Trends in Rheumatic Heart Disease Mortality in the United States of America from 1968 to 2023: A Nationwide Analysis

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In the United States of America (USA), RHD persists among socioeconomically disadvantaged populations. This study examines long-term trends in RHD mortality from 1968-2023, identifying associated demographic and geographic factors to guide targeted public health interventions. Methods This retrospective observational study examined death certificate data from the Centers for Disease Control and Prevention’s Wide-Ranging Online Data for Epidemiologic Research (WONDER) database, encompassing three different International Classification of Disease periods (ICD-8, 9, 10). Analysis included stratification by age, gender, race, ethnicity and urbanisation level. Joinpoint analysis and multiple regression analysis were used to identify trends. Results 365,298 cases of fatal RHD from 1968-2023 were included. Independent of definitional changes following transitions in ICD coding, RHD mortality decreased from 1968-2012 (16,325 annual deaths vs 3,083 annual deaths, annual percentage change -1.8%, p<0.0001) with subsequent rise from 2013 to 2023 (APC +3.1%, p<0.0001). Females represented 62.4% of overall cases, with consistently higher RHD CMR than men (CMR ratio range 1.09-2.43). Minimal differences in valvular patterns of damage between sexes were observed. Both male and female sex (p=0.024 and p=0.022 respectively), Hispanic/Latino status (p=0.044) and age 35-54 years (p=0.003) were most associated with increasing RHD CMR from 2012 to 2023. Conclusion Following decades of decline RHD mortality in the USA has increased from 2012 to 2023. Disproportionate increases in American Indian and rural populations underscore need for targeted strengthened surveillance and initiatives in these groups. Rheumatic heart disease epidemiology mortality United States of America Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Rheumatic heart disease (RHD) is a chronic and preventable cardiac condition resulting from autoimmune damage to heart valves following one or more episodes of acute rheumatic fever (ARF). 1 Cumulative damage can lead to heart failure, stroke, pulmonary hypertension and arrhythmias - all of which may eventually lead to death. 2 Importantly, RHD is highly preventable with prompt diagnosis, treatment and secondary prevention of ARF. 3 Given that RHD is preventable, effective public health initiatives focused on increasing awareness, disease control and secondary prevention can theoretically lead to elimination of this disease. In the United States of America (USA/US), the incidence of RHD has significantly decreased since the early 20th century. 4 In the 21 st century, the burden of disease within the US is disproportionately concentrated in socioeconomically disadvantaged groups. 5 Ongoing epidemiological surveillance is essential to not only monitor progression toward RHD elimination, but to address persistent health inequities and to ensure that mortality in vulnerable groups is not forgotten. This need is particularly urgent in the US, where there is currently no national RHD strategy in place. This contrasts to other regions such as Australia 6 and Africa 7 , which have implemented coordinated public health initiatives to tackle RHD. Previous studies have described temporal trends in RHD mortality but have not incorporated updated data through to 2023. 5,8 Moreover, most analyses are restricted to a single ICD coding period beginning in 1999. This offers a limited snapshot of RHD mortality rather than a more comprehensive historical perspective. No studies to date have applied multivariable modelling to identify the demographic or geographic drivers associated with observed changes in absolute death counts and mortality rates. This study reports trends in RHD mortality in the US from 1968 to 2023, using both absolute death counts and crude mortality rates (CMR). Our study compares mortality patterns across age, sex, race, ethnicity and urbanisation. By identifying key demographic and geographic factors associated with RHD mortality, this study aims to inform targeted public health interventions that reduce RHD mortality in the most affected populations. Methods This retrospective observational study was reported in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement ( Supplementary Table 1 ) and the REporting of studies Conducted using Observational Routinely-collected Data (RECORD) guidelines ( Supplementary Table 2 ). CDC-Wonder CDC-Wonder is an open-access public health resource containing de-identified mortality data based on death certificates for US residents. Each death certificate identifies a single underlying cause of death in conjunction with demographic data. The ‘Underlying Cause of Death’ dataset was accessed, containing mortality and population counts for all counties in the US. 9 This dataset includes all recorded deaths from 1968 onwards, including year of death, sex, racial category, ethnicity and degree of urbanisation. Data relating to all RHD-related mortality from 1968-2023 were extracted, including absolute number of deaths, population, crude mortality rate per 100,000, age-adjusted mortality rate (when applicable) and percentage of total deaths. Data extraction Age groups were extracted in 5-year blocks and grouped into the following categories: 0-34 years, 35-54 years, 55-74 years, 75-84 years and >85 years. Racial categories were defined as: “American Indian or Alaskan Native”, “Asian or Pacific Islander”, “Black or African American”, “White and Hispanic”. Urbanisation was grouped in the following categories 9 Large Central Metro (counties in metropolitan statistical areas (MSAs) with ≥1 million population, including the core urban centre) , Large Fringe Metro (surrounding suburban counties in MSAs with ≥1 million population) , Medium Metro (counties in MSAs with 250,000 to 999,999 population), S mall Metro (counties in MSAs with <250,000 population), Micropolitan (Nonmetro) (Counties with urban clusters of 10,000 to 49,999 population) and Non-Core (Nonmetro) (all other rural counties with no urban cluster of ≥10,000 population). RHD Coding Definitions The time-period 1968-2023 encompassed three different ICD coding periods and consequently different ICD codes were used to identify RHD in each time-period: ICD-8 (1968-1978): 391 (Rheumatic fever with heart involvement), 393-398 (Chronic rheumatic heart diseases) ICD-9 (1979-1998): 391, (Rheumatic fever with heart involvement), 393-398 (Chronic rheumatic heart diseases) ICD-10 (1999-onwards): I01(Rheumatic fever with heart involvement), I05-9 (Chronic rheumatic heart diseases) Statistical Analysis Joinpoint analysis was performed to identify significant periods of transition over time. 10 Significant periods of change in reported RHD death rate within one year of ICD code change were discounted from analysis as being likely artefactual and related to definitional changes. Significant changes deemed likely non-artefactual were interrogated further. Univariate linear regression assessed trends over time in both absolute death counts and CMR for each subgroup. Results were displayed in a faceted forest plot. Multiple regression with backward selection was then used within cohorts to identify independent time-trend significance of potential demographic contributing factors. A p value of <0.05 was considered significant. Statistical analyses were performed using STATA/MP version 17.0 and Joinpoint Trend Analysis Software (Version 5.2.0, National Cancer Institute, USA) to evaluate time trends and identify significant changes in mortality rates. Ethical approval This study was exempt from institutional ethics review as the CDC WONDER database contains anonymised, publicly available data. To preserve confidentiality, group counts representing <10 persons were suppressed and for analysis assumed as 0. Results Overall data A total of 365,298 cases of fatal RHD from 1968-2023 were included. The annual number of RHD-related deaths decreased markedly from 16,325 in 1968 to 2,983 in 2010 followed by an increase to 4,332 in 2023 ( Figure 1A). Joinpoint analysis demonstrated significant periods of change at each period of ICD coding change and these changes were excluded as likely relating to definitional rather than epidemiological changes. Excluding these ICD-code related changes, significant decline in RHD mortality was observed from 1968-2012 (annual percentage change (APC) -1.8%, p<0.0001) with a subsequent increase in RHD mortality from 2012 onwards (APC +3.1%, p<0.0001). Females accounted for 62.4% of RHD-related deaths (n = 227,849) throughout the entire study period. Our study identified a significant gender disparity, in which females had consistently higher total number of deaths per year ( Figure 1B) and higher CMR for RHD than males, with CMR ratio ranging from 1.09-2.43 depending on the year ( Supplementary Figure 1 ). With regards to age groups, the proportion of RHD mortality changed from individuals 35-64 years (46% of RHD deaths in 1968 vs 18% in 2023) to those aged ≥75 years (17% of deaths in 1968 vs 61% of deaths in 2023) (Figure 1C and Supplementary Figure 2 ). The White population consistently accounted for most RHD-related deaths each year by race, with a mean proportion of 89.9% (SD 1.7%), ranging from 85.2% to 92.3% depending on the year ( Figure 1D ). Using the ICD codes, the sex-based differences in the pattern of valvular involvement at the time of death between 2018-2023 were minimal ( Figure 2 ). Figure 1. Trends in annual rheumatic heart disease-related deaths in the United States from 1968 to 2023. Annual number of RHD-related deaths from CDC WONDER death certificate data. Panels show: (A) overall trend, (B) stratification by sex, (C) age group, and (D) race. RHD = rheumatic heart disease. Figure 2. Sex-specific comparison of valve involvement among rheumatic heart disease-related deaths in the United States of America, 2018–2023. Figure 3. A) Crude mortality rate and B) age-adjusted mortality rate per 100,000 for rheumatic heart disease-related deaths in the United States (2000–2023) Decline in RHD mortality 1968-2011: RHD mortality reduced from 1968-2012 (p85-year-olds (p<0.0001). Multiple regression using backward selection demonstrated that female sex (p=0.046) and White race (p=0.029) remained independently associated with reducing RHD CMR. Rural location (p=0.010) and age >85 years (p=0.003) were independently associated with increasing RHD CMR. Rising RHD mortality 2012-2023 : RHD mortality increased from 3,083 in 2012 to 4,332 in 2023, with CMR rising from 0.98 to 1.29 per 100,000 (p < 0.0001), indicating population growth was not a confounder ( Figure 3) . Age-adjusted mortality rate per 100,000 also rose from 0.88 to 1.04 (p < 0.0001). Univariate linear regression showed increases across both sexes, all racial groups, all age categories (except 75-84) and all urbanisation groups for both number of death and crude rates ( Figure 4) . Multiple regression using backward selection demonstrated both male and female sex (p=0.024 and p=0.022 respectively), Hispanic/Latino status (p=0.044) and age 35-54 years (p=0.003) were most associated with increasing RHD CMR. Figure 4. Forest plot of regression slopes (β) for annual changes in crude mortality rate per 100,000 for rheumatic heart disease related deaths across subgroups, 2012–2023. Discussion Ongoing epidemiological studies are critical for tracking progress towards the elimination of RHD in the US. This is the largest nationwide retrospective analysis of RHD mortality in the US published spanning three ICD coding periods from 1968-2023. Our findings report a marked decline in the number of RHD-related deaths from 1968 to 2012, followed by an increase from 2012 onwards. Females accounted for 62.4% of all deaths, with mortality shifting from middle-aged adults to the elderly over the study period, reflecting a reduction in premature RHD-related mortality. RHD prevalence, incidence and mortality has declined substantially since the 20 th century. 11 The availability of penicillin since the 1940s enabled effective treatment of ARF with oral penicillin V and long-term secondary prophylaxis with monthly benzathine penicillin G injections. 12 Concurrently, socioeconomic improvements reduced overcrowding, improved sanitation and increased educational awareness of RHD supporting primary prevention. 13 From a diagnostic point of view, the introduction of the Jones Criteria in 1944, screening with auscultation and the increasingly widespread use of echocardiography facilitated prompt diagnosis and treatment reducing the risk of ARF progressing to RHD. 14,15 Collectively, these measures have improved RHD prevention, detection and management contributing to our findings of reduced overall and premature RHD mortality in the US between 1968-2012. The disproportionate burden of RHD among females is consistent with prior studies across multiple countries. Our calculated female-to-male mortality ratio of 1.66:1 falls within the range described in the literature at 1.5-2:1 depending on the study and country. 16–20 The underlying mechanisms of this disparity remains poorly understood. Proposed explanations include smaller chamber sizes leading to amplified impact of valvular lesions, 21 or hormonal influences that may enhance autoimmune mechanisms leading to valvular damage, 22 while others suggest socioeconomic factors predispose women to streptococcal exposure. 17 Our study identified minimal sex-based difference in the distribution of valvular involvement as reported by the ICD codes. The literature reports mitral and tricuspid valve involvement more common in females and aortic involvement more prominent in males. 23,24 Indeed, while our findings reflect this, the observed effect sizes were minimal and unlikely to hold meaningful clinical significance. Future research to identify the underlying contributors to increased female risk is warranted to inform sex-specific identification and treatment strategies. The recent rise in RHD mortality has been previously reported by Salman et al. for the period 2017 to 2020. 5 Importantly, the recent rise is not directly attributable to population growth as the CMR in 2023 has returned to higher levels comparable to those observed in 2000. While the ageing American population likely contributes to the increase in overall RHD-related deaths, the declining proportion of deaths among individuals aged ≥75 years since 2014 and rising age-adjusted mortality rate since 2017 suggests involvement of unknown factors ( Supplementary Figure 3 ). Furthermore, the upward trends since 2012 in RHD mortality are evident across both sexes, all races, ethnicities, urbanisation groups and all age groups except the 75-84 years group. Our analysis identifies males, adults aged 35-54 years, American Indian individuals, and Hispanic populations as subgroups with fastest increasing risk for RHD mortality ( Supplementary Materials Table 3 ). This significantly contrasts with the largest contributors to total RHD mortality: females, individuals aged ≥75 years, White populations, non-Hispanics, and residents of large metropolitan areas. Milutinovic et al.’s 2024 study using the Global Disease Burden data identified an increase in RHD prevalence and incidence in the USA since 2007-2021, contrasting with the EU’s continued decline since 1993. 25 Combining our findings, the rising prevalence, incidence and mortality of RHD suggest that RHD is not being adequately controlled in the US. While Milutinovic et al. propose immigration from high-RHD-burden regions to the US as a potential factor, this does not explain the widespread increase across all demographics, which may indicate broader systemic factors at play. The worsening income inequality in the US may be a considerable factor given the growth of the low-income population and a rising Gini coefficient in recent years. 26–28 For race, ethnicity and urbanisation, the rise in RHD-mortality warrants attention given established socioeconomic disparities in these communities. American Indian, 29,30 African American 31,32 and Hispanic 33,34 populations face well-documented socioeconomic disadvantages which include but are not limited to poor housing conditions, homelessness, overcrowding and subpar sanitation services, all of which increase the likelihood of Group A streptococcus infections. Additionally, disproportionate burdens of poverty and unemployment can exacerbate these disparities, leading to difficulty and failure to access healthcare. 35–37 Rural communities face similar structural barriers, preventing timely accessing to healthcare. 30 In the absence of targeted interventions, the disease burden may shift towards a pattern mirrored in Australia where disadvantaged communities populations and those in rural or remote areas bear the larger total population percentage of RHD. 38,39 Our study has several important limitations. Firstly, our study spans three ICD codes introducing a clear transitional artifact, most notably between ICD-8 and ICD-9 in 1979. This is attributable to a change in classification where under ICD-8, mitral and aortic valve diseases were assumed to be rheumatic unless explicated mentioned otherwise. 40 In contrast, ICD-9 reversed this assumption classifying these valvular conditions as non-rheumatic unless specifically attributed to rheumatic fever, except mitral stenosis remained rheumatic in origin. Approximately 4,015 deaths were reclassified according to the new classification. Reliance on death certificate data is inherently limited by potential misclassification. The “Underlying Cause of Death” dataset captures a singular cause of death determined by the certifying physician, which may be affected to by subjectivity and variable reporting practices. CDC WONDER dataset suppresses death counts below 10 to protect confidentiality. In our analysis, suppressed entries were treated as zero, likely leading to underestimation of mortality for the affected groups. This approach was only applied in 1999-2023 for the relevant groups aged 0-24. Conclusion Rheumatic heart disease mortality in the United States of America has increased from 2012 to 2023, interrupting a decades-long decline. Disproportionate increases observed among American Indian and rural populations underscore the need for strengthened surveillance and targeted public health initiatives in these groups. Abbreviations ARF Acute rheumatic fever RHD Rheumatic heart disease HF Heart failure CMR Crude mortality rate ICD International Classification of Diseases USA / US United States of America CDC Centers for Disease Control and Prevention CDC WONDER Centers for Disease Control and Prevention Wide-ranging Online Data for Epidemiologic Research STROBE Strengthening the Reporting of Observational Studies in Epidemiology RECORD REporting of studies Conducted using Observational Routinely-collected Data MSA Metropolitan statistical area APC Annual percentage change Declarations Acknowledgements: The authors gratefully acknowledge the Centers for Disease Control and Prevention Wide-Ranging Online Data for Epidemiologic Research (CDC WONDER) platform for providing open access to the mortality data used in this study. Conflict of Interest Statement: The authors declare that they have no conflicts of interest to disclose. CRediT Author Statement Jacky Chen : Writing - Original draft preparation, Data curation, Investigation, Visualization, Formal analysis. Dean Nelson : Supervision, Project administration, Writing – Review & Editing Adam Trytell: Supervision, Project administration, Writing – Review & Editing. Andrew Burns: Supervision, Project administration, Writing – Review & Editing. Noel Bayley :Supervision, Project administration, Writing – Review & Editing. Elizabeth Paratz: Conceptualization, Validation, Supervision, Project administration, Formal analysis, Writing – Review & Editing Conflict of Interest Statement: The authors report no relationships that could be construed as a conflict of interest. Funding source: Nil Data Availability Statement: The data that support the findings of this study are available from the corresponding author upon request. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8237353","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":554952172,"identity":"3326e1d6-74fb-48cb-98c6-b52897764ddf","order_by":0,"name":"Jacky 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1","display":"","copyAsset":false,"role":"figure","size":93211,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTrends in annual rheumatic heart disease-related deaths in the United States from 1968 to 2023.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAnnual number of RHD-related deaths from CDC WONDER death certificate data. Panels show: (A) overall trend, (B) stratification by sex, (C) age group, and (D) race.\u003c/p\u003e\n\u003cp\u003eRHD = rheumatic heart disease.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8237353/v1/020c038d082aa130ca3c4b07.jpg"},{"id":97894190,"identity":"f6b97dd3-fc8f-437e-9810-c23d14b54d99","added_by":"auto","created_at":"2025-12-10 15:32:01","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":52292,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSex-specific comparison of valve involvement among rheumatic heart disease-related deaths in the United States of America, 2018–2023.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8237353/v1/c503b25fc5e82fe41c0a6ece.jpg"},{"id":97692828,"identity":"e0ee7e7e-fd17-410b-bd40-67f9ef6dff4e","added_by":"auto","created_at":"2025-12-08 11:14:48","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":83218,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA) Crude mortality rate and B) age-adjusted mortality rate per 100,000 for rheumatic heart disease-related deaths in the United States (2000–2023)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8237353/v1/30ed97dc211d7f12dc8c4106.jpg"},{"id":97692830,"identity":"a180a277-1480-4514-b245-f6f672322d96","added_by":"auto","created_at":"2025-12-08 11:14:48","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":96624,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eForest plot of regression slopes (β) for annual changes in crude mortality rate per 100,000 for rheumatic heart disease related deaths across subgroups, 2012–2023.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8237353/v1/515ff2ce0d6751c6ea825cbc.jpg"},{"id":98622128,"identity":"a98c6231-0e83-4e3c-b365-7eb5612eda7c","added_by":"auto","created_at":"2025-12-19 16:45:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1444886,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8237353/v1/c26e6f3f-a866-42ac-910f-e71363b632d7.pdf"},{"id":97893806,"identity":"29e03428-82e6-4eca-8b4e-66c28559780d","added_by":"auto","created_at":"2025-12-10 15:31:16","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":296336,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementary.docx","url":"https://assets-eu.researchsquare.com/files/rs-8237353/v1/08bf13ae75e9bf2a72a2d4bb.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Trends in Rheumatic Heart Disease Mortality in the United States of America from 1968 to 2023: A Nationwide Analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eRheumatic heart disease (RHD) is a chronic and preventable cardiac condition resulting from autoimmune damage to heart valves following one or more episodes of acute rheumatic fever (ARF).\u003csup\u003e1\u003c/sup\u003e Cumulative damage can lead to heart failure, stroke, pulmonary hypertension and arrhythmias - all of which may eventually lead to death.\u003csup\u003e2\u003c/sup\u003e Importantly, RHD is highly preventable with prompt diagnosis, treatment and secondary prevention of ARF.\u003csup\u003e3\u003c/sup\u003e Given that RHD is preventable, effective public health initiatives focused on increasing awareness, disease control and secondary prevention can theoretically lead to elimination of this disease.\u003c/p\u003e\n\u003cp\u003eIn the United States of America (USA/US), the incidence of RHD has significantly decreased since the early 20th century.\u003csup\u003e4\u003c/sup\u003e In the 21\u003csup\u003est\u003c/sup\u003e century, the burden of disease within the US is disproportionately concentrated in socioeconomically disadvantaged groups.\u003csup\u003e5\u003c/sup\u003e Ongoing epidemiological surveillance is essential to not only monitor progression toward RHD elimination, but to address persistent health inequities and to ensure that mortality in vulnerable groups is not forgotten. This need is particularly urgent in the US, where there is currently no national RHD strategy in place. This contrasts to other regions such as Australia\u003csup\u003e6\u003c/sup\u003e and Africa\u003csup\u003e7\u003c/sup\u003e, which have implemented coordinated public health initiatives to tackle RHD.\u003c/p\u003e\n\u003cp\u003ePrevious studies have described temporal trends in RHD mortality but have not incorporated updated data through to 2023.\u003csup\u003e5,8\u003c/sup\u003e Moreover, most analyses are restricted to a single ICD coding period beginning in 1999. This offers a limited snapshot of RHD mortality rather than a more comprehensive historical perspective. No studies to date have applied multivariable modelling to identify the demographic or geographic drivers associated with observed changes in absolute death counts and mortality rates.\u003c/p\u003e\n\u003cp\u003eThis study reports trends in RHD mortality in the US from 1968 to 2023, using both absolute death counts and crude mortality rates (CMR). Our study compares mortality patterns across age, sex, race, ethnicity and urbanisation. By identifying key demographic and geographic factors associated with RHD mortality, this study aims to inform targeted public health interventions that reduce RHD mortality in the most affected populations.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThis retrospective observational study was reported in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement (\u003cstrong\u003eSupplementary Table 1\u003c/strong\u003e) and the REporting of studies Conducted using Observational Routinely-collected Data (RECORD) guidelines (\u003cstrong\u003eSupplementary Table 2\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCDC-Wonder\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCDC-Wonder is an open-access public health resource containing de-identified mortality data based on death certificates for US residents. Each death certificate identifies a single underlying cause of death in conjunction with demographic data. The ‘Underlying Cause of Death’ dataset was accessed, containing mortality and population counts for all counties in the US.\u003csup\u003e9\u003c/sup\u003e This dataset includes all recorded deaths from 1968 onwards, including year of death, sex, racial category, ethnicity and degree of urbanisation. Data relating to all RHD-related mortality from 1968-2023 were extracted, including absolute number of deaths, population, crude mortality rate per 100,000, age-adjusted mortality rate (when applicable) and percentage of total deaths.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData extraction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAge groups were extracted in 5-year blocks and grouped into the following categories: 0-34 years, 35-54 years, 55-74 years, 75-84 years and \u0026gt;85 years. Racial categories were defined as: “American Indian or Alaskan Native”, “Asian or Pacific Islander”, “Black or African American”, “White and Hispanic”. \u0026nbsp;Urbanisation was grouped in the following categories\u003csup\u003e9\u003c/sup\u003e Large Central Metro (counties in metropolitan statistical areas (MSAs) with ≥1 million population, including the core urban centre)\u003cem\u003e,\u0026nbsp;\u003c/em\u003eLarge Fringe Metro (surrounding suburban counties in MSAs with ≥1 million population)\u003cem\u003e,\u0026nbsp;\u003c/em\u003eMedium Metro (counties in MSAs with 250,000 to 999,999 population), \u003cem\u003eS\u003c/em\u003emall Metro (counties in MSAs with \u0026lt;250,000 population), Micropolitan (Nonmetro) (Counties with urban clusters of 10,000 to 49,999 population) and\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNon-Core (Nonmetro) (all other rural counties with no urban cluster of ≥10,000 population).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRHD Coding Definitions \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe time-period 1968-2023 encompassed three different ICD coding periods and consequently different ICD codes were used to identify RHD in each time-period:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eICD-8 (1968-1978): 391 (Rheumatic fever with heart involvement), 393-398 (Chronic rheumatic heart diseases)\u003c/li\u003e\n \u003cli\u003eICD-9 (1979-1998): 391, (Rheumatic fever with heart involvement), 393-398 (Chronic rheumatic heart diseases)\u003c/li\u003e\n \u003cli\u003eICD-10 (1999-onwards): I01(Rheumatic fever with heart involvement), I05-9 (Chronic rheumatic heart diseases)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJoinpoint analysis was performed to identify significant periods of transition over time.\u003csup\u003e10\u003c/sup\u003e Significant periods of change in reported RHD death rate within one year of ICD code change were discounted from analysis as being likely artefactual and related to definitional changes. Significant changes deemed likely non-artefactual were interrogated further. Univariate linear regression assessed trends over time in both absolute death counts and CMR for each subgroup. Results were displayed in a faceted forest plot. Multiple regression with backward selection was then used within cohorts to identify independent time-trend significance of potential demographic contributing factors. A p value of \u0026lt;0.05 was considered significant. Statistical analyses were performed using STATA/MP version 17.0 \u0026nbsp; and Joinpoint Trend Analysis Software (Version 5.2.0, National Cancer Institute, USA) to evaluate time trends and identify significant changes in mortality rates.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was exempt from institutional ethics review as the CDC WONDER database contains anonymised, publicly available data. To preserve confidentiality, group counts representing \u0026lt;10 persons were suppressed and for analysis assumed as 0. \u003cstrong\u003e\u003cbr\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eOverall data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 365,298 cases of fatal RHD from 1968-2023 were included. The annual number of RHD-related deaths decreased markedly from 16,325 in 1968 to 2,983 in 2010 followed by an increase to 4,332 in 2023 (\u003cstrong\u003eFigure 1A).\u003c/strong\u003e Joinpoint analysis demonstrated significant periods of change at each period of ICD coding change and these changes were excluded as likely relating to definitional rather than epidemiological changes. Excluding these ICD-code related changes, significant decline in RHD mortality was observed from 1968-2012 (annual percentage change (APC) -1.8%, p\u0026lt;0.0001) with a subsequent increase in RHD mortality from 2012 onwards (APC +3.1%, p\u0026lt;0.0001).\u003c/p\u003e\n\u003cp\u003eFemales accounted for 62.4% of RHD-related deaths (n = 227,849) throughout the entire study period. Our study identified a significant gender disparity, in which females had consistently higher total number of deaths per year (\u003cstrong\u003eFigure 1B)\u0026nbsp;\u003c/strong\u003eand higher CMR for RHD than males, with CMR ratio ranging from 1.09-2.43 depending on the year (\u003cstrong\u003eSupplementary Figure 1\u003c/strong\u003e). With regards to age groups, the proportion of RHD mortality changed from individuals 35-64 years (46% of RHD deaths in 1968 vs 18% in 2023) to those aged \u0026ge;75 years (17% of deaths in 1968 vs 61% of deaths in 2023) \u003cstrong\u003e(Figure 1C and Supplementary Figure 2\u003c/strong\u003e). The White population consistently accounted for most RHD-related deaths each year by race, with a mean proportion of 89.9% (SD 1.7%), ranging from 85.2% to 92.3% depending on the year (\u003cstrong\u003eFigure 1D\u003c/strong\u003e). Using the ICD codes, the sex-based differences in the pattern of valvular involvement at the time of death between 2018-2023 were minimal (\u003cstrong\u003eFigure 2\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 1.\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eTrends in annual rheumatic heart disease-related deaths in the United States from 1968 to 2023.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAnnual number of RHD-related deaths from CDC WONDER death certificate data. Panels show: (A) overall trend, (B) stratification by sex, (C) age group, and (D) race.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRHD = rheumatic heart disease.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 2.\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eSex-specific comparison of valve involvement among rheumatic heart disease-related deaths in the United States of America, 2018\u0026ndash;2023.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 3. A) Crude mortality rate and B) age-adjusted mortality rate per 100,000 for rheumatic heart disease-related deaths in the United States (2000\u0026ndash;2023)\u003c/strong\u003e\u003cstrong\u003e\u003cbr\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDecline in RHD mortality 1968-2011:\u0026nbsp;\u003c/strong\u003eRHD mortality reduced from 1968-2012 (p\u0026lt;0.0001). \u0026nbsp;Subgroup analysis, identified a reduction RHD CMR for both sexes, in Black / African Americans and Whites race, urban and rural demographics and across all age groups except \u0026gt;85-year-olds (p\u0026lt;0.0001). Multiple regression using backward selection demonstrated that female sex (p=0.046) and White race (p=0.029) remained independently associated with reducing RHD CMR. Rural location (p=0.010) and age \u0026gt;85 years (p=0.003) were independently associated with increasing RHD CMR.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRising RHD mortality 2012-2023\u003c/strong\u003e: RHD mortality increased from 3,083 in 2012 to 4,332 in 2023, with CMR rising from 0.98 to 1.29 per 100,000 (p \u0026lt; 0.0001), indicating population growth was not a confounder (\u003cstrong\u003eFigure 3)\u003c/strong\u003e. Age-adjusted mortality rate per 100,000 also rose from 0.88 to 1.04 (p \u0026lt; 0.0001). Univariate linear regression showed increases across both sexes, all racial groups, all age categories (except 75-84) and all urbanisation groups for both number of death and crude rates (\u003cstrong\u003eFigure 4)\u003c/strong\u003e. Multiple regression using backward selection demonstrated both male and female sex (p=0.024 and p=0.022 respectively), Hispanic/Latino status (p=0.044) and age 35-54 years (p=0.003) were most associated with increasing RHD CMR.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eFigure 4. Forest plot of\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;regression slopes (\u0026beta;) for annual changes in crude mortality rate per 100,000 for rheumatic heart disease related deaths across subgroups, 2012\u0026ndash;2023.\u003c/strong\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOngoing epidemiological studies are critical for tracking progress towards the elimination of RHD in the US. This is the largest nationwide retrospective analysis of RHD mortality in the US published spanning three ICD coding periods from 1968-2023. Our findings report a marked decline in the number of RHD-related deaths from 1968 to 2012, followed by an increase from 2012 onwards. Females accounted for 62.4% of all deaths, with mortality shifting from middle-aged adults to the elderly over the study period, reflecting a reduction in premature RHD-related mortality.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRHD prevalence, incidence and mortality has declined substantially since the 20\u003csup\u003eth\u003c/sup\u003e century.\u003csup\u003e11\u003c/sup\u003e The availability of penicillin since the 1940s enabled effective treatment of ARF with oral penicillin V and long-term secondary prophylaxis with monthly benzathine penicillin G injections.\u003csup\u003e12\u003c/sup\u003e Concurrently, socioeconomic improvements reduced overcrowding, improved sanitation and increased educational awareness of RHD supporting primary prevention.\u003csup\u003e13\u003c/sup\u003e From a diagnostic point of view, the introduction of the Jones Criteria in 1944, screening with auscultation and the increasingly widespread use of echocardiography facilitated prompt diagnosis and treatment reducing the risk of ARF progressing to RHD.\u003csup\u003e14,15\u003c/sup\u003e Collectively, these measures have improved RHD prevention, detection and management contributing to our findings of reduced overall and premature RHD mortality in the US between 1968-2012.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe disproportionate burden of RHD among females is consistent with prior studies across multiple countries. Our calculated female-to-male mortality ratio of 1.66:1 falls within the range described in the literature at 1.5-2:1 depending on the study and country.\u003csup\u003e16–20\u003c/sup\u003e The underlying mechanisms of this disparity remains poorly understood. Proposed explanations include smaller chamber sizes leading to amplified impact of valvular lesions,\u003csup\u003e21\u003c/sup\u003e or hormonal influences that may enhance autoimmune mechanisms leading to valvular damage,\u003csup\u003e22\u003c/sup\u003e while others suggest socioeconomic factors predispose women to streptococcal exposure.\u003csup\u003e17\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur study identified minimal sex-based difference in the distribution of valvular involvement as reported by the ICD codes. The literature reports mitral and tricuspid valve involvement more common in females and aortic involvement more prominent in males.\u003csup\u003e23,24\u003c/sup\u003e Indeed, while our findings reflect this, the observed effect sizes were minimal and unlikely to hold meaningful clinical significance. Future research to identify the underlying contributors to increased female risk is warranted to inform sex-specific identification and treatment strategies.\u003c/p\u003e\n\u003cp\u003eThe recent rise in RHD mortality has been previously reported by Salman et al. for the period 2017 to 2020.\u003csup\u003e5\u003c/sup\u003e Importantly, the recent rise is not directly attributable to population growth as the CMR in 2023 has returned to higher levels comparable to those observed in 2000. While the ageing American population likely contributes to the increase in overall RHD-related deaths, the declining proportion of deaths among individuals aged ≥75 years since 2014 and rising age-adjusted mortality rate since 2017 suggests involvement of unknown factors (\u003cstrong\u003eSupplementary Figure 3\u003c/strong\u003e). Furthermore, the upward trends since 2012 in RHD mortality are evident across both sexes, all races, ethnicities, urbanisation groups and all age groups except the 75-84 years group. Our analysis identifies males, adults aged 35-54 years, American Indian individuals, and Hispanic populations as subgroups with fastest increasing risk for RHD mortality (\u003cstrong\u003eSupplementary Materials Table 3\u003c/strong\u003e). This significantly contrasts with the largest contributors to total RHD mortality: females, individuals aged ≥75 years, White populations, non-Hispanics, and residents of large metropolitan areas.\u003c/p\u003e\n\u003cp\u003eMilutinovic et al.’s 2024 study using the Global Disease Burden data identified an increase in RHD prevalence and incidence in the USA since 2007-2021, contrasting with the EU’s continued decline since 1993.\u003csup\u003e25\u003c/sup\u003e Combining our findings, the rising prevalence, incidence and mortality of RHD suggest that RHD is not being adequately controlled in the US. While Milutinovic et al. propose immigration from high-RHD-burden regions to the US as a potential factor, this does not explain the widespread increase across all demographics, which may indicate broader systemic factors at play. The worsening income inequality in the US may be a considerable factor given the growth of the low-income population and a rising Gini coefficient in recent years.\u003csup\u003e26–28\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor race, ethnicity and urbanisation, the rise in RHD-mortality warrants attention given established socioeconomic disparities in these communities. American Indian,\u003csup\u003e29,30\u003c/sup\u003e African American\u003csup\u003e31,32\u003c/sup\u003e and Hispanic\u003csup\u003e33,34\u003c/sup\u003e populations face well-documented socioeconomic disadvantages which include but are not limited to poor housing conditions, homelessness, overcrowding and subpar sanitation services, all of which increase the likelihood of Group A streptococcus infections. Additionally, disproportionate burdens of poverty and unemployment can exacerbate these disparities, leading to difficulty and failure to access healthcare.\u003csup\u003e35–37\u003c/sup\u003e Rural communities face similar structural barriers, preventing timely accessing to healthcare.\u003csup\u003e30\u003c/sup\u003e In the absence of targeted interventions, the disease burden may shift towards a pattern mirrored in Australia where disadvantaged communities populations and those in rural or remote areas bear the larger total population percentage of RHD.\u003csup\u003e38,39\u003c/sup\u003e\u0026nbsp;\u003cbr\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur study has several important limitations. Firstly, our study spans three ICD codes introducing a clear transitional artifact, most notably between ICD-8 and ICD-9 in 1979. This is attributable to a change in classification where under ICD-8, mitral and aortic valve diseases were assumed to be rheumatic unless explicated mentioned otherwise.\u003csup\u003e40\u003c/sup\u003e In contrast, ICD-9 reversed this assumption classifying these valvular conditions as non-rheumatic unless specifically attributed to rheumatic fever, except mitral stenosis remained rheumatic in origin. Approximately 4,015 deaths were reclassified according to the new classification. Reliance on death certificate data is inherently limited by potential misclassification. The “Underlying Cause of Death” dataset captures a singular cause of death determined by the certifying physician, which may be affected to by subjectivity and variable reporting practices. CDC WONDER dataset suppresses death counts below 10 to protect confidentiality. In our analysis, suppressed entries were treated as zero, likely leading to underestimation of mortality for the affected groups. This approach was only applied in 1999-2023 for the relevant groups aged 0-24.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eRheumatic heart disease mortality in the United States of America has increased from 2012 to 2023, interrupting a decades-long decline. Disproportionate increases observed among American Indian and rural populations underscore the need for strengthened surveillance and targeted public health initiatives in these groups.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eARF\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAcute rheumatic fever\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eRHD\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eRheumatic heart disease\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eHF\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eHeart failure\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eCMR\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eCrude mortality rate\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eICD\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eInternational Classification of Diseases\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eUSA / US\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eUnited States of America\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eCDC\u003c/b\u003e\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\"\u003e\u003cb\u003eCDC WONDER\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eCenters for Disease Control and Prevention Wide-ranging Online Data for Epidemiologic Research\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eSTROBE\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eStrengthening the Reporting of Observational Studies in Epidemiology\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eRECORD\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eREporting of studies Conducted using Observational Routinely-collected Data\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eMSA\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eMetropolitan statistical area\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eAPC\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAnnual percentage change\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors gratefully acknowledge the Centers for Disease Control and Prevention Wide-Ranging Online Data for Epidemiologic Research (CDC WONDER) platform for providing open access to the mortality data used in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest Statement:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflicts of interest to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCRediT Author Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJacky Chen\u003c/strong\u003e: Writing - Original draft preparation, Data curation, Investigation, Visualization, Formal analysis. \u003cstrong\u003eDean Nelson\u003c/strong\u003e: Supervision, Project administration, Writing \u0026ndash; Review \u0026amp; Editing \u003cstrong\u003eAdam Trytell:\u003c/strong\u003e Supervision, Project administration, Writing \u0026ndash; Review \u0026amp; Editing. \u003cstrong\u003eAndrew Burns:\u003c/strong\u003e Supervision, Project administration, Writing \u0026ndash; Review \u0026amp; Editing. \u003cstrong\u003eNoel Bayley\u003c/strong\u003e:Supervision, Project administration, Writing \u0026ndash; Review \u0026amp; Editing. \u003cstrong\u003eElizabeth Paratz:\u0026nbsp;\u003c/strong\u003eConceptualization, Validation, Supervision, Project administration, Formal analysis, Writing \u0026ndash; Review \u0026amp; Editing\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest Statement:\u003c/strong\u003e The authors report no relationships that could be construed as a conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding source:\u003c/strong\u003e Nil\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement:\u003c/strong\u003e The data that support the findings of this study are available from the corresponding author upon request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Approval Statement\u003c/strong\u003e: This study was exempt from institutional ethics review as the CDC WONDER database contains anonymised, publicly available data.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eDooley LM, Ahmad TB, Pandey M, Good MF, Kotiw M. Rheumatic heart disease: A review of the current status of global research activity. Autoimmun Rev. 2021;20(2):102740. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.autrev.2020.102740\u003c/span\u003e\u003cspan address=\"10.1016/j.autrev.2020.102740\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eStacey I, Hung J, Cannon J, et al. Long-term outcomes following rheumatic heart disease diagnosis in Australia. Eur Heart J Open. 2021;1(3):oeab035. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/ehjopen/oeab035\u003c/span\u003e\u003cspan address=\"10.1093/ehjopen/oeab035\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShimanda PP, Shumba TW, Brunstr\u0026ouml;m M, et al. 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Accessed July 27, 2025. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://stacks.cdc.gov/view/cdc/107996\u003c/span\u003e\u003cspan address=\"https://stacks.cdc.gov/view/cdc/107996\" targettype=\"URL\" 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":false,"hideJournal":false,"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":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Rheumatic heart disease, epidemiology, mortality, United States of America","lastPublishedDoi":"10.21203/rs.3.rs-8237353/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8237353/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Background\nRheumatic heart disease (RHD) is a preventable sequela of acute rheumatic fever causing chronic valvular damage to the heart. In the United States of America (USA), RHD persists among socioeconomically disadvantaged populations. This study examines long-term trends in RHD mortality from 1968-2023, identifying associated demographic and geographic factors to guide targeted public health interventions.\n\nMethods \nThis retrospective observational study examined death certificate data from the Centers for Disease Control and Prevention’s Wide-Ranging Online Data for Epidemiologic Research (WONDER) database, encompassing three different International Classification of Disease periods (ICD-8, 9, 10). Analysis included stratification by age, gender, race, ethnicity and urbanisation level. Joinpoint analysis and multiple regression analysis were used to identify trends. \n\nResults\n365,298 cases of fatal RHD from 1968-2023 were included. Independent of definitional changes following transitions in ICD coding, RHD mortality decreased from 1968-2012 (16,325 annual deaths vs 3,083 annual deaths, annual percentage change -1.8%, p\u003c0.0001) with subsequent rise from 2013 to 2023 (APC +3.1%, p\u003c0.0001). Females represented 62.4% of overall cases, with consistently higher RHD CMR than men (CMR ratio range 1.09-2.43). Minimal differences in valvular patterns of damage between sexes were observed. Both male and female sex (p=0.024 and p=0.022 respectively), Hispanic/Latino status (p=0.044) and age 35-54 years (p=0.003) were most associated with increasing RHD CMR from 2012 to 2023.\n\nConclusion\nFollowing decades of decline RHD mortality in the USA has increased from 2012 to 2023. Disproportionate increases in American Indian and rural populations underscore need for targeted strengthened surveillance and initiatives in these groups.","manuscriptTitle":"Trends in Rheumatic Heart Disease Mortality in the United States of America from 1968 to 2023: A Nationwide Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-08 11:14:44","doi":"10.21203/rs.3.rs-8237353/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-19T09:24:16+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-05T17:46:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"26629456889330815415161300598342107772","date":"2026-04-14T17:50:18+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-31T03:14:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"85482434184522183041843977652511230225","date":"2025-12-15T00:21:31+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-04T01:18:42+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-12-03T08:03:23+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-02T03:35:21+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-02T03:34:29+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2025-11-29T13:20:35+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f0dc156d-acb9-4b33-a37c-30e67b2a0e48","owner":[],"postedDate":"December 8th, 2025","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-19T09:24:16+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-05T17:46:15+00:00","index":29,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-19T09:41:02+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-08 11:14:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8237353","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8237353","identity":"rs-8237353","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}