Advancing Molecular Insights: A Global Systematic Review and Meta-analysis of Epidemiology and Drug Resistance Patterns of Mycobacterium tuberculosis in Sputum Samples

Advancing Molecular Insights: A Global Systematic Review and Meta-analysis of Epidemiology and Drug Resistance Patterns of Mycobacterium tuberculosis in Sputum Samples | 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 Advancing Molecular Insights: A Global Systematic Review and Meta-analysis of Epidemiology and Drug Resistance Patterns of Mycobacterium tuberculosis in Sputum Samples Madan Singh Bohara, Dwij Raj Bhatta This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5798511/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Tuberculosis (TB), caused by Mycobacterium tuberculosis (Mtb), remains a leading cause of morbidity and mortality globally. The emergence of multidrug-resistant (MDR-TB) and extensively drug-resistant TB (XDR-TB) presents significant challenges for TB control. Molecular diagnostics and epidemiological studies provide critical insights into the genetic diversity and drug resistance of Mtb, yet regional variability and fragmented data complicate global understanding. Materials & Methods Following PRISMA guidelines, a systematic search of PubMed, Google Scholar, and ScienceDirect identified peer-reviewed articles published between 2018 and 2023. Thirteen studies met the inclusion criteria, encompassing 3469 isolates from diverse regions. Key variables included drug resistance patterns, phylogenetic lineages, and demographic data. Statistical analyses included meta-analysis of proportions, heterogeneity assessments, and publication bias evaluation. Findings: MDR-TB prevalence ranged from 1.5% in Kenya and Mexico to 34.4% in India. Resistance to rifampicin and isoniazid showed pooled prevalence rates of 2.9% and 6.2%, respectively, with significant geographical variability. Phylogenetic analyses revealed distinct lineage distributions: lineage 3 predominated in India, lineage 2 was prevalent in China, and lineage 4 dominated in Ethiopia and Ghana. Age and gender analysis indicated a higher proportion of male TB patients, with significant variability across studies. Conclusion This study highlights the global heterogeneity in TB drug resistance and genetic diversity. Tailored regional strategies, informed by molecular epidemiology, are essential to address the rising threat of MDR-TB and enhance TB control efforts. Multidrug-resistant Tuberculosis Molecular Epidemiology Phylogeny Lineage Drug-resistant Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Mycobacterium tuberculosis (Mtb) is bacterium caused tuberculosis (TB) which remains the utmost shared root of death from any single infectious [ 1 ]. The emergence of extensively drug-resistant (XDR) and multidrug-resistant (MDR) TB strains poses a major challenge to TB eradication, with an estimated 400,000 MDR/RR-TB cases worldwide in 2023. Global TB incidence rose to 10.8 million cases in 2023, reversing prior declines, with 1.09 million deaths among HIV-negative individuals and 161,000 deaths among people with HIV [ 2 ]. A serious public health issue is the rising number of extrapulmonary TB (EPTB), and multidrug-resistant TB (MDR-TB) cases that are reported in Nepal each year [ 3 ]. Molecular tests for TB detection and drug susceptibility are transforming TB care by providing faster, more accurate diagnostics, especially for underserved populations and high-burden regions. WHO-endorsed nucleic acid amplification tests (NAATs) have proven superior to traditional smear microscopy, enabling tailored treatments through rapid detection of drug resistance. Ongoing innovations, including point-of-care tests and next-generation sequencing, hold promise for improving global TB control and addressing the challenges of undiagnosed and drug-resistant cases [ 4 ]. To improve the management of the disease, Mycobacterium tuberculosis Complex (MTBC) genotyping and drug susceptibility are crucial [ 5 ]. Genotyping of MTBC differentiates recently transmitted and reactivated TB disease, with clustered isolates being epidemiologically linked, and unique isolates resulting from latent infection reactivation outside the population [ 6 ]. There are direct and indirect methods for diagnosing TB, including microscopy, culture, antigen detection, and nucleic acid detection. However, sputum smear microscopy has poor sensitivity and specificity, and mycobacterial culture is the most reliable reference standard but take longer time. Molecular diagnosis, a new direct method, offers simplicity, rapidity, and accuracy [ 7 ]. Sputum is the most common diagnostic sample for tuberculosis, tested through smear microscopy, culture, or NAAT. Urine, a point-of-care test for advanced HIV, is recommended for adults [ 8 ]. Microscopic examination is the primary diagnostic method in low- and middle-income countries, but its sensitivity is low, especially in cases of confirmed pulmonary TB and HIV/Immunosuppression [ 9 ]. The disease is a leading cause of morbidity and mortality, especially in low- and middle-income countries [ 10 ]. Despite global efforts, the emergence of drug-resistant TB strains, including multidrug-resistant (MDR) and extensively drug-resistant (XDR) strains, has complicated TB control strategies [ 11 ]. Understanding the molecular characteristics of MTB is crucial for developing targeted diagnostic, therapeutic, and control measures. However, data from different studies on molecular markers, mutations associated with drug resistance, and strain diversity of MTB are often fragmented, region-specific, or inconsistent. This makes it challenging to form a comprehensive understanding of the global or regional molecular epidemiology of MTB [ 12 ]. The purpose of this systematic review and meta-analysis was to find and evaluate previous studies that looked at the molecular prevalence and drug resistance pattern of pulmonary tuberculosis. Obtaining the global pooled prevalence of TB phenotypes and genotypes was another goal. Methods This study protocol was designed in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines for systematic reviews and meta-analyses. A comprehensive search was conducted across PubMed, Google Scholar, and ScienceDirect databases to identify eligible peer-reviewed articles for inclusion. The database search strategy was rigorously applied, focusing on original research articles published in the English language between 2018 and 2023. Titles, abstracts, and full-text articles were systematically screened and evaluated for eligibility. The scope of the review was restricted to scientific studies conducted during the period from 2014 to 2023. The keywords used for searching articles were “Multidrug-resistant tuberculosis” or “MDR-TB” or “Drug-resistant TB” or “Risk factors of MDR-TB” or “Predictors of MDR-TB”, "Molecular epidemiology", or "Phylogeny", or "Lineage", or " Mycobacterium tuberculosis ". Search results were compiled using a citation management software Zotero. In addition to databases used, we explored references of selected studies to incorporate all potential pertinent articles to construct our summary estimate. Inclusion and Exclusion Criteria Studies conducted in hospital or community settings that analysed human TB presumptive cases and Mycobacterium tuberculosis (MTB) isolates from sputum samples were included. Articles deemed irrelevant or lacking sufficient information were excluded from the analysis. Data Extraction Key variables such as age, sex, methods of TB diagnosis and drug susceptibility testing (DST), prevalence of drug-resistant TB (DR-TB) and multidrug-resistant TB (MDR-TB), tools for molecular analysis, and TB lineages were extracted. Additional information on authors, country, study design, study area, sample size, year of publication, and study duration were also collected. For studies with insufficient or missing data, or where full-text access was unavailable, corresponding authors were contacted via email to obtain the relevant information. Data Synthesis and Analysis The studies were categorized based on their design, and the study characteristics were summarized using percentages and frequencies for categorical variables. A proportion meta-analysis was performed using MedCalc-version 23.0.9 software and JASP 0.14.3 to analyse pooled data. Statistical Analysis Statistical analyses included meta-analysis of proportions to assess heterogeneity and publication bias. Forest plots and funnel plots were generated for MDR-TB, rifampicin-resistant TB, and isoniazid-resistant TB. One-way ANOVA was used to compare mean age across studies, and chi-squared tests were applied to evaluate associations between gender and TB prevalence. Quality Assessment This study included adherence to PRISMA guidelines, rigorous database searches with standardized eligibility criteria, and independent screening and data extraction by multiple reviewers to minimize bias. Study quality was assessed using standardized tools, and missing data were addressed by contacting corresponding authors. Statistical analyses, including heterogeneity and publication bias assessments, were conducted using robust methods such as meta-analysis of proportions, sensitivity analyses, and appropriate statistical tests. All steps were thoroughly documented to ensure transparency, reproducibility, and scientific rigor. This systematic review was not registered. Results We identified a total of 745 studies of them, about 565, 13, 167, and 167 studies respectively from the PubMed, ScienceDirect, Google Scholar Science searches. The reviewers read titles, abstracts, and keywords to assess for duplicates and adherence to inclusion and exclusion criteria. Records that were not excluded at this stage were reviewed in full text to assess the same; records were excluded leaving a total of 13 studies for inclusion in this review. Of the 745 studies, 13 included studies were conducted in various countries, including India, Mexico, Ghana, Ethiopia, China, and Kenya. The study sites included diverse locations such as Andhra Pradesh, Monterrey, Eastern region of Ghana, Addis Ababa, Sikkim, Jigjiga city, Fujian province, Yunnan province, Nairobi, Jiangxi province, Volta, and South Xinjiang worldwide. The study period ranged from one to five years. Mycobacterial cultures were primarily grown on Lowenstein-Jensen (LJ) media, with a few studies used Middlebrook 7H9 broth (MGIT). A total of 3469 individual isolates analyzed varied across studies, ranging from 104 to 1071. Age ranges were reported in some studies, with variations observed across different populations with age ranges spanning from 0–95 years. Mean age was reported in some studies, varying from 26.4 years [13] to 50 years [14] with mean of average age was 37.76 years. Most of the used for studies molecular techniques characterization were spoligotyping and MIRU-VNTR typing given in table 1. Table 1 Characteristics of selected studies (2018-2023) Reference Country Study site Study period Culture on Isolates Age range Molecular analysis Srikar et al .,2023 India Andhra Pradesh 2018 – 2019 LJ media 104 0-90 Spoligo, MIRU-VNTR Flore et al . 2023 Mexico Monterrey 2017 – 2021 LJ media 190 0-61 Spoligo, MIRU-VNTR Benjamin et al . 2021 Ghana Eastern region of Ghana 2017 LJ media 143 17-89 Spoligo, MIRU-VNTR Diriba et al . 2020 Ethiopia Addis Ababa NR LJ media 153 NR Spoligo, MIRU-VNTR Devi et al . 2021 India Sikkim 2016 – 2018 LJ media 399 NR Spoligo, MIRU-VNTR Worku et al . 2022 Ethiopia Jigjiga city 2018 – 2019 LJ media 323 15-80 Spoligo, MIRU-VNTR Lin et al . 2021 China Fujian province 2016 – 2017 LJ media 119 NR Spoligo, MIRU-VNTR Bai et al . 2019 China Yunnan province 2014 – 2016 MGIT 270 NR Spoligo, SIVIT database Ogari et al . 2019 Kenya Nairobi 2015 – 2016 MGIT 132 18-60 NR Luo et al . 2019 China Jiangxi province 2014 – 2016 LJ media 1071 NR MIRU-VNTR Ameke et al . 2021 Ghana Volta 2016 – 2017 LJ media 115 13-86 IS6110 Gupta et al . 2019 India Madhya Pradesh 2014 – 2017 LJ media 103 17-95 Spoligotyping Yin et al . 2023 China South Xinjiang 2017 – 2019 LJ media 347 NR Spoligo, MIRU-VNTR LJ: Lowenstein-Jensen Medium, NR: Not Reported, Spoligo: Spoligotyping, MIRU-VNTR: Mycobacterial Interspersed Repetitive Unit-Variable Number Tandem Repeat The age and gender distribution among TB patients in selected studies reveals significant variability given in table 2. The number of isolates ranged from 103 to 347. Across all studies, the proportion of male patients was consistently higher than females, with male percentages ranging from 39.42% [14] to 78.64% [15]. Female representation ranged from 21.36% (Gupta et al ., 2019) to 60.58% [14]. Statistical tests showed high heterogeneity in the data, with an I² inconsistency of 90.86% (P<0.001; 95% CI: 85.67–94.17). Publication bias, assessed via Kendall's Tau test, indicated non-significant values for male (P=0.2429) and female (P=0.1857) proportions. One-way ANOVA (F=2.208, P=0.474) did not reveal significant variation in mean age across studies. However, the Chi-squared test demonstrated significant variability in gender distribution (P=0.0282), suggesting potential differences in TB prevalence between male and female patients across the studies. These findings underscore the importance of considering demographic heterogeneity in TB research and management strategies. Table 2 Statistical inference for gender of TB patients in selected studies Test for heterogeneity Significance level/I 2 (Inconsistency) P<0.001/90.86% P<0.001/90.86% 95% CI for I 2 85.67 to 94.17 90.11 to 95.62 Publication bias Kendall's Tau test 0.2727 -0.3091 Significance level P=0.2429 P=0.1857 One way ANOVA F-ratio 2.208 Significance level P=0.474 Chi-Squared test Significance level P=0.0282 The data in Table 3 highlighted the prevalence of drug-resistant tuberculosis across various countries, illustrating significant geographical variability in resistance to first-line anti-tuberculosis drugs and multidrug resistance (MDR). In India [15] reported resistance rates of 28.9% for rifampicin (RIF), 47.12% for isoniazid (INH), and 23% for MDR-TB, while [16] recorded an MDR rate of 34.4%. Resistance in Ethiopia was notably lower, with [17] documenting RIF resistance at 8% and an MDR rate of 19.2%. Meanwhile, [18] observed a high rate of resistance to ethambutol (EMB) at 62.2%. China displayed moderate resistance levels, with [19]) reporting RIF resistance at 20.3% and MDR-TB at 7.7%, while [20] observed higher MDR prevalence at 19.3%. In Ghana [21] recorded RIF resistance at 36.7% and MDR-TB at 32.9%, representing one of the highest MDR rates in the dataset. Mexico [22] and Kenya [23] showed relatively low resistance rates, reporting MDR-TB rates of 1.5% and 1.6%, respectively. Table 3 Prevalence of drug resistance tuberculosis in selected studies Reference Country Isolates RIF INH SM EMB PZA MDR No % No % No % No % N0 % No. % Srikar et al ., 2023 India 104 30 28.9 49 47.12 36 34.6 6 5.77 0 NR 24 23 Flores et al ., 2023 Mexico 190 5 2.6 13 6.9 NR NR NR NR NR NR 3 1.5 Benjamin et al ., 2021 Ghana 143 29 36.7 45 57 40 50.6 14 17.7 NR NR 26 32.9 Diriba et al ., 2020 Ethiopia 153 11 8 19 13.8 7 5.1 3 2.2 19 13.9 29 19.2 Worku et al ., 2022 Ethiopia 323 NR NR NR NR NR 58 NR 62.2 37 NR NR NR Lin et al ., 2021 China 119 56 20.7 34 27.8 39 14.4 56 20.7 50 18.5 NR NR Bai et al ., 2019 China 270 56 37.8 74.25 27.8 39 14.4 34 12.6 50 18.5 52 19.3 Ogari et al ., 2019 Kenya 132 1 0.8 1 0.8 NR NR NR NR NR NR 2 1.6 Ameke et al ., 2021 Ghana 115 5 5.1 6.1 NR NR NR NR NR NR NR 3 3 Gupta et al ., 2019 India 103 13 12.6 NR NR NR NR NR NR NR NR 5 4.8 Yin et al ., 2023 China 347 61 20.3 98 32.7 67 22.3 26 8.7 NR NR 23 7.7 Devi et al ., 2021 India 399 NA NR NR NR NR NR NR NR NR NR 81 34.4 Luo et al ., 2019 China 1071 28 60 60 2 NR NR NR 157 NR Note: RIF: Rifampicin, INH: Isoniazid, SM: Streptomycin, EMB: Ethambutol, PZA: Pyrazinamide, MD: Multidrug-resistance, NR: No record The table 4 showed the distribution of Mycobacterium tuberculosis phylogenetic lineages varies significantly across geographical regions, reflecting the complex epidemiology of tuberculosis (TB). In India [14] found that lineage 3 (East-African Indian lineage) predominated with 55.8% of isolates, followed by lineage 1 (Indo-Oceanic) at 21.2%. Conversely [15] reported a slightly higher prevalence of lineage 1 at 37.9%, with lineage 3 accounting for 44.7%. In Ethiopia [17] observed that lineage 4 (Euro-American lineage) was dominant, representing 72.5% of isolates, while lineage 3 accounted for 19%. Similarly, [18] identified lineage 4 as the most prevalent (53.9%), but with a higher proportion of lineage 3 (25.7%). In China, lineage 2 (East Asian/Beijing lineage) was the most prevalent, accounting for 67.2% [24] and 73.7% [20] of isolates in studies. Lineage 3 was less common, with [24] reporting 29.4% and [20] finding no isolates from this lineage. In Mexico, [22] reported lineage 5 (West African lineage) as the most common, comprising 44.7% of isolates, followed by lineage 4 at 40.5%. In Ghana, lineage 4 predominated, with [21] and [25] recording 55.9% and 71.3%, respectively, while lineage 3 represented a smaller fraction. Table 4 Phylogenic lineages of Mycobacterium tuberculosis in isolates of selected studies Reference Country Isolate L1 L2 L3 L4 L5 Others No No % No % No % No % No % No % Srikar et al ., 2023 India 104 22 21.2 12 11.5 58 55.8 6 5.8 6 5.8 6 5.8 Flores et al ., 2023 Mexico 190 10 5.3 18 9.5 0 0.0 77 40.5 85 44.7 NR NR Benjamin et al ., 2021 Ghana 143 1 0.7 1 0.7 52 36.4 80 55.9 1 0.7 8 5.6 Diriba et al ., 2020 Ethiopia 153 0 0.0 1 0.7 29 19.0 111 72.5 3 2.0 9 5.9 Worku et al ., 2022 Ethiopia 323 16 5.0 48 14.9 83 25.7 174 53.9 0 0.0 2 0.6 Lin et al ., 2021 China 119 0 0.0 80 67.2 35 29.4 2 1.7 0 0.0 2 1.7 Bai et al ., 2019 China 270 11 4.1 199 73.7 0 0.0 22 8.1 0 0.0 38 14.1 Ameke et al ., 2021 Ghana 115 0 0.0 3 2.6 1 0.9 82 71.3 24 20.9 5 4.3 Gupta et al ., 2019 India 103 39 37.9 0 0.0 46 44.7 4 3.9 14 13.6 0 0.0 Yin et al ., 2023 China 347 16 4.6 208 59.9 47 13.5 50 14.4 0 0.0 26 7.5 L1: Indo-Oceanic lineage or East African Indian lineage, L2: East Asian lineage, (Beijing), L3: East-African Indian lineage, L4: Euro-American lineage, L5: West African lineage The meta-analysis summarizes the prevalence of multidrug-resistant tuberculosis (MDR-TB) across 11 studies, including 3027 isolates. The fixed-effects model estimated an overall MDR-TB prevalence of 12.52% (95% CI: 11.37–13.75%), while the random-effects model showed a slightly lower prevalence of 10.76% (95% CI: 6.62–15.75%). Individual studies reported a wide range of prevalence values [14] from India reported a relatively high MDR-TB prevalence of 23.08% (95% CI: 15.38–32.36%), while [22] in Mexico observed a much lower prevalence of 1.58% (95% CI: 0.33–4.55%). Similarly, [26] in China, with the largest sample size of 1071 isolates, reported a prevalence of 14.66% (95% CI: 12.59–16.92%), contributing significantly to the overall weight. Heterogeneity among the studies was substantial, with an I² statistic of 93.44% (95% CI: 90.15–95.64%), indicating high inconsistency in the reported results. This heterogeneity was statistically significant (Q = 152.52, p < 0.00001). Publication bias was assessed using Egger’s and Begg’s tests. Egger’s test showed no statistically significant bias (intercept = -3.2444, 95% CI: -10.15 to 3.66, p = 0.3154), and Begg’s test also indicated no significant bias (Kendall’s Tau = -0.2364, p = 0.3115). These findings highlight notable variability in MDR-TB prevalence across regions, emphasizing the need for tailored interventions to address MDR-TB in different contexts. High heterogeneity suggests potential differences in study populations, diagnostic methods, and healthcare settings. Table 5 Meta- analysis for prevalence of MDR TB in selected studies Study References Isolates Proportion (%) 95% CI Weight (%) Fixed Random Srikar et al ., 2023 104 23.077 15.380 to 32.363 3.46 8.57 Flores et al ., 2023 190 1.579 0.327 to 4.545 6.29 9.16 Benjamin et al ., 2021 143 18.182 12.234 to 25.494 4.74 8.91 Diriba et al ., 2020 153 18.954 13.077 to 26.074 5.07 8.97 Bai et al ., 2019 270 19.259 14.731 to 24.476 8.92 9.39 Ogari et al ., 2019 132 1.515 0.184 to 5.366 4.38 8.83 Ameke et al ., 2021 115 2.609 0.541 to 7.435 3.82 8.68 Gupta et al ., 2019 103 4.854 1.595 to 10.966 3.42 8.55 Yin et al ., 2023 347 6.628 4.248 to 9.780 11.45 9.52 Devi et al ., 2021 399 20.301 16.462 to 24.587 13.17 9.58 Luo et al ., 2019 1071 14.659 12.594 to 16.921 35.29 9.84 Total (fixed effects) 3027 12.524 11.367 to 13.753 100.0 100.00 Total (random effects) 3027 10.756 6.616 to 15.753 0 100.00 Heterogeneity test Q I 2 (Inconsistency) 95% CI for I 2 DF P-value 152.5158 93.44% 90.15 to 95.64 10 <0.00001 Egger's test Intercept 95% CI Significance level -3.2444 10.1470 to 3.6582 P=0.3154 Begg's test Kendall's Tau= -0.2364 Significance level (P=0.3115) In the figure 3, the forest plot indicates a pooled prevalence of MDR-TB ranging from 10.8% (random-effects) to 12.5% (fixed-effects), with significant heterogeneity among studies (I² = 93.44%), reflecting variability in MDR-TB prevalence across different regions and populations. The funnel plot suggests potential asymmetry, indicating possible publication bias or small-study effects, although Egger's and Begg's tests show no statistically significant evidence of bias (p > 0.05). These results highlight global disparities in MDR-TB prevalence and the need for standardized methodologies to improve comparability. The meta-analysis of rifampicin resistance in TB reveals a pooled prevalence of 2.7% (fixed-effects) and 2.9% (random-effects), with a 95% CI of 2.0%–3.6% (fixed) and 1.5%–4.8% (random). Significant heterogeneity is observed among studies (I² = 74.74%, p < 0.0001), indicating variability in rifampicin resistance prevalence across regions and populations. Publication bias assessment shows no significant bias, as evidenced by Egger's test (p = 0.5508) and Begg's test (p = 0.4042). These findings underscore the moderate prevalence of rifampicin resistance and the need for tailored regional strategies to combat TB drug resistance. T able 6 Meta-Analysis of Rifampicin Resistance in TB Study References Total Proportion (%) 95% CI Weight (%) Fixed Random Srikar et al ., 2023 104 3.846 1.058 to 9.556 6.24 8.97 Flores et al ., 2023 190 2.632 0.860 to 6.034 11.34 10.62 Benjamin et al ., 2021 143 0.699 0.0177 to 3.835 8.55 9.90 Diriba et al ., 2020 151 7.285 3.692 to 12.661 9.03 10.04 Lin et al ., 2021 119 0.000 0.000 to 3.052 7.13 9.37 Bai et al ., 2019 270 4.815 2.588 to 8.093 16.09 11.38 Ogari et al ., 2019 132 0.758 0.0192 to 4.149 7.90 9.67 Ameke et al ., 2021 115 4.348 1.427 to 9.855 6.89 9.27 Gupta et al ., 2019 103 6.796 2.776 to 13.502 6.18 8.94 Yin et al ., 2023 347 0.865 0.179 to 2.506 20.67 11.83 Total (fixed effects) 1674 2.744 2.017 to 3.641 100.00 100.00 Total (random effects) 1674 2.904 1.495 to 4.759 100.00 100.00 Test for heterogeneity Q I 2 95% CI DF P value 35.6281 74.74% 52.83 to 86.47 9 P<0.0001 Publication bias Egger's test Intercept 95% Significance level 1.9641 -5.3090 to 9.2372 P= 0.5508 Begg's test Kendall's Tau 0.200 Significance level 0.2000 P=0.4042 Isoniazid resistance in tuberculosis (TB) revealed an overall resistance rate of 6.746% (95% CI: 5.559% to 8.097%) under the fixed-effects model and 6.186% (95% CI: 3.358% to 9.799%) under the random-effects model. Substantial heterogeneity was observed among the included studies, as evidenced by a Q-statistic of 56.8352, and I² value of 85.92% (95% CI: 75.21% to 92.01%), and a P-value of <0.0001. The highest reported resistance was by [ 14] at 15.385% (95% CI: 9.057% to 23.778%), while the lowest was reported by [23]) at 0.758% (95% CI: 0.0192% to 4.149%). In terms of study weight, [19] contributed the most (22.03%) under the fixed-effects model, followed [20] at 17.15%. Assessment of publication bias through Egger’s test (P = 0.3573) and Begg’s test (P = 0.4042) showed no significant evidence of bias, supporting the robustness of the findings. The variability in resistance rates across studies highlights the need for region-specific surveillance and interventions to address isoniazid resistance effectively. Table 7 Metanalysis for isoniazid resistance TB in selected studies Study Sample (N) Proportion (%) 95% CI Weight (%) Fixed Random Srikar et al ., 2023 104 15.385 9.057 to 23.778 6.65 10.36 Flores et al ., 2023 190 6.842 3.693 to 11.416 12.09 11.45 Benjamin et al ., 2021 143 4.196 1.555 to 8.909 9.11 10.99 Diriba et al ., 2020 151 12.583 7.749 to 18.950 9.62 11.09 Lin et al ., 2021 119 0.000 0.000 to 3.052 7.59 10.64 Bai et al ., 2019 270 8.519 5.477 to 12.508 17.15 11.90 Ogari et al ., 2019 132 0.758 0.0192 to 4.149 8.42 10.84 Ameke et al ., 2021 115 5.217 1.939 to 11.010 7.34 10.57 Yin et al ., 2023 347 8.934 6.151 to 12.441 22.03 12.15 Total (fixed effects) 1571 6.746 5.559 to 8.097 100.00 100.00 Total (random effects) 1571 6.186 3.358 to 9.799 100.00 100.00 Test for heterogeneity Q I 2 95% CI DF P value 56.8352 85.92% 75.21 to 92.01 8 P<0.0001 Publication bias Egger's test Intercept 95% Significance level -4.3250 -14.7045 to 6.0545 P= 0.3573 Begg's test Kandall's Tau Significance level -0.2222 P=0.4042 The random-effects (RE) model summary effect size is 25.81 (95% CI: -59.12 to 110.74), shown by the diamond at the bottom of the forest plot. This result suggests considerable heterogeneity among studies. The scatter of points appears symmetric around the vertical line of funnel plot (representing the overall effect size), suggesting no significant publication bias. Discussion The findings of this systematic review and meta-analysis provide an insightful overview of the epidemiological, molecular, and phenotypic characteristics of Mycobacterium tuberculosis across various regions and studies from 2018 to 2023. These studies highlight the diversity in drug resistance patterns, phylogenetic lineages, and demographic distribution of tuberculosis (TB) cases globally. The demographic distribution of TB patients reveals significant variability across studies. The predominance of male patients in most of the studies aligns with existing literature that suggests higher TB incidence among men, the Chi-squared test (p = 0.0282) confirms significant variability in gender distribution. This is agreed by the previous studies conducted by [ 27 – 31 ]. The age range varied widely across studies, but ANOVA did not reveal significant variation in mean age, underscoring the disease's impact across age groups. The prevalence of drug-resistant TB demonstrated substantial geographical variability in this study is supported by [ 32 , 33 ]. The global pooled prevalence of the various drug-resistant tuberculosis likes MDR, Isoniazid (INH), and Rifampcin (RIF) was determined to be 12.5%, 6.2.%, and 2.9%, respectively, based on the published findings of all included studies are lines with the previous study by [ 33 ] but lower than [ 34 , 35 ]. High multidrug resistance (MDR-TB) rates were observed in India, Ghana, and parts of China, highlighting significant public health challenges. For instance [ 16 ] reported the highest MDR-TB rate of 34.4% in India, while [ 21 ] observed 32.9% MDR-TB in Ghana. These high resistance rates could be attributed to inconsistent treatment adherence, suboptimal drug supply systems, and variations in healthcare infrastructure. Conversely, Mexico [ 22 ] and Kenya [ 23 ] reported relatively low MDR-TB rates (1.5% and 1.6%, respectively), reflecting possible differences in treatment policies or surveillance systems. For instance [ 18 ] in Ethiopia noted exceptionally high ethambutol (EMB) resistance (62.2%), which could have implications for first-line treatment regimens in the region. Phylogenetic analyses reveal distinct lineage distributions across regions, reflecting the genetic diversity of M. tuberculosis and its adaptation to different host populations. Lineage 3 (East-African Indian lineage) predominated in studies from India, with [ 14 ] reporting 55.8% prevalence is similar to finding of previous study [ 36 ], whereas lineage 4 (Euro-American lineage) dominated in Ethiopia [ 17 ] and Ghana [ 21 ], as observed that is similar to study conducted in Ethiopia by [ 37 ] and in Ghana [ 38 ]. In China, lineage 2 (East Asian/Beijing lineage) was the most prevalent, reported at 67.2% by [ 24 ] Land 73.7% by [ 20 ]. The high prevalence of lineage 2 in China aligns with its well-documented association with drug resistance and high transmissibility is lining with previous studies [39, 40]. In Mexico [ 22 ], lineage 5 (West African lineage) was predominant, highlighting regional specificity in lineage distribution. This diversity emphasizes the importance of molecular typing in understanding TB transmission dynamics and guiding regional TB control strategies. Conclusion This study reviewed 745 articles, ultimately including 13 studies from diverse regions such as India, China, Ghana, Ethiopia, Kenya, and Mexico, focusing on tuberculosis (TB) epidemiology, drug resistance, and phylogenetic lineages from 2018–2023. Analysis of 3469 isolates revealed significant variability in age, gender distribution, and drug resistance. Resistance to first-line drugs, including rifampicin (RIF) and isoniazid (INH), varied widely, with multidrug-resistant TB (MDR-TB) rates ranging from 1.5–34.4%. The phylogenetic analysis highlighted geographical diversity, with predominant lineages being East-Asian/Beijing in China, Euro-American in Ethiopia and Ghana, and East-African Indian in India. These findings underscore the importance of tailored TB management strategies based on regional demographic and molecular epidemiological profiles. Declarations Conflict of interest The authors declare no conflict of interest related to this systematic review. All authors contributed equally to data extraction, review analysis, and manuscript preparation. The work was conducted independently, and no external influences, financial or personal, have affected the objectivity or integrity of this review. Funding No Author Contribution Declaration by the AuthorsCorresponding Author: Madan Singh Bohara (M.S.B.)Co-Author: Prof. Dr. Dwij Raj Bhatta (D.R.B)Author Contributions: Conceptualization, M.S.B.; data curation, D.R.B.; formal analysis, MSB.; investigation, MSB. and D.R.B.; methodology, M.S.B.; supervision, D. R.B.; visualisation, M.S.B.; writing—original draft, M.S.B. and writing review and editing, D.R B. All authors have read and agreed to the published version of the manuscript.The authors confirm that the data supporting this study's findings are available within the article.Conflict of Interest/Competing Interests:The authors declare that no conflicts of interest or competing interests are associated with this study.Acknowledgements: We extend our gratitude to the Central Department of Microbiology, Tribhuvan University, Nepal for their invaluable assistance in providing access to relevant literature and library access to complete this study. Data Availability:The authors confirm that the data that support the findings of this study are available within the article. Datasets are available through the corresponding author upon reasonable request. References Mann BC, Loubser J, Omar S, Glanz C, Ektefaie Y, Jacobson KR, et al. Systematic review and meta-analysis of protocols and yield of direct from sputum sequencing of Mycobacterium tuberculosis . Microbiology. 2024. https://doi.org/10.1101/2024.12.04.625621 World Health Organization. Global tuberculosis report. Geneva; 2024. Available from: https://www.who.int/teams/global-tuberculosis-programme/tb-reports/global-tuberculosis-report-2024 Shah Y, Paudel S, Pandey K, Gupta GP, Solo ES, Joshi J, et al. Insights into transmission dynamics of Mycobacterium tuberculosis complex in Nepal. Trop Med Health. 2022;50(1):8. https://doi.org/10.1186/s41182-022-00400-z MacLean E, Kohli M, Weber SF, Suresh A, Schumacher SG, Denkinger CM, et al. Advances in molecular diagnosis of tuberculosis. J Clin Microbiol . 2020;58(e01582-19). https://doi.org/10.1128/JCM.01582-19 Pokam BDT, Yeboah-Manu D, Lawson L, Guemdjom PW, Okonu R, Madukaji L, et al. Molecular analysis of Mycobacterium tuberculosis isolated in the North Central Zone of Nigeria. J Epidemiol Glob Health. 2019;9(4):259. https://doi.org/10.2991/jegh.k.191015.001 Kato-Maeda M, Metcalfe JZ, Flores L. Genotyping of Mycobacterium tuberculosis : Application in epidemiologic studies. Future Microbiol. 2011;6:203–16. https://doi.org/10.2217/fmb.10.165 Deng S, Sun Y, Xia H, Liu Z, Gao L, Yang J, et al. Accuracy of commercial molecular diagnostics for the detection of pulmonary tuberculosis in China: A systematic review. Sci Rep. 2019;9(1):4553. https://doi.org/10.1038/s41598-019-41074-8 Savage HR, Rickman HM, Burke RM, Odland ML, Savio M, Ringwald B, et al. Accuracy of upper respiratory tract samples to diagnose Mycobacterium tuberculosis : A systematic review and meta-analysis. Lancet Microbe. 2023;4(10):e811–e821. https://doi.org/10.1016/S2666-5247(23)00190-8 Sester M, Giehl C, McNerney R, Kampmann B, Walzl G, et al. Challenges and perspectives for improved management of HIV/ Mycobacterium tuberculosis co-infection. Eur Respir J. 2010;36(6):1242–7. https://doi.org/10.1183/09031936.00055910 World Health Organization (WHO). Global Tuberculosis Report 2023. Geneva, Switzerland: WHO; 2023. Available from: https://www.who.int/tb/publications/global_report/en/ Dheda K, Gumbo T, Maartens G, Dooley KE, McNerney R, Murray M, et al. The epidemiology, pathogenesis, transmission, diagnosis, and management of multidrug-resistant, extensively drug-resistant, and incurable tuberculosis. Lancet Respir Med. 2017;5(4):291–360. https://doi.org/10.1016/S2213-2600(17)30079-6 Zhang Y, Yew WW. Mechanisms of drug resistance in Mycobacterium tuberculosis : Update 2015. Int J Tuberc Lung Dis. 2015;19(11):1276–89. https://doi.org/10.5588/ijtld.15.0389 Ogari CO, Nyamache AK, Nonoh J, Amukoye E. Prevalence and detection of drug resistant mutations in Mycobacterium tuberculosis among drug naïve patients in Nairobi, Kenya. BMC Infect Dis. 2019;19:279. https://doi.org/10.1186/s12879-019-3911-9 Srikar A, Venkataramana B, Mohan A, Sarma P, Rekha Devi K, Narain K, et al. Molecular diversity of Mycobacterium tuberculosis isolates in treatment-experienced patients from Andhra Pradesh, India. J Infect Dev Ctries. 2023;17(08):1114–24. https://doi.org/10.3855/jidc.17757 Gupta R, Amrathlal RS, Prakash R, Jain S, Tiwari PK. Spoligotyping, phenotypic and genotypic characterization of katG and rpoB genes of Mycobacterium tuberculosis isolates from the Sahariya tribe of Madhya Pradesh, India. J Infect Public Health . 2019;12(3):395–402. https://doi.org/10.1016/j.jiph.2018.12.009 Devi KR, Pradhan J, Bhutia R, Dadul P, Sarkar A, Gohain N, Narain K. Molecular diversity of Mycobacterium tuberculosis complex in Sikkim, India, and prediction of dominant spoligotypes using artificial intelligence. Sci Rep. 2021;11(1):1–16. https://doi.org/10.1038/s41598-021-86626-z Diriba G, Kebede A, Tola HH, Yenew B, Moga S, Addise D, et al. Molecular characterization and drug resistance patterns of Mycobacterium tuberculosis complex in extrapulmonary tuberculosis patients in Addis Ababa, Ethiopia. PLoS One. 2020;15(12):1–13. https://doi.org/10.1371/journal.pone.0243493 Worku G, Gumi B, Girma M, Mohammedbirhan B, Diriba G, Seid G, et al. Drug sensitivity of clinical isolates of Mycobacterium tuberculosis and its association with bacterial genotype in the Somali region, Eastern Ethiopia. Front Public Health. 2022;10:942618. https://doi.org/10.3389/fpubh.2022.942618 Yin C, Mijiti X, Liu H, Wang Q, Cao B, Anwaierjiang A, et al. Molecular epidemiology of clinical Mycobacterium tuberculosis isolates from Southern Xinjiang, China using spoligotyping and 15-locus MIRU-VNTR typing. Infect Drug Resist. 2023;16:1313–26. https://doi.org/10.2147/IDR.S393192 Bai R, Chi S, Li X, Dai X, Ji Z, Jian M, et al. Genetic diversity and drug susceptibility patterns of the Mycobacterium tuberculosis complex in Yunnan, China. Biosci Rep. 2019;39(5):BSR20181746. https://doi.org/10.1042/BSR20181746 Benjamin Thumamo Pokam Y, Yeboah-Manu D, Amiteye D, Asare P, Guemdjom PW, Yhiler NY, et al. Molecular epidemiology and multidrug resistance of Mycobacterium tuberculosis complex from pulmonary tuberculosis patients in the Eastern region of Ghana. Heliyon. 2021;7(10):e08152. https://doi.org/10.1016/j.heliyon.2021.e08152 Flores-Aréchiga A, Zacarías-Hernández JL, Vázquez-Cortés CG, Reyes S, De O. Cavazos M, Rivera-Morales LG, et al. Molecular epidemiology and drug resistance of Mycobacterium tuberculosis in a tertiary care hospital in northeastern Mexico. J Infect Dev Ctries. [Epub ahead of print]. https://doi.org/10.3855/jidc.18026 Lin Y, Lin H, Xiao L, Chen Y, Meng X, Zeng X, et al. Tuberculosis recurrence over a 7-year follow-up period in successfully treated patients in a routine program setting in China: A prospective longitudinal study. Int J Infect Dis. 2021;110:403–9. Ameke S, Asare P, Aboagye SY, Otchere ID, Osei-Wusu S, Yeboah-Manu D, et al. Molecular epidemiology of Mycobacterium tuberculosis complex in the Volta Region of Ghana. PLoS One. 2021;16(3):1–12. https://doi.org/10.1371/journal.pone.0238898 Luo D, Chen Q, Xiong G, Peng Y, Liu T, Chen X, et al. Prevalence and molecular characterization of multidrug-resistant M. tuberculosis in Jiangxi province, China. Sci Rep. 2019;9(1):1–8. https://doi.org/10.1038/s41598-019-43547-2 Ulasi A, Nwachukwu N, Onyeagba R, Umeham S, Amadi A. Prevalence of rifampicin-resistant tuberculosis among pulmonary tuberculosis patients in Enugu, Nigeria. Afr Health Sci. 2022;22(2):156. https://doi.org/10.4314/ahs.v22i2.18 Rasaki SO, Ajibola AA, Musa SA, Moradeyo AK, Odeigah LO, Abdullateef SG, et al. Rifampicin-resistant tuberculosis in a secondary health institution in Nigeria, West Africa. J Infect Dis Ther. 2014;2:139. Nair SA, Raizada N, Sachdeva KS, Dankinger C, Schumacher S, Dewan P, et al. Factors associated with tuberculosis and rifampicin-resistant tuberculosis amongst symptomatic patients in India: A retrospective analysis. PLoS One. 2016;1(11):e0150054. https://doi.org/10.1371/journal.pone.0150054 Bohara MS, Ojha RC. Rifampicin-resistant tuberculosis and associated factors among pulmonary tuberculosis patients in Mahakali Provincial Hospital, Nepal. Far West Rev. 2024;2(1):220–34. https://doi.org/10.3126/fwr.v2i1.70540 Humayun M, Chirenda J, Ye W, Mukeredzi I, Mujuru HA, Yang Z. Effect of gender on clinical presentation of tuberculosis (TB) and age-specific risk of TB, and TB-HIV coinfection. Open Forum Infect Dis. 2022;9(10):ofac512. https://doi.org/10.1093/ofid/ofac512 Molla KA, Reta MA, Ayene YY. Prevalence of multidrug-resistant tuberculosis in East Africa: A systematic review and meta-analysis. PLoS One. 2022;17(6):e0270272. https://doi.org/10.1371/journal.pone.0270272 Salari N, Kanjoori AH, Hosseinian-Far A, Hasheminezhad R, Mansouri K, Mohammadi M. Global prevalence of drug-resistant tuberculosis: A systematic review and meta-analysis. Infect Dis Poverty. 2023;12(57). https://doi.org/10.1186/s40249-023-01107- Akalu TY, Clements AC, Gebreyohannes EA, Gilmour B, Alene KA. Prevalence of tuberculosis infection among contacts of drug-resistant tuberculosis patients: A systematic review and meta-analysis. J Infect. 2024;89(2):106198. https://doi.org/10.1016/j.jinf.2024.106198 Shah NS, Yuen CM, Heo M, Tolman AW, Becerra MC. Yield of contact investigations in households of patients with drug-resistant tuberculosis: Systematic review and meta-analysis. Clin Infect Dis. 2014;58(3):381–91. Singh AV, Singh S, Yadav A, Kushwah S, Yadav R, Sai DK, Chauhan DS. Genetic variability in multidrug-resistant Mycobacterium tuberculosis isolates from patients with pulmonary tuberculosis in North India. BMC Microbiol. 2021;21(1):123. https://doi.org/10.1186/s12866-021-02174-6 Mekonnen A, Merker M, Collins JM, Addise D, Aseffa A, Petros B, et al. Molecular epidemiology and drug resistance patterns of Mycobacterium tuberculosis complex isolates from university students and the local community in Eastern Ethiopia. PLoS One. 2018;13(9):e0198054. https://doi.org/10.1371/journal.pone.0198054 Asante-Poku A, Otchere ID, Osei-Wusu S, Sarpong E, Baddoo A, Forson A, et al. Molecular epidemiology of Mycobacterium africanum in Ghana. BMC Infect Dis. 2016;16:385. https://doi.org/10.1186/s12879-016-1739-3 Zhu C, Yang T, Yin J, Jiang H, Takiff HE, Gao Q, et al. The global success of Mycobacterium tuberculosis modern Beijing family is driven by a few recently emerged strains. Microbiol Spectr. 2023;11(4):e03339-22. https://doi.org/10.1128/spectrum.03339-22 Holt KE, McAdam P, Thai PVT, Thuong NT, Minh Ha DT, Lan NN, et al. Frequent transmission of the Mycobacterium tuberculosis Beijing lineage and positive selection for EsxW Beijing variant in Vietnam. Nat Genet. 2018;50(6):849. https://doi.org/10.1038/s41588-018-0117-9 Additional Declarations No competing interests reported. Supplementary Files Addtionalfile.xlsx 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. 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studies\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"04.png","url":"https://assets-eu.researchsquare.com/files/rs-5798511/v1/5573b234e1b76d44302e8783.png"},{"id":76282756,"identity":"12a580a5-a32a-4216-a450-ca1d083a16b2","added_by":"auto","created_at":"2025-02-14 10:50:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2319611,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5798511/v1/3886e1c3-b467-4dc4-a3c0-6800761cf607.pdf"},{"id":76278666,"identity":"9e272732-3ab4-46c8-9e77-a1954938ccc2","added_by":"auto","created_at":"2025-02-14 10:18:00","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":26386,"visible":true,"origin":"","legend":"","description":"","filename":"Addtionalfile.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5798511/v1/70916b3e49a40719742d4c00.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Advancing Molecular Insights: A Global Systematic Review and Meta-analysis of Epidemiology and Drug Resistance Patterns of Mycobacterium tuberculosis in Sputum Samples","fulltext":[{"header":"Introduction","content":"\u003cp\u003e \u003cem\u003eMycobacterium tuberculosis (Mtb)\u003c/em\u003e is bacterium caused tuberculosis (TB) which remains the utmost shared root of death from any single infectious [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The emergence of extensively drug-resistant (XDR) and multidrug-resistant (MDR) TB strains poses a major challenge to TB eradication, with an estimated 400,000 MDR/RR-TB cases worldwide in 2023. Global TB incidence rose to 10.8\u0026nbsp;million cases in 2023, reversing prior declines, with 1.09\u0026nbsp;million deaths among HIV-negative individuals and 161,000 deaths among people with HIV [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. A serious public health issue is the rising number of extrapulmonary TB (EPTB), and multidrug-resistant TB (MDR-TB) cases that are reported in Nepal each year [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMolecular tests for TB detection and drug susceptibility are transforming TB care by providing faster, more accurate diagnostics, especially for underserved populations and high-burden regions. WHO-endorsed nucleic acid amplification tests (NAATs) have proven superior to traditional smear microscopy, enabling tailored treatments through rapid detection of drug resistance. Ongoing innovations, including point-of-care tests and next-generation sequencing, hold promise for improving global TB control and addressing the challenges of undiagnosed and drug-resistant cases [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. To improve the management of the disease, \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e Complex (MTBC) genotyping and drug susceptibility are crucial [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Genotyping of MTBC differentiates recently transmitted and reactivated TB disease, with clustered isolates being epidemiologically linked, and unique isolates resulting from latent infection reactivation outside the population [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThere are direct and indirect methods for diagnosing TB, including microscopy, culture, antigen detection, and nucleic acid detection. However, sputum smear microscopy has poor sensitivity and specificity, and mycobacterial culture is the most reliable reference standard but take longer time. Molecular diagnosis, a new direct method, offers simplicity, rapidity, and accuracy [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Sputum is the most common diagnostic sample for tuberculosis, tested through smear microscopy, culture, or NAAT. Urine, a point-of-care test for advanced HIV, is recommended for adults [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Microscopic examination is the primary diagnostic method in low- and middle-income countries, but its sensitivity is low, especially in cases of confirmed pulmonary TB and HIV/Immunosuppression [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe disease is a leading cause of morbidity and mortality, especially in low- and middle-income countries [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Despite global efforts, the emergence of drug-resistant TB strains, including multidrug-resistant (MDR) and extensively drug-resistant (XDR) strains, has complicated TB control strategies [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Understanding the molecular characteristics of MTB is crucial for developing targeted diagnostic, therapeutic, and control measures. However, data from different studies on molecular markers, mutations associated with drug resistance, and strain diversity of MTB are often fragmented, region-specific, or inconsistent. This makes it challenging to form a comprehensive understanding of the global or regional molecular epidemiology of MTB [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe purpose of this systematic review and meta-analysis was to find and evaluate previous studies that looked at the molecular prevalence and drug resistance pattern of pulmonary tuberculosis. Obtaining the global pooled prevalence of TB phenotypes and genotypes was another goal.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThis study protocol was designed in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines for systematic reviews and meta-analyses. A comprehensive search was conducted across PubMed, Google Scholar, and ScienceDirect databases to identify eligible peer-reviewed articles for inclusion. The database search strategy was rigorously applied, focusing on original research articles published in the English language between 2018 and 2023. Titles, abstracts, and full-text articles were systematically screened and evaluated for eligibility. The scope of the review was restricted to scientific studies conducted during the period from 2014 to 2023. The keywords used for searching articles were \u0026ldquo;Multidrug-resistant tuberculosis\u0026rdquo; or \u0026ldquo;MDR-TB\u0026rdquo; or \u0026ldquo;Drug-resistant TB\u0026rdquo; or \u0026ldquo;Risk factors of MDR-TB\u0026rdquo; or \u0026ldquo;Predictors of MDR-TB\u0026rdquo;, \"Molecular epidemiology\", or \"Phylogeny\", or \"Lineage\", or \"\u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e\". Search results were compiled using a citation management software Zotero. In addition to databases used, we explored references of selected studies to incorporate all potential pertinent articles to construct our summary estimate.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eInclusion and Exclusion Criteria\u003c/h2\u003e \u003cp\u003eStudies conducted in hospital or community settings that analysed human TB presumptive cases and \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e (MTB) isolates from sputum samples were included. Articles deemed irrelevant or lacking sufficient information were excluded from the analysis.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData Extraction\u003c/h3\u003e\n\u003cp\u003eKey variables such as age, sex, methods of TB diagnosis and drug susceptibility testing (DST), prevalence of drug-resistant TB (DR-TB) and multidrug-resistant TB (MDR-TB), tools for molecular analysis, and TB lineages were extracted. Additional information on authors, country, study design, study area, sample size, year of publication, and study duration were also collected. For studies with insufficient or missing data, or where full-text access was unavailable, corresponding authors were contacted via email to obtain the relevant information.\u003c/p\u003e\n\u003ch3\u003eData Synthesis and Analysis\u003c/h3\u003e\n\u003cp\u003eThe studies were categorized based on their design, and the study characteristics were summarized using percentages and frequencies for categorical variables. A proportion meta-analysis was performed using MedCalc-version 23.0.9 software and JASP 0.14.3 to analyse pooled data.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses included meta-analysis of proportions to assess heterogeneity and publication bias. Forest plots and funnel plots were generated for MDR-TB, rifampicin-resistant TB, and isoniazid-resistant TB. One-way ANOVA was used to compare mean age across studies, and chi-squared tests were applied to evaluate associations between gender and TB prevalence.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eQuality Assessment\u003c/h3\u003e\n\u003cp\u003eThis study included adherence to PRISMA guidelines, rigorous database searches with standardized eligibility criteria, and independent screening and data extraction by multiple reviewers to minimize bias. Study quality was assessed using standardized tools, and missing data were addressed by contacting corresponding authors. Statistical analyses, including heterogeneity and publication bias assessments, were conducted using robust methods such as meta-analysis of proportions, sensitivity analyses, and appropriate statistical tests. All steps were thoroughly documented to ensure transparency, reproducibility, and scientific rigor. This systematic review was not registered.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eWe identified a total of 745 studies of them, about 565, 13, 167, and 167 studies respectively from the PubMed, ScienceDirect, Google Scholar Science searches. \u0026nbsp; The reviewers read titles, abstracts, and keywords to assess for duplicates and adherence to inclusion and exclusion criteria. Records that were not excluded at this stage were reviewed in full text to assess the same; records were excluded leaving a total of 13 studies for inclusion in this review.\u003c/p\u003e\n\u003cp\u003eOf the 745 studies, 13 included studies were conducted in various countries, including India, Mexico, Ghana, Ethiopia, China, and Kenya. The study sites included diverse locations such as Andhra Pradesh, Monterrey, Eastern region of Ghana, Addis Ababa, Sikkim, Jigjiga city, Fujian province, Yunnan province, Nairobi, Jiangxi province, Volta, and South Xinjiang worldwide. The study period ranged from one to five years. Mycobacterial cultures were primarily grown on Lowenstein-Jensen (LJ) media, with a few studies used Middlebrook 7H9 broth (MGIT). A total of 3469 individual isolates analyzed varied across studies, ranging from 104 to 1071. Age ranges were reported in some studies, with variations observed across different populations with age ranges spanning from 0\u0026ndash;95 years. \u0026nbsp;Mean age was reported in some studies, varying from 26.4 years [13] to 50 years [14] with mean of average age was 37.76 years. \u0026nbsp;Most of the used for studies molecular techniques characterization were spoligotyping and MIRU-VNTR typing given in table 1.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1 Characteristics of selected studies (2018-2023)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"725\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eReference\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCountry\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStudy site\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStudy period\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCulture on\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIsolates\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge range\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMolecular analysis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSrikar \u003cem\u003eet al\u003c/em\u003e.,2023\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eIndia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003eAndhra Pradesh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e2018 \u0026ndash; 2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eLJ media\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0-90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003eSpoligo, MIRU-VNTR\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFlore \u003cem\u003eet al\u003c/em\u003e. 2023\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eMexico\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003eMonterrey\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e2017 \u0026ndash; 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eLJ media\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0-61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003eSpoligo, MIRU-VNTR\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBenjamin \u003cem\u003eet al\u003c/em\u003e. 2021\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eGhana\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003eEastern region of Ghana\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eLJ media\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e17-89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003eSpoligo, MIRU-VNTR\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiriba \u003cem\u003eet al\u003c/em\u003e. 2020\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eEthiopia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003eAddis Ababa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eLJ media\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003eSpoligo, MIRU-VNTR\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDevi \u003cem\u003eet al\u003c/em\u003e. 2021\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eIndia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003eSikkim\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e2016 \u0026ndash; 2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eLJ media\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e399\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003eSpoligo, MIRU-VNTR\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWorku \u003cem\u003eet al\u003c/em\u003e. 2022\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eEthiopia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003eJigjiga city\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e2018 \u0026ndash; 2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eLJ media\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e323\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e15-80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003eSpoligo, MIRU-VNTR\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLin \u003cem\u003eet al\u003c/em\u003e. 2021\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003eFujian province\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e2016 \u0026ndash; 2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eLJ media\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003eSpoligo, MIRU-VNTR\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBai \u003cem\u003eet al\u003c/em\u003e. 2019\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003eYunnan province\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e2014 \u0026ndash; 2016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eMGIT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e270\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003eSpoligo, SIVIT database\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOgari \u003cem\u003eet al\u003c/em\u003e. 2019\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eKenya\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003eNairobi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e2015 \u0026ndash; 2016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eMGIT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e18-60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLuo \u003cem\u003eet al\u003c/em\u003e. 2019\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003eJiangxi province\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e2014 \u0026ndash; 2016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eLJ media\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003eMIRU-VNTR\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAmeke \u003cem\u003eet al\u003c/em\u003e. 2021\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eGhana\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003eVolta\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e2016 \u0026ndash; 2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eLJ media\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e13-86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003eIS6110\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGupta \u003cem\u003eet al\u003c/em\u003e. 2019\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eIndia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003eMadhya Pradesh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e2014 \u0026ndash; 2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eLJ media\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e17-95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003eSpoligotyping\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYin \u003cem\u003eet al\u003c/em\u003e. 2023\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003eSouth Xinjiang\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e2017 \u0026ndash; 2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eLJ media\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e347\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003eSpoligo, MIRU-VNTR\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eLJ: Lowenstein-Jensen Medium, NR: Not Reported, Spoligo: Spoligotyping, MIRU-VNTR: Mycobacterial Interspersed Repetitive Unit-Variable Number Tandem Repeat\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe age and gender distribution among TB patients in selected studies reveals significant variability given in table 2. The number of isolates ranged from 103 to 347. Across all studies, the proportion of male patients was consistently higher than females, with male percentages ranging from 39.42% [14] to 78.64% [15]. Female representation ranged from 21.36% (Gupta \u003cem\u003eet al\u003c/em\u003e., 2019) to 60.58% [14]. Statistical tests showed high heterogeneity in the data, with an I\u0026sup2; inconsistency of 90.86% (P\u0026lt;0.001; 95% CI: 85.67\u0026ndash;94.17). Publication bias, assessed via Kendall\u0026apos;s Tau test, indicated non-significant values for male (P=0.2429) and female (P=0.1857) proportions. One-way ANOVA (F=2.208, P=0.474) did not reveal significant variation in mean age across studies. However, the Chi-squared test demonstrated significant variability in gender distribution (P=0.0282), suggesting potential differences in TB prevalence between male and female patients across the studies. These findings underscore the importance of considering demographic heterogeneity in TB research and management strategies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2 Statistical inference for gender of TB patients in selected studies\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"659\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTest for heterogeneity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 248px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSignificance level/I\u003csup\u003e2\u003c/sup\u003e(Inconsistency)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u0026lt;0.001/90.86%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u0026lt;0.001/90.86%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 248px;\"\u003e\n \u003cp\u003e95% CI for I\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e85.67 to 94.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e90.11 to 95.62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePublication bias\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 248px;\"\u003e\n \u003cp\u003eKendall\u0026apos;s Tau test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e0.2727\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e-0.3091\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 248px;\"\u003e\n \u003cp\u003eSignificance level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003eP=0.2429\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003eP=0.1857\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOne way ANOVA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003eF-ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e2.208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003eSignificance level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eP=0.474\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eChi-Squared test\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003eSignificance level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 266px;\"\u003e\n \u003cp\u003eP=0.0282\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe data in Table 3 highlighted the prevalence of drug-resistant tuberculosis across various countries, illustrating significant geographical variability in resistance to first-line anti-tuberculosis drugs and multidrug resistance (MDR). In India [15] reported resistance rates of 28.9% for rifampicin (RIF), 47.12% for isoniazid (INH), and 23% for MDR-TB, while [16] recorded an MDR rate of 34.4%. Resistance in Ethiopia was notably lower, with [17] documenting RIF resistance at 8% and an MDR rate of 19.2%. Meanwhile, [18] observed a high rate of resistance to ethambutol (EMB) at 62.2%.\u003c/p\u003e\n\u003cp\u003eChina displayed moderate resistance levels, with [19]) reporting RIF resistance at 20.3% and MDR-TB at 7.7%, while [20] observed higher MDR prevalence at 19.3%. In Ghana [21] recorded RIF resistance at 36.7% and MDR-TB at 32.9%, representing one of the highest MDR rates in the dataset. Mexico [22] and Kenya [23] showed relatively low resistance rates, reporting MDR-TB rates of 1.5% and 1.6%, respectively.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3 Prevalence of drug resistance tuberculosis in selected studies\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"727\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eReference\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCountry\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIsolates\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRIF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eINH\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEMB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePZA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMDR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u003cs\u003e%\u003c/s\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u003cs\u003e%\u003c/s\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u003cs\u003e%\u003c/s\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u003cs\u003e%\u003c/s\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003eN0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u003cs\u003e%\u003c/s\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003eNo.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u003cs\u003e%\u003c/s\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSrikar \u003cem\u003eet al\u003c/em\u003e., 2023\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003eIndia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e28.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e47.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e34.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e5.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFlores \u003cem\u003eet al\u003c/em\u003e., 2023\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026nbsp;Mexico\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e2.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e6.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBenjamin \u003cem\u003eet al\u003c/em\u003e., 2021\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003eGhana\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e36.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e50.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e17.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e32.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiriba \u003cem\u003eet al\u003c/em\u003e., 2020\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003eEthiopia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e13.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e5.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e13.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e19.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWorku \u003cem\u003eet al\u003c/em\u003e., 2022\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003eEthiopia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e323\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e62.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLin \u003cem\u003eet al\u003c/em\u003e., 2021\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e20.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e27.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e14.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e20.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e18.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBai \u003cem\u003eet al\u003c/em\u003e., 2019\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e270\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e37.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e74.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e27.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e14.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e12.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e18.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e19.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOgari \u003cem\u003eet al\u003c/em\u003e., 2019\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003eKenya\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAmeke \u003cem\u003eet al\u003c/em\u003e., 2021\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003eGhana\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e5.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e6.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGupta \u003cem\u003eet al\u003c/em\u003e., 2019\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003eIndia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e12.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e4.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYin \u003cem\u003eet al\u003c/em\u003e., 2023\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e347\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e20.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e32.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e22.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e8.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e7.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDevi \u003cem\u003eet al\u003c/em\u003e., 2021\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003eIndia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e399\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e34.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLuo \u003cem\u003eet al\u003c/em\u003e., 2019\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e1071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e157\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: RIF: Rifampicin, INH: Isoniazid, SM: Streptomycin, EMB: Ethambutol, PZA: Pyrazinamide, MD: Multidrug-resistance, NR: No record\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe table 4 showed the \u0026nbsp;distribution of \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e phylogenetic lineages varies significantly across geographical regions, reflecting the complex epidemiology of tuberculosis (TB). In India [14] found that lineage 3 (East-African Indian lineage) predominated with 55.8% of isolates, followed by lineage 1 (Indo-Oceanic) at 21.2%. Conversely [15] reported a slightly higher prevalence of lineage 1 at 37.9%, with lineage 3 accounting for 44.7%.\u003c/p\u003e\n\u003cp\u003eIn Ethiopia [17] observed that lineage 4 (Euro-American lineage) was dominant, representing 72.5% of isolates, while lineage 3 accounted for 19%. Similarly, [18] identified lineage 4 as the most prevalent (53.9%), but with a higher proportion of lineage 3 (25.7%).\u003c/p\u003e\n\u003cp\u003eIn China, lineage 2 (East Asian/Beijing lineage) was the most prevalent, accounting for 67.2% [24] and 73.7% [20] of isolates in studies. Lineage 3 was less common, with [24] reporting 29.4% and [20] finding no isolates from this lineage.\u003c/p\u003e\n\u003cp\u003eIn Mexico, [22] reported lineage 5 (West African lineage) as the most common, comprising 44.7% of isolates, followed by lineage 4 at 40.5%. In Ghana, lineage 4 predominated, with [21] and [25] recording 55.9% and 71.3%, respectively, while lineage 3 represented a smaller fraction.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ePhylogenic lineages of \u003cem\u003eMycobacterium\u0026nbsp;\u003c/em\u003etuberculosis in\u0026nbsp;isolates\u0026nbsp;of selected studies\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"673\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 6.0033%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eReference\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 4.677%;\"\u003e\n \u003cp\u003eCountry\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.8393%;\"\u003e\n \u003cp\u003eIsolate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 5.1657%;\"\u003e\n \u003cp\u003eL1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 5.7939%;\"\u003e\n \u003cp\u003eL2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 5.1657%;\"\u003e\n \u003cp\u003eL3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.5828%;\"\u003e\n \u003cp\u003eL4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 5.1657%;\"\u003e\n \u003cp\u003eL5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 8.1673%;\"\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 3.8393%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.3036%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.8621%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.5828%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.2111%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.3036%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.8621%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.5828%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.8621%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.3036%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.7922%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.513%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.8621%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.0033%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSrikar \u003cem\u003eet al\u003c/em\u003e., 2023\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4.677%;\"\u003e\n \u003cp\u003eIndia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.8393%;\"\u003e\n \u003cp\u003e104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.3036%;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.8621%;\"\u003e\n \u003cp\u003e21.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.5828%;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.2111%;\"\u003e\n \u003cp\u003e11.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.3036%;\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.8621%;\"\u003e\n \u003cp\u003e55.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.5828%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.8621%;\"\u003e\n \u003cp\u003e5.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.3036%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.7922%;\"\u003e\n \u003cp\u003e5.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.513%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.8621%;\"\u003e\n \u003cp\u003e5.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.0033%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFlores \u003cem\u003eet al\u003c/em\u003e., 2023\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4.677%;\"\u003e\n \u003cp\u003eMexico\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.8393%;\"\u003e\n \u003cp\u003e190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.3036%;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.8621%;\"\u003e\n \u003cp\u003e5.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.5828%;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.2111%;\"\u003e\n \u003cp\u003e9.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.3036%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.8621%;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.5828%;\"\u003e\n \u003cp\u003e77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.8621%;\"\u003e\n \u003cp\u003e40.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.3036%;\"\u003e\n \u003cp\u003e85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.7922%;\"\u003e\n \u003cp\u003e44.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.513%;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.8621%;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.0033%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBenjamin \u003cem\u003eet al\u003c/em\u003e., 2021\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4.677%;\"\u003e\n \u003cp\u003eGhana\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.8393%;\"\u003e\n \u003cp\u003e143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.3036%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.8621%;\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.5828%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.2111%;\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.3036%;\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.8621%;\"\u003e\n \u003cp\u003e36.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.5828%;\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.8621%;\"\u003e\n \u003cp\u003e55.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.3036%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.7922%;\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.513%;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.8621%;\"\u003e\n \u003cp\u003e5.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.0033%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiriba \u003cem\u003eet al\u003c/em\u003e., 2020\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4.677%;\"\u003e\n \u003cp\u003eEthiopia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.8393%;\"\u003e\n \u003cp\u003e153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.3036%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.8621%;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.5828%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.2111%;\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.3036%;\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.8621%;\"\u003e\n \u003cp\u003e19.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.5828%;\"\u003e\n \u003cp\u003e111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.8621%;\"\u003e\n \u003cp\u003e72.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.3036%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.7922%;\"\u003e\n \u003cp\u003e2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.513%;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.8621%;\"\u003e\n \u003cp\u003e5.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.0033%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWorku \u003cem\u003eet al\u003c/em\u003e., 2022\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4.677%;\"\u003e\n \u003cp\u003eEthiopia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.8393%;\"\u003e\n \u003cp\u003e323\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.3036%;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.8621%;\"\u003e\n \u003cp\u003e5.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.5828%;\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.2111%;\"\u003e\n \u003cp\u003e14.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.3036%;\"\u003e\n \u003cp\u003e83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.8621%;\"\u003e\n \u003cp\u003e25.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.5828%;\"\u003e\n \u003cp\u003e174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.8621%;\"\u003e\n \u003cp\u003e53.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.3036%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.7922%;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.513%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.8621%;\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.0033%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLin \u003cem\u003eet al\u003c/em\u003e., 2021\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4.677%;\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.8393%;\"\u003e\n \u003cp\u003e119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.3036%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.8621%;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.5828%;\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.2111%;\"\u003e\n \u003cp\u003e67.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.3036%;\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.8621%;\"\u003e\n \u003cp\u003e29.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.5828%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.8621%;\"\u003e\n \u003cp\u003e1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.3036%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.7922%;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.513%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.8621%;\"\u003e\n \u003cp\u003e1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.0033%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBai \u003cem\u003eet al\u003c/em\u003e., 2019\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4.677%;\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.8393%;\"\u003e\n \u003cp\u003e270\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.3036%;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.8621%;\"\u003e\n \u003cp\u003e4.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.5828%;\"\u003e\n \u003cp\u003e199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.2111%;\"\u003e\n \u003cp\u003e73.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.3036%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.8621%;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.5828%;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.8621%;\"\u003e\n \u003cp\u003e8.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.3036%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.7922%;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.513%;\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.8621%;\"\u003e\n \u003cp\u003e14.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.0033%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAmeke \u003cem\u003eet al\u003c/em\u003e., 2021\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4.677%;\"\u003e\n \u003cp\u003eGhana\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.8393%;\"\u003e\n \u003cp\u003e115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.3036%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.8621%;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.5828%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.2111%;\"\u003e\n \u003cp\u003e2.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.3036%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.8621%;\"\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.5828%;\"\u003e\n \u003cp\u003e82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.8621%;\"\u003e\n \u003cp\u003e71.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.3036%;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.7922%;\"\u003e\n \u003cp\u003e20.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.513%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.8621%;\"\u003e\n \u003cp\u003e4.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.0033%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGupta \u003cem\u003eet al\u003c/em\u003e., 2019\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4.677%;\"\u003e\n \u003cp\u003eIndia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.8393%;\"\u003e\n \u003cp\u003e103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.3036%;\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.8621%;\"\u003e\n \u003cp\u003e37.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.5828%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.2111%;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.3036%;\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.8621%;\"\u003e\n \u003cp\u003e44.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.5828%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.8621%;\"\u003e\n \u003cp\u003e3.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.3036%;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.7922%;\"\u003e\n \u003cp\u003e13.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.513%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.8621%;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.0033%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYin \u003cem\u003eet al\u003c/em\u003e., 2023\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4.677%;\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.8393%;\"\u003e\n \u003cp\u003e347\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.3036%;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.8621%;\"\u003e\n \u003cp\u003e4.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.5828%;\"\u003e\n \u003cp\u003e208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.2111%;\"\u003e\n \u003cp\u003e59.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.3036%;\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.8621%;\"\u003e\n \u003cp\u003e13.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.5828%;\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.8621%;\"\u003e\n \u003cp\u003e14.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.3036%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.7922%;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.513%;\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.8621%;\"\u003e\n \u003cp\u003e7.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"15\" valign=\"top\" style=\"width: 46.9097%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eL1: Indo-Oceanic lineage or East African Indian lineage, L2: East Asian lineage, (Beijing), L3: East-African Indian lineage, L4: Euro-American lineage, L5: West African lineage\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe meta-analysis summarizes the prevalence of multidrug-resistant tuberculosis (MDR-TB) across 11 studies, including 3027 isolates. The fixed-effects model estimated an overall MDR-TB prevalence of 12.52% (95% CI: 11.37\u0026ndash;13.75%), while the random-effects model showed a slightly lower prevalence of 10.76% (95% CI: 6.62\u0026ndash;15.75%).\u003c/p\u003e\n\u003cp\u003eIndividual studies reported a wide range of prevalence values [14] from India reported a relatively high MDR-TB prevalence of 23.08% (95% CI: 15.38\u0026ndash;32.36%), while [22] in Mexico observed a much lower prevalence of 1.58% (95% CI: 0.33\u0026ndash;4.55%). Similarly, [26] in China, with the largest sample size of 1071 isolates, reported a prevalence of 14.66% (95% CI: 12.59\u0026ndash;16.92%), contributing significantly to the overall weight.\u003c/p\u003e\n\u003cp\u003eHeterogeneity among the studies was substantial, with an I\u0026sup2; statistic of 93.44% (95% CI: 90.15\u0026ndash;95.64%), indicating high inconsistency in the reported results. This heterogeneity was statistically significant (Q = 152.52, p \u0026lt; 0.00001).\u003c/p\u003e\n\u003cp\u003ePublication bias was assessed using Egger\u0026rsquo;s and Begg\u0026rsquo;s tests. Egger\u0026rsquo;s test showed no statistically significant bias (intercept = -3.2444, 95% CI: -10.15 to 3.66, p = 0.3154), and Begg\u0026rsquo;s test also indicated no significant bias (Kendall\u0026rsquo;s Tau = -0.2364, p = 0.3115).\u003c/p\u003e\n\u003cp\u003eThese findings highlight notable variability in MDR-TB prevalence across regions, emphasizing the need for tailored interventions to address MDR-TB in different contexts. High heterogeneity suggests potential differences in study populations, diagnostic methods, and healthcare settings.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5 Meta- analysis for prevalence of MDR TB in selected studies\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"637\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eStudy References\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eIsolates\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003eProportion (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 159px;\"\u003e\n \u003cp\u003eWeight (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003eFixed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003eRandom\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eSrikar \u003cem\u003eet al\u003c/em\u003e., 2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e23.077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e15.380 to 32.363\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e3.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e8.57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eFlores \u003cem\u003eet al\u003c/em\u003e., 2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e1.579\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e0.327 to 4.545\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e6.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e9.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eBenjamin \u003cem\u003eet al\u003c/em\u003e., 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e18.182\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e12.234 to 25.494\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e4.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e8.91\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eDiriba \u003cem\u003eet al\u003c/em\u003e., 2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e18.954\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e13.077 to 26.074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e5.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e8.97\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eBai \u003cem\u003eet al\u003c/em\u003e., 2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e270\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e19.259\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e14.731 to 24.476\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e8.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e9.39\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eOgari \u003cem\u003eet al\u003c/em\u003e., 2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e1.515\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e0.184 to 5.366\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e4.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e8.83\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eAmeke \u003cem\u003eet al\u003c/em\u003e., 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e2.609\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e0.541 to 7.435\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e3.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e8.68\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eGupta \u003cem\u003eet al\u003c/em\u003e., 2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e4.854\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e1.595 to 10.966\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e3.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e8.55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eYin \u003cem\u003eet al\u003c/em\u003e., 2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e347\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e6.628\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e4.248 to 9.780\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e11.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e9.52\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eDevi \u003cem\u003eet al\u003c/em\u003e., 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e399\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e20.301\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e16.462 to 24.587\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e13.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e9.58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eLuo \u003cem\u003eet al\u003c/em\u003e., 2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e1071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e14.659\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e12.594 to 16.921\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e35.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e9.84\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eTotal (fixed effects)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e3027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e12.524\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e11.367 to 13.753\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e100.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e100.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eTotal (random effects)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e3027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e10.756\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e6.616 to 15.753\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e100.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;Heterogeneity test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003eI\u003csup\u003e2\u003c/sup\u003e(Inconsistency)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e95% CI for I\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003eDF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e152.5158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e93.44%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e90.15 to 95.64\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026lt;0.00001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eEgger\u0026apos;s test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eIntercept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 256px;\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 159px;\"\u003e\n \u003cp\u003eSignificance level\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-3.2444\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 256px;\"\u003e\n \u003cp\u003e10.1470 to 3.6582\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 159px;\"\u003e\n \u003cp\u003eP=0.3154\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eBegg\u0026apos;s test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 183px;\"\u003e\n \u003cp\u003eKendall\u0026apos;s Tau= -0.2364\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 303px;\"\u003e\n \u003cp\u003eSignificance level (P=0.3115)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;In the figure 3, the forest plot indicates a pooled prevalence of MDR-TB ranging from 10.8% (random-effects) to 12.5% (fixed-effects), with significant heterogeneity among studies (I\u0026sup2; = 93.44%), reflecting variability in MDR-TB prevalence across different regions and populations. The funnel plot suggests potential asymmetry, indicating possible publication bias or small-study effects, although Egger\u0026apos;s and Begg\u0026apos;s tests show no statistically significant evidence of bias (p \u0026gt; 0.05). These results highlight global disparities in MDR-TB prevalence and the need for standardized methodologies to improve comparability.\u003c/p\u003e\n\u003cp\u003eThe meta-analysis of rifampicin resistance in TB reveals a pooled prevalence of 2.7% (fixed-effects) and 2.9% (random-effects), with a 95% CI of 2.0%\u0026ndash;3.6% (fixed) and 1.5%\u0026ndash;4.8% (random). Significant heterogeneity is observed among studies (I\u0026sup2; = 74.74%, p \u0026lt; 0.0001), indicating variability in rifampicin resistance prevalence across regions and populations. Publication bias assessment shows no significant bias, as evidenced by Egger\u0026apos;s test (p = 0.5508) and Begg\u0026apos;s test (p = 0.4042). These findings underscore the moderate prevalence of rifampicin resistance and the need for tailored regional strategies to combat TB drug resistance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eT\u003c/strong\u003e\u003cstrong\u003eable 6 Meta-Analysis of Rifampicin Resistance in TB\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"635\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eStudy References\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eProportion (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eWeight (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFixed\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eRandom\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSrikar \u003cem\u003eet al\u003c/em\u003e., 2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.846\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.058 to 9.556\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8.97\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFlores \u003cem\u003eet al\u003c/em\u003e., 2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.632\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.860 to 6.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10.62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBenjamin \u003cem\u003eet al\u003c/em\u003e., 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.699\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0177 to 3.835\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9.90\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDiriba \u003cem\u003eet al\u003c/em\u003e., 2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.285\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.692 to 12.661\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLin \u003cem\u003eet al\u003c/em\u003e., 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.000 to 3.052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9.37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBai \u003cem\u003eet al\u003c/em\u003e., 2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e270\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.815\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.588 to 8.093\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e16.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11.38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOgari \u003cem\u003eet al\u003c/em\u003e., 2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.758\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0192 to 4.149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9.67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAmeke \u003cem\u003eet al\u003c/em\u003e., 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.348\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.427 to 9.855\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9.27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGupta \u003cem\u003eet al\u003c/em\u003e., 2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.796\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.776 to 13.502\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8.94\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYin \u003cem\u003eet al\u003c/em\u003e., 2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e347\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.865\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.179 to 2.506\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e20.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11.83\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTotal (fixed effects)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1674\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.744\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.017 to 3.641\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e100.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e100.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTotal (random effects)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1674\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.904\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.495 to 4.759\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e100.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e100.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eTest for heterogeneity\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eI\u003csup\u003e2\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e35.6281\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e74.74%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e52.83 to 86.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eP\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003ePublication bias\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEgger\u0026apos;s test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIntercept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e95%\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSignificance level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.9641\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-5.3090 to 9.2372\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eP= 0.5508\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBegg\u0026apos;s test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eKendall\u0026apos;s Tau\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSignificance level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.2000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eP=0.4042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eIsoniazid resistance in tuberculosis (TB) revealed an overall resistance rate of 6.746% (95% CI: 5.559% to 8.097%) under the fixed-effects model and 6.186% (95% CI: 3.358% to 9.799%) under the random-effects model. Substantial heterogeneity was observed among the included studies, as evidenced by a Q-statistic of 56.8352, and I\u0026sup2; value of 85.92% (95% CI: 75.21% to 92.01%), and a P-value of \u0026lt;0.0001. The highest reported resistance was by [ 14] at 15.385% (95% CI: 9.057% to 23.778%), while the lowest was reported by [23]) at 0.758% (95% CI: 0.0192% to 4.149%). In terms of study weight, [19] contributed the most (22.03%) under the fixed-effects model, followed [20] at 17.15%. Assessment of publication bias through Egger\u0026rsquo;s test (P = 0.3573) and Begg\u0026rsquo;s test (P = 0.4042) showed no significant evidence of bias, supporting the robustness of the findings. The variability in resistance rates across studies highlights the need for region-specific surveillance and interventions to address isoniazid resistance effectively.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 7 Metanalysis for isoniazid resistance TB in selected studies\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"584\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eStudy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003eSample\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(N)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eProportion\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003eWeight (%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003eFixed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003eRandom\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eSrikar \u003cem\u003eet al\u003c/em\u003e., 2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e15.385\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e9.057 to 23.778\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e6.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e10.36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eFlores \u003cem\u003eet al\u003c/em\u003e., 2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e6.842\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e3.693 to 11.416\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e12.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e11.45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eBenjamin \u003cem\u003eet al\u003c/em\u003e., 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e4.196\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e1.555 to 8.909\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e9.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e10.99\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eDiriba \u003cem\u003eet al\u003c/em\u003e., 2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e12.583\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e7.749 to 18.950\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e9.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e11.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eLin \u003cem\u003eet al\u003c/em\u003e., 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e0.000 to 3.052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e7.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e10.64\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eBai \u003cem\u003eet al\u003c/em\u003e., 2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e270\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e8.519\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e5.477 to 12.508\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e17.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e11.90\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eOgari \u003cem\u003eet al\u003c/em\u003e., 2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e0.758\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e0.0192 to 4.149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e8.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e10.84\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eAmeke \u003cem\u003eet al\u003c/em\u003e., 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e5.217\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e1.939 to 11.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e7.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e10.57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eYin \u003cem\u003eet al\u003c/em\u003e., 2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e347\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e8.934\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e6.151 to 12.441\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e22.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e12.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eTotal (fixed effects)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e1571\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e6.746\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e5.559 to 8.097\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e100.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e100.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eTotal (random effects)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e1571\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e6.186\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e3.358 to 9.799\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e100.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e100.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eTest for heterogeneity\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003eQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eI\u003csup\u003e2\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003eDF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e56.8352\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e85.92%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e75.21 to 92.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003eP\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003ePublication bias\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003eEgger\u0026apos;s test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eIntercept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e95%\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003eSignificance level\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e-4.3250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e-14.7045 to 6.0545\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003eP= 0.3573\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003eBegg\u0026apos;s test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eKandall\u0026apos;s Tau\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003eSignificance level\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e-0.2222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003eP=0.4042\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe random-effects (RE) model summary effect size is 25.81 (95% CI: -59.12 to 110.74), shown by the diamond at the bottom of the forest plot. This result suggests considerable heterogeneity among studies. The scatter of points appears symmetric around the vertical line of funnel plot (representing the overall effect size), suggesting no significant publication bias.\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe findings of this systematic review and meta-analysis provide an insightful overview of the epidemiological, molecular, and phenotypic characteristics of \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e across various regions and studies from 2018 to 2023. These studies highlight the diversity in drug resistance patterns, phylogenetic lineages, and demographic distribution of tuberculosis (TB) cases globally. The demographic distribution of TB patients reveals significant variability across studies. The predominance of male patients in most of the studies aligns with existing literature that suggests higher TB incidence among men, the Chi-squared test (p\u0026thinsp;=\u0026thinsp;0.0282) confirms significant variability in gender distribution. This is agreed by the previous studies conducted by [\u003cspan additionalcitationids=\"CR28 CR29 CR30\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The age range varied widely across studies, but ANOVA did not reveal significant variation in mean age, underscoring the disease's impact across age groups.\u003c/p\u003e \u003cp\u003eThe prevalence of drug-resistant TB demonstrated substantial geographical variability in this study is supported by [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The global pooled prevalence of the various drug-resistant tuberculosis likes MDR, Isoniazid (INH), and Rifampcin (RIF) was determined to be 12.5%, 6.2.%, and 2.9%, respectively, based on the published findings of all included studies are lines with the previous study by [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] but lower than [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. High multidrug resistance (MDR-TB) rates were observed in India, Ghana, and parts of China, highlighting significant public health challenges. For instance [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] reported the highest MDR-TB rate of 34.4% in India, while [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] observed 32.9% MDR-TB in Ghana. These high resistance rates could be attributed to inconsistent treatment adherence, suboptimal drug supply systems, and variations in healthcare infrastructure. Conversely, Mexico [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] and Kenya [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] reported relatively low MDR-TB rates (1.5% and 1.6%, respectively), reflecting possible differences in treatment policies or surveillance systems. For instance [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] in Ethiopia noted exceptionally high ethambutol (EMB) resistance (62.2%), which could have implications for first-line treatment regimens in the region.\u003c/p\u003e \u003cp\u003ePhylogenetic analyses reveal distinct lineage distributions across regions, reflecting the genetic diversity of \u003cem\u003eM. tuberculosis\u003c/em\u003e and its adaptation to different host populations. Lineage 3 (East-African Indian lineage) predominated in studies from India, with [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] reporting 55.8% prevalence is similar to finding of previous study [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], whereas lineage 4 (Euro-American lineage) dominated in Ethiopia [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] and Ghana [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], as observed that is similar to study conducted in Ethiopia by [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] and in Ghana [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. In China, lineage 2 (East Asian/Beijing lineage) was the most prevalent, reported at 67.2% by [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] Land 73.7% by [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The high prevalence of lineage 2 in China aligns with its well-documented association with drug resistance and high transmissibility is lining with previous studies [39, 40]. In Mexico [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], lineage 5 (West African lineage) was predominant, highlighting regional specificity in lineage distribution. This diversity emphasizes the importance of molecular typing in understanding TB transmission dynamics and guiding regional TB control strategies.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study reviewed 745 articles, ultimately including 13 studies from diverse regions such as India, China, Ghana, Ethiopia, Kenya, and Mexico, focusing on tuberculosis (TB) epidemiology, drug resistance, and phylogenetic lineages from 2018\u0026ndash;2023. Analysis of 3469 isolates revealed significant variability in age, gender distribution, and drug resistance. Resistance to first-line drugs, including rifampicin (RIF) and isoniazid (INH), varied widely, with multidrug-resistant TB (MDR-TB) rates ranging from 1.5\u0026ndash;34.4%. The phylogenetic analysis highlighted geographical diversity, with predominant lineages being East-Asian/Beijing in China, Euro-American in Ethiopia and Ghana, and East-African Indian in India. These findings underscore the importance of tailored TB management strategies based on regional demographic and molecular epidemiological profiles.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eConflict of interest\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest related to this systematic review. All authors contributed equally to data extraction, review analysis, and manuscript preparation. The work was conducted independently, and no external influences, financial or personal, have affected the objectivity or integrity of this review.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003cp\u003eAuthor Contribution\u003c/p\u003e\n\u003cp\u003eDeclaration by the AuthorsCorresponding Author: Madan Singh Bohara (M.S.B.)Co-Author: Prof. Dr. Dwij Raj Bhatta (D.R.B)Author Contributions: Conceptualization, M.S.B.; data curation, D.R.B.; formal analysis, MSB.; investigation, MSB. and D.R.B.; methodology, M.S.B.; supervision, D. R.B.; visualisation, M.S.B.; writing\u0026mdash;original draft, M.S.B. and writing review and editing, D.R B. All authors have read and agreed to the published version of the manuscript.The authors confirm that the data supporting this study\u0026apos;s findings are available within the article.Conflict of Interest/Competing Interests:The authors declare that no conflicts of interest or competing interests are associated with this study.Acknowledgements: We extend our gratitude to the Central Department of Microbiology, Tribhuvan University, Nepal for their invaluable assistance in providing access to relevant literature and library access to complete this study. Data Availability:The authors confirm that the data that support the findings of this study are available within the article. Datasets are available through the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMann BC, Loubser J, Omar S, Glanz C, Ektefaie Y, Jacobson KR, et al. Systematic review and meta-analysis of protocols and yield of direct from sputum sequencing of \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e. Microbiology. 2024. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1101/2024.12.04.625621\u003c/span\u003e\u003cspan address=\"10.1101/2024.12.04.625621\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Health Organization. Global tuberculosis report. Geneva; 2024. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.who.int/teams/global-tuberculosis-programme/tb-reports/global-tuberculosis-report-2024\u003c/span\u003e\u003cspan address=\"https://www.who.int/teams/global-tuberculosis-programme/tb-reports/global-tuberculosis-report-2024\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShah Y, Paudel S, Pandey K, Gupta GP, Solo ES, Joshi J, et al. Insights into transmission dynamics of \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e complex in Nepal. Trop Med Health. 2022;50(1):8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s41182-022-00400-z\u003c/span\u003e\u003cspan address=\"10.1186/s41182-022-00400-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMacLean E, Kohli M, Weber SF, Suresh A, Schumacher SG, Denkinger CM, et al. Advances in molecular diagnosis of tuberculosis. \u003cem\u003eJ Clin Microbiol\u003c/em\u003e. 2020;58(e01582-19). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1128/JCM.01582-19\u003c/span\u003e\u003cspan address=\"10.1128/JCM.01582-19\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePokam BDT, Yeboah-Manu D, Lawson L, Guemdjom PW, Okonu R, Madukaji L, et al. Molecular analysis of \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e isolated in the North Central Zone of Nigeria. J Epidemiol Glob Health. 2019;9(4):259. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2991/jegh.k.191015.001\u003c/span\u003e\u003cspan address=\"10.2991/jegh.k.191015.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKato-Maeda M, Metcalfe JZ, Flores L. Genotyping of \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e: Application in epidemiologic studies. Future Microbiol. 2011;6:203\u0026ndash;16. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2217/fmb.10.165\u003c/span\u003e\u003cspan address=\"10.2217/fmb.10.165\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDeng S, Sun Y, Xia H, Liu Z, Gao L, Yang J, et al. Accuracy of commercial molecular diagnostics for the detection of pulmonary tuberculosis in China: A systematic review. Sci Rep. 2019;9(1):4553. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41598-019-41074-8\u003c/span\u003e\u003cspan address=\"10.1038/s41598-019-41074-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSavage HR, Rickman HM, Burke RM, Odland ML, Savio M, Ringwald B, et al. Accuracy of upper respiratory tract samples to diagnose \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e: A systematic review and meta-analysis. Lancet Microbe. 2023;4(10):e811\u0026ndash;e821. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S2666-5247(23)00190-8\u003c/span\u003e\u003cspan address=\"10.1016/S2666-5247(23)00190-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSester M, Giehl C, McNerney R, Kampmann B, Walzl G, et al. Challenges and perspectives for improved management of HIV/\u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e co-infection. Eur Respir J. 2010;36(6):1242\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1183/09031936.00055910\u003c/span\u003e\u003cspan address=\"10.1183/09031936.00055910\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Health Organization (WHO). Global Tuberculosis Report 2023. Geneva, Switzerland: WHO; 2023. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.who.int/tb/publications/global_report/en/\u003c/span\u003e\u003cspan address=\"https://www.who.int/tb/publications/global_report/en/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDheda K, Gumbo T, Maartens G, Dooley KE, McNerney R, Murray M, et al. The epidemiology, pathogenesis, transmission, diagnosis, and management of multidrug-resistant, extensively drug-resistant, and incurable tuberculosis. Lancet Respir Med. 2017;5(4):291\u0026ndash;360. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S2213-2600(17)30079-6\u003c/span\u003e\u003cspan address=\"10.1016/S2213-2600(17)30079-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang Y, Yew WW. Mechanisms of drug resistance in \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e: Update 2015. Int J Tuberc Lung Dis. 2015;19(11):1276\u0026ndash;89. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5588/ijtld.15.0389\u003c/span\u003e\u003cspan address=\"10.5588/ijtld.15.0389\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOgari CO, Nyamache AK, Nonoh J, Amukoye E. Prevalence and detection of drug resistant mutations in \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e among drug na\u0026iuml;ve patients in Nairobi, Kenya. BMC Infect Dis. 2019;19:279. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12879-019-3911-9\u003c/span\u003e\u003cspan address=\"10.1186/s12879-019-3911-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSrikar A, Venkataramana B, Mohan A, Sarma P, Rekha Devi K, Narain K, et al. Molecular diversity of \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e isolates in treatment-experienced patients from Andhra Pradesh, India. J Infect Dev Ctries. 2023;17(08):1114\u0026ndash;24. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3855/jidc.17757\u003c/span\u003e\u003cspan address=\"10.3855/jidc.17757\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGupta R, Amrathlal RS, Prakash R, Jain S, Tiwari PK. Spoligotyping, phenotypic and genotypic characterization of \u003cem\u003ekatG\u003c/em\u003e and \u003cem\u003erpoB\u003c/em\u003e genes of \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e isolates from the Sahariya tribe of Madhya Pradesh, India. \u003cem\u003eJ Infect Public Health\u003c/em\u003e. 2019;12(3):395\u0026ndash;402. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jiph.2018.12.009\u003c/span\u003e\u003cspan address=\"10.1016/j.jiph.2018.12.009\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDevi KR, Pradhan J, Bhutia R, Dadul P, Sarkar A, Gohain N, Narain K. Molecular diversity of \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e complex in Sikkim, India, and prediction of dominant spoligotypes using artificial intelligence. Sci Rep. 2021;11(1):1\u0026ndash;16. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41598-021-86626-z\u003c/span\u003e\u003cspan address=\"10.1038/s41598-021-86626-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDiriba G, Kebede A, Tola HH, Yenew B, Moga S, Addise D, et al. Molecular characterization and drug resistance patterns of \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e complex in extrapulmonary tuberculosis patients in Addis Ababa, Ethiopia. PLoS One. 2020;15(12):1\u0026ndash;13. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0243493\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0243493\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorku G, Gumi B, Girma M, Mohammedbirhan B, Diriba G, Seid G, et al. Drug sensitivity of clinical isolates of \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e and its association with bacterial genotype in the Somali region, Eastern Ethiopia. Front Public Health. 2022;10:942618. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fpubh.2022.942618\u003c/span\u003e\u003cspan address=\"10.3389/fpubh.2022.942618\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYin C, Mijiti X, Liu H, Wang Q, Cao B, Anwaierjiang A, et al. Molecular epidemiology of clinical \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e isolates from Southern Xinjiang, China using spoligotyping and 15-locus MIRU-VNTR typing. Infect Drug Resist. 2023;16:1313\u0026ndash;26. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2147/IDR.S393192\u003c/span\u003e\u003cspan address=\"10.2147/IDR.S393192\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBai R, Chi S, Li X, Dai X, Ji Z, Jian M, et al. Genetic diversity and drug susceptibility patterns of the \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e complex in Yunnan, China. Biosci Rep. 2019;39(5):BSR20181746. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1042/BSR20181746\u003c/span\u003e\u003cspan address=\"10.1042/BSR20181746\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBenjamin Thumamo Pokam Y, Yeboah-Manu D, Amiteye D, Asare P, Guemdjom PW, Yhiler NY, et al. Molecular epidemiology and multidrug resistance of \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e complex from pulmonary tuberculosis patients in the Eastern region of Ghana. Heliyon. 2021;7(10):e08152. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.heliyon.2021.e08152\u003c/span\u003e\u003cspan address=\"10.1016/j.heliyon.2021.e08152\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFlores-Ar\u0026eacute;chiga A, Zacar\u0026iacute;as-Hern\u0026aacute;ndez JL, V\u0026aacute;zquez-Cort\u0026eacute;s CG, Reyes S, De O. Cavazos M, Rivera-Morales LG, et al. Molecular epidemiology and drug resistance of \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e in a tertiary care hospital in northeastern Mexico. J Infect Dev Ctries. [Epub ahead of print]. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3855/jidc.18026\u003c/span\u003e\u003cspan address=\"10.3855/jidc.18026\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLin Y, Lin H, Xiao L, Chen Y, Meng X, Zeng X, et al. Tuberculosis recurrence over a 7-year follow-up period in successfully treated patients in a routine program setting in China: A prospective longitudinal study. Int J Infect Dis. 2021;110:403\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAmeke S, Asare P, Aboagye SY, Otchere ID, Osei-Wusu S, Yeboah-Manu D, et al. Molecular epidemiology of \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e complex in the Volta Region of Ghana. PLoS One. 2021;16(3):1\u0026ndash;12. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0238898\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0238898\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLuo D, Chen Q, Xiong G, Peng Y, Liu T, Chen X, et al. Prevalence and molecular characterization of multidrug-resistant \u003cem\u003eM. tuberculosis\u003c/em\u003e in Jiangxi province, China. Sci Rep. 2019;9(1):1\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41598-019-43547-2\u003c/span\u003e\u003cspan address=\"10.1038/s41598-019-43547-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUlasi A, Nwachukwu N, Onyeagba R, Umeham S, Amadi A. Prevalence of rifampicin-resistant tuberculosis among pulmonary tuberculosis patients in Enugu, Nigeria. Afr Health Sci. 2022;22(2):156. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4314/ahs.v22i2.18\u003c/span\u003e\u003cspan address=\"10.4314/ahs.v22i2.18\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRasaki SO, Ajibola AA, Musa SA, Moradeyo AK, Odeigah LO, Abdullateef SG, et al. Rifampicin-resistant tuberculosis in a secondary health institution in Nigeria, West Africa. J Infect Dis Ther. 2014;2:139.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNair SA, Raizada N, Sachdeva KS, Dankinger C, Schumacher S, Dewan P, et al. Factors associated with tuberculosis and rifampicin-resistant tuberculosis amongst symptomatic patients in India: A retrospective analysis. PLoS One. 2016;1(11):e0150054. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0150054\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0150054\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBohara MS, Ojha RC. Rifampicin-resistant tuberculosis and associated factors among pulmonary tuberculosis patients in Mahakali Provincial Hospital, Nepal. Far West Rev. 2024;2(1):220\u0026ndash;34. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3126/fwr.v2i1.70540\u003c/span\u003e\u003cspan address=\"10.3126/fwr.v2i1.70540\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHumayun M, Chirenda J, Ye W, Mukeredzi I, Mujuru HA, Yang Z. Effect of gender on clinical presentation of tuberculosis (TB) and age-specific risk of TB, and TB-HIV coinfection. Open Forum Infect Dis. 2022;9(10):ofac512. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/ofid/ofac512\u003c/span\u003e\u003cspan address=\"10.1093/ofid/ofac512\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMolla KA, Reta MA, Ayene YY. Prevalence of multidrug-resistant tuberculosis in East Africa: A systematic review and meta-analysis. PLoS One. 2022;17(6):e0270272. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0270272\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0270272\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSalari N, Kanjoori AH, Hosseinian-Far A, Hasheminezhad R, Mansouri K, Mohammadi M. Global prevalence of drug-resistant tuberculosis: A systematic review and meta-analysis. Infect Dis Poverty. 2023;12(57). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s40249-023-01107-\u003c/span\u003e\u003cspan address=\"10.1186/s40249-023-01107-\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAkalu TY, Clements AC, Gebreyohannes EA, Gilmour B, Alene KA. Prevalence of tuberculosis infection among contacts of drug-resistant tuberculosis patients: A systematic review and meta-analysis. J Infect. 2024;89(2):106198. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jinf.2024.106198\u003c/span\u003e\u003cspan address=\"10.1016/j.jinf.2024.106198\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShah NS, Yuen CM, Heo M, Tolman AW, Becerra MC. Yield of contact investigations in households of patients with drug-resistant tuberculosis: Systematic review and meta-analysis. Clin Infect Dis. 2014;58(3):381\u0026ndash;91.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSingh AV, Singh S, Yadav A, Kushwah S, Yadav R, Sai DK, Chauhan DS. Genetic variability in multidrug-resistant \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e isolates from patients with pulmonary tuberculosis in North India. BMC Microbiol. 2021;21(1):123. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12866-021-02174-6\u003c/span\u003e\u003cspan address=\"10.1186/s12866-021-02174-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMekonnen A, Merker M, Collins JM, Addise D, Aseffa A, Petros B, et al. Molecular epidemiology and drug resistance patterns of \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e complex isolates from university students and the local community in Eastern Ethiopia. PLoS One. 2018;13(9):e0198054. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0198054\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0198054\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAsante-Poku A, Otchere ID, Osei-Wusu S, Sarpong E, Baddoo A, Forson A, et al. Molecular epidemiology of \u003cem\u003eMycobacterium africanum\u003c/em\u003e in Ghana. BMC Infect Dis. 2016;16:385. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12879-016-1739-3\u003c/span\u003e\u003cspan address=\"10.1186/s12879-016-1739-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhu C, Yang T, Yin J, Jiang H, Takiff HE, Gao Q, et al. The global success of \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e modern Beijing family is driven by a few recently emerged strains. Microbiol Spectr. 2023;11(4):e03339-22. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1128/spectrum.03339-22\u003c/span\u003e\u003cspan address=\"10.1128/spectrum.03339-22\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHolt KE, McAdam P, Thai PVT, Thuong NT, Minh Ha DT, Lan NN, et al. Frequent transmission of the \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e Beijing lineage and positive selection for EsxW Beijing variant in Vietnam. Nat Genet. 2018;50(6):849. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41588-018-0117-9\u003c/span\u003e\u003cspan address=\"10.1038/s41588-018-0117-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\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":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Multidrug-resistant, Tuberculosis, Molecular Epidemiology, Phylogeny, Lineage, Drug-resistant","lastPublishedDoi":"10.21203/rs.3.rs-5798511/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5798511/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eTuberculosis (TB), caused by \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e (Mtb), remains a leading cause of morbidity and mortality globally. The emergence of multidrug-resistant (MDR-TB) and extensively drug-resistant TB (XDR-TB) presents significant challenges for TB control. Molecular diagnostics and epidemiological studies provide critical insights into the genetic diversity and drug resistance of Mtb, yet regional variability and fragmented data complicate global understanding.\u003c/p\u003e\u003ch2\u003eMaterials \u0026amp; Methods\u003c/h2\u003e \u003cp\u003eFollowing PRISMA guidelines, a systematic search of PubMed, Google Scholar, and ScienceDirect identified peer-reviewed articles published between 2018 and 2023. Thirteen studies met the inclusion criteria, encompassing 3469 isolates from diverse regions. Key variables included drug resistance patterns, phylogenetic lineages, and demographic data. Statistical analyses included meta-analysis of proportions, heterogeneity assessments, and publication bias evaluation.\u003c/p\u003e\u003ch2\u003eFindings:\u003c/h2\u003e \u003cp\u003eMDR-TB prevalence ranged from 1.5% in Kenya and Mexico to 34.4% in India. Resistance to rifampicin and isoniazid showed pooled prevalence rates of 2.9% and 6.2%, respectively, with significant geographical variability. Phylogenetic analyses revealed distinct lineage distributions: lineage 3 predominated in India, lineage 2 was prevalent in China, and lineage 4 dominated in Ethiopia and Ghana. Age and gender analysis indicated a higher proportion of male TB patients, with significant variability across studies.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThis study highlights the global heterogeneity in TB drug resistance and genetic diversity. Tailored regional strategies, informed by molecular epidemiology, are essential to address the rising threat of MDR-TB and enhance TB control efforts.\u003c/p\u003e","manuscriptTitle":"Advancing Molecular Insights: A Global Systematic Review and Meta-analysis of Epidemiology and Drug Resistance Patterns of Mycobacterium tuberculosis in Sputum Samples","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-14 10:17:55","doi":"10.21203/rs.3.rs-5798511/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":"10957238-3cff-43aa-94f2-0a86537a4d58","owner":[],"postedDate":"February 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-02-14T10:17:55+00:00","versionOfRecord":[],"versionCreatedAt":"2025-02-14 10:17:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5798511","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5798511","identity":"rs-5798511","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}