Epidemiology of Unspecified Degenerative Nervous System Disorders: Mortality Trends via CDC WONDER (1999-2025)

Epidemiology of Unspecified Degenerative Nervous System Disorders: Mortality Trends via CDC WONDER (1999-2025) | 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 Epidemiology of Unspecified Degenerative Nervous System Disorders: Mortality Trends via CDC WONDER (1999-2025) Palwasha Asghar, Muhammad Jawad, Kinza Irshad, Wajeeha Iftikhar Shah, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8958661/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 27 Apr, 2026 Read the published version in BMC Neurology → Version 1 posted 16 You are reading this latest preprint version Abstract Background: Unspecified degenerative nervous system disorders (ICD-10 G31.9) increasingly contribute to neurological mortality, yet national temporal patterns remain unclear. Methods: We analyzed U.S. mortality data (1999–2025) from CDC WONDER. Age-adjusted mortality rates (AAMRs) were analyzed using Joinpoint regression to estimate annual percent changes (APCs), stratified by sex, race, urbanization, and census regions. Sensitivity analyses assessed stability. Results: Overall, AAMR increased from 1999 to 2011 (APC 1.34%) and sharply from 2011–2014 (15.79%), declined from 2014 to 2018 (− 3.14%), rose from 2018–2021 (13.38%), and decreased thereafter (− 1.89%). Females showed early increases, whereas males were stable before 2011. Racial and regional analyses revealed heterogeneity, with post-2010 increases in several groups. Metropolitan areas showed persistent increases, while nonmetropolitan trends fluctuated. Sensitivity analyses confirmed findings. Conclusion: Mortality from G31.9 disorders shows changing trends, varying across demographics and regions. These differences highlight gaps in diagnosis and care, emphasizing the need for targeted public health strategies. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction The ICD-10 code G31.9 designates unspecified degenerative nervous system disorders, including senile brain degeneration and other progressive neural atrophies that lack defined medical classifications. The US population faces significant health impacts from these disorders because more than 54% of Americans experience neurological diseases, according to a recent study [1]. The aging population has driven up disease rates, which increased disability-adjusted life years from 1990 to 2021 by 55% [2]. The disease cycle starts with continuous nerve cell death resulting in cognitive deterioration, motor dysfunction, and patient dependence, while cardiovascular disease and other health conditions worsen the symptoms. The age-standardized prevalence rates for neurodegenerative diseases showed a 16.2% increase in Parkinson's disease from 1990 to 2017, which indicates a larger societal pattern. The progressive nature of this condition creates higher demands for caregivers while driving up national healthcare costs [1]. The condition remains underdiagnosed because of its unusual symptoms resembling other illnesses and because diagnostic testing takes multiple years to complete in NMOSD cases and similar disorders. Advanced imaging technologies and biomarker tests remain inaccessible to many patients, creating obstacles for their identification. The condition remains untreated because its existence remains hidden from recognition, leading to worse health results [3].​ The age-adjusted mortality rates (AAMR) for neurological diseases experienced a substantial decline from 98.6 per 100,000 in 1999 to 84.2 in 2013 because of better medical treatments. Neurodegenerative disorders experienced an increase from 16.9 to 36.8 by 2017. Unspecified degenerative nervous system disorders, which doctors classify with G31 codes, have become a major contributor to increasing healthcare expenses. Alzheimer's dementia shows a U-shaped mortality pattern, explaining the increasing health costs. The overall reductions create a persistent challenge, leading to increased regional effects of these unknown medical conditions [4]​. The pattern of death from unknown degenerative brain diseases shows that rural areas and nonmetropolitan areas experience higher death rates. The first group of people needs to travel up to 4.01 times longer to reach neurologists when compared to people who live in metropolitan areas. The first group of people needs to travel between 1.14 and 3.32 times longer to reach primary care doctors when compared to people who live in metropolitan areas. The problem of access to specialists exists because American Indian/Alaska Native and Hispanic groups face greater difficulties reaching medical experts, which leads to more cases of undiagnosed diseases and worse health results when compared to non-Hispanic White individuals. Nonmetropolitan residence establishes deep social inequalities reflecting the national pattern that shows rural areas experience higher death rates while facing restrictions on access to culturally competent medical services, which become especially critical during the rising death rates from Alzheimer's disease and related disorders in these communities [5]. This study uses Joinpoint regression on CDC WONDER data to analyze AAMR trends and study disorder patterns that affected the period from 1999 to 2025. The national strategies will use our findings about stagnant areas and distributional disparities to create more specialized treatment plans. The study results provide policy guidance to address the growing challenges in public health [6]. Methodology Data Source WE employed the CDC WONDER (Centers for Disease Control and Prevention Wide-Ranging Online Data for Epidemiologic Research) database for the evaluation of the mortalities that have happened within the United States related to unspecified degenerative nervous system disorders. We utilized the death certificates derived from the Multiple Cause‐of‐Death Public Use Record database and determined unspecified degenerative nervous system disorders as an underlying or contributing factor towards deaths ( 7 – 9 ). The CDC WONDER database has also been employed before in many other research studies for the analysis of temporal mortality trends related to unspecified degenerative nervous system disorders at the national level. Unspecified degenerative nervous system disorders-related mortalities were recognized using the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD‐10‐CM) codes G31.9, in individuals of all age groups. We enacted STROBE guidelines to maintain transparency and clarity in the analytical process. The study did not receive approval from a local institutional review board since it was using data from the CDC WONDER database, which involves publicly available de-identified data. Data Extraction Unspecified degenerative nervous system disorders-related mortalities and population sizes were retrieved for analysis from 1999 to 2025. Demographic variables such as sex, race and ethnicity, age, and geographic variables such as urban-rural, place of death, and state were also extracted as mentioned on the death certificates ( 8 , 10 , 11 ). Racial demographic characteristics were defined as Asian or Pacific Islander, Black or African American, Hispanic or Latino, and White. Racial and ethnic characteristics were used as categories in CDC WONDER data analyses, which depended largely on the information collected from death certificates. The National Center for Health Statistics Urban-Rural Classification Scheme was used to classify the study population into metropolitan and nonmetropolitan areas, per the 2013 US Census classification ( 12 ). Statistical Analysis: We determined crude and age-adjusted mortality (AAMR) rates per 1 million people. The crude mortality rate (CMR) for a given year was calculated by dividing the total number of unspecified degenerative nervous system disorders-related deaths by the corresponding US population for that year. AAMRs were determined by direct standardization of the unspecified degenerative nervous system disorders-related deaths to the US population for the corresponding year 1999, as already explained ( 13 ). In this study, 27 years were included. For trend description concerning CMR and AAMR for unspecified degenerative nervous system disorders-related mortality, the Joinpoint regression software package (version 4.9.0.0; National Cancer Institute) was used ( 14 ). Given the existing temporal trends in CMR and AAMR, log-linear regression models were established. The methodologies discussed in the literature were applied to find the inflection points of the temporal trends of CMR and AAMR for unspecified degenerative nervous system disorders between 1999 and 2025 using Joinpoint regression ( 15 ). To estimate the annual percent change (APC) and the associated 95% confidence intervals (CIs), we used the permutation test. APC was considered as increasing or decreasing if the slope describing the change in mortality was significantly different from zero, determined by two-tailed t testing. Statistical significance for both APC and AAPC was set at an alpha level of 0.05, meaning trends were considered significant if their corresponding p-values were less than 0.05. We executed sensitivity analyses as a method to verify their results. We conducted a second mortality analysis, which examined cases with unspecified degenerative nervous system disorders (ICD-10-CM code G31.9) as their primary cause of death. The research team conducted subgroup analyses that separated participants based on their gender and racial/ethnic backgrounds to study how temporal trends moved between different demographic groups. We confirmed trend stability through age-adjusted mortality rate calculations, which used different standard population structures to calculate their results. We conducted an examination of APC and AAPC estimates from various models to determine whether different case definitions and standardization methods affected their main research conclusions. Results Unspecified degenerative nervous system disorders (ICD-10 code G31.9) accounted for deaths across all ages in the United States from 1999 to 2025. Temporal analysis demonstrated multiple joinpoints, indicating fluctuating mortality trends over the study period (Central Illustration). Annual Trends for Degenerative Nervous System Disorder–Related AAMR The overall Age-Adjusted Mortality Rate (AAMR) showed four join points. From 1999 to 2011, mortality increased significantly (APC 1.34%). This was followed by a sharp, significant rise from 2011 to 2014 (APC 15.79%). Subsequently, the trend declined from 2014 to 2018 (APC − 3.14%). Mortality again increased significantly between 2018 and 2021 (APC 13.38%), followed by a decline from 2021 to 2025 (APC − 1.89%) (Fig. 1 )(Table 1 ). Table 1 Age-adjusted mortality rates per 100,000, stratified by sex in the United States, 1999 to 2025. Year Age-Adjusted Mortality Rate (95% CI) Overall Age-Adjusted Mortality Rate (95% CI) Male Age-Adjusted Mortality Rate (95% CI) Female 1999 0.67 (0.64, 0.70) 0.78 (0.72, 0.83) 0.60 (0.56, 0.64) 2000 0.71 (0.67, 0.74) 0.84 (0.78, 0.90) 0.61 (0.57, 0.65) 2001 0.64 (0.61, 0.67) 0.72 (0.67, 0.77) 0.57 (0.53, 0.60) 2002 0.65 (0.62, 0.68) 0.73 (0.68, 0.78) 0.58 (0.55, 0.62) 2003 0.63 (0.60, 0.66) 0.68 (0.63, 0.73) 0.59 (0.55, 0.62) 2004 0.67 (0.64, 0.70) 0.72 (0.67, 0.77) 0.63 (0.59, 0.67) 2005 0.69 (0.66, 0.72) 0.74 (0.69, 0.79) 0.65 (0.61, 0.69) 2006 0.70 (0.67, 0.73) 0.75 (0.70, 0.80) 0.69 (0.65, 0.72) 2007 0.72 (0.69, 0.76) 0.75 (0.70, 0.80) 0.70 (0.66, 0.74) 2008 0.74 (0.71, 0.77) 0.76 (0.71, 0.81) 0.73 (0.70, 0.77) 2009 0.75 (0.72, 0.78) 0.74 (0.69, 0.79) 0.73 (0.70, 0.77) 2010 0.73 (0.70, 0.76) 0.75 (0.70, 0.79) 0.72 (0.69, 0.76) 2011 0.75 (0.72, 0.78) 0.79 (0.74, 0.84) 0.72 (0.68, 0.75) 2012 0.80 (0.77, 0.83) 0.78 (0.73, 0.83) 0.80 (0.76, 0.84) 2013 1.08 (1.05, 1.12) 1.06 (1.00, 1.11) 1.06 (1.02, 1.11) 2014 1.13 (1.10, 1.17) 1.07 (1.02, 1.13) 1.15 (1.10, 1.19) 2015 1.11 (1.08, 1.15) 1.10 (1.05, 1.16) 1.13 (1.08, 1.17) 2016 1.05 (1.02, 1.09) 1.04 (0.99, 1.09) 1.06 (1.02, 1.10) 2017 1.10 (1.07, 1.14) 1.02 (0.97, 1.08) 1.14 (1.09, 1.18) 2018 1.01 (0.98, 1.05) 0.95 (0.90, 0.99) 1.03 (0.99, 1.08) 2019 1.10 (1.07, 1.13) 1.07 (1.02, 1.12) 1.09 (1.05, 1.13) 2020 1.37 (1.33, 1.41) 1.30 (1.24, 1.35) 1.38 (1.34, 1.43) 2021 1.50 (1.46, 1.54) 1.40 (1.35, 1.46) 1.56 (1.51, 1.61) 2022 1.44 (1.40, 1.48) 1.37 (1.31, 1.43) 1.49 (1.44, 1.53) 2023 1.40 (1.36, 1.44) 1.25 (1.20, 1.31) 1.47 (1.42, 1.52) 2024 1.33 (1.30, 1.37) 1.23 (1.18, 1.29) 1.41 (1.37, 1.46) 2025 1.44 (1.40, 1.48) 1.36 (1.31, 1.42) 1.47 (1.43, 1.52) Abbreviations: CI, Confidence Interval Degenerative Nervous System Disorder–Related AAMR Stratified by Sex Among females, four join points were identified. Mortality increased significantly from 1999 to 2011 (APC 2.13%), followed by a marked rise from 2011 to 2014 (APC 16.40%). A decline occurred from 2014 to 2018 (APC − 3.05%), after which rates increased significantly from 2018 to 2021 (APC 13.85%). The trend then decreased from 2021 to 2025 (APC − 1.55%). Among males however, mortality remained stable from 1999 to 2011 (APC − 0.14%). Rates increased from 2011 to 2014 (APC 14.37%), declined from 2014 to 2018 (APC − 3.50%), rose again from 2018 to 2021 (APC 12.89%), and declined from 2021 to 2025 (APC − 2.10%) (Fig. 1 )(Table 1 ). Degenerative Nervous System Disorder–Related AAMR Stratified by Race/Ethnicity Race-stratified analysis demonstrated heterogeneous patterns. Among Asian or Pacific Islander individuals (two joinpoints), mortality declined from 1999 to 2011 (APC − 1.84%), followed by a sharp rise from 2011 to 2014 (APC 90.24%). Thereafter, rates declined significantly from 2014 to 2025 (APC − 2.41%). Among Black or African American individuals (three joinpoints), mortality increased significantly from 1999 to 2013 (APC 3.80%), declined from 2013 to 2017 (APC − 6.33%), increased from 2017 to 2020 (APC 21.89%), and declined significantly from 2020 to 2025 (APC − 5.75%). Among White individuals (four joinpoints), mortality remained relatively stable from 1999 to 2011 (APC − 0.26%), increased significantly from 2011 to 2014 (APC 16.36%), declined from 2014 to 2018 (APC − 0.65%), rose again from 2018 to 2021 (APC 12.31%), and decreased from 2021 to 2025 (APC − 1.11%). Among Hispanic or Latino individuals (two joinpoints), mortality increased significantly from 1999 to 2013 (APC 10.94%), declined sharply from 2013 to 2016 (APC − 20.44%), and subsequently increased significantly from 2016 to 2025 (APC 3.88%) (Fig. 2 )(Table 2 ). Table 2 Age-adjusted mortality rates per 100,000, stratified by race in the United States, 1999 to 2025. Year Age-Adjusted Mortality Rate (95% CI) NH Asian or Pacific Islander Age-Adjusted Mortality Rate (95% CI) NH Black or African American Age-Adjusted Mortality Rate (95% CI) NH White Age-Adjusted Mortality Rate (95% CI) Hispanic or Latino 1999 0.52 (0.35, 0.76) 0.56 (0.46, 0.66) 0.70 (0.67, 0.73) 0.53 (0.40, 0.68) 2000 0.37 (0.22, 0.58) 0.56 (0.47, 0.66) 0.71 (0.67, 0.74) 0.64 (0.50, 0.80) 2001 0.40 (0.26, 0.59) 0.67 (0.57, 0.78) 0.64 (0.61, 0.67) 0.68 (0.54, 0.84) 2002 0.39 (0.25, 0.58) 0.62 (0.52, 0.73) 0.66 (0.62, 0.69) 0.87 (0.70, 1.04) 2003 0.43 (0.30, 0.61) 0.65 (0.55, 0.76) 0.62 (0.59, 0.65) 0.91 (0.74, 1.07) 2004 0.30 (0.20, 0.45) 0.76 (0.64, 0.87) 0.64 (0.61, 0.67) 1.07 (0.89, 1.24) 2005 0.37 (0.25, 0.52) 0.79 (0.67, 0.90) 0.67 (0.64, 0.70) 1.08 (0.91, 1.25) 2006 0.26 (0.16, 0.38) 0.70 (0.60, 0.81) 0.68 (0.65, 0.71) 1.75 (1.54, 1.97) 2007 0.22 (0.14, 0.34) 0.74 (0.64, 0.85) 0.67 (0.64, 0.70) 1.82 (1.61, 2.03) 2008 0.28 (0.19, 0.40) 0.71 (0.60, 0.81) 0.69 (0.66, 0.73) 2.04 (1.82, 2.25) 2009 0.27 (0.18, 0.39) 0.73 (0.63, 0.84) 0.70 (0.67, 0.74) 1.72 (1.53, 1.92) 2010 0.40 (0.29, 0.53) 0.74 (0.64, 0.85) 0.66 (0.63, 0.69) 2.01 (1.81, 2.22) 2011 0.24 (0.16, 0.34) 0.94 (0.82, 1.05) 0.65 (0.62, 0.68) 2.24 (2.04, 2.45) 2012 0.44 (0.33, 0.57) 1.07 (0.95, 1.19) 0.66 (0.63, 0.69) 2.47 (2.26, 2.68) 2013 1.80 (1.57, 2.03) 0.95 (0.84, 1.07) 0.95 (0.91, 0.99) 2.60 (2.40, 2.81) 2014 2.10 (1.86, 2.35) 0.91 (0.80, 1.02) 1.00 (0.96, 1.04) 2.41 (2.22, 2.60) 2015 2.30 (2.06, 2.54) 0.81 (0.71, 0.91) 1.04 (1.00, 1.08) 1.71 (1.55, 1.87) 2016 2.25 (2.02, 2.48) 0.76 (0.66, 0.85) 1.00 (0.97, 1.04) 1.36 (1.22, 1.49) 2017 1.97 (1.76, 2.19) 0.84 (0.74, 0.94) 1.08 (1.04, 1.12) 1.46 (1.32, 1.60) 2018 1.51 (1.33, 1.68) 0.84 (0.74, 0.94) 0.99 (0.95, 1.02) 1.45 (1.32, 1.59) 2019 1.37 (1.21, 1.54) 0.96 (0.86, 1.07) 1.06 (1.02, 1.09) 1.42 (1.29, 1.55) 2020 1.91 (1.72, 2.10) 1.38 (1.26, 1.50) 1.32 (1.28, 1.36) 1.59 (1.45, 1.72) 2021 2.05 (1.85, 2.26) 1.30 (1.17, 1.42) 1.45 (1.41, 1.50) 2.18 (2.02, 2.34) 2022 2.05 (1.86, 2.24) 1.22 (1.10, 1.34) 1.38 (1.34, 1.43) 1.95 (1.80, 2.09) 2023 1.67 (1.50, 1.84) 1.16 (1.05, 1.28) 1.39 (1.34, 1.43) 1.82 (1.68, 1.96) 2024 1.63 (1.47, 1.79) 0.95 (0.85, 1.05) 1.35 (1.30, 1.39) 1.78 (1.64, 1.91) 2025 1.86 (1.69, 2.04) 1.02 (0.91, 1.12) 1.40 (1.36, 1.44) 1.88 (1.74, 2.02) Abbreviations: CI, Confidence Interval; NH, Non-Hispanic Degenerative Nervous System Disorder–Related AAMR Stratified by Urbanization In metropolitan areas (three joinpoints), mortality increased significantly from 1999 to 2011 (APC 2.23%) and from 2011 to 2014 (APC 16.64%). Rates continued rising from 2014 to 2018 (APC 4.60%) and increased significantly from 2018 to 2020 (APC 15.17%). In non-metropolitan areas (one joinpoint), mortality declined significantly from 1999 to 2012 (APC − 5.23%), followed by a significant increase from 2012 to 2020 (APC 9.35%) (Fig. 3 )(Table 3 ). Table 3 Age-adjusted mortality rates per 100,000, stratified by urbanization in the United States, 1999 to 2020. Year Age-Adjusted Mortality Rate (95% CI) Metropolitan Age-Adjusted Mortality Rate (95% CI) Non-Metropolitan 1999 0.66 (0.62, 0.69) 0.77 (0.69, 0.84) 2000 0.69 (0.66, 0.73) 0.73 (0.66, 0.80) 2001 0.64 (0.61, 0.67) 0.61 (0.55, 0.68) 2002 0.64 (0.61, 0.67) 0.61 (0.55, 0.68) 2003 0.66 (0.63, 0.69) 0.54 (0.48, 0.61) 2004 0.69 (0.66, 0.73) 0.59 (0.52, 0.65) 2005 0.74 (0.70, 0.77) 0.50 (0.44, 0.57) 2006 0.77 (0.74, 0.81) 0.45 (0.39, 0.51) 2007 0.77 (0.73, 0.80) 0.48 (0.42, 0.54) 2008 0.83 (0.80, 0.87) 0.40 (0.34, 0.45) 2009 0.81 (0.77, 0.84) 0.43 (0.38, 0.49) 2010 0.79 (0.76, 0.82) 0.44 (0.38, 0.49) 2011 0.83 (0.80, 0.87) 0.42 (0.37, 0.48) 2012 0.87 (0.84, 0.91) 0.38 (0.33, 0.43) 2013 1.23 (1.19, 1.27) 0.35 (0.30, 0.41) 2014 1.27 (1.23, 1.31) 0.44 (0.39, 0.50) 2015 1.27 (1.22, 1.31) 0.48 (0.43, 0.54) 2016 1.17 (1.13, 1.21) 0.49 (0.43, 0.55) 2017 1.22 (1.18, 1.26) 0.55 (0.49, 0.60) 2018 1.10 (1.06, 1.14) 0.62 (0.55, 0.68) 2019 1.16 (1.13, 1.20) 0.65 (0.59, 0.72) 2020 1.49 (1.45, 1.53) 0.76 (0.69, 0.82) Abbreviations: CI, Confidence Interval Degenerative Nervous System Disorder–Related AAMR Stratified by Census Region Regional analysis demonstrated distinct temporal patterns. In the Northeast (one joinpoint), mortality declined from 1999 to 2009 (APC − 5.50%), followed by a significant increase from 2009 to 2025 (APC 3.14%). In the Midwest (one joinpoint), rates declined significantly from 1999 to 2012 (APC − 5.22%), then increased significantly from 2012 to 2025 (APC 8.14%). In the South (four joinpoints), mortality increased significantly from 1999 to 2008 (APC 8.66%), remained stable from 2008 to 2012 (APC 0.76%), decreased from 2012 to 2016 (APC − 9.44%), increased significantly from 2016 to 2021 (APC 13.12%), and declined again significantly from 2021 to 2025 (APC − 7.12%). In the West (two joinpoints), mortality declined from 1999 to 2010 (APC − 5.40%), increased sharply from 2010 to 2013 (APC 83.00%), and then rose modestly from 2013 to 2025 (APC 1.43%) (Fig. 4 )(Table 4 ). Table 4 Age-adjusted mortality rates per 100,000, stratified by census region in the United States, 1999 to 2025. Year Age-Adjusted Mortality Rate (95% CI) Northeast Age-Adjusted Mortality Rate (95% CI) Midwest Age-Adjusted Mortality Rate (95% CI) South Age-Adjusted Mortality Rate (95% CI) West 1999 0.49 (0.43, 0.55) 0.82 (0.75, 0.89) 0.67 (0.62, 0.72) 0.65 (0.58, 0.72) 2000 0.54 (0.48, 0.60) 0.70 (0.63, 0.76) 0.76 (0.70, 0.81) 0.66 (0.59, 0.73) 2001 0.46 (0.41, 0.52) 0.66 (0.60, 0.72) 0.79 (0.73, 0.85) 0.49 (0.44, 0.55) 2002 0.51 (0.45, 0.57) 0.65 (0.59, 0.71) 0.81 (0.76, 0.87) 0.53 (0.47, 0.59) 2003 0.44 (0.39, 0.49) 0.62 (0.56, 0.68) 0.85 (0.79, 0.90) 0.46 (0.41, 0.52) 2004 0.43 (0.37, 0.48) 0.60 (0.54, 0.66) 0.98 (0.92, 1.04) 0.48 (0.42, 0.54) 2005 0.43 (0.38, 0.48) 0.52 (0.47, 0.58) 1.11 (1.05, 1.18) 0.48 (0.43, 0.54) 2006 0.34 (0.29, 0.38) 0.51 (0.45, 0.56) 1.22 (1.16, 1.29) 0.49 (0.43, 0.54) 2007 0.31 (0.27, 0.36) 0.51 (0.45, 0.56) 1.28 (1.22, 1.35) 0.41 (0.36, 0.46) 2008 0.34 (0.30, 0.39) 0.44 (0.39, 0.49) 1.39 (1.32, 1.46) 0.45 (0.40, 0.50) 2009 0.30 (0.26, 0.35) 0.46 (0.41, 0.51) 1.42 (1.35, 1.49) 0.42 (0.37, 0.47) 2010 0.32 (0.27, 0.36) 0.42 (0.37, 0.47) 1.40 (1.33, 1.47) 0.44 (0.39, 0.49) 2011 0.35 (0.30, 0.39) 0.40 (0.35, 0.44) 1.42 (1.35, 1.49) 0.46 (0.41, 0.51) 2012 0.30 (0.26, 0.34) 0.44 (0.40, 0.49) 1.44 (1.37, 1.50) 0.61 (0.56, 0.67) 2013 0.34 (0.29, 0.38) 0.39 (0.35, 0.44) 1.31 (1.25, 1.38) 2.15 (2.05, 2.26) 2014 0.36 (0.31, 0.40) 0.47 (0.42, 0.52) 1.19 (1.13, 1.25) 2.43 (2.33, 2.54) 2015 0.39 (0.35, 0.44) 0.44 (0.39, 0.49) 1.06 (1.00, 1.11) 2.62 (2.51, 2.74) 2016 0.38 (0.33, 0.43) 0.50 (0.45, 0.54) 0.91 (0.86, 0.96) 2.48 (2.37, 2.58) 2017 0.36 (0.31, 0.40) 0.55 (0.49, 0.60) 1.17 (1.11, 1.23) 2.35 (2.24, 2.45) 2018 0.38 (0.33, 0.43) 0.57 (0.52, 0.62) 1.20 (1.14, 1.26) 1.72 (1.63, 1.80) 2019 0.43 (0.38, 0.48) 0.59 (0.54, 0.64) 1.35 (1.29, 1.41) 1.76 (1.67, 1.85) 2020 0.53 (0.48, 0.58) 0.92 (0.85, 0.98) 1.66 (1.60, 1.73) 2.11 (2.01, 2.20) 2021 0.55 (0.50, 0.60) 0.79 (0.73, 0.86) 1.77 (1.70, 1.84) 2.61 (2.50, 2.72) 2022 0.54 (0.49, 0.60) 0.64 (0.59, 0.69) 1.64 (1.58, 1.71) 2.68 (2.57, 2.79) 2023 0.42 (0.38, 0.47) 0.83 (0.77, 0.90) 1.56 (1.50, 1.63) 2.44 (2.34, 2.55) 2024 0.44 (0.40, 0.49) 0.93 (0.87, 1.00) 1.35 (1.29, 1.41) 2.44 (2.34, 2.54) 2025 0.49 (0.44, 0.55) 1.12 (1.05, 1.19) 1.39 (1.33, 1.44) 2.67 (2.56, 2.77) Abbreviations: CI, Confidence Interval Degenerative Nervous System Disorder–Related Mortality Stratified by State Analysis of State age-adjusted mortality rates (AAMRs) demonstrated significant inter-state differences in added mortality burden from 1999–2025. The highest AAMRs were observed in Florida, with an AAMR of 2.99, and California (AAMR 2.49), with West Virginia, Washington, and Utah also displaying elevated rates. However, the lowest AAMRs were seen in New York and Connecticut (AAMRs of 0.29), followed by Arkansas and New Jersey (AAMRs of 0.33), and finally Delaware (AAMR of 0.36). These findings indicate marked differences between states in cumulative mortality burden across the study period. (Fig. 7 ) (Table 7 ). Table 7 Age-adjusted mortality rates per 100,000, stratified by state in the United States, 1999 to 2025. State 1999–2020 AAMR (95% CI) 2021–2025 AAMR (95% CI) Alabama 0.53 (0.48, 0.57) 1.18 (1.06, 1.30) Alaska 0.43 (0.30, 0.59) Unreliable Arizona 0.41 (0.38, 0.44) 1.58 (1.47, 1.69) Arkansas 0.34 (0.30, 0.39) 0.28 (0.20, 0.37) California 1.91 (1.88, 1.94) 3.57 (3.50, 3.65) Colorado 0.51 (0.46, 0.55) 1.03 (0.91, 1.14) Connecticut 0.28 (0.25, 0.31) 0.32 (0.25, 0.40) Delaware 0.37 (0.29, 0.46) 0.30 (0.18, 0.47) District of Columbia 0.44 (0.33, 0.57) N/A Florida 3.00 (2.96, 3.04) 2.94 (2.86, 3.02) Georgia 0.54 (0.51, 0.58) 0.43 (0.38, 0.49) Hawaii 0.47 (0.40, 0.55) 0.47 (0.35, 0.62) Idaho 0.73 (0.64, 0.82) 0.62 (0.49, 0.78) Illinois 0.52 (0.49, 0.54) 0.60 (0.54, 0.65) Indiana 0.48 (0.45, 0.52) 0.43 (0.37, 0.49) Iowa 0.54 (0.49, 0.59) 0.46 (0.37, 0.57) Kansas 0.72 (0.65, 0.78) 0.61 (0.49, 0.72) Kentucky 0.41 (0.37, 0.45) 0.27 (0.21, 0.34) Louisiana 0.37 (0.33, 0.41) 0.67 (0.57, 0.77) Maine 0.71 (0.62, 0.80) 0.95 (0.77, 1.14) Maryland 0.39 (0.36, 0.43) 0.53 (0.45, 0.61) Massachusetts 0.54 (0.50, 0.57) 0.42 (0.36, 0.48) Michigan 0.42 (0.39, 0.45) 0.74 (0.67, 0.81) Minnesota 0.77 (0.72, 0.82) 1.24 (1.13, 1.36) Mississippi 0.70 (0.64, 0.77) 1.38 (1.20, 1.56) Missouri 0.65 (0.61, 0.70) 2.44 (2.28, 2.59) Montana 0.65 (0.55, 0.75) 0.59 (0.43, 0.78) Nebraska 0.52 (0.45, 0.58) 0.51 (0.39, 0.66) Nevada 0.46 (0.40, 0.52) 0.63 (0.52, 0.75) New Hampshire 0.42 (0.35, 0.50) 0.52 (0.39, 0.68) New Jersey 0.27 (0.25, 0.30) 0.46 (0.40, 0.51) New Mexico 0.41 (0.35, 0.47) 0.52 (0.40, 0.66) New York 0.27 (0.25, 0.28) 0.34 (0.31, 0.38) North Carolina 0.47 (0.44, 0.50) 0.43 (0.38, 0.48) North Dakota 0.51 (0.41, 0.63) 0.55 (0.36, 0.81) Ohio 0.56 (0.53, 0.59) 0.66 (0.60, 0.72) Oklahoma 0.37 (0.33, 0.41) 0.48 (0.39, 0.57) Oregon 0.59 (0.54, 0.63) 1.46 (1.32, 1.61) Pennsylvania 0.55 (0.53, 0.58) 0.72 (0.67, 0.78) Rhode Island 0.47 (0.38, 0.55) 0.40 (0.27, 0.57) South Carolina 0.72 (0.67, 0.78) 1.42 (1.28, 1.55) South Dakota 0.52 (0.43, 0.62) 0.50 (0.33, 0.72) Tennessee 0.56 (0.52, 0.60) 1.75 (1.61, 1.88) Texas 0.98 (0.95, 1.01) 1.85 (1.78, 1.92) Utah 0.74 (0.66, 0.82) 2.38 (2.12, 2.64) Vermont 0.83 (0.68, 0.98) 0.95 (0.68, 1.29) Virginia 0.39 (0.36, 0.42) 0.65 (0.58, 0.72) Washington 0.64 (0.60, 0.68) 3.22 (3.05, 3.39) West Virginia 1.12 (1.02, 1.21) 3.48 (3.16, 3.81) Wisconsin 0.62 (0.58, 0.66) 1.20 (1.09, 1.31) Wyoming 0.53 (0.40, 0.67) 0.62 (0.39, 0.94) Abbreviations: CI, Confidence Interval * Indicates suppressed/unreliable estimate due to small counts Degenerative Nervous System Disorder–Related Mortality Stratified by Place of Death Analysis of place of death revealed that the largest proportion of deaths occurred in nursing homes or long-term care facilities (36.27%), followed by deaths at the decedent’s home (30.70%). Hospital inpatient deaths accounted for 13,343 deaths (14.05%). Hospice facility deaths represented 7,870 deaths (8.29%), while deaths classified as “Other” accounted for 8,114 deaths (8.55%). A smaller proportion of deaths occurred in medical facilities designated as outpatient or emergency departments (1,772 deaths; 1.87%) or dead on arrival (133 deaths; 0.14%). Deaths with unknown places of death were less than 0.2% of cases. These findings illustrate that 2/3rd of the deaths occurred either in long-term care facilities or at home, suggesting increased mortality outside the inpatient hospital settings. (Table 6 ) (Fig. 6 ) Table 6 Place of Death. Place of Death Total Deaths % of Total Deaths Decedent's home 29,146 30.70% Hospice facility 7,870 8.29% Medical Facility - Dead on Arrival 133 0.14% Medical Facility - Inpatient 13,343 14.05% Medical Facility - Outpatient or ER 1,772 1.87% Medical Facility - Status unknown 24 0.03% Nursing home/long term care 34,436 36.27% Other 8,114 8.55% Place of death unknown 99 0.10% Total 94,937 100.00% Degenerative Nervous System Disorder–Related Mortality Stratified by Age Groups Analysis of age-stratified crude rates revealed distinct age-dependent trends. Between 1999 and 2025, mortality was lower for those without joinpoints who were 5–14 years old (APC − 2.08%), 35–44 years old (APC − 1.68%), and 45–54 years old (APC − 2.12%). Adults between the ages of 55 and 64, however, experienced a decline from 1999 to 2011 with APC − 2.33% and a significant increase from 2011 to 2025 (APC 1.69%). Those between the ages of 65 and 74 had a decrease in APC from 1999 to 2010 (− 3.37%) and a significant increase from 2010 to 2025 (APC 3.43%). Over the course of the study, mortality among people 75–84 years old increased overall. The ≥ 85-year group showed the most volatile pattern with considerable increases from 1999 to 2014 (APC 4.78% and 20.34%), a decline from 2014 to 2018 (APC − 3.68%), a significant increase from 2018 to 2021 (APC 15.63%), and a decline from 2021 to 2025 (APC − 2.23%).(Fig. 5 )(Table 5 )(Fig. 8 ). Table 5 Crude mortality rates per 100,000, stratified by age group in the United States, 1999 to 2025. Year Crude Rate (95% CI) 5–14 years Crude Rate (95% CI) 35–44 years Crude Rate (95% CI) 45–54 years Crude Rate (95% CI) 55–64 years Crude Rate (95% CI) 65–74 years Crude Rate (95% CI) 75–84 years Crude Rate (95% CI) 85 + years 1999 0.08 (0.06, 0.12) 0.09 (0.07, 0.13) 0.19 (0.14, 0.24) 0.53 (0.44, 0.63) 1.68 (1.50, 1.87) 4.65 (4.26, 5.03) 14.49 (13.33, 15.65) 2000 0.06 (0.04, 0.08) 0.08 (0.06, 0.11) 0.19 (0.15, 0.24) 0.50 (0.41, 0.59) 1.68 (1.49, 1.87) 4.80 (4.41, 5.18) 15.12 (13.95, 16.29) 2001 0.06 (0.04, 0.08) 0.10 (0.07, 0.13) 0.20 (0.16, 0.25) 0.52 (0.43, 0.61) 1.56 (1.38, 1.74) 3.95 (3.60, 4.29) 14.49 (13.36, 15.63) 2002 0.09 (0.07, 0.13) 0.06 (0.04, 0.08) 0.16 (0.12, 0.20) 0.50 (0.41, 0.58) 1.48 (1.30, 1.65) 4.39 (4.02, 4.75) 15.50 (14.33, 16.66) 2003 0.06 (0.04, 0.09) 0.09 (0.07, 0.13) 0.20 (0.16, 0.25) 0.51 (0.43, 0.60) 1.42 (1.24, 1.59) 4.04 (3.69, 4.39) 15.43 (14.28, 16.58) 2004 0.09 (0.06, 0.12) 0.08 (0.06, 0.12) 0.17 (0.13, 0.21) 0.48 (0.40, 0.56) 1.46 (1.28, 1.63) 4.43 (4.06, 4.79) 16.52 (15.34, 17.70) 2005 0.07 (0.05, 0.10) 0.10 (0.07, 0.13) 0.19 (0.15, 0.23) 0.52 (0.44, 0.60) 1.55 (1.37, 1.73) 4.23 (3.88, 4.58) 18.24 (17.02, 19.46) 2006 0.07 (0.03, 0.07) 0.08 (0.05, 0.11) 0.20 (0.16, 0.24) 0.49 (0.41, 0.56) 1.27 (1.11, 1.42) 4.64 (4.27, 5.00) 20.10 (18.84, 21.36) 2007 0.06 (0.04, 0.09) 0.06 (0.04, 0.09) 0.17 (0.13, 0.21) 0.45 (0.38, 0.53) 1.19 (1.04, 1.34) 4.98 (4.60, 5.36) 19.82 (18.59, 21.05) 2008 0.07 (0.05, 0.10) 0.05 (0.03, 0.08) 0.13 (0.10, 0.17) 0.40 (0.33, 0.47) 1.18 (1.03, 1.33) 5.06 (4.67, 5.44) 23.25 (21.94, 24.56) 2009 0.06 (0.04, 0.09) 0.07 (0.04, 0.09) 0.12 (0.09, 0.16) 0.42 (0.36, 0.49) 1.21 (1.06, 1.35) 4.74 (4.36, 5.11) 23.21 (21.93, 24.50) 2010 0.05 (0.03, 0.08) 0.07 (0.05, 0.10) 0.14 (0.11, 0.18) 0.44 (0.37, 0.50) 1.18 (1.03, 1.32) 4.52 (4.16, 4.89) 23.32 (22.04, 24.60) 2011 0.07 (0.05, 0.10) 0.06 (0.04, 0.09) 0.13 (0.10, 0.17) 0.35 (0.29, 0.41) 1.18 (1.04, 1.33) 5.06 (4.68, 5.45) 23.58 (22.33, 24.84) 2012 0.06 (0.02, 0.07) 0.05 (0.03, 0.08) 0.16 (0.12, 0.20) 0.44 (0.37, 0.50) 1.19 (1.05, 1.33) 5.22 (4.83, 5.61) 25.48 (24.19, 26.77) 2013 0.05 (0.02, 0.05) 0.07 (0.05, 0.11) 0.17 (0.13, 0.21) 0.38 (0.32, 0.44) 1.27 (1.13, 1.41) 7.09 (6.64, 7.54) 37.98 (36.42, 39.53) 2014 0.05 (0.03, 0.08) 0.06 (0.04, 0.09) 0.14 (0.11, 0.19) 0.46 (0.40, 0.53) 1.36 (1.22, 1.50) 7.03 (6.59, 7.48) 40.36 (38.77, 41.94) 2015 0.05 (0.03, 0.08) 0.06 (0.04, 0.09) 0.11 (0.08, 0.14) 0.42 (0.35, 0.48) 1.34 (1.20, 1.48) 6.89 (6.45, 7.32) 41.66 (40.06, 43.25) 2016 0.06 (0.04, 0.09) 0.06 (0.04, 0.09) 0.12 (0.09, 0.15) 0.43 (0.37, 0.49) 1.46 (1.32, 1.60) 6.03 (5.62, 6.43) 38.68 (37.16, 40.21) 2017 0.06 (0.02, 0.06) 0.05 (0.03, 0.08) 0.12 (0.09, 0.16) 0.47 (0.41, 0.54) 1.40 (1.27, 1.54) 7.17 (6.74, 7.61) 40.10 (38.56, 41.64) 2018 0.06 (0.04, 0.09) 0.05 (0.03, 0.07) 0.09 (0.07, 0.13) 0.46 (0.39, 0.52) 1.47 (1.34, 1.61) 6.35 (5.95, 6.74) 35.51 (34.07, 36.95) 2019 0.05 (0.03, 0.08) 0.05 (0.03, 0.07) 0.15 (0.12, 0.19) 0.48 (0.41, 0.54) 1.44 (1.31, 1.57) 6.56 (6.17, 6.96) 39.80 (38.28, 41.32) 2020 0.04 (0.01, 0.04) 0.06 (0.04, 0.09) 0.13 (0.10, 0.17) 0.53 (0.46, 0.60) 1.80 (1.66, 1.95) 8.65 (8.20, 9.10) 50.55 (48.84, 52.26) 2021 0.04 (0.02, 0.06) 0.06 (0.04, 0.08) 0.15 (0.11, 0.19) 0.55 (0.48, 0.62) 1.85 (1.71, 2.00) 8.91 (8.45, 9.37) 57.52 (55.59, 59.44) 2022 0.04 (0.02, 0.06) 0.08 (0.05, 0.11) 0.14 (0.10, 0.18) 0.50 (0.43, 0.57) 1.86 (1.71, 2.00) 8.86 (8.42, 9.30) 53.72 (51.93, 55.50) 2023 0.05 (0.03, 0.08) 0.06 (0.04, 0.08) 0.15 (0.12, 0.19) 0.50 (0.43, 0.57) 1.76 (1.62, 1.90) 7.98 (7.57, 8.38) 53.45 (51.63, 55.27) 2024 0.05 (0.03, 0.08) 0.06 (0.04, 0.08) 0.09 (0.06, 0.12) 0.44 (0.37, 0.50) 1.66 (1.53, 1.80) 8.36 (7.95, 8.77) 50.22 (48.49, 51.96) 2025 0.04 (0.02, 0.07) 0.06 (0.04, 0.09) 0.11 (0.08, 0.15) 0.51 (0.44, 0.58) 1.85 (1.71, 1.99) 8.97 (8.55, 9.39) 53.63 (51.84, 55.42) Abbreviations: CI, Confidence Interval Sensitivity Analysis Sensitivity analyses demonstrated similar overall patterns. Overall mortality showed significant increases from 1999 to 2011 (APC 1.35%) and from 2011 to 2014 (APC 15.53%), a decline from 2014 to 2018 (APC − 2.47%), and a significant increase from 2018 to 2025 (APC 4.89%). Sex, race, region, and urbanization-specific sensitivity analyses demonstrated consistent directional trends compared with the primary models.(Table 8 ). Table 8 Total Deaths, population, AAPC and AAPC p values. Category Total Deaths (1999–2025) Population (1999–2025) AAPC (95% CI) AAPC p-value Overall 94,938 8,426,674,820 3.0148* (0.8443, 5.2319) 0.006260 Sex Male 37,177 4,149,446,302 2.0272 (-0.2890, 4.3972) 0.086730 Female 57,761 4,277,228,518 3.5678* (1.9488, 5.2125) 0.000013 Race/Ethnicity NH Asian or Pacific Islander 4,999 462,823,040 5.6886 (-2.5263, 14.5959) 0.180193 NH Black or African American 7,259 1,076,077,095 2.1710 (-1.0972, 5.5471) 0.195379 NH White 71,040 5,373,539,687 2.7332* (1.0079, 4.4879) 0.001805 Hispanic or Latino 11,128 1,394,229,761 4.3611* (0.4816, 8.3905) 0.027207 Census Region Northeast 7,738 1,499,844,553 -0.2721 (-1.3970, 0.8655) 0.637747 Midwest 13,122 1,811,852,704 1.2400* (0.1023, 2.3907) 0.032568 South 42,738 3,149,216,278 2.7369* (1.1030, 4.3972) 0.000963 West 31,340 1,965,761,285 5.4229 (-4.3972, 16.2518) 0.289794 Age Group 5–14 years 612 1,107,046,640 -2.0774* (-2.8628, -1.2857) 0.000014 35–44 years 776 1,153,855,648 -1.6791* (-2.4608, -0.8911) 0.000191 45–54 years 1,670 1,130,750,794 -2.1195* (-2.8360, -1.3977) 0.000003 55–64 years 4,560 976,491,209 -0.1852 (-0.8990, 0.5337) 0.612660 65–74 years 10,266 683,488,110 0.4958 (-0.2881, 1.2858) 0.215798 75–84 years 24,396 389,198,776 3.3221* (2.7689, 3.8783) < 0.000001 85 + years 51,102 151,040,781 5.1730* (3.2159, 7.1673) < 0.000001 Urbanization Metropolitan 58,586 5,739,475,649 3.9836* (2.1204, 5.8809) 0.000023 Non-Metropolitan 6,804 1,006,871,652 0.0784 (-0.8513, 1.0168) 0.869324 Abbreviations: AAPC, Average Annual Percentage Change; CI, Confidence Interval; NH, Non-Hispanic. * Indicates AAPC significantly differs from zero at the alpha = 0.05 level. DISCUSSION In our analysis of nationwide death certificate data obtained via the publicly available CDC WONDER database related to the ICD code: G31.9, accounting for unspecified neurodegenerative disorders within the citizens of the United States, we demonstrate an overall rise in the unspecified NDDs mortality from 1999 to 2025. Although an overall rise in the AAMR since 1999 has been observed, fluctuations have also been reported, given the considerable declining trends in the APC between the years 2014–2018 and then again between 2021–2025. Similar shifting trends were also reported in both sexes, with females having a very slightly higher (1.47) AAMR than men (1.36) by 2025. Across the years, rising and falling mortality trends were present in all ethnic backgrounds analyzed, with marked increases especially during the years 2017–2021 in most of them. An interestingly huge spike in the APC (90.24%) was observed in Asian or Pacific Islander populations from 2011–2014. The highest AAMR was seen in the West, particularly, with the metropolitan areas possessing a higher APC than their non-metropolitan counterparts, and the Northeast had the lowest AAMR. The Midwest was observed to have the highest APC by 2025. Rising mortality recorded in our analysis is also reflected by an overall global health loss of 15% related to just brain diseases, which was even more than cancer and cardiovascular-related health burden ( 1 ). There has been a total increase of 63% in the prevalence of neurologic disorders, with a concerning prediction of a rise to 4.9 billion affected patients by 2050, with Alzheimer’s disease, a NDD, being one of the contributors to this disease burden ( 16 ). Disorders related to neurodegeneration are vastly characterized by their clinical manifestations, mostly presenting as pyramidal and extrapyramidal movement disorders, and also cognitive or behavioural syndromes ( 17 ), and include Alzheimer’s disease and other dementias along with Parkinson’s disease most commonly ( 21 ). Compared to an AAMR of 0.66 in 1999, the overall AAMR had risen to 1.44 in 2025 in our analysis, which is reaffirmed by previous literature showing that almost 24.9 million people had dementia worldwide by 1990, and in 2021, this number had increased to 56.9 million ( 21 ). A good amount of changeable factors, including smoking, sedentary lifestyle, diabetes, and cardiac health, and nonchangeable factors, like age and genes, are linked to dementia ( 21 ). Even though specific abnormal protein aggregations and regional vulnerability give rise to neurodegenerative disorders, they all also share core pathologic mechanisms related to gradually increasing nervous dysfunction and neuronal loss, such as proteotoxic stress, and dysfunction in ubiquitin–proteasome and autophagy–lysosomal pathways, oxidative stress, apoptosis, and inflammation in the brain ( 17 ). Within our bodies, reactive oxygen and nitrogen species (RNOS) build up and result in oxidative stress and thus exacerbate aging and age-related diseases ( 22 ), since oxidative stress is crucial in the development of neurodegeneration ( 23 ). Likewise, these stressful conditions can also promote neuronal senescence, which is evident in neurodegenerative diseases like AD ( 39 ). Such mechanisms can help us understand the rise in crude rate associated with increasing ages seen in our study, since individuals aged 85 years and above had the highest proportion of deaths as well as crude rate, followed by those between the ages of 75–84 years. This is evident by changes related to autophagy, an intricate degradative process triggered by various bodily responses to stress, such as reduced insulin and ATP concentration, while maintaining protein homeostasis ( 38 ). Increased mTOR stimulation and alteration in signalling cascades associated with aging impair autophagosome formation and thus autophagy, resulting in several nervous system changes, such as increased aggregation of tubular ER within axons and therefore greater calcium release from the ER, and enhanced neuronal activity ( 38 ). These underlying processes may help explain the rising burden of nervous system degeneration. While it is fortunate to note a decline in the APC between the years 2021 and 2025, prior marked rises should not be ignored. The rise in the APC to 13.38% specially during the years 2028 − 2021 could have been influenced by COVID-19 pandemic and associated mortality as well, since during 2020 specifically when the population was not as widely vaccinated, slowly rising mortality percentage was observed for the elderly that was higher in men (20%) than women (15%), with an overall higher COVID-19 mortality for the elderly ( 24 ). Higher COVID-related AAMR was observed in Hispanic and black individuals compared to white people as well ( 24 ), signifying marked racial/ethnic disparities. These findings validate our study, considering the fact that ging is a foremost underlying factor responsible for the development of NDDs ( 25 ). Recent declines in the APC could be secondary to advancements in healthcare, as life expectancy in both men and women is reported to increase by 1.1–1.5 years at age 65 and 0.6–0.8 years at age 85 in the presence of efficiently accessible healthcare ( 26 ), and increases are also noted across both urban and rural regions ( 26 ). The older men had a 10–14% increase in their life expectancy, in contrast with the women who had 6–8%, generally suggesting a significant relationship between elderly mortality and access to healthcare ( 26 ). Although overall APC in both females and males was approximately similar in our analysis by 2025, females exhibited a modestly higher AAMR by 2025 in comparison with their male counterparts. This finding also aligns with a cohort study in Spain carried out by Maitee Rosende-Roca et al., which not only had an overall greater proportion of female cohorts (71.6%) than male cohorts (28.4%), but the authors also noticed a rapid progression of disease in women diagnosed with mild Alzheimer’s disease dementia than men ( 18 ). Additionally, sharp and statistically significant rises observed in both sexes, particularly during the years 2018 to 2021, can be subject to the emergence of COVID-19 pandemic. Research shows that women are more susceptible to developing long-COVID syndrome and thus neurological symptoms associated with it, such as depression, headaches, fatigue with foggy brain and anosmia, with a slightly greater prevalence of anxiety, PTSD, dysgeusia, and vertigo as well ( 19 ). These sex-based disparities can also be due to females exhibiting a heightened immune response to COVID-19 infection, with reportedly higher seropositivity and antibody titers ( 20 ). Although a considerable disparity was not really reported in our analysis, studies have confirmed a higher rate of age-related atrophy in men than women, as well as cognitive decline at old age (at almost 65 years) ( 27 ). However, women are more likely to have higher rates of atrophy in the white matter ( 27 ). This can also be because of anatomical differences apparent between the two genders, as men display greater disparities in total gray matter of the brain, with pronounced volumes of the cingulate gyrus in their female counterparts ( 27 ). We also believe it is crucial to study modifiable risk factors associated with gendered differences and neurodegeneration, as loneliness was greater in Hispanic women (32.2%) than in Hispanic men (15.9%), even though men exhibited a greater link between loneliness and the occurrence of dementia ( 29 ). Regarding race, our analysis revealed similar AAMRs by 2025 in Asian or Pacific Islander populations (1.86) and Hispanic or Latino populations (1.88). A steadily declining trend in the APC was observed for Asians or Pacific Islanders from 2014 to 2025 in comparison with Hispanics or Latinos, who depicted a significant increase (from − 20.44% to 3.88%) in the APC during the same time period. Even though this is contrasted previous research that showed a greater proportion of white individuals affected with Frontotemporal Dementia, a neurodegenerative disorder, when compared with Black and Asian populations ( 28 ), it should also be noted that these disparities may be subject to underrepresentation of racial/ethnic minorities and inadequate sample size ( 28 ). Additionally, we now know that social determinants of health like socio-economic background, environment and health-associated experiences differ across varying genders and ethnicities/races, with alterable risk factors being responsible for around 35% of dementia cases ( 29 ). Moreover, noted disparity across demographics can be influenced by discrepancy in utilization of medical services as Altaaf Saadi et al. discovered that Black people were almost 30% less likely to seek neurologic outpatient consultation in comparison with white people, despite having a higher rate of hospitalization and ER visits ( 30 ). The likelihood dropped to 40% for Hispanics ( 30 ), suggesting a potential association between mortality rate and healthcare access in the US. Interestingly, a possible association has been observed between educational status and cerebral pathology, further reaffirming brain reserve theory ( 31 ), as more well-educated individuals were seen to tolerate a greater extent of gray matter volume atrophy of dACC, thus depicting resistance against AD pathology, with bilinguals displaying a higher stimulation of ACC (anterior cingulate cortex) and more gray matter ( 31 ). These factors can account for educational differences across racial minorities and thus affect the mortality data associated with neurodegeneration, as differences in educational program enrollments and degrees, and learning abilities have been reported by previous studies across varying races and genders ( 32 ). White people were observed with a higher increase in access to early educational programs, with the greatest inclining trends in learning disabilities too ( 32 ). The findings of our study indicated a higher mortality rate for the Midwest regions of the US (APC of 8.14%), especially in the areas with the highest levels of urbanisation. South exhibited significant fluctuations, with a marked decline in the APC during the years 2021 to 2025. Urban populations generally tend to have better living standards and a decreased crude death rate ( 33 ). Although various factors can account for the disparities, rural populations have a higher percentage of people at 65 years of age and above, and they are less likely to have hospital visits with a higher incidence of poverty ( 34 ) despite having an overall greater burden of modifiable risk factors, including cardiometabolic, psychosocial, and behavioural factors ( 34 ). The South and the Midwest were noted to have a higher ratio of these disparities ( 34 ), which aligns with our findings. These underlying elements may account for less documentation of the NDDs and thus mortality. Furthermore, research has also suggested outdoor air pollution as a risk factor for the development of NDDs, and an overall reduced total cerebral brain volume ( 41 ), which also explains the greater extent of mortality observed in urban populations. Additionally, residency near major roadways has a stronger positive correlation with dementia incidence, with stronger associations among urban dwellers ( 35 ). Among states, our analysis revealed that California and West Virginia were the states with the highest crude rates and age-adjusted mortality rate, reflecting regional disparities which may be secondary to varying lifestyles and thus modifiable risk factors across various states as a 2021 GBD study discovered that not only life expectancy of the US declined in 2021, Mississippi was ranked as the state with worst life expectancy in general for men, and West Virginia for women ( 36 ). West Virginia also had the overall highest age-standardised rate of years lived with disability ( 36 ). Regional differences can also be due to differing prevalence of underlying chronic health conditions, as well as socioeconomic factors and differences in healthcare access ( 37 ). Southeast was found to have the highest prevalence for chronic illnesses, with more prevalent areas depicting a large proportion of elderly and socioeconomically impaired individuals ( 37 ). A higher percentage of racial/ethnic minorities was also noted ( 37 ), further emphasizing the need for considering social demographics when addressing disease courses and prevalence. In our analysis, nursing homes and decedents’ homes were observed with the highest number of deaths among places of death. These findings are reaffirmed by research showing that most of the people residing in nursing homes are 65 years or above, with one third of people being older than 85 years of age ( 40 ). A total of 1.3 million US citizens reside in nursing homes ( 40 ). A decision to move to nursing homes in old age can be secondary to several reasons, including lack of independence, hospitalisations and declining health status ( 42 ), which are also observed in neurodegenerative diseases and age-related cognitive decline. Inability to carry out daily life tasks, disappointing home-based care, occupational deprivation, and family choices are also some of the reasons why the elderly wish to move to a nursing home ( 42 ), reflecting rising mortality within nursing home residents discovered in our analysis. Strengths and limitations we utilized the publicly available CDC Wonder database and evaluated mortality trends across various demographics and US states to create a more extensive and broad understanding of disease nature and progression, especially in underrepresented demographics. Our study spanned over 26 years, allowing for careful evaluation of long-term mortality trends and potential social determinants. However, we used the ICD codes, which may be subject to human error and misclassification as they are entered by the physicians, resulting in underreporting. Our CDC wonder study also does not account for underlying factors associated with mortality, especially in old age, such as comorbidities. Real-life applications Our study helps identify various gaps and factors across multiple demographics that may be increasing the disease burden, and therefore, these modifiable factors can be identified to generate more public health screening and treatment programs for unspecified and underreported NDDs, especially while also enhancing workforce planning in neurology for geriatrics. Incorporating a more patient-centered and holistic approach can also help us combat social inequities not just in healthcare but also in general, and reduce the overall disease and economic burden. Conclusion In summary, our US-based national analysis from 1999–2025 presents an overall rising trend in the AAMR of the unspecified nervous system degenerative disorders (ICD code G31.9). We have discovered marked discrepancies in our analysis across multiple demographics, signifying crucial insights for health ministries, policymakers, and healthcare workers. High mortality was observed for increasingly older age groups, racial minorities such as Asian and Hispanic populations, females, and metropolitan regions, particularly in the Midwest. Even though further research is necessary to determine modifiable risk factors, we identify several social determinants associated with the NDD mortality. Abbreviations AAMR – Age-Adjusted Mortality Rate APC – Annual Percent Change AAPC – Average Annual Percent Change CDC – Centres for Disease Control and Prevention ICD-10 – International Classification of Diseases, 10th Revision NH – Non-Hispanic Declarations Ethics Approval and Consent to Participate This study used publicly available, de-identified data from CDC WONDER and was exempt from institutional review board approval. Consent for Publication Not applicable. Availability of Data and Materials The dataset analyzed in this study is publicly available on the CDC WONDER online database (https://wonder.cdc.gov/). No special access permissions were required. Conflict of Interests The authors declare that they have no conflicts of interests. Funding The authors received no specific funding for this work. Authors’ Contributions Palwasha Asghar conceptualized the article, critically evaluated the literature, and supervised the study. Muhammad Jawad handled data extraction and analysis. Razeena Zahid, Kinza Irshad, and Muhammad Talha contributed to manuscript writing. Wajeeha Iftikhar Shah contributed to the making of supplementary files: Asad Khan and Raghabendra kumar made the Figures and the central illustration. All authors reviewed and approved the final version of the manuscript. Acknowledgments Not applicable. Peer and provenance statement: Not Applicable Artificial Intelligence Use: No artificial intelligence tools were used in the study design, data analysis, or manuscript writing. References GBD 2017 US Neurological Disorders Collaborators, Feigin VL, Vos T, Alahdab F, Amit AM, Bärnighausen TW, Beghi E, Beheshti M, Chavan PP, Criqui MH, Desai R. Burden of neurological disorders across the US from 1990-2017: a global burden of disease study. JAMA Neurology. 2021 Feb;78(2):165-76. Steinmetz JD, Seeher KM, Schiess N, Nichols E, Cao B, Servili C, Cavallera V, Cousin E, Hagins H, Moberg ME, Mehlman ML. 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Supplementary Files supplementaryTables16.docx Cite Share Download PDF Status: Published Journal Publication published 27 Apr, 2026 Read the published version in BMC Neurology → Version 1 posted Editorial decision: Revision requested 30 Mar, 2026 Reviews received at journal 30 Mar, 2026 Reviewers agreed at journal 26 Mar, 2026 Reviews received at journal 26 Mar, 2026 Reviewers agreed at journal 26 Mar, 2026 Reviewers agreed at journal 26 Mar, 2026 Reviewers agreed at journal 26 Mar, 2026 Reviews received at journal 20 Mar, 2026 Reviewers agreed at journal 20 Mar, 2026 Reviews received at journal 18 Mar, 2026 Reviewers agreed at journal 18 Mar, 2026 Reviewers invited by journal 18 Mar, 2026 Editor invited by journal 03 Mar, 2026 Editor assigned by journal 25 Feb, 2026 Submission checks completed at journal 25 Feb, 2026 First submitted to journal 24 Feb, 2026 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. 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(1999-2020)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8958661/v1/d2c2372225fb8b54ddfb76d3.png"},{"id":105727812,"identity":"e28e8564-ba3f-49ec-98d0-402b75fb9c62","added_by":"auto","created_at":"2026-03-30 11:04:13","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":93812,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCensus Region Stratified Mortality Trends (1999-2025)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8958661/v1/8793e5ad13d8e99ce12f5475.png"},{"id":105562870,"identity":"0f25d0e5-f2cd-445d-9d93-40e794f5721e","added_by":"auto","created_at":"2026-03-27 12:45:02","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":73278,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAge Stratified Mortality Trends (1999-2025)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8958661/v1/587937e0166dfac190479335.png"},{"id":105061901,"identity":"8691b292-6f2d-4dae-9676-9c2439da7954","added_by":"auto","created_at":"2026-03-20 13:01:17","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":92622,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePlaces of Death\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-8958661/v1/6628a857a62cb217e942f724.png"},{"id":105061903,"identity":"17824ab7-bdb8-4c05-81d4-8d774dbd743e","added_by":"auto","created_at":"2026-03-20 13:01:17","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":68287,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEpidemiology of Unspecified Degenerative Nervous System Disorders, States (1999-2025)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-8958661/v1/6998cee878ea813320d44e74.png"},{"id":105061904,"identity":"e4afaa97-93cd-4803-8867-6bf3b49a2144","added_by":"auto","created_at":"2026-03-20 13:01:17","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":391916,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCentral Illustration\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-8958661/v1/db443892860a404ccce4784b.png"},{"id":108439273,"identity":"34352dfa-30e4-47c1-93cd-636a4e3d3c77","added_by":"auto","created_at":"2026-05-04 16:18:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1600422,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8958661/v1/756fb2f8-dfc9-46c8-8e84-6b36ebf627c2.pdf"},{"id":105061897,"identity":"e5233536-bcb6-4bfb-bc67-bbb41cc5426c","added_by":"auto","created_at":"2026-03-20 13:01:17","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":40207,"visible":true,"origin":"","legend":"","description":"","filename":"supplementaryTables16.docx","url":"https://assets-eu.researchsquare.com/files/rs-8958661/v1/d1ef1c2e6c9ddb69f11c98c4.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Epidemiology of Unspecified Degenerative Nervous System Disorders: Mortality Trends via CDC WONDER (1999-2025)","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe ICD-10 code G31.9 designates unspecified degenerative nervous system disorders, including senile brain degeneration and other progressive neural atrophies that lack defined medical classifications. The US population faces significant health impacts from these disorders because more than 54% of Americans experience neurological diseases, according to a recent study [1]. The aging population has driven up disease rates, which increased disability-adjusted life years from 1990 to 2021 by 55% [2]. The disease cycle starts with continuous nerve cell death resulting in cognitive deterioration, motor dysfunction, and patient dependence, while cardiovascular disease and other health conditions worsen the symptoms. The age-standardized prevalence rates for neurodegenerative diseases showed a 16.2% increase in Parkinson's disease from 1990 to 2017, which indicates a larger societal pattern. The progressive nature of this condition creates higher demands for caregivers while driving up national healthcare costs [1].\u003c/p\u003e \u003cp\u003eThe condition remains underdiagnosed because of its unusual symptoms resembling other illnesses and because diagnostic testing takes multiple years to complete in NMOSD cases and similar disorders. Advanced imaging technologies and biomarker tests remain inaccessible to many patients, creating obstacles for their identification. The condition remains untreated because its existence remains hidden from recognition, leading to worse health results [3].​ The age-adjusted mortality rates (AAMR) for neurological diseases experienced a substantial decline from 98.6 per 100,000 in 1999 to 84.2 in 2013 because of better medical treatments. Neurodegenerative disorders experienced an increase from 16.9 to 36.8 by 2017. Unspecified degenerative nervous system disorders, which doctors classify with G31 codes, have become a major contributor to increasing healthcare expenses. Alzheimer's dementia shows a U-shaped mortality pattern, explaining the increasing health costs. The overall reductions create a persistent challenge, leading to increased regional effects of these unknown medical conditions [4]​.\u003c/p\u003e \u003cp\u003eThe pattern of death from unknown degenerative brain diseases shows that rural areas and nonmetropolitan areas experience higher death rates. The first group of people needs to travel up to 4.01 times longer to reach neurologists when compared to people who live in metropolitan areas. The first group of people needs to travel between 1.14 and 3.32 times longer to reach primary care doctors when compared to people who live in metropolitan areas. The problem of access to specialists exists because American Indian/Alaska Native and Hispanic groups face greater difficulties reaching medical experts, which leads to more cases of undiagnosed diseases and worse health results when compared to non-Hispanic White individuals. Nonmetropolitan residence establishes deep social inequalities reflecting the national pattern that shows rural areas experience higher death rates while facing restrictions on access to culturally competent medical services, which become especially critical during the rising death rates from Alzheimer's disease and related disorders in these communities [5].\u003c/p\u003e \u003cp\u003eThis study uses Joinpoint regression on CDC WONDER data to analyze AAMR trends and study disorder patterns that affected the period from 1999 to 2025. The national strategies will use our findings about stagnant areas and distributional disparities to create more specialized treatment plans. The study results provide policy guidance to address the growing challenges in public health [6].\u003c/p\u003e \n\n "},{"header":"Methodology","content":"\u003ch3\u003eData Source\u003c/h3\u003e\u003cp\u003eWE employed the CDC WONDER (Centers for Disease Control and Prevention Wide-Ranging Online Data for Epidemiologic Research) database for the evaluation of the mortalities that have happened within the United States related to unspecified degenerative nervous system disorders. We utilized the death certificates derived from the Multiple Cause‐of‐Death Public Use Record database and determined unspecified degenerative nervous system disorders as an underlying or contributing factor towards deaths (\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e–\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e). The CDC WONDER database has also been employed before in many other research studies for the analysis of temporal mortality trends related to unspecified degenerative nervous system disorders at the national level. Unspecified degenerative nervous system disorders-related mortalities were recognized using the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD‐10‐CM) codes G31.9, in individuals of all age groups. We enacted STROBE guidelines to maintain transparency and clarity in the analytical process. The study did not receive approval from a local institutional review board since it was using data from the CDC WONDER database, which involves publicly available de-identified data.\u003c/p\u003e\u003ch2\u003eData Extraction\u003c/h2\u003e\u003cp\u003eUnspecified degenerative nervous system disorders-related mortalities and population sizes were retrieved for analysis from 1999 to 2025. Demographic variables such as sex, race and ethnicity, age, and geographic variables such as urban-rural, place of death, and state were also extracted as mentioned on the death certificates (\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e). Racial demographic characteristics were defined as Asian or Pacific Islander, Black or African American, Hispanic or Latino, and White. Racial and ethnic characteristics were used as categories in CDC WONDER data analyses, which depended largely on the information collected from death certificates. The National Center for Health Statistics Urban-Rural Classification Scheme was used to classify the study population into metropolitan and nonmetropolitan areas, per the 2013 US Census classification (\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e\u003ch2\u003eStatistical Analysis:\u003c/h2\u003e\u003cp\u003eWe determined crude and age-adjusted mortality (AAMR) rates per 1\u0026nbsp;million people. The crude mortality rate (CMR) for a given year was calculated by dividing the total number of unspecified degenerative nervous system disorders-related deaths by the corresponding US population for that year. AAMRs were determined by direct standardization of the unspecified degenerative nervous system disorders-related deaths to the US population for the corresponding year 1999, as already explained (\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e). In this study, 27 years were included. For trend description concerning CMR and AAMR for unspecified degenerative nervous system disorders-related mortality, the Joinpoint regression software package (version 4.9.0.0; National Cancer Institute) was used (\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e). Given the existing temporal trends in CMR and AAMR, log-linear regression models were established. The methodologies discussed in the literature were applied to find the inflection points of the temporal trends of CMR and AAMR for unspecified degenerative nervous system disorders between 1999 and 2025 using Joinpoint regression (\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e). To estimate the annual percent change (APC) and the associated 95% confidence intervals (CIs), we used the permutation test. APC was considered as increasing or decreasing if the slope describing the change in mortality was significantly different from zero, determined by two-tailed t testing. Statistical significance for both APC and AAPC was set at an alpha level of 0.05, meaning trends were considered significant if their corresponding p-values were less than 0.05.\u003c/p\u003e\u003cp\u003eWe executed sensitivity analyses as a method to verify their results. We conducted a second mortality analysis, which examined cases with unspecified degenerative nervous system disorders (ICD-10-CM code G31.9) as their primary cause of death. The research team conducted subgroup analyses that separated participants based on their gender and racial/ethnic backgrounds to study how temporal trends moved between different demographic groups. We confirmed trend stability through age-adjusted mortality rate calculations, which used different standard population structures to calculate their results. We conducted an examination of APC and AAPC estimates from various models to determine whether different case definitions and standardization methods affected their main research conclusions.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eUnspecified degenerative nervous system disorders (ICD-10 code G31.9) accounted for deaths across all ages in the United States from 1999 to 2025. Temporal analysis demonstrated multiple joinpoints, indicating fluctuating mortality trends over the study period (Central Illustration).\u003c/p\u003e\n\u003ch3\u003eAnnual Trends for Degenerative Nervous System Disorder–Related AAMR\u003c/h3\u003e\n\u003cp\u003eThe overall Age-Adjusted Mortality Rate (AAMR) showed four join points. From 1999 to 2011, mortality increased significantly (APC 1.34%). This was followed by a sharp, significant rise from 2011 to 2014 (APC 15.79%). Subsequently, the trend declined from 2014 to 2018 (APC\u0026thinsp;\u0026minus;\u0026thinsp;3.14%). Mortality again increased significantly between 2018 and 2021 (APC 13.38%), followed by a decline from 2021 to 2025 (APC\u0026thinsp;\u0026minus;\u0026thinsp;1.89%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e)(Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAge-adjusted mortality rates per 100,000, stratified by sex in the United States, 1999 to 2025.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge-Adjusted Mortality Rate (95% CI)\u003c/p\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAge-Adjusted Mortality Rate (95% CI)\u003c/p\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAge-Adjusted Mortality Rate (95% CI)\u003c/p\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.67 (0.64, 0.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.78 (0.72, 0.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.60 (0.56, 0.64)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.71 (0.67, 0.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.84 (0.78, 0.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.61 (0.57, 0.65)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.64 (0.61, 0.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.72 (0.67, 0.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.57 (0.53, 0.60)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.65 (0.62, 0.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.73 (0.68, 0.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.58 (0.55, 0.62)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.63 (0.60, 0.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.68 (0.63, 0.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.59 (0.55, 0.62)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.67 (0.64, 0.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.72 (0.67, 0.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.63 (0.59, 0.67)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.69 (0.66, 0.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.74 (0.69, 0.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.65 (0.61, 0.69)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.70 (0.67, 0.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.75 (0.70, 0.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.69 (0.65, 0.72)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.72 (0.69, 0.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.75 (0.70, 0.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.70 (0.66, 0.74)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.74 (0.71, 0.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.76 (0.71, 0.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.73 (0.70, 0.77)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.75 (0.72, 0.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.74 (0.69, 0.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.73 (0.70, 0.77)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.73 (0.70, 0.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.75 (0.70, 0.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.72 (0.69, 0.76)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.75 (0.72, 0.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.79 (0.74, 0.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.72 (0.68, 0.75)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.80 (0.77, 0.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.78 (0.73, 0.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.80 (0.76, 0.84)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.08 (1.05, 1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.06 (1.00, 1.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.06 (1.02, 1.11)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.13 (1.10, 1.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.07 (1.02, 1.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.15 (1.10, 1.19)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.11 (1.08, 1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.10 (1.05, 1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.13 (1.08, 1.17)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.05 (1.02, 1.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.04 (0.99, 1.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.06 (1.02, 1.10)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.10 (1.07, 1.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.02 (0.97, 1.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.14 (1.09, 1.18)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.01 (0.98, 1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.95 (0.90, 0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.03 (0.99, 1.08)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.10 (1.07, 1.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.07 (1.02, 1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.09 (1.05, 1.13)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.37 (1.33, 1.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.30 (1.24, 1.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.38 (1.34, 1.43)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.50 (1.46, 1.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.40 (1.35, 1.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.56 (1.51, 1.61)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.44 (1.40, 1.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.37 (1.31, 1.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.49 (1.44, 1.53)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.40 (1.36, 1.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.25 (1.20, 1.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.47 (1.42, 1.52)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.33 (1.30, 1.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.23 (1.18, 1.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.41 (1.37, 1.46)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.44 (1.40, 1.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.36 (1.31, 1.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.47 (1.43, 1.52)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cem\u003eAbbreviations: CI, Confidence Interval\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eDegenerative Nervous System Disorder–Related AAMR Stratified by Sex\u003c/h3\u003e\n\u003cp\u003eAmong females, four join points were identified. Mortality increased significantly from 1999 to 2011 (APC 2.13%), followed by a marked rise from 2011 to 2014 (APC 16.40%). A decline occurred from 2014 to 2018 (APC\u0026thinsp;\u0026minus;\u0026thinsp;3.05%), after which rates increased significantly from 2018 to 2021 (APC 13.85%). The trend then decreased from 2021 to 2025 (APC\u0026thinsp;\u0026minus;\u0026thinsp;1.55%).\u003c/p\u003e \u003cp\u003eAmong males however, mortality remained stable from 1999 to 2011 (APC\u0026thinsp;\u0026minus;\u0026thinsp;0.14%). Rates increased from 2011 to 2014 (APC 14.37%), declined from 2014 to 2018 (APC\u0026thinsp;\u0026minus;\u0026thinsp;3.50%), rose again from 2018 to 2021 (APC 12.89%), and declined from 2021 to 2025 (APC\u0026thinsp;\u0026minus;\u0026thinsp;2.10%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e)(Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eDegenerative Nervous System Disorder\u0026ndash;Related AAMR Stratified by Race/Ethnicity\u003c/h2\u003e \u003cp\u003eRace-stratified analysis demonstrated heterogeneous patterns.\u003c/p\u003e \u003cp\u003eAmong Asian or Pacific Islander individuals (two joinpoints), mortality declined from 1999 to 2011 (APC\u0026thinsp;\u0026minus;\u0026thinsp;1.84%), followed by a sharp rise from 2011 to 2014 (APC 90.24%). Thereafter, rates declined significantly from 2014 to 2025 (APC\u0026thinsp;\u0026minus;\u0026thinsp;2.41%).\u003c/p\u003e \u003cp\u003eAmong Black or African American individuals (three joinpoints), mortality increased significantly from 1999 to 2013 (APC 3.80%), declined from 2013 to 2017 (APC\u0026thinsp;\u0026minus;\u0026thinsp;6.33%), increased from 2017 to 2020 (APC 21.89%), and declined significantly from 2020 to 2025 (APC\u0026thinsp;\u0026minus;\u0026thinsp;5.75%).\u003c/p\u003e \u003cp\u003eAmong White individuals (four joinpoints), mortality remained relatively stable from 1999 to 2011 (APC\u0026thinsp;\u0026minus;\u0026thinsp;0.26%), increased significantly from 2011 to 2014 (APC 16.36%), declined from 2014 to 2018 (APC\u0026thinsp;\u0026minus;\u0026thinsp;0.65%), rose again from 2018 to 2021 (APC 12.31%), and decreased from 2021 to 2025 (APC\u0026thinsp;\u0026minus;\u0026thinsp;1.11%).\u003c/p\u003e \u003cp\u003eAmong Hispanic or Latino individuals (two joinpoints), mortality increased significantly from 1999 to 2013 (APC 10.94%), declined sharply from 2013 to 2016 (APC\u0026thinsp;\u0026minus;\u0026thinsp;20.44%), and subsequently increased significantly from 2016 to 2025 (APC 3.88%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e)(Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAge-adjusted mortality rates per 100,000, stratified by race in the United States, 1999 to 2025.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge-Adjusted Mortality Rate (95% CI)\u003c/p\u003e \u003cp\u003eNH Asian or Pacific Islander\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAge-Adjusted Mortality Rate (95% CI)\u003c/p\u003e \u003cp\u003eNH Black or African American\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAge-Adjusted Mortality Rate (95% CI)\u003c/p\u003e \u003cp\u003eNH White\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAge-Adjusted Mortality Rate (95% CI)\u003c/p\u003e \u003cp\u003eHispanic or Latino\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.52 (0.35, 0.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.56 (0.46, 0.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.70 (0.67, 0.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.53 (0.40, 0.68)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.37 (0.22, 0.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.56 (0.47, 0.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.71 (0.67, 0.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.64 (0.50, 0.80)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.40 (0.26, 0.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.67 (0.57, 0.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.64 (0.61, 0.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.68 (0.54, 0.84)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.39 (0.25, 0.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.62 (0.52, 0.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.66 (0.62, 0.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.87 (0.70, 1.04)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.43 (0.30, 0.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.65 (0.55, 0.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.62 (0.59, 0.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.91 (0.74, 1.07)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.30 (0.20, 0.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.76 (0.64, 0.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.64 (0.61, 0.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.07 (0.89, 1.24)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.37 (0.25, 0.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.79 (0.67, 0.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.67 (0.64, 0.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.08 (0.91, 1.25)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.26 (0.16, 0.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.70 (0.60, 0.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.68 (0.65, 0.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.75 (1.54, 1.97)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.22 (0.14, 0.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.74 (0.64, 0.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.67 (0.64, 0.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.82 (1.61, 2.03)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.28 (0.19, 0.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.71 (0.60, 0.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.69 (0.66, 0.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.04 (1.82, 2.25)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.27 (0.18, 0.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.73 (0.63, 0.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.70 (0.67, 0.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.72 (1.53, 1.92)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.40 (0.29, 0.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.74 (0.64, 0.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.66 (0.63, 0.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.01 (1.81, 2.22)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.24 (0.16, 0.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.94 (0.82, 1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.65 (0.62, 0.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.24 (2.04, 2.45)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.44 (0.33, 0.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.07 (0.95, 1.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.66 (0.63, 0.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.47 (2.26, 2.68)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.80 (1.57, 2.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.95 (0.84, 1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.95 (0.91, 0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.60 (2.40, 2.81)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.10 (1.86, 2.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.91 (0.80, 1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00 (0.96, 1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.41 (2.22, 2.60)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.30 (2.06, 2.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.81 (0.71, 0.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.04 (1.00, 1.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.71 (1.55, 1.87)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.25 (2.02, 2.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.76 (0.66, 0.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00 (0.97, 1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.36 (1.22, 1.49)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.97 (1.76, 2.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.84 (0.74, 0.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.08 (1.04, 1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.46 (1.32, 1.60)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.51 (1.33, 1.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.84 (0.74, 0.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.99 (0.95, 1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.45 (1.32, 1.59)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.37 (1.21, 1.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.96 (0.86, 1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.06 (1.02, 1.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.42 (1.29, 1.55)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.91 (1.72, 2.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.38 (1.26, 1.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.32 (1.28, 1.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.59 (1.45, 1.72)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.05 (1.85, 2.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.30 (1.17, 1.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.45 (1.41, 1.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.18 (2.02, 2.34)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.05 (1.86, 2.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.22 (1.10, 1.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.38 (1.34, 1.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.95 (1.80, 2.09)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.67 (1.50, 1.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.16 (1.05, 1.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.39 (1.34, 1.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.82 (1.68, 1.96)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.63 (1.47, 1.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.95 (0.85, 1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.35 (1.30, 1.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.78 (1.64, 1.91)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.86 (1.69, 2.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.02 (0.91, 1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.40 (1.36, 1.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.88 (1.74, 2.02)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003eAbbreviations: CI, Confidence Interval; NH, Non-Hispanic\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDegenerative Nervous System Disorder–Related AAMR Stratified by Urbanization\u003c/h3\u003e\n\u003cp\u003eIn metropolitan areas (three joinpoints), mortality increased significantly from 1999 to 2011 (APC 2.23%) and from 2011 to 2014 (APC 16.64%). Rates continued rising from 2014 to 2018 (APC 4.60%) and increased significantly from 2018 to 2020 (APC 15.17%). In non-metropolitan areas (one joinpoint), mortality declined significantly from 1999 to 2012 (APC\u0026thinsp;\u0026minus;\u0026thinsp;5.23%), followed by a significant increase from 2012 to 2020 (APC 9.35%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e)(Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAge-adjusted mortality rates per 100,000, stratified by urbanization in the United States, 1999 to 2020.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge-Adjusted Mortality Rate (95% CI)\u003c/p\u003e \u003cp\u003eMetropolitan\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAge-Adjusted Mortality Rate (95% CI)\u003c/p\u003e \u003cp\u003eNon-Metropolitan\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.66 (0.62, 0.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.77 (0.69, 0.84)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.69 (0.66, 0.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.73 (0.66, 0.80)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.64 (0.61, 0.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.61 (0.55, 0.68)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.64 (0.61, 0.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.61 (0.55, 0.68)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.66 (0.63, 0.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.54 (0.48, 0.61)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.69 (0.66, 0.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.59 (0.52, 0.65)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.74 (0.70, 0.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.50 (0.44, 0.57)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.77 (0.74, 0.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.45 (0.39, 0.51)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.77 (0.73, 0.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.48 (0.42, 0.54)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.83 (0.80, 0.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.40 (0.34, 0.45)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.81 (0.77, 0.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.43 (0.38, 0.49)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.79 (0.76, 0.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.44 (0.38, 0.49)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.83 (0.80, 0.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.42 (0.37, 0.48)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.87 (0.84, 0.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.38 (0.33, 0.43)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.23 (1.19, 1.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.35 (0.30, 0.41)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.27 (1.23, 1.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.44 (0.39, 0.50)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.27 (1.22, 1.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.48 (0.43, 0.54)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.17 (1.13, 1.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.49 (0.43, 0.55)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.22 (1.18, 1.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.55 (0.49, 0.60)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.10 (1.06, 1.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.62 (0.55, 0.68)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.16 (1.13, 1.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.65 (0.59, 0.72)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.49 (1.45, 1.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.76 (0.69, 0.82)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003cem\u003eAbbreviations: CI, Confidence Interval\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eDegenerative Nervous System Disorder–Related AAMR Stratified by Census Region\u003c/h3\u003e\n\u003cp\u003eRegional analysis demonstrated distinct temporal patterns. In the Northeast (one joinpoint), mortality declined from 1999 to 2009 (APC\u0026thinsp;\u0026minus;\u0026thinsp;5.50%), followed by a significant increase from 2009 to 2025 (APC 3.14%). In the Midwest (one joinpoint), rates declined significantly from 1999 to 2012 (APC\u0026thinsp;\u0026minus;\u0026thinsp;5.22%), then increased significantly from 2012 to 2025 (APC 8.14%). In the South (four joinpoints), mortality increased significantly from 1999 to 2008 (APC 8.66%), remained stable from 2008 to 2012 (APC 0.76%), decreased from 2012 to 2016 (APC\u0026thinsp;\u0026minus;\u0026thinsp;9.44%), increased significantly from 2016 to 2021 (APC 13.12%), and declined again significantly from 2021 to 2025 (APC\u0026thinsp;\u0026minus;\u0026thinsp;7.12%). In the West (two joinpoints), mortality declined from 1999 to 2010 (APC\u0026thinsp;\u0026minus;\u0026thinsp;5.40%), increased sharply from 2010 to 2013 (APC 83.00%), and then rose modestly from 2013 to 2025 (APC 1.43%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e)(Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAge-adjusted mortality rates per 100,000, stratified by census region in the United States, 1999 to 2025.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge-Adjusted Mortality Rate (95% CI)\u003c/p\u003e \u003cp\u003eNortheast\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAge-Adjusted Mortality Rate (95% CI)\u003c/p\u003e \u003cp\u003eMidwest\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAge-Adjusted Mortality Rate (95% CI)\u003c/p\u003e \u003cp\u003eSouth\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAge-Adjusted Mortality Rate (95% CI)\u003c/p\u003e \u003cp\u003eWest\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.49 (0.43, 0.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.82 (0.75, 0.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.67 (0.62, 0.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.65 (0.58, 0.72)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.54 (0.48, 0.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.70 (0.63, 0.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.76 (0.70, 0.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.66 (0.59, 0.73)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.46 (0.41, 0.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.66 (0.60, 0.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.79 (0.73, 0.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.49 (0.44, 0.55)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.51 (0.45, 0.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.65 (0.59, 0.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.81 (0.76, 0.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.53 (0.47, 0.59)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.44 (0.39, 0.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.62 (0.56, 0.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.85 (0.79, 0.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.46 (0.41, 0.52)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.43 (0.37, 0.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.60 (0.54, 0.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.98 (0.92, 1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.48 (0.42, 0.54)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.43 (0.38, 0.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.52 (0.47, 0.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.11 (1.05, 1.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.48 (0.43, 0.54)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.34 (0.29, 0.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.51 (0.45, 0.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.22 (1.16, 1.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.49 (0.43, 0.54)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.31 (0.27, 0.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.51 (0.45, 0.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.28 (1.22, 1.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.41 (0.36, 0.46)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.34 (0.30, 0.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.44 (0.39, 0.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.39 (1.32, 1.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.45 (0.40, 0.50)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.30 (0.26, 0.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.46 (0.41, 0.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.42 (1.35, 1.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.42 (0.37, 0.47)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.32 (0.27, 0.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.42 (0.37, 0.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.40 (1.33, 1.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.44 (0.39, 0.49)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.35 (0.30, 0.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.40 (0.35, 0.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.42 (1.35, 1.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.46 (0.41, 0.51)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.30 (0.26, 0.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.44 (0.40, 0.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.44 (1.37, 1.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.61 (0.56, 0.67)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.34 (0.29, 0.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.39 (0.35, 0.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.31 (1.25, 1.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.15 (2.05, 2.26)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.36 (0.31, 0.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.47 (0.42, 0.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.19 (1.13, 1.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.43 (2.33, 2.54)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.39 (0.35, 0.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.44 (0.39, 0.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.06 (1.00, 1.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.62 (2.51, 2.74)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.38 (0.33, 0.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.50 (0.45, 0.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.91 (0.86, 0.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.48 (2.37, 2.58)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.36 (0.31, 0.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.55 (0.49, 0.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.17 (1.11, 1.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.35 (2.24, 2.45)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.38 (0.33, 0.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.57 (0.52, 0.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.20 (1.14, 1.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.72 (1.63, 1.80)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.43 (0.38, 0.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.59 (0.54, 0.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.35 (1.29, 1.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.76 (1.67, 1.85)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.53 (0.48, 0.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.92 (0.85, 0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.66 (1.60, 1.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.11 (2.01, 2.20)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.55 (0.50, 0.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.79 (0.73, 0.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.77 (1.70, 1.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.61 (2.50, 2.72)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.54 (0.49, 0.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.64 (0.59, 0.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.64 (1.58, 1.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.68 (2.57, 2.79)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.42 (0.38, 0.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.83 (0.77, 0.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.56 (1.50, 1.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.44 (2.34, 2.55)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.44 (0.40, 0.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.93 (0.87, 1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.35 (1.29, 1.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.44 (2.34, 2.54)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.49 (0.44, 0.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.12 (1.05, 1.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.39 (1.33, 1.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.67 (2.56, 2.77)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003eAbbreviations: CI, Confidence Interval\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eDegenerative Nervous System Disorder\u0026ndash;Related Mortality Stratified by State\u003c/h2\u003e \u003cp\u003eAnalysis of State age-adjusted mortality rates (AAMRs) demonstrated significant inter-state differences in added mortality burden from 1999\u0026ndash;2025. The highest AAMRs were observed in Florida, with an AAMR of 2.99, and California (AAMR 2.49), with West Virginia, Washington, and Utah also displaying elevated rates. However, the lowest AAMRs were seen in New York and Connecticut (AAMRs of 0.29), followed by Arkansas and New Jersey (AAMRs of 0.33), and finally Delaware (AAMR of 0.36). These findings indicate marked differences between states in cumulative mortality burden across the study period. (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e7\u003c/span\u003e) (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAge-adjusted mortality rates per 100,000, stratified by state in the United States, 1999 to 2025.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eState\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1999\u0026ndash;2020 AAMR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2021\u0026ndash;2025 AAMR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlabama\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.53 (0.48, 0.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.18 (1.06, 1.30)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlaska\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.43 (0.30, 0.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnreliable\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArizona\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.41 (0.38, 0.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.58 (1.47, 1.69)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArkansas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.34 (0.30, 0.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.28 (0.20, 0.37)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCalifornia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.91 (1.88, 1.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.57 (3.50, 3.65)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eColorado\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.51 (0.46, 0.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.03 (0.91, 1.14)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConnecticut\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.28 (0.25, 0.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.32 (0.25, 0.40)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDelaware\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.37 (0.29, 0.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.30 (0.18, 0.47)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistrict of Columbia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.44 (0.33, 0.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFlorida\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.00 (2.96, 3.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.94 (2.86, 3.02)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGeorgia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.54 (0.51, 0.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.43 (0.38, 0.49)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHawaii\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.47 (0.40, 0.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.47 (0.35, 0.62)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIdaho\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.73 (0.64, 0.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.62 (0.49, 0.78)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIllinois\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.52 (0.49, 0.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.60 (0.54, 0.65)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndiana\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.48 (0.45, 0.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.43 (0.37, 0.49)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIowa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.54 (0.49, 0.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.46 (0.37, 0.57)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKansas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.72 (0.65, 0.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.61 (0.49, 0.72)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKentucky\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.41 (0.37, 0.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.27 (0.21, 0.34)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLouisiana\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.37 (0.33, 0.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.67 (0.57, 0.77)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.71 (0.62, 0.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.95 (0.77, 1.14)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaryland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.39 (0.36, 0.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.53 (0.45, 0.61)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMassachusetts\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.54 (0.50, 0.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.42 (0.36, 0.48)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMichigan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.42 (0.39, 0.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.74 (0.67, 0.81)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMinnesota\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.77 (0.72, 0.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.24 (1.13, 1.36)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMississippi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.70 (0.64, 0.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.38 (1.20, 1.56)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissouri\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.65 (0.61, 0.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.44 (2.28, 2.59)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMontana\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.65 (0.55, 0.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.59 (0.43, 0.78)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNebraska\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.52 (0.45, 0.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.51 (0.39, 0.66)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNevada\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.46 (0.40, 0.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.63 (0.52, 0.75)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNew Hampshire\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.42 (0.35, 0.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.52 (0.39, 0.68)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNew Jersey\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.27 (0.25, 0.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.46 (0.40, 0.51)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNew Mexico\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.41 (0.35, 0.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.52 (0.40, 0.66)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNew York\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.27 (0.25, 0.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.34 (0.31, 0.38)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorth Carolina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.47 (0.44, 0.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.43 (0.38, 0.48)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorth Dakota\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.51 (0.41, 0.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.55 (0.36, 0.81)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOhio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.56 (0.53, 0.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.66 (0.60, 0.72)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOklahoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.37 (0.33, 0.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.48 (0.39, 0.57)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOregon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.59 (0.54, 0.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.46 (1.32, 1.61)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePennsylvania\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.55 (0.53, 0.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.72 (0.67, 0.78)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRhode Island\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.47 (0.38, 0.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.40 (0.27, 0.57)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouth Carolina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.72 (0.67, 0.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.42 (1.28, 1.55)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouth Dakota\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.52 (0.43, 0.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.50 (0.33, 0.72)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTennessee\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.56 (0.52, 0.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.75 (1.61, 1.88)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTexas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.98 (0.95, 1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.85 (1.78, 1.92)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUtah\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.74 (0.66, 0.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.38 (2.12, 2.64)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVermont\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.83 (0.68, 0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.95 (0.68, 1.29)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVirginia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.39 (0.36, 0.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.65 (0.58, 0.72)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWashington\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.64 (0.60, 0.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.22 (3.05, 3.39)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWest Virginia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.12 (1.02, 1.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.48 (3.16, 3.81)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWisconsin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.62 (0.58, 0.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.20 (1.09, 1.31)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWyoming\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.53 (0.40, 0.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.62 (0.39, 0.94)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003cem\u003eAbbreviations: CI, Confidence Interval\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003cem\u003e* Indicates suppressed/unreliable estimate due to small counts\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eDegenerative Nervous System Disorder\u0026ndash;Related Mortality Stratified by Place of Death\u003c/h2\u003e \u003cp\u003eAnalysis of place of death revealed that the largest proportion of deaths occurred in nursing homes or long-term care facilities (36.27%), followed by deaths at the decedent\u0026rsquo;s home (30.70%). Hospital inpatient deaths accounted for 13,343 deaths (14.05%). Hospice facility deaths represented 7,870 deaths (8.29%), while deaths classified as \u0026ldquo;Other\u0026rdquo; accounted for 8,114 deaths (8.55%). A smaller proportion of deaths occurred in medical facilities designated as outpatient or emergency departments (1,772 deaths; 1.87%) or dead on arrival (133 deaths; 0.14%). Deaths with unknown places of death were less than 0.2% of cases. These findings illustrate that 2/3rd of the deaths occurred either in long-term care facilities or at home, suggesting increased mortality outside the inpatient hospital settings. (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePlace of Death.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlace of Death\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal Deaths\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e% of Total Deaths\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDecedent's home\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e29,146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30.70%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHospice facility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7,870\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.29%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedical Facility - Dead on Arrival\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.14%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedical Facility - Inpatient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13,343\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.05%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedical Facility - Outpatient or ER\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,772\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.87%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedical Facility - Status unknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.03%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNursing home/long term care\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e34,436\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36.27%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8,114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.55%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlace of death unknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.10%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e94,937\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e100.00%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eDegenerative Nervous System Disorder\u0026ndash;Related Mortality Stratified by Age Groups\u003c/h2\u003e \u003cp\u003eAnalysis of age-stratified crude rates revealed distinct age-dependent trends. Between 1999 and 2025, mortality was lower for those without joinpoints who were 5\u0026ndash;14 years old (APC\u0026thinsp;\u0026minus;\u0026thinsp;2.08%), 35\u0026ndash;44 years old (APC\u0026thinsp;\u0026minus;\u0026thinsp;1.68%), and 45\u0026ndash;54 years old (APC\u0026thinsp;\u0026minus;\u0026thinsp;2.12%). Adults between the ages of 55 and 64, however, experienced a decline from 1999 to 2011 with APC\u0026thinsp;\u0026minus;\u0026thinsp;2.33% and a significant increase from 2011 to 2025 (APC 1.69%). Those between the ages of 65 and 74 had a decrease in APC from 1999 to 2010 (\u0026minus;\u0026thinsp;3.37%) and a significant increase from 2010 to 2025 (APC 3.43%). Over the course of the study, mortality among people 75\u0026ndash;84 years old increased overall. The \u0026ge;\u0026thinsp;85-year group showed the most volatile pattern with considerable increases from 1999 to 2014 (APC 4.78% and 20.34%), a decline from 2014 to 2018 (APC\u0026thinsp;\u0026minus;\u0026thinsp;3.68%), a significant increase from 2018 to 2021 (APC 15.63%), and a decline from 2021 to 2025 (APC\u0026thinsp;\u0026minus;\u0026thinsp;2.23%).(Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e5\u003c/span\u003e)(Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e5\u003c/span\u003e)(Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCrude mortality rates per 100,000, stratified by age group in the United States, 1999 to 2025.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCrude Rate (95% CI)\u003c/p\u003e \u003cp\u003e5\u0026ndash;14 years\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCrude Rate (95% CI)\u003c/p\u003e \u003cp\u003e35\u0026ndash;44 years\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCrude Rate (95% CI)\u003c/p\u003e \u003cp\u003e45\u0026ndash;54 years\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCrude Rate (95% CI)\u003c/p\u003e \u003cp\u003e55\u0026ndash;64 years\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCrude Rate (95% CI)\u003c/p\u003e \u003cp\u003e65\u0026ndash;74 years\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCrude Rate (95% CI)\u003c/p\u003e \u003cp\u003e75\u0026ndash;84 years\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCrude Rate (95% CI)\u003c/p\u003e \u003cp\u003e85\u0026thinsp;+\u0026thinsp;years\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.08 (0.06, 0.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.09 (0.07, 0.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.19 (0.14, 0.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.53 (0.44, 0.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.68 (1.50, 1.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.65 (4.26, 5.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e14.49 (13.33, 15.65)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.06 (0.04, 0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.08 (0.06, 0.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.19 (0.15, 0.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.50 (0.41, 0.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.68 (1.49, 1.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.80 (4.41, 5.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e15.12 (13.95, 16.29)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.06 (0.04, 0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.10 (0.07, 0.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.20 (0.16, 0.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.52 (0.43, 0.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.56 (1.38, 1.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.95 (3.60, 4.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e14.49 (13.36, 15.63)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.09 (0.07, 0.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.06 (0.04, 0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.16 (0.12, 0.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.50 (0.41, 0.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.48 (1.30, 1.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.39 (4.02, 4.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e15.50 (14.33, 16.66)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.06 (0.04, 0.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.09 (0.07, 0.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.20 (0.16, 0.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.51 (0.43, 0.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.42 (1.24, 1.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.04 (3.69, 4.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e15.43 (14.28, 16.58)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.09 (0.06, 0.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.08 (0.06, 0.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.17 (0.13, 0.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.48 (0.40, 0.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.46 (1.28, 1.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.43 (4.06, 4.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e16.52 (15.34, 17.70)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.07 (0.05, 0.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.10 (0.07, 0.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.19 (0.15, 0.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.52 (0.44, 0.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.55 (1.37, 1.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.23 (3.88, 4.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e18.24 (17.02, 19.46)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.07 (0.03, 0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.08 (0.05, 0.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.20 (0.16, 0.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.49 (0.41, 0.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.27 (1.11, 1.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.64 (4.27, 5.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e20.10 (18.84, 21.36)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.06 (0.04, 0.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.06 (0.04, 0.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.17 (0.13, 0.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.45 (0.38, 0.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.19 (1.04, 1.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.98 (4.60, 5.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e19.82 (18.59, 21.05)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.07 (0.05, 0.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.05 (0.03, 0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.13 (0.10, 0.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.40 (0.33, 0.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.18 (1.03, 1.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5.06 (4.67, 5.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e23.25 (21.94, 24.56)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.06 (0.04, 0.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.07 (0.04, 0.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.12 (0.09, 0.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.42 (0.36, 0.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.21 (1.06, 1.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.74 (4.36, 5.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e23.21 (21.93, 24.50)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.05 (0.03, 0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.07 (0.05, 0.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.14 (0.11, 0.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.44 (0.37, 0.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.18 (1.03, 1.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.52 (4.16, 4.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e23.32 (22.04, 24.60)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.07 (0.05, 0.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.06 (0.04, 0.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.13 (0.10, 0.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.35 (0.29, 0.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.18 (1.04, 1.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5.06 (4.68, 5.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e23.58 (22.33, 24.84)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.06 (0.02, 0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.05 (0.03, 0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.16 (0.12, 0.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.44 (0.37, 0.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.19 (1.05, 1.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5.22 (4.83, 5.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e25.48 (24.19, 26.77)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.05 (0.02, 0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.07 (0.05, 0.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.17 (0.13, 0.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.38 (0.32, 0.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.27 (1.13, 1.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e7.09 (6.64, 7.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e37.98 (36.42, 39.53)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.05 (0.03, 0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.06 (0.04, 0.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.14 (0.11, 0.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.46 (0.40, 0.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.36 (1.22, 1.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e7.03 (6.59, 7.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e40.36 (38.77, 41.94)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.05 (0.03, 0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.06 (0.04, 0.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.11 (0.08, 0.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.42 (0.35, 0.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.34 (1.20, 1.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6.89 (6.45, 7.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e41.66 (40.06, 43.25)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.06 (0.04, 0.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.06 (0.04, 0.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.12 (0.09, 0.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.43 (0.37, 0.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.46 (1.32, 1.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6.03 (5.62, 6.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e38.68 (37.16, 40.21)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.06 (0.02, 0.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.05 (0.03, 0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.12 (0.09, 0.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.47 (0.41, 0.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.40 (1.27, 1.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e7.17 (6.74, 7.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e40.10 (38.56, 41.64)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.06 (0.04, 0.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.05 (0.03, 0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.09 (0.07, 0.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.46 (0.39, 0.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.47 (1.34, 1.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6.35 (5.95, 6.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e35.51 (34.07, 36.95)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.05 (0.03, 0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.05 (0.03, 0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.15 (0.12, 0.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.48 (0.41, 0.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.44 (1.31, 1.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6.56 (6.17, 6.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e39.80 (38.28, 41.32)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.04 (0.01, 0.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.06 (0.04, 0.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.13 (0.10, 0.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.53 (0.46, 0.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.80 (1.66, 1.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e8.65 (8.20, 9.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e50.55 (48.84, 52.26)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.04 (0.02, 0.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.06 (0.04, 0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.15 (0.11, 0.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.55 (0.48, 0.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.85 (1.71, 2.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e8.91 (8.45, 9.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e57.52 (55.59, 59.44)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.04 (0.02, 0.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.08 (0.05, 0.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.14 (0.10, 0.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.50 (0.43, 0.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.86 (1.71, 2.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e8.86 (8.42, 9.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e53.72 (51.93, 55.50)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.05 (0.03, 0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.06 (0.04, 0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.15 (0.12, 0.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.50 (0.43, 0.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.76 (1.62, 1.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e7.98 (7.57, 8.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e53.45 (51.63, 55.27)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.05 (0.03, 0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.06 (0.04, 0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.09 (0.06, 0.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.44 (0.37, 0.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.66 (1.53, 1.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e8.36 (7.95, 8.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e50.22 (48.49, 51.96)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.04 (0.02, 0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.06 (0.04, 0.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.11 (0.08, 0.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.51 (0.44, 0.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.85 (1.71, 1.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e8.97 (8.55, 9.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e53.63 (51.84, 55.42)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003cem\u003eAbbreviations: CI, Confidence Interval\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eSensitivity Analysis\u003c/h2\u003e \u003cp\u003eSensitivity analyses demonstrated similar overall patterns. Overall mortality showed significant increases from 1999 to 2011 (APC 1.35%) and from 2011 to 2014 (APC 15.53%), a decline from 2014 to 2018 (APC\u0026thinsp;\u0026minus;\u0026thinsp;2.47%), and a significant increase from 2018 to 2025 (APC 4.89%).\u003c/p\u003e \u003cp\u003eSex, race, region, and urbanization-specific sensitivity analyses demonstrated consistent directional trends compared with the primary models.(Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTotal Deaths, population, AAPC and AAPC p values.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal Deaths\u003c/p\u003e \u003cp\u003e(1999\u0026ndash;2025)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePopulation\u003c/p\u003e \u003cp\u003e(1999\u0026ndash;2025)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAAPC (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAAPC p-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e94,938\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8,426,674,820\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.0148* (0.8443, 5.2319)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.006260\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37,177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,149,446,302\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.0272 (-0.2890, 4.3972)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.086730\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57,761\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,277,228,518\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.5678* (1.9488, 5.2125)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRace/Ethnicity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNH Asian or Pacific Islander\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4,999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e462,823,040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.6886 (-2.5263, 14.5959)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.180193\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNH Black or African American\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7,259\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,076,077,095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.1710 (-1.0972, 5.5471)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.195379\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNH White\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e71,040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5,373,539,687\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.7332* (1.0079, 4.4879)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001805\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHispanic or Latino\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11,128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,394,229,761\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.3611* (0.4816, 8.3905)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.027207\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCensus Region\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNortheast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7,738\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,499,844,553\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.2721 (-1.3970, 0.8655)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.637747\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMidwest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13,122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,811,852,704\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.2400* (0.1023, 2.3907)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.032568\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42,738\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,149,216,278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.7369* (1.1030, 4.3972)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000963\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31,340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,965,761,285\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.4229 (-4.3972, 16.2518)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.289794\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge Group\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u0026ndash;14 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e612\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,107,046,640\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.0774* (-2.8628, -1.2857)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35\u0026ndash;44 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e776\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,153,855,648\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.6791* (-2.4608, -0.8911)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000191\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e45\u0026ndash;54 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,670\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,130,750,794\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.1195* (-2.8360, -1.3977)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e55\u0026ndash;64 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4,560\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e976,491,209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.1852 (-0.8990, 0.5337)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.612660\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e65\u0026ndash;74 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10,266\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e683,488,110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.4958 (-0.2881, 1.2858)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.215798\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e75\u0026ndash;84 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24,396\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e389,198,776\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.3221* (2.7689, 3.8783)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.000001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e85\u0026thinsp;+\u0026thinsp;years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51,102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e151,040,781\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.1730* (3.2159, 7.1673)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.000001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUrbanization\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetropolitan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58,586\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5,739,475,649\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.9836* (2.1204, 5.8809)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Metropolitan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6,804\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,006,871,652\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0784 (-0.8513, 1.0168)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.869324\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003eAbbreviations: AAPC, Average Annual Percentage Change; CI, Confidence Interval; NH, Non-Hispanic.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003e* Indicates AAPC significantly differs from zero at the alpha\u0026thinsp;=\u0026thinsp;0.05 level.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eIn our analysis of nationwide death certificate data obtained via the publicly available CDC WONDER database related to the ICD code: G31.9, accounting for unspecified neurodegenerative disorders within the citizens of the United States, we demonstrate an overall rise in the unspecified NDDs mortality from 1999 to 2025. Although an overall rise in the AAMR since 1999 has been observed, fluctuations have also been reported, given the considerable declining trends in the APC between the years 2014\u0026ndash;2018 and then again between 2021\u0026ndash;2025. Similar shifting trends were also reported in both sexes, with females having a very slightly higher (1.47) AAMR than men (1.36) by 2025. Across the years, rising and falling mortality trends were present in all ethnic backgrounds analyzed, with marked increases especially during the years 2017\u0026ndash;2021 in most of them. An interestingly huge spike in the APC (90.24%) was observed in Asian or Pacific Islander populations from 2011\u0026ndash;2014. The highest AAMR was seen in the West, particularly, with the metropolitan areas possessing a higher APC than their non-metropolitan counterparts, and the Northeast had the lowest AAMR. The Midwest was observed to have the highest APC by 2025. Rising mortality recorded in our analysis is also reflected by an overall global health loss of 15% related to just brain diseases, which was even more than cancer and cardiovascular-related health burden (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). There has been a total increase of 63% in the prevalence of neurologic disorders, with a concerning prediction of a rise to 4.9\u0026nbsp;billion affected patients by 2050, with Alzheimer\u0026rsquo;s disease, a NDD, being one of the contributors to this disease burden (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDisorders related to neurodegeneration are vastly characterized by their clinical manifestations, mostly presenting as pyramidal and extrapyramidal movement disorders, and also cognitive or behavioural syndromes (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e), and include Alzheimer\u0026rsquo;s disease and other dementias along with Parkinson\u0026rsquo;s disease most commonly (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Compared to an AAMR of 0.66 in 1999, the overall AAMR had risen to 1.44 in 2025 in our analysis, which is reaffirmed by previous literature showing that almost 24.9\u0026nbsp;million people had dementia worldwide by 1990, and in 2021, this number had increased to 56.9\u0026nbsp;million (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). A good amount of changeable factors, including smoking, sedentary lifestyle, diabetes, and cardiac health, and nonchangeable factors, like age and genes, are linked to dementia (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Even though specific abnormal protein aggregations and regional vulnerability give rise to neurodegenerative disorders, they all also share core pathologic mechanisms related to gradually increasing nervous dysfunction and neuronal loss, such as proteotoxic stress, and dysfunction in ubiquitin\u0026ndash;proteasome and autophagy\u0026ndash;lysosomal pathways, oxidative stress, apoptosis, and inflammation in the brain (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Within our bodies, reactive oxygen and nitrogen species (RNOS) build up and result in oxidative stress and thus exacerbate aging and age-related diseases (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e), since oxidative stress is crucial in the development of neurodegeneration (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Likewise, these stressful conditions can also promote neuronal senescence, which is evident in neurodegenerative diseases like AD (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). Such mechanisms can help us understand the rise in crude rate associated with increasing ages seen in our study, since individuals aged 85 years and above had the highest proportion of deaths as well as crude rate, followed by those between the ages of 75\u0026ndash;84 years. This is evident by changes related to autophagy, an intricate degradative process triggered by various bodily responses to stress, such as reduced insulin and ATP concentration, while maintaining protein homeostasis (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). Increased mTOR stimulation and alteration in signalling cascades associated with aging impair autophagosome formation and thus autophagy, resulting in several nervous system changes, such as increased aggregation of tubular ER within axons and therefore greater calcium release from the ER, and enhanced neuronal activity (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). These underlying processes may help explain the rising burden of nervous system degeneration.\u003c/p\u003e \u003cp\u003eWhile it is fortunate to note a decline in the APC between the years 2021 and 2025, prior marked rises should not be ignored. The rise in the APC to 13.38% specially during the years 2028\u0026thinsp;\u0026minus;\u0026thinsp;2021 could have been influenced by COVID-19 pandemic and associated mortality as well, since during 2020 specifically when the population was not as widely vaccinated, slowly rising mortality percentage was observed for the elderly that was higher in men (20%) than women (15%), with an overall higher COVID-19 mortality for the elderly (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Higher COVID-related AAMR was observed in Hispanic and black individuals compared to white people as well (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e), signifying marked racial/ethnic disparities. These findings validate our study, considering the fact that ging is a foremost underlying factor responsible for the development of NDDs (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Recent declines in the APC could be secondary to advancements in healthcare, as life expectancy in both men and women is reported to increase by 1.1\u0026ndash;1.5 years at age 65 and 0.6\u0026ndash;0.8 years at age 85 in the presence of efficiently accessible healthcare (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e), and increases are also noted across both urban and rural regions (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). The older men had a 10\u0026ndash;14% increase in their life expectancy, in contrast with the women who had 6\u0026ndash;8%, generally suggesting a significant relationship between elderly mortality and access to healthcare (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAlthough overall APC in both females and males was approximately similar in our analysis by 2025, females exhibited a modestly higher AAMR by 2025 in comparison with their male counterparts. This finding also aligns with a cohort study in Spain carried out by Maitee Rosende-Roca et al., which not only had an overall greater proportion of female cohorts (71.6%) than male cohorts (28.4%), but the authors also noticed a rapid progression of disease in women diagnosed with mild Alzheimer\u0026rsquo;s disease dementia than men (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Additionally, sharp and statistically significant rises observed in both sexes, particularly during the years 2018 to 2021, can be subject to the emergence of COVID-19 pandemic. Research shows that women are more susceptible to developing long-COVID syndrome and thus neurological symptoms associated with it, such as depression, headaches, fatigue with foggy brain and anosmia, with a slightly greater prevalence of anxiety, PTSD, dysgeusia, and vertigo as well (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). These sex-based disparities can also be due to females exhibiting a heightened immune response to COVID-19 infection, with reportedly higher seropositivity and antibody titers (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Although a considerable disparity was not really reported in our analysis, studies have confirmed a higher rate of age-related atrophy in men than women, as well as cognitive decline at old age (at almost 65 years) (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). However, women are more likely to have higher rates of atrophy in the white matter (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). This can also be because of anatomical differences apparent between the two genders, as men display greater disparities in total gray matter of the brain, with pronounced volumes of the cingulate gyrus in their female counterparts (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). We also believe it is crucial to study modifiable risk factors associated with gendered differences and neurodegeneration, as loneliness was greater in Hispanic women (32.2%) than in Hispanic men (15.9%), even though men exhibited a greater link between loneliness and the occurrence of dementia (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRegarding race, our analysis revealed similar AAMRs by 2025 in Asian or Pacific Islander populations (1.86) and Hispanic or Latino populations (1.88). A steadily declining trend in the APC was observed for Asians or Pacific Islanders from 2014 to 2025 in comparison with Hispanics or Latinos, who depicted a significant increase (from \u0026minus;\u0026thinsp;20.44% to 3.88%) in the APC during the same time period. Even though this is contrasted previous research that showed a greater proportion of white individuals affected with Frontotemporal Dementia, a neurodegenerative disorder, when compared with Black and Asian populations (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e), it should also be noted that these disparities may be subject to underrepresentation of racial/ethnic minorities and inadequate sample size (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Additionally, we now know that social determinants of health like socio-economic background, environment and health-associated experiences differ across varying genders and ethnicities/races, with alterable risk factors being responsible for around 35% of dementia cases (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Moreover, noted disparity across demographics can be influenced by discrepancy in utilization of medical services as Altaaf Saadi et al. discovered that Black people were almost 30% less likely to seek neurologic outpatient consultation in comparison with white people, despite having a higher rate of hospitalization and ER visits (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). The likelihood dropped to 40% for Hispanics (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e), suggesting a potential association between mortality rate and healthcare access in the US. Interestingly, a possible association has been observed between educational status and cerebral pathology, further reaffirming brain reserve theory (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e), as more well-educated individuals were seen to tolerate a greater extent of gray matter volume atrophy of dACC, thus depicting resistance against AD pathology, with bilinguals displaying a higher stimulation of ACC (anterior cingulate cortex) and more gray matter (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). These factors can account for educational differences across racial minorities and thus affect the mortality data associated with neurodegeneration, as differences in educational program enrollments and degrees, and learning abilities have been reported by previous studies across varying races and genders (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). White people were observed with a higher increase in access to early educational programs, with the greatest inclining trends in learning disabilities too (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe findings of our study indicated a higher mortality rate for the Midwest regions of the US (APC of 8.14%), especially in the areas with the highest levels of urbanisation. South exhibited significant fluctuations, with a marked decline in the APC during the years 2021 to 2025. Urban populations generally tend to have better living standards and a decreased crude death rate (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Although various factors can account for the disparities, rural populations have a higher percentage of people at 65 years of age and above, and they are less likely to have hospital visits with a higher incidence of poverty (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e) despite having an overall greater burden of modifiable risk factors, including cardiometabolic, psychosocial, and behavioural factors (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). The South and the Midwest were noted to have a higher ratio of these disparities (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e), which aligns with our findings. These underlying elements may account for less documentation of the NDDs and thus mortality. Furthermore, research has also suggested outdoor air pollution as a risk factor for the development of NDDs, and an overall reduced total cerebral brain volume (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e), which also explains the greater extent of mortality observed in urban populations. Additionally, residency near major roadways has a stronger positive correlation with dementia incidence, with stronger associations among urban dwellers (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAmong states, our analysis revealed that California and West Virginia were the states with the highest crude rates and age-adjusted mortality rate, reflecting regional disparities which may be secondary to varying lifestyles and thus modifiable risk factors across various states as a 2021 GBD study discovered that not only life expectancy of the US declined in 2021, Mississippi was ranked as the state with worst life expectancy in general for men, and West Virginia for women (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). West Virginia also had the overall highest age-standardised rate of years lived with disability (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). Regional differences can also be due to differing prevalence of underlying chronic health conditions, as well as socioeconomic factors and differences in healthcare access (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). Southeast was found to have the highest prevalence for chronic illnesses, with more prevalent areas depicting a large proportion of elderly and socioeconomically impaired individuals (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). A higher percentage of racial/ethnic minorities was also noted (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e), further emphasizing the need for considering social demographics when addressing disease courses and prevalence. In our analysis, nursing homes and decedents\u0026rsquo; homes were observed with the highest number of deaths among places of death. These findings are reaffirmed by research showing that most of the people residing in nursing homes are 65 years or above, with one third of people being older than 85 years of age (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). A total of 1.3\u0026nbsp;million US citizens reside in nursing homes (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). A decision to move to nursing homes in old age can be secondary to several reasons, including lack of independence, hospitalisations and declining health status (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e), which are also observed in neurodegenerative diseases and age-related cognitive decline. Inability to carry out daily life tasks, disappointing home-based care, occupational deprivation, and family choices are also some of the reasons why the elderly wish to move to a nursing home (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e), reflecting rising mortality within nursing home residents discovered in our analysis.\u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and limitations\u003c/h2\u003e \u003cp\u003ewe utilized the publicly available CDC Wonder database and evaluated mortality trends across various demographics and US states to create a more extensive and broad understanding of disease nature and progression, especially in underrepresented demographics. Our study spanned over 26 years, allowing for careful evaluation of long-term mortality trends and potential social determinants. However, we used the ICD codes, which may be subject to human error and misclassification as they are entered by the physicians, resulting in underreporting. Our CDC wonder study also does not account for underlying factors associated with mortality, especially in old age, such as comorbidities.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eReal-life applications\u003c/strong\u003e \u003cp\u003eOur study helps identify various gaps and factors across multiple demographics that may be increasing the disease burden, and therefore, these modifiable factors can be identified to generate more public health screening and treatment programs for unspecified and underreported NDDs, especially while also enhancing workforce planning in neurology for geriatrics. Incorporating a more patient-centered and holistic approach can also help us combat social inequities not just in healthcare but also in general, and reduce the overall disease and economic burden.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, our US-based national analysis from 1999\u0026ndash;2025 presents an overall rising trend in the AAMR of the unspecified nervous system degenerative disorders (ICD code G31.9). We have discovered marked discrepancies in our analysis across multiple demographics, signifying crucial insights for health ministries, policymakers, and healthcare workers. High mortality was observed for increasingly older age groups, racial minorities such as Asian and Hispanic populations, females, and metropolitan regions, particularly in the Midwest. Even though further research is necessary to determine modifiable risk factors, we identify several social determinants associated with the NDD mortality.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u0026nbsp;AAMR – Age-Adjusted Mortality Rate\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;APC – Annual Percent Change\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;AAPC – Average Annual Percent Change\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;CDC – Centres for Disease Control and Prevention\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;ICD-10 – International Classification of Diseases, 10th Revision\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;NH – Non-Hispanic\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cu\u003eEthics Approval and Consent to Participate\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study used publicly available, de-identified data from CDC WONDER and was exempt from institutional review board approval.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cu\u003eConsent for Publication\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cu\u003eAvailability of Data and Materials\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe dataset analyzed in this study is publicly available on the CDC WONDER online database (https://wonder.cdc.gov/). No special access permissions were required.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cu\u003eConflict of Interests\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflicts of interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cu\u003eFunding\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors received no specific funding for this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cu\u003eAuthors’ Contributions\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePalwasha Asghar\u003c/em\u003e conceptualized the article, critically evaluated the literature, and supervised the study. \u003cem\u003eMuhammad Jawad\u003c/em\u003e handled data extraction and analysis. \u003cem\u003eRazeena Zahid, Kinza Irshad, and Muhammad Talha\u003c/em\u003e contributed to manuscript writing.\u003cem\u003eWajeeha Iftikhar Shah\u003c/em\u003e contributed to the making of supplementary files: \u003cem\u003eAsad Khan and\u0026nbsp;\u003c/em\u003eRaghabendra kumar made the Figures and the central illustration.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll authors reviewed and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cu\u003eAcknowledgments\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cu\u003ePeer and provenance statement:\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cu\u003eArtificial Intelligence Use:\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo artificial intelligence tools were used in the study design, data analysis, or manuscript writing.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGBD 2017 US Neurological Disorders Collaborators, Feigin VL, Vos T, Alahdab F, Amit AM, B\u0026auml;rnighausen TW, Beghi E, Beheshti M, Chavan PP, Criqui MH, Desai R. 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PMID: 39642048.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-neurology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nurl","sideBox":"Learn more about [BMC Neurology](http://bmcneurol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nurl","title":"BMC Neurology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-8958661/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8958661/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground:\u003c/h2\u003e \u003cp\u003eUnspecified degenerative nervous system disorders (ICD-10 G31.9) increasingly contribute to neurological mortality, yet national temporal patterns remain unclear.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e \u003cp\u003eWe analyzed U.S. mortality data (1999\u0026ndash;2025) from CDC WONDER. Age-adjusted mortality rates (AAMRs) were analyzed using Joinpoint regression to estimate annual percent changes (APCs), stratified by sex, race, urbanization, and census regions. Sensitivity analyses assessed stability.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e \u003cp\u003eOverall, AAMR increased from 1999 to 2011 (APC 1.34%) and sharply from 2011\u0026ndash;2014 (15.79%), declined from 2014 to 2018 (\u0026minus;\u0026thinsp;3.14%), rose from 2018\u0026ndash;2021 (13.38%), and decreased thereafter (\u0026minus;\u0026thinsp;1.89%). Females showed early increases, whereas males were stable before 2011. Racial and regional analyses revealed heterogeneity, with post-2010 increases in several groups. Metropolitan areas showed persistent increases, while nonmetropolitan trends fluctuated. Sensitivity analyses confirmed findings.\u003c/p\u003e\u003ch2\u003eConclusion:\u003c/h2\u003e \u003cp\u003eMortality from G31.9 disorders shows changing trends, varying across demographics and regions. These differences highlight gaps in diagnosis and care, emphasizing the need for targeted public health strategies.\u003c/p\u003e","manuscriptTitle":"Epidemiology of Unspecified Degenerative Nervous System Disorders: Mortality Trends via CDC WONDER (1999-2025)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-20 13:01:07","doi":"10.21203/rs.3.rs-8958661/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-30T13:33:59+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-30T05:19:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"174732107386648123666455499059523206159","date":"2026-03-27T02:07:03+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-26T12:35:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"323139655838632349008646070139889192348","date":"2026-03-26T12:11:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"245192243574318855702766112763070320287","date":"2026-03-26T08:27:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"182567953108858031692717162097742853382","date":"2026-03-26T05:26:26+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-20T20:05:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"168218904670578395784575153704834459503","date":"2026-03-20T19:52:05+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-18T23:52:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"235099050762609677032457044089442983753","date":"2026-03-18T23:49:31+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-18T04:06:10+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-03T14:02:43+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-26T01:06:07+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-26T01:05:01+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Neurology","date":"2026-02-24T14:25:13+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-neurology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nurl","sideBox":"Learn more about [BMC Neurology](http://bmcneurol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nurl","title":"BMC Neurology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4802f092-647a-4d9d-979e-33546fb9dd05","owner":[],"postedDate":"March 20th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-05-04T16:18:46+00:00","versionOfRecord":{"articleIdentity":"rs-8958661","link":"https://doi.org/10.1186/s12883-026-04925-2","journal":{"identity":"bmc-neurology","isVorOnly":false,"title":"BMC Neurology"},"publishedOn":"2026-04-27 15:57:17","publishedOnDateReadable":"April 27th, 2026"},"versionCreatedAt":"2026-03-20 13:01:07","video":"","vorDoi":"10.1186/s12883-026-04925-2","vorDoiUrl":"https://doi.org/10.1186/s12883-026-04925-2","workflowStages":[]},"version":"v1","identity":"rs-8958661","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8958661","identity":"rs-8958661","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}