Marcus Ebeling, Neil K Mehta, Leah Abrams, Mikko Myrskylä
{"title":"Sicker at all ages? Two decades of changing disease histories and mortality in the United States.","authors":"Marcus Ebeling, Neil K Mehta, Leah Abrams, Mikko Myrskylä","doi":"10.1093/ije/dyag217","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Stagnating cardiovascular disease mortality and rising drug- and suicide-related deaths are key drivers of stalled US mortality improvements. However, prior research has largely overlooked disease histories preceding death, despite increasing multimorbidity. We examined changes in disease-history profiles in the US population and assessed how shifts in profile composition and within-profile mortality shaped mortality trends.</p><p><strong>Methods: </strong>We analysed adults aged ≥30 years (N = 510 372) from the IPUMS-NHIS (National Health Interview Survey) 1997-2018 linked mortality files (follow-up through 2019). Latent class analysis on the pooled sample across all ages and years identified disease-history profiles based on self-reported cancer, diabetes, cardiovascular disease, mental health, internal diseases, obesity, and physical limitations. Logistic regression was used to estimate profile-specific 5-year mortality over time, and counterfactual decomposition was used to quantify the contributions of compositional change and within-profile mortality improvement to overall mortality trends.</p><p><strong>Results: </strong>Five disease-history profiles were identified. Prevalence and 5-year death probability ranged from disease-free (62.4%; men 3.7% [95% confidence interval (CI) 3.5-3.8], women 2.4% [95% CI 2.3-2.5]) to high-burden multimorbidity (5.9%; men 17.8% [95% CI 16.8-18.8], women 12.6% [95% CI 11.9-13.3]), with substantial gradients across intermediate profiles. Profile composition varied substantially by gender, education, and race/ethnicity. The disease-free population declined by nearly 1% annually, replaced primarily by cardiometabolic and high-burden multimorbidity profiles. Most profiles experienced stagnating or increasing mortality after the mid-2000s. The decomposition shows that within-profile mortality improvement was the dominant driver of declining mortality; compositional shifts, mostly driven by the declining disease-free share, partially offset these gains.</p><p><strong>Conclusion: </strong>Stagnating US mortality reflects both the growing prevalence of high-morbidity profiles and worsening or stagnating survival within them, highlighting the limited reach of prevention and disease-management efforts.</p>","PeriodicalId":14147,"journal":{"name":"International journal of epidemiology","volume":"55 5","pages":""},"PeriodicalIF":5.7000,"publicationDate":"2026-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13654198/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International journal of epidemiology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1093/ije/dyag217","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH","Score":null,"Total":0}
引用次数: 0
Abstract
Background: Stagnating cardiovascular disease mortality and rising drug- and suicide-related deaths are key drivers of stalled US mortality improvements. However, prior research has largely overlooked disease histories preceding death, despite increasing multimorbidity. We examined changes in disease-history profiles in the US population and assessed how shifts in profile composition and within-profile mortality shaped mortality trends.
Methods: We analysed adults aged ≥30 years (N = 510 372) from the IPUMS-NHIS (National Health Interview Survey) 1997-2018 linked mortality files (follow-up through 2019). Latent class analysis on the pooled sample across all ages and years identified disease-history profiles based on self-reported cancer, diabetes, cardiovascular disease, mental health, internal diseases, obesity, and physical limitations. Logistic regression was used to estimate profile-specific 5-year mortality over time, and counterfactual decomposition was used to quantify the contributions of compositional change and within-profile mortality improvement to overall mortality trends.
Results: Five disease-history profiles were identified. Prevalence and 5-year death probability ranged from disease-free (62.4%; men 3.7% [95% confidence interval (CI) 3.5-3.8], women 2.4% [95% CI 2.3-2.5]) to high-burden multimorbidity (5.9%; men 17.8% [95% CI 16.8-18.8], women 12.6% [95% CI 11.9-13.3]), with substantial gradients across intermediate profiles. Profile composition varied substantially by gender, education, and race/ethnicity. The disease-free population declined by nearly 1% annually, replaced primarily by cardiometabolic and high-burden multimorbidity profiles. Most profiles experienced stagnating or increasing mortality after the mid-2000s. The decomposition shows that within-profile mortality improvement was the dominant driver of declining mortality; compositional shifts, mostly driven by the declining disease-free share, partially offset these gains.
Conclusion: Stagnating US mortality reflects both the growing prevalence of high-morbidity profiles and worsening or stagnating survival within them, highlighting the limited reach of prevention and disease-management efforts.
期刊介绍:
The International Journal of Epidemiology is a vital resource for individuals seeking to stay updated on the latest advancements and emerging trends in the field of epidemiology worldwide.
The journal fosters communication among researchers, educators, and practitioners involved in the study, teaching, and application of epidemiology pertaining to both communicable and non-communicable diseases. It also includes research on health services and medical care.
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Overall, this journal is an indispensable tool for staying informed and connected within the dynamic realm of epidemiology.