Trends in Acute Myocardial Infarction–Related Mortality Among US Adults with Respiratory Diseases from 1999 to 2023: A Cross-Sectional Analysis of the CDC WONDER Database

Trends in Acute Myocardial Infarction–Related Mortality Among US Adults with Respiratory Diseases from 1999 to 2023: A Cross-Sectional Analysis of the CDC WONDER Database | 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 Trends in Acute Myocardial Infarction–Related Mortality Among US Adults with Respiratory Diseases from 1999 to 2023: A Cross-Sectional Analysis of the CDC WONDER Database Wei Yan, Yuan Zeng, Xinrui Xue, Lei Liu, Xuefeng Wang, Hongqiang Ren This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7957244/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: Acute myocardial infarction (AMI) is a leading cause of death in the United States. However, the long-term trends in AMI-related mortality among adults with respiratory diseases have not been thoroughly investigated. This study aimed to analyze these trends from 1999 to 2023 and to identify high-risk subgroups. Method: Data were obtained from the CDC WONDER database for a retrospective cohort study. The age-adjusted mortality rate (AAMR per 100,000) of acute myocardial infarction (ICD-10 I21) was analyzed among adults aged ≥25 years diagnosed with respiratory diseases (ICD-10 J00-J98). Joinpoint regression modeled temporal trends, generating the average annual percentage change (AAPC) and annual percentage change (APC). To model future trends, the best-fitting autoregressive integrated moving average (ARIMA) model was used to project mortality rates for the coming decade. Result: A total of 775,365 deaths from acute myocardial infarction (AMI) were recorded among US adults with respiratory diseases during the study period (1999-2023). The overall age-adjusted mortality rate (AAMR) fell from 20.20 (95% CI: 19.99–20.41) to 10.58 per 100,000, corresponding to an average annual percentage change (AAPC) of -2.77% (95% CI: -3.18 to -2.51). This downward trend was interrupted by a sharp, transient rise coinciding with the COVID-19 pandemic (APC 2018–2021 = 8.63%; 95% CI: 4.81–11.00). Furthermore, stark disparities were evident, as mortality rates consistently remained elevated for males, non-Hispanic American Indian/Alaska Native persons, individuals aged 85 and above, and inhabitants of the Southern US and rural communities. Conclusion: The decline in AMI mortality among adults with respiratory diseases notwithstanding, this issue continues to pose a major public health challenge in the United States—a challenge that was severely intensified by the exacerbation of pre-existing disparities during the COVID-19 pandemic. Our study reveals that patients with respiratory diseases constitute a distinct high-risk population for AMI, whose gains in survival are precarious and inequitably distributed. These findings mandate a paradigm shift towards integrated cardiopulmonary care and equity-focused public health interventions to reduce the burden of coronary artery disease in this vulnerable group. Acute myocardial infarction Mortality rate Respiratory diseases Health disparities Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Cardiovascular diseases (CVDs), particularly acute myocardial infarction (AMI), remain one of the leading causes of death worldwide.[1, 2] Concurrently, respiratory infections such as influenza also contribute to substantial morbidity and mortality, resulting in millions of hospitalizations annually.[3] Robust epidemiological evidence has established an association between respiratory diseases and an increased risk of adverse cardiovascular events, including AMI.[4] For instance, a meta-analysis demonstrated that the risk of AMI doubles following a recent respiratory infection.[5] Despite this well-established link, comprehensive long-term data on AMI-related mortality trends among susceptible US populations with pre-existing respiratory diseases remain scarce. Therefore, utilizing national mortality data from the Centers for Disease Control and Prevention (CDC) WONDER database (1999–2023), this study was designed to delineate the long-term trends in acute myocardial infarction (AMI)-related mortality among US adults with respiratory diseases and to investigate disparities by gender, race/ethnicity, age, and geographic location. The findings are intended to inform the formulation of tailored public health interventions for vulnerable subgroups. Method This study is a retrospective analysis of trends in acute myocardial infarction (AMI)-related mortality among patients with respiratory diseases in the United States from 1999 to 2023. The data were obtained from the CDC WONDER database, an extensive online database for epidemiologic research provided by the Centers for Disease Control and Prevention.The database employs an on-demand query system for the analysis of healthcare data. The CDC WONDER database was used to identify AMI-related deaths occurring among patients with respiratory diseases in the United States. To ascertain these deaths, where a respiratory disease was listed as a contributing cause of death alongside AMI on the national death certificates, the multiple cause-of-death public-use records were utilized. For analyzing AMI-related mortality in this population, International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) codes I21 (for AMI) and J00-J98 (for respiratory diseases) were applied to patients aged 25 years and older. The CDC WONDER database contains only anonymized, publicly available data; therefore, this study was exempt from institutional review board approval. Data on the number of deaths and the corresponding population sizes for individuals with respiratory diseases and AMI were extracted for the period from 1999 to 2023. The extracted dataset included further classifications based on demographic and regional groups. This additional stratification comprised the following variables: sex, race/ethnicity, age, urban-rural classification, geographic region, and state. In this study, racial and ethnic groups were defined as Non-Hispanic (NH) White, Non-Hispanic Black, Non-Hispanic American Indian or Alaska Native, Non-Hispanic Asian or Pacific Islander, and Hispanic, as identified on the death certificate. Age was categorized into the following groups: 25-34, 35-44, 45-54, 55-64, 65-74, 75-84, and 85 years and older. According to the National Center for Health Statistics urban-rural classification scheme and based on the 2013 U.S Census criteria[6], the data were categorized into urban (large metropolitan areas [population ≥1 million]), medium/small metropolitan areas [population 50,000–999,999]), and rural (population <50,000) counties. Geographic region was classified as Northeast, Midwest, South, or West based on the U.S. Census Bureau scheme. From the extracted data, the crude mortality rate and the age-adjusted mortality rate for AMI among patients with respiratory diseases were calculated. The crude mortality rate was computed by dividing the number of deaths associated with both respiratory diseases and AMI by the corresponding US population. We standardized the age-adjusted mortality rate (AAMR) per 100,000 population using the 2000 US standard population. To determine mortality trends during the study period, we used the Joinpoint Regression Program (Version 5.1.0, National Cancer Institute, Bethesda, MD). The annual percentage change (APC) and the average annual percentage change (AAPC) along with their 95% confidence intervals (CIs) were calculated using the Monte Carlo permutation method, which connects trend segments by identifying inflection points (joinpoints) in the regression model. A trend was considered statistically significant (either increasing or decreasing) if the slope of the mortality trend over time was determined to be non-zero by a two-tailed t-test, with the significance level set at p ≤ 0.05. Throughout the results, asterisks are used to denote statistically significant differences. For predictive time series analysis, the Autoregressive Integrated Moving Average (ARIMA) model was utilized to forecast mortality rates through 2033 using non-stationary data. The ARIMA model was selected due to its effectiveness in handling non-stationary time series data and its widespread application in healthcare forecasting. Compared to alternative models, it provides a more detailed understanding of temporal patterns.[7] The optimal ARIMA model was determined using an automated function based on the Bayesian Information Criterion (BIC) and was subsequently fitted to the data. The Ljung-Box test was applied to assess whether the model residuals constituted white noise[7]. Additionally, the robustness of the model was validated through time-series cross-validation, and the root mean square error (RMSE) was reported to indicate forecast accuracy[8]. Result 1. Overall From 1999 to 2023, a total of 775,365 patients with respiratory diseases died from acute myocardial infarction (AMI) in the United States( Table 1 ). During this period, the age-adjusted mortality rate (AAMR) decreased from 20.20 (95% CI: 19.99 to 20.41) per 100,000 in 1999 to 10.58 (95% CI: 10.45 to 10.70) in 2023. The average annual percentage change (AAPC) was -2.77% (95% CI: -3.18 to -2.51). The annual percentage change (APC) in AAMR was -4.40% (95% CI: -6.92 to -3.73) from 1999 to 2009, -2.07% (95% CI: -3.26 to -0.15) from 2009 to 2018, increased to 8.63% (95% CI: 4.81 to 11.00) from 2018 to 2021, and then decreased to -13.25% (95% CI: -17.93 to -8.71) from 2021 to 2023. Overall, the AAMR declined from 20.20 (95% CI: 19.99 to 20.41) in 1999 to 10.58 (95% CI: 10.45 to 10.70) in 2023 ( Supplementary Table S8 ). 2. Demographics 2.1 Gender During the period 1999–2023, AMI in the context of respiratory disease was responsible for 427,685 male deaths (55.1%) and 347,680 female deaths (44.9%) in the US( Table 1 ). The male AAMR exhibited a pronounced overall decline from 28.50 to 13.59 per 100,000 (AAPC = -3.14%)( Supplementary Table S1,S8 ). However, this trajectory was not linear, featuring a rapid initial decrease (1999-2009 APC = -5.12%), a slowed rate of decline (2009-2018 APC = -2.12%), a notable pandemic-period reversal (2018-2021 APC = 9.61%), and a final, precipitous drop (2021-2023 APC = -14.93%).( Supplementary Table S1,S8 ) The AAMR among females fell from 14.99 (95% CI: 14.76-15.22) in 1999 to 8.18 (95% CI: 8.04-8.32) per 100,000 in 2023, corresponding to an AAPC of -2.61% (95% CI: -3.02, -2.33)( Supplementary Table S1,S8 ). The temporal trend was segmented into four periods: a decline of -3.86% per year (1999-2009), a slower decline of -2.33% (2009-2018), a rise of 7.12% (2018-2021), and a final period of steep decline at -11.05% per year (2021-2023) ( Figure 1, Supplementary Table S1,S8 2.2 Race The highest AAMR was initially observed among the Non-Hispanic White population (1999-2006) and subsequently among the Non-Hispanic American Indian or Alaska Native population (2006-2023)( Figure 2,Supplementary Table S2 ). For Non-Hispanic White individuals, the AAMR decreased from 20.56 (95% CI: 20.33-20.79) in 1999 to 11.32 (95% CI: 11.17-11.45) in 2023, which yielded an AAPC of -2.55%(Supplementary Table S2,S8). The joinpoint analysis for this group was characterized by four distinct phases: a period of steep decline (1999-2009; APC = -4.24%), a period of more modest decline (2009-2018; APC = -1.83%), a pandemic-associated upturn (2018-2021; APC = 7.58%), and a recent period of rapid decline (2021-2023; APC = -11.28%).( Supplementary Table S2,S8 ) In the Non-Hispanic American Indian or Alaska Native group, the AAMR decreased from 15.55 (95% CI: 12.46-18.63) to 10.74 (95% CI: 9.17-12.31) per 100,000 between 1999 and 2023(Supplementary Table S2,S8). The overall average annual percent change (AAPC) was -1.76% (95% CI: -3.29 to 1.12), which was not statistically significant. The joinpoint analysis revealed a period of non-significant change from 1999 to 2018 (APC = -1.56%; 95% CI: -11.13 to 29.35), a period of increase from 2018 to 2021 (APC = 11.24%), and a final period of substantial, albeit not statistically significant, decline from 2021 to 2023 (APC = -20.05%; 95% CI: -34.35 to 1.95). ( Supplementary Table S2,S8 ) The Non-Hispanic Asian or Pacific Islander group demonstrated the most favorable mortality profile, with the lowest AAMR across all years. From 1999 to 2023, the AAMR in this group fell from 14.47 (95% CI: 13.17-15.78) to 5.72 (95% CI: 5.33-6.11) per 100,000, yielding an AAPC of -3.76% (95% CI: -4.48, -3.25). Joinpoint analysis delineated a prolonged period of significant decline until 2017 (APC = -4.46%), a transient reversal and increase through 2021 (APC = 6.61%), and a final segment of the steepest decline observed in the most recent years (2021-2023; APC = -16.32%).( Supplementary Table S2,S8 ) The Non-Hispanic Black population experienced a decline in AAMR from 20.14 (95% CI: 19.40-20.87) to 10.83 (95% CI: 10.43-11.23) per 100,000 between 1999 and 2023, with an AAPC of -2.68%. This overall trend masked significant variations: a period of steep decline until 2009 (APC = -4.63%) was followed by a plateau phase from 2009-2018 (APC = -2.23%; 95% CI: -3.41, 0.13). A dramatic reversal then occurred from 2018-2021, with mortality rising sharply (APC = 12.81%), before the steepest decline of the entire series commenced from 2021-2023 (APC = -15.48%).( Supplementary Table S2,S8 ) The Hispanic population exhibited a pronounced overall reduction in AAMR from 15.95 (95% CI: 15.03-16.87) to 8.06 (95% CI: 7.71-8.40) per 100,000 between 1999 and 2023 (AAPC = -2.77%). However, this group demonstrated the most volatile trend, characterized by a significant long-term decline (1999-2018; APC = -3.29%), a dramatic surge coinciding with the pandemic (2018-2021; APC = 17.88%), followed by the steepest mortality decrease observed in the study (2021-2023; APC = -23.37%) ( Figure 2, Supplementary Table S2,S8 ). 2.3 Age group Analysis by age group revealed substantial disparities( Table 1 ). The crude mortality rate was highest among those aged 85 and older, declining from 224.55 to 116.22 per 100,000, in contrast to the 25-34 year age group, which consistently had the lowest rates (0.08 to 0.27 per 100,000)( Supplementary Table S3 ). Notably, the mortality trends differed across age groups. While the 25-34 and 35-44 year groups showed non-significant or slightly increasing trends, all older age groups experienced significant declines, with AAPCs of -0.45 (45-54 years), -1.51 (55-64 years), -2.84 (65-74 years), -3.32 (75-84 years), and -2.60 (85+ years), respectively ( Figure 3, Supplementary Table S3,S8 ). 2.4 Region 2.4.1 Census area Among the U.S. Census regions, the South had the highest average AAMR during the study period( Table 1 ). Its peak AAMR in 1999 was 20.84 (95% CI: 20.48 to 21.20) per 100,000, and the mortality trend had an AAPC of -2.31 (95% CI: -2.76 to -2.01). The APC was -4.08 (95% CI: -7.21 to -3.32) from 1999 to 2009, -1.64 (95% CI: -2.96 to 0.60) from 2009 to 2018, 10.21 (95% CI: 5.98 to 12.84) from 2018 to 2021, and -13.31 (95% CI: -18.16 to -8.45) from 2021 to 2023.( Supplementary Table S4,S8 ) The mortality trend in the Midwest, which had the second highest AAMR, was characterized by an overall significant decline (AAPC = -3.00%; 95% CI: -3.33, -2.76) from a peak of 21.66 in 1999. Joinpoint analysis identified an initial period of steep decline until 2007 (APC = -5.22%), a subsequent period of more gradual reduction (2007-2018; APC = -1.94%), a distinct pandemic-associated upturn (2018-2021; APC = 7.25%), and a final segment of precipitous decline (2021-2023; APC = -13.71%). ( Supplementary Table S4,S8 ) The Western US region exhibited an initial period of sustained, steady decline in AAMR from 1999 to 2018 (APC = -3.29%), leading to an overall AAPC of -2.47% from a peak of 18.25 in 1999. This long-term trend was subsequently interrupted by a period of pronounced volatility, characterized by a sharp pandemic-era surge (2018-2021; APC = 11.42%) and a subsequent rapid decline (2021-2023; APC = -13.42%). ( Supplementary Table S4,S8 ) The Northeast was characterized by the lowest average AAMR among all regions. The trend, which peaked at 19.21 in 2000, showed a significant overall decline (AAPC = -3.88%). Joinpoint analysis delineated a period of consistent and substantial reduction from 1999-2018 (APC = -4.33%), a transient pandemic-period interruption (2018-2021; APC = 6.15%), and a final segment of rapid decline (2021-2023; APC = -13.42%) ( Figure 4, Supplementary Table S4,S8 ). 2.4.2 State An analysis of state-level trends from 2019 to 2023 confirmed an overall decline in the age-adjusted mortality rate (AAMR) across most states. The most pronounced AAPC decreases were identified in Delaware (-5.82), Connecticut (-5.45), and Rhode Island (-4.63). However, a distinct cluster of states with rising mortality emerged, led by South Dakota (AAPC = 1.37) and followed by Mississippi (0.57), Kentucky (0.56), and Arkansas (0.31).( Figure 5, Supplementary Table S6 ). 2.4.3 Urban and rural Analysis by urbanization level demonstrated a striking inverse correlation between development and mortality( Table 1 ). Rural areas bore the highest burden, with an AAMR declining only modestly from 24.40 to 20.79 (AAPC = -1.41%). A steep gradient was evident, as medium/small metropolitan areas saw a greater decline from 19.39 to 13.40 (AAPC = -2.31%), and large metropolitan areas experienced the most substantial improvement, with rates falling from 19.12 to 10.51 (AAPC = -3.33%) between 1999 and 2020 ( Figure 6, Supplementary Table S5,S8 ). 2.5 Forecast of Age-Adjusted Mortality. (The observed trend (1999–2023) and projected values (2024–2033) for the AAMR are detailed in Figure 7 and Supplemental Table S7.) The optimal ARIMA model was selected based on the lowest Bayesian Information Criterion (BIC) and a satisfactory Ljung-Box test result, indicating that the residuals were independently distributed. This model was preferred as it minimizes information loss while adequately capturing the underlying trend in the data. In our study, the ARIMA (0,1,0) model was selected, with a BIC of 69.32. The Ljung-Box test confirmed that the residuals were white noise (p = 0.8696). The model was cross-validated using a time-series cross-validation approach, yielding a mean root mean square error (RMSE) of 0.88. This model was then used to forecast the age-adjusted mortality rate from 2024 to 2033. The projected rate for 2024 is 10.17 (95% CI: 8.38-11.97), and it is expected to decline to 6.57 (95% CI: 0.88-12.25) by 2033. The forecasts indicate a continuing downward trend in the age-adjusted mortality rate ( Figure 7; Supplementary Table S7 ). Discussion Our systematic analysis of CDC WONDER data (1999-2023) revealed that, despite an overall significant decline in AMI-related mortality (AAPC = -2.77) among US adults with respiratory diseases, this trend was interrupted by a transient increase during 2018-2021. Furthermore, we identified substantial disparities across gender, racial/ethnic, age, and geographic subgroups. These insights provide an evidence base for refining cardiovascular risk management strategies targeting this vulnerable population. Respiratory diseases are closely associated with acute myocardial infarction. Beyond shared risk factors, pathophysiological interactions, such as systemic inflammation and hypoxia, form the underlying connection between respiratory conditions and AMI.[9] In our study, the AMI-related age-adjusted mortality rate among patients with respiratory diseases declined from 20.20 to 10.58 per 100,000 between 1999 and 2023. This trend corresponds closely with the overall progress in cardiovascular disease prevention and management in the United States. From a clinical perspective, advancements in two key domains are likely the primary drivers:First, innovations in cardiovascular treatment, including the widespread use of statins, the popularization of percutaneous coronary intervention (shortening AMI treatment time), and the standardized application of antiplatelet agents, have significantly reduced mortality risk in AMI patients.[10-12].Second, optimized management of respiratory diseases, particularly chronic obstructive pulmonary disease and asthma, through the standardized use of long-acting bronchodilators and inhaled corticosteroids, alongside the promotion of influenza and pneumococcal vaccinations, has reduced the frequency and severity of respiratory infections. Barnes et al. demonstrated that respiratory infections can trigger AMI through mechanisms such as the activation of inflammatory responses and increased thrombotic risk (pooled OR = 2.01)[5]. Similarly, an analysis of a national inpatient sample from 2000 to 2017 indicated that patients hospitalized with respiratory infections were generally older, had more comorbidities, exhibited a higher incidence of non-ST-segment elevation MI, and that respiratory infection significantly impacted AMI admission rates, complication rates, and mortality[13]. A meta-analysis further confirmed that influenza can precipitate AMI (incidence rate ratio, 5.37; 95% CI, 3.48-8.28; I² = 69.4%)[14]. Therefore, improved control of respiratory diseases has indirectly contributed to the reduction in AMI-related mortality. Our study found that the AAMR was consistently higher in males than in females (13.59 vs. 8.18 per 100,000 in 2023, respectively), with a greater magnitude of decline in males (AAPC = -3.14 vs. -2.61). This phenomenon may be related to the cardio-protective effects of estrogen in females,[15-17] as well as higher exposure to risk factors among males, such as smoking,[18, 19] alcohol consumption[20], and unhealthy dietary habits[21]. Regarding racial/ethnic disparities, the Non-Hispanic American Indian or Alaska Native population had the highest AAMR after 2006 (10.74 per 100,000 in 2023) and the smallest decline (AAPC = -1.76), forming a stark contrast with the Non-Hispanic White (AAPC = -2.55) and Asian/Pacific Islander (AAPC = -3.76) populations. This persistently elevated AMI mortality risk among Non-Hispanic American Indian or Alaska Native individuals is likely attributable to several factors: they are more likely to reside in remote areas with scarce healthcare resources, have lower control rates for underlying conditions like hypertension and diabetes, and demonstrate insufficient awareness of cardiovascular disease prevention knowledge[22-26] We identified a striking age-stratified disparity, with marked variations in both the baseline mortality burden and the rate of its decline across age groups. The most pronounced burden was unsurprisingly observed among adults aged ≥85 years (crude mortality: 116.22 in 2023). This group exhibited a paradoxical but explicable pattern: a steeper overall decline (AAPC=-2.60) than some younger cohorts, likely reflecting intense clinical focus, yet this occurs against a backdrop of extreme vulnerability. This vulnerability stems from the high prevalence of multimorbidity (e.g., hypertension, diabetes, renal insufficiency), age-related immunosenescence that predisposes them to severe AMI triggered by respiratory infection, and poor tolerance to standard AMI interventions due to diminished physiological reserve,[27, 28]—a finding consistent with literature on age and comorbidity.[29] Conversely, a troubling reversal of trend was detected in young adults aged 25-44, who showed slight mortality increases (AAPCs: 1.92 and 0.11). This signals a potential "rejuvenation" of cardiovascular risk, plausibly fueled by the escalating epidemic of adverse lifestyle choices—including obesity, sleep deprivation, and poor diet—in this demographic, thereby compounding the risk for young individuals already burdened by respiratory disease .[30-33] Our findings reveal a pronounced geographic gradient in mortality improvements. The Southern US and rural communities experienced the highest baseline mortality and the most attenuated declines (AAPCs: -2.31 and -1.41, respectively), a pattern directly attributable to systemic healthcare access barriers. In the rural South, limited primary care infrastructure is compounded by a critical shortage of specialist providers (including cardiologists and pulmonologists), leading to systematic delays in critical care metrics like door-to-balloon time and consequently, missed therapeutic opportunities for AMI patients.[34] The stark contrast with the rapid improvement seen in large metropolitan areas (AAPC=-3.33) powerfully affirms the decisive role of healthcare resource availability and the dissemination of healthy lifestyle practices in achieving mortality reduction.[35] This study identified a significant increase in AMI-related mortality across all subgroups during 2018-2021 (overall APC=8.63; male APC=9.61; female APC=7.12). This period closely coincided with the global pandemic of COVID-19, suggesting the pandemic was a key driver of this inflection point. The underlying mechanisms likely involve three primary aspects: First, the direct cardiovascular injury from COVID-19 infection—the SARS-CoV-2 virus can invade cardiomyocytes via the ACE2 receptor, causing myocarditis and myocardial injury, while the systemic inflammatory response it induces can accelerate the rupture of atherosclerotic plaques, triggering AMI.[36, 37] As noted in one study, levels of platelet-derived microparticles (MPs) were 2.3 times higher in COVID-19 patients than in those with conventional pneumonia, directly promoting the conversion of fibrinogen to fibrin and accelerating coronary thrombosis.[38] Second, the disruption of routine medical services—during the pandemic, many patients with respiratory diseases delayed seeking care due to fear of infection, leading to worsened respiratory infections, while AMI patients experienced treatment delays as emergency resources were prioritized for COVID-19 cases, for instance, a multi-center retrospective study in India reported that treatment delays for myocardial infarction increased during the COVID-19 pandemic.[39-41] Third, the deterioration of lifestyle habits related to the pandemic—during lockdowns, reduced physical activity, unbalanced diets, and heightened psychological stress may have increased blood pressure and blood glucose levels, indirectly elevating AMI risk.[42-44] Conversely, the significant mortality decline from 2021-2023 (overall APC=-13.25) occurred alongside the widespread rollout of COVID-19 vaccinations and the restoration of routine medical services, leading to a marked decrease in patient mortality compared to the pandemic peak .[45, 46] Despite substantial reductions in AMI mortality among US respiratory disease patients over 25 years, the COVID-19 pandemic caused a severe reversal, highlighting profound population vulnerability and entrenched disparities. These results compel a paradigm shift from broad strategies to targeted interventions that remedy resource gaps, ensure equitable care, and integrate cardiovascular risk management into respiratory disease protocols, which is essential for alleviating the cardiopulmonary disease burden and advancing health equity. 4. Limitations Despite being a large-scale retrospective analysis, this study has several limitations. First, the reliance on death certificate coding from the CDC WONDER database introduces the potential for cause-of-death misclassification. Furthermore, the lack of data on individual-level confounders, such as smoking history, hypertension, and diabetes, prevents us from adjusting for their potential influence on mortality rates. Second, the classification of respiratory diseases is non-specific. By using the broad ICD-10 codes J00-J98 to define "respiratory diseases," we did not distinguish between acute and chronic conditions, or between infectious and non-infectious diseases. The risk of AMI triggered by different types of respiratory illnesses may vary significantly. Third, this study lacks an evaluation of intervention effectiveness. Our analysis describes mortality trends but does not assess the impact of specific interventions, such as vaccinations or pharmacotherapies, on mortality; therefore, it cannot directly validate the effectiveness of existing prevention and control strategies. Abbreviations AMI Acute Myocardial Infarction AAMR Age-Adjusted Mortality Rate AAPC Average Annual Percentage Change APC Annual Percentage Change ARIMA Autoregressive Integrated Moving Average CDC Centers for Disease Control and Prevention CI Confidence Interval ICD-10 International Classification of Diseases, Tenth Revision NH Non-Hispanic US United States Declarations - Ethics approval and consent to participate Not applicable. This study utilized exclusively de-identified, publicly available data from the CDC WONDER database. According to the policies of our institutional review board, research involving such data does not constitute human subjects research and is exempt from ethics approval and the requirement for informed consent. - Consent for publication Not applicable. - Availability of data and materials The datasets generated and/or analyzed during the current study are publicly available in the CDC WONDER database (https://wonder.cdc.gov/). - Competing interests The authors declare that they have no competing interests. - Funding This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. - Authors' contributions WY and YZ contributed to the study conception and design. Material preparation, data collection and analysis were performed by WY, XX, and LL. The first draft of the manuscript was written by WY, and all authors (WY, YZ, XX, LL, XW, HR) commented on previous versions of the manuscript. All authors read and approved the final manuscript. - Acknowledgements Not applicable. - Authors' information (optional) Not applicable. - Clinical trial number Not applicable. References Roth GA, Mensah GA, Johnson CO, Addolorato G, Ammirati E, Baddour LM, et al. Global Burden of Cardiovascular Diseases and Risk Factors, 1990-2019: Update From the GBD 2019 Study. J Am Coll Cardiol 2020;76(25):2982-3021. Lozano R, Naghavi M, Foreman K, Lim S, Shibuya K, Aboyans V, et al. Global and regional mortality from 235 causes of death for 20 age groups in 1990 and 2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancet 2012;380(9859):2095-128. Lafond KE, Porter RM, Whaley MJ, Suizan Z, Ran Z, Aleem MA, et al. Global burden of influenza-associated lower respiratory tract infections and hospitalizations among adults: A systematic review and meta-analysis. PLoS Med 2021;18(3):e1003550. Veizades S, Tso A, Nguyen PK. 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COVID-19 Vaccination Coverage, Behaviors, and Intentions among Adults with Previous Diagnosis, United States. Emerg Infect Dis 2022;28(3):631-8. Table Table1: Demographic Characteristics of Acute Myocardial Infarction–Related Deaths Among US Adults with Respiratory Diseases, 1999–2023. Characteristics Deaths (%) Entire Cohort 775365 Gender Female 347680(44.8) Male 427685(55.2) Census Region Northeast 133574(17.2) Midwest 179989(23.2) South 309699(39.9) West 152103(19.6) Race/Ethnicity NH American Indian or Alaska Native 4329(0.6) NH Asian or Pacific Islander 17632(2.3) NH Black or African American 68101(8.8) NH White 639543(82.5) Hispanic or Latino 43617(5.6) Urbanization Large Metro 298240(38.5) Medium/Small Metro 208365(26.9) Nonmetro 166746(21.5) Ten-Year Age Groups 25–34 years 1417(0.2) 35–44 years 6825(0.9) 45–54 years 31274(4.0) 55–64 years 94619(12.2) 65–74 years 178144(23.0) 75–84 years 248855(32.1) 85+ years 214231(27.6) Additional Declarations No competing interests reported. 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3","display":"","copyAsset":false,"role":"figure","size":134856,"visible":true,"origin":"","legend":"\u003cp\u003eAge-Standardized Mortality Rates (AAMRs) per 100,000 for Acute Myocardial Infarction Related to Respiratory Diseases among US Adults, Stratified by Age Group, 1999–2023.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7957244/v1/880c3911af16c2f5ef4a586c.png"},{"id":95103593,"identity":"a4b5b07c-8bb5-4ede-855f-da5f6f5d9536","added_by":"auto","created_at":"2025-11-04 10:24:27","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":92440,"visible":true,"origin":"","legend":"\u003cp\u003eAge-Standardized Mortality Rates (AAMRs) per 100,000 for Acute Myocardial Infarction Related to Respiratory Diseases among US Adults, Stratified by U.S. Census Region, 1999–2023.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7957244/v1/307152985b9311d71802400a.png"},{"id":95103571,"identity":"ae4b709b-7f6a-4395-a9bd-f864100bedf5","added_by":"auto","created_at":"2025-11-04 10:24:22","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":134826,"visible":true,"origin":"","legend":"\u003cp\u003eAge-Standardized Mortality Rates (AAMRs) per 100,000 for Acute Myocardial Infarction Related to Respiratory Diseases among US Adults, Stratified by State, 1999–2023.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7957244/v1/a6a0a0df21dcd3ea189c53ca.png"},{"id":95103596,"identity":"8a31220d-8144-4818-881b-9e16c2a94925","added_by":"auto","created_at":"2025-11-04 10:24:27","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":62676,"visible":true,"origin":"","legend":"\u003cp\u003eAge-Standardized Mortality Rates (AAMRs) per 100,000 for Acute Myocardial Infarction Related to Respiratory Diseases among US Adults, Stratified by Urban-Rural Classification, 1999–2023.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7957244/v1/654a6e42d175ebca717b5773.png"},{"id":95103637,"identity":"9c5dc188-6fc6-426b-bf00-8fb99759103f","added_by":"auto","created_at":"2025-11-04 10:24:30","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":59092,"visible":true,"origin":"","legend":"\u003cp\u003eAge-standardized mortality rate (AAMR) for acute myocardial infarction related to respiratory diseases from 1999 to 2023, with the peak during the COVID-19 pandemic highlighted by a red line. The AAMR from 2024 to 2033 was projected using an ARIMA model fitted to the entire observed data series (1999–2023).\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-7957244/v1/71107e968d65bdd5100202dd.png"},{"id":104404043,"identity":"1d76e2ec-2c40-4ef5-9692-cba23eb00fc4","added_by":"auto","created_at":"2026-03-11 12:19:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1466848,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7957244/v1/d42cf424-26d6-4e1a-b76e-c044565b79fb.pdf"},{"id":95103645,"identity":"28499f4b-c13a-42e7-b146-c544c9fa5d32","added_by":"auto","created_at":"2025-11-04 10:24:32","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":57142,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-7957244/v1/e95e4bb1f5176613416884a0.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Trends in Acute Myocardial Infarction–Related Mortality Among US Adults with Respiratory Diseases from 1999 to 2023: A Cross-Sectional Analysis of the CDC WONDER Database","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCardiovascular diseases (CVDs), particularly acute myocardial infarction (AMI), remain one of the leading causes of death worldwide.[1, 2] Concurrently, respiratory infections such as influenza also contribute to substantial morbidity and mortality, resulting in millions of hospitalizations annually.[3] Robust epidemiological evidence has established an association between respiratory diseases and an increased risk of adverse cardiovascular events, including AMI.[4] For instance, a meta-analysis demonstrated that the risk of AMI doubles following a recent respiratory infection.[5] Despite this well-established link, comprehensive long-term data on AMI-related mortality trends among susceptible US populations with pre-existing respiratory diseases remain scarce.\u003c/p\u003e\u003cp\u003eTherefore, utilizing national mortality data from the Centers for Disease Control and Prevention (CDC) WONDER database (1999\u0026ndash;2023), this study was designed to delineate the long-term trends in acute myocardial infarction (AMI)-related mortality among US adults with respiratory diseases and to investigate disparities by gender, race/ethnicity, age, and geographic location. The findings are intended to inform the formulation of tailored public health interventions for vulnerable subgroups.\u003c/p\u003e"},{"header":"Method","content":"\u003cp\u003eThis study is a retrospective analysis of trends in acute myocardial infarction (AMI)-related mortality among patients with respiratory diseases in the United States from 1999 to 2023. The data were obtained from the CDC WONDER database, an extensive online database for epidemiologic research provided by the Centers for Disease Control and Prevention.The database employs an on-demand query system for the analysis of healthcare data. The CDC WONDER database was used to identify AMI-related deaths occurring among patients with respiratory diseases in the United States. To ascertain these deaths, where a respiratory disease was listed as a contributing cause of death alongside AMI on the national death certificates, the multiple cause-of-death public-use records were utilized. For analyzing AMI-related mortality in this population, International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) codes I21 (for AMI) and J00-J98 (for respiratory diseases) were applied to patients aged 25 years and older. The CDC WONDER database contains only anonymized, publicly available data; therefore, this study was exempt from institutional review board approval.\u003c/p\u003e\n\u003cp\u003eData on the number of deaths and the corresponding population sizes for individuals with respiratory diseases and AMI were extracted for the period from 1999 to 2023. The extracted dataset included further classifications based on demographic and regional groups. This additional stratification comprised the following variables: sex, race/ethnicity, age, urban-rural classification, geographic region, and state. In this study, racial and ethnic groups were defined as Non-Hispanic (NH) White, Non-Hispanic Black, Non-Hispanic American Indian or Alaska Native, Non-Hispanic Asian or Pacific Islander, and Hispanic, as identified on the death certificate. Age was categorized into the following groups: 25-34, 35-44, 45-54, 55-64, 65-74, 75-84, and 85 years and older. According to the National Center for Health Statistics urban-rural classification scheme and based on the 2013 U.S Census criteria[6], the data were categorized into urban (large metropolitan areas [population \u0026ge;1 million]), medium/small metropolitan areas [population 50,000\u0026ndash;999,999]), and rural (population \u0026lt;50,000) counties. Geographic region was classified as Northeast, Midwest, South, or West based on the U.S. Census Bureau scheme.\u003c/p\u003e\n\u003cp\u003eFrom the extracted data, the crude mortality rate and the age-adjusted mortality rate for AMI among patients with respiratory diseases were calculated. The crude mortality rate was computed by dividing the number of deaths associated with both respiratory diseases and AMI by the corresponding US population. We standardized the age-adjusted mortality rate (AAMR) per 100,000 population using the 2000 US standard population. To determine mortality trends during the study period, we used the Joinpoint Regression Program (Version 5.1.0, National Cancer Institute, Bethesda, MD). The annual percentage change (APC) and the average annual percentage change (AAPC) along with their 95% confidence intervals (CIs) were calculated using the Monte Carlo permutation method, which connects trend segments by identifying inflection points (joinpoints) in the regression model. A trend was considered statistically significant (either increasing or decreasing) if the slope of the mortality trend over time was determined to be non-zero by a two-tailed t-test, with the significance level set at p \u0026le; 0.05. Throughout the results, asterisks are used to denote statistically significant differences.\u003c/p\u003e\n\u003cp\u003eFor predictive time series analysis, the Autoregressive Integrated Moving Average (ARIMA) model was utilized to forecast mortality rates through 2033 using non-stationary data. The ARIMA model was selected due to its effectiveness in handling non-stationary time series data and its widespread application in healthcare forecasting. Compared to alternative models, it provides a more detailed understanding of temporal patterns.[7]\u003c/p\u003e\n\u003cp\u003eThe optimal ARIMA model was determined using an automated function based on the Bayesian Information Criterion (BIC) and was subsequently fitted to the data. The Ljung-Box test was applied to assess whether the model residuals constituted white noise[7]. Additionally, the robustness of the model was validated through time-series cross-validation, and the root mean square error (RMSE) was reported to indicate forecast accuracy[8].\u003c/p\u003e"},{"header":"Result","content":"\u003cp\u003e\u003cstrong\u003e1.\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eOverall\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFrom 1999 to 2023, a total of 775,365 patients with respiratory diseases died from acute myocardial infarction (AMI) in the United States(\u003cstrong\u003eTable 1\u003c/strong\u003e). During this period, the age-adjusted mortality rate (AAMR) decreased from 20.20 (95% CI: 19.99 to 20.41) per 100,000 in 1999 to 10.58 (95% CI: 10.45 to 10.70) in 2023. The average annual percentage change (AAPC) was -2.77% (95% CI: -3.18 to -2.51). The annual percentage change (APC) in AAMR was -4.40% (95% CI: -6.92 to -3.73) from 1999 to 2009, -2.07% (95% CI: -3.26 to -0.15) from 2009 to 2018, increased to 8.63% (95% CI: 4.81 to 11.00) from 2018 to 2021, and then decreased to -13.25% (95% CI: -17.93 to -8.71) from 2021 to 2023. Overall, the AAMR declined from 20.20 (95% CI: 19.99 to 20.41) in 1999 to 10.58 (95% CI: 10.45 to 10.70) in 2023 (\u003cstrong\u003eSupplementary Table S8\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eDemographics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.1\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDuring the period 1999\u0026ndash;2023, AMI in the context of respiratory disease was responsible for 427,685 male deaths (55.1%) and 347,680 female deaths (44.9%) in the US(\u003cstrong\u003eTable 1\u003c/strong\u003e). The male AAMR exhibited a pronounced overall decline from 28.50 to 13.59 per 100,000 (AAPC = -3.14%)(\u003cstrong\u003eSupplementary Table S1,S8\u003c/strong\u003e). However, this trajectory was not linear, featuring a rapid initial decrease (1999-2009 APC = -5.12%), a slowed rate of decline (2009-2018 APC = -2.12%), a notable pandemic-period reversal (2018-2021 APC = 9.61%), and a final, precipitous drop (2021-2023 APC = -14.93%).(\u003cstrong\u003eSupplementary Table S1,S8\u003c/strong\u003e)\u003c/p\u003e\n\u003cp\u003eThe AAMR among females fell from 14.99 (95% CI: 14.76-15.22) in 1999 to 8.18 (95% CI: 8.04-8.32) per 100,000 in 2023, corresponding to an AAPC of -2.61% (95% CI: -3.02, -2.33)(\u003cstrong\u003eSupplementary Table S1,S8\u003c/strong\u003e). The temporal trend was segmented into four periods: a decline of -3.86% per year (1999-2009), a slower decline of -2.33% (2009-2018), a rise of 7.12% (2018-2021), and a final period of steep decline at -11.05% per year (2021-2023) (\u003cstrong\u003eFigure 1,\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eSupplementary Table S1,S8\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eRace\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe highest AAMR was initially observed among the Non-Hispanic White population (1999-2006) and subsequently among the Non-Hispanic American Indian or Alaska Native population (2006-2023)(\u0026nbsp;\u003cstrong\u003eFigure 2,Supplementary Table S2\u003c/strong\u003e). For Non-Hispanic White individuals, the AAMR decreased from 20.56 (95% CI: 20.33-20.79) in 1999 to 11.32 (95% CI: 11.17-11.45) in 2023, which yielded an AAPC of -2.55%(Supplementary Table S2,S8). The joinpoint analysis for this group was characterized by four distinct phases: a period of steep decline (1999-2009; APC = -4.24%), a period of more modest decline (2009-2018; APC = -1.83%), a pandemic-associated upturn (2018-2021; APC = 7.58%), and a recent period of rapid decline (2021-2023; APC = -11.28%).(\u003cstrong\u003e\u0026nbsp;Supplementary Table S2,S8\u003c/strong\u003e)\u003c/p\u003e\n\u003cp\u003eIn the Non-Hispanic American Indian or Alaska Native group, the AAMR decreased from 15.55 (95% CI: 12.46-18.63) to 10.74 (95% CI: 9.17-12.31) per 100,000 between 1999 and 2023(Supplementary Table S2,S8). The overall average annual percent change (AAPC) was -1.76% (95% CI: -3.29 to 1.12), which was not statistically significant. The joinpoint analysis revealed a period of non-significant change from 1999 to 2018 (APC = -1.56%; 95% CI: -11.13 to 29.35), a period of increase from 2018 to 2021 (APC = 11.24%), and a final period of substantial, albeit not statistically significant, decline from 2021 to 2023 (APC = -20.05%; 95% CI: -34.35 to 1.95).\u0026nbsp;(\u003cstrong\u003eSupplementary Table S2,S8\u003c/strong\u003e)\u003c/p\u003e\n\u003cp\u003eThe Non-Hispanic Asian or Pacific Islander group demonstrated the most favorable mortality profile, with the lowest AAMR across all years. From 1999 to 2023, the AAMR in this group fell from 14.47 (95% CI: 13.17-15.78) to 5.72 (95% CI: 5.33-6.11) per 100,000, yielding an AAPC of -3.76% (95% CI: -4.48, -3.25). Joinpoint analysis delineated a prolonged period of significant decline until 2017 (APC = -4.46%), a transient reversal and increase through 2021 (APC = 6.61%), and a final segment of the steepest decline observed in the most recent years (2021-2023; APC = -16.32%).(\u003cstrong\u003eSupplementary Table S2,S8\u003c/strong\u003e)\u003c/p\u003e\n\u003cp\u003eThe Non-Hispanic Black population experienced a decline in AAMR from 20.14 (95% CI: 19.40-20.87) to 10.83 (95% CI: 10.43-11.23) per 100,000 between 1999 and 2023, with an AAPC of -2.68%. This overall trend masked significant variations: a period of steep decline until 2009 (APC = -4.63%) was followed by a plateau phase from 2009-2018 (APC = -2.23%; 95% CI: -3.41, 0.13). A dramatic reversal then occurred from 2018-2021, with mortality rising sharply (APC = 12.81%), before the steepest decline of the entire series commenced from 2021-2023 (APC = -15.48%).(\u003cstrong\u003eSupplementary Table S2,S8\u003c/strong\u003e)\u003c/p\u003e\n\u003cp\u003eThe Hispanic population exhibited a pronounced overall reduction in AAMR from 15.95 (95% CI: 15.03-16.87) to 8.06 (95% CI: 7.71-8.40) per 100,000 between 1999 and 2023 (AAPC = -2.77%). However, this group demonstrated the most volatile trend, characterized by a significant long-term decline (1999-2018; APC = -3.29%), a dramatic surge coinciding with the pandemic (2018-2021; APC = 17.88%), followed by the steepest mortality decrease observed in the study (2021-2023; APC = -23.37%) (\u003cstrong\u003eFigure 2,\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eSupplementary Table S2,S8\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Age group\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAnalysis by age group revealed substantial disparities(\u003cstrong\u003eTable 1\u003c/strong\u003e). The crude mortality rate was highest among those aged 85 and older, declining from 224.55 to 116.22 per 100,000, in contrast to the 25-34 year age group, which consistently had the lowest rates (0.08 to 0.27 per 100,000)( \u003cstrong\u003eSupplementary\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Table S3\u003c/strong\u003e). Notably, the mortality trends differed across age groups. While the 25-34 and 35-44 year groups showed non-significant or slightly increasing trends, all older age groups experienced significant declines, with AAPCs of -0.45 (45-54 years), -1.51 (55-64 years), -2.84 (65-74 years), -3.32 (75-84 years), and -2.60 (85+ years), respectively (\u003cstrong\u003eFigure 3,\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eSupplementary Table S3,S8\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eRegion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4.1 Census area\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAmong the U.S. Census regions, the South had the highest average AAMR during the study period(\u003cstrong\u003eTable 1\u003c/strong\u003e). Its peak AAMR in 1999 was 20.84 (95% CI: 20.48 to 21.20) per 100,000, and the mortality trend had an AAPC of -2.31 (95% CI: -2.76 to -2.01). The APC was -4.08 (95% CI: -7.21 to -3.32) from 1999 to 2009, -1.64 (95% CI: -2.96 to 0.60) from 2009 to 2018, 10.21 (95% CI: 5.98 to 12.84) from 2018 to 2021, and -13.31 (95% CI: -18.16 to -8.45) from 2021 to 2023.(\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eSupplementary\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Table S4,S8\u003c/strong\u003e)\u003c/p\u003e\n\u003cp\u003eThe mortality trend in the Midwest, which had the second highest AAMR, was characterized by an overall significant decline (AAPC = -3.00%; 95% CI: -3.33, -2.76) from a peak of 21.66 in 1999. Joinpoint analysis identified an initial period of steep decline until 2007 (APC = -5.22%), a subsequent period of more gradual reduction (2007-2018; APC = -1.94%), a distinct pandemic-associated upturn (2018-2021; APC = 7.25%), and a final segment of precipitous decline (2021-2023; APC = -13.71%).\u0026nbsp;(\u003cstrong\u003eSupplementary\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Table S4,S8\u003c/strong\u003e)\u003c/p\u003e\n\u003cp\u003eThe Western US region exhibited an initial period of sustained, steady decline in AAMR from 1999 to 2018 (APC = -3.29%), leading to an overall AAPC of -2.47% from a peak of 18.25 in 1999. This long-term trend was subsequently interrupted by a period of pronounced volatility, characterized by a sharp pandemic-era surge (2018-2021; APC = 11.42%) and a subsequent rapid decline (2021-2023; APC = -13.42%).\u0026nbsp;(\u003cstrong\u003eSupplementary\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Table S4,S8\u003c/strong\u003e)\u003c/p\u003e\n\u003cp\u003eThe Northeast was characterized by the lowest average AAMR among all regions.\u0026nbsp;The trend, which peaked at 19.21 in 2000, showed a significant overall decline (AAPC = -3.88%). Joinpoint analysis delineated a period of consistent and substantial reduction from 1999-2018 (APC = -4.33%), a transient pandemic-period interruption (2018-2021; APC = 6.15%), and a final segment of rapid decline (2021-2023; APC = -13.42%) (\u003cstrong\u003eFigure 4,\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eSupplementary Table S4,S8\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4.2 State\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAn analysis of state-level trends from 2019 to 2023 confirmed an overall decline in the age-adjusted mortality rate (AAMR) across most states. The most pronounced AAPC decreases were identified in Delaware (-5.82), Connecticut (-5.45), and Rhode Island (-4.63). However, a distinct cluster of states with rising mortality emerged, led by South Dakota (AAPC = 1.37) and followed by Mississippi (0.57), Kentucky (0.56), and Arkansas (0.31).(\u003cstrong\u003e\u0026nbsp;Figure\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;5,\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Supplementary\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Table S6\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4.3\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eUrban and rural\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAnalysis by urbanization level demonstrated a striking inverse correlation between development and mortality(\u003cstrong\u003eTable 1\u003c/strong\u003e). Rural areas bore the highest burden, with an AAMR declining only modestly from 24.40 to 20.79 (AAPC = -1.41%). A steep gradient was evident, as medium/small metropolitan areas saw a greater decline from 19.39 to 13.40 (AAPC = -2.31%), and large metropolitan areas experienced the most substantial improvement, with rates falling from 19.12 to 10.51 (AAPC = -3.33%) between 1999 and 2020 (\u003cstrong\u003eFigure 6,\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eSupplementary Table S5,S8\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eForecast of Age-Adjusted Mortality.\u003c/strong\u003e (The observed trend (1999\u0026ndash;2023) and projected values (2024\u0026ndash;2033) for the AAMR are detailed in Figure 7 and Supplemental Table S7.)\u003c/p\u003e\n\u003cp\u003eThe optimal ARIMA model was selected based on the lowest Bayesian Information Criterion (BIC) and a satisfactory Ljung-Box test result, indicating that the residuals were independently distributed. This model was preferred as it minimizes information loss while adequately capturing the underlying trend in the data. In our study, the ARIMA (0,1,0) model was selected, with a BIC of 69.32. The Ljung-Box test confirmed that the residuals were white noise (p = 0.8696). The model was cross-validated using a time-series cross-validation approach, yielding a mean root mean square error (RMSE) of 0.88. This model was then used to forecast the age-adjusted mortality rate from 2024 to 2033. The projected rate for 2024 is 10.17 (95% CI: 8.38-11.97), and it is expected to decline to 6.57 (95% CI: 0.88-12.25) by 2033. The forecasts indicate a continuing downward trend in the age-adjusted mortality rate (\u003cstrong\u003eFigure 7; Supplementary Table S7\u003c/strong\u003e).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur systematic analysis of CDC WONDER data (1999-2023) revealed that, despite an overall significant decline in AMI-related mortality (AAPC = -2.77) among US adults with respiratory diseases, this trend was interrupted by a transient increase during 2018-2021. Furthermore, we identified substantial disparities across gender, racial/ethnic, age, and geographic subgroups. These insights provide an evidence base for refining cardiovascular risk management strategies targeting this vulnerable population.\u003c/p\u003e\n\u003cp\u003eRespiratory diseases are closely associated with acute myocardial infarction. Beyond shared risk factors, pathophysiological interactions, such as systemic inflammation and hypoxia, form the underlying connection between respiratory conditions and AMI.[9] In our study, the AMI-related age-adjusted mortality rate among patients with respiratory diseases declined from 20.20 to 10.58 per 100,000 between 1999 and 2023. This trend corresponds closely with the overall progress in cardiovascular disease prevention and management in the United States. From a clinical perspective, advancements in two key domains are likely the primary drivers:First, innovations in cardiovascular treatment, including the widespread use of statins, the popularization of percutaneous coronary intervention (shortening AMI treatment time), and the standardized application of antiplatelet agents, have significantly reduced mortality risk in AMI patients.[10-12].Second, optimized management of respiratory diseases, particularly chronic obstructive pulmonary disease and asthma, through the standardized use of long-acting bronchodilators and inhaled corticosteroids, alongside the promotion of influenza and pneumococcal vaccinations, has reduced the frequency and severity of respiratory infections. Barnes et al. demonstrated that respiratory infections can trigger AMI through mechanisms such as the activation of inflammatory responses and increased thrombotic risk (pooled OR = 2.01)[5]. Similarly, an analysis of a national inpatient sample from 2000 to 2017 indicated that patients hospitalized with respiratory infections were generally older, had more comorbidities, exhibited a higher incidence of non-ST-segment elevation MI, and that respiratory infection significantly impacted AMI admission rates, complication rates, and mortality[13]. A meta-analysis further confirmed that influenza can precipitate AMI (incidence rate ratio, 5.37; 95% CI, 3.48-8.28; I\u0026sup2; = 69.4%)[14]. Therefore, improved control of respiratory diseases has indirectly contributed to the reduction in AMI-related mortality.\u003c/p\u003e\n\u003cp\u003eOur study found that the AAMR was consistently higher in males than in females (13.59 vs. 8.18 per 100,000 in 2023, respectively), with a greater magnitude of decline in males (AAPC = -3.14 vs. -2.61). This phenomenon may be related to the cardio-protective effects of estrogen in females,[15-17] as well as higher exposure to risk factors among males, such as smoking,[18, 19] alcohol consumption[20], and unhealthy dietary habits[21]. Regarding racial/ethnic disparities, the Non-Hispanic American Indian or Alaska Native population had the highest AAMR after 2006 (10.74 per 100,000 in 2023) and the smallest decline (AAPC = -1.76), forming a stark contrast with the Non-Hispanic White (AAPC = -2.55) and Asian/Pacific Islander (AAPC = -3.76) populations. This persistently elevated AMI mortality risk among Non-Hispanic American Indian or Alaska Native individuals is likely attributable to several factors: they are more likely to reside in remote areas with scarce healthcare resources, have lower control rates for underlying conditions like hypertension and diabetes, and demonstrate insufficient awareness of cardiovascular disease prevention knowledge[22-26]\u003c/p\u003e\n\u003cp\u003eWe identified a striking age-stratified disparity, with marked variations in both the baseline mortality burden and the rate of its decline across age groups. The most pronounced burden was unsurprisingly observed among adults aged \u0026ge;85 years (crude mortality: 116.22 in 2023). This group exhibited a paradoxical but explicable pattern: a steeper overall decline (AAPC=-2.60) than some younger cohorts, likely reflecting intense clinical focus, yet this occurs against a backdrop of extreme vulnerability. This vulnerability stems from the high prevalence of multimorbidity (e.g., hypertension, diabetes, renal insufficiency), age-related immunosenescence that predisposes them to severe AMI triggered by respiratory infection, and poor tolerance to standard AMI interventions due to diminished physiological reserve,[27, 28]\u0026mdash;a finding consistent with literature on age and comorbidity.[29] Conversely, a troubling reversal of trend was detected in young adults aged 25-44, who showed slight mortality increases (AAPCs: 1.92 and 0.11). This signals a potential \u0026quot;rejuvenation\u0026quot; of cardiovascular risk, plausibly fueled by the escalating epidemic of adverse lifestyle choices\u0026mdash;including obesity, sleep deprivation, and poor diet\u0026mdash;in this demographic, thereby compounding the risk for young individuals already burdened by respiratory disease .[30-33]\u003c/p\u003e\n\u003cp\u003eOur findings reveal a pronounced geographic gradient in mortality improvements. The Southern US and rural communities experienced the highest baseline mortality and the most attenuated declines (AAPCs: -2.31 and -1.41, respectively), a pattern directly attributable to systemic healthcare access barriers. In the rural South, limited primary care infrastructure is compounded by a critical shortage of specialist providers (including cardiologists and pulmonologists), leading to systematic delays in critical care metrics like door-to-balloon time and consequently, missed therapeutic opportunities for AMI patients.[34] The stark contrast with the rapid improvement seen in large metropolitan areas (AAPC=-3.33) powerfully affirms the decisive role of healthcare resource availability and the dissemination of healthy lifestyle practices in achieving mortality reduction.[35]\u003c/p\u003e\n\u003cp\u003eThis study identified a significant increase in AMI-related mortality across all subgroups during 2018-2021 (overall APC=8.63; male APC=9.61; female APC=7.12). This period closely coincided with the global pandemic of COVID-19, suggesting the pandemic was a key driver of this inflection point. The underlying mechanisms likely involve three primary aspects: First, the direct cardiovascular injury from COVID-19 infection\u0026mdash;the SARS-CoV-2 virus can invade cardiomyocytes via the ACE2 receptor, causing myocarditis and myocardial injury, while the systemic inflammatory response it induces can accelerate the rupture of atherosclerotic plaques, triggering AMI.[36, 37] As noted in one study, levels of platelet-derived microparticles (MPs) were 2.3 times higher in COVID-19 patients than in those with conventional pneumonia, directly promoting the conversion of fibrinogen to fibrin and accelerating coronary thrombosis.[38] Second, the disruption of routine medical services\u0026mdash;during the pandemic, many patients with respiratory diseases delayed seeking care due to fear of infection, leading to worsened respiratory infections, while AMI patients experienced treatment delays as emergency resources were prioritized for COVID-19 cases, for instance, a multi-center retrospective study in India reported that treatment delays for myocardial infarction increased during the COVID-19 pandemic.[39-41] Third, the deterioration of lifestyle habits related to the pandemic\u0026mdash;during lockdowns, reduced physical activity, unbalanced diets, and heightened psychological stress may have increased blood pressure and blood glucose levels, indirectly elevating AMI risk.[42-44] Conversely, the significant mortality decline from 2021-2023 (overall APC=-13.25) occurred alongside the widespread rollout of COVID-19 vaccinations and the restoration of routine medical services, leading to a marked decrease in patient mortality compared to the pandemic peak .[45, 46]\u003c/p\u003e\n\u003cp\u003eDespite substantial reductions in AMI mortality among US respiratory disease patients over 25 years, the COVID-19 pandemic caused a severe reversal, highlighting profound population vulnerability and entrenched disparities. These results compel a paradigm shift from broad strategies to targeted interventions that remedy resource gaps, ensure equitable care, and integrate cardiovascular risk management into respiratory disease protocols, which is essential for alleviating the cardiopulmonary disease burden and advancing health equity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4. \u003c/strong\u003e\u003cstrong\u003eLimitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDespite being a large-scale retrospective analysis, this study has several limitations. First, the reliance on death certificate coding from the CDC WONDER database introduces the potential for cause-of-death misclassification. Furthermore, the lack of data on individual-level confounders, such as smoking history, hypertension, and diabetes, prevents us from adjusting for their potential influence on mortality rates. Second, the classification of respiratory diseases is non-specific. By using the broad ICD-10 codes J00-J98 to define \u0026quot;respiratory diseases,\u0026quot; we did not distinguish between acute and chronic conditions, or between infectious and non-infectious diseases. The risk of AMI triggered by different types of respiratory illnesses may vary significantly. Third, this study lacks an evaluation of intervention effectiveness. Our analysis describes mortality trends but does not assess the impact of specific interventions, such as vaccinations or pharmacotherapies, on mortality; therefore, it cannot directly validate the effectiveness of existing prevention and control strategies.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAMI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAcute Myocardial Infarction\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAAMR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAge-Adjusted Mortality Rate\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAAPC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAverage Annual Percentage Change\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAPC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAnnual Percentage Change\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eARIMA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAutoregressive Integrated Moving Average\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCDC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eCenters for Disease Control and Prevention\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eConfidence Interval\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eICD-10\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eInternational Classification of Diseases, Tenth Revision\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eNH\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eNon-Hispanic\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eUS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eUnited States\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e-\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp;Not applicable. This study utilized exclusively de-identified, publicly available data from the CDC WONDER database. According to the policies of our institutional review board, research involving such data does not constitute human subjects research and is exempt from ethics approval and the requirement for informed consent.\u003c/p\u003e\n\u003cp\u003e- \u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp;Not applicable.\u003c/p\u003e\n\u003cp\u003e- \u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp;The datasets generated and/or analyzed during the current study are publicly available in the CDC WONDER database (https://wonder.cdc.gov/).\u003c/p\u003e\n\u003cp\u003e- \u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp;The authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e- \u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp;This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e- \u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp;WY and YZ contributed to the study conception and design. Material preparation, data collection and analysis were performed by WY, XX, and LL. The first draft of the manuscript was written by WY, and all authors (WY, YZ, XX, LL, XW, HR) commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e- \u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp;Not applicable.\u003c/p\u003e\n\u003cp\u003e-\u003cstrong\u003eAuthors' information (optional)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp;Not applicable.\u003c/p\u003e\n\u003cp\u003e-\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":" References","content":"\u003col\u003e\n\u003cli\u003eRoth GA, Mensah GA, Johnson CO, Addolorato G, Ammirati E, Baddour LM, et al. Global Burden of Cardiovascular Diseases and Risk Factors, 1990-2019: Update From the GBD 2019 Study. 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Int Heart J 2024;65(6):969-77.\u003c/li\u003e\n\u003cli\u003eGodfrey TM, Cordova-Marks FM, Jones D, Melton F, Breathett K. Metabolic Syndrome Among American Indian and Alaska Native Populations: Implications for Cardiovascular Health. Curr Hypertens Rep 2022;24(5):107-14.\u003c/li\u003e\n\u003cli\u003eManson SM, Buchwald DS. Aging and Health of American Indians and Alaska Natives: Contributions from the Native Investigator Development Program. J Aging Health 2021;33(7-8_suppl):3s-9s.\u003c/li\u003e\n\u003cli\u003eVillarroel MA, Clarke TC, Norris T. Health of American Indian and Alaska Native Adults, by Urbanization Level: United States, 2014-2018. NCHS Data Brief 2020(372):1-8.\u003c/li\u003e\n\u003cli\u003eKaur N, Esie P, Finsaas MC, Mauro PM, Keyes KM. Trends in Racial-Ethnic Disparities in Adult Mental Health Treatment Use From 2005 to 2019. Psychiatr Serv 2023;74(5):455-62.\u003c/li\u003e\n\u003cli\u003eAbedi V, Olulana O, Avula V, Chaudhary D, Khan A, Shahjouei S, et al. 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Cardiovasc Res 2022;118(10):2281-92.\u003c/li\u003e\n\u003cli\u003eGulati R, Behfar A, Narula J, Kanwar A, Lerman A, Cooper L, et al. Acute Myocardial Infarction in Young Individuals. Mayo Clin Proc 2020;95(1):136-56.\u003c/li\u003e\n\u003cli\u003eJortveit J, Pripp AH, Lang\u0026oslash;rgen J, Halvorsen S. Incidence, risk factors and outcome of young patients with myocardial infarction. Heart 2020;106(18):1420-6.\u003c/li\u003e\n\u003cli\u003eTea V, Danchin N, Puymirat E. [Myocardial infarction in young patient: Epidemiological specificities and risk factors]. Presse Med 2019;48(12):1383-6.\u003c/li\u003e\n\u003cli\u003eMiller CE, Vasan RS. The southern rural health and mortality penalty: A review of regional health inequities in the United States. Soc Sci Med 2021;268:113443.\u003c/li\u003e\n\u003cli\u003eJames CV, Moonesinghe R, Wilson-Frederick SM, Hall JE, Penman-Aguilar A, Bouye K. Racial/Ethnic Health Disparities Among Rural Adults - United States, 2012-2015. 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Heartbeat: delayed and inadequate treatment of acute coronary syndromes during the COVID-19 pandemic. Heart 2022;108(6):407-9.\u003c/li\u003e\n\u003cli\u003eMenon JC, Ms A, S H, Janakiram C, James A, Sreedevi A, et al. Assessing health system preparedness from trends and time delays in the management of myocardial infarctions during the COVID-19 pandemic in India: a multicentre retrospective cohort study. BMJ Open 2025;15(9):e094109.\u003c/li\u003e\n\u003cli\u003eVosko I, Zirlik A, Bugger H. Impact of COVID-19 on Cardiovascular Disease. Viruses 2023;15(2).\u003c/li\u003e\n\u003cli\u003eHulscher N, Hodkinson R, Makis W, McCullough PA. Autopsy findings in cases of fatal COVID-19 vaccine-induced myocarditis. ESC Heart Fail 2025;12(5):3212-25.\u003c/li\u003e\n\u003cli\u003eAlShahrani I, Hosmani J, Shankar VG, AlShahrani A, Togoo RA, Yassin SM, et al. COVID-19 and cardiovascular system-a comprehensive review. Rev Cardiovasc Med 2021;22(2):343-51.\u003c/li\u003e\n\u003cli\u003eNguyen KH, Mansfield KA, Xie CY, Corlin L, Niska RW. COVID-19 Adult, Childhood, and Adolescent Vaccination Coverage Among Military and Civilian Families, United States. Mil Med 2023;188(7-8):e2651-e60.\u003c/li\u003e\n\u003cli\u003eNguyen KH, Huang J, Mansfield K, Corlin L, Allen JD. COVID-19 Vaccination Coverage, Behaviors, and Intentions among Adults with Previous Diagnosis, United States. Emerg Infect Dis 2022;28(3):631-8.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table","content":"\u003cp\u003eTable1: Demographic\u0026nbsp;Characteristics of Acute Myocardial Infarction\u0026ndash;Related Deaths Among US Adults with Respiratory Diseases, 1999\u0026ndash;2023.\u003c/p\u003e\n\u003cdiv align=\"Left\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"566\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 366px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 201px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDeaths (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 366px;\"\u003e\n \u003cp\u003eEntire Cohort\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 201px;\"\u003e\n \u003cp\u003e775365\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 366px;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 201px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 366px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 201px;\"\u003e\n \u003cp\u003e347680(44.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 366px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 201px;\"\u003e\n \u003cp\u003e427685(55.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 366px;\"\u003e\n \u003cp\u003eCensus Region\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 201px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 366px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Northeast\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 201px;\"\u003e\n \u003cp\u003e133574(17.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 366px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Midwest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 201px;\"\u003e\n \u003cp\u003e179989(23.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 366px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;South\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 201px;\"\u003e\n \u003cp\u003e309699(39.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 366px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;West\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 201px;\"\u003e\n \u003cp\u003e152103(19.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 366px;\"\u003e\n \u003cp\u003eRace/Ethnicity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 201px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 366px;\"\u003e\n \u003cp\u003e\u0026nbsp; NH American Indian or Alaska\u003c/p\u003e\n \u003cp\u003eNative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 201px;\"\u003e\n \u003cp\u003e4329(0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 366px;\"\u003e\n \u003cp\u003e\u0026nbsp; NH Asian or Pacific Islander\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 201px;\"\u003e\n \u003cp\u003e17632(2.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 366px;\"\u003e\n \u003cp\u003e\u0026nbsp; NH Black or African American\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 201px;\"\u003e\n \u003cp\u003e68101(8.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 366px;\"\u003e\n \u003cp\u003e\u0026nbsp; NH White\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 201px;\"\u003e\n \u003cp\u003e639543(82.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 366px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Hispanic or Latino\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 201px;\"\u003e\n \u003cp\u003e43617(5.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 366px;\"\u003e\n \u003cp\u003eUrbanization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 201px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 366px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Large Metro\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 201px;\"\u003e\n \u003cp\u003e298240(38.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 366px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Medium/Small Metro\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 201px;\"\u003e\n \u003cp\u003e208365(26.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 366px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Nonmetro\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 201px;\"\u003e\n \u003cp\u003e166746(21.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 366px;\"\u003e\n \u003cp\u003eTen-Year Age Groups\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 201px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 366px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;25\u0026ndash;34 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 201px;\"\u003e\n \u003cp\u003e1417(0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 366px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;35\u0026ndash;44 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 201px;\"\u003e\n \u003cp\u003e6825(0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 366px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;45\u0026ndash;54 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 201px;\"\u003e\n \u003cp\u003e31274(4.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 366px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;55\u0026ndash;64 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 201px;\"\u003e\n \u003cp\u003e94619(12.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 366px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;65\u0026ndash;74 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 201px;\"\u003e\n \u003cp\u003e178144(23.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 366px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;75\u0026ndash;84 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 201px;\"\u003e\n \u003cp\u003e248855(32.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 366px;\"\u003e\n \u003cp\u003e\u0026nbsp; 85+ years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 201px;\"\u003e\n \u003cp\u003e214231(27.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\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":"Acute myocardial infarction, Mortality rate, Respiratory diseases, Health disparities","lastPublishedDoi":"10.21203/rs.3.rs-7957244/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7957244/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eAcute myocardial infarction (AMI) is a leading cause of death in the United States. However, the long-term trends in AMI-related mortality among adults with respiratory diseases have not been thoroughly investigated. This study aimed to analyze these trends from 1999 to 2023 and to identify high-risk subgroups.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethod: \u003c/strong\u003eData were obtained from the CDC WONDER database for a retrospective cohort study. The age-adjusted mortality rate (AAMR per 100,000) of acute myocardial infarction (ICD-10 I21) was analyzed among adults aged ≥25 years diagnosed with respiratory diseases (ICD-10 J00-J98). Joinpoint regression modeled temporal trends, generating the average annual percentage change (AAPC) and annual percentage change (APC). To model future trends, the best-fitting autoregressive integrated moving average (ARIMA) model was used to project mortality rates for the coming decade.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResult: \u003c/strong\u003eA total of 775,365 deaths from acute myocardial infarction (AMI) were recorded among US adults with respiratory diseases during the study period (1999-2023). The overall age-adjusted mortality rate (AAMR) fell from 20.20 (95% CI: 19.99–20.41) to 10.58 per 100,000, corresponding to an average annual percentage change (AAPC) of -2.77% (95% CI: -3.18 to -2.51). This downward trend was interrupted by a sharp, transient rise coinciding with the COVID-19 pandemic (APC 2018–2021 = 8.63%; 95% CI: 4.81–11.00). Furthermore, stark disparities were evident, as mortality rates consistently remained elevated for males, non-Hispanic American Indian/Alaska Native persons, individuals aged 85 and above, and inhabitants of the Southern US and rural communities.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eThe decline in AMI mortality among adults with respiratory diseases notwithstanding, this issue continues to pose a major public health challenge in the United States—a challenge that was severely intensified by the exacerbation of pre-existing disparities during the COVID-19 pandemic. Our study reveals that patients with respiratory diseases constitute a distinct high-risk population for AMI, whose gains in survival are precarious and inequitably distributed. These findings mandate a paradigm shift towards integrated cardiopulmonary care and equity-focused public health interventions to reduce the burden of coronary artery disease in this vulnerable group.\u003c/p\u003e","manuscriptTitle":"Trends in Acute Myocardial Infarction–Related Mortality Among US Adults with Respiratory Diseases from 1999 to 2023: A Cross-Sectional Analysis of the CDC WONDER Database","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-04 10:23:36","doi":"10.21203/rs.3.rs-7957244/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":"3132cd2d-6618-45e7-bcb3-d13948d0c09d","owner":[],"postedDate":"November 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-08T14:55:16+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-04 10:23:36","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7957244","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7957244","identity":"rs-7957244","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}