Global Burden of Drug-Resistant Tuberculosis and HIV Co-Infection and Its Attributable Risk Factors, 1990 to 2021, with Projections to 2031

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The objective of this study was to analyze the global burden of HIV and drug-resistant tuberculosis co-infection across different age groups, genders, and Socio-demographic Index (SDI) regions and attributable risk factors. Methods Data from the GBD 2021 and joinpoint regression analysis were utilized to examine trends from 1990 to 2021 across different genders and SDI regions. The A BAPC model was employed to forecast trends up to 2031. Results Co-infection of HIV with Multidrug-resistant tuberculosis (MDR-TB) shows no gender difference, while extensively drug-resistant tuberculosis (XDR-TB) is more prevalent in men (all P < 0.01). The burden varies by SDI, with low SDI regions having more MDR-TB and high-middle SDI regions more XDR-TB. Unsafe sex was the primary risk factor for HIV co-infection with DR-TB, with drug use being the second major contributor in men and intimate partner violence in women. Projections of DR-TB by HIV status indicate a gradual decline from 2019 to 2031. Conclusion Despite declining trends, the burden remains substantial, especially in resource-limited areas. To combat co-infections of HIV and XDR-TB, in addition to reducing unsafe sexual practices, it is necessary to implement measures to curb drug abuse among men and protect women from intimate partner violence. Clinical trial number: not applicable. Global Burden of Disease (GBD) HIV drug-resistant tuberculosis (DR-TB) risk factor prediction Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Background Multidrug-resistant (MDR) and extensively drug-resistant tuberculosis (XDR-TB), a growing antimicrobial resistance threat, undermine treatment efficacy and strain healthcare systems, particularly in endemic areas [ 1 , 2 ] . The treatment of drug-resistant cases is costly, prolonged and toxic, with an average treatment success rate of about 56%, while the treatment success rate for drug-susceptible tuberculosis is 85% [ 3 ] . The rampant spread of MDR-TB and XDR-TB is exacerbated by deficiencies in detection and prevention, models of care, poor compliance with treatment, and limited treatment options within the health system [ 4 – 6 ] . In addition to drug resistance, co-infection with human immunodeficiency virus (HIV) is another major driver of poor outcomes in TB [ 7 ] . An estimated 38 million people currently live with HIV worldwide, over two-thirds of whom are in Africa [ 8 ] . HIV is transmitted via body fluids and secretions and if unchecked this can lead to an immune-deficient state and demise over a period of 2–10 years [ 9 ] . Infection with HIV is the most powerful known risk factor predisposing for TB infection and progression to active disease, which increases the risk of latent TB reactivation 20-fold [ 10 ] . The annual risk of TB reactivation in individuals with HIV is estimated to be as high as 10%, while those without HIV face a lifetime reactivation risk of only 5–10% [ 8 ] . TB is also the most common cause of AIDS-related death [ 11 ] . The ongoing spread of HIV and DR-TB co-infection poses a major challenge to global TB control. While research on their epidemiology is growing, data on gender and regional differences, especially at varying Socio-demographic Index (SDI) levels, is scarce. Understanding the prevalence and distribution of DR-TB by HIV-status is crucial for assessing the disease burden and informing public health strategies to mitigate the TB epidemic. METHODS Data source The Global burden of disease study database (GBD) 2021 study represents one of the largest publicly accessible global epidemiological surveys [ 12 ] . The methodologies and findings of the GBD 2019 study have been extensively documented in prior publications [ 13 – 15 ] . Building upon the GBD 2019 framework, the GBD 2021 study incorporates new methodologies and processes to deliver detailed estimates of incidence, prevalence, and Disability-Adjusted Life Years (DALYs) for 371 health conditions. This data is segmented by sex, age, and the SDI, which reflects disparities in income, education, and fertility across different populations [ 13 ] . We accessed data through the Global Health Data Exchange (GHDx) query tool ( http://ghdx.healthdata.org/gbd-results-tool ), a user-friendly web-based platform, to gather information on population data, as well as the incidence, prevalence, mortality, DALYs and its attributable risk factors associated with HIV and DR-TB co-infections and age-standardized rates. Joinpoint regression analysis To analyze the temporal trend across different genders and SDI quintiles from 1990 to 2021, we employed joinpoint regression analyses. This method not only allows for a deeper understanding of the distribution of HIV and DR-TB co-infections across SDI quintiles but also helps identify significant joinpoints, where the trend undergoes a notable change. The analysis segments the trend into multiple phases and calculates the annual percentage change (APC), along with its 95% confidence interval ( CI ), for each phase within different SDI strata. We then assessed the average annual percentage change (AAPC) in age-standardized DALYs rates, as well as incidence, prevalence, and mortality rates, across different SDI regions from 1990 to 2021. Joinpoint software was used to structure the analysis and test the significance of trends between joinpoints [ 16 ] . An APC greater than zero indicates an upward trend, while a negative APC signifies a downward trend. Bayesian age-period-cohort (BAPC) analysis The Bayesian age–period–cohort (BAPC) model has been employed for projections and is recognized for its superiority over many linear power models in delivering more accurate and sensible forecasts. It has been confirmed that probabilistic forecasts generated using the BAPC model are well-calibrated and appropriately ranged [ 17 ] . Statistical analysis Statistical analyses and graphing for this study were conducted using R software (version 4.4.1). Additionally, Joinpoint regression software (version 4.9.1.0) was employed for temporal trend analysis. A Bayesian age-period-cohort (BAPC) analysis was performed in R using the BAPC and INLA packages to project future trends in disease burden. For the analysis of differences between groups, two-way ANOVA will be used if the data are normally distributed. If the data are not normally distributed, the Scheirer-Ray-Hare test will be applied. A p -value threshold of 0.05 was applied, with values below this cutoff considered statistically significant. RESULTS The population distribution of HIV and DR-TB co-infection Figure 1 and Table 1 illustrated the trends of DR-TB co-infected with HIV from 1990 to 2021, categorized by gender, for the rates of prevalence, incidence, mortality, and DALYs. HIV co-infected with MDR-TB exhibits no significant gender differences in prevalence, incidence, mortality, or DALYs ( P = 0.053, 0.277, 0.354, 0.212). However, males experience notably higher rates of co-infection with XDR-TB (all P < 0.01) (Supplementary tabl e1 ). The incidence, prevalence, and mortality rates were higher among individuals aged 25–45 (Figure S1 -2). Additionally, in the age group under 5 years, there is a noticeable small peak in the incidence and mortality rates of HIV co-infected with MDR-TB (Figure S1 ). Table 1 ASPR, ASIR, ASDR and ASR of DALYs for HIV and DR-TB co-infection, 1990–2021 rates (per 100,000) No. (95% CI) HIV and MDR-TB co-infection HIV and XDR-TB co-infection 1990 2021 1990 2021 Prevalence Male 0.05 (0.03, 0.08) 0.79 (0.53, 1.15) 0.00 (0.00, 0.00) 0.02 (0.02, 0.04) Female 0.05 (0.03, 0.09) 0.95 (0.65, 1.40) 0.00 (0.00, 0.00) 0.02 (0.01, 0.02) Both 0.05 (0.03, 0.08) 0.87 (0.59, 1.29) 0.00 (0.00, 0.00) 0.02 (0.02, 0.03) Incidence Male 0.04 (0.03, 0.06) 0.52 (0.36, 0.76) 0.00 (0.00, 0.00) 0.02 (0.02, 0.03) Female 0.03 (0.02, 0.06) 0.59 (0.41, 0.87) 0.00 (0.00, 0.00) 0.02 (0.01, 0.02) Both 0.04 (0.02, 0.06) 0.55 (0.38, 0.81) 0.00 (0.00, 0.00) 0.02 (0.01, 0.03) Deaths Male 0.02 (0.01, 0.03) 0.2 (0.09, 0.37) 0.00 (0.00, 0.00) 0.01 (0.01, 0.02) Female 0.01 (0.00, 0.03) 0.23 (0.1, 0.42) 0.00 (0.00, 0.00) 0.01 (0.00, 0.01) Both 0.01 (0.01, 0.03) 0.21 (0.09, 0.39) 0.00 (0.00, 0.00) 0.01 (0.00, 0.02) DALYs Male 0.82 (0.35, 1.63) 10.42 (4.75, 18.87) 0.00 (0.00, 0.00) 0.60 (0.28, 1.06) Female 0.71 (0.25, 1.57) 12.59 (5.59, 23.19) 0.00 (0.00, 0.00) 0.42 (0.19, 0.74) Both 0.76 (0.30, 1.58) 11.48 (5.13, 20.78) 0.00 (0.00, 0.00) 0.51 (0.24, 0.91) Note: DALYs, Disability-adjusted life years; DR-TB, drug-resistant tuberculosis; MDR-TB, Multidrug Drug-Resistant Tuberculosis; XDR-TB, Extensively Drug-Resistant Tuberculosis; HIV, Human Immunodeficiency Virus. Note DALYs, Disability-adjusted life years; DR-TB, drug-resistant tuberculosis; HIV, Human Immunodeficiency Virus. The temporal trends of HIV and DR-TB co-infection from 1990 to 2021 Based on GBD 2021 data, the global age-standardized prevalence rate (ASPR) of HIV co-infected with MDR-TB increased from 0.051 per 100,000 (95% CI : 0.032–0.084) in 1990 to 0.868 per 100,000 ( 95% CI : 0.595–1.286) in 2021, with an average annual percentage change (AAPC) of 9.102% ( 95% CI : 9.308 to 9.902) (Table S2 and Figure S3A). The temporal trends of prevalence follows an inverted "V" pattern, as illustrated in Figure S3B-D, and the turning point values occurred in 2005. The age-standardized incidence rate (ASIR), age-standardized deaths rate (ASDR) and age-standardized rate (ASR) of DALYs of HIV co-infected with MDR-TB showed a similar pattern to ASPR, with an AAPC of 9.303% ( 95% CI : 8.540 to 9.522), 9.163% ( 95% CI : 8.510 to 9.819), 9.040% ( 95% CI : 8.366 to 9.718), respectively (Table S2). The overall trends in the ASPR, ASIR, ASDR, and ASR of DALYs associated with HIV and XDR-TB co-infection showed a significant increase from 1990 to 2021 (Table S2 and Fig. 2 ). The AAPC for ASPR, ASIR, ASDR, and ASR of DALYs were 18.444% ( 95% CI : 16.368 to 20.558), 19.041% ( 95% CI : 17.376 to 20.729), 15.365% ( 95% CI : 14.283 to 16.458) and 41.220% ( 95% CI : 35.104 to 47.613), respectively. The SDI regions distribution of HIV and DR-TB co-infection The ASPR, ASIR, ASDR, and ASR of DALYs for HIV co-infected with MDR-TB varied significantly across different SDI regions (all p < 0.001). Regions with high SDI exhibited the lowest disease burden, while the burden progressively increased with decreasing SDI levels (Fig. 3 ). From 1990 to 2021, the overall trends in ASPR, ASIR, ASDR, and ASR of DALYs for HIV co-infected with MDR-TB showed a decline in high SDI regions, with AAPCs of -2.547% ( 95% CI : -2.770 to -2.322), -3.163% ( 95% CI : -3.793 to -2.529), -5.070% ( 95% CI : -5.613 to -4.524), and − 5.348% ( 95% CI : -5.866 to -4.828), respectively. In contrast, these rates increased in other SDI regions as demonstrated in Table S3. From 1990 to 2021, the disease burden of HIV co-infected with XDR-TB demonstrated an overall increasing trend across SDI quintiles. This burden was most substantial in the high-middle SDI regions, whereas high SDI regions exhibited the lowest burden (Fig. 3 ). The regions with low-middle SDI experienced the most pronounced AAPCs in ASPR, ASIR, and ASDR, at 20.858% ( 95% CI : 18.633 to 23.124), 21.026% ( 95% CI : 18.913 to 23.177), 19.019% ( 95% CI : 17.008 to 21.064), and 40.099% (95% CI: 34.090 to 46.378), respectively (Table S3). Attributable risk factors for HIV and DR-TB co-infection All level 2 risk factors associated with HIV and DR-TB co-infection included unsafe sex, drug use, and intimate partner violence. Unsafe sex was the main risk factor for both genders, with 0.18 (0.08–0.33) and 0.01 (0.00–0.01) per 100 000 for ASDR of HIV co-infected with MDR-TB and XDR-TB in 2021, respectively. Unsafe sex also contributed the most to the ASR of DALYs attributed to risk factors, with 9.24 (4.14, 16.87) and 0.34 (0.15, 0.61) per 100 000 for HIV co-infected with MDR-TB and XDR-TB, respectively. Drug use was the sencond major contributor to the HIV and DR-TB co-infection burden in men attributable to risk factors, while in female, intimate partner violence was the second contributor to the HIV and DR-TB co-infection burden attributable to risk factors (Fig. 4 ). Prediction of disease burden for HIV co-infection with DR-TB The BAPC analysis indicated that the ASPR value for HIV co-infected with MDR-TB is projected to gradually decrease to (7.88E-06 ± 1.20E-05) by 2031 (Fig. 5 A and Table S4). The prediction results for ASIR, ASDR, and ASR of DALYs showed a similar trend (Fig. 5 B, C, D and Table S4). The trends in ASPR, ASIR, ASDR, and ASR of DALYs for HIV co-infected with XDR-TB are similar to those for HIV co-infected with MDR-TB. Refer to Fig. 5 E-F and Table S5 for details. DISCUSSION TB is one of the major public health concerns globally [ 18 ] . Studies have indicated that individuals living with HIV co-infected with DR-TB experience poor outcomes and strikingly high mortality rates [ 19 ] . Notably, the incidence, prevalence, mortality, and DALYs for HIV co-infected with DR-TB peaked around 2005, coinciding with a peak in the overall HIV mortality rate [ 20 ] . The reason for the subsequent decrease in HIV co-infection with DR-TB may be partly associated with improved HIV control measures. Between 2000 and 2015, the global focus on the Millennium Development Goals resulted in over US $ 500 billion being spent on HIV/AIDS prevention, care, and treatment, which contributed to a reduction in overall HIV-related mortality [ 21 ] . It has been observed that among people living with HIV, the risk of MDR and the risk of primary multi-drug resistance (multi-drug resistance associated with transmission) was 1.42-fold and 2.7-fold higher than for those not living with HIV, respectively [ 22 ] . However, HIV coinfection does not directly drive the evolution or transmission of drug-resistant strains. HIV-infected individuals often have negative sputum smears for TB, which leads to missed opportunities for early diagnosis and timely treatment [ 23 ] . This increases their risk of drug resistance and mortality. Younger adults (aged 25–45 years) bore a significant disease burden of HIV co-infected with DR-TB [ 24 ] , which aligned with the epidemiological trends of HIV-related deaths in this age group compared to all other age groups. This phenomenon can be credited to a rise in social engagements, elevated exposure levels, engagement in precarious activities, and enhanced mobility characteristic of this demographic. The high incidence and mortality rates among young children can be attributed to their underdeveloped immune systems [ 25 ] . Our study found the disease burden of HIV co-infection with MDR-TB is slightly higher in women, but the difference is not statistically significant. Previous studies [ 15 ] have shown that HIV-positive individuals experience a greater disease burden among females. However, in the case of DR-TB, the diagnosis and management protocols are generally more stringent, which may mitigate gender disparities. The complex and prolonged treatment required for DR-TB often necessitates a more equitable distribution of resources by families and social systems, thereby further reducing gender differences. However, the disease burden of HIV co-infection with XDR-TB is significantly higher in men compared to women. This might be related to certain social factors. These issues, such as promiscuity, drug use, smoking, and poor adherence to treatment, may be more pronounced among men. However, itimate partner violence was one of the major contributor to the HIV and DR-TB co-infection burden in female attributable to risk factors and women generally face greater barriers to accessing appropriate tuberculosis treatment [ 26 ] , more policies are needed to protect women from harm. The impact of SDI on HIV co-infection with DR-TB is multifaceted. In regions with high SDI, there has been a general decrease in the disease burden associated with HIV and MDR-TB. In contrast, regions with low SDI, particularly sub-Saharan Africa, have experienced an increasing burden. Previous studies have shown that the burden of HIV and TB co-infection is particularly high in sub-Saharan Africa, which accounts for over 75% of global HIV and TB co-infection cases [ 27 ] . These regions typically face a greater array of challenges, including inadequate infrastructure, scarcity of diagnostic reagents and effective treatment drugs, as well as a lack of public health education and community engagement. Poor environments, malnutrition, HIV and TB stigma, and lack of protection were even more severe in low-income countries [ 28 – 31 ] . Prioritizing the integration of TB and HIV services and promoting educational initiatives is particularly crucial in these regions. In regions with high to middle SDI, the co-infection of HIV and XDR-TB remains a significant issue, posing a heavy disease burden. Efforts in these areas should focus on strengthening TB drug management, standardizing medication practices, and implementing effective measures to actively curb the spread of drug resistance. Our study, based on the GBD database, provides a global perspective on the disease burden of HIV co-infection with DR-TB. However, it has limitations, notably the significant underestimation of TB-related DALYs due to the lack of consideration for post-TB disability [ 32 ] . Additionally, data remain limited, especially in resource-constrained regions with presumed high disease burdens. CONCLUSION Although the ASPR, ASIR, ASDR, and DALYs for HIV co-infection with DR-TB show a downward trend, the overall disease burden remains significant, particularly in resource-limited regions. Consistent vigilance and tailored interventions are essential for effectively tackling this challenge. A concerted effort to control unsafe sexual practices are required to reduce HIV and DR-TB co-infection burden. Moreover, special attention should be devoted to the transmission of HIV co-infected with XDR-TB among men. It is crucial to develop and enforce strategies to combat drug misuse among men and to protect women from the scourge of intimate partner violence. Abbreviations GBD Global Burden of Disease APC annual percentage change BAPC Bayesian age-period-cohort SDI Socio-demographic Index CI confidence interval HIV Human Immunodeficiency Virus DR-TB Drug-Resistant Tuberculosis MDR-TB Multidrug Drug-Resistant Tuberculosis XDR-TB Extensively Drug-Resistant Tuberculosis ASPR age-standardized prevalence rate ASIR age-standardized incidence rate ASDR age-standardized deaths rate ASR age-standardized rate DALYs Disability-Adjusted Life Years. Declarations Ethics approval and consent to participate: As it utilized publicly accessible secondary data which were anonymized and compiled, the need for informed Consent was waived for this research The authors confirm that there are no competing interests to disclose. Consent for publication Not applicable. Availability of data and materials The study's datasets are available on the IHME's GHDx query tool: [http://ghdx.healthdata.org/gbd-results-tool]. Competing interests The authors confirm that there are no competing interests to disclose. Funding No funding available. Authors' contributions Liting Feng: Writing - original draft, Software, Data curation; Bob Wang: Writing - review & editing; Li Li: editing, Funding acquisition; Jing Feng: Editing, Conceptualization, Resources; Xing Wang: Editing, Validation, Funding acquisition, Conceptualization. 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Supplementary Files SupplementaryFiguresandtable1.docx Cite Share Download PDF Status: Published Journal Publication published 07 Nov, 2025 Read the published version in BMC Infectious Diseases → Version 1 posted Editorial decision: Revision requested 21 May, 2025 Reviews received at journal 21 May, 2025 Reviewers agreed at journal 02 May, 2025 Reviews received at journal 01 Jan, 2025 Reviewers agreed at journal 12 Dec, 2024 Reviewers invited by journal 18 Nov, 2024 Editor assigned by journal 12 Nov, 2024 Submission checks completed at journal 11 Nov, 2024 First submitted to journal 10 Nov, 2024 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5424376","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":380438947,"identity":"89bfdeed-4ae4-48bd-a9cd-b0886503a6fd","order_by":0,"name":"Liting Feng","email":"","orcid":"","institution":"Haihe Clinical School, Tianjin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Liting","middleName":"","lastName":"Feng","suffix":""},{"id":380438948,"identity":"cd0f949c-0b48-4e5f-aab2-050412b355a6","order_by":1,"name":"Yubao Wang","email":"","orcid":"","institution":"Tianjin Medical University General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yubao","middleName":"","lastName":"Wang","suffix":""},{"id":380438949,"identity":"c8cc90b0-f3f7-4271-bd60-2047e48588ae","order_by":2,"name":"Li Li","email":"","orcid":"","institution":"Tianjin University","correspondingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Li","suffix":""},{"id":380438950,"identity":"9c4dbdbc-3e56-4e3b-bf9e-2df7eb78d462","order_by":3,"name":"Xing Wang","email":"","orcid":"","institution":"Haihe Clinical School, Tianjin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xing","middleName":"","lastName":"Wang","suffix":""},{"id":380438951,"identity":"b28244fc-b825-4ab7-8e5c-f0f0287db6e1","order_by":4,"name":"Jing Feng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAsklEQVRIiWNgGAWjYBAC+xs5hp95GP7z8zMzH3xAlBY2mTfG0jwMB5gl29mSDYjTIv/GjBmkxeA8j5kAcVqkcyBajA8zmDEw1NhEE6flD8MfZrPDDGkPGI6l5TYQo8UaZAtQy3EDxobDRGiRyDEDe9+4mbFNgiQtygbMzGzEaklLBmkxkzjMxmyQQJRfJJIPAqPygAx///mPDz7U2BDWAgaM/6CMBKKUj4JRMApGwSggCACfYTaAwxK7UQAAAABJRU5ErkJggg==","orcid":"","institution":"Tianjin Medical University General Hospital","correspondingAuthor":true,"prefix":"","firstName":"Jing","middleName":"","lastName":"Feng","suffix":""}],"badges":[],"createdAt":"2024-11-10 05:38:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5424376/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5424376/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12879-025-11830-5","type":"published","date":"2025-11-07T15:58:06+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":70177322,"identity":"a21d39d7-5a61-4877-8830-1acf536a06a6","added_by":"auto","created_at":"2024-11-29 07:46:57","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":167427,"visible":true,"origin":"","legend":"\u003cp\u003eGlobal population distribution of incidence(A), prevalence(B), death(C) and DALYs rate (D) for HIV and DR-TB co-infection stratified by sex, from 1990 to 2021.\u003c/p\u003e\n\u003cp\u003eNote: DALYs, Disability-adjusted life years; DR-TB, drug-resistant tuberculosis; HIV, Human Immunodeficiency Virus.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5424376/v1/5cd61fb54756e09b91d10593.jpeg"},{"id":70177325,"identity":"99bb98bb-8e17-4350-b17a-30551ab6dec0","added_by":"auto","created_at":"2024-11-29 07:46:57","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":980617,"visible":true,"origin":"","legend":"\u003cp\u003eJoinpoint regression analysis of ASPR, ASIR, ASDR, and ASR of DALYs for HIV co-infection with MDR-TB (panels A, B, C, D) or XDR-TB (panels E, F, G, H) stratified by sex, from 1990 to 2021.\u003c/p\u003e\n\u003cp\u003eNote: ASPR, age-standardized prevalence rate; ASIR, age-standardized incidence rate; ASDR, age-standardized deaths rate; ASR, age-standardized rate; HIV, Human Immunodeficiency Virus; MDR-TB, Multidrug Drug-Resistant Tuberculosis; XDR-TB, Extensively Drug-Resistant Tuberculosis; DALYs,Disability-Adjusted Life Years.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-5424376/v1/3c9567b919514ff492eb9392.png"},{"id":70178543,"identity":"aa552dee-15d8-47fa-9801-8792456311db","added_by":"auto","created_at":"2024-11-29 08:02:57","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":3187400,"visible":true,"origin":"","legend":"\u003cp\u003eJoinpoint regression analysis of ASPR, ASIR, ASDR, and ASR of DALYs for HIV co-infection with MDR-TB (panels A, B, C, D) or XDR-TB (panels E, F, G, H) across different SDI quintiles from 1990 to 2021.\u003c/p\u003e\n\u003cp\u003eNote: ASPR, age-standardized prevalence rate; ASIR, age-standardized incidence rate; ASDR, age-standardized deaths rate; ASR, age-standardized rate; HIV, Human Immunodeficiency Virus; MDR-TB, Multidrug Drug-Resistant Tuberculosis; XDR-TB, Extensively Drug-Resistant Tuberculosis; DALYs,Disability-Adjusted Life Years.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-5424376/v1/f303bccd078e0f85eea65e44.png"},{"id":70178330,"identity":"0b3128b0-3bb5-4df9-b1d2-7a7f83415a28","added_by":"auto","created_at":"2024-11-29 07:54:57","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":272812,"visible":true,"origin":"","legend":"\u003cp\u003eThe global ASDR (A,C) and ASR of DALYs (B,D) for HIV and DR-TB co-infection attributed to risk factors in 1990 and 2021.\u003c/p\u003e\n\u003cp\u003eNote: ASDR, age-standardized deaths rate; ASR, age-standardized rate; DALYs,Disability-Adjusted Life Years; HIV, Human Immunodeficiency Virus; DR-TB, Drug-Resistant Tuberculosis.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5424376/v1/334ea5d81dee4e967edad172.jpeg"},{"id":70177321,"identity":"732661ae-3d7e-481d-9a24-69e8471bf6d7","added_by":"auto","created_at":"2024-11-29 07:46:57","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1115004,"visible":true,"origin":"","legend":"\u003cp\u003eA ten-year forecast of ASPR, ASIR, ASDR, and ASR of DALYs for HIV co-infection with MDR-TB (panels A, B, C, D) or XDR-TB (panels E, F, G, H).\u003c/p\u003e\n\u003cp\u003eNote: ASPR, age-standardized prevalence rate; ASIR, age-standardized incidence rate; ASDR, age-standardized deaths rate; ASR, age-standardized rate; HIV, Human Immunodeficiency Virus; MDR-TB, Multidrug Drug-Resistant Tuberculosis; XDR-TB, Extensively Drug-Resistant Tuberculosis; DALYs,Disability-Adjusted Life Years.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-5424376/v1/b027ec2a03362949d75c1b77.png"},{"id":95564269,"identity":"a104177e-0e63-4ea1-b52f-b0666e7415b9","added_by":"auto","created_at":"2025-11-10 16:09:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5763494,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5424376/v1/1ace6481-745e-4ed8-80a7-88ef3131ab60.pdf"},{"id":70177326,"identity":"6e6a8fd6-b3f3-4873-8f87-70189305de1e","added_by":"auto","created_at":"2024-11-29 07:46:57","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1670466,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFiguresandtable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-5424376/v1/f7c2150d23a5396f3811e8b9.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Global Burden of Drug-Resistant Tuberculosis and HIV Co-Infection and Its Attributable Risk Factors, 1990 to 2021, with Projections to 2031","fulltext":[{"header":"Background","content":"\u003cp\u003eMultidrug-resistant (MDR) and extensively drug-resistant tuberculosis (XDR-TB), a growing antimicrobial resistance threat, undermine treatment efficacy and strain healthcare systems, particularly in endemic areas\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. The treatment of drug-resistant cases is costly, prolonged and toxic, with an average treatment success rate of about 56%, while the treatment success rate for drug-susceptible tuberculosis is 85%\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. The rampant spread of MDR-TB and XDR-TB is exacerbated by deficiencies in detection and prevention, models of care, poor compliance with treatment, and limited treatment options within the health system\u003csup\u003e[\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. In addition to drug resistance, co-infection with human immunodeficiency virus (HIV) is another major driver of poor outcomes in TB\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAn estimated 38\u0026nbsp;million people currently live with HIV worldwide, over two-thirds of whom are in Africa\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. HIV is transmitted via body fluids and secretions and if unchecked this can lead to an immune-deficient state and demise over a period of 2\u0026ndash;10 years\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. Infection with HIV is the most powerful known risk factor predisposing for TB infection and progression to active disease, which increases the risk of latent TB reactivation 20-fold\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. The annual risk of TB reactivation in individuals with HIV is estimated to be as high as 10%, while those without HIV face a lifetime reactivation risk of only 5\u0026ndash;10%\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. TB is also the most common cause of AIDS-related death\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe ongoing spread of HIV and DR-TB co-infection poses a major challenge to global TB control. While research on their epidemiology is growing, data on gender and regional differences, especially at varying Socio-demographic Index (SDI) levels, is scarce. Understanding the prevalence and distribution of DR-TB by HIV-status is crucial for assessing the disease burden and informing public health strategies to mitigate the TB epidemic.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData source\u003c/h2\u003e \u003cp\u003eThe Global burden of disease study database (GBD) 2021 study represents one of the largest publicly accessible global epidemiological surveys\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. The methodologies and findings of the GBD 2019 study have been extensively documented in prior publications\u003csup\u003e[\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. Building upon the GBD 2019 framework, the GBD 2021 study incorporates new methodologies and processes to deliver detailed estimates of incidence, prevalence, and Disability-Adjusted Life Years (DALYs) for 371 health conditions. This data is segmented by sex, age, and the SDI, which reflects disparities in income, education, and fertility across different populations\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWe accessed data through the Global Health Data Exchange (GHDx) query tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://ghdx.healthdata.org/gbd-results-tool\u003c/span\u003e\u003cspan address=\"http://ghdx.healthdata.org/gbd-results-tool\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), a user-friendly web-based platform, to gather information on population data, as well as the incidence, prevalence, mortality, DALYs and its attributable risk factors associated with HIV and DR-TB co-infections and age-standardized rates.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eJoinpoint regression analysis\u003c/h2\u003e \u003cp\u003eTo analyze the temporal trend across different genders and SDI quintiles from 1990 to 2021, we employed joinpoint regression analyses. This method not only allows for a deeper understanding of the distribution of HIV and DR-TB co-infections across SDI quintiles but also helps identify significant joinpoints, where the trend undergoes a notable change. The analysis segments the trend into multiple phases and calculates the annual percentage change (APC), along with its 95% confidence interval (\u003cem\u003eCI\u003c/em\u003e), for each phase within different SDI strata. We then assessed the average annual percentage change (AAPC) in age-standardized DALYs rates, as well as incidence, prevalence, and mortality rates, across different SDI regions from 1990 to 2021. Joinpoint software was used to structure the analysis and test the significance of trends between joinpoints\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. An APC greater than zero indicates an upward trend, while a negative APC signifies a downward trend.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eBayesian age-period-cohort (BAPC) analysis\u003c/h2\u003e \u003cp\u003eThe Bayesian age\u0026ndash;period\u0026ndash;cohort (BAPC) model has been employed for projections and is recognized for its superiority over many linear power models in delivering more accurate and sensible forecasts. It has been confirmed that probabilistic forecasts generated using the BAPC model are well-calibrated and appropriately ranged \u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses and graphing for this study were conducted using R software (version 4.4.1). Additionally, Joinpoint regression software (version 4.9.1.0) was employed for temporal trend analysis. A Bayesian age-period-cohort (BAPC) analysis was performed in R using the BAPC and INLA packages to project future trends in disease burden. For the analysis of differences between groups, two-way ANOVA will be used if the data are normally distributed. If the data are not normally distributed, the Scheirer-Ray-Hare test will be applied. A \u003cem\u003ep\u003c/em\u003e-value threshold of 0.05 was applied, with values below this cutoff considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eThe population distribution of HIV and DR-TB co-infection\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e illustrated the trends of DR-TB co-infected with HIV from 1990 to 2021, categorized by gender, for the rates of prevalence, incidence, mortality, and DALYs. HIV co-infected with MDR-TB exhibits no significant gender differences in prevalence, incidence, mortality, or DALYs (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.053, 0.277, 0.354, 0.212). However, males experience notably higher rates of co-infection with XDR-TB (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) (Supplementary tabl\u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003ee1\u003c/span\u003e). The incidence, prevalence, and mortality rates were higher among individuals aged 25\u0026ndash;45 (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e-2). Additionally, in the age group under 5 years, there is a noticeable small peak in the incidence and mortality rates of HIV co-infected with MDR-TB (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eASPR, ASIR, ASDR and ASR of DALYs for HIV and DR-TB co-infection, 1990\u0026ndash;2021\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e \u003cp\u003erates (per 100,000) No. (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eHIV and MDR-TB co-infection\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eHIV and XDR-TB co-infection\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1990\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1990\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePrevalence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.05 (0.03, 0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.79 (0.53, 1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00 (0.00, 0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.02 (0.02, 0.04)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.05 (0.03, 0.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.95 (0.65, 1.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00 (0.00, 0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.02 (0.01, 0.02)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBoth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.05 (0.03, 0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.87 (0.59, 1.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00 (0.00, 0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.02 (0.02, 0.03)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eIncidence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.04 (0.03, 0.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.52 (0.36, 0.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00 (0.00, 0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.02 (0.02, 0.03)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.03 (0.02, 0.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.59 (0.41, 0.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00 (0.00, 0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.02 (0.01, 0.02)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBoth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.04 (0.02, 0.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.55 (0.38, 0.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00 (0.00, 0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.02 (0.01, 0.03)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eDeaths\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.02 (0.01, 0.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.2 (0.09, 0.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00 (0.00, 0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.01 (0.01, 0.02)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.01 (0.00, 0.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.23 (0.1, 0.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00 (0.00, 0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.01 (0.00, 0.01)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBoth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.01 (0.01, 0.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.21 (0.09, 0.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00 (0.00, 0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.01 (0.00, 0.02)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eDALYs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.82 (0.35, 1.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.42 (4.75, 18.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00 (0.00, 0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.60 (0.28, 1.06)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.71 (0.25, 1.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.59 (5.59, 23.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00 (0.00, 0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.42 (0.19, 0.74)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBoth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.76 (0.30, 1.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.48 (5.13, 20.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00 (0.00, 0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.51 (0.24, 0.91)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eNote: DALYs, Disability-adjusted life years; DR-TB, drug-resistant tuberculosis; MDR-TB, Multidrug Drug-Resistant Tuberculosis; XDR-TB, Extensively Drug-Resistant Tuberculosis; HIV, Human Immunodeficiency Virus.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eNote\u003c/strong\u003e \u003cp\u003eDALYs, Disability-adjusted life years; DR-TB, drug-resistant tuberculosis; HIV, Human Immunodeficiency Virus.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eThe temporal trends of HIV and DR-TB co-infection from 1990 to 2021\u003c/h2\u003e \u003cp\u003eBased on GBD 2021 data, the global age-standardized prevalence rate (ASPR) of HIV co-infected with MDR-TB increased from 0.051 per 100,000 (95% \u003cem\u003eCI\u003c/em\u003e: 0.032\u0026ndash;0.084) in 1990 to 0.868 per 100,000 (\u003cem\u003e95% CI\u003c/em\u003e: 0.595\u0026ndash;1.286) in 2021, with an average annual percentage change (AAPC) of 9.102% (\u003cem\u003e95% CI\u003c/em\u003e: 9.308 to 9.902) (Table S2 and Figure S3A). The temporal trends of prevalence follows an inverted \"V\" pattern, as illustrated in Figure S3B-D, and the turning point values occurred in 2005. The age-standardized incidence rate (ASIR), age-standardized deaths rate (ASDR) and age-standardized rate (ASR) of DALYs of HIV co-infected with MDR-TB showed a similar pattern to ASPR, with an AAPC of 9.303% (\u003cem\u003e95% CI\u003c/em\u003e: 8.540 to 9.522), 9.163% (\u003cem\u003e95% CI\u003c/em\u003e: 8.510 to 9.819), 9.040% (\u003cem\u003e95% CI\u003c/em\u003e: 8.366 to 9.718), respectively (Table S2).\u003c/p\u003e \u003cp\u003eThe overall trends in the ASPR, ASIR, ASDR, and ASR of DALYs associated with HIV and XDR-TB co-infection showed a significant increase from 1990 to 2021 (Table S2 and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The AAPC for ASPR, ASIR, ASDR, and ASR of DALYs were 18.444% (\u003cem\u003e95% CI\u003c/em\u003e: 16.368 to 20.558), 19.041% (\u003cem\u003e95% CI\u003c/em\u003e: 17.376 to 20.729), 15.365% (\u003cem\u003e95% CI\u003c/em\u003e: 14.283 to 16.458) and 41.220% (\u003cem\u003e95% CI\u003c/em\u003e: 35.104 to 47.613), respectively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eThe SDI regions distribution of HIV and DR-TB co-infection\u003c/h2\u003e \u003cp\u003eThe ASPR, ASIR, ASDR, and ASR of DALYs for HIV co-infected with MDR-TB varied significantly across different SDI regions (all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Regions with high SDI exhibited the lowest disease burden, while the burden progressively increased with decreasing SDI levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). From 1990 to 2021, the overall trends in ASPR, ASIR, ASDR, and ASR of DALYs for HIV co-infected with MDR-TB showed a decline in high SDI regions, with AAPCs of -2.547% (\u003cem\u003e95% CI\u003c/em\u003e: -2.770 to -2.322), -3.163% (\u003cem\u003e95% CI\u003c/em\u003e: -3.793 to -2.529), -5.070% (\u003cem\u003e95% CI\u003c/em\u003e: -5.613 to -4.524), and \u0026minus;\u0026thinsp;5.348% (\u003cem\u003e95% CI\u003c/em\u003e: -5.866 to -4.828), respectively. In contrast, these rates increased in other SDI regions as demonstrated in Table S3.\u003c/p\u003e \u003cp\u003eFrom 1990 to 2021, the disease burden of HIV co-infected with XDR-TB demonstrated an overall increasing trend across SDI quintiles. This burden was most substantial in the high-middle SDI regions, whereas high SDI regions exhibited the lowest burden (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The regions with low-middle SDI experienced the most pronounced AAPCs in ASPR, ASIR, and ASDR, at 20.858% (\u003cem\u003e95% CI\u003c/em\u003e: 18.633 to 23.124), 21.026% (\u003cem\u003e95% CI\u003c/em\u003e: 18.913 to 23.177), 19.019% (\u003cem\u003e95% CI\u003c/em\u003e: 17.008 to 21.064), and 40.099% (95% CI: 34.090 to 46.378), respectively (Table S3).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eAttributable risk factors for HIV and DR-TB co-infection\u003c/h2\u003e\u003cp\u003eAll level 2 risk factors associated with HIV and DR-TB co-infection included unsafe sex, drug use, and intimate partner violence. Unsafe sex was the main risk factor for both genders, with 0.18 (0.08\u0026ndash;0.33) and 0.01 (0.00\u0026ndash;0.01) per 100 000 for ASDR of HIV co-infected with MDR-TB and XDR-TB in 2021, respectively. Unsafe sex also contributed the most to the ASR of DALYs attributed to risk factors, with 9.24 (4.14, 16.87) and 0.34 (0.15, 0.61) per 100 000 for HIV co-infected with MDR-TB and XDR-TB, respectively. Drug use was the sencond major contributor to the HIV and DR-TB co-infection burden in men attributable to risk factors, while in female, intimate partner violence was the second contributor to the HIV and DR-TB co-infection burden attributable to risk factors (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003ePrediction of disease burden for HIV co-infection with DR-TB\u003c/h2\u003e \u003cp\u003eThe BAPC analysis indicated that the ASPR value for HIV co-infected with MDR-TB is projected to gradually decrease to (7.88E-06\u0026thinsp;\u0026plusmn;\u0026thinsp;1.20E-05) by 2031 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA and Table S4). The prediction results for ASIR, ASDR, and ASR of DALYs showed a similar trend (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB, C, D and Table S4).\u003c/p\u003e \u003cp\u003eThe trends in ASPR, ASIR, ASDR, and ASR of DALYs for HIV co-infected with XDR-TB are similar to those for HIV co-infected with MDR-TB. Refer to Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE-F and Table S5 for details.\u003c/p\u003e\u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eTB is one of the major public health concerns globally\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. Studies have indicated that individuals living with HIV co-infected with DR-TB experience poor outcomes and strikingly high mortality rates\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. Notably, the incidence, prevalence, mortality, and DALYs for HIV co-infected with DR-TB peaked around 2005, coinciding with a peak in the overall HIV mortality rate\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. The reason for the subsequent decrease in HIV co-infection with DR-TB may be partly associated with improved HIV control measures. Between 2000 and 2015, the global focus on the Millennium Development Goals resulted in over US\u003cspan\u003e$\u003c/span\u003e500\u0026nbsp;billion being spent on HIV/AIDS prevention, care, and treatment, which contributed to a reduction in overall HIV-related mortality\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. It has been observed that among people living with HIV, the risk of MDR and the risk of primary multi-drug resistance (multi-drug resistance associated with transmission) was 1.42-fold and 2.7-fold higher than for those not living with HIV, respectively\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. However, HIV coinfection does not directly drive the evolution or transmission of drug-resistant strains. HIV-infected individuals often have negative sputum smears for TB, which leads to missed opportunities for early diagnosis and timely treatment\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. This increases their risk of drug resistance and mortality.\u003c/p\u003e\u003cp\u003eYounger adults (aged 25\u0026ndash;45 years) bore a significant disease burden of HIV co-infected with DR-TB\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e, which aligned with the epidemiological trends of HIV-related deaths in this age group compared to all other age groups. This phenomenon can be credited to a rise in social engagements, elevated exposure levels, engagement in precarious activities, and enhanced mobility characteristic of this demographic. The high incidence and mortality rates among young children can be attributed to their underdeveloped immune systems\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOur study found the disease burden of HIV co-infection with MDR-TB is slightly higher in women, but the difference is not statistically significant. Previous studies\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e have shown that HIV-positive individuals experience a greater disease burden among females. However, in the case of DR-TB, the diagnosis and management protocols are generally more stringent, which may mitigate gender disparities. The complex and prolonged treatment required for DR-TB often necessitates a more equitable distribution of resources by families and social systems, thereby further reducing gender differences. However, the disease burden of HIV co-infection with XDR-TB is significantly higher in men compared to women. This might be related to certain social factors. These issues, such as promiscuity, drug use, smoking, and poor adherence to treatment, may be more pronounced among men. However, itimate partner violence was one of the major contributor to the HIV and DR-TB co-infection burden in female attributable to risk factors and women generally face greater barriers to accessing appropriate tuberculosis treatment\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e, more policies are needed to protect women from harm.\u003c/p\u003e \u003cp\u003eThe impact of SDI on HIV co-infection with DR-TB is multifaceted. In regions with high SDI, there has been a general decrease in the disease burden associated with HIV and MDR-TB. In contrast, regions with low SDI, particularly sub-Saharan Africa, have experienced an increasing burden. Previous studies have shown that the burden of HIV and TB co-infection is particularly high in sub-Saharan Africa, which accounts for over 75% of global HIV and TB co-infection cases\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. These regions typically face a greater array of challenges, including inadequate infrastructure, scarcity of diagnostic reagents and effective treatment drugs, as well as a lack of public health education and community engagement. Poor environments, malnutrition, HIV and TB stigma, and lack of protection were even more severe in low-income countries\u003csup\u003e[\u003cspan additionalcitationids=\"CR29 CR30\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e. Prioritizing the integration of TB and HIV services and promoting educational initiatives is particularly crucial in these regions. In regions with high to middle SDI, the co-infection of HIV and XDR-TB remains a significant issue, posing a heavy disease burden. Efforts in these areas should focus on strengthening TB drug management, standardizing medication practices, and implementing effective measures to actively curb the spread of drug resistance.\u003c/p\u003e \u003cp\u003eOur study, based on the GBD database, provides a global perspective on the disease burden of HIV co-infection with DR-TB. However, it has limitations, notably the significant underestimation of TB-related DALYs due to the lack of consideration for post-TB disability\u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e. Additionally, data remain limited, especially in resource-constrained regions with presumed high disease burdens.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eAlthough the ASPR, ASIR, ASDR, and DALYs for HIV co-infection with DR-TB show a downward trend, the overall disease burden remains significant, particularly in resource-limited regions. Consistent vigilance and tailored interventions are essential for effectively tackling this challenge. A concerted effort to control unsafe sexual practices are required to reduce HIV and DR-TB co-infection burden. Moreover, special attention should be devoted to the transmission of HIV co-infected with XDR-TB among men. It is crucial to develop and enforce strategies to combat drug misuse among men and to protect women from the scourge of intimate partner violence.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGBD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGlobal Burden of Disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAPC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eannual percentage change\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBAPC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBayesian age-period-cohort\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSDI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSocio-demographic Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003econfidence interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHIV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHuman Immunodeficiency Virus\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDR-TB\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDrug-Resistant Tuberculosis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMDR-TB\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMultidrug Drug-Resistant Tuberculosis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eXDR-TB\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eExtensively Drug-Resistant Tuberculosis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eASPR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eage-standardized prevalence rate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eASIR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eage-standardized incidence rate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eASDR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eage-standardized deaths rate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eASR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eage-standardized rate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDALYs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDisability-Adjusted Life Years.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u003c/strong\u003e As it utilized publicly accessible secondary data which were anonymized and compiled, the need for informed \u003cstrong\u003eConsent was waived for this research\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors confirm that there are no competing interests to disclose.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study\u0026apos;s datasets are available on the IHME\u0026apos;s GHDx query tool: [http://ghdx.healthdata.org/gbd-results-tool].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors confirm that there are no competing interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding available.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLiting Feng: Writing - original draft, Software, Data curation; Bob Wang: Writing - review \u0026amp; editing; Li Li: editing, Funding acquisition; Jing Feng: Editing, Conceptualization, Resources; Xing Wang: Editing, Validation, Funding acquisition, Conceptualization.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors are thankful to the Institute for Health Metrics and Evaluation for providing access to the significant Global Burden of Disease data.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLiebenberg D, Gordhan BG, Kana BD. 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Global, regional, and national sex differences in the global burden of tuberculosis by HIV status, 1990\u0026ndash;2019: results from the Global Burden of Disease Study 2019. Lancet Infect Dis. 2022;22(2):222\u0026ndash;41.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLong D, Mao C, Zhang Z, Liu Y, Li J, Xu Y, Zhu Y. Long-term trends in the burden of colorectal cancer in Europe over three decades: a joinpoint regression and age-period-cohort analysis. Front Oncol. 2023;13:1287653.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRiebler A, Held L. Projecting the future burden of cancer: Bayesian age-period-cohort analysis with integrated nested Laplace approximations. Biom J. 2017;59(3):531\u0026ndash;49.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMacNeil A, Glaziou P, Sismanidis C, Date A, Maloney S, Floyd K. Global Epidemiology of Tuberculosis and Progress Toward Meeting Global Targets - Worldwide, 2018. MMWR Morb Mortal Wkly Rep. 2020;69(11):281\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSingh A, Prasad R, Balasubramanian V, Gupta N. Drug-Resistant Tuberculosis and HIV Infection: Current Perspectives. HIV AIDS (Auckl). 2020;12:9\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eof HIV. 1980\u0026ndash;2017, and forecasts to 2030, for 195 countries and territories: a systematic analysis for the Global Burden of Diseases, Injuries, and Risk Factors Study 2017. \u003cem\u003eLancet HIV\u003c/em\u003e 2019, 6(12):e831-e859.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGlobal Burden of Disease Health Financing Collaborator N. Spending on health and HIV/AIDS: domestic health spending and development assistance in 188 countries, 1995\u0026ndash;2015. Lancet. 2018;391(10132):1799\u0026ndash;829.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSultana ZZ, Hoque FU, Beyene J, Akhlak-Ul-Islam M, Khan MHR, Ahmed S, Hawlader DH, Hossain A. HIV infection and multidrug resistant tuberculosis: a systematic review and meta-analysis. BMC Infect Dis. 2021;21(1):51.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVittor AY, Garland JM, Gilman RH. Molecular Diagnosis of TB in the HIV Positive Population. Ann Glob Health. 2014;80(6):476\u0026ndash;85.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePontali E, Sotgiu G, Centis R, D'Ambrosio L, Spanevello A, Migliori GB. Management of drug resistantTB in patients with HIV co-infection. Expert Opin Pharmacother. 2015;16(18):2737\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen J, Liu D, Zeng L, Jia ZJ, Cheng G, Xiao X, Zhang L. Disease burden and risk factors of children aged 0\u0026ndash;14 years in China: a retrospective study on data from the Global Burden of Disease Study 2019. BMJ Open. 2024;14(5):e076013.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTurusbekova N, Celan C, Caraulan L, Rucsineanu O, Jibuti M, Ibragimova O, Saidova N. Gender-related factors associated with delayed diagnosis of tuberculosis in Eastern Europe and Central Asia. BMC Public Health. 2022;22(1):1999.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWondmeneh TG, Mekonnen AT. The incidence rate of tuberculosis and its associated factors among HIV-positive persons in Sub-Saharan Africa: a systematic review and meta-analysis. BMC Infect Dis. 2023;23(1):613.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAhmed A, Mekonnen D, Shiferaw AM, Belayneh F, Yenit MK. Incidence and determinants of tuberculosis infection among adult patients with HIV attending HIV care in north-east Ethiopia: a retrospective cohort study. BMJ Open. 2018;8(2):e016961.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNg R, Kendall CE, Burchell AN, Bayoumi AM, Loutfy MR, Raboud J, Glazier RH, Rourke S, Antoniou T. Emergency department use by people with HIV in Ontario: a population-based cohort study. CMAJ Open. 2016;4(2):E240\u0026ndash;248.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePradeilles R, Baye K, Holdsworth M. Addressing malnutrition in low- and middle-income countries with double-duty actions. Proc Nutr Soc. 2019;78(3):388\u0026ndash;97.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWouters E, Sommerland N, Masquillier C, Rau A, Engelbrecht M, Van Rensburg AJ, Kigozi G, Ponnet K, Van Damme W. Unpacking the dynamics of double stigma: how the HIV-TB co-epidemic alters TB stigma and its management among healthcare workers. BMC Infect Dis. 2020;20(1):106.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTomeny EM, Nightingale R, Chinoko B, Nikolaidis GF, Madan JJ, Worrall E, Ngwira LG, Banda NP, Lonnroth K, Evans D et al. TB morbidity estimates overlook the contribution of post-TB disability: evidence from urban Malawi. BMJ Glob Health 2022, 7(5).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Global Burden of Disease (GBD), HIV, drug-resistant tuberculosis (DR-TB), risk factor, prediction","lastPublishedDoi":"10.21203/rs.3.rs-5424376/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5424376/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe continuous spread of HIV and drug-resistant tuberculosis (DR-TB) co-infection is a significant challenge and threatens global tuberculosis (TB) control. The objective of this study was to analyze the global burden of HIV and drug-resistant tuberculosis co-infection across different age groups, genders, and Socio-demographic Index (SDI) regions and attributable risk factors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData from the GBD 2021 and joinpoint regression analysis were utilized to examine trends from 1990 to 2021 across different genders and SDI regions. The A BAPC model was employed to forecast trends up to 2031.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCo-infection of HIV with Multidrug-resistant tuberculosis (MDR-TB) shows no gender difference, while extensively drug-resistant tuberculosis (XDR-TB) is more prevalent in men (all \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01). The burden varies by SDI, with low SDI regions having more MDR-TB and high-middle SDI regions more XDR-TB. Unsafe sex was the primary risk factor for HIV co-infection with DR-TB, with drug use being the second major contributor in men and intimate partner violence in women. Projections of DR-TB by HIV status indicate a gradual decline from 2019 to 2031.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDespite declining trends, the burden remains substantial, especially in resource-limited areas. To combat co-infections of HIV and XDR-TB, in addition to reducing unsafe sexual practices, it is necessary to implement measures to curb drug abuse among men and protect women from intimate partner violence.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number:\u003c/strong\u003e not applicable.\u003c/p\u003e","manuscriptTitle":"Global Burden of Drug-Resistant Tuberculosis and HIV Co-Infection and Its Attributable Risk Factors, 1990 to 2021, with Projections to 2031","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-29 07:46:52","doi":"10.21203/rs.3.rs-5424376/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-05-21T19:36:14+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-21T11:45:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"53091301093160155456331152654631626771","date":"2025-05-02T10:39:42+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-01-02T04:47:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"339712647861825478542208977003574248864","date":"2024-12-12T09:49:40+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-11-18T09:07:04+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-11-12T07:04:31+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-11-12T03:54:06+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Infectious Diseases","date":"2024-11-10T05:36:05+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7b9a9317-4a46-4fc9-81ca-2198e0c33d46","owner":[],"postedDate":"November 29th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-11-10T16:07:55+00:00","versionOfRecord":{"articleIdentity":"rs-5424376","link":"https://doi.org/10.1186/s12879-025-11830-5","journal":{"identity":"bmc-infectious-diseases","isVorOnly":false,"title":"BMC Infectious Diseases"},"publishedOn":"2025-11-07 15:58:06","publishedOnDateReadable":"November 7th, 2025"},"versionCreatedAt":"2024-11-29 07:46:52","video":"","vorDoi":"10.1186/s12879-025-11830-5","vorDoiUrl":"https://doi.org/10.1186/s12879-025-11830-5","workflowStages":[]},"version":"v1","identity":"rs-5424376","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5424376","identity":"rs-5424376","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}