The Association Between Tuberculosis/HIV Incidence and Mortality in Nigeria: A Retrospective Analysis from 2000 to 2023

The Association Between Tuberculosis/HIV Incidence and Mortality in Nigeria: A Retrospective Analysis from 2000 to 2023 | 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 The Association Between Tuberculosis/HIV Incidence and Mortality in Nigeria: A Retrospective Analysis from 2000 to 2023 Uthman Olalekan Al-Ameen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6787707/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 and Human Immunodeficiency Virus (HIV) co-infection remains a major public health challenge, particularly in high-burden countries like Nigeria. Understanding incidence–mortality dynamics is essential for guiding control strategies. Objective: To analyze the association between TB/HIV incidence and TB/HIV-related mortality in Nigeria from 2000 to 2023, accounting for temporal trends. Methods: A retrospective secondary data analysis was conducted using UNAIDS estimates. Correlation, linear regression, and segmented time-series regression were applied to quantify associations and detect mortality trend changes. Analyses were performed in R version 4.4.2. Results: TB/HIV incidence and deaths were strongly correlated (r = 0.991, p < 0.001). Linear regression confirmed a significant association, with each additional case of TB/HIV incidence linked to ~0.67 deaths (95% CI: 0.63–0.70, p < 0.001). The model explained 98.2% of mortality variance (adjusted R² = 0.982), though the negative intercept reflected a model limitation. Segmented regression identified two breakpoints (2003 and 2011), marking shifts in mortality: declines (–2800 deaths/year, 2000–2003), sharp increases (+3250 deaths/year, 2003–2011), and subsequent steep declines (–4484 deaths/year, 2011–2023). These transitions coincided with programmatic changes, including ART scale-up and strengthened TB/HIV collaboration. Conclusion: TB/HIV incidence strongly predicts mortality in Nigeria, but the very high R² should be interpreted cautiously given shared UNAIDS modeling assumptions. Combining linear and time-series approaches provides a more nuanced understanding of Nigeria’s TB/HIV epidemic and underscores the need for adaptive, evidence-based control strategies. A key limitation is reliance on secondary UNAIDS estimates, which may introduce modeling biases and restrict causal inference. Tuberculosis HIV AIDS TB/HIV co-infection Nigeria Incidence Mortality Epidemiology Figures Figure 1 Figure 2 1. Introduction Tuberculosis (TB) remains one of the leading causes of death globally, particularly among individuals living with Human Immunodeficiency Virus (HIV) infection (1). The dual burden of TB and HIV represents a major public health challenge, especially in sub-Saharan Africa, where the two diseases interact synergistically to worsen morbidity and mortality outcomes. Nigeria, in particular, bears a substantial share of this burden (2, 3). The co-epidemic of TB and HIV continues to hinder progress toward global health targets, despite extensive control efforts at both international and national levels. The epidemiological relationship between TB and HIV is well-documented, but most studies have been descriptive in nature, with limited quantification of long-term incidence–mortality dynamics using inferential modeling. This study addresses that gap by applying both linear and segmented regression analyses to evaluate how changes in TB and HIV incidence predict mortality trends in Nigeria between 2000 and 2023. By explicitly modeling both overall linear associations and potential non-linear shifts over time, this work provides a more nuanced understanding of the TB/HIV epidemic in Nigeria and its public health implications. Understanding the epidemiology of TB/HIV co-infection is essential for designing effective prevention and control strategies (4). Nigeria currently has the third-largest HIV epidemic globally (5). In 2018, the national HIV prevalence among adults aged 15–49 years was estimated at 1.4% (6). Concurrently, Nigeria remains among the 30 high TB burden countries identified by the World Health Organization (1). The convergence of these two epidemics creates a complex public health challenge, driven by socioeconomic disparities (7), limited access to quality healthcare (8), and inadequate implementation of TB/HIV collaborative interventions. Over the past decade, Nigeria has made notable progress in reducing HIV incidence through improved case finding, expanded treatment coverage, and increased awareness (4). The country has adopted the Joint United Nations Programme on HIV/AIDS (UNAIDS) 95-95-95 targets, which aim for 95% of people living with HIV to know their status, 95% of those diagnosed to receive treatment, and 95% of those on treatment to achieve viral suppression by 2030 (4). To accelerate case detection, the Nigerian government introduced the Enhanced Community Case-Finding Package, an innovative approach to improve identification of undiagnosed HIV-positive individuals (9). Despite these gains, TB continues to be the leading opportunistic infection and cause of death among people living with HIV in Nigeria (10). The dual burden of TB/HIV extends beyond health outcomes to impose significant social and economic costs. Families and communities bear the brunt of lost productivity, while the healthcare system faces mounting demands for diagnosis, treatment, and integrated care (11, 12). Addressing these challenges requires evidence-based, data-driven strategies that can inform policy and optimize resource allocation. This study, therefore, seeks to analyze national-level trends in TB and HIV incidence and mortality from 2000 to 2023 to identify key patterns and relationships. By examining these trends, we aim to generate actionable insights that can guide future interventions and strengthen TB/HIV control programs in Nigeria (13, 14, 6). Additionally, the study highlights the value of integrating statistical modeling, such as linear and segmented regression, to assess long-term epidemiological patterns. The potential application of Bayesian statistical frameworks to synthesize multiple datasets also presents an opportunity for cost-effective, integrated surveillance systems to improve infectious disease response at both national and sub national levels (15,16,6). 2. Methods 2.1 Study Design and Data Source: This study employed a retrospective secondary data analysis design using national-level annual data from 2000 to 2023 . Data on TB/HIV incidence and deaths in Nigeria were obtained from the UNAIDS Global Database (https://aidsinfo.unaids.org). To ensure completeness and accuracy, the dataset was cross-validated with the World Health Organization (WHO) Global Tuberculosis Reports , confirming consistency of reported trends. Although UNAIDS estimates are model-based, they represent the most reliable and widely used source for monitoring global TB/HIV statistics. 2.2 Ethical Considerations This study was based on secondary, aggregated, and publicly available data obtained from the UNAIDS database. As no human participants were directly involved, institutional review board (IRB) approval was not required. Ethical principles of transparency and responsible data use were upheld throughout the study. 2.3 Variables The primary variables of interest were: Independent Variable: TB/HIV Incidence (number of new TB cases among HIV-positive individuals) Dependent Variable: TB/HIV Deaths (number of deaths attributed to TB among HIV-positive individuals) 2.4 Statistical Analysis Data analysis was conducted using R programming version 4.4.2. The following statistical methods were employed: Descriptive Statistics: Summary statistics were calculated for both TB/HIV incidence and deaths to understand the distribution and trends over time. Correlation Analysis: Pearson correlation coefficient was calculated to assess the strength and direction of the linear relationship between TB/HIV incidence and deaths. Linear Regression Analysis: Simple linear regression was performed to model the relationship between TB/HIV incidence (independent variable) and TB/HIV deaths (dependent variable). The regression model was used to estimate the slope, intercept, and coefficient of determination (R 2 ) Segmented (time-series) regression was conducted to identify structural changes in mortality trends over time. Two breakpoints were estimated (2003 and 2011), and slopes for each segment were compared. This approach allowed assessment of non-linearity and periods of intensified decline. Although tuberculosis and HIV data are inherently count-based, this study utilized national-level annual aggregate data from 2000–2023 obtained from WHO and UNAIDS reports. These yearly incidence and mortality estimates behave as continuous variables and are suitable for linear modeling. Linear regression was therefore applied to assess the overall strength and direction of the association between incidence and mortality. Prior to analysis, linearity and normality assumptions were examined and found reasonably satisfied at the aggregate level. This approach is consistent with methodologies used in WHO Global Tuberculosis Reports and other epidemiological trend analyses. Additionally, segmented regression was employed to detect and model potential non-linear changes in mortality trends over time. 2.5 Visualization: Time-series line plots and scatter plots with regression lines were generated to visualize the trends and relationship between the variables. 3. Results Descriptive Statistics: TB/HIV incidence in Nigeria showed a fluctuating pattern from 2000 to 2023, with the highest incidence observed in 2011 (97,000 cases) and the lowest in 2023 (25,000 cases). Similarly, TB/HIV deaths peaked in 2011 (59,000 deaths) and declined to 6,700 deaths in 2023. Correlation Between TB/HIV Incidence and Deaths Pearson correlation analysis revealed a strong positive correlation between TB/HIV incidence and deaths ( r = 0.991, p < 0.001). This indicates a close relationship between the two variables, with higher incidence rates associated with higher mortality rates. Association Between TB/HIV Incidence and Deaths Simple linear regression revealed a very strong positive association between TB/HIV incidence and TB/HIV deaths. For every additional case of TB/HIV incidence, mortality increased by approximately 0.67 deaths (95% CI: 0.63–0.70; p < 0.001). The model accounted for 98.2% of the variance in TB/HIV deaths (adjusted R² = 0.982), confirming a robust linear relationship. Trends in TB/HIV Deaths A segmented regression analysis was conducted to examine changes in TB/HIV deaths over time. The model identified two statistically significant breakpoints in the series: the first in 2003 (95% CI: 2003.0–2004.0) and the second in 2011 (95% CI: 2011.0–2011.5) . Prior to 2003, TB/HIV deaths declined at an average rate of –2,800 deaths per year (95% CI: –5,023 to –577; p=0.016) . From 2003 to 2011, the trend reversed, with deaths increasing by approximately +3,250 deaths per year (95% CI: 2,483–4,017; p<0.001) . Following 2011, a sharp decline was observed, with TB/HIV deaths decreasing by –4,484 deaths annually (95% CI: –4,899 to –4,068; p<0.001) . The segmented regression model demonstrated excellent fit, explaining 97.3% of the variance in deaths (adjusted R²=0.973) . Visualizations Figure 1: Time-Series Line Plot of TB/HIV Incidence and Deaths in Nigeria (2000–2023) Time-series line plots were generated to illustrate the trends in TB/HIV incidence and deaths in Nigeria from 2000 to 2023 as shown in figure 1. Figure 2: Scatter plot of TB/HIV Deaths vs. TB/HIV Incidence with Regression Line Scatter plots with regression lines were generated to visualize the relationship between TB/HIV deaths and TB/HIV incidence as shown figure 2. Table 1: TB/HIV Deaths and Incidence in Nigeria (2000-2023) Year TB_HIV Deaths TB_HIV Incidence 2000 47 000 79 000 2001 40 000 71 000 2002 39 000 65 000 2003 38 000 62 000 2004 37 000 61 000 2005 40 000 64 000 2006 43 000 69 000 2007 45 000 74 000 2008 56 000 94 000 2009 54 000 91 000 2010 55 000 90 000 2011 59 000 97 000 2012 54 000 90 000 2013 52 000 87 000 2014 48 000 78 000 2015 44 000 71 000 2016 41 000 67 000 2017 37 000 61 000 2018 33 000 55 000 2019 29 000 48 000 2020 21 000 35 000 2021 14 000 28 000 2022 11 000 28 000 2023 6700 25 000 4. Discussion The findings of this study highlight the strong relationship between TB/HIV incidence and deaths in Nigeria from 2000 to 2023. The significant positive correlation (r = 0.991, p < 0.001) indicates that changes in TB/HIV incidence are closely associated with corresponding changes in mortality rates. Simple linear regression further quantified this relationship, showing that each additional TB/HIV case was associated with 0.67 deaths (95% CI: 0.63–0.70), with the model explaining 98.2% of the variance in deaths (adjusted R² = 0.982). This underscores the interconnectedness of these two epidemics and the need for integrated approaches to address them. However, the very high R² should be interpreted cautiously, as both incidence and mortality data originate from UNAIDS models, and shared assumptions may inflate the apparent strength of association. The negative intercept also indicates limitations of linear extrapolation, emphasizing the importance of cautious interpretation. Segmented regression analysis provided deeper insights by showing that mortality trends were not uniform across the study period. Breakpoints in 2003 and 2011 coincided with programmatic changes, particularly the rapid scale-up of ART and the strengthening of TB/HIV collaborative activities. These shifts suggest that national health policies significantly influenced mortality trends beyond what incidence alone predicted. Nigeria has the tenth largest burden of TB cases in the world (17), and TB/HIV co-infection remains a major public health challenge (18). Among the estimated 3.4 million people living with HIV in Nigeria, less than 800,000 are on treatment (19). Despite progress in HIV control, Nigeria still has the second largest global HIV epidemic and one of the highest rates of new infections in sub-Saharan Africa (11). Our findings are consistent with this evidence, as the observed decline in mortality after 2011 aligns with improvements in ART coverage and collaborative TB/HIV programs. Historical data also support this, with HIV prevalence rising sharply from <0.1% in 1987 to 5.8% in 2001, before stabilizing at 5.0% in 2003 (20). This context helps explain why mortality was initially high but later stabilized as ART scale-up gained momentum. Nevertheless, challenges persist. Access to quality TB/HIV services remains limited in conflict-affected areas, and funding gaps constrain prevention and treatment for key populations (4, 11). Studies have shown that HIV-positive TB patients have lower treatment success rates and higher risks of treatment failure and default (18). Strengthening surveillance systems, expanding ART coverage, and ensuring consistent clinical and laboratory monitoring are essential for improving outcomes (1, 4). The COVID-19 pandemic has further strained HIV financing in Nigeria, raising concerns about sustaining progress (5). The integration of linear and time-series methods in this study highlights the importance of combining cross-sectional association with trend analysis. While incidence strongly predicts mortality, programmatic and contextual shifts such as ART expansion, surveillance improvements, and policy interventions significantly shaped long-term outcomes. This underscores that effective epidemic control requires adaptive, evidence-driven strategies. Continued investment in TB/HIV research and integrated service delivery models is essential to sustain progress and address remaining challenges (12, 21). Our findings therefore provide strong evidence that reducing incidence through ART scale-up, improved surveillance, and strengthened TB/HIV integration will have a direct and measurable impact on mortality reduction. 5. Conclusion This study demonstrates a strong positive association between TB/HIV incidence and TB/HIV-related deaths in Nigeria from 2000 to 2023. The strong correlation and significant regression results underscore the close relationship between these two variables. The findings highlight the critical importance of addressing the TB/HIV co-epidemic through integrated and intensified control efforts. Scaling up access to HIV testing and treatment, strengthening TB control programs, and addressing the underlying social determinants of health are essential for reducing the burden of TB/HIV co-infection in Nigeria. The results underscore the need for policymakers and healthcare providers to prioritize TB/HIV control and allocate resources effectively to improve the health outcomes of affected populations. Further research is needed to evaluate the impact of specific interventions and identify strategies for optimizing TB/HIV control efforts in Nigeria. 6 Limitation: This study relied on secondary data obtained from UNAIDS, which may be subject to limitations in data completeness, accuracy, and reporting consistency across years. Additionally, the analysis focused on national-level data, which may overlook some regional disparities and local contextual factors influencing TB/HIV trends. 7 Recommendations Efforts should be intensified to strengthen integrated TB/HIV surveillance and improve data quality across regions. Access to HIV testing, antiretroviral therapy, and TB preventive treatment should be expanded, especially in high-burden areas. Investments in health systems and community outreach are essential to improve service delivery. Future studies should explore regional variations. Declarations Conflict of interest : The author declares that there is no conflict of interest Author contributions : Uthman Olalekan Al-Ameen is the sole author and contributed to data analysis and manuscript writing Funding : This research was self-funded. Acknowledgments : The author would like to thank the UNAIDS organization for providing the data set used for the study. Data Availability : The data used in this study were obtained from UNAIDS, as detailed in the manuscript. For further information on data access, please refer to UNAIDS at [https://aidsinfo.unaids.org/]. Ethical Approval: This study did not require ethical approval as it involved the use of secondary data. Patient Consent: This study did not involve direct interaction with human participants; therefore, patient consent was not applicable. Consent to Participate declaration: not applicable. Clinical Trial number: not applicable. Consent to Publish declaration: not applicable. Appendix: Full meanings of Abbreviations: TB: Tuberculosis UNAIDS: Joint United Nations Program on HIV/AIDS ART: Antiretroviral therapy HIV: Human Immunodeficiency Virus AIDS: Acquired Immunodeficiency Syndrome References Chang, C. A., Meloni, S. T., Eisen, G., Chaplin, B., Akande, P., Okonkwo, P., Rawizza, H. E., Tchetgen Tchetgen, E., & Kanki, P. J. (2015). Tuberculosis incidence and risk factors among human immunodeficiency virus (HIV)-infected adults receiving antiretroviral therapy in a large HIV program in Nigeria. Open Forum Infectious Diseases, 2 (4), ofv154. https://doi.org/10.1093/ofid/ofv154 Aliyu, N., & Magaji, B. A. (2019). Prevalence and factors associated with TB-HIV co-infection among tuberculosis patients treated at Specialist Hospital, Sokoto State, Nigeria (2014–2017). Texila International Journal of Clinical Research, 6 (2). https://doi.org/10.21522/TIJCR.2014.06.02.Art002 Okonko, I. O., Ejike, I. U., Innocent-Adiele, C., & Cookey, T. I. (2020). HIV coinfections with tuberculosis among HIV-1 infected individuals in old Cross River State, Nigeria. Journal of Immunoassay & Immunochemistry, 41 (3), 245–256. https://doi.org/10.1080/15321819.2020.1717527 Bassey, A. E., & Miteu, G. D. (2023). A review of current trends in HIV epidemiology, surveillance, and control in Nigeria. Annals of Medicine and Surgery, 85 , 1790–1795. https://doi.org/10.1097/MS9.0000000000000604 Oladele, T. T., Olakunde, B. O., Oladele, E. A., Ogbuoji, O., & Yamey, G. (2020). The impact of COVID-19 on HIV financing in Nigeria: A call for proactive measures. BMJ Global Health, 5 (5), e002718. https://doi.org/10.1136/bmjgh-2020-002718 Onovo, A. A., Adeyemi, A., Onime, D., Kalnoky, M., Kagniniwa, B., Dessie, M., Lee, L., Parrish, D., Adebobola, B., Ashefor, G., Ogorry, O., Goldstein, R., & Meri, H. (2023). Estimation of HIV prevalence and burden in Nigeria: A Bayesian predictive modelling study. EClinicalMedicine, 62 , 102098. https://doi.org/10.1016/j.eclinm.2023.102098 Nwosu, C. O., & Ataguba, J. E. (2019). Socioeconomic inequalities in maternal health service utilisation: A case of antenatal care in Nigeria using a decomposition approach. International Journal of Health Policy and Management, 8 (6), 362–371. https://doi.org/10.15171/ijhpm.2019.13 Oga‐Omenka, C., Bada, F., Agbaje, A., & Mavodza, C. (2020). Ease and equity of access to free DR-TB services in Nigeria—A qualitative analysis of policies, structures and processes. International Journal of Tuberculosis and Lung Disease, 24 (5), 510–517. https://doi.org/10.5588/ijtld.19.0710 Jahun, I., Dirlikov, E., Odafe, S., Yakubu, A., Boyd, A. T., Bachanas, P., Nzelu, C., Aliyu, G., Ellerbrock, T., Swaminathan, M., & CDC Nigeria ART Surge Team. (2021). Ensuring optimal community HIV testing services in Nigeria using an enhanced community case-finding package (ECCP), October 2019–March 2020: Acceleration to HIV epidemic control. HIV/AIDS (Auckland, N.Z.), 13 , 839–850. https://doi.org/10.2147/HIV.S316480 Yunusa, F., Bello, M., Kayode, G. A., Adegboye, A., Abone, G., Murtala-Ibrahim, F., Okposo, M., Anjorin, A., Dutt, S., Yusuf, O., Sam-Agudu, N., & Dakum, P. (2020). Uptake of tuberculosis preventive therapy in people living with HIV/AIDS in northern Nigeria: A programme to increase use of isoniazid preventive therapy. The Lancet Global Health, 8 (S1), S37. https://doi.org/10.1016/S2214-109X(20)30178-9 Oyebode, T., Hassan, Z., Afolaranmi, T., Umar, M., Magaji, F., Pawa, M., Akande, P., Sagay, S., Gwamna, J., & Kanki, P. (2021). Implementation of context-specific diagnostics, strategies and resources for PMTCT scale-up programming in conflict-affected communities in Jos, Nigeria. International Journal of HIV/AIDS Prevention, Education and Behavioural Science, 7 (1), 75–88. https://doi.org/10.11648/j.ijhpebs.20210701.13 Okoroiwu, H. U., Umoh, E. A., Asanga, E. E., & Eze, E. S. (2022). Thirty-five years (1986–2021) of HIV/AIDS in Nigeria: Bibliometric and scoping analysis. AIDS Research and Therapy, 19 (1), 64. https://doi.org/10.1186/s12981-022-00489-6 Dye, C., & Williams, B. G. (2007). Eliminating human tuberculosis in the twenty-first century. Journal of the Royal Society Interface, 4 (Suppl 6), 653–662. https://doi.org/10.1098/rsif.2007.0240.focus Ogbo, F. A., Ogeleka, P., Okoro, A., Olusanya, B. O., Olusanya, J., Ifegwu, I. K., Awosemo, A. O., Eastwood, J., & Page, A. (2018). Tuberculosis disease burden and attributable risk factors in Nigeria, 1990–2016. Tropical Medicine and Health, 46 , 34. https://doi.org/10.1186/s41182-018-0114-9 Adebayo, S. B., Gayawan, E., Fagbamigbe, A. F., & Bello, F. W. (2019). Bayesian geo-additive spatial modelling of HIV prevalence using data from population-based surveys. HIV/AIDS Review, 18 (4), 229–240. Dale, R. (2020). Parameter estimation and optimization for biological mathematical models using Bayesian statistics (Master's thesis, Louisiana State University). LSU Digital Commons. https://doi.org/10.31390/gradschool_theses.5249 Otu, A. A. (2013). A review of the National Tuberculosis and Leprosy Control Programme (NTBLCP) of Nigeria: Challenges and prospects. Annals of Tropical Medicine and Public Health, 6 (5), 491–500. https://doi.org/10.4103/1755-6783.133685 Ofoegbu, O. S., & Odume, B. B. (2015). Treatment outcome of tuberculosis patients at National Hospital Abuja, Nigeria: A five-year retrospective study. South African Family Practice, 57 (1), 50–56. https://doi.org/10.1080/20786190.2014.995913 Adamu, A. L., Gadanya, M. A., Abubakar, I. S., Jibo, A. M., Bello, M. M., Gajida, A. U., Babashani, M. M., & Abubakar, I. (2017). High mortality among tuberculosis patients on treatment in Nigeria: A retrospective cohort study. BMC Infectious Diseases, 17 (1), 170. https://doi.org/10.1186/s12879-017-2249-4 Entonu, P. E., & Agwale, S. M. (2007). A review of the epidemiology, pathogenesis, and immunology of tuberculosis in Nigeria. Journal of Infection in Developing Countries, 1 (1), 27–34. https://doi.org/10.3855/jidc.276 Odaibo, G. N., Okonkwo, P., Lawal, O. M., & Olaleye, D. O. (2013). HIV infection among newly diagnosed TB patients in southwestern Nigeria: A multi-DOTS center study. World Journal of AIDS, 3 (2), 132–138. https://doi.org/10.4236/wja.2013.32018 Adedokun, O., Badru, T., Khamofu, H., Negedu-Momoh, O. R., Iwara, E., Agbakwuru, C., Atobatele, A., Merrigan, M., Ukpong, D., Nzelu, C., Ashefor, G., Pandey, S. R., & Torpey, K. (2020). Akwa Ibom AIDS indicator survey: Key findings and lessons learnt. PLOS ONE, 15 (6), e0234079. https://doi.org/10.1371/journal.pone.0234079 Chijioke-Akaniro, O., Ubochioma, E., Omoniyi, A., Omosebi, O., Olarewaju, O., Etolue, M., Asuke, S., Aniwada, E., Ndubuisi, A. U., Ombeka, V., Agbaje, A., Lawanson, A., & Anyaike, C. (2022). Strategic engagement of private facilities to increase public-private mix (PPM) contribution to Nigeria tuberculosis case notification. Journal of Tuberculosis Research, 10 (3), 99–110. https://doi.org/10.4236/jtr.2022.103008 Additional Declarations No competing interests reported. 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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-6787707","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":542554226,"identity":"3264e63c-b67f-47db-8087-234a345bc72e","order_by":0,"name":"Uthman Olalekan Al-Ameen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAElEQVRIiWNgGAWjYFACHjApA+EYSMiBqAMP8Olg40HSyFBhYQzWkkC8ljMViQ0gGp8Wg/u9Rzf83GHDwz+7+fGHj20S6fPDDj8E2mInp9uAQ8sxvrSbvWfSeCTuHDOTnNkmkbvxdpoBUEuysdkBXFp4zG7wth3mYbiRYMbMC9IyOwGk5UDiNjxabv4FapG/kf7581+gwwxnp38gqOU2yBaDGzkG0gxnJBLkpXPw2yJ5LMfstmxbGo/hjZwyyZ4KCcMN0jkFBxIMcPuF7/AZs5tv22zk5G6kb/7ww6BOXn42kPGhwk4OlxYsTgWrNCBWOQjIN5CiehSMglEwCkYCAAC/aGQUZSIGagAAAABJRU5ErkJggg==","orcid":"","institution":"University of Ibadan","correspondingAuthor":true,"prefix":"","firstName":"Uthman","middleName":"Olalekan","lastName":"Al-Ameen","suffix":""}],"badges":[],"createdAt":"2025-05-30 22:23:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6787707/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6787707/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":95677485,"identity":"112f70f2-660a-4dae-9b7c-ae3900832a6c","added_by":"auto","created_at":"2025-11-11 19:02:30","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":99822,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTime-Series Line Plot of TB/HIV Incidence and Deaths in Nigeria (2000–2023)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTime-series line plots were generated to illustrate the trends in TB/HIV incidence and deaths in Nigeria from 2000 to 2023 as shown in figure 1.\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6787707/v1/b23d12f2d6a8dc9ee28d919f.jpg"},{"id":95799224,"identity":"0fe64df2-047e-494b-99f0-d3e912a414e3","added_by":"auto","created_at":"2025-11-13 08:19:12","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":37439,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eScatter plot of TB/HIV Deaths vs. TB/HIV Incidence with Regression Line\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eScatter plots with regression lines were generated to visualize the relationship between TB/HIV deaths and TB/HIV incidence as shown figure 2.\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6787707/v1/1e56f8373c2ca76cbe8c4668.jpg"},{"id":98382143,"identity":"e423439c-2cad-4c92-a21c-fc5f46676087","added_by":"auto","created_at":"2025-12-17 07:55:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1259817,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6787707/v1/ebe24141-4b4d-476a-93fc-0a58f0358b8d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Association Between Tuberculosis/HIV Incidence and Mortality in Nigeria: A Retrospective Analysis from 2000 to 2023","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eTuberculosis (TB) remains one of the leading causes of death globally, particularly among individuals living with Human Immunodeficiency Virus (HIV) infection (1). The dual burden of TB and HIV represents a major public health challenge, especially in sub-Saharan Africa, where the two diseases interact synergistically to worsen morbidity and mortality outcomes. Nigeria, in particular, bears a substantial share of this burden (2, 3). The co-epidemic of TB and HIV continues to hinder progress toward global health targets, despite extensive control efforts at both international and national levels.\u003c/p\u003e\n\u003cp\u003eThe epidemiological relationship between TB and HIV is well-documented, but most studies have been descriptive in nature, with limited quantification of long-term incidence–mortality dynamics using inferential modeling. This study addresses that gap by applying both linear and segmented regression analyses to evaluate how changes in TB and HIV incidence predict mortality trends in Nigeria between 2000 and 2023. By explicitly modeling both overall linear associations and potential non-linear shifts over time, this work provides a more nuanced understanding of the TB/HIV epidemic in Nigeria and its public health implications.\u003c/p\u003e\n\u003cp\u003eUnderstanding the epidemiology of TB/HIV co-infection is essential for designing effective prevention and control strategies (4). Nigeria currently has the third-largest HIV epidemic globally (5). In 2018, the national HIV prevalence among adults aged 15–49 years was estimated at 1.4% (6). Concurrently, Nigeria remains among the 30 high TB burden countries identified by the World Health Organization (1). The convergence of these two epidemics creates a complex public health challenge, driven by socioeconomic disparities (7), limited access to quality healthcare (8), and inadequate implementation of TB/HIV collaborative interventions.\u003c/p\u003e\n\u003cp\u003eOver the past decade, Nigeria has made notable progress in reducing HIV incidence through improved case finding, expanded treatment coverage, and increased awareness (4). The country has adopted the Joint United Nations Programme on HIV/AIDS (UNAIDS) 95-95-95 targets, which aim for 95% of people living with HIV to know their status, 95% of those diagnosed to receive treatment, and 95% of those on treatment to achieve viral suppression by 2030 (4). To accelerate case detection, the Nigerian government introduced the Enhanced Community Case-Finding Package, an innovative approach to improve identification of undiagnosed HIV-positive individuals (9). Despite these gains, TB continues to be the leading opportunistic infection and cause of death among people living with HIV in Nigeria (10).\u003c/p\u003e\n\u003cp\u003eThe dual burden of TB/HIV extends beyond health outcomes to impose significant social and economic costs. Families and communities bear the brunt of lost productivity, while the healthcare system faces mounting demands for diagnosis, treatment, and integrated care (11, 12). Addressing these challenges requires evidence-based, data-driven strategies that can inform policy and optimize resource allocation. This study, therefore, seeks to analyze national-level trends in TB and HIV incidence and mortality from 2000 to 2023 to identify key patterns and relationships.\u003c/p\u003e\n\u003cp\u003eBy examining these trends, we aim to generate actionable insights that can guide future interventions and strengthen TB/HIV control programs in Nigeria (13, 14, 6). Additionally, the study highlights the value of integrating statistical modeling, such as linear and segmented regression, to assess long-term epidemiological patterns. The potential application of Bayesian statistical frameworks to synthesize multiple datasets also presents an opportunity for cost-effective, integrated surveillance systems to improve infectious disease response at both national and sub national levels (15,16,6).\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cp\u003e\u003cstrong\u003e2.1 Study Design and Data Source:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study employed a \u003cstrong\u003eretrospective secondary data analysis design\u003c/strong\u003e using national-level annual data from \u003cstrong\u003e2000 to 2023\u003c/strong\u003e. Data on TB/HIV incidence and deaths in Nigeria were obtained from the \u003cstrong\u003eUNAIDS Global Database\u003c/strong\u003e (https://aidsinfo.unaids.org). To ensure completeness and accuracy, the dataset was cross-validated with the \u003cstrong\u003eWorld Health Organization (WHO) Global Tuberculosis Reports\u003c/strong\u003e, confirming consistency of reported trends. Although UNAIDS estimates are model-based, they represent the most reliable and widely used source for monitoring global TB/HIV statistics.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Ethical Considerations\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;This study was based on secondary, aggregated, and publicly available data obtained from the UNAIDS database. As no human participants were directly involved, institutional review board (IRB) approval was not required. Ethical principles of transparency and responsible data use were upheld throughout the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Variables\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe primary variables of interest were:\u003c/p\u003e\n\u003cp\u003eIndependent Variable: TB/HIV Incidence (number of new TB cases among HIV-positive individuals)\u003c/p\u003e\n\u003cp\u003eDependent Variable: TB/HIV Deaths (number of deaths attributed to TB among HIV-positive individuals)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4 Statistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData analysis was conducted using R programming version 4.4.2. The following statistical methods were employed:\u003c/p\u003e\n\u003cp\u003eDescriptive Statistics: Summary statistics were calculated for both TB/HIV incidence and deaths to understand the distribution and trends over time.\u003c/p\u003e\n\u003cp\u003eCorrelation Analysis: Pearson correlation coefficient was calculated to assess the strength and direction of the linear relationship between TB/HIV incidence and deaths.\u003c/p\u003e\n\u003cp\u003eLinear Regression Analysis: Simple linear regression was performed to model the relationship between TB/HIV incidence (independent variable) and TB/HIV deaths (dependent variable). The regression model was used to estimate the slope, intercept, and coefficient of determination (R\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n\u003cp\u003eSegmented (time-series) regression was conducted to identify structural changes in mortality trends over time. Two breakpoints were estimated (2003 and 2011), and slopes for each segment were compared. This approach allowed assessment of non-linearity and periods of intensified decline.\u003c/p\u003e\n\u003cp\u003eAlthough tuberculosis and HIV data are inherently count-based, this study utilized national-level annual aggregate data from 2000\u0026ndash;2023 obtained from WHO and UNAIDS reports. These yearly incidence and mortality estimates behave as continuous variables and are suitable for linear modeling. Linear regression was therefore applied to assess the overall strength and direction of the association between incidence and mortality. Prior to analysis, linearity and normality assumptions were examined and found reasonably satisfied at the aggregate level. This approach is consistent with methodologies used in WHO Global Tuberculosis Reports and other epidemiological trend analyses. Additionally, segmented regression was employed to detect and model potential non-linear changes in mortality trends over time.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5 Visualization:\u003c/strong\u003e Time-series line plots and scatter plots with regression lines were generated to visualize the trends and relationship between the variables.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cstrong\u003eDescriptive Statistics:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTB/HIV incidence in Nigeria showed a fluctuating pattern from 2000 to 2023, with the highest incidence observed in 2011 (97,000 cases) and the lowest in 2023 (25,000 cases). Similarly, TB/HIV deaths peaked in 2011 (59,000 deaths) and declined to 6,700 deaths in 2023.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorrelation Between TB/HIV Incidence and Deaths\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePearson correlation analysis revealed a strong positive correlation between TB/HIV incidence and deaths (\u003cem\u003er\u003c/em\u003e = 0.991, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001). This indicates a close relationship between the two variables, with higher incidence rates associated with higher mortality rates.\u003c/p\u003e\n\u003ch3\u003eAssociation Between TB/HIV Incidence and Deaths\u003c/h3\u003e\n\u003cp\u003eSimple linear regression revealed a very strong positive association between TB/HIV incidence and TB/HIV deaths. For every additional case of TB/HIV incidence, mortality increased by approximately 0.67 deaths (95% CI: 0.63\u0026ndash;0.70; p \u0026lt; 0.001). The model accounted for 98.2% of the variance in TB/HIV deaths (adjusted R\u0026sup2; = 0.982), confirming a robust linear relationship.\u003c/p\u003e\n\u003ch3\u003eTrends in TB/HIV Deaths\u003c/h3\u003e\n\u003cp\u003eA segmented regression analysis was conducted to examine changes in TB/HIV deaths over time. The model identified\u0026nbsp;\u003cstrong\u003etwo statistically significant breakpoints\u003c/strong\u003e in the series: the first in\u0026nbsp;\u003cstrong\u003e2003 (95% CI: 2003.0\u0026ndash;2004.0)\u003c/strong\u003e and the second in\u0026nbsp;\u003cstrong\u003e2011 (95% CI: 2011.0\u0026ndash;2011.5)\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePrior to 2003, TB/HIV deaths declined at an average rate of\u0026nbsp;\u003cstrong\u003e\u0026ndash;2,800 deaths per year (95% CI: \u0026ndash;5,023 to \u0026ndash;577; p=0.016)\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e From 2003 to 2011, the trend reversed, with deaths increasing by approximately\u0026nbsp;\u003cstrong\u003e+3,250 deaths per year (95% CI: 2,483\u0026ndash;4,017; p\u0026lt;0.001)\u003c/strong\u003e. Following 2011, a sharp decline was observed, with TB/HIV deaths decreasing by\u0026nbsp;\u003cstrong\u003e\u0026ndash;4,484 deaths annually (95% CI: \u0026ndash;4,899 to \u0026ndash;4,068; p\u0026lt;0.001)\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e The segmented regression model demonstrated excellent fit, explaining\u0026nbsp;\u003cstrong\u003e97.3% of the variance in deaths (adjusted R\u0026sup2;=0.973)\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVisualizations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 1: Time-Series Line Plot of TB/HIV Incidence and Deaths in Nigeria (2000\u0026ndash;2023)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTime-series line plots were generated to illustrate the trends in TB/HIV incidence and deaths in Nigeria from 2000 to 2023 as shown in figure 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 2: Scatter plot of TB/HIV Deaths vs. TB/HIV Incidence with Regression Line\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eScatter plots with regression lines were generated to visualize the relationship between TB/HIV deaths and TB/HIV incidence as shown figure 2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1: TB/HIV Deaths and Incidence in Nigeria (2000-2023)\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\u003c/div\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003eYear\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003eTB_HIV Deaths\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"105\"\u003e\n\u003cp\u003eTB_HIV Incidence\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e2000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e47 000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"105\"\u003e\n\u003cp\u003e79 000\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e2001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e40 000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"105\"\u003e\n\u003cp\u003e71 000\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e2002\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e39 000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"105\"\u003e\n\u003cp\u003e65 000\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e2003\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e38 000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"105\"\u003e\n\u003cp\u003e62 000\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e2004\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e37 000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"105\"\u003e\n\u003cp\u003e61 000\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e2005\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e40 000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"105\"\u003e\n\u003cp\u003e64 000\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e2006\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e43 000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"105\"\u003e\n\u003cp\u003e69 000\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e2007\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e45 000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"105\"\u003e\n\u003cp\u003e74 000\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e2008\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e56 000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"105\"\u003e\n\u003cp\u003e94 000\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e2009\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e54 000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"105\"\u003e\n\u003cp\u003e91 000\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e2010\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e55 000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"105\"\u003e\n\u003cp\u003e90 000\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e2011\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e59 000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"105\"\u003e\n\u003cp\u003e97 000\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e2012\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e54 000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"105\"\u003e\n\u003cp\u003e90 000\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e2013\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e52 000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"105\"\u003e\n\u003cp\u003e87 000\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e2014\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e48 000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"105\"\u003e\n\u003cp\u003e78 000\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e2015\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e44 000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"105\"\u003e\n\u003cp\u003e71 000\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e2016\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e41 000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"105\"\u003e\n\u003cp\u003e67 000\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e2017\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e37 000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"105\"\u003e\n\u003cp\u003e61 000\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e2018\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e33 000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"105\"\u003e\n\u003cp\u003e55 000\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e2019\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e29 000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"105\"\u003e\n\u003cp\u003e48 000\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e2020\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e21 000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"105\"\u003e\n\u003cp\u003e35 000\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e2021\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e14 000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"105\"\u003e\n\u003cp\u003e28 000\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e2022\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e11 000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"105\"\u003e\n\u003cp\u003e28 000\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e2023\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e6700\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"105\"\u003e\n\u003cp\u003e25 000\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe findings of this study highlight the strong relationship between TB/HIV incidence and deaths in Nigeria from 2000 to 2023. The significant positive correlation (r = 0.991, p \u0026lt; 0.001) indicates that changes in TB/HIV incidence are closely associated with corresponding changes in mortality rates. Simple linear regression further quantified this relationship, showing that each additional TB/HIV case was associated with 0.67 deaths (95% CI: 0.63–0.70), with the model explaining 98.2% of the variance in deaths (adjusted R² = 0.982). This underscores the interconnectedness of these two epidemics and the need for integrated approaches to address them.\u003c/p\u003e\n\u003cp\u003eHowever, the very high R² should be interpreted cautiously, as both incidence and mortality data originate from UNAIDS models, and shared assumptions may inflate the apparent strength of association. The negative intercept also indicates limitations of linear extrapolation, emphasizing the importance of cautious interpretation. Segmented regression analysis provided deeper insights by showing that mortality trends were not uniform across the study period. Breakpoints in 2003 and 2011 coincided with programmatic changes, particularly the rapid scale-up of ART and the strengthening of TB/HIV collaborative activities. These shifts suggest that national health policies significantly influenced mortality trends beyond what incidence alone predicted.\u003c/p\u003e\n\u003cp\u003eNigeria has the tenth largest burden of TB cases in the world (17), and TB/HIV co-infection remains a major public health challenge (18). Among the estimated 3.4 million people living with HIV in Nigeria, less than 800,000 are on treatment (19). Despite progress in HIV control, Nigeria still has the second largest global HIV epidemic and one of the highest rates of new infections in sub-Saharan Africa (11). Our findings are consistent with this evidence, as the observed decline in mortality after 2011 aligns with improvements in ART coverage and collaborative TB/HIV programs. Historical data also support this, with HIV prevalence rising sharply from \u0026lt;0.1% in 1987 to 5.8% in 2001, before stabilizing at 5.0% in 2003 (20). This context helps explain why mortality was initially high but later stabilized as ART scale-up gained momentum.\u003c/p\u003e\n\u003cp\u003eNevertheless, challenges persist. Access to quality TB/HIV services remains limited in conflict-affected areas, and funding gaps constrain prevention and treatment for key populations (4, 11). Studies have shown that HIV-positive TB patients have lower treatment success rates and higher risks of treatment failure and default (18). Strengthening surveillance systems, expanding ART coverage, and ensuring consistent clinical and laboratory monitoring are essential for improving outcomes (1, 4). The COVID-19 pandemic has further strained HIV financing in Nigeria, raising concerns about sustaining progress (5).\u003c/p\u003e\n\u003cp\u003eThe integration of linear and time-series methods in this study highlights the importance of combining cross-sectional association with trend analysis. While incidence strongly predicts mortality, programmatic and contextual shifts such as ART expansion, surveillance improvements, and policy interventions significantly shaped long-term outcomes. This underscores that effective epidemic control requires adaptive, evidence-driven strategies.\u003c/p\u003e\n\u003cp\u003eContinued investment in TB/HIV research and integrated service delivery models is essential to sustain progress and address remaining challenges (12, 21). Our findings therefore provide strong evidence that reducing incidence through ART scale-up, improved surveillance, and strengthened TB/HIV integration will have a direct and measurable impact on mortality reduction.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study demonstrates a strong positive association between TB/HIV incidence and TB/HIV-related deaths in Nigeria from 2000 to 2023. The strong correlation and significant regression results underscore the close relationship between these two variables. The findings highlight the critical importance of addressing the TB/HIV co-epidemic through integrated and intensified control efforts. Scaling up access to HIV testing and treatment, strengthening TB control programs, and addressing the underlying social determinants of health are essential for reducing the burden of TB/HIV co-infection in Nigeria. The results underscore the need for policymakers and healthcare providers to prioritize TB/HIV control and allocate resources effectively to improve the health outcomes of affected populations. Further research is needed to evaluate the impact of specific interventions and identify strategies for optimizing TB/HIV control efforts in Nigeria.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e6 \u0026nbsp;Limitation:\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;This study relied on secondary data obtained from UNAIDS, which may be subject to limitations in data completeness, accuracy, and reporting consistency across years. Additionally, the analysis focused on national-level data, which may overlook some regional disparities and local contextual factors influencing TB/HIV trends.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003e7 \u0026nbsp;Recommendations\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eEfforts should be intensified to strengthen integrated TB/HIV surveillance and improve data quality across regions. Access to HIV testing, antiretroviral therapy, and TB preventive treatment should be expanded, especially in high-burden areas. Investments in health systems and community outreach are essential to improve service delivery. Future studies should explore regional variations.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e: The author declares that there is no conflict of interest\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e: Uthman Olalekan Al-Ameen is the sole author and contributed to data analysis and manuscript writing\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e: This research was self-funded.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e: The author would like to thank the UNAIDS organization for providing the data set used for the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e: The data used in this study were obtained from UNAIDS, as detailed in the manuscript. For further information on data access, please refer to UNAIDS at [https://aidsinfo.unaids.org/].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval:\u003c/strong\u003e This study did not require ethical approval as it involved the use of secondary data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePatient Consent:\u003c/strong\u003e This study did not involve direct interaction with human participants; therefore, patient consent was not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate declaration: not applicable.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial number: not applicable.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish declaration: not applicable.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAppendix: Full meanings of Abbreviations:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTB:\u0026nbsp;\u003c/strong\u003eTuberculosis\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUNAIDS:\u003c/strong\u003e \u003cem\u003eJoint United Nations Program on HIV/AIDS\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eART:\u0026nbsp;\u003c/strong\u003eAntiretroviral therapy\u003c/p\u003e\n\u003cp\u003eHIV: Human Immunodeficiency Virus\u003c/p\u003e\n\u003cp\u003eAIDS: Acquired Immunodeficiency Syndrome\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eChang, C. A., Meloni, S. T., Eisen, G., Chaplin, B., Akande, P., Okonkwo, P., Rawizza, H. E., Tchetgen Tchetgen, E., \u0026amp; Kanki, P. J. (2015). Tuberculosis incidence and risk factors among human immunodeficiency virus (HIV)-infected adults receiving antiretroviral therapy in a large HIV program in Nigeria. \u003cem\u003eOpen Forum Infectious Diseases, 2\u003c/em\u003e(4), ofv154. https://doi.org/10.1093/ofid/ofv154\u003c/li\u003e\n \u003cli\u003eAliyu, N., \u0026amp; Magaji, B. A. (2019). Prevalence and factors associated with TB-HIV co-infection among tuberculosis patients treated at Specialist Hospital, Sokoto State, Nigeria (2014\u0026ndash;2017). \u003cem\u003eTexila International Journal of Clinical Research, 6\u003c/em\u003e(2). https://doi.org/10.21522/TIJCR.2014.06.02.Art002\u003c/li\u003e\n \u003cli\u003eOkonko, I. O., Ejike, I. U., Innocent-Adiele, C., \u0026amp; Cookey, T. I. (2020). HIV coinfections with tuberculosis among HIV-1 infected individuals in old Cross River State, Nigeria. \u003cem\u003eJournal of Immunoassay \u0026amp; Immunochemistry, 41\u003c/em\u003e(3), 245\u0026ndash;256. https://doi.org/10.1080/15321819.2020.1717527\u003c/li\u003e\n \u003cli\u003eBassey, A. E., \u0026amp; Miteu, G. D. (2023). A review of current trends in HIV epidemiology, surveillance, and control in Nigeria. \u003cem\u003eAnnals of Medicine and Surgery, 85\u003c/em\u003e, 1790\u0026ndash;1795. https://doi.org/10.1097/MS9.0000000000000604\u003c/li\u003e\n \u003cli\u003eOladele, T. T., Olakunde, B. O., Oladele, E. A., Ogbuoji, O., \u0026amp; Yamey, G. (2020). The impact of COVID-19 on HIV financing in Nigeria: A call for proactive measures. \u003cem\u003eBMJ Global Health, 5\u003c/em\u003e(5), e002718. https://doi.org/10.1136/bmjgh-2020-002718\u003c/li\u003e\n \u003cli\u003eOnovo, A. A., Adeyemi, A., Onime, D., Kalnoky, M., Kagniniwa, B., Dessie, M., Lee, L., Parrish, D., Adebobola, B., Ashefor, G., Ogorry, O., Goldstein, R., \u0026amp; Meri, H. (2023). Estimation of HIV prevalence and burden in Nigeria: A Bayesian predictive modelling study. \u003cem\u003eEClinicalMedicine, 62\u003c/em\u003e, 102098. https://doi.org/10.1016/j.eclinm.2023.102098\u003c/li\u003e\n \u003cli\u003eNwosu, C. O., \u0026amp; Ataguba, J. E. (2019). Socioeconomic inequalities in maternal health service utilisation: A case of antenatal care in Nigeria using a decomposition approach. \u003cem\u003eInternational Journal of Health Policy and Management, 8\u003c/em\u003e(6), 362\u0026ndash;371. https://doi.org/10.15171/ijhpm.2019.13\u003c/li\u003e\n \u003cli\u003eOga‐Omenka, C., Bada, F., Agbaje, A., \u0026amp; Mavodza, C. (2020). Ease and equity of access to free DR-TB services in Nigeria\u0026mdash;A qualitative analysis of policies, structures and processes. \u003cem\u003eInternational Journal of Tuberculosis and Lung Disease, 24\u003c/em\u003e(5), 510\u0026ndash;517. https://doi.org/10.5588/ijtld.19.0710\u003c/li\u003e\n \u003cli\u003eJahun, I., Dirlikov, E., Odafe, S., Yakubu, A., Boyd, A. T., Bachanas, P., Nzelu, C., Aliyu, G., Ellerbrock, T., Swaminathan, M., \u0026amp; CDC Nigeria ART Surge Team. (2021). Ensuring optimal community HIV testing services in Nigeria using an enhanced community case-finding package (ECCP), October 2019\u0026ndash;March 2020: Acceleration to HIV epidemic control. \u003cem\u003eHIV/AIDS (Auckland, N.Z.), 13\u003c/em\u003e, 839\u0026ndash;850. https://doi.org/10.2147/HIV.S316480\u003c/li\u003e\n \u003cli\u003eYunusa, F., Bello, M., Kayode, G. A., Adegboye, A., Abone, G., Murtala-Ibrahim, F., Okposo, M., Anjorin, A., Dutt, S., Yusuf, O., Sam-Agudu, N., \u0026amp; Dakum, P. (2020). Uptake of tuberculosis preventive therapy in people living with HIV/AIDS in northern Nigeria: A programme to increase use of isoniazid preventive therapy. \u003cem\u003eThe Lancet Global Health, 8\u003c/em\u003e(S1), S37. https://doi.org/10.1016/S2214-109X(20)30178-9\u003c/li\u003e\n \u003cli\u003eOyebode, T., Hassan, Z., Afolaranmi, T., Umar, M., Magaji, F., Pawa, M., Akande, P., Sagay, S., Gwamna, J., \u0026amp; Kanki, P. (2021). Implementation of context-specific diagnostics, strategies and resources for PMTCT scale-up programming in conflict-affected communities in Jos, Nigeria. \u003cem\u003eInternational Journal of HIV/AIDS Prevention, Education and Behavioural Science, 7\u003c/em\u003e(1), 75\u0026ndash;88. https://doi.org/10.11648/j.ijhpebs.20210701.13\u003c/li\u003e\n \u003cli\u003eOkoroiwu, H. U., Umoh, E. A., Asanga, E. E., \u0026amp; Eze, E. S. (2022). Thirty-five years (1986\u0026ndash;2021) of HIV/AIDS in Nigeria: Bibliometric and scoping analysis. \u003cem\u003eAIDS Research and Therapy, 19\u003c/em\u003e(1), 64. https://doi.org/10.1186/s12981-022-00489-6\u003c/li\u003e\n \u003cli\u003eDye, C., \u0026amp; Williams, B. G. (2007). Eliminating human tuberculosis in the twenty-first century. \u003cem\u003eJournal of the Royal Society Interface, 4\u003c/em\u003e(Suppl 6), 653\u0026ndash;662. https://doi.org/10.1098/rsif.2007.0240.focus\u003c/li\u003e\n \u003cli\u003eOgbo, F. A., Ogeleka, P., Okoro, A., Olusanya, B. O., Olusanya, J., Ifegwu, I. K., Awosemo, A. O., Eastwood, J., \u0026amp; Page, A. (2018). Tuberculosis disease burden and attributable risk factors in Nigeria, 1990\u0026ndash;2016. \u003cem\u003eTropical Medicine and Health, 46\u003c/em\u003e, 34. https://doi.org/10.1186/s41182-018-0114-9\u003c/li\u003e\n \u003cli\u003eAdebayo, S. B., Gayawan, E., Fagbamigbe, A. F., \u0026amp; Bello, F. W. (2019). Bayesian geo-additive spatial modelling of HIV prevalence using data from population-based surveys. \u003cem\u003eHIV/AIDS Review, 18\u003c/em\u003e(4), 229\u0026ndash;240.\u003c/li\u003e\n \u003cli\u003eDale, R. (2020). Parameter estimation and optimization for biological mathematical models using Bayesian statistics (Master\u0026apos;s thesis, Louisiana State University). LSU Digital Commons. https://doi.org/10.31390/gradschool_theses.5249\u003c/li\u003e\n \u003cli\u003eOtu, A. A. (2013). A review of the National Tuberculosis and Leprosy Control Programme (NTBLCP) of Nigeria: Challenges and prospects. \u003cem\u003eAnnals of Tropical Medicine and Public Health, 6\u003c/em\u003e(5), 491\u0026ndash;500. https://doi.org/10.4103/1755-6783.133685\u003c/li\u003e\n \u003cli\u003eOfoegbu, O. S., \u0026amp; Odume, B. B. (2015). Treatment outcome of tuberculosis patients at National Hospital Abuja, Nigeria: A five-year retrospective study. \u003cem\u003eSouth African Family Practice, 57\u003c/em\u003e(1), 50\u0026ndash;56. https://doi.org/10.1080/20786190.2014.995913\u003c/li\u003e\n \u003cli\u003eAdamu, A. L., Gadanya, M. A., Abubakar, I. S., Jibo, A. M., Bello, M. M., Gajida, A. U., Babashani, M. M., \u0026amp; Abubakar, I. (2017). High mortality among tuberculosis patients on treatment in Nigeria: A retrospective cohort study. \u003cem\u003eBMC Infectious Diseases, 17\u003c/em\u003e(1), 170. https://doi.org/10.1186/s12879-017-2249-4\u003c/li\u003e\n \u003cli\u003eEntonu, P. E., \u0026amp; Agwale, S. M. (2007). A review of the epidemiology, pathogenesis, and immunology of tuberculosis in Nigeria. \u003cem\u003eJournal of Infection in Developing Countries, 1\u003c/em\u003e(1), 27\u0026ndash;34. https://doi.org/10.3855/jidc.276\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eOdaibo, G. N., Okonkwo, P., Lawal, O. M., \u0026amp; Olaleye, D. O. (2013).\u003c/strong\u003e HIV infection among newly diagnosed TB patients in southwestern Nigeria: A multi-DOTS center study. \u003cem\u003eWorld Journal of AIDS, 3\u003c/em\u003e(2), 132\u0026ndash;138. https://doi.org/10.4236/wja.2013.32018\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eAdedokun, O., Badru, T., Khamofu, H., Negedu-Momoh, O. R., Iwara, E., Agbakwuru, C., Atobatele, A., Merrigan, M., Ukpong, D., Nzelu, C., Ashefor, G., Pandey, S. R., \u0026amp; Torpey, K. (2020).\u003c/strong\u003e Akwa Ibom AIDS indicator survey: Key findings and lessons learnt. \u003cem\u003ePLOS ONE, 15\u003c/em\u003e(6), e0234079. https://doi.org/10.1371/journal.pone.0234079\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eChijioke-Akaniro, O., Ubochioma, E., Omoniyi, A., Omosebi, O., Olarewaju, O., Etolue, M., Asuke, S., Aniwada, E., Ndubuisi, A. U., Ombeka, V., Agbaje, A., Lawanson, A., \u0026amp; Anyaike, C. (2022).\u003c/strong\u003e Strategic engagement of private facilities to increase public-private mix (PPM) contribution to Nigeria tuberculosis case notification. \u003cem\u003eJournal of Tuberculosis Research, 10\u003c/em\u003e(3), 99\u0026ndash;110. https://doi.org/10.4236/jtr.2022.103008\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Tuberculosis, HIV, AIDS, TB/HIV co-infection, Nigeria, Incidence, Mortality, Epidemiology","lastPublishedDoi":"10.21203/rs.3.rs-6787707/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6787707/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Tuberculosis and Human Immunodeficiency Virus (HIV) co-infection remains a major public health challenge, particularly in high-burden countries like Nigeria. Understanding incidence–mortality dynamics is essential for guiding control strategies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjective:\u003c/strong\u003e To analyze the association between TB/HIV incidence and TB/HIV-related mortality in Nigeria from 2000 to 2023, accounting for temporal trends.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e A retrospective secondary data analysis was conducted using UNAIDS estimates. Correlation, linear regression, and segmented time-series regression were applied to quantify associations and detect mortality trend changes. Analyses were performed in R version 4.4.2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e TB/HIV incidence and deaths were strongly correlated (r = 0.991, p \u0026lt; 0.001). Linear regression confirmed a significant association, with each additional case of TB/HIV incidence linked to ~0.67 deaths (95% CI: 0.63–0.70, p \u0026lt; 0.001). The model explained 98.2% of mortality variance (adjusted R² = 0.982), though the negative intercept reflected a model limitation. Segmented regression identified two breakpoints (2003 and 2011), marking shifts in mortality: declines (–2800 deaths/year, 2000–2003), sharp increases (+3250 deaths/year, 2003–2011), and subsequent steep declines (–4484 deaths/year, 2011–2023). These transitions coincided with programmatic changes, including ART scale-up and strengthened TB/HIV collaboration.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e TB/HIV incidence strongly predicts mortality in Nigeria, but the very high R² should be interpreted cautiously given shared UNAIDS modeling assumptions. Combining linear and time-series approaches provides a more nuanced understanding of Nigeria’s TB/HIV epidemic and underscores the need for adaptive, evidence-based control strategies. \u003cstrong\u003eA key limitation is reliance on secondary UNAIDS estimates, which may introduce modeling biases and restrict causal inference.\u003c/strong\u003e\u003c/p\u003e","manuscriptTitle":"The Association Between Tuberculosis/HIV Incidence and Mortality in Nigeria: A Retrospective Analysis from 2000 to 2023","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-11 19:02:25","doi":"10.21203/rs.3.rs-6787707/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":"e7f77983-c27b-4ff2-b413-fec740d2ab07","owner":[],"postedDate":"November 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-17T07:55:03+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-11 19:02:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6787707","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6787707","identity":"rs-6787707","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}