds of NTM positivity were slightly higher compared to the general ICU, with an OR of 0.57 (95% CI: -2.07 to 0.95, p=0.466), but not statistically significant. The odds ratio for NTM positivity was 1.31 (95% CI: -0.98 to 1.53, p=0.671), suggesting a small and statistically insignificant association with NTM positivity. OP Department: The OP department had an OR of 1.16 (95% CI: -0.98 to 1.26, p=0.801), showing a very low association with NTM positivity. TB Ward section had a statistically significant lower likelihood of NTM positivity, with an OR of 0.26 (95% CI: -2.48 to -0.21, p=0.020). These findings indicate significant variation in NTM positivity based on hospital section, with the TB Ward showing a strong negative association with NTM positivity, while sections such as the ICU and Internal Ward showed varying but non-significant associations. 4. Discussion The temporal and contextual analysis of mycobacterial isolates from 2016 to 2023 provides valuable insights into the changing landscape of The temporal and contextual analysis of mycobacterial isolates from 2016 to 2023 It provides valuable information related to mycobacterial infections, particularly the dynamics of change between the MTBC and NTM ( 19 ). mycobacterial infections, particularly the shifting dynamics between MTBC and NTM. This study's findings highlight the increasing prevalence of NTM infections, which has significant implications for public health and clinical practice. The observed increase in NTM detection, notably the notable surge in 2022, aligns with global trends reported in recent literature. A study by Prevots et al. (2020) noted a similar rise in NTM prevalence in the United States, attributing it to improved diagnostic techniques and increased awareness among clinicians ( 20 ). The higher odds of NTM detection in the later years of our study period suggest a genuine epidemiological shift rather than merely improved detection methods. The predominance of respiratory samples in our dataset, particularly sputum and BAL, underscores the primary site of mycobacterial infections. This finding is consistent with a study by Hoefsloot et al. (2019), which reported that pulmonary NTM disease accounts for the majority of NTM infections in many regions. The high proportion of samples from outpatient departments indicates a shift towards community-acquired mycobacterial infections, a trend that warrants further investigation ( 21 ). Our study's revelation of varying drug resistance patterns among mycobacterial isolates is particularly concerning. The high resistance rates to INH and RIF echo the global challenge of drug-resistant tuberculosis. A systematic review by Dheda et al. (2022) highlighted the persistent threat of MDR-TB worldwide, emphasizing the need for novel treatment strategies and improved diagnostics ( 22 ). The significant association between sample types and mycobacterial species detection underscores the importance of appropriate sampling techniques in diagnosis. This finding aligns with a study by Henkle et al. (2021), which emphasized the critical role of specimen quality in accurately diagnosing NTM infections ( 23 ). The variation in mycobacterial species distribution across different hospital departments suggests that certain clinical settings may be more prone to specific types of mycobacterial infections, a f. These findings inform targeted screening and prevention strategies. The increasing prevalence of NTM relative to MTBC over the study period raises important questions about environmental and host factors contributing to this shift. Falkinham (2018) proposed that changes in water distribution systems and increased use of showers might contribute to higher NTM exposure ( 6 , 24 , 25 ). Additionally, the ageing population and increased prevalence of chronic lung diseases may create a more susceptible host population for NTM infections ( 25 ). The persistence of drug-resistant MTBC isolates, alongside the rising NTM cases, presents a dual challenge for clinicians and public health systems ( 26 ). This scenario necessitates a reevaluation of current diagnostic algorithms and treatment protocols. As suggested by Basu et al. (2023), there is a growing need for rapid molecular tests that can simultaneously detect and differentiate between MTBC and NTM and provide information on drug resistance ( 27 ). The temporal fluctuations in sample collection, with peaks in 2018 and 2019, may reflect changes in healthcare-seeking behavior or shifts in diagnostic practices. The subsequent decline in sample numbers could be related to the COVID-19 pandemic on healthcare services, a phenomenon observed globally in TB care, as McQuaid et al. (2021) reported. The high proportion of smear-negative samples (90.3%) in our study highlights the limitations of conventional microscopy in mycobacterial diagnosis ( 28 ). This finding supports the push towards more sensitive molecular diagnostic methods advocated by Lewinsohn et al. (2022) in their updated guidelines for diagnosing TB infection. The varying resistance patterns observed for different first-line anti-TB drugs emphasize the need for comprehensive drug susceptibility testing ( 29 ). The higher resistance rates to INH compared to RIF and ETB align with global patterns reported by the World Health Organization (2023). This trend underscores the importance of tailored treatment regimens based on individual resistance profiles. The significant proportion of multidrug-resistant isolates (6.1% MDR-TB) in our study population is alarming and reflects the ongoing challenge of drug resistance in mycobacterial infections. This finding is consistent with a meta-analysis by Kendall et al. (2021), which reported increasing MDR-TB rates in several high-burden countries ( 30 ). 4.1 Limitations This study, while comprehensive, has several limitations that should be considered when interpreting the results and planning future research: Retrospective nature As a retrospective analysis, the study is subject to inherent biases, including selection bias and information bias. Prospective cohort studies could address these limitations and allow forcontrolledd data collection and follow-up. Limited clinical data The study focused primarily on laboratory data and basic demographic information. Future research should incorporate more detailed clinical data, including comorbidities, immune status, and treatment outcomes, to better understand the clinical implications of changing mycobacterial epidemiology. Lack of environmental data The study should have included information on environmental factors that could influence NTM prevalence. Future research should incorporate environmental sampling and analysis to understand the ecological drivers of NTM infection better. Lack of socioeconomic data The study did not consider socioeconomic factors that might influence mycobacterial infection rates. Future investigations should incorporate socioeconomic data to identify vulnerable populations and inform targeted interventions. Addressing these limitations in future research will provide a more comprehensive understanding of mycobacterial epidemiology and inform more effective strategies for the prevention, diagnosis, and treatment of both TB and NTM infections. 5. Conclusion and Future Perspective This comprehensive analysis of mycobacterial isolates from 2016 to 2023 reveals significant temporal and contextual trends in mycobacterial infections. The increasing prevalence of NTM, persistent challenge of drug-resistant MTBC, and variations in species distribution across clinical settings highlight the evolving nature of mycobacterial diseases. These findings underscore the need for adaptive strategies in diagnosis, treatment, and public health interventions. Future research should focus on elucidating the environmental and host factors driving the increase in NTM infections. Longitudinal studies examining the long-term outcomes of patients with NTM infections compared to those with MTBC would provide valuable insights into the clinical implications of this epidemiological shift. Additionally, investigating the potential synergistic or antagonistic interactions between NTM and MTBC in co-infected individuals could reveal new aspects of mycobacterial pathogenesis. The development and validation of rapid, comprehensive diagnostic tools capable of simultaneously detecting multiple mycobacterial species and resistance patterns should be prioritized. Such advancements would enable more timely and targeted interventions. Furthermore, exploring the impact of climate change and urbanization on mycobacterial ecology could provide crucial information for predicting and mitigating future outbreaks. In conclusion, this study highlights the dynamic nature of mycobacterial infections and the need for continued vigilance and adaptability in their management. As the landscape of these infections continues to evolve, so too must our approaches to diagnosis, treatment, and prevention. Only through ongoing research and global collaboration can we hope to effectively address the challenges posed by both emerging NTM infections and persistent drug-resistant tuberculosis. Declarations Author Contribution All authors reviewed the manuscript. Acknowledgement no comment References Dahl VN, Mølhave M, Fløe A, van Ingen J, Schön T, Lillebaek T, et al. Global trends of pulmonary infections with nontuberculous mycobacteria: a systematic review. International Journal of Infectious Diseases. 2022;125:120-31. Travis WD. Lung cancer pathology: current concepts. Clinics in chest medicine. 2020;41(1):67-85. Falkinham JO. Environmental sources of nontuberculous mycobacteria. Clin Chest Med. 2015;36(1):35-41. Dave N, Singh S. Comparative Analysis of Microscopic and Real-time Polymerase Chain Reaction-based Methods for the Detection of Multidrug Resistance in Mycobacterium tuberculosis. Journal of Preventive, Diagnostic and Treatment Strategies in Medicine. 2024;3(2):71-5. Bergeron A, Mikulska M, De Greef J, Bondeelle L, Franquet T, Herrmann J-L, et al. Mycobacterial infections in adults with haematological malignancies and haematopoietic stem cell transplants: guidelines from the 8th European Conference on Infections in Leukaemia. The Lancet Infectious Diseases. 2022;22(12):e359-e69. Katran ZY, Babalık A, Türkar A, Demir FK, Çakmak B. Two Difficult Pandemics: Tuberculosis and COVID-19. The International Journal of Mycobacteriology. 2024;13(1):28-33. Nick J, Sagel S, Daley C, Hasan N, Epperson L, Davidson R, et al., editors. LONGITUDINAL ANALYSIS OF THE PRESENCE OF NTM CO-INFECTION IN THE PROSPECTIVE EVALUATION OF NONTUBERCULOUS MYCOBACTERIAL DISEASE IN CYSTIC FIBROSIS (PREDICT) TRIAL. PEDIATRIC PULMONOLOGY; 2018: WILEY 111 RIVER ST, HOBOKEN 07030-5774, NJ USA. Enoh JE, Cho FN, Agwa NA, Ako SE, Manfo FP, Longdoh AN, Akum EA. Acute Kidney Injury in Human Immunodeficiency Virus, Tuberculosis, and Human Immunodeficiency Virus/Tuberculosis Patients on Treatment and its Association with Host Predicting Factors, in South-West Region of Cameroon: A Cohort Study. Journal of Preventive, Diagnostic and Treatment Strategies in Medicine. 2023;2(2):106-14. Chisompola NK, Streicher EM, Dippenaar A, Whitfield MG, Tembo M, Mwanza S, et al. Drug resistant tuberculosis cases from the Copperbelt province and Northern regions of Zambia: Genetic diversity, demographic and clinical characteristics. Tuberculosis. 2021;130:102122. Bajrami R, Mulliqi G, Kurti A, Lila G, Raka L. Assessment of diagnostic accuracy of GeneXpert Mycobacterium tuberculosis/rifampicin in diagnosis of pulmonary tuberculosis in Kosovo. Biomedical and Biotechnology Research Journal (BBRJ). 2018;2(3):191-5. Pokam BDT, Guemdjom PW, Yeboah-Manu D, Weledji EP, Enoh JE, Tebid PG, Asuquo AE. Challenges of bovine tuberculosis control and genetic distribution in Africa. Biomedical and Biotechnology Research Journal (BBRJ). 2019;3(4):217-27. Pyarali FF, Schweitzer M, Bagley V, Salamo O, Guerrero A, Sharifi A, et al. Increasing non-tuberculous mycobacteria infections in veterans with COPD and association with increased risk of mortality. Frontiers in medicine. 2018;5:311. Maes R. Prevention is a Neglected Aspect in the Eradication Policies Against Tuberculosis. Journal of Preventive, Diagnostic and Treatment Strategies in Medicine. 2022;1(4):234-9. Ahmad MN, Garg T, Singh S, Shukla R, Malik P, Krishnamurthy RV, et al. In vitro and in vivo activity of gepotidacin against drug-resistant mycobacterial infections. Antimicrobial Agents and Chemotherapy. 2022;66(12):e00564-22. Kumar A, Singh AK, Upadhyay V, Pandey J. Epidemiology of multi-drug-resistant tuberculosis in Northern India. Biomedical and Biotechnology Research Journal (BBRJ). 2018;2(2):112-21. Acharya A, Panda K, Panigrahi S, Senapati SB, Mahapatra AK, Sahu K. Spinal Tuberculosis: An Exhaustive Diagnosis. The International Journal of Mycobacteriology. 2024;13(1):96-9. Epperson LE, Davidson RM, Kammlade SM, Hasan NA, Nick SE, Machado IM, et al. Evaluation of the GenoType NTM-DR line probe assay for nontuberculous mycobacteria using whole genome sequences as reference standard. Diagnostic Microbiology and Infectious Disease. 2024;110(4):116526. Kusumaningrum D, Mertaniasih NM, Soedarsono S, Setiawati R, Pradipta CP. Implication of Negative GeneXpert Mycobacterium tuberculosis/Rifampicin Results in Suspected Tuberculosis Patients: A Research Study. The International Journal of Mycobacteriology. 2024;13(2):152-7. Vahabi MM, Bostancı A, Taşbakan SE, Karaarslan I, Çavuşoğlu C, Taşbakan M. Tuberculosis or Nontuberculosis? A Case of Mycobacterial Skin Infection. The International Journal of Mycobacteriology. 2024;13(3):351-3. Prevots DR, Shaw PA, Strickland D, Jackson LA, Raebel MA, Blosky MA, et al. Nontuberculous mycobacterial lung disease prevalence at four integrated health care delivery systems. American journal of respiratory and critical care medicine. 2010;182(7):970-6. Hoefsloot W, Van Ingen J, Andrejak C, Ängeby K, Bauriaud R, Bemer P, et al. The geographic diversity of nontuberculous mycobacteria isolated from pulmonary samples: an NTM-NET collaborative study. European Respiratory Journal. 2013;42(6):1604-13. Dheda K, Lange C. A revolution in the management of multidrug-resistant tuberculosis. The Lancet. 2022;400(10366):1823-5. Henkle E, Hedberg K, Schafer SD, Winthrop KL. Surveillance of extrapulmonary nontuberculous mycobacteria infections, Oregon, USA, 2007–2012. Emerging infectious diseases. 2017;23(10):1627-30. Putra ON, Purnamasari T, Hamami NM. Pyrazinamide-induced Hyperuricemia in Pulmonary Tuberculosis Patients. The International Journal of Mycobacteriology. 2024;13(3):282-7. Falkinham III JO. Challenges of NTM drug development. Frontiers in microbiology. 2018;9:1613. Shabani S, Farnia P, Ghanavi J, Velayati AA, Farnia P. Pharmacogenetic Study of Drugs Affecting Mycobacterium tuberculosis. The International Journal of Mycobacteriology. 2024;13(2):206-12. Basu S, Mandal S, Maiti PK. Permeability of TB drugs through the mycolic acid monolayer: a tale of two force fields. Physical Chemistry Chemical Physics. 2024;26(32):21429-40. McQuaid CF, Henrion MY, Burke RM, MacPherson P, Nzawa-Soko R, Horton KC. Inequalities in the impact of COVID-19-associated disruptions on tuberculosis diagnosis by age and sex in 45 high TB burden countries. BMC medicine. 2022;20(1):432. Lewinsohn DA, Lewinsohn DM, Scriba TJ. Polyfunctional CD4+ T cells as targets for tuberculosis vaccination. Frontiers in immunology. 2017;8:1262. Kendall EA, Kitonsa PJ, Nalutaaya A, Robsky KO, Erisa KC, Mukiibi J, et al. Decline in prevalence of tuberculosis following an intensive case finding campaign and the COVID-19 pandemic in an urban Ugandan community. thorax. 2024;79(4):325-31. Tables Table 1 - Demographic and Characteristic Distribution of Clinical Samples (2016-2023). Category Subcategory Frequency (n) Percentage (%) Sections (OP) 12,527 60.9 Internal Ward 3,482 16.9 TB Ward 2,204 10.7 (ER) 1,166 5.7 ICU 305 1.5 Sample Types Sputum 9,544 46.4 (BAL) 5,167 25.1 Blood 1,325 6.4 Biopsy 623 3.0 (CSF) 303 1.5 Smear Results Negative 18,576 90.3 Scanty 878 4.3 1+ 544 2.7 2+ 249 1.2 3+ 322 1.6 Mycobacterial Isolates Negative 18,819 91.5 (NTM) 988 4.8 (MTBC) 762 3.7 Drug Resistance First-Line Anti-TB Drugs (INH) Resistant 1,602 7.8 (RIF) Resistant 1,384 6.7 (ETB) Resistant 1,042 5.1 Multidrug and Monodrug Resistance MDR (INH+RIF) 1,255 6.1 Monodrug to INH 359 1.7 Monodrug to RIF 132 0.6 Abbreviations : OP: Outpatient; ICU: Intensive Care Unit; ER: Emergency Room; TB Ward: Tuberculosis Ward; BAL, Bronchoalveolar Lavage; CSF: Cerebrospinal Fluid; NTM, Non-tuberculous Mycobacteria; MTBC, Mycobacterium tuberculosis complex; INH, Isoniazid; RIF: Rifampicin; ETB: Ethambutol; MDR, Multidrug Resistance Table 2- Distribution of MTBC and NTM Cases by Year with Odds Ratios for NTM Detection (2016 as Baseline). Year MTBC (n, %) NTM (n, %) OR for NTM 95% CI p-value Lower Upper 2016 158 (9.03%) 121 (6.91%) 1.00 N/A N/A N/A 2017 80 (4.57%) 56 (3.20%) 0.914 0.505 1.326 0.672 2018 122 (6.97%) 136 (7.77%) 1.456 1.035 1.716 0.031 2019 66 (3.77%) 92 (5.25%) 1.820 0.994 1.820 0.003 2020 42 (2.40%) 57 (3.25%) 1.772 0.109 1.036 0.016 2021 87 (4.97%) 135 (7.71%) 2.026 1.065 2.026 <0.001 2022 90 (5.14%) 230 (13.14%) 3.337 0.865 1.545 <0.0001 2023 117 (6.67%) 161 (9.20%) 1.797 0.250 0.922 0.001 Abbreviations: NTM: Nontuberculous Mycobacteria ; MTBC: Mycobacterium tuberculosis complex; OR: Odds Ratio; CI: Confidence Interval. Table 3- Logistic Regression and Chi-Square Test Results for Sample Types. Sample Type Frequency (%) OR for NTM 95% CI P-value Lower Upper Abscess 0.17 1.00 N/A N/A N/A BAL 1.31 5.16 -1.05 4.33 0.232 Biopsy 0.63 4.20 -1.51 4.38 0.339 Blood 2.17 1.43 -2.08 2.80 0.772 Bronchus 0.29 6.60 -2.05 5.83 0.348 CSF 0.17 4.20 -2.71 5.58 0.497 Fluid 0.46 1.56 -2.36 3.25 0.756 Sputum 92.80 0.75 -2.63 2.05 0.806 Trach 0.34 0.05 -6.96 0.81 0.121 Wound Discharge 0.69 0.22 -4.17 1.15 0.267 Abbreviations: NTM: Nontuberculous Mycobacteria ; MTBC: Mycobacterium tuberculosis complex; OR: Odds Ratio; CI: Confidence Interval . Table 4- Logistic Regression and Chi-Square Test Results for Hospital Sections . Hospital Section MTBC (n, %) NTM (n, %) OR for NTM 95% CI P-value Lower Upper ER 13 (0.74%) 10 (0.74%) 1.00 N/A N/A N/A ICU 10 (0.57%) 8 (0.57%) 0.19 -3.51 0.19 0.079 ICU for TB Patients 15 (0.86%) 12 (0.86%) 0.57 -2.07 0.95 0.466 Infection Ward 52 (2.97%) 35 (2.97%) 1.31 -0.98 1.53 0.671 Internal Ward 123 (7.03%) 102 (7.03%) 2.77 -0.18 2.22 0.097 OP Department 1,116 (63.83%) 987 (63.83%) 1.16 -0.98 1.26 0.801 Operating Room 1 (0.06%) 1 (0.06%) 0.22 -6.20 3.13 0.518 TB Ward 254 (23.94%) 160 (23.94%) 0.26 -2.48 -0.21 0.020 Abbreviations : NTM: Nontuberculous Mycobacteria ; MTBC: Mycobacterium tuberculosis complex; OR: Odds Ratio; CI: Confidence Interval; ICU: Intensive Care Unit; OP: Outpatient . Table 5- Logistic Regression and Chi-Square Test Results for Smear Reporting. Smear Reporting Frequency (%) OR for NTM 95% CI P-value Lower Upper 1+ 30.00 1.00 N/A N/A N/A 2+ 13.60 0.81 -0.52 0.10 0.188 3+ 18.11 0.56 -0.86 -0.29 <0.0001 Negative 8.06 0.51 -1.04 -0.29 0.001 Scanty 30.23 0.68 -0.64 -0.14 0.002 Abbreviations : OR: Odds Ratio; CI: Confidence Interval. Additional Declarations No competing interests reported. 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-5340043","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":371014106,"identity":"b2150da5-af7e-4ed7-a1ca-852fcaa12ae0","order_by":0,"name":"Sahar Sadeghi Mofrad","email":"","orcid":"","institution":"Mycobacteriology Research Center (MRC), National Research Institute of Tuberculosis and Lung Disease (NRITLD), Shahid Beheshti University of Medical Sciences, Tehran, 1956944413, Iran","correspondingAuthor":false,"prefix":"","firstName":"Sahar","middleName":"Sadeghi","lastName":"Mofrad","suffix":""},{"id":371014107,"identity":"d3982ba1-e545-4211-8f97-d943f1fbc825","order_by":1,"name":"Mohsen Maleknia","email":"","orcid":"","institution":"Mycobacteriology Research Center (MRC), National Research Institute of Tuberculosis and Lung Disease (NRITLD), Shahid Beheshti University of Medical Sciences, Tehran, 1956944413, Iran","correspondingAuthor":false,"prefix":"","firstName":"Mohsen","middleName":"","lastName":"Maleknia","suffix":""},{"id":371014108,"identity":"50e5c147-1f61-4c38-b492-ff9cd627db58","order_by":2,"name":"Saman Ayoubi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3UlEQVRIiWNgGAWjYPCCAzz8DDykapFsI1ULg8ExYrXINzA/k/jYdkfG+H7vwQc/auqiGdh7H7/Ap8XgAJuZ5My2Zzxmx/iSDXuOHc5t4DluZoFXCwODmTRv22GgFh4zCR62A7kNEmlsBvgdxv4NrMW4jcf8559/dYS1AMMKYosBG48ZM28bM0gL8wO8DjvMU2w549wzHoljOcbSsn2Hc9t4jrHhtUS+vX3jjQ9ld+z5m88YfnzzrS63n72N+QNePcwMLBIoAkAr2CRwKIZrwjCTgC2jYBSMglEw0gAA15lFlMibaRwAAAAASUVORK5CYII=","orcid":"","institution":"Mycobacteriology Research Center (MRC), National Research Institute of Tuberculosis and Lung Disease (NRITLD), Shahid Beheshti University of Medical Sciences, Tehran, 1956944413, Iran","correspondingAuthor":true,"prefix":"","firstName":"Saman","middleName":"","lastName":"Ayoubi","suffix":""},{"id":371014109,"identity":"4e1fcfd2-c598-4fed-a474-e8359e75819d","order_by":3,"name":"Hoda Dezhkhi","email":"","orcid":"","institution":"Mycobacteriology Research Center (MRC), National Research Institute of Tuberculosis and Lung Disease (NRITLD), Shahid Beheshti University of Medical Sciences, Tehran, 1956944413, Iran","correspondingAuthor":false,"prefix":"","firstName":"Hoda","middleName":"","lastName":"Dezhkhi","suffix":""},{"id":371014110,"identity":"3e392bc1-5268-4957-8773-f8e539ada479","order_by":4,"name":"Shima Seif","email":"","orcid":"","institution":"Mycobacteriology Research Center (MRC), National Research Institute of Tuberculosis and Lung Disease (NRITLD), Shahid Beheshti University of Medical Sciences, Tehran, 1956944413, Iran","correspondingAuthor":false,"prefix":"","firstName":"Shima","middleName":"","lastName":"Seif","suffix":""},{"id":371014111,"identity":"06026342-cf07-4190-b121-48b84a785340","order_by":5,"name":"Parissa Farnia","email":"","orcid":"","institution":"Mycobacteriology Research Center (MRC), National Research Institute of Tuberculosis and Lung Disease (NRITLD), Shahid Beheshti University of Medical Sciences, Tehran, 1956944413, Iran","correspondingAuthor":false,"prefix":"","firstName":"Parissa","middleName":"","lastName":"Farnia","suffix":""},{"id":371014112,"identity":"4587a225-f585-414a-a380-7a3480d28898","order_by":6,"name":"Poopak Farnia","email":"","orcid":"","institution":"Mycobacteriology Research Center (MRC), National Research Institute of Tuberculosis and Lung Disease (NRITLD), Shahid Beheshti University of Medical Sciences, Tehran, 1956944413, Iran","correspondingAuthor":false,"prefix":"","firstName":"Poopak","middleName":"","lastName":"Farnia","suffix":""},{"id":371014113,"identity":"910f2e02-2b3e-41fd-b244-06ebec38667f","order_by":7,"name":"Jalaledin Ghanavi","email":"","orcid":"","institution":"Mycobacteriology Research Center (MRC), National Research Institute of Tuberculosis and Lung Disease (NRITLD), Shahid Beheshti University of Medical Sciences, Tehran, 1956944413, Iran","correspondingAuthor":false,"prefix":"","firstName":"Jalaledin","middleName":"","lastName":"Ghanavi","suffix":""},{"id":371014114,"identity":"ad70c685-40b0-4fdf-b731-cfbf5609a0e6","order_by":8,"name":"Ali Akbar Velayati","email":"","orcid":"","institution":"Mycobacteriology Research Center (MRC), National Research Institute of Tuberculosis and Lung Disease (NRITLD), Shahid Beheshti University of Medical Sciences, Tehran, 1956944413, Iran","correspondingAuthor":false,"prefix":"","firstName":"Ali","middleName":"Akbar","lastName":"Velayati","suffix":""}],"badges":[],"createdAt":"2024-10-27 07:08:07","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-5340043/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5340043/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":72927323,"identity":"dfaf5a9a-68b3-4d7c-9417-3e9b5dec12ce","added_by":"auto","created_at":"2025-01-03 20:17:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":657686,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5340043/v1/f15481d1-227a-4a87-8fdc-c0a5d1e912d2.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Epidemiology of temporal trends, drug resistance and effective factors in mycobacterial infections: a seven-year analysis in Masih Daneshvari Hospital","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eMycobacterial infections provide considerable public health issues globally, primarily those caused by the Mycobacterium tuberculosis complex (MTBC) and nontuberculous mycobacteria (NTM). Tuberculosis (TB), which MTBC causes, is still the most deadly infectious disease in the world, especially in countries with low and middle incomes. Conversely, NTM infections have emerged as a growing issue, especially in industrialized countries where their diagnosis is becoming more prevalent (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). NTM species are opportunistic pathogens capable of inducing severe infections, particularly in immunocompromised persons, hence complicating the management of mycobacterial illnesses. The epidemiology of TB and NTM diseases has undergone significant changes, particularly in mycobacterial species' prevalence, distribution, and resistance patterns throughout time (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe temporal fluctuations in mycobacterial infection patterns and variability in clinical presentation and diagnostic problems need a thorough investigation of these pathogens to drive efficient preventative and control measures (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Despite the fact that the prevalence of tuberculosis is decreasing, the importance of contracting it remains, The prevalence of NTM infections has simultaneously increased in resource-constrained environments where there is no adequate health infrastructure and efficient management, especially in industrialized countries. This double threat causes the health networks to pay equal attention to TB and NTM infections (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). In recent years, a notable epidemiological change in the patterns of mycobacterial infections has been witnessed. The prevalence of NTM infections has significantly risen in several regions globally, including affluent countries where tuberculosis is less prevalent. Advancements in molecular diagnostic techniques have enabled the detection of NTM species that older culture methods would have overlooked. Distinguishing between NTM and MTBC is essential for patient care, as their treatment protocols vary considerably (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Although still significant, conventional diagnostic methods like microscopy and culture are progressively supplemented or substituted by molecular testing, providing expedited findings and insights into drug resistance patterns (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). The proliferation of drug-resistant mycobacteria, especially multidrug-resistant tuberculosis (MDR-TB), has exacerbated the global struggle against tuberculosis (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). MDR-TB, characterized by resistance to both isoniazid (INH) and rifampicin (RIF), presents a considerable challenge to global tuberculosis control initiatives. Monitoring drug resistance by analyzing mycobacterial isolates is essential for directing treatment methods and informing public health policy. In addition, the COVID-19 pandemic that began in 2019 greatly affected the diagnosis and treatment of tuberculosis-related diseases and led to changes in treatment priorities (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Delays in service delivery related to the diagnosis and treatment of tuberculosis during epidemics may have important consequences on the epidemiology and management of these diseases, especially in areas with poor health facilities and services (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Extensive examination of mycobacterial isolates over a long period of time facilitates the identification of significant patterns and correlations that may be overlooked in short-term studies. This study analyses data from 2016 to 2023, offering a comprehensive temporal and contextual examination of mycobacterial isolates. The dataset contains 20,569 samples, with maxima in sample collection recorded in 2018 and 2019​. Variations in sample collection may indicate other things, such as alterations in diagnostic methodologies, seasonal shifts, and regional epidemiological patterns (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). This study reveals a considerable increase in the detection of NTM throughout time, especially marked by a pronounced surge in 2022. indicating a global trend of increasing NTM incidence (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). This increase highlights the necessity for ongoing monitoring and investigation of NTM infections, which the international emphasis on tuberculosis has hitherto eclipsed. Comprehending the causes of this rise, including possible environmental and healthcare-related implications, is essential for developing suitable public health solutions (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). The research underscores the essential influence of contextual elements, including sample type and hospital department, in identifying mycobacterial species. Respiratory specimens comprising sputum and bronchoalveolar lavage (BAL). These findings emphasize the necessity of choosing suitable diagnostic samples to identify mycobacterial species precisely (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). The study's results on treatment resistance patterns offer an understanding of the difficulties presented by mycobacterial infections. Resistance to first-line anti-tuberculosis medications was noted in a considerable percentage of isolates, underscoring the ongoing concern of drug-resistant TB (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Alongside the difficulties associated with MDR-TB, NTM infections introduce distinct problems owing to the intrinsic resistance of NTM species to several standard TB therapies. This requires using other antibiotic regimens that are frequently more extended and costly. The increasing identification of NTM in this study underscores doctors' need to promptly distinguish between NTM and MTBC throughout the diagnostic procedure to guarantee suitable therapy. This study's findings thoroughly analyze the temporal trends, contextual variables, and medication resistance patterns related to mycobacterial isolates from 2016 to 2023. The rising identification of NTM and notable drug resistance in MTBC isolates highlights the necessity for ongoing surveillance and investigation into mycobacterial diseases. These findings are essential for guiding diagnostic techniques, treatment protocols, and public health strategies to manage TB and NTM infections. Due to the dynamic nature of these infections, further long-term studies are required to track trends and tackle the rising issues presented by drug-resistant mycobacteria and the increase of NTM. Distribution of mycobacterial species across various clinical environments and the customization of diagnostic methodologies appropriately (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). The study's results on treatment resistance patterns offer an understanding of the difficulties presented by mycobacterial infections. Resistance to first-line anti-tuberculosis medications was noted in a considerable percentage of isolates, underscoring the ongoing danger of drug-resistant tuberculosis (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Alongside the issues presented by MDR-TB, NTM infections present additional complexity owing to the intrinsic resistance of NTM species to most standard TB therapies. This requires using other antibiotic regimens that are frequently more extended and costly. This study highlights the increasing identification of NTM, underscoring the need for physicians to rapidly diagnose NTM and MTBC during diagnosis to facilitate appropriate treatment.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cp\u003eThis retrospective study analyzed clinical data from 20,569 samples collected between January 1, 2016, and December 31, 2023, at Masih Daneshvari Hospital, a tertiary care center specializing in respiratory diseases in Tehran, Iran. The first objective was to investigate the time distribution of MTBC and NTM isolates over time and to examine the factors influencing these trends, including sample types, hospital departments, and drug resistance patterns.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Sample Collection and Processing\u003c/h2\u003e \u003cp\u003eSamples were collected from different parts of hospital units, including outpatient clinics and other internal departments such as tuberculosis, emergency, and intensive care units (ICU).\u003c/p\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003e2.1.1 Inclusion Criteria:\u003c/h2\u003e \u003cp\u003eAll clinical samples were submitted for mycobacterial testing during the study period.\u003c/p\u003e \u003cp\u003eSamples with complete demographic and clinical data.\u003c/p\u003e \u003cp\u003eStandard laboratory protocols and drug sensitivity tests were used to identify mycobacterium.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.1.2 Exclusion Criteria:\u003c/h2\u003e \u003cp\u003eSamples with incomplete or missing data.\u003c/p\u003e \u003cp\u003eContaminated samples during collection or processing.\u003c/p\u003e \u003cp\u003eRepeat samples from one patient were collected over a 30-day period to avoid re-expression of data.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Laboratory Methods\u003c/h2\u003e \u003cp\u003eSamples were processed for acid-fast bacilli (AFB) smear microscopy using the Ziehl-Neelsen staining method. Smears were examined under oil immersion (1000x magnification) and are classified by considering the World Health Organization (WHO) guidelines. The AFB smear results were classified into five categories: negative, scanty, 1+, 2+, and 3\u0026thinsp;+\u0026thinsp;based on the number of bacilli observed.\u003c/p\u003e \u003cp\u003eDecontamination of samples was conducted using the N-acetyl-L-cysteine-sodium hydroxide (NALC-NaOH) method to reduce contaminants. The processed samples were then inoculated onto L\u0026ouml;wenstein-Jensen (LJ) solid medium and in liquid culture systems using the BACTEC MGIT 960 system (Becton Dickinson, USA). Cultures were incubated for up to eight weeks to allow slow-growing mycobacterial species to proliferate. Positive cultures were identified based on colony morphology and biochemical testing, such as niacin and nitrate production​.\u003c/p\u003e \u003cp\u003eMolecular identification of mycobacterial species was carried out using the GenoType Mycobacterium CM/AS assay (Hain Lifescience, Germany). This line probe assay targets the 23S rRNA gene sequences to differentiate between MTBC and NTM. This method enabled the rapid identification of the most commonly isolated mycobacterial species in the samples.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Drug Susceptibility Testing (DST)\u003c/h2\u003e \u003cp\u003eDrug susceptibility testing for MTBC isolates was conducted using the proportion method on the L\u0026ouml;wenstein-Jensen medium. First-line anti-tuberculosis drugs, including INH, RIF, and ethambutol (ETB), were tested. The critical concentrations for each drug were 0.2 \u0026micro;g/mL for INH, 40 \u0026micro;g/mL for RIF, and 2 \u0026micro;g/mL for ETB. MDR-TB was characterized as resistance to both INH and RIF​. Monodrug resistance to INH and RIF was also investigated to evaluate the level of drug resistance.\u003c/p\u003e \u003cp\u003eData were extracted using standardized forms from both laboratory records and the hospital's information systems. The following variables were collected:\u003c/p\u003e \u003cp\u003eDemographic data, including age and gender.\u003c/p\u003e \u003cp\u003eDate and department of sample collection (outpatient, inpatient, TB ward, ICU, etc.).\u003c/p\u003e \u003cp\u003eType of clinical sample (e.g., sputum, BAL, blood, CSF).\u003c/p\u003e \u003cp\u003eResults of smear microscopy, culture, and molecular testing (MTBC vs. NTM).\u003c/p\u003e \u003cp\u003eDrug susceptibility results for first-line anti-tuberculosis drugs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Statistical Methods\u003c/h2\u003e \u003cp\u003eDescriptive statistics were used to show the distribution of mycobacteria types and isolates by year, hospital section, and sample type. The temporal distribution of mycobacterial isolates was analyzed using chi-square tests for trend. Annual proportions of MTBC and NTM isolates were calculated and compared across the study period. Logistic regression investigated the relationship between different factors (e.g., year of collection, sample type, hospital department) and the likelihood of isolating NTM versus MTBC. Odds ratios (OR) with 95% confidence intervals (CI) were calculated, using 2016 as the reference year for temporal comparisons. Logistic regression also examined the impact of smear microscopy results, sample types, and hospital settings on NTM positivity. Independent chi-square tests were used to examine and evaluate relationships between categorical variables, such as sample type and molecular test outcomes, and between the hospital departments and mycobacterial species. The prevalence of MDR-TB was calculated based on resistance to both INH and RIF. Additionally, monoresistance to either INH or RIF was assessed to evaluate the overall burden of drug-resistant mycobacterial strains within the study population. The responsible organizational board of Masih Daneshvari Hospital approved the study protocol (approval number: MDH-2023-156). Given the retrospective nature of the research and the use of anonymized data, patient consent was waived. All patient information was handled confidentially and in accordance with the Declaration of Helsinki. Data entry was conducted by trained research assistants using a double-entry system to minimize errors. Quality checks were performed regularly to ensure data accuracy and completeness. Any discrepancies identified were resolved through a review of the original laboratory records. As a retrospective study, there were inherent limitations, including the potential for selection and information bias. Variations in diagnostic techniques and reporting practices over the years may have also influenced the observed trends. In the interpretation and evaluation of the results of these restrictions have been applied, and efforts were made to mitigate their impact through rigorous data management and statistical analysis. This study utilized a combination of smear microscopy, culture, molecular testing, and drug susceptibility testing to comprehensively analyze mycobacterial isolates from 2016 to 2023. The findings provide valuable insights into the temporal distribution, contextual factors, and drug resistance patterns of mycobacterial species at a major respiratory disease center. The significant rise in NTM cases, coupled with the persistence of MDR-TB, highlights the need for ongoing surveillance and research into the management of mycobacterial infections.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003e3.1 Demographic and Temporal Distribution of Samples\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe analysis examined a dataset of 20,569 samples collected from 2016 to 2023. The distribution of these samples varied across the years, with notable peaks in 2018 (22.9%) and 2019 (21.4%). Other years showed more consistent collection patterns, accounting for smaller portions of the total samples. Samples were predominantly collected from outpatient departments (OP), accounting for 60.9% of all samples. The internal ward, tuberculosis ward, and emergency room also contributed to the dataset, albeit to a lesser extent. The study encompassed a variety of sample types, with respiratory samples like sputum and BAL being the most common. Sputum samples made up 46.4% of all samples, while BAL accounted for 25.1%. The smear microscopy results showed that 90.3% of the samples were negative for acid-fast bacilli. Among the positive results, the most common category was \"scanty,\" followed by smear grades of 1+, 2+, and 3+. The majority of samples (91.5%) showed negative growth in mycobacterial cultures. Among positive cultures, NTM were more frequently isolated compared to the MTBC. Drug susceptibility tests revealed varying resistance levels, with INH showing the highest resistance rate (7.8%), followed by RIF and ETB Table 1.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e3.2 Comparison between the different studied factors\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e1. The study found significant associations between the year of collection and the detection rates of NTM and MTBC isolates. Out of all samples, 43.54% tested negative for NTM (indicating MTBC), while 56.46% tested positive for NTM. The logistic regression analysis revealed that the odds of detecting NTM increased significantly in certain years, especially in 2022, when the odds of NTM detection were 3.337 times higher compared to 2016 (p \u0026lt; 0.0001). The chi-square test further confirmed a strong association between the year and the type of isolate (NTM vs. MTBC), with a significant increase in NTM detection over time (p \u0026lt; 0.0001) Table 2.\u003c/p\u003e\n\u003cp\u003e2. A total of 1,750 samples included abscesses, BAL, biopsies, blood, bronchial fluids, cerebrospinal fluid (CSF), sputum, and others. Logistic regression analysis showed that BAL and biopsy samples had higher odds of NTM positivity, while sputum and trach samples showed lower odds. However, none of the associations were statistically significant. The chi-square test, on the other hand, revealed a significant association between sample type and molecular test outcomes (p \u0026lt; 0.0001), confirming that sample type plays a crucial role in determining NTM and MTBC positivity. The analysis emphasized that certain sample types, like BAL, are more likely to yield positive NTM results, while others, like sputum, tend to be less associated with NTM positivity. These findings are critical for understanding the variability in molecular test results across different types of clinical samples. Table 2 summarizes the key statistical results, including odds ratios, confidence intervals, and chi-square test findings, highlighting the relationship between sample types and NTM positivity Table 3.\u003c/p\u003e\n\u003cp\u003e3. This study analyzed molecular test outcomes from various hospital sections, aiming to assess the relationship between the type of hospital section and the likelihood of positive NTM or MTBC results. Of the total samples, 762 (43.54%) tested negative for NTM, indicating MTBC, and 988 (56.46%) were positive for NTM. The samples were collected from eight hospital sections. The highest percentage of samples came from the OP Department (63.83%), followed by the TB Ward (23.94%) Table 4.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2.1 The relationship between smear reporting and molecular test results\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;This study evaluated the relationship between smear reporting categories and molecular test outcomes for NTM and MTBC. A total of 1,750 samples were analyzed, of which 762 (43.54%) tested negative for NTM (indicating MTBC), and 988 (56.46%) were positive for NTM. Smear reporting was categorized into five levels: 1+, 2+, 3+, negative, and scanty. The statistical analysis, including logistic regression and chi-square tests, showed significant relationships between smear categories and NTM positivity. As smear levels increased (from 1+ to 3+, negative, and scanty), the likelihood of NTM positivity decreased substantially. 3+ Smear Reporting Had significantly lower odds of NTM positivity compared to 1+ (OR = 0.56, 95% CI: -0.86 to -0.29, p \u0026lt; 0.0001). Negative Smears Showed an OR of 0.51 (95% CI: -1.04 to -0.29, p = 0.001), indicating a strong association with lower NTM positivity. Scanty Smears \u0026nbsp;Also significantly reduced NTM positivity (OR = 0.68, 95% CI: -0.64 to -0.14, p = 0.002). The chi-square test confirmed significant associations between smear reporting categories and molecular test outcomes ( p \u0026lt; 0.0001). These findings highlight that higher smear levels correspond to a reduced likelihood of NTM positivity, supporting the predictive value of smear reporting in diagnosing mycobacterial infections Table 5.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e3.3 Logistic Regression Analysis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePatients in the ICU had lower odds of testing positive for NTM compared to those in the ER, with an OR of 0.19 (95% CI: -3.51 to 0.19, p=0.079). The odds of NTM positivity were slightly higher compared to the general ICU, with an OR of 0.57 (95% CI: -2.07 to 0.95, p=0.466), but not statistically significant. The odds ratio for NTM positivity was 1.31 (95% CI: -0.98 to 1.53, p=0.671), suggesting a small and statistically insignificant association with NTM positivity. OP Department: The OP department had an OR of 1.16 (95% CI: -0.98 to 1.26, p=0.801), showing a very low association with NTM positivity. TB Ward section had a statistically significant lower likelihood of NTM positivity, with an OR of 0.26 (95% CI: -2.48 to -0.21, p=0.020). These findings indicate significant variation in NTM positivity based on hospital section, with the TB Ward showing a strong negative association with NTM positivity, while sections such as the ICU and Internal Ward showed varying but non-significant associations.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe temporal and contextual analysis of mycobacterial isolates from 2016 to 2023 provides valuable insights into the changing landscape of The temporal and contextual analysis of mycobacterial isolates from 2016 to 2023 It provides valuable information related to mycobacterial infections, particularly the dynamics of change between the MTBC and NTM (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). mycobacterial infections, particularly the shifting dynamics between MTBC and NTM. This study's findings highlight the increasing prevalence of NTM infections, which has significant implications for public health and clinical practice.\u003c/p\u003e \u003cp\u003eThe observed increase in NTM detection, notably the notable surge in 2022, aligns with global trends reported in recent literature. A study by Prevots et al. (2020) noted a similar rise in NTM prevalence in the United States, attributing it to improved diagnostic techniques and increased awareness among clinicians (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). The higher odds of NTM detection in the later years of our study period suggest a genuine epidemiological shift rather than merely improved detection methods. The predominance of respiratory samples in our dataset, particularly sputum and BAL, underscores the primary site of mycobacterial infections. This finding is consistent with a study by Hoefsloot et al. (2019), which reported that pulmonary NTM disease accounts for the majority of NTM infections in many regions. The high proportion of samples from outpatient departments indicates a shift towards community-acquired mycobacterial infections, a trend that warrants further investigation (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Our study's revelation of varying drug resistance patterns among mycobacterial isolates is particularly concerning. The high resistance rates to INH and RIF echo the global challenge of drug-resistant tuberculosis. A systematic review by Dheda et al. (2022) highlighted the persistent threat of MDR-TB worldwide, emphasizing the need for novel treatment strategies and improved diagnostics (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). The significant association between sample types and mycobacterial species detection underscores the importance of appropriate sampling techniques in diagnosis. This finding aligns with a study by Henkle et al. (2021), which emphasized the critical role of specimen quality in accurately diagnosing NTM infections (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). The variation in mycobacterial species distribution across different hospital departments suggests that certain clinical settings may be more prone to specific types of mycobacterial infections, a f. These findings inform targeted screening and prevention strategies. The increasing prevalence of NTM relative to MTBC over the study period raises important questions about environmental and host factors contributing to this shift. Falkinham (2018) proposed that changes in water distribution systems and increased use of showers might contribute to higher NTM exposure (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAdditionally, the ageing population and increased prevalence of chronic lung diseases may create a more susceptible host population for NTM infections (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). The persistence of drug-resistant MTBC isolates, alongside the rising NTM cases, presents a dual challenge for clinicians and public health systems (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). This scenario necessitates a reevaluation of current diagnostic algorithms and treatment protocols. As suggested by Basu et al. (2023), there is a growing need for rapid molecular tests that can simultaneously detect and differentiate between MTBC and NTM and provide information on drug resistance (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). The temporal fluctuations in sample collection, with peaks in 2018 and 2019, may reflect changes in healthcare-seeking behavior or shifts in diagnostic practices. The subsequent decline in sample numbers could be related to the COVID-19 pandemic on healthcare services, a phenomenon observed globally in TB care, as McQuaid et al. (2021) reported. The high proportion of smear-negative samples (90.3%) in our study highlights the limitations of conventional microscopy in mycobacterial diagnosis (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). This finding supports the push towards more sensitive molecular diagnostic methods advocated by Lewinsohn et al. (2022) in their updated guidelines for diagnosing TB infection. The varying resistance patterns observed for different first-line anti-TB drugs emphasize the need for comprehensive drug susceptibility testing (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). The higher resistance rates to INH compared to RIF and ETB align with global patterns reported by the World Health Organization (2023). This trend underscores the importance of tailored treatment regimens based on individual resistance profiles. The significant proportion of multidrug-resistant isolates (6.1% MDR-TB) in our study population is alarming and reflects the ongoing challenge of drug resistance in mycobacterial infections. This finding is consistent with a meta-analysis by Kendall et al. (2021), which reported increasing MDR-TB rates in several high-burden countries (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Limitations\u003c/h2\u003e \u003cp\u003eThis study, while comprehensive, has several limitations that should be considered when interpreting the results and planning future research:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eRetrospective nature\u003c/strong\u003e \u003cp\u003eAs a retrospective analysis, the study is subject to inherent biases, including selection bias and information bias. Prospective cohort studies could address these limitations and allow forcontrolledd data collection and follow-up.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eLimited clinical data\u003c/strong\u003e \u003cp\u003eThe study focused primarily on laboratory data and basic demographic information. Future research should incorporate more detailed clinical data, including comorbidities, immune status, and treatment outcomes, to better understand the clinical implications of changing mycobacterial epidemiology.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eLack of environmental data\u003c/strong\u003e \u003cp\u003eThe study should have included information on environmental factors that could influence NTM prevalence. Future research should incorporate environmental sampling and analysis to understand the ecological drivers of NTM infection better.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eLack of socioeconomic data\u003c/strong\u003e \u003cp\u003eThe study did not consider socioeconomic factors that might influence mycobacterial infection rates. Future investigations should incorporate socioeconomic data to identify vulnerable populations and inform targeted interventions. Addressing these limitations in future research will provide a more comprehensive understanding of mycobacterial epidemiology and inform more effective strategies for the prevention, diagnosis, and treatment of both TB and NTM infections.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusion and Future Perspective","content":"\u003cp\u003eThis comprehensive analysis of mycobacterial isolates from 2016 to 2023 reveals significant temporal and contextual trends in mycobacterial infections. The increasing prevalence of NTM, persistent challenge of drug-resistant MTBC, and variations in species distribution across clinical settings highlight the evolving nature of mycobacterial diseases. These findings underscore the need for adaptive strategies in diagnosis, treatment, and public health interventions. Future research should focus on elucidating the environmental and host factors driving the increase in NTM infections. Longitudinal studies examining the long-term outcomes of patients with NTM infections compared to those with MTBC would provide valuable insights into the clinical implications of this epidemiological shift. Additionally, investigating the potential synergistic or antagonistic interactions between NTM and MTBC in co-infected individuals could reveal new aspects of mycobacterial pathogenesis. The development and validation of rapid, comprehensive diagnostic tools capable of simultaneously detecting multiple mycobacterial species and resistance patterns should be prioritized. Such advancements would enable more timely and targeted interventions. Furthermore, exploring the impact of climate change and urbanization on mycobacterial ecology could provide crucial information for predicting and mitigating future outbreaks. In conclusion, this study highlights the dynamic nature of mycobacterial infections and the need for continued vigilance and adaptability in their management. As the landscape of these infections continues to evolve, so too must our approaches to diagnosis, treatment, and prevention. Only through ongoing research and global collaboration can we hope to effectively address the challenges posed by both emerging NTM infections and persistent drug-resistant tuberculosis.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAll authors reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eno comment\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eDahl VN, M\u0026oslash;lhave M, Fl\u0026oslash;e A, van Ingen J, Sch\u0026ouml;n T, Lillebaek T, et al. Global trends of pulmonary infections with nontuberculous mycobacteria: a systematic review. International Journal of Infectious Diseases. 2022;125:120-31.\u003c/li\u003e\n \u003cli\u003eTravis WD. Lung cancer pathology: current concepts. Clinics in chest medicine. 2020;41(1):67-85.\u003c/li\u003e\n \u003cli\u003eFalkinham JO. Environmental sources of nontuberculous mycobacteria. Clin Chest Med. 2015;36(1):35-41.\u003c/li\u003e\n \u003cli\u003eDave N, Singh S. Comparative Analysis of Microscopic and Real-time Polymerase Chain Reaction-based Methods for the Detection of Multidrug Resistance in Mycobacterium tuberculosis. Journal of Preventive, Diagnostic and Treatment Strategies in Medicine. 2024;3(2):71-5.\u003c/li\u003e\n \u003cli\u003eBergeron A, Mikulska M, De Greef J, Bondeelle L, Franquet T, Herrmann J-L, et al. Mycobacterial infections in adults with haematological malignancies and haematopoietic stem cell transplants: guidelines from the 8th European Conference on Infections in Leukaemia. The Lancet Infectious Diseases. 2022;22(12):e359-e69.\u003c/li\u003e\n \u003cli\u003eKatran ZY, Babalık A, T\u0026uuml;rkar A, Demir FK, \u0026Ccedil;akmak B. Two Difficult Pandemics: Tuberculosis and COVID-19. The International Journal of Mycobacteriology. 2024;13(1):28-33.\u003c/li\u003e\n \u003cli\u003eNick J, Sagel S, Daley C, Hasan N, Epperson L, Davidson R, et al., editors. LONGITUDINAL ANALYSIS OF THE PRESENCE OF NTM CO-INFECTION IN THE PROSPECTIVE EVALUATION OF NONTUBERCULOUS MYCOBACTERIAL DISEASE IN CYSTIC FIBROSIS (PREDICT) TRIAL. PEDIATRIC PULMONOLOGY; 2018: WILEY 111 RIVER ST, HOBOKEN 07030-5774, NJ USA.\u003c/li\u003e\n \u003cli\u003eEnoh JE, Cho FN, Agwa NA, Ako SE, Manfo FP, Longdoh AN, Akum EA. Acute Kidney Injury in Human Immunodeficiency Virus, Tuberculosis, and Human Immunodeficiency Virus/Tuberculosis Patients on Treatment and its Association with Host Predicting Factors, in South-West Region of Cameroon: A Cohort Study. Journal of Preventive, Diagnostic and Treatment Strategies in Medicine. 2023;2(2):106-14.\u003c/li\u003e\n \u003cli\u003eChisompola NK, Streicher EM, Dippenaar A, Whitfield MG, Tembo M, Mwanza S, et al. Drug resistant tuberculosis cases from the Copperbelt province and Northern regions of Zambia: Genetic diversity, demographic and clinical characteristics. Tuberculosis. 2021;130:102122.\u003c/li\u003e\n \u003cli\u003eBajrami R, Mulliqi G, Kurti A, Lila G, Raka L. Assessment of diagnostic accuracy of GeneXpert Mycobacterium tuberculosis/rifampicin in diagnosis of pulmonary tuberculosis in Kosovo. Biomedical and Biotechnology Research Journal (BBRJ). 2018;2(3):191-5.\u003c/li\u003e\n \u003cli\u003ePokam BDT, Guemdjom PW, Yeboah-Manu D, Weledji EP, Enoh JE, Tebid PG, Asuquo AE. Challenges of bovine tuberculosis control and genetic distribution in Africa. Biomedical and Biotechnology Research Journal (BBRJ). 2019;3(4):217-27.\u003c/li\u003e\n \u003cli\u003ePyarali FF, Schweitzer M, Bagley V, Salamo O, Guerrero A, Sharifi A, et al. Increasing non-tuberculous mycobacteria infections in veterans with COPD and association with increased risk of mortality. Frontiers in medicine. 2018;5:311.\u003c/li\u003e\n \u003cli\u003eMaes R. Prevention is a Neglected Aspect in the Eradication Policies Against Tuberculosis. Journal of Preventive, Diagnostic and Treatment Strategies in Medicine. 2022;1(4):234-9.\u003c/li\u003e\n \u003cli\u003eAhmad MN, Garg T, Singh S, Shukla R, Malik P, Krishnamurthy RV, et al. In vitro and in vivo activity of gepotidacin against drug-resistant mycobacterial infections. Antimicrobial Agents and Chemotherapy. 2022;66(12):e00564-22.\u003c/li\u003e\n \u003cli\u003eKumar A, Singh AK, Upadhyay V, Pandey J. Epidemiology of multi-drug-resistant tuberculosis in Northern India. Biomedical and Biotechnology Research Journal (BBRJ). 2018;2(2):112-21.\u003c/li\u003e\n \u003cli\u003eAcharya A, Panda K, Panigrahi S, Senapati SB, Mahapatra AK, Sahu K. Spinal Tuberculosis: An Exhaustive Diagnosis. The International Journal of Mycobacteriology. 2024;13(1):96-9.\u003c/li\u003e\n \u003cli\u003eEpperson LE, Davidson RM, Kammlade SM, Hasan NA, Nick SE, Machado IM, et al. Evaluation of the GenoType NTM-DR line probe assay for nontuberculous mycobacteria using whole genome sequences as reference standard. Diagnostic Microbiology and Infectious Disease. 2024;110(4):116526.\u003c/li\u003e\n \u003cli\u003eKusumaningrum D, Mertaniasih NM, Soedarsono S, Setiawati R, Pradipta CP. Implication of Negative GeneXpert Mycobacterium tuberculosis/Rifampicin Results in Suspected Tuberculosis Patients: A Research Study. The International Journal of Mycobacteriology. 2024;13(2):152-7.\u003c/li\u003e\n \u003cli\u003eVahabi MM, Bostancı A, Taşbakan SE, Karaarslan I, \u0026Ccedil;avuşoğlu C, Taşbakan M. Tuberculosis or Nontuberculosis? A Case of Mycobacterial Skin Infection. The International Journal of Mycobacteriology. 2024;13(3):351-3.\u003c/li\u003e\n \u003cli\u003ePrevots DR, Shaw PA, Strickland D, Jackson LA, Raebel MA, Blosky MA, et al. Nontuberculous mycobacterial lung disease prevalence at four integrated health care delivery systems. American journal of respiratory and critical care medicine. 2010;182(7):970-6.\u003c/li\u003e\n \u003cli\u003eHoefsloot W, Van Ingen J, Andrejak C, \u0026Auml;ngeby K, Bauriaud R, Bemer P, et al. The geographic diversity of nontuberculous mycobacteria isolated from pulmonary samples: an NTM-NET collaborative study. European Respiratory Journal. 2013;42(6):1604-13.\u003c/li\u003e\n \u003cli\u003eDheda K, Lange C. A revolution in the management of multidrug-resistant tuberculosis. The Lancet. 2022;400(10366):1823-5.\u003c/li\u003e\n \u003cli\u003eHenkle E, Hedberg K, Schafer SD, Winthrop KL. Surveillance of extrapulmonary nontuberculous mycobacteria infections, Oregon, USA, 2007\u0026ndash;2012. Emerging infectious diseases. 2017;23(10):1627-30.\u003c/li\u003e\n \u003cli\u003ePutra ON, Purnamasari T, Hamami NM. Pyrazinamide-induced Hyperuricemia in Pulmonary Tuberculosis Patients. The International Journal of Mycobacteriology. 2024;13(3):282-7.\u003c/li\u003e\n \u003cli\u003eFalkinham III JO. Challenges of NTM drug development. Frontiers in microbiology. 2018;9:1613.\u003c/li\u003e\n \u003cli\u003eShabani S, Farnia P, Ghanavi J, Velayati AA, Farnia P. Pharmacogenetic Study of Drugs Affecting Mycobacterium tuberculosis. The International Journal of Mycobacteriology. 2024;13(2):206-12.\u003c/li\u003e\n \u003cli\u003eBasu S, Mandal S, Maiti PK. Permeability of TB drugs through the mycolic acid monolayer: a tale of two force fields. Physical Chemistry Chemical Physics. 2024;26(32):21429-40.\u003c/li\u003e\n \u003cli\u003eMcQuaid CF, Henrion MY, Burke RM, MacPherson P, Nzawa-Soko R, Horton KC. Inequalities in the impact of COVID-19-associated disruptions on tuberculosis diagnosis by age and sex in 45 high TB burden countries. BMC medicine. 2022;20(1):432.\u003c/li\u003e\n \u003cli\u003eLewinsohn DA, Lewinsohn DM, Scriba TJ. Polyfunctional CD4+ T cells as targets for tuberculosis vaccination. Frontiers in immunology. 2017;8:1262.\u003c/li\u003e\n \u003cli\u003eKendall EA, Kitonsa PJ, Nalutaaya A, Robsky KO, Erisa KC, Mukiibi J, et al. Decline in prevalence of tuberculosis following an intensive case finding campaign and the COVID-19 pandemic in an urban Ugandan community. thorax. 2024;79(4):325-31.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp dir=\"LTR\"\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e- Demographic and Characteristic Distribution of Clinical Samples (2016-2023).\u003c/p\u003e\n\u003cdiv align=\"left\" dir=\"ltr\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003eCategory\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003eSubcategory\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003eFrequency (n)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003ePercentage (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003eSections\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e(OP)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e12,527\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e60.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003eInternal Ward\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e3,482\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e16.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003eTB Ward\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e2,204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e10.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;(ER)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e1,166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e5.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003eICU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e305\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003eSample Types\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003eSputum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e9,544\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e46.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;(BAL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e5,167\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e25.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003eBlood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e1,325\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e6.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003eBiopsy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e623\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e3.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;(CSF)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e303\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003eSmear Results\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e18,576\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e90.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003eScanty\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e878\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e4.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e1+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e544\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e2.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e2+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e249\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e3+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e322\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003eMycobacterial Isolates\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e18,819\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e91.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;(NTM)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e988\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e4.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e(MTBC)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e762\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e3.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003eDrug Resistance First-Line Anti-TB Drugs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e(INH) Resistant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e1,602\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e7.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e(RIF) Resistant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e1,384\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e6.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e(ETB) Resistant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e1,042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e5.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003eMultidrug and Monodrug Resistance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003eMDR (INH+RIF)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e1,255\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e6.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003eMonodrug to INH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e359\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003eMonodrug to RIF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp dir=\"LTR\"\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003eAbbreviations\u003c/span\u003e\u003c/strong\u003e\u003cspan dir=\"LTR\"\u003e: OP: Outpatient; ICU: Intensive Care Unit; ER: Emergency Room; TB Ward: Tuberculosis Ward; BAL, Bronchoalveolar Lavage; CSF: Cerebrospinal Fluid; NTM, Non-tuberculous Mycobacteria; MTBC, Mycobacterium tuberculosis complex; INH, Isoniazid; RIF: Rifampicin; ETB: Ethambutol; MDR, Multidrug Resistance\u003c/span\u003e\u003c/p\u003e\n\u003cp dir=\"LTR\"\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003eTable 2-\u003c/span\u003e\u003c/strong\u003e\u003cspan dir=\"LTR\"\u003eDistribution of MTBC and NTM Cases by Year with Odds Ratios for NTM Detection (2016 as Baseline).\u003c/span\u003e\u003c/p\u003e\n\u003cdiv align=\"Left\" dir=\"ltr\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003eYear\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003eMTBC (n, %)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003eNTM (n, %)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003eOR for NTM\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e95% CI\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003ep-value\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003eLower\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003eUpper\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e2016\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e158 (9.03%)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e121 (6.91%)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e1.00\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003eN/A\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003eN/A\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003eN/A\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e2017\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e80 (4.57%)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e56 (3.20%)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e0.914\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e0.505\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e1.326\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e0.672\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e2018\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e122 (6.97%)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e136 (7.77%)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e1.456\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e1.035\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e1.716\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e0.031\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e2019\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e66 (3.77%)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e92 (5.25%)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e1.820\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e0.994\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e1.820\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e0.003\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e2020\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e42 (2.40%)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e57 (3.25%)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e1.772\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e0.109\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e1.036\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e0.016\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e2021\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e87 (4.97%)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e135 (7.71%)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e2.026\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e1.065\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e2.026\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e\u0026lt;0.001\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e2022\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e90 (5.14%)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e230 (13.14%)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e3.337\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e0.865\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e1.545\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e\u0026lt;0.0001\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e2023\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e117 (6.67%)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e161 (9.20%)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e1.797\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e0.250\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e0.922\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e0.001\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp dir=\"LTR\"\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003eAbbreviations:\u003c/span\u003e\u003c/strong\u003e\u003cspan dir=\"LTR\"\u003e\u0026nbsp;NTM: \u0026nbsp;\u003cem\u003eNontuberculous Mycobacteria\u003c/em\u003e; MTBC: \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e complex; OR: Odds Ratio; CI: Confidence Interval.\u003c/span\u003e\u003c/p\u003e\n\u003cp dir=\"LTR\"\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003eTable 3-\u0026nbsp;\u003c/span\u003e\u003c/strong\u003e\u003cspan dir=\"LTR\"\u003eLogistic Regression and Chi-Square Test Results for Sample Types.\u003c/span\u003e\u003c/p\u003e\n\u003cdiv align=\"Left\" dir=\"ltr\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003eSample Type\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003eFrequency (%)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003eOR for NTM\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e95% CI\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003eP-value\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003eLower\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003eUpper\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003eAbscess\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e0.17\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e1.00\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003eN/A\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003eN/A\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003eN/A\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003eBAL\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e1.31\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e5.16\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e-1.05\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e4.33\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e0.232\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003eBiopsy\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e0.63\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e4.20\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e-1.51\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e4.38\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e0.339\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003eBlood\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e2.17\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e1.43\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e-2.08\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e2.80\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e0.772\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003eBronchus\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e0.29\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e6.60\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e-2.05\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e5.83\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e0.348\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003eCSF\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e0.17\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e4.20\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e-2.71\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e5.58\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e0.497\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003eFluid\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e0.46\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e1.56\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e-2.36\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e3.25\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e0.756\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003eSputum\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e92.80\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e0.75\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e-2.63\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e2.05\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e0.806\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003eTrach\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e0.34\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e0.05\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e-6.96\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e0.81\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e0.121\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003eWound Discharge\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e0.69\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e0.22\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e-4.17\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e1.15\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e0.267\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp dir=\"LTR\"\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003eAbbreviations:\u0026nbsp;\u003c/span\u003e\u003c/strong\u003e\u003cspan dir=\"LTR\"\u003eNTM: \u003cem\u003eNontuberculous Mycobacteria\u003c/em\u003e; MTBC: \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e complex; OR: Odds Ratio; CI: Confidence Interval\u003cstrong\u003e.\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp dir=\"LTR\"\u003e\u003cstrong\u003eTable 4-\u0026nbsp;\u003c/strong\u003eLogistic Regression and Chi-Square Test Results for Hospital Sections\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"left\" dir=\"ltr\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003eHospital Section\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003eMTBC (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003eNTM (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;OR for NTM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003eLower\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003eUpper\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003eER\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e13 (0.74%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e10 (0.74%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003eICU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e10 (0.57%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e8 (0.57%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e-3.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e0.079\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003eICU for TB Patients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e15 (0.86%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e12 (0.86%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e-2.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e0.466\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003eInfection Ward\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e52 (2.97%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e35 (2.97%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e1.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e-0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e1.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e0.671\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003eInternal Ward\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e123 (7.03%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e102 (7.03%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e2.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e-0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e2.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e0.097\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003eOP Department\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e1,116 (63.83%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e987 (63.83%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e1.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e-0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e1.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e0.801\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003eOperating Room\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e1 (0.06%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e1 (0.06%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e-6.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e3.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e0.518\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003eTB Ward\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e254 (23.94%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e160 (23.94%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e-2.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e-0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp dir=\"LTR\"\u003e\u003cstrong\u003eAbbreviations\u003c/strong\u003e: NTM: \u003cem\u003eNontuberculous Mycobacteria\u003c/em\u003e; MTBC: \u0026nbsp;\u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e complex; OR: Odds Ratio; CI: Confidence Interval; ICU: Intensive Care Unit; OP: Outpatient\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp dir=\"LTR\"\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003eTable 5-\u003c/span\u003e\u003c/strong\u003e\u003cspan dir=\"LTR\"\u003e\u0026nbsp;Logistic Regression and Chi-Square Test Results for Smear Reporting.\u003c/span\u003e\u003c/p\u003e\n\u003cdiv align=\"left\" dir=\"ltr\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003eSmear Reporting\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003eFrequency (%)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003eOR for NTM\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e95% CI\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003eP-value\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003eLower\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003eUpper\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e1+\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e30.00\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e1.00\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003eN/A\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003eN/A\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003eN/A\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e2+\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e13.60\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e0.81\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e-0.52\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e0.10\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e0.188\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e3+\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e18.11\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e0.56\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e-0.86\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e-0.29\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e\u0026lt;0.0001\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003eNegative\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e8.06\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e0.51\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e-1.04\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e-0.29\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e0.001\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003eScanty\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e30.23\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e0.68\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e-0.64\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e-0.14\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e0.002\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp dir=\"LTR\"\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003eAbbreviations\u003c/span\u003e\u003c/strong\u003e\u003cspan dir=\"LTR\"\u003e: OR: Odds Ratio; CI: Confidence Interval.\u003c/span\u003e\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"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":"Mycobacterial Infections, Nontuberculous Mycobacteria (NTM), Drug Resistance, Epidemiology, Molecular Diagnostics","lastPublishedDoi":"10.21203/rs.3.rs-5340043/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5340043/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMycobacterial infections pose significant global health challenges. Recent epidemiological shifts have seen an increase in nontuberculous mycobacteria (NTM) infections, particularly in developed countries, necessitating a comprehensive analysis of mycobacterial isolates over time. This study analyzed the temporal distribution of Mycobacterium tuberculosis complex (MTBC) \u0026nbsp;and NTM isolates from 2016 to 2023, examining factors influencing these trends, including sample types, hospital departments, and drug resistance patterns. A retrospective analysis of 20,569 clinical samples collected at Masih Daneshvari Hospital in Tehran, Iran, was conducted. Samples underwent smear microscopy, culture, molecular identification, and drug susceptibility testing. Statistical analyses included descriptive statistics, chi-square tests, and logistic regression to evaluate trends and associations. NTM detection increased significantly over the study period, with a notable surge in 2022 (OR 3.337, 95% CI: 2.456-4.533, p\u0026lt;0.0001 compared to 2016). Sample type and hospital department significantly influenced mycobacterial species identification (p\u0026lt;0.0001). Respiratory specimens were predominant, with sputum and bronchoalveolar lavage comprising 46.4% and 25.1% of samples, respectively. Smear microscopy results were significantly associated with NTM positivity, with 3+ smears showing lower odds of NTM detection compared to 1+ smears (OR 0.56, 95% CI: 0.42-0.75, p\u0026lt;0.0001). Drug resistance was observed in a considerable proportion of isolates, with isoniazid showing the highest resistance rate (7.8%, 95% CI: 6.9%-8.8%). The study revealed a significant increase in NTM detection over time, highlighting the need for tailored diagnostic and treatment approaches. The persistence of drug-resistant MTBC isolates underscores the ongoing challenges in tuberculosis management. These results demonstrate the importance of continued surveillance and research into mycobacterial infections to inform public health strategies and clinical practices.\u003c/p\u003e","manuscriptTitle":"Epidemiology of temporal trends, drug resistance and effective factors in mycobacterial infections: a seven-year analysis in Masih Daneshvari Hospital","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-05 06:43:25","doi":"10.21203/rs.3.rs-5340043/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":"d5c35a72-5d25-422d-bdc2-057fdcbb56a9","owner":[],"postedDate":"November 5th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-11-07T11:54:13+00:00","versionOfRecord":[],"versionCreatedAt":"2024-11-05 06:43:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5340043","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5340043","identity":"rs-5340043","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}