A 25-Year Analysis of Liver Disease–Hypertension Comorbid Mortality Trends in the United States, 1999–2023

A 25-Year Analysis of Liver Disease–Hypertension Comorbid Mortality Trends in the United States, 1999–2023 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A 25-Year Analysis of Liver Disease–Hypertension Comorbid Mortality Trends in the United States, 1999–2023 Muhammad Atif Mazhar, Shama Parveen, Eshal Atif, Heena Parveen, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7586042/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 Death certificates frequently list both liver disease and hypertension codes, yet trends in this co-occurrence have been underexplored. This study analyzed 25 years of U.S. data to identify patterns in deaths that list both conditions. Methods We conducted a descriptive analysis of mortality data from CDC WONDER for 1999–2023, focusing on adults aged ≥ 25 years. Deaths that listed both liver diseases (K70–K76) and hypertensive diseases (I10–I15) were identified using ICD-10 codes. Age-adjusted mortality rates (AAMRs) per 100,000 were standardized to the 2000 U.S. population. Temporal trends were assessed with Joinpoint regression, which estimated annual percent changes (APCs) with 95% confidence intervals. Analyses were stratified by age, sex, race/ethnicity, region, state, and urbanization level. Results Between 1999 and 2023, deaths with both conditions listed increased from 2,307 to 18,769, totaling 208,666 deaths. The national AAMR rose from 1.30 per 100,000 in 1999 to 6.75 in 2023, with an average annual percent change (AAPC) of + 6.71% (95% CI: 5.64–7.80). Joinpoint regression identified four phases: rapid early growth (1999–2001, APC 19.50%), steady increase (2001–2018, APC 4.27%), sharp acceleration (2018–2021, APC 17.64%), and stabilization (2021–2023, APC 0.28%). In 2023, males had higher rates than females (9.02 vs. 4.71 per 100,000). Non-Hispanic (NH) American Indian or Alaska Native populations had the highest rates (12.74 per 100,000), followed by Hispanic (8.15), NH White (6.95), and NH Black (6.84), while NH Asian or Pacific Islander individuals had the lowest (3.05). Geographic variation was substantial, with rates ranging from 2.87 (Connecticut) to 21.54 (Oklahoma) per 100,000. Conclusion Deaths that list both liver disease and hypertension codes increased substantially over 25 years (AAPC + 6.71%), with notable acceleration during 2018–2021. These documentation patterns warrant continued surveillance to understand evolving mortality trends. Liver disease hypertension mortality trends multiple cause of death health surveillance epidemiology United States Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Liver disease and hypertensive disorders are significant contributors to global mortality and account collectively for more than 3.3 million deaths [ 1 , 2 ]. In 2021, liver disease was responsible for approximately 1.26 million deaths, which represented a 13% increase since 1990 [ 3 ], whereas hypertensive heart disease resulted in approximately 1.33 million fatalities [ 2 ]. Hypertension affects more than 1 billion individuals worldwide [ 4 ]. The global burden of metabolic dysfunction-associated steatotic liver disease (MASLD) has reached epidemic proportions and impacts approximately 25% of the global population [ 5 ]. Standard mortality reports generally address these two conditions separately, which may limit understanding of deaths where both conditions are documented. Clinical studies show that liver disease and hypertension frequently appear together in patient populations. In MASLD, previously known as nonalcoholic fatty liver disease (NAFLD), the prevalence of hypertension typically ranges from 39% to 69%. A meta-analysis of 86 studies reported a prevalence of 39.34% and identified a 1.4-fold increased risk compared with individuals without liver disease [ 5 , 6 ]. The relationship between MASLD and cardiovascular disease has been documented, with MASLD patients who exhibit elevated cardiovascular mortality [ 7 ]. Among individuals with hypertension, the prevalence of MASLD approaches 50%, nearly double the global average of approximately 25% [ 8 ]. In cases of cirrhosis, systemic hypertension is less common due to hyperdynamic physiology, whereas portal hypertension affects most patients prior to symptom onset [ 9 , 10 ]. Portal hypertension, a significant complication of advanced liver disease, serves as a prognostic marker in patients with cirrhosis [ 11 ]. Multimorbidity datasets demonstrate frequent clusters of liver disease, hypertension, and metabolic disorders [ 12 ]. 2. Methods 2.1. Study design and data sources We conducted a retrospective, population-based analysis of U.S. mortality data from the Multiple Cause of Death (MCOD) Database for 1999 to 2023, consistent with STROBE guidelines [ 38 , 39 ]. 2.2. Study population and case definitions We included decedents aged 25 years or older to capture the primary age range for chronic liver disease and hypertension mortality and exclude younger deaths that may represent different etiologies. Underlying and contributing causes of death used International Classification of Diseases 10th Revision (ICD-10) codes, which the U.S. has used since 1999 [ 40 ]. Liver diseases used ICD-10 codes K70-K76: alcoholic liver disease (K70), toxic liver disease (K71), hepatic failure (K72), chronic hepatitis (K73), fibrosis and cirrhosis of the liver (K74), other inflammatory liver diseases (K75), other diseases of the liver (K76). Hypertensive diseases included codes I10–I15: essential hypertension (I10), hypertensive heart disease (I11), hypertensive chronic kidney disease (I12), hypertensive heart and chronic kidney disease (I13), and secondary hypertension (I15). Pulmonary hypertension (I27.x) was excluded as it represents a distinct pathophysiological entity primarily related to pulmonary vascular disease rather than systemic hypertension. The primary outcome was death certificates that listed at least one liver disease code (K70–K76) and at least one hypertensive disease code (I10–I15), regardless of whether either was the underlying cause of death. This approach captures deaths where both conditions were documented by certifying physicians. Race and ethnicity classifications used National Center for Health Statistics (NCHS) bridged-race categories: non-Hispanic (NH) White, non-Hispanic Black, NH American Indian or Alaska Native, NH Asian or Pacific Islander, and Hispanic [ 41 ]. 2.3. Outcome measures and rate calculation The primary measure was age-adjusted mortality rates (AAMRs) per 100,000 population, calculated overall and stratified by sex, race/ethnicity, U.S. Census region, state, and urbanization category. Rates were directly standardized to the 2000 U.S. standard population [ 42 ]. For AAMR 95% confidence intervals, we used intervals provided by CDC WONDER, which employs the gamma method for age-adjusted rates [ 43 ]. 2.4. Temporal trend analysis Temporal trends in co-listing rates used Joinpoint Regression Program version 5.0.2. A log-linear model with heteroscedastic errors and permutation tests allowed up to 4 joinpoints to estimate annual percent changes (APCs) with 95% confidence intervals for each segment. Average annual percent changes (AAPCs) summarized overall trends across the full study period, consistent with standard Joinpoint methodology [ 44 , 45 ]. 2.5. Recent trend analysis We compared mortality rates from 2020 to 2023 with a pooled baseline from 2017 to 2019 and calculated rate ratios (RRs) and absolute rate differences. Confidence intervals for rate ratios used the delta method in R statistical software [ 46 ]. The 2017–2019 period served as baseline to establish a pre-2020 reference period. 2.6. Geographic analysis AAMRs were compiled by U.S. Census regions and states. Regional and state APCs used identical joinpoint configurations. Urban and rural area classifications used the 2013 NCHS Urban–Rural Classification [ 47 ], and Census regions and divisions used Census Bureau standards [ 48 ]. 2.7. Data quality, software, and ethics Counts below 10 were suppressed per CDC WONDER policy; rates based on fewer than 20 deaths were flagged as statistically unreliable. AAMRs and confidence intervals used CDC WONDER; temporal trends used Joinpoint v5.0.2; rate ratios and absolute rate differences used spreadsheet software [ 39 , 44 ]. The dataset is publicly available and deidentified; therefore, institutional review board approval was not required [ 49 ]. 3. Results 3.1. National Temporal Trends Between 1999 and 2023, a total of 208,666 deaths were recorded with both liver disease and hypertension codes listed (Supplementary Table 1). Annual deaths increased from 2,307 in 1999 to 18,769 in 2023. The national age-adjusted mortality rate (AAMR) rose from 1.30 per 100,000 (95% CI: 1.25–1.36) to 6.75 per 100,000 (95% CI: 6.66–6.85) over the same period (Supplementary Table 3). By 2019, the AAMR reached 4.59 per 100,000 (95% CI: 4.50–4.67), a 253% increase from 1999. The largest short-term rise occurred between 2019 and 2021, when rates increased from 4.59 to 6.70 per 100,000 (95% CI: 6.60–6.80)—a 46% jump. From 2021 to 2023, AAMRs showed little additional change and remained at 6.75 per 100,000 (95% CI: 6.66–6.85) in 2023. 3.2. Temporal Phase Analysis Joinpoint regression identified four distinct temporal phases in national mortality trends (Supplementary Table 2). The age-adjusted mortality rate increased at an annual percent change (APC) of + 19.50% (95% CI: 7.57–32.76) from 1999 to 2001. Between 2001 and 2018, the increase continued at a slower pace with an APC of + 4.27% (95% CI: 4.00–4.63). From 2018 to 2021, the APC rose to + 17.64% (95% CI: 12.42–23.11). No statistically significant change occurred between 2021 and 2023 (APC + 0.28%, 95% CI: −3.56–4.29). The average annual percent change (AAPC) across the entire 1999–2023 period was + 6.71% (95% CI: 5.64–7.80). All APCs except the 2021–2023 period were statistically significant (p < 0.05). 3.3. Recent Trends (2019–2023) Between 2019 and 2020, the national AAMR increased from 4.59 per 100,000 (95% CI: 4.50–4.67) to 5.86 per 100,000 (95% CI: 5.77–5.95). Annual deaths rose from 12,230 to 15,709 (Supplementary Table 1). Mortality rates remained elevated through 2023, with AAMRs of 6.69 per 100,000 (95% CI: 6.59–6.79) in 2022 and 6.75 per 100,000 (95% CI: 6.66–6.85) in 2023. Annual deaths reached 18,769 in 2023. Compared with pooled 2017–2019 rates, mortality rates were 1.36 times higher in 2020, 1.56 times higher in 2021–2022, and 1.57 times higher in 2023 (Supplementary Table 3). Joinpoint regression showed no statistically significant change in national mortality rates between 2021 and 2023 (APC + 0.28%, 95% CI: -3.56 to 4.29). Subgroup analyses revealed non-significant changes for NH White (+ 1.54%), NH Black (-2.21%), and Hispanic (-3.89%) adults, while the NH Others category had a significant increase (APC + 7.24%, 95% CI: 4.22–10.34) (Supplementary Table 2). 3.4. Demographic disparities 3.4.1. Sex differences In 2023, death certificates that listed both conditions had higher rates among males (9.02 per 100,000; 95% CI: 8.85–9.19) compared to females (4.71 per 100,000; 95% CI: 4.60–4.83). Males accounted for 11,693 deaths (62.3%) while females accounted for 7,076 deaths (37.7%) (Supplementary Tables 1 and 3). Over the 25-year period, males had an AAPC of + 7.07% (95% CI: 5.69–8.47) while females had an AAPC of + 6.49% (95% CI: 5.07–7.92). Both sexes had steady increases from 2001–2018: males increased 4.50% annually (95% CI: 4.17–4.83) and females 3.81% annually (95% CI: 3.44–4.18). From 2018–2021, rates accelerated for both groups. No significant changes occurred from 2021–2023 (Fig. 1 ). 3.4.2. Race and Ethnicity In 2023, co-listing rates varied substantially across racial and ethnic groups (Fig. 2 ; Supplementary Table 5). Non-Hispanic American Indian or Alaska Native individuals had the highest rates at 12.74 per 100,000 (95% CI: 11.38–14.09). Hispanic individuals followed at 8.15 per 100,000 (95% CI: 7.82–8.48). Non-Hispanic White individuals recorded 6.95 per 100,000 (95% CI: 6.84–7.06) and represented the largest absolute number with 13,101 deaths. Non-Hispanic Black individuals had rates of 6.84 per 100,000 (95% CI: 6.55–7.14), while Non-Hispanic Asian or Pacific Islander individuals had the lowest rates at 3.05 per 100,000 (95% CI: 2.77–3.34). The 25-year AAPC varied by race/ethnicity: Non-Hispanic White adults + 7.53% (95% CI: 6.19–8.89), Hispanic adults + 4.12% (95% CI: 2.57–5.69), Non-Hispanic Black adults + 3.11% (95% CI: 1.22–5.04), and Non-Hispanic Others + 3.42% (95% CI: 1.95–4.91). 3.6. Geographic Patterns 3.6.1. Regional Analysis In 2023, co-listing rates differed across U.S. Census regions. The South had the highest rates at 17.75 per 100,000 (95% CI: 17.50-18.01). The West followed at 16.65 (95% CI: 16.33–16.97), then the Midwest at 15.21 (95% CI: 14.89–15.53), and the Northeast at 13.78 (95% CI: 13.46–14.11) (Supplementary Table 6). Regional AAPC values over 25 years were: South + 8.11% (95% CI: 7.24–8.99), Northeast + 7.43% (95% CI: 5.91–8.97), West + 6.96% (95% CI: 6.08–7.85), and Midwest + 6.59% (95% CI: 5.68–7.50). All regions had increases during 2017–2021, with varying patterns in recent years (Fig. 3 ; Supplementary Table 2). 3.6.2. State-level Analysis In 2023, state-level co-listing rates varied widely (Supplementary Tables 5 and 9). Rates ranged from approximately 3 per 100,000 in some Northeastern states to over 20 per 100,000 in some South-Central states (Fig. 4 ). These patterns may reflect differences in healthcare systems, population demographics, and documentation practices across states. 3.6.3. Urban-Rural Disparities In 2020, co-listing rates varied across urbanization categories. Rates were lowest in large fringe metropolitan areas (3.9 per 100,000; 95% CI: 3.8-4.0) and highest in micropolitan areas (6.1 per 100,000; 95% CI: 5.8–6.4). Intermediate values occurred across other metropolitan categories (Fig. 5 ; Supplementary Table 8). 3.7. Place of Death Patterns Place of death changed between 2019 and 2023 (Fig. 6 ; Supplementary Table 7). Deaths at home increased from 4,755 in 2019 to 7,636 in 2023. Hospice facility deaths rose from 1,007 to 1,698. Inpatient hospital deaths increased from 3,500 in 2019 to 5,600 in 2021, then declined to 5,370 in 2023. Deaths in nursing homes or long-term care facilities increased from 1,507 to 1,956. These patterns accompanied the overall increase in total deaths from 12,230 in 2019 to 18,769 in 2023. 4. Discussion 4.1. Principal Findings Over twenty-five years, deaths that listed both liver disease and hypertension codes increased from 2,307 to 18,769. The national AAMR rose from 1.30 to 6.75 per 100,000 individuals, with an overall AAPC of + 6.71% (95% CI: 5.64–7.80). Joinpoint analysis identified four distinct segments: rapid increase (1999–2001), steady rise (2001–2018), pronounced acceleration (2018–2021), and stabilization (2021–2023). The increase from 2019 to 2021 was evident in both rates and counts. Rate ratios relative to the baseline period of 2017–2019 remained above 1.5 throughout 2023. These documentation patterns occurred when metabolic disorders and cardiometabolic risk factors gained increased recognition in liver-related outcomes [ 8 , 18 ], though this analysis examines co-listing patterns rather than clinical causality. Globally, these temporal changes align with NAFLD's emergence as a common cause of chronic liver disease. Prevalence increased from 10.5% in 1990 to 16.0% in 2019 among all age groups, representing over 1.2 billion cases worldwide [ 23 ]. The acceleration in co-listing during the early 2000s also coincides with metabolic syndrome recognition within clinical guidelines [ 50 ] and NAFLD's emergence as a prominent hepatic condition [ 5 , 6 ]. These co-listing patterns may reflect complex interactions between evolving diagnostic practices, population health trends, documentation practices, and increased recognition of multimorbidity in clinical care. Global burden patterns show regional variations in disease etiology and progression [ 1 ]. However, the extent to which these trends represent changes in disease patterns versus documentation practices remains uncertain and requires further investigation. 4.2. Demographic Disparities In 2023, males had approximately twofold higher co-listing rates compared to females, consistent with previous findings in liver disease mortality surveillance [ 51 ]. From 2018 to 2021, APCs increased for both sexes, with females having a marginally higher APC. This occurred during a period of reported increases in alcohol-related harm and metabolic risks within the female demographic [ 52 , 53 ]. These sex-based differences in co-listing patterns may be influenced by multiple factors: biological susceptibility, behavioral patterns, healthcare utilization practices, and documentation patterns [ 54 ]. Globally, liver disease mortality varies by sociodemographic characteristics. Low- and middle-income countries experience burdens of viral hepatitis, whereas high-income regions face increasing rates of NAFLD and alcohol-related liver disease [ 23 ]. Racial and ethnic differences in co-listing rates persisted. Non-Hispanic American Indian or Alaska Native adults had the highest rates in 2023. Joinpoint regression revealed a significant increase from 2000–2017, a nonsignificant rise from 2017–2020, and a nonsignificant decline from 2020–2023. This trajectory indicates sustained elevation in this group despite recent stabilization. Hispanic adults had significant increases until 2018, a further rise from 2018–2021, and a nonsignificant decline thereafter. Non-Hispanic White and Non-Hispanic Black adults had similar 2023 rates, with modest or negative APCs between 2021 and 2023. These patterns may reflect multiple factors: access to healthcare, socioeconomic status, underlying health conditions, and documentation practices [ 54 , 55 ]. The relationship between race/ethnicity and co-listing patterns likely reflects multiple intersecting factors that encompass historical inequities, social determinants of health, and healthcare access [ 56 ]. The pattern among non-Hispanic Black individuals may reflect competing risk factors, diagnostic pathways, or documentation differences. Prior research suggests multiple explanations [ 57 , 58 ]. However, these interpretations remain hypotheses that require further investigation, as this analysis cannot distinguish between disease patterns and documentation variations. 4.3. Geographic Patterns Geographic variation occurred across regions and states. Higher rates in parts of the southern and western regions appeared in areas with documented gradients in obesity, diabetes, and alcohol-related outcomes, as well as differences in policy and environmental factors [ 59 , 60 ]. These regional variations may also reflect differences in healthcare infrastructure, provider availability, state-level health policies, and documentation practices, along with geographic disparities in access to specialized liver care [ 61 ]. Rural areas had higher rates compared to large metropolitan regions, which aligns with documented barriers in specialty healthcare access, transportation, and chronic disease diagnosis [ 62 , 63 ]. The urban-rural gradient may represent access-related factors, underlying population health differences, documentation practices, or combinations thereof. These patterns warrant further investigation, particularly given the concentration of specialized hepatology services in urban centers and potential variations in death certificate completion practices across different healthcare settings. 4.4. Recent Trends (2019–2023) From 2019 to 2023, deaths that listed both conditions increased by 53%. A notable rise during 2020–2021 persisted through 2023. The distribution of deaths by place shifted toward residences and hospice facilities. These changes occurred during a period characterized by reports of care delays, service reallocation, and disruptions in chronic disease management [ 26 , 28 ]. This period coincided with documented challenges for chronic liver disease patients, including increased mortality risks and delayed care across both viral and nonviral liver diseases. Global data show similar patterns of healthcare disruption [ 23 , 33 ]. Healthcare disruptions during this period were multifaceted: delayed screening, reduced access to specialized care, and reported increases in alcohol consumption in some populations. Studies documented changes in outcomes related to alcohol-induced liver conditions during this period [ 30 , 31 ], and multimorbidity was associated with increased mortality risk [ 35 ]. Patients with cirrhosis had higher rates of complications and mortality, with decompensated cirrhosis associated with the highest risk [ 37 ]. While these temporal associations are noteworthy, our study design documents co-listing trends rather than causal mechanisms. The increases may reflect multiple factors: changes in disease patterns, healthcare delivery, documentation practices, or combinations thereof. These merit further investigation through dedicated studies designed to examine causal pathways. 4.5. Value of Multiple-Cause Surveillance MCOD analysis provides additional surveillance value beyond single-cause tabulations; however, it primarily reflects what certifiers document rather than clinical disease interactions. Documentation practices vary across settings and over time [ 20 ]. Single-cause tabulations may underestimate deaths where multiple chronic diseases are present. Notable differences, ranging from two- to three-fold, occur when using any-mention definitions compared to underlying cause only [ 21 , 22 ]. Documentation practices may influence these estimates, as variations in multiple-cause listing and certifier behavior have been documented [ 20 , 24 , 25 ]. Techniques to weight multiple causes or summarize MCOD patterns are available and can serve as sensitivity analyses [ 24 , 25 ]. MCOD data provides a useful surveillance indicator for examining co-occurrence patterns in mortality data. However, results should be interpreted cautiously due to inherent limitations of death certificate documentation. These include potential variations in coding practices, diagnostic recognition, and documentation completeness across different healthcare settings and time periods. Ongoing methodological development aims to enhance the analysis and interpretation of multiple causes of death data [ 25 ]. 4.6. Surveillance and Research Implications The scale of co-listing patterns suggests potential areas for enhanced surveillance and research. While our findings reflect documentation patterns rather than validated clinical interactions, they may inform surveillance strategies and hypothesis generation for future studies. Given the observational nature of our study and its reliance on death certificate documentation, these findings should be interpreted cautiously and cannot directly inform clinical practice guidelines. The documented co-listing patterns may warrant further investigation through prospective clinical studies designed to examine whether integrated assessment approaches could benefit patients with multiple chronic conditions. Such research could explore systematic screening protocols, care coordination models, and shared decision-making tools, consistent with established frameworks for multimorbidity care [ 64 ]. However, the effectiveness of such approaches would require validation through dedicated clinical trials before implementation. At the population level, areas with elevated rates may benefit from enhanced surveillance systems and research into underlying factors. These could include studies that examine healthcare access, documentation practices, and population health determinants. The documented geographic variation in co-listing rates suggests the need for targeted surveillance efforts, particularly given documented disparities in access to specialized care. 4.7. Limitations This study has several important limitations. Death certificates may underreport or misclassify conditions, and multiple cause co-listing reflects what certifiers document at the time of death rather than lifelong disease interactions. This fundamental limitation means that patterns may reflect coding practices, end-of-life documentation patterns, or temporal changes in diagnostic recognition rather than actual disease relationships, potentially leading to both over- and under-estimation of co-occurrence. Documentation practices vary across settings and among certifiers, which can introduce selection or documentation bias [ 20 , 22 ]. Socioeconomic factors may influence both disease development and the certification process (location of death, certifier identity, recorded details), meaning estimates may reflect access and documentation factors alongside biological considerations [ 20 , 22 ]. Additionally, coding modifications, evolving diagnostic awareness, and changing healthcare access from 1999 to 2023 may impact trends [ 40 ]. Although ICD-10 coding provides standardized definitions, the specificity of liver disease (K70–K76) and hypertension (I10–I15) categories may not fully capture disease subtypes, and misclassification remains possible. Data reliability varied across racial and ethnic groups, with additional complications from classification changes over time. Race and ethnicity classifications changed during the study period, with bridged-race categories used from 1999–2009 and single-race classifications from 2010–2023. Asian and Pacific Islander populations were combined pre-2010 but separated thereafter. These classification changes may contribute to apparent temporal trends and limit the precision of long-term racial/ethnic comparisons. For NH American Indian or Alaska Native populations, mortality counts were unstable in 1999, requiring analyses from 2000–2023. For Asian or Pacific Islander populations, early years had small counts and wide confidence intervals, limiting precision. These differences mean long-term comparisons across groups should be interpreted cautiously, as early instability and methodological changes may exaggerate temporal shifts. Small population states had frequent data suppression, creating geographic bias toward larger states and limiting rural health assessment. Geographic comparisons are ecological and cannot assign area-level factors to individuals. Results emphasize mortality rather than incidence or nonfatal disease [ 13 , 65 ]. Small number suppression and "unreliable" flags may introduce imprecision. Rate-ratio confidence intervals used approximation methods rather than formal time-series approaches. Given extensive stratification, multiple testing adjustments were not performed; subgroup findings should be interpreted as descriptive rather than definitive hypothesis tests. Finally, competing risks may influence patterns: co-listing rates could appear lower in populations with high competing cardiovascular mortality. These limitations necessitate cautious interpretation and highlight the need for complementary study designs. 4.8. Research Priorities Future research should focus on methodological advances to better distinguish documentation artifacts from clinically meaningful patterns in MCOD data. Validation studies that integrate MCOD with clinical registries, claims data, or longitudinal cohorts could improve accuracy and enable causal inference. Machine learning methods could help identify meaningful co-occurrence patterns within MCOD data, consistent with recent methodological advances [ 25 ]. Prospective cohort studies are needed to examine potential causal pathways and clinical relationships that cannot be assessed through death certificate analysis. Health services research could evaluate integrated care models while accounting for the limitations identified in surveillance data. International collaborative research may help understand regional variations in both disease patterns and documentation practices [ 23 , 1 ]. The temporal changes during 2018–2023 warrant dedicated investigation through studies designed to examine multiple potential explanations: healthcare delivery changes, behavioral modifications, and documentation practice evolution. Such research should use study designs capable of distinguishing between these competing explanations. 5. Conclusion Deaths that listed both liver disease and hypertension codes increased substantially from 1999 to 2023 (AAPC + 6.71%), with notable acceleration during 2018–2021 and subsequent stabilization through 2023. Geographic and demographic variation in co-listing patterns occurred. While these findings reflect documentation patterns rather than validated disease interactions, they provide valuable surveillance insights into evolving mortality trends. MCOD-based surveillance, despite inherent limitations in death certificate documentation, offers a population-level perspective on co-occurrence patterns that complements other surveillance approaches. The development of enhanced analytical methods and validation studies could improve understanding of the relationship between documentation patterns and underlying health trends. Continued surveillance of these patterns, combined with methodologically appropriate research designs, may inform our understanding of evolving mortality trends and guide future epidemiological investigations. Abbreviations CDC WONDER Centers for Disease Control and Prevention Wide-Ranging Online Data for Epidemiologic Research AAMR Age-Adjusted Mortality Rates APC Annual Percentage Change AAPC Average Annual Percentage change CI Confidence Interval MASLD Metabolic dysfunction-associated steatotic liver disease (MASLD) NAFLD Nonalcoholic fatty liver disease (NAFLD), MCOD Multiple Cause of Death STROBE Strengthening the Reporting of Observational Studies in Epidemiology NCHS National Center for Health Statistics RR Rate Ratio ARD Absolute rate differences Declarations Data availability The dataset supporting the conclusions of this article is included in its additional file Funding None of the authors have any financial disclosures and no funding was received for this project. Contributions S.P. and S.Q. contributed to the study conception and design. S.P. and H.P. extracted the data, and S.Q. and E.A. performed all the statistical analyses. S.P. and H.P. generated the figures. E.A., S.Q., S.P., H.P., and M.A.M. drafted the manuscript and prepared successive revisions. A.O. supervised the project, provided critical reviews, and managed the administration. All the authors have read and approved the final manuscript. Ethics declarations Conflict of interest The authors declare no competing interests. Supplementary Information The supporting tables and graphs can be found in the supplementary file. Rights and permissions Institutional Review Board Statement: Ethical review and approval were waived for this study as we used publicly available deidentified mortality data from the CDC WONDER database. No individual consent was required because the study used aggregate, deidentified data. This study adhered to the principles of the Declaration of Helsinki, and all analyses were conducted in accordance with CDC guidelines for mortality data use and interpretation. Informed Consent Statement: Not applicable. 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Circulation. 2004 Jan 27;109(3):433-8. https://doi.org/10.1161/01.CIR.0000111245.75752.C6 Additional Declarations No competing interests reported. Supplementary Files 3SeptCDCsupplementaryfiles1.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7586042","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":513749908,"identity":"a6b9f876-4d14-4f12-8620-26d5ecab118a","order_by":0,"name":"Muhammad Atif Mazhar","email":"","orcid":"","institution":"Alfaisal University","correspondingAuthor":false,"prefix":"","firstName":"Muhammad","middleName":"Atif","lastName":"Mazhar","suffix":""},{"id":513749909,"identity":"57dccf47-bc77-4db8-96ed-dd8b8249ef45","order_by":1,"name":"Shama Parveen","email":"","orcid":"","institution":"Alfaisal University","correspondingAuthor":false,"prefix":"","firstName":"Shama","middleName":"","lastName":"Parveen","suffix":""},{"id":513749910,"identity":"6d5c62e5-962e-474c-89f7-1727dc63611e","order_by":2,"name":"Eshal Atif","email":"","orcid":"","institution":"Alfaisal University","correspondingAuthor":false,"prefix":"","firstName":"Eshal","middleName":"","lastName":"Atif","suffix":""},{"id":513749911,"identity":"f3a81241-e787-4408-8774-27ecd7aac517","order_by":3,"name":"Heena Parveen","email":"","orcid":"","institution":"Alfaisal University","correspondingAuthor":false,"prefix":"","firstName":"Heena","middleName":"","lastName":"Parveen","suffix":""},{"id":513749912,"identity":"3cc628fe-205b-4007-b04d-21ba8ef80db9","order_by":4,"name":"Sadia Qazi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3ElEQVRIiWNgGAWjYLCChAoGGfYGxgYGhgIg7wBRWs4w8PAcAGkxIFYLYxtIC4hFjBb59uTHHx7Os+PhYT/c/OGHAYMc340E/FoMzjwzMEjclszDw5PYJtljwGAsSVCLRIJBQuI2Zh57hkSg8wwYEjcQ0iI/I/3DgcQ59Tw8/A+bP/4xYKgnqIXhRo5hQ2LDYR4eicQGaaAtCQaE/fKmmCHh2HGglodt0jIGEoYzzzwg4LD29M0ff9RUy/Hwpz/++KbCRp7vOCGHMaAqkCCkHFPLKBgFo2AUjAJMAACWFkRqKyLh0gAAAABJRU5ErkJggg==","orcid":"","institution":"Alfaisal University","correspondingAuthor":true,"prefix":"","firstName":"Sadia","middleName":"","lastName":"Qazi","suffix":""},{"id":513749913,"identity":"c320097f-d87a-4122-bade-247eb5666923","order_by":5,"name":"Akef Obeidat","email":"","orcid":"","institution":"Alfaisal University","correspondingAuthor":false,"prefix":"","firstName":"Akef","middleName":"","lastName":"Obeidat","suffix":""}],"badges":[],"createdAt":"2025-09-10 21:08:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7586042/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7586042/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":91151667,"identity":"658ae72a-7454-473a-b8bc-cf3272b4adfe","added_by":"auto","created_at":"2025-09-12 07:18:20","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":108203,"visible":true,"origin":"","legend":"\u003cp\u003eAge-Adjusted Mortality Rates for Liver Disease and Hypertension Comorbid Mortality, Stratified by Sex, 1999-2023\u003c/p\u003e\n\u003cp\u003eLegend: Age-adjusted mortality rates (AAMRs) per 100,000 population are standardized to the 2000 U.S. standard population. Lines represent Joinpoint regression fits for overall (orange), male (blue), and female (green) populations. Annual percent change (APC) values appear for each segment, with an asterisk (*) that indicates statistical significance at p \u0026lt; 0.05. Joinpoints denote statistically significant inflection years where mortality trends changed slope. Males consistently had higher rates than females throughout the study period, with both sexes showing similar temporal patterns: steady increases from 2001-2018 and sharp acceleration from 2018-2021, followed by stabilization from 2021-2023. Estimates were generated using the National Cancer Institute's Joinpoint Regression Program (version 5.0.2) with mortality data from CDC WONDER Multiple Cause-of-Death files, 1999–2023.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7586042/v1/77447cecf62759acf54a5c6b.jpg"},{"id":91151668,"identity":"bd5fa203-36ee-4ff5-9198-147bd2fa5b31","added_by":"auto","created_at":"2025-09-12 07:18:20","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":123174,"visible":true,"origin":"","legend":"\u003cp\u003eAge-Adjusted Mortality Rates for Liver Disease and Hypertension Comorbid Mortality, Stratified by Race, 1999-2023\u003c/p\u003e\n\u003cp\u003eLegend: Age-adjusted mortality rates (AAMRs) per 100,000 population are standardized to the 2000 U.S. standard population. Lines show trends for Non-Hispanic (NH) White (green), NH Black or African American (blue), Hispanic or Latino (purple), and NH Others (orange) populations. Annual percent change (APC) values with 95% confidence intervals are displayed in the legend for key time periods, with an asterisk (*) that indicates statistical significance at p \u0026lt; 0.05. Notable disparities exist across racial/ethnic groups, with Hispanic populations showing the highest rates by 2023, followed by NH Black, NH White, and NH Others populations. All groups demonstrated acceleration during 2018-2021, with varying patterns in recent years. The NH Others category includes NH American Indian or Alaska Native and NH Asian or Pacific Islander populations. Data source: CDC WONDER Multiple Cause-of-Death database, 1999–2023.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7586042/v1/717edf449f995238a28a81d3.jpg"},{"id":91153039,"identity":"a84f9e61-35b3-48e5-9982-0f6ef7b28bda","added_by":"auto","created_at":"2025-09-12 07:26:20","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":139770,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAge-Adjusted Mortality Rates for Liver Disease and Hypertension Comorbid Mortality, Stratified by Census Region, 1999-2023\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLegend: Age-adjusted mortality rates (AAMRs) per 100,000 population are standardized to the 2000 U.S. standard population. Lines show trends for Northeast (orange), Midwest (green), South (blue), and West (purple) regions. Annual percent change (APC) values with 95% confidence intervals are displayed in the legend for key time periods, with an asterisk (*) that indicates statistical significance at p \u0026lt; 0.05. The South had the highest rates by 2023, followed by the West, Midwest, and Northeast regions. All regions demonstrated acceleration during the 2018-2021 period, with varying recovery patterns in recent years. The Midwest showed a significant decline from 2020-2023, while other regions showed more modest changes. Data source: CDC WONDER Multiple Cause-of-Death database, 1999–2023.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7586042/v1/597f51647d919a9387236b84.jpg"},{"id":91151675,"identity":"c7a483a3-192c-4317-9617-d7c99b22a147","added_by":"auto","created_at":"2025-09-12 07:18:20","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":122073,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAge-Adjusted Mortality Rates for Liver Disease–Hypertension Comorbid Mortality, by State, United States, 1999–2023\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLegend: This map shows the average age-adjusted mortality rates (AAMRs) per 100,000 population, standardized to the 2000 U.S. standard population. These rates represent the mean rates over a 25-year period (1999–2023). The color intensity on the map corresponds to the magnitude of the mortality rate, with \u0026nbsp;darker blue indicating higher average mortality rates. The data reveals significant geographic variation, with higher rates generally concentrated in the South-Central and Western states. Oklahoma, for instance, shows the highest average rates, while several Northeastern states have the lowest. States shown in white on the map have insufficient data for a reliable rate calculation. The data for this map was sourced from the \u0026nbsp;CDC WONDER Multiple Cause-of-Death database, 1999–2023. The map was created using Datawrapper.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7586042/v1/185af482a0727a4616720228.jpg"},{"id":91153360,"identity":"9d091da1-d54c-438e-96f8-275d8f1e3fd7","added_by":"auto","created_at":"2025-09-12 07:34:20","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":150084,"visible":true,"origin":"","legend":"\u003cp\u003eAge-Adjusted Mortality Rates for Liver Disease–Hypertension Comorbid Mortality, by 2013 Urbanization Category, United States, 1999–2020\u003c/p\u003e\n\u003cp\u003eLegend: Age-adjusted mortality rates (AAMRs) per 100,000 population are standardized to the 2000 U.S. standard population. The lines show trends for six different urbanization categories from 1999 to 2020: Large Central Metro (orange), Large Fringe Metro (blue), Medium Metro (light blue), Small Metro (purple), Micropolitan (Nonmetro) (green), and NonCore (Nonmetro) (dark green). Annual percent change (APC) values with 95% confidence intervals (CI) are displayed in the legend for key time periods, with an asterisk (*) indicating statistical significance at p \u0026lt; 0.05. The data reveals an overall increase in mortality rates across all urbanization categories, with a particularly sharp rise observed from 2018 to 2020. The Micropolitan (Nonmetro) and Non-Core (Nonmetro) areas showed significant increases in mortality rates during the latter part of the study period. Data source: CDC WONDER Multiple Cause-of-Death database, 1999–2020.\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7586042/v1/31255d9e8d5ed83ec91b1302.jpg"},{"id":91151671,"identity":"db1e29c4-c4f5-4193-bd83-d1a93154d64a","added_by":"auto","created_at":"2025-09-12 07:18:20","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":56707,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLiver Disease–Hypertension Comorbid Mortality, by Place of Death, United States, 1999–2023\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLegend: This figure displays the total number of deaths by place of death. The horizontal bar graph shows that most deaths occurred in Decedent's home and Medical Facility - Inpatient. Other places of death include Medical Facility - Outpatient or ER, Hospice facility, Nursing home/long term care, and Other. The data represents liver disease-hypertension comorbid mortality from 1999–2023.\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7586042/v1/144d5803ff160f9eda469daa.jpg"},{"id":92594317,"identity":"bb8b6d8c-6ea1-4061-9a4b-800d9a125641","added_by":"auto","created_at":"2025-10-01 12:47:04","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1766938,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7586042/v1/f4fee4ee-3b5e-4301-bcbf-a22e44893892.pdf"},{"id":91151684,"identity":"679b8e47-f5a3-43ee-9494-d3f1976e1dc9","added_by":"auto","created_at":"2025-09-12 07:18:21","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":4502787,"visible":true,"origin":"","legend":"","description":"","filename":"3SeptCDCsupplementaryfiles1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7586042/v1/44e080c15146e6a5514d4687.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"A 25-Year Analysis of Liver Disease–Hypertension Comorbid Mortality Trends in the United States, 1999–2023","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eLiver disease and hypertensive disorders are significant contributors to global mortality and account collectively for more than 3.3\u0026nbsp;million deaths [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In 2021, liver disease was responsible for approximately 1.26\u0026nbsp;million deaths, which represented a 13% increase since 1990 [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], whereas hypertensive heart disease resulted in approximately 1.33\u0026nbsp;million fatalities [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Hypertension affects more than 1\u0026nbsp;billion individuals worldwide [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The global burden of metabolic dysfunction-associated steatotic liver disease (MASLD) has reached epidemic proportions and impacts approximately 25% of the global population [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Standard mortality reports generally address these two conditions separately, which may limit understanding of deaths where both conditions are documented.\u003c/p\u003e\u003cp\u003eClinical studies show that liver disease and hypertension frequently appear together in patient populations. In MASLD, previously known as nonalcoholic fatty liver disease (NAFLD), the prevalence of hypertension typically ranges from 39% to 69%. A meta-analysis of 86 studies reported a prevalence of 39.34% and identified a 1.4-fold increased risk compared with individuals without liver disease [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The relationship between MASLD and cardiovascular disease has been documented, with MASLD patients who exhibit elevated cardiovascular mortality [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Among individuals with hypertension, the prevalence of MASLD approaches 50%, nearly double the global average of approximately 25% [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In cases of cirrhosis, systemic hypertension is less common due to hyperdynamic physiology, whereas portal hypertension affects most patients prior to symptom onset [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Portal hypertension, a significant complication of advanced liver disease, serves as a prognostic marker in patients with cirrhosis [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Multimorbidity datasets demonstrate frequent clusters of liver disease, hypertension, and metabolic disorders [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Study design and data sources\u003c/h2\u003e\u003cp\u003eWe conducted a retrospective, population-based analysis of U.S. mortality data from the Multiple Cause of Death (MCOD) Database for 1999 to 2023, consistent with STROBE guidelines [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2. Study population and case definitions\u003c/h2\u003e\u003cp\u003eWe included decedents aged 25 years or older to capture the primary age range for chronic liver disease and hypertension mortality and exclude younger deaths that may represent different etiologies. Underlying and contributing causes of death used International Classification of Diseases 10th Revision (ICD-10) codes, which the U.S. has used since 1999 [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eLiver diseases used ICD-10 codes K70-K76: alcoholic liver disease (K70), toxic liver disease (K71), hepatic failure (K72), chronic hepatitis (K73), fibrosis and cirrhosis of the liver (K74), other inflammatory liver diseases (K75), other diseases of the liver (K76). Hypertensive diseases included codes I10\u0026ndash;I15: essential hypertension (I10), hypertensive heart disease (I11), hypertensive chronic kidney disease (I12), hypertensive heart and chronic kidney disease (I13), and secondary hypertension (I15). Pulmonary hypertension (I27.x) was excluded as it represents a distinct pathophysiological entity primarily related to pulmonary vascular disease rather than systemic hypertension.\u003c/p\u003e\u003cp\u003eThe primary outcome was death certificates that listed at least one liver disease code (K70\u0026ndash;K76) and at least one hypertensive disease code (I10\u0026ndash;I15), regardless of whether either was the underlying cause of death. This approach captures deaths where both conditions were documented by certifying physicians.\u003c/p\u003e\u003cp\u003eRace and ethnicity classifications used National Center for Health Statistics (NCHS) bridged-race categories: non-Hispanic (NH) White, non-Hispanic Black, NH American Indian or Alaska Native, NH Asian or Pacific Islander, and Hispanic [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3. Outcome measures and rate calculation\u003c/h2\u003e\u003cp\u003eThe primary measure was age-adjusted mortality rates (AAMRs) per 100,000 population, calculated overall and stratified by sex, race/ethnicity, U.S. Census region, state, and urbanization category. Rates were directly standardized to the 2000 U.S. standard population [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. For AAMR 95% confidence intervals, we used intervals provided by CDC WONDER, which employs the gamma method for age-adjusted rates [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4. Temporal trend analysis\u003c/h2\u003e\u003cp\u003eTemporal trends in co-listing rates used Joinpoint Regression Program version 5.0.2. A log-linear model with heteroscedastic errors and permutation tests allowed up to 4 joinpoints to estimate annual percent changes (APCs) with 95% confidence intervals for each segment. Average annual percent changes (AAPCs) summarized overall trends across the full study period, consistent with standard Joinpoint methodology [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e\u003cb\u003e2.5. Recent trend analysis\u003c/b\u003e\u003c/h2\u003e\u003cp\u003eWe compared mortality rates from 2020 to 2023 with a pooled baseline from 2017 to 2019 and calculated rate ratios (RRs) and absolute rate differences. Confidence intervals for rate ratios used the delta method in R statistical software [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. The 2017\u0026ndash;2019 period served as baseline to establish a pre-2020 reference period.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6. Geographic analysis\u003c/h2\u003e\u003cp\u003eAAMRs were compiled by U.S. Census regions and states. Regional and state APCs used identical joinpoint configurations. Urban and rural area classifications used the 2013 NCHS Urban\u0026ndash;Rural Classification [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e], and Census regions and divisions used Census Bureau standards [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.7. Data quality, software, and ethics\u003c/h2\u003e\u003cp\u003eCounts below 10 were suppressed per CDC WONDER policy; rates based on fewer than 20 deaths were flagged as statistically unreliable. AAMRs and confidence intervals used CDC WONDER; temporal trends used Joinpoint v5.0.2; rate ratios and absolute rate differences used spreadsheet software [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. The dataset is publicly available and deidentified; therefore, institutional review board approval was not required [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.1. National Temporal Trends\u003c/h2\u003e\u003cp\u003eBetween 1999 and 2023, a total of 208,666 deaths were recorded with both liver disease and hypertension codes listed (Supplementary Table\u0026nbsp;1). Annual deaths increased from 2,307 in 1999 to 18,769 in 2023. The national age-adjusted mortality rate (AAMR) rose from 1.30 per 100,000 (95% CI: 1.25\u0026ndash;1.36) to 6.75 per 100,000 (95% CI: 6.66\u0026ndash;6.85) over the same period (Supplementary Table\u0026nbsp;3).\u003c/p\u003e\u003cp\u003eBy 2019, the AAMR reached 4.59 per 100,000 (95% CI: 4.50\u0026ndash;4.67), a 253% increase from 1999. The largest short-term rise occurred between 2019 and 2021, when rates increased from 4.59 to 6.70 per 100,000 (95% CI: 6.60\u0026ndash;6.80)\u0026mdash;a 46% jump. From 2021 to 2023, AAMRs showed little additional change and remained at 6.75 per 100,000 (95% CI: 6.66\u0026ndash;6.85) in 2023.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.2. Temporal Phase Analysis\u003c/h2\u003e\u003cp\u003eJoinpoint regression identified four distinct temporal phases in national mortality trends (Supplementary Table\u0026nbsp;2). The age-adjusted mortality rate increased at an annual percent change (APC) of +\u0026thinsp;19.50% (95% CI: 7.57\u0026ndash;32.76) from 1999 to 2001. Between 2001 and 2018, the increase continued at a slower pace with an APC of +\u0026thinsp;4.27% (95% CI: 4.00\u0026ndash;4.63).\u003c/p\u003e\u003cp\u003eFrom 2018 to 2021, the APC rose to +\u0026thinsp;17.64% (95% CI: 12.42\u0026ndash;23.11). No statistically significant change occurred between 2021 and 2023 (APC\u0026thinsp;+\u0026thinsp;0.28%, 95% CI: \u0026minus;3.56\u0026ndash;4.29). The average annual percent change (AAPC) across the entire 1999\u0026ndash;2023 period was +\u0026thinsp;6.71% (95% CI: 5.64\u0026ndash;7.80). All APCs except the 2021\u0026ndash;2023 period were statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.3. Recent Trends (2019\u0026ndash;2023)\u003c/h2\u003e\u003cp\u003eBetween 2019 and 2020, the national AAMR increased from 4.59 per 100,000 (95% CI: 4.50\u0026ndash;4.67) to 5.86 per 100,000 (95% CI: 5.77\u0026ndash;5.95). Annual deaths rose from 12,230 to 15,709 (Supplementary Table\u0026nbsp;1). Mortality rates remained elevated through 2023, with AAMRs of 6.69 per 100,000 (95% CI: 6.59\u0026ndash;6.79) in 2022 and 6.75 per 100,000 (95% CI: 6.66\u0026ndash;6.85) in 2023. Annual deaths reached 18,769 in 2023.\u003c/p\u003e\u003cp\u003eCompared with pooled 2017\u0026ndash;2019 rates, mortality rates were 1.36 times higher in 2020, 1.56 times higher in 2021\u0026ndash;2022, and 1.57 times higher in 2023 (Supplementary Table\u0026nbsp;3). Joinpoint regression showed no statistically significant change in national mortality rates between 2021 and 2023 (APC\u0026thinsp;+\u0026thinsp;0.28%, 95% CI: -3.56 to 4.29). Subgroup analyses revealed non-significant changes for NH White (+\u0026thinsp;1.54%), NH Black (-2.21%), and Hispanic (-3.89%) adults, while the NH Others category had a significant increase (APC\u0026thinsp;+\u0026thinsp;7.24%, 95% CI: 4.22\u0026ndash;10.34) (Supplementary Table\u0026nbsp;2).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.4. Demographic disparities\u003c/h2\u003e\u003cdiv id=\"Sec15\" class=\"Section3\"\u003e\u003ch2\u003e3.4.1. Sex differences\u003c/h2\u003e\u003cp\u003eIn 2023, death certificates that listed both conditions had higher rates among males (9.02 per 100,000; 95% CI: 8.85\u0026ndash;9.19) compared to females (4.71 per 100,000; 95% CI: 4.60\u0026ndash;4.83). Males accounted for 11,693 deaths (62.3%) while females accounted for 7,076 deaths (37.7%) (Supplementary Tables\u0026nbsp;1 and 3).\u003c/p\u003e\u003cp\u003eOver the 25-year period, males had an AAPC of +\u0026thinsp;7.07% (95% CI: 5.69\u0026ndash;8.47) while females had an AAPC of +\u0026thinsp;6.49% (95% CI: 5.07\u0026ndash;7.92). Both sexes had steady increases from 2001\u0026ndash;2018: males increased 4.50% annually (95% CI: 4.17\u0026ndash;4.83) and females 3.81% annually (95% CI: 3.44\u0026ndash;4.18). From 2018\u0026ndash;2021, rates accelerated for both groups. No significant changes occurred from 2021\u0026ndash;2023 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section3\"\u003e\u003ch2\u003e3.4.2. Race and Ethnicity\u003c/h2\u003e\u003cp\u003eIn 2023, co-listing rates varied substantially across racial and ethnic groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; Supplementary Table\u0026nbsp;5). Non-Hispanic American Indian or Alaska Native individuals had the highest rates at 12.74 per 100,000 (95% CI: 11.38\u0026ndash;14.09). Hispanic individuals followed at 8.15 per 100,000 (95% CI: 7.82\u0026ndash;8.48). Non-Hispanic White individuals recorded 6.95 per 100,000 (95% CI: 6.84\u0026ndash;7.06) and represented the largest absolute number with 13,101 deaths.\u003c/p\u003e\u003cp\u003eNon-Hispanic Black individuals had rates of 6.84 per 100,000 (95% CI: 6.55\u0026ndash;7.14), while Non-Hispanic Asian or Pacific Islander individuals had the lowest rates at 3.05 per 100,000 (95% CI: 2.77\u0026ndash;3.34). The 25-year AAPC varied by race/ethnicity: Non-Hispanic White adults\u0026thinsp;+\u0026thinsp;7.53% (95% CI: 6.19\u0026ndash;8.89), Hispanic adults\u0026thinsp;+\u0026thinsp;4.12% (95% CI: 2.57\u0026ndash;5.69), Non-Hispanic Black adults\u0026thinsp;+\u0026thinsp;3.11% (95% CI: 1.22\u0026ndash;5.04), and Non-Hispanic Others\u0026thinsp;+\u0026thinsp;3.42% (95% CI: 1.95\u0026ndash;4.91).\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e3.6. Geographic Patterns\u003c/h2\u003e\u003cdiv id=\"Sec18\" class=\"Section3\"\u003e\u003ch2\u003e3.6.1. Regional Analysis\u003c/h2\u003e\u003cp\u003eIn 2023, co-listing rates differed across U.S. Census regions. The South had the highest rates at 17.75 per 100,000 (95% CI: 17.50-18.01). The West followed at 16.65 (95% CI: 16.33\u0026ndash;16.97), then the Midwest at 15.21 (95% CI: 14.89\u0026ndash;15.53), and the Northeast at 13.78 (95% CI: 13.46\u0026ndash;14.11) (Supplementary Table\u0026nbsp;6).\u003c/p\u003e\u003cp\u003eRegional AAPC values over 25 years were: South\u0026thinsp;+\u0026thinsp;8.11% (95% CI: 7.24\u0026ndash;8.99), Northeast\u0026thinsp;+\u0026thinsp;7.43% (95% CI: 5.91\u0026ndash;8.97), West\u0026thinsp;+\u0026thinsp;6.96% (95% CI: 6.08\u0026ndash;7.85), and Midwest\u0026thinsp;+\u0026thinsp;6.59% (95% CI: 5.68\u0026ndash;7.50). All regions had increases during 2017\u0026ndash;2021, with varying patterns in recent years (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e; Supplementary Table\u0026nbsp;2).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section3\"\u003e\u003ch2\u003e3.6.2. State-level Analysis\u003c/h2\u003e\u003cp\u003eIn 2023, state-level co-listing rates varied widely (Supplementary Tables\u0026nbsp;5 and 9). Rates ranged from approximately 3 per 100,000 in some Northeastern states to over 20 per 100,000 in some South-Central states (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). These patterns may reflect differences in healthcare systems, population demographics, and documentation practices across states.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section3\"\u003e\u003ch2\u003e3.6.3. Urban-Rural Disparities\u003c/h2\u003e\u003cp\u003eIn 2020, co-listing rates varied across urbanization categories. Rates were lowest in large fringe metropolitan areas (3.9 per 100,000; 95% CI: 3.8-4.0) and highest in micropolitan areas (6.1 per 100,000; 95% CI: 5.8\u0026ndash;6.4). Intermediate values occurred across other metropolitan categories (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e; Supplementary Table\u0026nbsp;8).\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003e3.7. Place of Death Patterns\u003c/h2\u003e\u003cp\u003ePlace of death changed between 2019 and 2023 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e; Supplementary Table\u0026nbsp;7). Deaths at home increased from 4,755 in 2019 to 7,636 in 2023. Hospice facility deaths rose from 1,007 to 1,698. Inpatient hospital deaths increased from 3,500 in 2019 to 5,600 in 2021, then declined to 5,370 in 2023. Deaths in nursing homes or long-term care facilities increased from 1,507 to 1,956. These patterns accompanied the overall increase in total deaths from 12,230 in 2019 to 18,769 in 2023.\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cdiv id=\"Sec23\" class=\"Section2\"\u003e\u003ch2\u003e4.1. Principal Findings\u003c/h2\u003e\u003cp\u003eOver twenty-five years, deaths that listed both liver disease and hypertension codes increased from 2,307 to 18,769. The national AAMR rose from 1.30 to 6.75 per 100,000 individuals, with an overall AAPC of +\u0026thinsp;6.71% (95% CI: 5.64\u0026ndash;7.80). Joinpoint analysis identified four distinct segments: rapid increase (1999\u0026ndash;2001), steady rise (2001\u0026ndash;2018), pronounced acceleration (2018\u0026ndash;2021), and stabilization (2021\u0026ndash;2023).\u003c/p\u003e\u003cp\u003eThe increase from 2019 to 2021 was evident in both rates and counts. Rate ratios relative to the baseline period of 2017\u0026ndash;2019 remained above 1.5 throughout 2023. These documentation patterns occurred when metabolic disorders and cardiometabolic risk factors gained increased recognition in liver-related outcomes [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], though this analysis examines co-listing patterns rather than clinical causality.\u003c/p\u003e\u003cp\u003eGlobally, these temporal changes align with NAFLD's emergence as a common cause of chronic liver disease. Prevalence increased from 10.5% in 1990 to 16.0% in 2019 among all age groups, representing over 1.2\u0026nbsp;billion cases worldwide [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The acceleration in co-listing during the early 2000s also coincides with metabolic syndrome recognition within clinical guidelines [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e] and NAFLD's emergence as a prominent hepatic condition [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThese co-listing patterns may reflect complex interactions between evolving diagnostic practices, population health trends, documentation practices, and increased recognition of multimorbidity in clinical care. Global burden patterns show regional variations in disease etiology and progression [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. However, the extent to which these trends represent changes in disease patterns versus documentation practices remains uncertain and requires further investigation.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\u003ch2\u003e4.2. Demographic Disparities\u003c/h2\u003e\u003cp\u003eIn 2023, males had approximately twofold higher co-listing rates compared to females, consistent with previous findings in liver disease mortality surveillance [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. From 2018 to 2021, APCs increased for both sexes, with females having a marginally higher APC. This occurred during a period of reported increases in alcohol-related harm and metabolic risks within the female demographic [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. These sex-based differences in co-listing patterns may be influenced by multiple factors: biological susceptibility, behavioral patterns, healthcare utilization practices, and documentation patterns [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eGlobally, liver disease mortality varies by sociodemographic characteristics. Low- and middle-income countries experience burdens of viral hepatitis, whereas high-income regions face increasing rates of NAFLD and alcohol-related liver disease [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eRacial and ethnic differences in co-listing rates persisted. Non-Hispanic American Indian or Alaska Native adults had the highest rates in 2023. Joinpoint regression revealed a significant increase from 2000\u0026ndash;2017, a nonsignificant rise from 2017\u0026ndash;2020, and a nonsignificant decline from 2020\u0026ndash;2023. This trajectory indicates sustained elevation in this group despite recent stabilization.\u003c/p\u003e\u003cp\u003eHispanic adults had significant increases until 2018, a further rise from 2018\u0026ndash;2021, and a nonsignificant decline thereafter. Non-Hispanic White and Non-Hispanic Black adults had similar 2023 rates, with modest or negative APCs between 2021 and 2023.\u003c/p\u003e\u003cp\u003eThese patterns may reflect multiple factors: access to healthcare, socioeconomic status, underlying health conditions, and documentation practices [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. The relationship between race/ethnicity and co-listing patterns likely reflects multiple intersecting factors that encompass historical inequities, social determinants of health, and healthcare access [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. The pattern among non-Hispanic Black individuals may reflect competing risk factors, diagnostic pathways, or documentation differences. Prior research suggests multiple explanations [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. However, these interpretations remain hypotheses that require further investigation, as this analysis cannot distinguish between disease patterns and documentation variations.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec25\" class=\"Section2\"\u003e\u003ch2\u003e4.3. Geographic Patterns\u003c/h2\u003e\u003cp\u003eGeographic variation occurred across regions and states. Higher rates in parts of the southern and western regions appeared in areas with documented gradients in obesity, diabetes, and alcohol-related outcomes, as well as differences in policy and environmental factors [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. These regional variations may also reflect differences in healthcare infrastructure, provider availability, state-level health policies, and documentation practices, along with geographic disparities in access to specialized liver care [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eRural areas had higher rates compared to large metropolitan regions, which aligns with documented barriers in specialty healthcare access, transportation, and chronic disease diagnosis [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. The urban-rural gradient may represent access-related factors, underlying population health differences, documentation practices, or combinations thereof. These patterns warrant further investigation, particularly given the concentration of specialized hepatology services in urban centers and potential variations in death certificate completion practices across different healthcare settings.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec26\" class=\"Section2\"\u003e\u003ch2\u003e4.4. Recent Trends (2019\u0026ndash;2023)\u003c/h2\u003e\u003cp\u003eFrom 2019 to 2023, deaths that listed both conditions increased by 53%. A notable rise during 2020\u0026ndash;2021 persisted through 2023. The distribution of deaths by place shifted toward residences and hospice facilities. These changes occurred during a period characterized by reports of care delays, service reallocation, and disruptions in chronic disease management [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThis period coincided with documented challenges for chronic liver disease patients, including increased mortality risks and delayed care across both viral and nonviral liver diseases. Global data show similar patterns of healthcare disruption [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Healthcare disruptions during this period were multifaceted: delayed screening, reduced access to specialized care, and reported increases in alcohol consumption in some populations. Studies documented changes in outcomes related to alcohol-induced liver conditions during this period [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], and multimorbidity was associated with increased mortality risk [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Patients with cirrhosis had higher rates of complications and mortality, with decompensated cirrhosis associated with the highest risk [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eWhile these temporal associations are noteworthy, our study design documents co-listing trends rather than causal mechanisms. The increases may reflect multiple factors: changes in disease patterns, healthcare delivery, documentation practices, or combinations thereof. These merit further investigation through dedicated studies designed to examine causal pathways.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec27\" class=\"Section2\"\u003e\u003ch2\u003e4.5. Value of Multiple-Cause Surveillance\u003c/h2\u003e\u003cp\u003eMCOD analysis provides additional surveillance value beyond single-cause tabulations; however, it primarily reflects what certifiers document rather than clinical disease interactions. Documentation practices vary across settings and over time [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Single-cause tabulations may underestimate deaths where multiple chronic diseases are present. Notable differences, ranging from two- to three-fold, occur when using any-mention definitions compared to underlying cause only [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eDocumentation practices may influence these estimates, as variations in multiple-cause listing and certifier behavior have been documented [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Techniques to weight multiple causes or summarize MCOD patterns are available and can serve as sensitivity analyses [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. MCOD data provides a useful surveillance indicator for examining co-occurrence patterns in mortality data.\u003c/p\u003e\u003cp\u003eHowever, results should be interpreted cautiously due to inherent limitations of death certificate documentation. These include potential variations in coding practices, diagnostic recognition, and documentation completeness across different healthcare settings and time periods. Ongoing methodological development aims to enhance the analysis and interpretation of multiple causes of death data [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec28\" class=\"Section2\"\u003e\u003ch2\u003e4.6. Surveillance and Research Implications\u003c/h2\u003e\u003cp\u003eThe scale of co-listing patterns suggests potential areas for enhanced surveillance and research. While our findings reflect documentation patterns rather than validated clinical interactions, they may inform surveillance strategies and hypothesis generation for future studies. Given the observational nature of our study and its reliance on death certificate documentation, these findings should be interpreted cautiously and cannot directly inform clinical practice guidelines.\u003c/p\u003e\u003cp\u003eThe documented co-listing patterns may warrant further investigation through prospective clinical studies designed to examine whether integrated assessment approaches could benefit patients with multiple chronic conditions. Such research could explore systematic screening protocols, care coordination models, and shared decision-making tools, consistent with established frameworks for multimorbidity care [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. However, the effectiveness of such approaches would require validation through dedicated clinical trials before implementation.\u003c/p\u003e\u003cp\u003eAt the population level, areas with elevated rates may benefit from enhanced surveillance systems and research into underlying factors. These could include studies that examine healthcare access, documentation practices, and population health determinants. The documented geographic variation in co-listing rates suggests the need for targeted surveillance efforts, particularly given documented disparities in access to specialized care.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec29\" class=\"Section2\"\u003e\u003ch2\u003e4.7. Limitations\u003c/h2\u003e\u003cp\u003eThis study has several important limitations. Death certificates may underreport or misclassify conditions, and multiple cause co-listing reflects what certifiers document at the time of death rather than lifelong disease interactions. This fundamental limitation means that patterns may reflect coding practices, end-of-life documentation patterns, or temporal changes in diagnostic recognition rather than actual disease relationships, potentially leading to both over- and under-estimation of co-occurrence.\u003c/p\u003e\u003cp\u003eDocumentation practices vary across settings and among certifiers, which can introduce selection or documentation bias [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Socioeconomic factors may influence both disease development and the certification process (location of death, certifier identity, recorded details), meaning estimates may reflect access and documentation factors alongside biological considerations [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Additionally, coding modifications, evolving diagnostic awareness, and changing healthcare access from 1999 to 2023 may impact trends [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Although ICD-10 coding provides standardized definitions, the specificity of liver disease (K70\u0026ndash;K76) and hypertension (I10\u0026ndash;I15) categories may not fully capture disease subtypes, and misclassification remains possible.\u003c/p\u003e\u003cp\u003eData reliability varied across racial and ethnic groups, with additional complications from classification changes over time. Race and ethnicity classifications changed during the study period, with bridged-race categories used from 1999\u0026ndash;2009 and single-race classifications from 2010\u0026ndash;2023. Asian and Pacific Islander populations were combined pre-2010 but separated thereafter. These classification changes may contribute to apparent temporal trends and limit the precision of long-term racial/ethnic comparisons.\u003c/p\u003e\u003cp\u003eFor NH American Indian or Alaska Native populations, mortality counts were unstable in 1999, requiring analyses from 2000\u0026ndash;2023. For Asian or Pacific Islander populations, early years had small counts and wide confidence intervals, limiting precision. These differences mean long-term comparisons across groups should be interpreted cautiously, as early instability and methodological changes may exaggerate temporal shifts. Small population states had frequent data suppression, creating geographic bias toward larger states and limiting rural health assessment.\u003c/p\u003e\u003cp\u003eGeographic comparisons are ecological and cannot assign area-level factors to individuals. Results emphasize mortality rather than incidence or nonfatal disease [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. Small number suppression and \"unreliable\" flags may introduce imprecision. Rate-ratio confidence intervals used approximation methods rather than formal time-series approaches. Given extensive stratification, multiple testing adjustments were not performed; subgroup findings should be interpreted as descriptive rather than definitive hypothesis tests.\u003c/p\u003e\u003cp\u003eFinally, competing risks may influence patterns: co-listing rates could appear lower in populations with high competing cardiovascular mortality. These limitations necessitate cautious interpretation and highlight the need for complementary study designs.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec30\" class=\"Section2\"\u003e\u003ch2\u003e4.8. Research Priorities\u003c/h2\u003e\u003cp\u003eFuture research should focus on methodological advances to better distinguish documentation artifacts from clinically meaningful patterns in MCOD data. Validation studies that integrate MCOD with clinical registries, claims data, or longitudinal cohorts could improve accuracy and enable causal inference. Machine learning methods could help identify meaningful co-occurrence patterns within MCOD data, consistent with recent methodological advances [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eProspective cohort studies are needed to examine potential causal pathways and clinical relationships that cannot be assessed through death certificate analysis. Health services research could evaluate integrated care models while accounting for the limitations identified in surveillance data. International collaborative research may help understand regional variations in both disease patterns and documentation practices [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe temporal changes during 2018\u0026ndash;2023 warrant dedicated investigation through studies designed to examine multiple potential explanations: healthcare delivery changes, behavioral modifications, and documentation practice evolution. Such research should use study designs capable of distinguishing between these competing explanations.\u003c/p\u003e\u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eDeaths that listed both liver disease and hypertension codes increased substantially from 1999 to 2023 (AAPC\u0026thinsp;+\u0026thinsp;6.71%), with notable acceleration during 2018\u0026ndash;2021 and subsequent stabilization through 2023. Geographic and demographic variation in co-listing patterns occurred. While these findings reflect documentation patterns rather than validated disease interactions, they provide valuable surveillance insights into evolving mortality trends.\u003c/p\u003e\u003cp\u003eMCOD-based surveillance, despite inherent limitations in death certificate documentation, offers a population-level perspective on co-occurrence patterns that complements other surveillance approaches. The development of enhanced analytical methods and validation studies could improve understanding of the relationship between documentation patterns and underlying health trends. Continued surveillance of these patterns, combined with methodologically appropriate research designs, may inform our understanding of evolving mortality trends and guide future epidemiological investigations.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"601\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCDC WONDER\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCenters for Disease Control and Prevention\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eWide-Ranging Online Data for\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eEpidemiologic Research\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAAMR\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAge-Adjusted Mortality Rates\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAPC\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAnnual Percentage Change\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAAPC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAverage Annual Percentage change\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCI\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eConfidence Interval\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMASLD\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMetabolic dysfunction-associated steatotic\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eliver disease (MASLD)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNAFLD\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNonalcoholic fatty liver disease (NAFLD),\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMCOD\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMultiple Cause of Death \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSTROBE\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eStrengthening the Reporting of\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eObservational Studies in Epidemiology\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNCHS\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNational Center for Health Statistics\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRR\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRate Ratio \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eARD\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAbsolute rate differences\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003eData availability\u003c/p\u003e\n\u003cp\u003eThe dataset supporting the conclusions of this article is \u0026nbsp;included in its additional file\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone of the authors have any financial disclosures and no funding was received for this project. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eContributions \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eS.P. and S.Q. contributed to the study conception and design. S.P. and H.P. extracted the data, and S.Q. and E.A. performed all the statistical analyses. S.P. and H.P. generated the figures. E.A., S.Q., S.P., H.P., and M.A.M. drafted the manuscript and prepared successive revisions. A.O. supervised the project, provided critical reviews, and managed the administration. All the authors have read and approved the final manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Information \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe supporting tables and graphs can be found in the supplementary file.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRights and permissions \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInstitutional Review Board Statement:\u0026nbsp;\u003c/strong\u003eEthical review and approval were waived for this study as we used publicly available deidentified mortality data from the CDC WONDER database. No individual consent was required because the study used aggregate, deidentified data.\u0026nbsp;This \u0026nbsp;study \u0026nbsp; adhered to the principles of the Declaration of Helsinki, and all analyses were conducted in accordance with CDC guidelines for mortality data use and interpretation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent Statement:\u003c/strong\u003e Not applicable. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement:\u003c/strong\u003e All data are publicly available at\u0026nbsp;https://wonder.cdc.gov/. \u003cstrong\u003eAcknowledgments:\u003c/strong\u003e The authors acknowledge the Centers for Disease Control and Prevention for maintaining the WONDER database and providing public access to vital statistics. \u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eDevarbhavi H, Asrani SK, Arab JP, Nartey YA, Pose E, Kamath PS. 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International Journal of Molecular Sciences. 2023 Nov 27;24(23):16805. https://doi.org/10.3390/ijms242316805 \u003c/li\u003e\n\u003cli\u003eFine JP, Gray RJ. A proportional hazards model for the sub-distribution of a competing risk. \u003cem\u003eJ Am Stat Assoc.\u003c/em\u003e 1999;94(446):496\u0026ndash;509. https://doi.org/10.1080/01621459.1999.10474144 \u003c/li\u003e\n\u003cli\u003eGrundy SM, Brewer Jr HB, Cleeman JI, Smith Jr SC, Lenfant C. Definition of metabolic syndrome: report of the National Heart, Lung, and Blood Institute/American Heart Association conference on scientific issues related to definition. Circulation. 2004 Jan 27;109(3):433-8. https://doi.org/10.1161/01.CIR.0000111245.75752.C6 \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"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":"Liver disease, hypertension, mortality trends, multiple cause of death, health surveillance, epidemiology, United States","lastPublishedDoi":"10.21203/rs.3.rs-7586042/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7586042/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eDeath certificates frequently list both liver disease and hypertension codes, yet trends in this co-occurrence have been underexplored. This study analyzed 25 years of U.S. data to identify patterns in deaths that list both conditions.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWe conducted a descriptive analysis of mortality data from CDC WONDER for 1999\u0026ndash;2023, focusing on adults aged\u0026thinsp;\u0026ge;\u0026thinsp;25 years. Deaths that listed both liver diseases (K70\u0026ndash;K76) and hypertensive diseases (I10\u0026ndash;I15) were identified using ICD-10 codes. Age-adjusted mortality rates (AAMRs) per 100,000 were standardized to the 2000 U.S. population. Temporal trends were assessed with Joinpoint regression, which estimated annual percent changes (APCs) with 95% confidence intervals. Analyses were stratified by age, sex, race/ethnicity, region, state, and urbanization level.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eBetween 1999 and 2023, deaths with both conditions listed increased from 2,307 to 18,769, totaling 208,666 deaths. The national AAMR rose from 1.30 per 100,000 in 1999 to 6.75 in 2023, with an average annual percent change (AAPC) of +\u0026thinsp;6.71% (95% CI: 5.64\u0026ndash;7.80). Joinpoint regression identified four phases: rapid early growth (1999\u0026ndash;2001, APC 19.50%), steady increase (2001\u0026ndash;2018, APC 4.27%), sharp acceleration (2018\u0026ndash;2021, APC 17.64%), and stabilization (2021\u0026ndash;2023, APC 0.28%). In 2023, males had higher rates than females (9.02 vs. 4.71 per 100,000). Non-Hispanic (NH) American Indian or Alaska Native populations had the highest rates (12.74 per 100,000), followed by Hispanic (8.15), NH White (6.95), and NH Black (6.84), while NH Asian or Pacific Islander individuals had the lowest (3.05). Geographic variation was substantial, with rates ranging from 2.87 (Connecticut) to 21.54 (Oklahoma) per 100,000.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eDeaths that list both liver disease and hypertension codes increased substantially over 25 years (AAPC\u0026thinsp;+\u0026thinsp;6.71%), with notable acceleration during 2018\u0026ndash;2021. These documentation patterns warrant continued surveillance to understand evolving mortality trends.\u003c/p\u003e","manuscriptTitle":"A 25-Year Analysis of Liver Disease–Hypertension Comorbid Mortality Trends in the United States, 1999–2023","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-12 07:18:15","doi":"10.21203/rs.3.rs-7586042/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":"fdf79e70-1f60-4f9f-8564-14c790a320c6","owner":[],"postedDate":"September 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-01T12:38:53+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-12 07:18:15","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7586042","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7586042","identity":"rs-7586042","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}