Tuberculosis Trends, Disparities, and Forecasts in the United States: Will the 2035 Elimination Goal Be Met?

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Daniyal Ali Khan, Syed Ali Tayyeb Hasan, Kantesh Kumar, Mustafa Aman, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7600721/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Tuberculosis (TB) remains a US public health concern, with persistent disparities, yet TB-related mortality trends, especially among older adults, remain understudied. Methods This cross-sectional study analysed CDC-WONDER mortality data (1999–2023) and National Notifiable Diseases Surveillance System incidence data (1999–2022) for adults aged ≥ 45 years. TB (ICD-10: A16-A19) incidence rates (IRs), age-adjusted and crude mortality rates (AAMRs, CMRs) were calculated per million population. Trends were analysed using Joinpoint regression, and ARIMA models were used to forecast mortality through 2035 in the context of the US elimination target. Results From 1999–2022, TB IRs declined from 64.3 to 25.0 per million. AAMRs fell from 17.4 to 7.5 between 1999–2023 (AAPC = -3.36%, 95% CI: -3.72 to -2.98). Of 26,341 deaths, 62.5% were male. By 2023, AAMRs in both genders had declined by over half. CMRs decreased across all age groups, with the largest decline in those ≥ 85 years, who also had the highest rates. Asian/Pacific Islander and American Indian/Alaska Native populations consistently had the highest AAMRs, while White individuals had the lowest. All regions except the West showed steady declines. Alaska, Hawaii, California, Mississippi, and Texas had the highest AAMRs. Large metropolitan areas consistently had higher AAMRs. Most deaths were due to respiratory TB, and 65.2% occurred in inpatient hospitals. Forecasting indicated that AAMRs are projected to remain above the US elimination threshold by 2035. Conclusion TB-related mortality in the US has declined over two decades, but recent resurgence and persistent demographic and geographic disparities highlight the need for targeted interventions to achieve elimination. Infectious Diseases Epidemiology Tuberculosis Epidemiology Health Inequities Mortality Forecasting United States Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Key Messages Question: What are the trends and disparities in US tuberculosis (TB) from 1999–2023, and is the nation on track to eliminate TB by 2035? Findings: Despite a decline in TB incidence and mortality rates over two decades, recent trends have plateaued or increased, with demographic and geographical disparities. Forecasting models suggest that, without renewed public health action, the US is unlikely to meet its 2035 TB elimination goal. Importance: There is an urgent need for targeted interventions to address persistent demographic and geographic disparities, enhance early diagnosis and treatment, and reinvigorate TB elimination efforts in the post-pandemic era. Introduction Tuberculosis (TB), caused by Mycobacterium tuberculosis, remains a major global public health threat. In 2022, an estimated 10.6 million people developed TB, and 1.3 million died worldwide despite decades of control efforts and effective treatments [ 1 , 2 ]. Although the highest burdens occur in low- and middle-income countries (LMICs), high-income nations such as the United States continue to report cases, with recent trends suggesting stalled progress. In 2023, 9633 TB cases were reported in the US, a 15.6% increase from 2022 and the highest since 2013, corresponding to an incidence of 2.9 per 100 000 population [ 3 ]. While incidence historically declined, recent fluctuations highlight persistent challenges in control and prevention. TB also imposes a burden of latent infection and long-term morbidity, particularly among immunocompromised and older adults [ 4 , 5 ]. Although TB morbidity is high, mortality remains a concern even in high-income settings. In the US, TB disproportionately affects marginalized groups: in 2023, 90.1% of cases occurred among racial and ethnic minorities, including 36.8% in Hispanics/Latinos, 30.0% in non-Hispanic (NH) Asians, and 17.6% in NH Blacks [ 6 ]. Males accounted for 62.3% of cases, and adults aged ≥ 65 years had the highest incidence at 4.3 per 100 000 [ 6 ]. Additionally, 75.8% of cases occurred in non–US-born individuals, with many linked to comorbidities such as HIV or diabetes [ 6 – 9 ]. Despite these risks, no prior study has comprehensively examined TB-related mortality trends in the US over time and across major demographic and geographic subgroups. While several clinical, demographic, and structural factors contribute to TB mortality, examining long-term trends can help illuminate disparities across subgroups and inform targeted public health efforts. This study aimed to characterize TB-related mortality patterns among US adults aged ≥ 45 years over 25 years, with detailed stratification by age, gender, race/ethnicity, and geography. We also evaluated shifts during the COVID-19 pandemic and incorporated forecast modelling to assess whether the US is on track to meet the WHO’s TB elimination goals, defined as fewer than one case per million population annually by 2035 [ 10 , 11 ]. By combining retrospective analysis with forward-looking projections, this study provides a comprehensive overview of TB mortality dynamics in an aging and increasingly high-risk population. Methods Study design and population This population-based, cross-sectional study analysed all TB-related deaths in adults aged ≥ 45 years using mortality data from the CDC Wide-ranging Online Data for Epidemiologic Research (CDC-WONDER) Multiple Cause of Death database (1999–2023) [ 12 , 13 ]. Deaths were identified by International Classification of Diseases, Tenth Revision (ICD-10) codes A16-A19, listed as either underlying or contributing causes on death certificates. Additionally, TB annual case counts and incidence rates (IRs) from 1999–2022, based on all ages and yearly total population, were obtained from the National Notifiable Diseases Surveillance System’s (NNDSS) Morbidity and Mortality Weekly Report summaries (1999–2015) and the CDC-WONDER NNDSS data platform (2016–2022) [ 14 , 15 ]. This study was exempt from ethical approval as a de-identified public database was used. All procedures conformed to the principles outlined in the Declaration of Helsinki, and the study was conducted per the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines [ 16 ]. Outcome Variables This study primarily analysed: (1) annual TB case counts and IRs (2) trends in overall age-adjusted mortality rates (AAMR) and IRs; (3) AAMR trends stratified by gender, race, region, urbanisation status, and ICD-10 codes (Supplementary Table S1) ; (4) age-stratified crude mortality rate (CMR) trends; (5) variation in state-level overall AAMR; (6) number of deaths by place of death; and (7) whether the US is on track to meet its 2035 target for TB elimination. To evaluate the projected trajectory of TB mortality, we employed autoregressive integrated moving average (ARIMA) modelling to generate three forecasts: (a) Forecast one: a 2020–2035 forecast trained on pre-pandemic data (2010–2019) also to assess the difference in expected and observed trends during COVID-19, (b) Forecast two: a 2024–2035 forecast using the full dataset (1999–2023), and (c) Forecast three: a 2024–2035 forecast based on the most recent 15 years of data (2009–2023). Data abstraction Age groups were stratified into 45–54, 55–64, 65–74, 75–84, and ≥ 85 years. Based on death certificate reporting, race/ethnicity classifications included Asian or Pacific Islander (A/PI), American Indian or Alaska Native (AI/AN), White, Black or African American, and Hispanic individuals. Places of death were categorized as inpatient hospital (IPH), outpatient/emergency room facility (OEF), medical facility-dead on arrival (MF-DoA), medical facility-status unknown (MF-SU), hospice, home, nursing home/long-term care facility, or other/unknown locations. Urbanisation status was defined using the 2013 National Center for Health Statistics Urban-Rural Classification: large metropolitan (≥ one million), medium/small metropolitan (50 000-999 999), and rural/non-metropolitan (< 50 000) [ 17 ]. Regional classifications followed the US Census Bureau definitions, dividing the nation into the Northeast, Midwest, South, and West. To protect confidentiality, CDC-WONDER suppresses death counts < 10 and marks rates < 20 as unreliable; these appear as dashes in tables and dotted lines in figures. Statistical analysis IRs, CMRs, and AAMRs were calculated per million individuals. AAMRs were standardised to the 2000 US standard population [ 18 ]. Annual and average annual percent changes (APCs and AAPCs), along with 95% confidence intervals (CIs), were computed using the Joinpoint Regression Program (version 5.4.0). This software identifies changes in mortality trends by applying log-linear regression models. APCs were estimated for each segment demarcated by joinpoints, with significance determined via the Monte Carlo permutation method. A trend was classified as increasing or decreasing if the APC differed from zero, as evaluated using two-tailed t-tests. Forecast analysis We conducted forecasting using the ARIMA model in R (version 4.5.1), as done previously [ 19 ]. ARIMA is suited for non-stationary time series, combining autoregressive and moving average terms to capture temporal patterns [ 20 ]. Optimal model parameters were automatically selected using auto.arima() based on the Bayesian Information Criterion (BIC) [ 20 – 22 ]. To assess stationarity, we applied the Augmented Dickey-Fuller (ADF) test. Model performance was evaluated using Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and the first-lag autocorrelation of residuals (ACF1). Residual patterns were further assessed using the Ljung-Box test, which tests for autocorrelation across several lags to determine whether the residuals approximate white noise [ 22 ]. We first calculated RMSE using one-step-ahead fitted values to evaluate how well the model reproduced historical trends. To further examine predictive accuracy, we employed time series cross-validation with the tsCV() function from the forecast package. This involved a 10-step forecast horizon, yielding a multi-step RMSE that reflected the model’s generalizability across different points in the time series [ 23 ]. All three ARIMA models required differencing, as the ADF test confirmed non-stationarity in the original data. A summary of model diagnostics and performance indicators is provided in Supplementary Table S2 . Results An overview of the main findings is presented in the graphical abstract ( Fig. 1 ). Overall From 1999–2022, 279 907 TB cases were reported across all ages. IRs declined from a peak of 64.3 per million in 1999 to 21.8 in 2020, followed by a rise to 25.0 in 2022 ( Fig. 2 , Supplementary Table S3) . Between 1999 and 2023, TB accounted for 26 341 deaths among adults aged ≥ 45 in the US (Supplementary Table S4) . TB-related AAMR declined by over half, from 17.4 per million in 1999 to 7.5 per million in 2023 (AAPC = -3.36%, 95% CI: -3.72 to -2.98) ( Fig. 3 ) . The steepest decline occurred between 1999 and 2004 (APC = -8.35%, 95% CI: -12.78 to -6.20), followed by a moderate decrease from 2004–2015 (APC = -4.87%, 95% CI: -5.86 to -1.19). From 2015–2023, however, there was a modest increase from 6.2 to 7.5 (APC = 2.09%, 95% CI: 0.54 to 4.80). Gender Stratified Males accounted for 16 481 (62.5%) of the total deaths and consistently exhibited higher AAMRs than females ( Fig. 3 , Supplementary Table S5) . From 1999–2023, male AAMRs declined 2.6-fold (26.8 to 10.3), compared to a two-fold drop in females (10.9 to 5.2). The overall decline was steeper in males (AAPC = -3.63%, 95% CI: -4.19 to -3.12) than in females (AAPC = -2.96%, 95% CI: -3.31 to -2.63). Among males, the steepest decrease occurred from 26.8 in 1999 to 10.4 in 2011 (APC = -6.96%, 95% CI: -8.99 to -5.82), with rates stabilizing thereafter (2011–2023 APC = 0.19%, 95% CI: -1.57 to 2.09). In contrast, females saw a substantial decline in AAMR from 10.9 in 1999 to 3.7 in 2015 (APC = -6.16%, 95% CI: -6.75 to -5.69), followed by a 1.5-fold increase to 5.2 in 2023 (APC = 3.78%, 95% CI: 2.10 to 5.94). Race Stratified Throughout the study period, A/PI and AI/AN populations exhibited the highest TB-related AAMRs, while White individuals consistently had the lowest ( Fig. 4 , Supplementary Table S5) . In 1999, A/PI individuals had the highest AAMR (76.1), followed by AI/AN (67.9), Black (47.8), Hispanics (37.4), and White (11.1), a nearly seven-fold difference between A/PI and White individuals. All racial groups had ≥ 2-fold AAMR declines from 1999–2023. Black individuals had the steepest drop, from 47.8 to 10.7 (AAPC = -5.83%, 95% CI: -6.45 to -5.32), with the steepest drop between 1999–2013 (APC = -9.36%, 95% CI: -10.70 to -8.45). For White individuals, AAMRs fell most rapidly from 1999–2007 (APC = -8.06%, 95% CI: -10.50 to -7.02), slowed from 2007–2016 (APC = -3.80%, 95% CI: -5.53 to -1.71), and then rose from 2016–2023 (APC = 2.62%, 95% CI: 0.90 to 6.01). A/PI and Hispanic populations saw their largest declines between 1999–2014, with APCs of -5.54% (95% CI: -7.98 to -4.18) and − 6.68% (95% CI: -12.35 to -5.29), respectively. By 2023, A/PI individuals had the highest AAMR (32.6), followed by AI/AN (23.7), Black (10.7), Hispanic (9.8), and White (4.6), an eight-fold disparity between A/PI and White populations. To protect confidentiality, CDC-WONDER suppresses death counts < 10 and marks rates < 20 as unreliable; this limitation has been indicated in the figure with dotted lines. Age Stratified TB-related CMRs declined across all age groups from 1999 to 2023, with rates increasing consistently by age ( Fig. 5 , Supplementary Table S6) . Most groups saw nearly a two-fold reduction, while individuals aged ≥ 85 experienced the greatest decline, from 81.1 in 1999 to 29.7 in 2023 (AAPC = − 4.42, 95% CI: − 4.98 to − 3.64), nearly a three-fold drop. The CMR gap between ages 45–54 and ≥ 85 remained substantial, narrowing slightly from 15-fold (5.5 vs 81.1) in 1999 to 12-fold (2.5 vs 29.7) in 2023. The CMR for the 65–74 group increased from 6.7 in 2014 to 8.7 in 2023 (APC = 3.68, 95% CI: 1.38–7.01). State Stratified States above the 90th percentile for AAMR (12.47) included Alaska (highest, 31.1), Hawaii (15.3), California (14.8), Mississippi (12.7), and Texas (12.5) ( Fig. 6 , Supplementary Table S7) . States below the 10th percentile (5.56) included Utah (4.1, lowest), Iowa (4.6), Kansas (5.0), Idaho (5.0), and West Virginia (5.5). To protect confidentiality, CDC-WONDER suppresses death counts < 10 and marks rates < 20 as unreliable; this limitation has been indicated in the figure. Region Stratified The Western region exhibited the highest AAMR for most of the 1999–2023 period ( Fig. 7 , Supplementary Table S8) , declining from a peak value of 21.2 in 1999 to 9.1 in 2016 (APC = -4.55%, 95% CI: -6.14 to -3.76). However, this was followed by a rise to 10.4 in 2023. Overall, it showed the smallest decline among all regions (AAPC = -2.70, 95% CI: -3.41 to -2.10). The South almost consistently had the second-highest AAMR, falling from 19.8 in 1999 to 7.0 in 2023 (AAPC = -3.92%, 95% CI: -4.32 to -3.53). Overall AAMRs for the Northeast and Midwest also dropped from 14.1 to 6.7 (AAPC = − 3.07%, 95% CI: − 3.76 to − 2.41) and from 13.7 to 6.2 (AAPC = − 3.20%, 95% CI: − 4.05 to − 2.44), respectively. Urbanisation Status Stratified Large metropolitan areas exhibited the highest AAMR of 19.1 in 1999 (Supplementary Figure S1, Supplementary Table S8) . These areas maintained the highest rates for most of the study period, despite a decline to 7.5 in 2020 (AAPC = -4.27%, 95% CI: -4.82 to -3.73). Medium/small metropolitan and non-metropolitan areas also showed declines from 15.9 and 15.2 in 1999 to 6.9 and 6.7 in 2020, with AAPCs of -4.04% (95% CI: -4.58 to -3.40) and − 4.60% (95% CI: -5.79 to -3.48), respectively. ICD-10 Codes Stratified The A16 cohort (respiratory TB) consistently had the highest AAMR, with values never falling below 4.0, in contrast to other cohorts, which remained below 2.0 throughout (Supplementary Figure S2, Supplementary Table S9) . A16 showed a sustained overall decline from 15.1 in 1999 to 5.4 in 2023 (AAPC = -4.15%, 95% CI: -4.52 to -3.63), with the steepest decline to 11.3 in 2001 (APC = -13.49%, 95% CI: -16.82 to -6.72). This was followed by a slower decline until 2015, reaching 4.9 (APC = -5.96%, 95% CI: -6.67 to -2.03). A17 (TB of the nervous system) was excluded from analysis due to unreliable or suppressed data. Place of Death Stratified Of the 26 341 TB-related deaths, more than half occurred in IPH (65.2%), followed by decedents' home (15.7%), nursing home (8.9%), OEF (4.2%), and hospice (2.5%) (Supplementary Table S10) . The remaining 3.6% were categorized as MF-DoA, MF-SU, or other/unknown locations. Forecasting Forecast one, based on pre-COVID-19 data, projected a steady decline in TB-related AAMRs, reaching 3.46 per million (95% CI: 1.25–5.67) by 2035 ( Fig. 8 , Supplementary Table S11) . Predicted rates for 2020–2023 were consistently lower than observed values during the same period. In contrast, forecast two, which incorporated the full dataset (1999–2023), predicted a persistent increase in AAMRs, reaching 9.48 by 2035, though with widening uncertainty intervals (e.g., -1.35 to 20.32 in 2035) (Supplementary Figure S3) . Forecast three maintained a constant projected AAMR of 7.5 from 2024 to 2035 (Supplementary Figure S4) . Discussion This 25-year analysis of TB trends in US adults aged ≥ 45 years showed IRs declined from 64.3 to 21.8 per million by 2020, then rebounded to 25.0 in 2022. AAMRs dropped by over half until 2015 but rose steeply through 2023. While AAMRs remained higher in males, females experienced a 1.5-fold increase from 2015 to 2023. All racial groups saw at least a two-fold decline in AAMRs from 1999–2023; A/PI and AI/AN populations had the highest rates, White individuals the lowest. A forecast based on pre-COVID-19 data projected a decline to 3.46 per million (95% CI: 1.25–5.67) by 2035 and predicted AAMRs for 2020–2023 were lower than observed. Our study showed a decline in TB IR over the past two decades, though the rate of decline slowed notably between 2013 and 2019. This mirrors findings by Salinas et al. and LoBue et al., who reported a plateau and even a slight increase in IR during that time, suggesting existing prevention strategies may have reached their limit [ 24 , 25 ]. Our study further shows a rise in IR between 2021 and 2022, reaching 25.0 per million. CDC reports show that 2023 TB case counts were 8.3% higher than in 2019, surpassing pre-pandemic levels [ 26 ]. These increases may be related to pandemic-associated healthcare disruptions. Notably, TB-related mortality began rising in 2015, coinciding with the IR plateau, and worsened during the pandemic. Swartwood et al. reported 17% and 21% increases in TB deaths in 2020 and 2021 compared to 2019 [ 27 ], a reversal also seen globally with 1.6 million TB deaths reported in 2021 [ 28 , 29 ]. These findings highlight TB control’s vulnerability to system disruptions and the need to revitalise elimination efforts. Males accounted for 60% of TB cases in our study, with consistently higher AAMRs compared to females. The decline in AAMR was greater for males (2.6-fold) than for females (two-fold), but males continued to bear a disproportionate burden of both incidence and mortality. This gender disparity is consistent with global trends and likely reflects a combination of biological, behavioral, and social factors [ 30 , 31 ]. Males face increased TB risk due to higher rates of smoking, alcohol use, incarceration, and homelessness, factors linked to immune suppression and greater transmission in overcrowded settings [ 32 – 34 ]. Broader social networks and high-risk occupations such as mining further contribute [ 35 , 36 ]. They also have higher prevalence of comorbidities such as HIV and substance use disorders, which elevate TB-related mortality [ 37 , 38 ]. Additionally, biological factors may increase male susceptibility to TB [ 35 – 39 ]. Although males consistently had higher AAMRs, the post-2015 rise in female TB mortality may indicate care gaps in older females, particularly as mortality increases steeply with age. Given females’ longer life expectancy and overrepresentation in older US populations [ 40 ], these trends highlight the need for gender-sensitive interventions [ 41 ]. Racial disparities in TB-related mortality were substantial in our study, with A/PI and AI/AN populations exhibiting the highest AAMRs and White individuals the lowest. One possible explanation is the higher burden among non-US-born individuals, who accounted for 75.8% of TB cases in 2023 [ 42 ]. We also observed higher TB-related mortality in the West and South, which may reflect immigration patterns, as these regions had larger immigrant populations from 2019–2023, potentially contributing to both regional and racial disparities [ 43 ]. However, a recent study analyzing the US TB data for 2011–2019, showed that racial disparities in TB outcomes are primarily seen in US-born individuals, not the non-US-born [ 44 ]. These disparities were largely driven by comorbidities (e.g., HIV, end-stage renal disease) and neighborhood-level vulnerabilities like poverty and racialized economic segregation [ 45 ]. Similarly, a 2011–2021 analysis reported the greatest relative disparities among AI/AN females, with incidence rate ratios up to 14.2 compared to White females [ 46 ]. Although our dataset lacked granularity to explore these contextual factors in detail, our findings reinforce the need for targeted, equity-oriented interventions that address the drivers of TB-related racial disparities. The US aims to eliminate TB by 2035 [ 11 ]. However, even our most optimistic forecast model, trained exclusively on pre-pandemic data, projected an AAMR of 3.46 by 2035. Forecast three predicted stable mortality trends, while forecast two suggested a possible increase. Collectively, these forecasts indicate that, based on current trends, the US is not on track to meet its TB elimination target. The Lancet Commission states that achieving TB elimination targets requires scaling up proven interventions, developing new tools (better diagnostics, shorter regimens, a more effective vaccine), expanding active case finding, addressing social determinants through multi-sectoral action, and improving person-centered care [ 47 , 48 ]. This study has several limitations. As CDC-WONDER relies on death certificates and ICD-10 codes, data may be subject to misclassification or incomplete reporting. Urban-rural analysis was limited to 2020 due to unavailable data beyond that year. To protect confidentiality, CDC-WONDER suppresses subgroups with < 10 deaths and flags counts < 20 as statistically unreliable, limiting interpretation for some racial groups, states, and ICD-10 subcategories in our analyses. Lastly, the cross-sectional design precludes causal inference. Conclusion Despite long-term progress, TB incidence and mortality in the US have plateaued in recent years, with concerning increases following the COVID-19 pandemic. Demographic and geographic disparities persist, with higher TB mortality observed among males, A/PI and AI/AN populations, older adults, and residents of the South and West. Forecasting models suggest that, without renewed public health action, the US is unlikely to meet its 2035 TB elimination goal. These findings highlight the urgent need for targeted interventions to address disparities, enhance early diagnosis and treatment, and reinvigorate TB elimination efforts in the post-pandemic era. Declarations Ethics approval: This study was exempt from ethical approval as a de-identified public database was used. All procedures conformed to the principles outlined in the Declaration of Helsinki, and the study was conducted per the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines. Acknowledgments: None. CRediT author statement All authors meet the authorship criteria outlined by the International Committee of Medical Journal Editors (ICMJE). Conceptualisation: Daniyal Ali Khan, Syed Ali Tayyeb Hasan; Methodology: Daniyal Ali Khan, Syed Ali Tayyeb Hasan, Kantesh Kumar, Mustafa Aman, Shahzaib Khan, Mahnoor Khan, Syed Haider Ali Gardezi, Ali Bin Abdul Jabbar, Syed Faisal Mahmood; Formal analysis and investigation: Shahzaib Khan, Mahnoor Khan; Data curation: Shahzaib Khan, Mahnoor Khan; Visualisation: Shahzaib Khan, Mahnoor Khan; Validation: Daniyal Ali Khan, Syed Ali Tayyeb Hasan; Writing - original draft preparation: Daniyal Ali Khan, Syed Ali Tayyeb Hasan, Kantesh Kumar, Mustafa Aman, Syed Haider Ali Gardezi; Writing - review and editing: Daniyal Ali Khan, Syed Ali Tayyeb Hasan, Kantesh Kumar, Mustafa Aman, Shahzaib Khan, Mahnoor Khan, Syed Haider Ali Gardezi, Ali Bin Abdul Jabbar, Syed Faisal Mahmood; Supervision: Ali Bin Abdul Jabbar, Syed Faisal Mahmood; Project administration: Daniyal Ali Khan, Syed Ali Tayyeb Hasan Daniyal Ali Khan and Syed Ali Tayyeb Hasan contributed equally. All authors had full access to the data, approved the final manuscript, and accept responsibility for its accuracy and integrity. Supplementary data: Supplementary data are available at IJE online. Conflict of Interest: None declared. Funding: None. Data availability statement: Data were abstracted from the Centers for Disease Control and Prevention’s Wide-Ranging Online Data for Epidemiologic Research (CDC-WONDER) platform. All relevant data are included in the manuscript and supplementary materials. CDC-WONDER Multiple Cause of Death 1999-2020: https://wonder.cdc.gov/mcd-icd10.html CDC-WONDER Multiple Cause of Death 2018-2023: https://wonder.cdc.gov/mcd-icd10-expanded.html Consent to participate and publish: No patient consent was needed for this study, as CDC-WONDER contains anonymized, publicly available data. References World Health Organization. 1.1 TB incidence. In: Global Tuberculosis Report 2023. 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Cancer Med 13(1):e6880. 10.1002/cam4.6880 Zhu B, Wu X, An W, Yao B, Liu Y (2022) The systematic analysis and 10-year prediction on disease burden of childhood cancer in China. Front Public Health 10:908955. 10.3389/fpubh.2022.908955 Lin H, Shi L, Zhang J, Zhang J, Zhang C (2021) Epidemiological characteristics and forecasting incidence for patients with breast cancer in Shantou, Southern China: 2006–2017. Cancer Med 10(8):2904–2913. 10.1002/cam4.3843 Watson L, Qi S, DeIure A et al (2021) Using Autoregressive Integrated Moving Average (ARIMA) Modelling to Forecast Symptom Complexity in an Ambulatory Oncology Clinic: Harnessing Predictive Analytics and Patient-Reported Outcomes. Int J Environ Res Public Health 18(16). 10.3390/ijerph18168365 Salinas JL, Mindra G, Haddad MB, Pratt R, Price SF, Langer AJ (2016) Leveling of Tuberculosis Incidence - United States, 2013–2015. MMWR Morb Mortal Wkly Rep 65(11):273–278. 10.15585/mmwr.mm6511a2 LoBue PA, Mermin JH (2017) Latent tuberculosis infection: the final frontier of tuberculosis elimination in the USA. Lancet Infect Dis 17(10):e327–e333. 10.1016/S1473-3099(17)30248-7 Centers for Disease Control and Prevention. National data. In: Reported Tuberculosis in the United States (2023) https://www.cdc.gov/tb-surveillance-report-2023/summary/national.html . Accessed August 7, 2025 Swartwood NA, Cohen T, Marks SM et al (2024) Effects of the COVID-19 pandemic on TB outcomes in the United States: a Bayesian analysis. MedRxiv Prepr Serv Health Sci Published online Oct 18:2024. 10.17.24315683 Falzon D, Zignol M, Bastard M, Floyd K, Kasaeva T (2023) The impact of the COVID-19 pandemic on the global tuberculosis epidemic. Front Immunol 14–2023. 10.3389/fimmu.2023.1234785 Yang H, Ruan X, Li W, Xiong J, Zheng Y (2024) Global, regional, and national burden of tuberculosis and attributable risk factors for 204 countries and territories, 1990–2021: a systematic analysis for the Global Burden of Diseases 2021 study. BMC Public Health 24(1):3111. 10.1186/s12889-024-20664-w Marçôa R, Ribeiro AI, Zão I, Duarte R (2018) Tuberculosis and gender – Factors influencing the risk of tuberculosis among men and women by age group. Pulmonology 24(3):199–202. 10.1016/j.pulmoe.2018.03.004 Horton KC, MacPherson P, Houben RMGJ, White RG, Corbett EL (2016) Sex Differences in Tuberculosis Burden and Notifications in Low- and Middle-Income Countries: A Systematic Review and Meta-analysis. PLOS Med 13(9):1–23. 10.1371/journal.pmed.1002119 Diwan VK, Thorson A (1999) Sex, gender, and tuberculosis. Lancet Lond Engl 353(9157):1000–1001. 10.1016/S0140-6736(99)01318-5 Holmes CB, Hausler H, Nunn P (1998) A review of sex differences in the epidemiology of tuberculosis. Int J Tuberc Lung Dis Off J Int Union Tuberc Lung Dis 2(2):96–104 Dabitao D, Bishai WR (2023) Sex and Gender Differences in Tuberculosis Pathogenesis and Treatment Outcomes. Curr Top Microbiol Immunol 441:139–183. 10.1007/978-3-031-35139-6_6 Nhamoyebonde S, Leslie A (2014) Biological differences between the sexes and susceptibility to tuberculosis. J Infect Dis 209(Suppl 3):S100–106. 10.1093/infdis/jiu147 Miller PB, Zalwango S, Galiwango R et al (2021) Association between tuberculosis in men and social network structure in Kampala, Uganda. BMC Infect Dis 21(1):1023. 10.1186/s12879-021-06475-z Global (2022) regional, and national sex differences in the global burden of tuberculosis by HIV status, 1990–2019: results from the Global Burden of Disease Study 2019. Lancet Infect Dis 22(2):222–241. 10.1016/S1473-3099(21)00449-7 Schmit KM, Shah N, Kammerer S, Bamrah Morris S, Marks SM (2020) Tuberculosis Transmission or Mortality Among Persons Living with HIV, USA, 2011–2016. J Racial Ethn Health Disparities 7(5):865–873. 10.1007/s40615-020-00709-7 Dibbern J, Eggers L, Schneider BE (2017) Sex differences in the C57BL/6 model of Mycobacterium tuberculosis infection. Sci Rep 7(1):10957. 10.1038/s41598-017-11438-z Guralnik JM, Balfour JL, Volpato S (2000) The ratio of older women to men: historical perspectives and cross-national comparisons. Aging Milan Italy 12(2):65–76. 10.1007/BF03339893 Rickman HM, Phiri MD, Feasey HRA et al (2025) Sex differences in the risk of Mycobacterium tuberculosis infection: a systematic review and meta-analysis of population-based immunoreactivity surveys. Lancet Public Health 10(7):e588–e598. 10.1016/S2468-2667(25)00120-3 Centers for Disease Control and Prevention. TB by origin of birth and reporting areas: 2023. In: Reported Tuberculosis in the United States (2023) https://www.cdc.gov/tb-surveillance-report-2023/tables/table-30.html . Accessed August 12, 2025 Migration Policy Institute (2025) U.S. immigrant population by state and county. https://www.migrationpolicy.org/programs/data-hub/charts/us-immigrant-population-state-and-county . Accessed August 12 Noppert GA, Hegde ST (2024) Racial and Ethnic Disparities in Tuberculosis—the Cost of Neglect. JAMA Netw Open 7(9):e2431908–e2431908. 10.1001/jamanetworkopen.2024.31908 Regan M, Barham T, Li Y et al (2024) Risk factors underlying racial and ethnic disparities in tuberculosis diagnosis and treatment outcomes, 2011–19: a multiple mediation analysis of national surveillance data. Lancet Public Health 9(8):e564–e572. 10.1016/S2468-2667(24)00151-8 Li Y, Regan M, Swartwood NA et al (2024) Disparities in Tuberculosis Incidence by Race and Ethnicity Among the U.S.-Born Population in the United States, 2011 to 2021: An Analysis of National Disease Registry Data. Ann Intern Med 177(4):418–427. 10.7326/M23-2975 Reid MJA, Arinaminpathy N, Bloom A et al (2019) Building a tuberculosis-free world: The Lancet Commission on tuberculosis. Lancet 393(10178):1331–1384. 10.1016/S0140-6736(19)30024-8 Chakaya JM, Harries AD, Marks GB (2020) Ending tuberculosis by 2030—Pipe dream or reality? Int J Infect Dis 92:S51–S54. 10.1016/j.ijid.2020.02.021 Additional Declarations The authors declare no competing interests. Supplementary Files SupplementaryAppendixTB.docx Supplementary Appendix Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7600721","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":514208499,"identity":"16e02772-9560-491d-a889-8b7adf65376f","order_by":0,"name":"Daniyal Ali 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Center","correspondingAuthor":false,"prefix":"","firstName":"Shahzaib","middleName":"","lastName":"Khan","suffix":""},{"id":514208504,"identity":"9948be60-dfba-4a4c-a473-bf798280712e","order_by":5,"name":"Mahnoor Khan","email":"","orcid":"https://orcid.org/0009-0009-8028-6814","institution":"Shaikh Khalifa Bin Zayed Al Nahyan Medical \u0026 Dental College","correspondingAuthor":false,"prefix":"","firstName":"Mahnoor","middleName":"","lastName":"Khan","suffix":""},{"id":514208505,"identity":"919cfad7-4ae9-4e26-bc8f-f140dcd57b9e","order_by":6,"name":"Syed Haider Ali Gardezi","email":"","orcid":"https://orcid.org/0009-0008-9871-1797","institution":"Aga Khan University","correspondingAuthor":false,"prefix":"","firstName":"Syed","middleName":"Haider Ali","lastName":"Gardezi","suffix":""},{"id":514208506,"identity":"0c37ac99-9c71-4e55-aae2-4445f773182c","order_by":7,"name":"Ali Bin Abdul Jabbar","email":"","orcid":"https://orcid.org/0000-0001-5145-8626","institution":"Creighton University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Ali","middleName":"Bin Abdul","lastName":"Jabbar","suffix":""},{"id":514208507,"identity":"823284d6-a7a6-44ae-a042-73ece2edc833","order_by":8,"name":"Syed Faisal Mahmood","email":"","orcid":"https://orcid.org/0000-0002-3390-7659","institution":"Aga Khan University","correspondingAuthor":false,"prefix":"","firstName":"Syed","middleName":"Faisal","lastName":"Mahmood","suffix":""}],"badges":[],"createdAt":"2025-09-12 12:44:19","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-7600721/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7600721/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":91366809,"identity":"82743eac-b821-4a62-aeb5-fbc37168d863","added_by":"auto","created_at":"2025-09-15 17:50:40","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":22777834,"visible":true,"origin":"","legend":"\u003cp\u003eGraphical abstract\u003c/p\u003e","description":"","filename":"FIG1.JPEG.TBCentralFigureNew01.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7600721/v1/712232f4deaceb0194631476.jpg"},{"id":91366780,"identity":"2a249df2-c4f7-4283-8bda-62bc16c045bd","added_by":"auto","created_at":"2025-09-15 17:50:40","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":261659,"visible":true,"origin":"","legend":"\u003cp\u003eIncidence rates per million of tuberculosis in the US from 1999 to 2022\u003c/p\u003e\n\u003cp\u003eAlt text: Line chart of tuberculosis incidence rates per million from 1999-2022.\u003c/p\u003e","description":"","filename":"FIG2.JPEG.TBIncidence.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7600721/v1/9bea1bd3e95b670e33dd4974.jpg"},{"id":91366778,"identity":"76041bd1-3362-4188-98d6-31058f9c754e","added_by":"auto","created_at":"2025-09-15 17:50:40","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":480462,"visible":true,"origin":"","legend":"\u003cp\u003eAge-adjusted mortality rates per million overall and stratified by gender\u003c/p\u003e\n\u003cp\u003eAlt text: Line chart of age-adjusted tuberculosis mortality rates per million from 1999-2023 showing three series: overall, males, and females.\u003c/p\u003e","description":"","filename":"FIG3.JPEG.OverallGender.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7600721/v1/04d9fc5cbba9652bf17194bd.jpg"},{"id":91367616,"identity":"8cd1c71a-bfdd-443d-ae12-ba648b939df6","added_by":"auto","created_at":"2025-09-15 17:58:40","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":812223,"visible":true,"origin":"","legend":"\u003cp\u003eAge-adjusted mortality rates per million stratified by race\u003c/p\u003e\n\u003cp\u003eTo protect confidentiality, CDC-WONDER suppresses death counts \u0026lt;10 and marks rates \u0026lt;20 as unreliable; this limitation has been indicated in the figure with dotted lines.\u003c/p\u003e\n\u003cp\u003eAlt text: Line chart of age-adjusted tuberculosis mortality rates per million from 1999-2023, stratified by race.\u003c/p\u003e","description":"","filename":"FIG4.JPEG.Race.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7600721/v1/bcbab1ef787f034dcbbcdb06.jpg"},{"id":91366802,"identity":"c83eeb4d-dc62-4b9a-8daf-b14ac6dfe3e3","added_by":"auto","created_at":"2025-09-15 17:50:40","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":755378,"visible":true,"origin":"","legend":"\u003cp\u003eCrude mortality rates per million stratified by 10-year age groups\u003c/p\u003e\n\u003cp\u003eAlt text: Line chart of crude tuberculosis mortality rates per million from 1999-2023, stratified by 10-year age groups.\u003c/p\u003e","description":"","filename":"FIG5.JPEG.Age.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7600721/v1/9c692cdd282c4d7f6bd376b0.jpg"},{"id":91366801,"identity":"e281ba00-f7d0-4005-9a59-3068be9a63e9","added_by":"auto","created_at":"2025-09-15 17:50:40","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":10434918,"visible":true,"origin":"","legend":"\u003cp\u003eOverall age-adjusted mortality rates per million from 1999-2023 stratified by states\u003c/p\u003e\n\u003cp\u003eTo protect confidentiality, CDC-WONDER suppresses death counts \u0026lt;10 and marks rates \u0026lt;20 as unreliable; this limitation has been indicated in the figure.\u003c/p\u003e\n\u003cp\u003eAlt text: Map of the US showing overall age-adjusted mortality rates per million from 1999-2023 for each state.\u003c/p\u003e","description":"","filename":"FIG6.JPEG.TBAAMRMap.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7600721/v1/5af1c8626cd4bafae2ea2b7a.jpg"},{"id":91368169,"identity":"d1427099-2db2-4ea2-8e9c-f426a50bb416","added_by":"auto","created_at":"2025-09-15 18:06:40","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":581870,"visible":true,"origin":"","legend":"\u003cp\u003eAge-adjusted mortality rates per million stratified by region\u003c/p\u003e\n\u003cp\u003eAlt text: Line chart of age-adjusted tuberculosis mortality rates per million from 1999-2023 stratified by region.\u003c/p\u003e","description":"","filename":"FIG7.JPEG.Regions.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7600721/v1/b4f1b10f6ea0712a931d4e7c.jpg"},{"id":91366790,"identity":"464274b1-b524-40ad-8e4d-af36a0446196","added_by":"auto","created_at":"2025-09-15 17:50:40","extension":"jpeg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":1415334,"visible":true,"origin":"","legend":"\u003cp\u003eForecast one: a 2020-2035 forecast trained on pre-pandemic data (2010-2019)\u003c/p\u003e\n\u003cp\u003eThe shaded areas represent the 95% confidence intervals.\u003c/p\u003e\n\u003cp\u003eAlt text: Line chart of age-adjusted tuberculosis mortality rates per million, showing Forecast one, a 2020-2035 projection based on pre-pandemic data (2010-2019).\u003c/p\u003e","description":"","filename":"FIG8.JPEG.ARIMAForecast1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7600721/v1/02b3cb4f6ba033a9256d2553.jpeg"},{"id":91368911,"identity":"411afc44-7d86-4bb0-a752-977c8870f7c2","added_by":"auto","created_at":"2025-09-15 18:23:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":15787507,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7600721/v1/fcdbce4a-d1b5-4a09-a22a-bc6143d34ea7.pdf"},{"id":91367618,"identity":"12da81e1-4980-4bee-a324-d6d8d017d5d5","added_by":"auto","created_at":"2025-09-15 17:58:40","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1325904,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Appendix\u003c/p\u003e","description":"","filename":"SupplementaryAppendixTB.docx","url":"https://assets-eu.researchsquare.com/files/rs-7600721/v1/3009882c61c5473ee1275b7f.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eTuberculosis Trends, Disparities, and Forecasts in the United States: Will the 2035 Elimination Goal Be Met?\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Key Messages","content":"\u003cp\u003e\u003cstrong\u003eQuestion:\u0026nbsp;\u003c/strong\u003eWhat are the trends and disparities in US tuberculosis (TB) from 1999\u0026ndash;2023, and is the nation on track to eliminate TB by 2035?\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFindings:\u0026nbsp;\u003c/strong\u003eDespite a decline in TB incidence and mortality rates over two decades, recent trends have plateaued or increased, with demographic and geographical disparities. Forecasting models suggest that, without renewed public health action, the US is unlikely to meet its 2035 TB elimination goal.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImportance:\u0026nbsp;\u003c/strong\u003eThere is an urgent need for targeted interventions to address persistent demographic and geographic disparities, enhance early diagnosis and treatment, and reinvigorate TB elimination efforts in the post-pandemic era.\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eTuberculosis (TB), caused by Mycobacterium tuberculosis, remains a major global public health threat. In 2022, an estimated 10.6\u0026nbsp;million people developed TB, and 1.3\u0026nbsp;million died worldwide despite decades of control efforts and effective treatments [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Although the highest burdens occur in low- and middle-income countries (LMICs), high-income nations such as the United States continue to report cases, with recent trends suggesting stalled progress. In 2023, 9633 TB cases were reported in the US, a 15.6% increase from 2022 and the highest since 2013, corresponding to an incidence of 2.9 per 100 000 population [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. While incidence historically declined, recent fluctuations highlight persistent challenges in control and prevention. TB also imposes a burden of latent infection and long-term morbidity, particularly among immunocompromised and older adults [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAlthough TB morbidity is high, mortality remains a concern even in high-income settings. In the US, TB disproportionately affects marginalized groups: in 2023, 90.1% of cases occurred among racial and ethnic minorities, including 36.8% in Hispanics/Latinos, 30.0% in non-Hispanic (NH) Asians, and 17.6% in NH Blacks [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Males accounted for 62.3% of cases, and adults aged\u0026thinsp;\u0026ge;\u0026thinsp;65 years had the highest incidence at 4.3 per 100 000 [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Additionally, 75.8% of cases occurred in non\u0026ndash;US-born individuals, with many linked to comorbidities such as HIV or diabetes [\u003cspan additionalcitationids=\"CR7 CR8\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Despite these risks, no prior study has comprehensively examined TB-related mortality trends in the US over time and across major demographic and geographic subgroups.\u003c/p\u003e\u003cp\u003eWhile several clinical, demographic, and structural factors contribute to TB mortality, examining long-term trends can help illuminate disparities across subgroups and inform targeted public health efforts. This study aimed to characterize TB-related mortality patterns among US adults aged\u0026thinsp;\u0026ge;\u0026thinsp;45 years over 25 years, with detailed stratification by age, gender, race/ethnicity, and geography. We also evaluated shifts during the COVID-19 pandemic and incorporated forecast modelling to assess whether the US is on track to meet the WHO\u0026rsquo;s TB elimination goals, defined as fewer than one case per million population annually by 2035 [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. By combining retrospective analysis with forward-looking projections, this study provides a comprehensive overview of TB mortality dynamics in an aging and increasingly high-risk population.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy design and population\u003c/h2\u003e\u003cp\u003eThis population-based, cross-sectional study analysed all TB-related deaths in adults aged\u0026thinsp;\u0026ge;\u0026thinsp;45 years using mortality data from the CDC Wide-ranging Online Data for Epidemiologic Research (CDC-WONDER) Multiple Cause of Death database (1999\u0026ndash;2023) [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Deaths were identified by International Classification of Diseases, Tenth Revision (ICD-10) codes A16-A19, listed as either underlying or contributing causes on death certificates. Additionally, TB annual case counts and incidence rates (IRs) from 1999\u0026ndash;2022, based on all ages and yearly total population, were obtained from the National Notifiable Diseases Surveillance System\u0026rsquo;s (NNDSS) Morbidity and Mortality Weekly Report summaries (1999\u0026ndash;2015) and the CDC-WONDER NNDSS data platform (2016\u0026ndash;2022) [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThis study was exempt from ethical approval as a de-identified public database was used. All procedures conformed to the principles outlined in the Declaration of Helsinki, and the study was conducted per the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eOutcome Variables\u003c/h3\u003e\n\u003cp\u003eThis study primarily analysed: (1) annual TB case counts and IRs (2) trends in overall age-adjusted mortality rates (AAMR) and IRs; (3) AAMR trends stratified by gender, race, region, urbanisation status, and ICD-10 codes \u003cb\u003e(Supplementary Table S1)\u003c/b\u003e; (4) age-stratified crude mortality rate (CMR) trends; (5) variation in state-level overall AAMR; (6) number of deaths by place of death; and (7) whether the US is on track to meet its 2035 target for TB elimination. To evaluate the projected trajectory of TB mortality, we employed autoregressive integrated moving average (ARIMA) modelling to generate three forecasts: (a) Forecast one: a 2020\u0026ndash;2035 forecast trained on pre-pandemic data (2010\u0026ndash;2019) also to assess the difference in expected and observed trends during COVID-19, (b) Forecast two: a 2024\u0026ndash;2035 forecast using the full dataset (1999\u0026ndash;2023), and (c) Forecast three: a 2024\u0026ndash;2035 forecast based on the most recent 15 years of data (2009\u0026ndash;2023).\u003c/p\u003e\n\u003ch3\u003eData abstraction\u003c/h3\u003e\n\u003cp\u003eAge groups were stratified into 45\u0026ndash;54, 55\u0026ndash;64, 65\u0026ndash;74, 75\u0026ndash;84, and \u0026ge;\u0026thinsp;85 years. Based on death certificate reporting, race/ethnicity classifications included Asian or Pacific Islander (A/PI), American Indian or Alaska Native (AI/AN), White, Black or African American, and Hispanic individuals. Places of death were categorized as inpatient hospital (IPH), outpatient/emergency room facility (OEF), medical facility-dead on arrival (MF-DoA), medical facility-status unknown (MF-SU), hospice, home, nursing home/long-term care facility, or other/unknown locations. Urbanisation status was defined using the 2013 National Center for Health Statistics Urban-Rural Classification: large metropolitan (\u0026ge;\u0026thinsp;one million), medium/small metropolitan (50 000-999 999), and rural/non-metropolitan (\u0026lt;\u0026thinsp;50 000) [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Regional classifications followed the US Census Bureau definitions, dividing the nation into the Northeast, Midwest, South, and West.\u003c/p\u003e\u003cp\u003eTo protect confidentiality, CDC-WONDER suppresses death counts\u0026thinsp;\u0026lt;\u0026thinsp;10 and marks rates\u0026thinsp;\u0026lt;\u0026thinsp;20 as unreliable; these appear as dashes in tables and dotted lines in figures.\u003c/p\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eIRs, CMRs, and AAMRs were calculated per million individuals. AAMRs were standardised to the 2000 US standard population [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Annual and average annual percent changes (APCs and AAPCs), along with 95% confidence intervals (CIs), were computed using the Joinpoint Regression Program (version 5.4.0). This software identifies changes in mortality trends by applying log-linear regression models. APCs were estimated for each segment demarcated by joinpoints, with significance determined via the Monte Carlo permutation method. A trend was classified as increasing or decreasing if the APC differed from zero, as evaluated using two-tailed t-tests.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eForecast analysis\u003c/h3\u003e\n\u003cp\u003eWe conducted forecasting using the ARIMA model in R (version 4.5.1), as done previously [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. ARIMA is suited for non-stationary time series, combining autoregressive and moving average terms to capture temporal patterns [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Optimal model parameters were automatically selected using auto.arima() based on the Bayesian Information Criterion (BIC) [\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTo assess stationarity, we applied the Augmented Dickey-Fuller (ADF) test. Model performance was evaluated using Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and the first-lag autocorrelation of residuals (ACF1). Residual patterns were further assessed using the Ljung-Box test, which tests for autocorrelation across several lags to determine whether the residuals approximate white noise [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eWe first calculated RMSE using one-step-ahead fitted values to evaluate how well the model reproduced historical trends. To further examine predictive accuracy, we employed time series cross-validation with the tsCV() function from the \u003cem\u003eforecast\u003c/em\u003e package. This involved a 10-step forecast horizon, yielding a multi-step RMSE that reflected the model\u0026rsquo;s generalizability across different points in the time series [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAll three ARIMA models required differencing, as the ADF test confirmed non-stationarity in the original data. A summary of model diagnostics and performance indicators is provided in \u003cb\u003eSupplementary Table S2\u003c/b\u003e.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eAn overview of the main findings is presented in the graphical abstract \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eOverall\u003c/h3\u003e\n\u003cp\u003eFrom 1999\u0026ndash;2022, 279 907 TB cases were reported across all ages. IRs declined from a peak of 64.3 per million in 1999 to 21.8 in 2020, followed by a rise to 25.0 in 2022 \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e, \u003cb\u003eSupplementary Table S3)\u003c/b\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eBetween 1999 and 2023, TB accounted for 26 341 deaths among adults aged\u0026thinsp;\u0026ge;\u0026thinsp;45 in the US \u003cb\u003e(Supplementary Table S4)\u003c/b\u003e. TB-related AAMR declined by over half, from 17.4 per million in 1999 to 7.5 per million in 2023 (AAPC = -3.36%, 95% CI: -3.72 to -2.98) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. The steepest decline occurred between 1999 and 2004 (APC = -8.35%, 95% CI: -12.78 to -6.20), followed by a moderate decrease from 2004\u0026ndash;2015 (APC = -4.87%, 95% CI: -5.86 to -1.19). From 2015\u0026ndash;2023, however, there was a modest increase from 6.2 to 7.5 (APC\u0026thinsp;=\u0026thinsp;2.09%, 95% CI: 0.54 to 4.80).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eGender Stratified\u003c/h3\u003e\n\u003cp\u003eMales accounted for 16 481 (62.5%) of the total deaths and consistently exhibited higher AAMRs than females \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003e, \u003cb\u003eSupplementary Table S5)\u003c/b\u003e. From 1999\u0026ndash;2023, male AAMRs declined 2.6-fold (26.8 to 10.3), compared to a two-fold drop in females (10.9 to 5.2). The overall decline was steeper in males (AAPC = -3.63%, 95% CI: -4.19 to -3.12) than in females (AAPC = -2.96%, 95% CI: -3.31 to -2.63).\u003c/p\u003e\u003cp\u003eAmong males, the steepest decrease occurred from 26.8 in 1999 to 10.4 in 2011 (APC = -6.96%, 95% CI: -8.99 to -5.82), with rates stabilizing thereafter (2011\u0026ndash;2023 APC\u0026thinsp;=\u0026thinsp;0.19%, 95% CI: -1.57 to 2.09). In contrast, females saw a substantial decline in AAMR from 10.9 in 1999 to 3.7 in 2015 (APC = -6.16%, 95% CI: -6.75 to -5.69), followed by a 1.5-fold increase to 5.2 in 2023 (APC\u0026thinsp;=\u0026thinsp;3.78%, 95% CI: 2.10 to 5.94).\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eRace Stratified\u003c/h2\u003e\u003cp\u003eThroughout the study period, A/PI and AI/AN populations exhibited the highest TB-related AAMRs, while White individuals consistently had the lowest \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e4\u003c/span\u003e, \u003cb\u003eSupplementary Table S5)\u003c/b\u003e. In 1999, A/PI individuals had the highest AAMR (76.1), followed by AI/AN (67.9), Black (47.8), Hispanics (37.4), and White (11.1), a nearly seven-fold difference between A/PI and White individuals.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAll racial groups had\u0026thinsp;\u0026ge;\u0026thinsp;2-fold AAMR declines from 1999\u0026ndash;2023. Black individuals had the steepest drop, from 47.8 to 10.7 (AAPC = -5.83%, 95% CI: -6.45 to -5.32), with the steepest drop between 1999\u0026ndash;2013 (APC = -9.36%, 95% CI: -10.70 to -8.45). For White individuals, AAMRs fell most rapidly from 1999\u0026ndash;2007 (APC = -8.06%, 95% CI: -10.50 to -7.02), slowed from 2007\u0026ndash;2016 (APC = -3.80%, 95% CI: -5.53 to -1.71), and then rose from 2016\u0026ndash;2023 (APC\u0026thinsp;=\u0026thinsp;2.62%, 95% CI: 0.90 to 6.01).\u003c/p\u003e\u003cp\u003eA/PI and Hispanic populations saw their largest declines between 1999\u0026ndash;2014, with APCs of -5.54% (95% CI: -7.98 to -4.18) and \u0026minus;\u0026thinsp;6.68% (95% CI: -12.35 to -5.29), respectively. By 2023, A/PI individuals had the highest AAMR (32.6), followed by AI/AN (23.7), Black (10.7), Hispanic (9.8), and White (4.6), an eight-fold disparity between A/PI and White populations.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTo protect confidentiality, CDC-WONDER suppresses death counts\u0026thinsp;\u0026lt;\u0026thinsp;10 and marks rates\u0026thinsp;\u0026lt;\u0026thinsp;20 as unreliable; this limitation has been indicated in the figure with dotted lines.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eAge Stratified\u003c/h2\u003e\u003cp\u003eTB-related CMRs declined across all age groups from 1999 to 2023, with rates increasing consistently by age \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e5\u003c/span\u003e, \u003cb\u003eSupplementary Table S6)\u003c/b\u003e. Most groups saw nearly a two-fold reduction, while individuals aged\u0026thinsp;\u0026ge;\u0026thinsp;85 experienced the greatest decline, from 81.1 in 1999 to 29.7 in 2023 (AAPC = \u0026minus;\u0026thinsp;4.42, 95% CI: \u0026minus;\u0026thinsp;4.98 to \u0026minus;\u0026thinsp;3.64), nearly a three-fold drop. The CMR gap between ages 45\u0026ndash;54 and \u0026ge;\u0026thinsp;85 remained substantial, narrowing slightly from 15-fold (5.5 vs 81.1) in 1999 to 12-fold (2.5 vs 29.7) in 2023. The CMR for the 65\u0026ndash;74 group increased from 6.7 in 2014 to 8.7 in 2023 (APC\u0026thinsp;=\u0026thinsp;3.68, 95% CI: 1.38\u0026ndash;7.01).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eState Stratified\u003c/h2\u003e\u003cp\u003eStates above the 90th percentile for AAMR (12.47) included Alaska (highest, 31.1), Hawaii (15.3), California (14.8), Mississippi (12.7), and Texas (12.5) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e6\u003c/span\u003e, \u003cb\u003eSupplementary Table S7)\u003c/b\u003e. States below the 10th percentile (5.56) included Utah (4.1, lowest), Iowa (4.6), Kansas (5.0), Idaho (5.0), and West Virginia (5.5).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTo protect confidentiality, CDC-WONDER suppresses death counts\u0026thinsp;\u0026lt;\u0026thinsp;10 and marks rates\u0026thinsp;\u0026lt;\u0026thinsp;20 as unreliable; this limitation has been indicated in the figure.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eRegion Stratified\u003c/h2\u003e\u003cp\u003eThe Western region exhibited the highest AAMR for most of the 1999\u0026ndash;2023 period \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e7\u003c/span\u003e, \u003cb\u003eSupplementary Table S8)\u003c/b\u003e, declining from a peak value of 21.2 in 1999 to 9.1 in 2016 (APC = -4.55%, 95% CI: -6.14 to -3.76). However, this was followed by a rise to 10.4 in 2023. Overall, it showed the smallest decline among all regions (AAPC = -2.70, 95% CI: -3.41 to -2.10). The South almost consistently had the second-highest AAMR, falling from 19.8 in 1999 to 7.0 in 2023 (AAPC = -3.92%, 95% CI: -4.32 to -3.53). Overall AAMRs for the Northeast and Midwest also dropped from 14.1 to 6.7 (AAPC = \u0026minus;\u0026thinsp;3.07%, 95% CI: \u0026minus;\u0026thinsp;3.76 to \u0026minus;\u0026thinsp;2.41) and from 13.7 to 6.2 (AAPC = \u0026minus;\u0026thinsp;3.20%, 95% CI: \u0026minus;\u0026thinsp;4.05 to \u0026minus;\u0026thinsp;2.44), respectively.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eUrbanisation Status Stratified\u003c/h2\u003e\u003cp\u003eLarge metropolitan areas exhibited the highest AAMR of 19.1 in 1999 \u003cb\u003e(Supplementary Figure S1, Supplementary Table S8)\u003c/b\u003e. These areas maintained the highest rates for most of the study period, despite a decline to 7.5 in 2020 (AAPC = -4.27%, 95% CI: -4.82 to -3.73). Medium/small metropolitan and non-metropolitan areas also showed declines from 15.9 and 15.2 in 1999 to 6.9 and 6.7 in 2020, with AAPCs of -4.04% (95% CI: -4.58 to -3.40) and \u0026minus;\u0026thinsp;4.60% (95% CI: -5.79 to -3.48), respectively.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eICD-10 Codes Stratified\u003c/h2\u003e\u003cp\u003eThe A16 cohort (respiratory TB) consistently had the highest AAMR, with values never falling below 4.0, in contrast to other cohorts, which remained below 2.0 throughout \u003cb\u003e(Supplementary Figure S2, Supplementary Table S9)\u003c/b\u003e. A16 showed a sustained overall decline from 15.1 in 1999 to 5.4 in 2023 (AAPC = -4.15%, 95% CI: -4.52 to -3.63), with the steepest decline to 11.3 in 2001 (APC = -13.49%, 95% CI: -16.82 to -6.72). This was followed by a slower decline until 2015, reaching 4.9 (APC = -5.96%, 95% CI: -6.67 to -2.03). A17 (TB of the nervous system) was excluded from analysis due to unreliable or suppressed data.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003ePlace of Death Stratified\u003c/h2\u003e\u003cp\u003eOf the 26 341 TB-related deaths, more than half occurred in IPH (65.2%), followed by decedents' home (15.7%), nursing home (8.9%), OEF (4.2%), and hospice (2.5%) \u003cb\u003e(Supplementary Table S10)\u003c/b\u003e. The remaining 3.6% were categorized as MF-DoA, MF-SU, or other/unknown locations.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eForecasting\u003c/h2\u003e\u003cp\u003eForecast one, based on pre-COVID-19 data, projected a steady decline in TB-related AAMRs, reaching 3.46 per million (95% CI: 1.25\u0026ndash;5.67) by 2035 \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003e8\u003c/span\u003e, \u003cb\u003eSupplementary Table S11)\u003c/b\u003e. Predicted rates for 2020\u0026ndash;2023 were consistently lower than observed values during the same period. In contrast, forecast two, which incorporated the full dataset (1999\u0026ndash;2023), predicted a persistent increase in AAMRs, reaching 9.48 by 2035, though with widening uncertainty intervals (e.g., -1.35 to 20.32 in 2035) \u003cb\u003e(Supplementary Figure S3)\u003c/b\u003e. Forecast three maintained a constant projected AAMR of 7.5 from 2024 to 2035 \u003cb\u003e(Supplementary Figure S4)\u003c/b\u003e.\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis 25-year analysis of TB trends in US adults aged\u0026thinsp;\u0026ge;\u0026thinsp;45 years showed IRs declined from 64.3 to 21.8 per million by 2020, then rebounded to 25.0 in 2022. AAMRs dropped by over half until 2015 but rose steeply through 2023. While AAMRs remained higher in males, females experienced a 1.5-fold increase from 2015 to 2023. All racial groups saw at least a two-fold decline in AAMRs from 1999\u0026ndash;2023; A/PI and AI/AN populations had the highest rates, White individuals the lowest. A forecast based on pre-COVID-19 data projected a decline to 3.46 per million (95% CI: 1.25\u0026ndash;5.67) by 2035 and predicted AAMRs for 2020\u0026ndash;2023 were lower than observed.\u003c/p\u003e\u003cp\u003eOur study showed a decline in TB IR over the past two decades, though the rate of decline slowed notably between 2013 and 2019. This mirrors findings by Salinas et al. and LoBue et al., who reported a plateau and even a slight increase in IR during that time, suggesting existing prevention strategies may have reached their limit [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Our study further shows a rise in IR between 2021 and 2022, reaching 25.0 per million. CDC reports show that 2023 TB case counts were 8.3% higher than in 2019, surpassing pre-pandemic levels [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. These increases may be related to pandemic-associated healthcare disruptions. Notably, TB-related mortality began rising in 2015, coinciding with the IR plateau, and worsened during the pandemic. Swartwood et al. reported 17% and 21% increases in TB deaths in 2020 and 2021 compared to 2019 [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], a reversal also seen globally with 1.6\u0026nbsp;million TB deaths reported in 2021 [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. These findings highlight TB control\u0026rsquo;s vulnerability to system disruptions and the need to revitalise elimination efforts.\u003c/p\u003e\u003cp\u003eMales accounted for 60% of TB cases in our study, with consistently higher AAMRs compared to females. The decline in AAMR was greater for males (2.6-fold) than for females (two-fold), but males continued to bear a disproportionate burden of both incidence and mortality. This gender disparity is consistent with global trends and likely reflects a combination of biological, behavioral, and social factors [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Males face increased TB risk due to higher rates of smoking, alcohol use, incarceration, and homelessness, factors linked to immune suppression and greater transmission in overcrowded settings [\u003cspan additionalcitationids=\"CR33\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Broader social networks and high-risk occupations such as mining further contribute [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. They also have higher prevalence of comorbidities such as HIV and substance use disorders, which elevate TB-related mortality [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Additionally, biological factors may increase male susceptibility to TB [\u003cspan additionalcitationids=\"CR36 CR37 CR38\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Although males consistently had higher AAMRs, the post-2015 rise in female TB mortality may indicate care gaps in older females, particularly as mortality increases steeply with age. Given females\u0026rsquo; longer life expectancy and overrepresentation in older US populations [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], these trends highlight the need for gender-sensitive interventions [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eRacial disparities in TB-related mortality were substantial in our study, with A/PI and AI/AN populations exhibiting the highest AAMRs and White individuals the lowest. One possible explanation is the higher burden among non-US-born individuals, who accounted for 75.8% of TB cases in 2023 [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. We also observed higher TB-related mortality in the West and South, which may reflect immigration patterns, as these regions had larger immigrant populations from 2019\u0026ndash;2023, potentially contributing to both regional and racial disparities [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. However, a recent study analyzing the US TB data for 2011\u0026ndash;2019, showed that racial disparities in TB outcomes are primarily seen in US-born individuals, not the non-US-born [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. These disparities were largely driven by comorbidities (e.g., HIV, end-stage renal disease) and neighborhood-level vulnerabilities like poverty and racialized economic segregation [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Similarly, a 2011\u0026ndash;2021 analysis reported the greatest relative disparities among AI/AN females, with incidence rate ratios up to 14.2 compared to White females [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Although our dataset lacked granularity to explore these contextual factors in detail, our findings reinforce the need for targeted, equity-oriented interventions that address the drivers of TB-related racial disparities.\u003c/p\u003e\u003cp\u003eThe US aims to eliminate TB by 2035 [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. However, even our most optimistic forecast model, trained exclusively on pre-pandemic data, projected an AAMR of 3.46 by 2035. Forecast three predicted stable mortality trends, while forecast two suggested a possible increase. Collectively, these forecasts indicate that, based on current trends, the US is not on track to meet its TB elimination target. The Lancet Commission states that achieving TB elimination targets requires scaling up proven interventions, developing new tools (better diagnostics, shorter regimens, a more effective vaccine), expanding active case finding, addressing social determinants through multi-sectoral action, and improving person-centered care [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThis study has several limitations. As CDC-WONDER relies on death certificates and ICD-10 codes, data may be subject to misclassification or incomplete reporting. Urban-rural analysis was limited to 2020 due to unavailable data beyond that year. To protect confidentiality, CDC-WONDER suppresses subgroups with \u0026lt;\u0026thinsp;10 deaths and flags counts\u0026thinsp;\u0026lt;\u0026thinsp;20 as statistically unreliable, limiting interpretation for some racial groups, states, and ICD-10 subcategories in our analyses. Lastly, the cross-sectional design precludes causal inference.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eDespite long-term progress, TB incidence and mortality in the US have plateaued in recent years, with concerning increases following the COVID-19 pandemic. Demographic and geographic disparities persist, with higher TB mortality observed among males, A/PI and AI/AN populations, older adults, and residents of the South and West. Forecasting models suggest that, without renewed public health action, the US is unlikely to meet its 2035 TB elimination goal. These findings highlight the urgent need for targeted interventions to address disparities, enhance early diagnosis and treatment, and reinvigorate TB elimination efforts in the post-pandemic era.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval:\u0026nbsp;\u003c/strong\u003eThis study was exempt from ethical approval as a de-identified public database was used. All procedures conformed to the principles outlined in the Declaration of Helsinki, and the study was conducted per the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u0026nbsp;\u003c/strong\u003eNone.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCRediT author statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors meet the authorship criteria outlined by the International Committee of Medical Journal Editors (ICMJE).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConceptualisation:\u003c/strong\u003e Daniyal Ali Khan, Syed Ali Tayyeb Hasan; \u003cstrong\u003eMethodology:\u003c/strong\u003e Daniyal Ali Khan, Syed Ali Tayyeb Hasan, Kantesh Kumar, Mustafa Aman, Shahzaib Khan, Mahnoor Khan, Syed Haider Ali Gardezi, Ali Bin Abdul Jabbar, Syed Faisal Mahmood; \u003cstrong\u003eFormal analysis and investigation:\u003c/strong\u003e Shahzaib Khan, Mahnoor Khan; \u003cstrong\u003eData curation:\u003c/strong\u003e Shahzaib Khan, Mahnoor Khan; \u003cstrong\u003eVisualisation:\u003c/strong\u003e Shahzaib Khan, Mahnoor Khan; \u003cstrong\u003eValidation:\u003c/strong\u003e Daniyal Ali Khan, Syed Ali Tayyeb Hasan; \u003cstrong\u003eWriting - original draft preparation:\u003c/strong\u003e Daniyal Ali Khan, Syed Ali Tayyeb Hasan, Kantesh Kumar, Mustafa Aman, Syed Haider Ali Gardezi; \u003cstrong\u003eWriting - review and editing:\u003c/strong\u003e Daniyal Ali Khan, Syed Ali Tayyeb Hasan, Kantesh Kumar, Mustafa Aman, Shahzaib Khan, Mahnoor Khan, Syed Haider Ali Gardezi, Ali Bin Abdul Jabbar, Syed Faisal Mahmood; \u003cstrong\u003eSupervision:\u003c/strong\u003e Ali Bin Abdul Jabbar, Syed Faisal Mahmood; \u003cstrong\u003eProject administration:\u003c/strong\u003e Daniyal Ali Khan, Syed Ali Tayyeb Hasan\u003c/p\u003e\n\u003cp\u003eDaniyal Ali Khan and Syed Ali Tayyeb Hasan contributed equally.\u003c/p\u003e\n\u003cp\u003eAll authors had full access to the data, approved the final manuscript, and accept responsibility for its accuracy and integrity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary data:\u0026nbsp;\u003c/strong\u003eSupplementary data are available at IJE online.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest:\u0026nbsp;\u003c/strong\u003eNone declared.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eNone.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement:\u0026nbsp;\u003c/strong\u003eData were abstracted from the Centers for Disease Control and Prevention\u0026rsquo;s Wide-Ranging Online Data for Epidemiologic Research (CDC-WONDER) platform. All relevant data are included in the manuscript and supplementary materials.\u003c/p\u003e\n\u003cp\u003eCDC-WONDER Multiple Cause of Death 1999-2020: https://wonder.cdc.gov/mcd-icd10.html\u003c/p\u003e\n\u003cp\u003eCDC-WONDER Multiple Cause of Death 2018-2023: https://wonder.cdc.gov/mcd-icd10-expanded.html\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate and publish:\u0026nbsp;\u003c/strong\u003eNo patient consent was needed for this study, as CDC-WONDER contains anonymized, publicly available data.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWorld Health Organization. 1.1 TB incidence. In: Global Tuberculosis Report 2023. 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Int J Infect Dis 92:S51\u0026ndash;S54. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.ijid.2020.02.021\u003c/span\u003e\u003cspan address=\"10.1016/j.ijid.2020.02.021\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Tuberculosis, Epidemiology, Health Inequities, Mortality, Forecasting, United States","lastPublishedDoi":"10.21203/rs.3.rs-7600721/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7600721/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eTuberculosis (TB) remains a US public health concern, with persistent disparities, yet TB-related mortality trends, especially among older adults, remain understudied.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThis cross-sectional study analysed CDC-WONDER mortality data (1999\u0026ndash;2023) and National Notifiable Diseases Surveillance System incidence data (1999\u0026ndash;2022) for adults aged\u0026thinsp;\u0026ge;\u0026thinsp;45 years. TB (ICD-10: A16-A19) incidence rates (IRs), age-adjusted and crude mortality rates (AAMRs, CMRs) were calculated per million population. Trends were analysed using Joinpoint regression, and ARIMA models were used to forecast mortality through 2035 in the context of the US elimination target.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eFrom 1999\u0026ndash;2022, TB IRs declined from 64.3 to 25.0 per million. AAMRs fell from 17.4 to 7.5 between 1999\u0026ndash;2023 (AAPC = -3.36%, 95% CI: -3.72 to -2.98). Of 26,341 deaths, 62.5% were male. By 2023, AAMRs in both genders had declined by over half. CMRs decreased across all age groups, with the largest decline in those\u0026thinsp;\u0026ge;\u0026thinsp;85 years, who also had the highest rates. Asian/Pacific Islander and American Indian/Alaska Native populations consistently had the highest AAMRs, while White individuals had the lowest. All regions except the West showed steady declines. Alaska, Hawaii, California, Mississippi, and Texas had the highest AAMRs. Large metropolitan areas consistently had higher AAMRs. Most deaths were due to respiratory TB, and 65.2% occurred in inpatient hospitals. Forecasting indicated that AAMRs are projected to remain above the US elimination threshold by 2035.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eTB-related mortality in the US has declined over two decades, but recent resurgence and persistent demographic and geographic disparities highlight the need for targeted interventions to achieve elimination.\u003c/p\u003e","manuscriptTitle":"Tuberculosis Trends, Disparities, and Forecasts in the United States: Will the 2035 Elimination Goal Be Met?","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-15 17:50:35","doi":"10.21203/rs.3.rs-7600721/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":"233e8c8f-9cd7-4a3d-9bf1-426d13b492f8","owner":[],"postedDate":"September 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":54631886,"name":"Infectious Diseases"},{"id":54631887,"name":"Epidemiology"}],"tags":[],"updatedAt":"2025-09-15T17:50:35+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-15 17:50:35","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7600721","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7600721","identity":"rs-7600721","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}