Mortality Trends in Heart Disease and COPD from 1999-2020: A CDC Wonder Analysis

Mortality Trends in Heart Disease and COPD from 1999-2020: A CDC Wonder Analysis | 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 Systematic Review Mortality Trends in Heart Disease and COPD from 1999-2020: A CDC Wonder Analysis Javeria Gul, Eishal Khan, Maria Campwala, Ibadullah Khan, Asad Ullah Farooq, and 10 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8822335/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Heart failure (HF) and Chronic Obstructive Pulmonary Disease (COPD) are leading causes of morbidity and mortality in older adults. Their coexistence poses major clinical challenges and complicates management. This study quantified and analyzed mortality trends highlighting disparities by sex, race/ethnicity, age, region, state, and urban-rural status. Methods The CDC WONDER multiple-cause mortality database was used (1999–2020), including deaths in which both HF and COPD were recorded on death certificates. Age-adjusted mortality rates (AAMRs) per 100,000, with 95% confidence intervals (CIs), were calculated across demographic, geographic, and temporal variables using ICD-10 codes. Joinpoint regression identified statistically significant (p < 0.05) trend changes and annual percent changes (APCs). Results A total o f 1,054,847 deaths occurred in adults ≥ 15 years with both HF and COPD. In males, AAMR declined from 24.01 in 1999 to 21.22 in 2012, followed by an increase to 26.23 in 2020; in females, from 13.91 in 1999 to 14.68 in 2012 and to 18.59 in 2020. White individuals had the highest racial AAMR (19.18). The Midwest reported the highest regional AAMR (20.75), while West Virginia ranked highest among states (32.40). Noncore and micropolitan areas showed higher AAMRs compared with metropolitan areas. Conclusions Mortality associated with the coexistence of heart failure and chronic obstructive pulmonary disease in the United States has increased substantially over the past decade Persistent disparities were observed across sex, race/ethnicity, geography, and urban-rural status. These findings underscore the need for targeted public health strategies to reduce mortality and narrow disparities in high risk groups. Heart Failure Chronic Obstructive Pulmonary Disease Mortality Trends Epidemiology Disparities CDC WONDER United States Comorbidity Public Health Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Heart failure (HF) represents an escalating public health challenge both in the United States and worldwide. In the U.S., the prevalence of HF is projected to rise from 2.4% in 2012 to 3.0% by 2030, impacting more than 8 million adults [1]. Alongside this growing prevalence, concerning trends in HF-related mortality have emerged, with rates increasing despite widespread implementation of guideline-directed therapies known to improve survival and clinical outcomes [2] (Fig. 1 ). From an economic perspective, HF imposes a substantial burden on healthcare systems, with direct medical costs rising from $ 20.9 billion in 2012 to an estimated $ 53.1 billion by 2030 [3]. Recent epidemiologic analyses highlight that age-adjusted mortality rates from HF have been increasing across diverse demographic groups, a trend evident not only among the elderly aged ≥ 75 years [4] but also in younger adults aged 15–44 years [5]. These patterns underscore the widening impact of HF across the lifespan and suggest that existing interventions may be insufficient to curb the rising mortality burden. Chronic obstructive pulmonary disease (COPD), characterized by progressive and generally irreversible airflow limitation resulting from an abnormal inflammatory response in the lungs, has become an increasingly prominent public health concern. In the United States, COPD affects over 12 million individuals and ranks as the third leading cause of death [6–8]. Globally, the World Health Organization (WHO) estimates that approximately 210 million people are affected by COPD, with 64 million exhibiting symptomatic disease [9]. In recent years, COPD has gained heightened attention due to its substantial contribution to both morbidity and mortality. Given its growing impact on population health, updated analyses of COPD-related mortality are essential to guide effective prevention strategies, inform healthcare planning, and support policy intervention. Materials and Methods Study design and Population We conducted an observational analysis using death records from the CDC WONDER Multiple Cause of Death database for the period 1999–2020 [10], focusing on individuals aged 15–85 + years in the United States. Mortality records were reviewed for cases in which both Heart Faliure and COPD were listed among the causes of death. The 10th edition of the International Classification of Diseases (ICD-10) was used to identify relevant cases. HF was classified under I50 codes, including congestive HF (I50.0), left ventricular failure (I50.1), and unspecified HF (I50.9). COPD was classified under J44 codes, encompassing acute lower respiratory infection (J44.0), acute exacerbation, unspecified (J44.1), other specified types (J44.8), and unspecified cases (J44.9) [11]. Deaths were included only if both HF and COPD codes appeared; those without both codes were excluded. Because this study used publicly available, de-identified government data, Institutional Review Board approval was not required. Data abstraction The CDC WONDER dataset includes information on sex, race/ethnicity, urban-rural classification, region, state, age group, and place of death [10]. Sex was classified as male or female based on death certificate records. Race/ethnicity followed the Office of Management and Budget (1997) standards: Hispanic or Latino, non-Hispanic (NH) White, NH Black or African American, NH American Indian or Alaska Native, and NH Asian or Pacific Islander. Urban-rural status was defined according to the National Center for Health Statistics (2013) scheme, which classifies urban areas as large metropolitan regions (≥ 1 million population) or medium/small metropolitan regions (50,000-999,999 population). Rural areas are defined as non-metropolitan regions with < 50,000 population. Geographic regions were categorized by the US Census Bureau (2013) as West, Midwest, South, or Northeast. Age groups were stratified as 65–74, 75–84, and ≥ 85 years. Place of death was grouped into healthcare settings (including outpatient, emergency department, inpatient, dead on arrival, or unknown status), home, hospice/palliative care, and nursing/extended care facilities. Data covered all 50 states and Washington, D.C. The mortality variables available through CDC WONDER have been widely used in prior epidemiologic studies and form the basis of the present analysis. Statistical analysis National trends were evaluated using age-adjusted mortality rates (AAMRs) per 100,000 individuals for HF and COPD, adjusted for population age structure using the 2000 U.S. standard population as the reference. Mortality rates were analyzed for 1999–2020 by sex, race/ethnicity, age group, urban-rural status, state, and year. Temporal patterns in AAMRs were assessed using the Joinpoint Regression Program (Version 5.3.0.0) [12], which applies log-linear regression models to identify statistically significant changes in trends. Annual percent changes (APCs) with 95% confidence intervals (CIs) were calculated, and trends were classified as increasing or decreasing when the slope significantly differed from zero. A p-value < 0.05 was considered statistically significant. Results The age-adjusted mortality rate (AAMR) for heart failure and COPD in patients aged 15 and above was calculated and is presented as deaths per 100,000 persons. Table 1 Deaths from Heart Failure and COPD stratified by sex, region, and race in adults aged 15 and above in the United States (1999–2020) Deaths Year Overall Women Men Northeast Midwest South West NH White NH Black or African American NH Asian or Pacific Islander NH American Indian or Alaska Native Hispanic or Latino Population 1999 37,737 18,529 19,208 6,993 10,147 13,164 7,433 35,114 2,262 252 109 829 219,084,800 2000 37,882 18,968 18,914 7,254 10,304 13,095 7,229 35,326 2,206 239 111 886 221,168,531 2001 38,542 19,483 19,059 7,143 10,387 13,407 7,605 35,885 2,275 270 112 885 224,518,698 2002 39,007 19,703 19,304 6,967 10,540 13,651 7,849 36,277 2,303 280 147 937 227,062,163 2003 39,951 20,215 19,736 6,863 10,864 14,177 8,047 37,107 2,370 329 145 1,015 229,479,283 2004 40,265 20,576 19,689 7,244 10,694 14,062 8,265 37,394 2,396 306 169 1,002 232,153,496 2005 42,819 21,878 20,941 7,428 11,412 15,248 8,731 39,527 2,771 328 193 1,081 234,997,553 2006 41,279 21,140 20,139 7,103 10,851 14,892 8,433 38,190 2,532 368 189 1,148 237,863,203 2007 41,067 20,967 20,100 6,969 10,629 14,979 8,490 37,947 2,560 357 203 1,114 240,549,592 2008 43,162 22,151 21011 7,385 11,237 15,816 8,724 39,753 2,811 404 194 1,131 243,186,582 2009 42,320 21,427 20893 7,144 10,970 15,774 8,432 38,796 2,864 413 247 1,149 245,683,948 2010 44,193 22,332 21861 7,471 11,200 16,558 8,964 40,622 2,901 458 212 1,302 247,518,325 2011 46,179 23,529 22650 7,643 12,092 17,006 9,438 42,404 3,082 448 245 1,365 250,390,811 2012 47,038 23806 23232 7,845 12,051 17,750 9,392 43,093 3,280 431 234 1,530 252769942 2013 49,794 24,920 24874 8,022 12,690 19,070 10,012 45,379 3,625 512 278 1,580 255,039,716 2014 50,306 25107 25199 7,952 13,071 19,227 10,056 45,800 3,662 533 311 1,620 257789101 2015 55,457 28,001 27456 8,704 14,228 21,352 11,173 50,320 4,172 629 336 1,887 260,402,033 2016 56,596 27977 28619 8,498 14,175 22,358 11,565 51,277 4,380 583 356 1,968 262152444 2017 61,075 30456 30619 9,116 15,328 24,175 12,456 55,146 4,858 681 390 2,175 264697626 2018 63,240 31230 32010 9,320 15,688 25,533 12,699 56,966 5,149 727 398 2,306 266281990 2019 65,133 32247 32886 9,501 16,082 26,509 13,041 58,573 5,392 754 414 2,389 267668677 2020 71,805 35,153 36,652 10,366 18,130 29,241 14,068 63,757 6,689 882 477 2,722 269,190,697 Total 1,054,847 529,795 525,052 172,931 272,770 397,044 212,102 964,653 74,540 10,184 5,470 32,021 5,409,649,211 Table 2 Trends in heart failure and COPD-related mortality among adults in the United States (1999–2020) Group Trend 1 Trend 2 Year APC (95% CI) P-value Year APC (95% CI) P-value Overall 1999–12 -0.24 (-0.59 to 0.12) 0.17 2012–20 2.80* (2.16 to 3.44) < 0.01 Sex Female 1999–12 0.21 (-0.20 to 0.62) 0.30 2012–20 2.78* (2.03 to 3.54) < 0.01 Male 1999–12 -0.94* (-1.28 to -0.59) < 0.01 2012–20 2.57* (1.95 to 3.20) < 0.01 Race/Ethnicity NH-AmericanIndian/AK Native 1999–20 1.08* (0.55 to 1.61) < 0.01 — — — NH-Asian/Pacific Islander 1999–20 -0.80* (-1.27 to -0.34) < 0.01 — — — NH-Black/African American 1999–14 0.58 (-0.04 to 1.21) 0.07 2014–20 5.90* (3.99 to 7.86) < 0.01 NH-White 1999–12 -0.15 (-0.48 to 0.19) 0.36 2012–20 2.86* (2.26 to 3.47) < 0.01 Hispanic/Latino 1999–11 -1.73* (-2.51 to -0.94) < 0.01 2011–20 1.79* (0.88 to 2.70) < 0.01 APC: annual percent change, NH: non-Hispanic. A total of 1,054,847 deaths occurred in patients aged 15 + with concurrent heart failure and COPD from 1999 to 2020 (Supplementary Table 1). 39.28% occurred at medical facility, 25.63% took place in nursing homes/long-term care facilities, 26.88% happened at home, 4.50% in hospice facility and 3.49% at other locations and 0.22% population place of death was known (Supplementary Table 7). Overall Trends in Age-Adjusted Mortality Rates (1999–2020). From 1999 to 2020, a total of 1,054,847 deaths related to heart failure and obstructive sleep apnea were recorded in the United States. Over the study period, the AAMR followed a biphasic temporal pattern, characterized by an early attenuation from 17.64 in 1999 to 17.30 in 2012 (annual percent change [APC]: −0.24; 95% CI: −0.59 to 0.12; p = 0.17), followed by a pronounced upward inflection, reaching 21.83 by 2020 (APC: 2.80; 95% CI: 2.16 to 3.44; p < 0.001). Across the entire study interval, the average annual percent change (AAPC) indicated an overall upward trajectory in mortality (AAPC: [insert AAPC value]; 95% CI: [insert CI]; p [insert p-value]) (Supplementary Table 1; Fig. 1 ). AAMRs: age-adjusted mortality rates, AAPC: average annual percentage change, APC: annual percent change. Gender-Based Stratification. Across the entire study period, males consistently experienced higher age-adjusted mortality rates (AAMRs) than females, indicating a sustained sex-based disparity in mortality burden (overall AAMR: males, 22.74 [95% CI: 22.68–22.81] vs females, 15.34 [95% CI: 15.30–15.39]). Among males, the AAMR exhibited an early attenuation, declining from 24.01 in 1999 to 21.22 in 2012 (annual percent change [APC]: −0.94; 95% CI: −1.28 to − 0.59; p < 0.001), followed by a pronounced upward inflection, reaching 26.23 by 2020 (APC: 2.57; 95% CI: 1.95 to 3.20; p < 0.001). Females displayed a broadly parallel temporal configuration, though at substantially lower absolute rates throughout the study period. The AAMR among females changed modestly from 13.91 in 1999 to 14.68 in 2012 (APC: 0.21; 95% CI: −0.20 to 0.62; p = 0.30), followed by a marked escalation to 18.59 in 2020 (APC: 2.78; 95% CI: 2.02 to 3.54; p < 0.001)..(supplementary table 2, Fig. 2 ) AAMRs: age-adjusted mortality rates, AAPC: average annual percentage change, APC: annual percent change. Race/Ethnicity–Based Stratification: When stratified by ethnicity, non-Hispanic populations consistently experienced markedly higher age-adjusted mortality rates (AAMRs) than Hispanic populations (overall AAMR: 18.97; 95% CI: 18.93–19.01 vs 8.57; 95% CI: 8.47–8.66). Across racial and ethnic groups, the greatest mortality burden was observed among White individuals, followed sequentially by American Indian/Alaska Native (AI/AN), Black, Hispanic, and Asian/Pacific Islander populations (overall AAMR: White, 19.18 [95% CI: 19.14–19.21]; AI/AN, 15.26 [95% CI: 14.84–15.68]; Black, 14.34 [95% CI: 14.24–14.45]; Hispanic, 8.57 [95% CI: 8.47–8.66]; Asian/Pacific Islander, 5.12 [95% CI: 5.02–5.22]). Over time, White individuals exhibited a modest early attenuation in AAMR from 18.35 in 1999 to 18.23 in 2012 (APC: −0.15; 95% CI: −0.48 to 0.19; p = 0.36), followed by a pronounced upward inflection reaching 22.96 by 2020 (APC: 2.86; 95% CI: 2.26–3.47; p < 0.001). In contrast, AI/AN populations showed a persistent upward trajectory across the study period, with AAMR increasing from 12.35 in 1999 to 17.14 in 2020 (APC: 1.08; 95% CI: 0.55–1.61; p < 0.01).(supplementary table 3 ,Fig. 3 ) NH: non-Hispanic, AAMR: age-adjusted mortality rate, APC: annual percent change. Among Black individuals, AAMRs rose gradually from 12.89 in 1999 to 13.69 in 2014 (APC: 0.58; 95% CI: −0.04 to 1.21; p = 0.07), followed by a steep acceleration to 20.25 in 2020 (APC: 5.90; 95% CI: 3.99–7.86; p < 0.001). Hispanic populations demonstrated a biphasic pattern, with an early contraction from 9.53 in 1999 to 7.95 in 2011 (APC: −1.73; 95% CI: −2.51 to − 0.94; p < 0.01), followed by a renewed upward shift to 9.48 in 2020 (APC: 1.79; 95% CI: 0.88–2.70; p < 0.01). Conversely, Asian/Pacific Islander populations displayed a sustained downward trajectory, with AAMR declining from 6.15 in 1999 to 5.35 in 2020 (APC: −0.80; 95% CI: −1.27 to − 0.36; p < 0.01). Urbanization-Based Stratification : Across the study period, non-metropolitan areas consistently experienced higher age-adjusted mortality rates (AAMRs) than metropolitan areas, reflecting a clear urban–rural gradient in mortality burden. Within non-metropolitan regions, the highest AAMRs were observed in noncore areas, followed by micropolitan areas. Among metropolitan regions, mortality burden was greatest in small metropolitan areas, followed sequentially by medium metropolitan, large fringe metropolitan, and large central metropolitan areas (overall AAMR: noncore, 26.19 [95% CI: 26.04–26.34]; micropolitan, 24.65 [95% CI: 24.52–24.78]; small metropolitan, 21.52 [95% CI: 21.40–21.64]; medium metropolitan, 19.27 [95% CI: 19.19–19.35]; large fringe metropolitan, 15.68 [95% CI: 15.62–15.75]; large central metropolitan, 14.02 [95% CI: 13.96–14.08]). Noncore areas exhibited an early period of relative stability, with AAMRs changing from 23.83 in 1999 to 25.69 in 2013 (annual percent change [APC]: 0.54; 95% CI: 0.25–0.83; p < 0.01), followed by a pronounced upward inflection to 32.23 by 2020 (APC: 3.29; 95% CI: 2.57–4.02; p < 0.001). Micropolitan areas demonstrated a modest early attenuation, with AAMRs shifting from 22.84 in 1999 to 23.15 in 2012 (APC: −0.08; 95% CI: −0.50 to 0.34; p = 0.69), followed by a steep rise to 31.51 in 2020 (APC: 3.57; 95% CI: 2.80–4.34; p < 0.001). Among small metropolitan areas, AAMRs increased from 19.82 in 1999 to 20.89 in 2013 (APC: 0.24; 95% CI: −0.13 to 0.62; p = 0.19), followed by a sharp escalation to 26.79 in 2020 (APC: 3.53; 95% CI: 2.63–4.43; p < 0.001). Medium metropolitan areas experienced a slight early shift, with AAMRs changing from 17.51 in 1999 to 17.84 in 2011 (APC: −0.01; 95% CI: −0.39 to 0.38; p = 0.96), followed by a pronounced increase to 23.62 by 2020 (APC: 2.94; 95% CI: 2.44–3.44; p < 0.001). Large fringe metropolitan areas displayed an early contraction, with AAMRs declining from 15.70 in 1999 to 14.84 in 2011 (APC: −0.81; 95% CI: −1.29 to − 0.34; p < 0.01), followed by a marked upward shift to 18.41 in 2020 (APC: 2.63; 95% CI: 2.01–3.25; p < 0.001). Similarly, large central metropolitan areas demonstrated an early decline, with AAMRs decreasing from 14.66 in 1999 to 13.21 in 2012 (APC: −0.71; 95% CI: −1.17 to − 0.25; p < 0.01), followed by a renewed rise to 15.75 by 2020 (APC: 1.82; 95% CI: 0.98–2.68; p < 0.001).(supplementary table 4, Fig. 4 ) AAMR: age-adjusted mortality rate, APC: annual percent change Geographic Region–Based Stratification: Across the study period, the highest age-adjusted mortality rates (AAMRs) were consistently observed in the Midwest, followed by the South, West, and Northeast, indicating marked regional variation in mortality burden (overall AAMR: Midwest, 20.75 [95% CI: 20.67–20.83]; South, 19.03 [95% CI: 18.97–19.09]; West, 17.39 [95% CI: 17.32–17.47]; Northeast, 14.89 [95% CI: 14.82–14.96]).(REFER TO FIGURE 5 ) At the state level, West Virginia, located in the South, recorded the highest AAMR (32.40), whereas Hawaii, in the West Census Region, exhibited the lowest (7.28). AAMR: age-adjusted mortality rate, AAPC: average annual percentage change, APC: annual percent change. Within the Southern region, states in the upper decile of mortality included West Virginia (32.40), Oklahoma (31.49), Kentucky (31.06), and Mississippi (28.74)—rates exceeding twofold those observed among states in the lowest decile, such as Florida (12.45) and the District of Columbia (10.07). The Midwest demonstrated comparatively narrower dispersion in AAMRs, with Indiana (24.74), Ohio (23.95), and Nebraska (23.14) among states with the highest mortality, while Wisconsin (18.82) and Illinois (16.31) occupied the lower end of the regional spectrum. In the Western region, the highest AAMRs were observed in Wyoming (25.97) and Oregon (24.17), whereas Arizona (11.00) and Hawaii (7.28) consistently reported the lowest rates. Within the Northeast, Vermont (23.67) and Rhode Island (23.33) exhibited the greatest mortality burden, in contrast to New York (12.81) and New Jersey (12.03), which reported the lowest AAMRs in the region.(supplementary table 5) Discussion In this comprehensive national analysis spanning more than two decades, we identified a substantial and growing mortality burden attributable to the coexistence of heart failure (HF) and chronic obstructive pulmonary disease (COPD) among individuals aged 15 years and older in the United States. Our findings demonstrate a clear biphasic temporal pattern in age adjusted mortality rates (AAMRs), with a period of relative stability or modest decline from 1999 through approximately 2012, followed by a pronounced and statistically significant increase through 2020. This inflection was consistently observed across sex, race and ethnicity, urbanization status, and geographic region, underscoring the pervasive and widening impact of HF and COPD comorbidity on population level mortality. The early attenuation in mortality observed during the first decade of the study period likely reflects improvements in cardiovascular and pulmonary care, including advancements in early detection, increased use of guideline directed medical therapy for HF, improved management of COPD exacerbations, and declining smoking prevalence during the late twentieth and early twenty first centuries ( 13 – 15 ). However, the subsequent reversal and acceleration in mortality after 2012 suggest that these gains have not been sustained. Several factors may account for this shift, including population aging, increasing multimorbidity, rising prevalence of obesity and metabolic disease, and the growing recognition of HF with preserved ejection fraction, a condition for which effective mortality reducing therapies have historically been limited. The sharp rise in mortality observed in the latter years of the study, particularly approaching 2020, may also reflect the direct and indirect effects of the COVID 19 pandemic, including disruptions in routine outpatient care, delayed presentation for acute decompensation, and heightened vulnerability among patients with chronic cardiopulmonary disease ( 16 , 17 ). Although CDC WONDER data do not permit causal attribution, the temporal concordance suggests that the pandemic may have amplified pre existing mortality trends in this high risk population. Consistent with prior epidemiologic studies, males experienced substantially higher AAMRs than females throughout the study period ( 18 , 19 ). While both sexes demonstrated similar biphasic trajectories, mortality rates among males remained persistently elevated, likely reflecting a higher cumulative burden of smoking exposure, occupational risk factors, and cardiometabolic disease. Socioeconomic factors also influence healthcare seeking behavior and access to preventive treatments, which may contribute to observed sex based differences in mortality outcomes ( 20 ). Notably, females exhibited a comparable relative acceleration in mortality after 2012, narrowing the sex gap in recent years. This pattern may reflect evolving risk factor profiles among women, including increased smoking prevalence in prior decades, as well as under recognition or undertreatment of HF and COPD in female patients ( 21 ). Marked racial and ethnic disparities were evident in both absolute mortality burden and temporal trends. White individuals exhibited the highest overall AAMRs, likely reflecting the older age structure of this population and a higher prevalence of diagnosed HF and COPD ( 22 ). However, the most concerning trend was observed among Black individuals, who experienced a dramatic acceleration in mortality after 2014, culminating in one of the steepest annual increases among all racial and ethnic groups. This pattern likely reflects structural inequities, including disparities in access to preventive care, disease modifying therapies, and timely management of acute cardiopulmonary exacerbations ( 23 – 25 ). American Indian and Alaska Native populations demonstrated a persistently rising mortality trajectory throughout the study period, highlighting a long standing and inadequately addressed public health crisis ( 23 , 25 ). In contrast, Hispanic populations exhibited a classic Hispanic paradox, with lower overall mortality rates and an initial decline, although this advantage diminished in later years ( 18 , 25 ). Asian and Pacific Islander populations were the only group to demonstrate a sustained decline in mortality, suggesting potential protective sociocultural factors, lower smoking prevalence, or differences in disease phenotype that warrant further investigation. A pronounced urban rural gradient in mortality was observed, with the highest AAMRs consistently documented in noncore and micropolitan areas. Rural populations experienced both higher baseline mortality and steeper increases in recent years. These disparities likely reflect reduced access to specialty care, higher prevalence of smoking and occupational lung disease, socioeconomic disadvantage, elevated cardiovascular risk factors, and limited availability of advanced HF therapies in rural settings ( 26 , 27 ). Policy initiatives aimed at improving healthcare access, workforce distribution, and chronic disease management in underserved areas are essential to mitigating these disparities. Geographically, the Midwest and South bore the greatest mortality burden, with particularly high rates observed in Appalachian and Southern states such as West Virginia, Kentucky, and Oklahoma. These regional patterns parallel known distributions of smoking prevalence, poverty, environmental exposures, and healthcare access inequities ( 28 , 29 ). Conversely, states in the West and Northeast, particularly Hawaii, Arizona, New York, and New Jersey, consistently demonstrated lower mortality rates, potentially reflecting differences in public health infrastructure, healthcare delivery systems, and population level health behaviors. Clinical and Public Health Implications The rising mortality associated with HF and COPD comorbidity has important clinical and public health implications. Individuals with coexisting HF and COPD represent a uniquely vulnerable population characterized by complex pathophysiology, diagnostic overlap, and therapeutic tradeoffs. Fragmented care models that address these conditions in isolation may be insufficient to reduce mortality risk. Integrated cardiopulmonary care strategies, improved transitional care, and targeted interventions in high risk populations, particularly rural communities and racial and ethnic minorities, are urgently needed. Additionally, the substantial proportion of deaths occurring outside acute care hospital settings, including at home and in long term care facilities, underscores the importance of timely palliative care integration, advance care planning, and enhanced outpatient disease management for patients with advanced cardiopulmonary disease. Limitations This study has several limitations. Mortality data derived from death certificates are subject to misclassification and coding variability, particularly for comorbid conditions such as HF and COPD. The CDC WONDER database lacks granular clinical information, including disease severity, treatment patterns, smoking history, and socioeconomic status, limiting causal inference. Additionally, observed trends may be influenced by changes in diagnostic practices, reporting accuracy, and ICD coding over time. Despite these limitations, the large nationally representative sample and extended study period strengthen the robustness and generalizability of our findings. Conclusion In conclusion, mortality associated with the coexistence of heart failure and chronic obstructive pulmonary disease in the United States has increased substantially over the past decade following an earlier period of relative stability. This rise has disproportionately affected males, Black and American Indian and Alaska Native populations, rural communities, and residents of the Midwest and South. These findings highlight an urgent need for renewed public health efforts, integrated care models, and equity focused interventions to address the growing mortality burden associated with cardiopulmonary multimorbidity. Declarations Funding: The authors did not receive support from any organization for the submitted work. Conflict of Interest: All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript. Ethics Approval: This study analyzed publicly available, de-identified data from the CDC WONDER database. As such, it was exempt from Institutional Review Board approval. Consent to Participate: Not applicable. Consent for Publication: Not applicable. Data Availability: The datasets generated and analyzed during the current study are publicly available in the CDC WONDER repository: https://wonder.cdc.gov/mcd.html Code Availability: Not applicable. Author Contributions: Javeria Gul: Methodology, Formal analysis, Data curation, Writing – original draft, Writing – review & editing, Visualization. Eishal Khan: Data curation, Formal analysis, Writing – review & editing. Maria Campwala: Conceptualization, Methodology, Validation, Writing – review & editing. Ibadullah Khan: Data curation, Writing – review & editing. Asad Ullah Farooq: Data curation, Writing – review & editing. Anid Hassan, M.D.: Conceptualization, Project administration, Visualization, Writing – review & editing. Rumman Javed: Methodology, Data curation, Writing – review & editing. Areeba Zia: Data curation, Writing – review & editing. Mahnoor Khuram: Data curation, Writing – review & editing. Sana Mehreen: Data curation, Writing – review & editing. Sadia Haleema: Data curation, Writing – review & editing. Hira Zeb: Data curation, Writing – review & editing. Hiba Noor: Data curation, Writing – review & editing. Iftekhar Khan: Data curation, Writing – review & editing. Hamid Shams: Data curation, Writing – review & editing. Acknowledgments: The authors thank the CDC for maintaining and providing access to the WONDER database. References Martin SS, Aday AW, Almarzooq ZI, Anderson CAM, Arora P, Avery CL, American Heart Association Council on Epidemiology and Prevention Statistics Committee and Stroke Statistics Subcommittee, et al. 2024 Heart Disease and Stroke Statistics: A Report of US and Global Data From the American Heart Association. Circulation. 2024;149(8):e347–913. 10.1161/CIR.0000000000001209 . Bozkurt B, Concerning Trends of Rising Heart Failure Mortality Rates. JACC Heart Fail. 2024;12(5):970–2. 10.1016/j.jchf.2024.04.001 . PMID: 38719388. https://www.jacc.org/doi/10.1016/j.jchf.2024.04.001 . 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Quiñones AR, Botoseneanu A, Markwardt S, Nagel CL, Newsom JT, Dorr DA, Allore HG. Racial/ethnic differences in multimorbidity development and chronic disease accumulation for middle-aged adults. PLoS ONE. 2019;14(6):e0218462. 10.1371/journal.pone.0218462 . PMID: 31206556; PMCID: PMC6576751. Price JH, Khubchandani J, McKinney M, Braun R. Racial/ethnic disparities in chronic diseases of youths and access to health care in the United States. Biomed Res Int. 2013;2013:787616. 10.1155/2013/787616 . Epub 2013 Sep 23. PMID: 24175301; PMCID: PMC3794652. 13), Yearby R, Clark B, Figueroa JF, Structural Racism In Historical And Modern US Health Care Policy. Health Aff (Millwood). 2022;41(2):187–194. 10.1377/hlthaff.2021.01466 . PMID: 35130059. History ARL. M by. America's Rural Hospitals Are in Crisis, That's Nothing New. TIME; 2024. https://time.com/7176024/rural-hospital‐crisis‐history/ . Harrington RA, Califf RM, Balamurugan A, Brown N, Benjamin RM, Braund WE, Hipp J, Konig M, Sanchez E, Joynt Maddox KE. Call to Action: Rural Health: A Presidential Advisory From the American Heart Association and American Stroke Association. Circulation. 2020;141(10):e615–44. 10.1161/CIR.0000000000000753 . Epub 2020 Feb 10. PMID: 32078375. Health Insurance Coverage of the Total Population. KFF accessed April 30, 2025, https://www.kff.org/other/state-indicator/total‐population/ Emerson J. States Ranked by Total Primary Care Physicians in 2024 | Becker's. Becker's Hosp Rev | Healthc News Anal, (2024), https://www.beckershospitalreview.com/rankings-and‐ratings/states‐ranked‐by‐total‐primary‐care‐physicians‐in‐2024/ Additional Declarations No competing interests reported. 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14:39:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8822335/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8822335/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102310380,"identity":"2e6c0119-d93c-40a0-b5b2-351c26de937b","added_by":"auto","created_at":"2026-02-10 11:53:45","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":32393,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOverall trends in AAMRs for heart failure and copd.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAAMRs: age-adjusted mortality rates, AAPC: average annual percentage change, APC: annual percent change.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8822335/v1/9a72aea9c02cbdf004999bf8.jpeg"},{"id":102310019,"identity":"e106d6ef-e8b5-445c-81a6-2bd65512159a","added_by":"auto","created_at":"2026-02-10 11:52:37","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":106196,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSex-specific trends in AAMRs for heart failure and COPD.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAAMRs: age-adjusted mortality rates, AAPC: average annual percentage change, APC: annual percent change.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8822335/v1/4d7699a198e5403074e181d6.jpeg"},{"id":102310256,"identity":"4b9c1eff-3066-4805-83ea-880ae17ec2ee","added_by":"auto","created_at":"2026-02-10 11:53:02","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":67842,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAAMR trends for heart failure and COPD stratified by race/ethnicity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNH: non-Hispanic, AAMR: age-adjusted mortality rate, APC: annual percent change.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8822335/v1/4e7aad96da14697920058f03.jpeg"},{"id":102310417,"identity":"c15c5095-5692-4e9e-bed2-91644c92e003","added_by":"auto","created_at":"2026-02-10 11:53:46","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":75687,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAAMR trends associated with urbanization\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAAMR: age-adjusted mortality rate, APC: annual percent change\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8822335/v1/00b3b6afe09b1d76c0d28de9.jpeg"},{"id":102309960,"identity":"9ba39899-f611-4f6f-8dd4-47de166bdf49","added_by":"auto","created_at":"2026-02-10 11:52:33","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":79976,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAAMR trends across different US census regions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAAMR: age-adjusted mortality rate, AAPC: average annual percentage change, APC: annual percent change.\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8822335/v1/7bacfebdcfb704896dbe5ce6.jpeg"},{"id":102310414,"identity":"5ab1daad-086b-4a1e-a9d8-38df4c5de4c4","added_by":"auto","created_at":"2026-02-10 11:53:46","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":246035,"visible":true,"origin":"","legend":"\u003cp\u003eCentral illustration showing mortality trends from heart failure and COPD among US adults aged ≥15 years\u003c/p\u003e\n\u003cp\u003eAAMR: age-adjusted mortality rate\u003c/p\u003e\n\u003cp\u003eImage Credit: This figure was created by the authors and has not been previously published.\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8822335/v1/ec74843350cff5e936402a23.jpeg"},{"id":105035700,"identity":"37b3a22c-8b2f-43cb-91c5-e4eef8f34ad7","added_by":"auto","created_at":"2026-03-20 07:26:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1808223,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8822335/v1/333902e3-1dd7-43e9-acaf-82173d903d2d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eMortality Trends in Heart Disease and COPD from 1999-2020: A CDC Wonder Analysis\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHeart failure (HF) represents an escalating public health challenge both in the United States and worldwide. In the U.S., the prevalence of HF is projected to rise from 2.4% in 2012 to 3.0% by 2030, impacting more than 8\u0026nbsp;million adults [1]. Alongside this growing prevalence, concerning trends in HF-related mortality have emerged, with rates increasing despite widespread implementation of guideline-directed therapies known to improve survival and clinical outcomes [2] (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). From an economic perspective, HF imposes a substantial burden on healthcare systems, with direct medical costs rising from \u003cspan\u003e$\u003c/span\u003e20.9\u0026nbsp;billion in 2012 to an estimated \u003cspan\u003e$\u003c/span\u003e53.1\u0026nbsp;billion by 2030 [3].\u003c/p\u003e \u003cp\u003eRecent epidemiologic analyses highlight that age-adjusted mortality rates from HF have been increasing across diverse demographic groups, a trend evident not only among the elderly aged\u0026thinsp;\u0026ge;\u0026thinsp;75 years [4] but also in younger adults aged 15\u0026ndash;44 years [5]. These patterns underscore the widening impact of HF across the lifespan and suggest that existing interventions may be insufficient to curb the rising mortality burden.\u003c/p\u003e \u003cp\u003eChronic obstructive pulmonary disease (COPD), characterized by progressive and generally irreversible airflow limitation resulting from an abnormal inflammatory response in the lungs, has become an increasingly prominent public health concern. In the United States, COPD affects over 12\u0026nbsp;million individuals and ranks as the third leading cause of death [6\u0026ndash;8]. Globally, the World Health Organization (WHO) estimates that approximately 210\u0026nbsp;million people are affected by COPD, with 64\u0026nbsp;million exhibiting symptomatic disease [9]. In recent years, COPD has gained heightened attention due to its substantial contribution to both morbidity and mortality. Given its growing impact on population health, updated analyses of COPD-related mortality are essential to guide effective prevention strategies, inform healthcare planning, and support policy intervention.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and Population\u003c/h2\u003e \u003cp\u003eWe conducted an observational analysis using death records from the CDC WONDER Multiple Cause of Death database for the period 1999\u0026ndash;2020 [10], focusing on individuals aged 15\u0026ndash;85\u0026thinsp;+\u0026thinsp;years in the United States. Mortality records were reviewed for cases in which both Heart Faliure and COPD were listed among the causes of death. The 10th edition of the International Classification of Diseases (ICD-10) was used to identify relevant cases. HF was classified under I50 codes, including congestive HF (I50.0), left ventricular failure (I50.1), and unspecified HF (I50.9). COPD was classified under J44 codes, encompassing acute lower respiratory infection (J44.0), acute exacerbation, unspecified (J44.1), other specified types (J44.8), and unspecified cases (J44.9) [11]. Deaths were included only if both HF and COPD codes appeared; those without both codes were excluded. Because this study used publicly available, de-identified government data, Institutional Review Board approval was not required.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData abstraction\u003c/h3\u003e\n\u003cp\u003eThe CDC WONDER dataset includes information on sex, race/ethnicity, urban-rural classification, region, state, age group, and place of death [10]. Sex was classified as male or female based on death certificate records. Race/ethnicity followed the Office of Management and Budget (1997) standards: Hispanic or Latino, non-Hispanic (NH) White, NH Black or African American, NH American Indian or Alaska Native, and NH Asian or Pacific Islander. Urban-rural status was defined according to the National Center for Health Statistics (2013) scheme, which classifies urban areas as large metropolitan regions (\u0026ge;\u0026thinsp;1\u0026nbsp;million population) or medium/small metropolitan regions (50,000-999,999 population). Rural areas are defined as non-metropolitan regions with \u0026lt;\u0026thinsp;50,000 population. Geographic regions were categorized by the US Census Bureau (2013) as West, Midwest, South, or Northeast. Age groups were stratified as 65\u0026ndash;74, 75\u0026ndash;84, and \u0026ge;\u0026thinsp;85 years. Place of death was grouped into healthcare settings (including outpatient, emergency department, inpatient, dead on arrival, or unknown status), home, hospice/palliative care, and nursing/extended care facilities. Data covered all 50 states and Washington, D.C. The mortality variables available through CDC WONDER have been widely used in prior epidemiologic studies and form the basis of the present analysis.\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eNational trends were evaluated using age-adjusted mortality rates (AAMRs) per 100,000 individuals for HF and COPD, adjusted for population age structure using the 2000 U.S. standard population as the reference. Mortality rates were analyzed for 1999\u0026ndash;2020 by sex, race/ethnicity, age group, urban-rural status, state, and year. Temporal patterns in AAMRs were assessed using the Joinpoint Regression Program (Version 5.3.0.0) [12], which applies log-linear regression models to identify statistically significant changes in trends. Annual percent changes (APCs) with 95% confidence intervals (CIs) were calculated, and trends were classified as increasing or decreasing when the slope significantly differed from zero. A p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThe age-adjusted mortality rate (AAMR) for heart failure and COPD in patients aged 15 and above was calculated and is presented as deaths per 100,000 persons.\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\u003eDeaths from Heart Failure and COPD stratified by sex, region, and race in adults aged 15 and above in the United States (1999\u0026ndash;2020)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"14\"\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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"13\" nameend=\"c14\" namest=\"c2\"\u003e \u003cp\u003eDeaths\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWomen\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMen\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNortheast\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMidwest\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSouth\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eWest\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNH White\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNH Black or African American\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNH Asian or Pacific Islander\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eNH American Indian or Alaska Native\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003eHispanic or Latino\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c14\"\u003e \u003cp\u003ePopulation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e1999\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e37,737\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18,529\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19,208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6,993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10,147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e13,164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7,433\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e35,114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e2,262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e829\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e219,084,800\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e37,882\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18,968\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18,914\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7,254\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10,304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e13,095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7,229\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e35,326\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e2,206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e886\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e221,168,531\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e38,542\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19,483\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19,059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7,143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10,387\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e13,407\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7,605\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e35,885\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e2,275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e270\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e885\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e224,518,698\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e39,007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19,703\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19,304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6,967\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10,540\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e13,651\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7,849\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e36,277\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e2,303\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e280\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e937\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e227,062,163\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e39,951\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20,215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19,736\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6,863\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10,864\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e14,177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8,047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e37,107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e2,370\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e329\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e1,015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e229,479,283\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e40,265\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20,576\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19,689\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7,244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10,694\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e14,062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8,265\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e37,394\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e2,396\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e1,002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e232,153,496\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2005\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e42,819\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21,878\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20,941\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7,428\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11,412\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e15,248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8,731\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e39,527\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e2,771\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e328\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e1,081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e234,997,553\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2006\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e41,279\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21,140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20,139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7,103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10,851\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e14,892\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8,433\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e38,190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e2,532\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e368\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e1,148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e237,863,203\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2007\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e41,067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20,967\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20,100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6,969\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10,629\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e14,979\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8,490\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e37,947\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e2,560\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e357\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e203\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e1,114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e240,549,592\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2008\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e43,162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22,151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7,385\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11,237\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e15,816\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8,724\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e39,753\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e2,811\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e404\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e1,131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e243,186,582\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2009\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e42,320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21,427\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20893\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7,144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10,970\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e15,774\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8,432\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e38,796\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e2,864\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e413\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e1,149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e245,683,948\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2010\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e44,193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22,332\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21861\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7,471\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11,200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e16,558\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8,964\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e40,622\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e2,901\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e458\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e212\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e1,302\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e247,518,325\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2011\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e46,179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23,529\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22650\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7,643\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12,092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e17,006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e9,438\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e42,404\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e3,082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e448\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e1,365\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e250,390,811\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2012\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e47,038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23806\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23232\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7,845\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12,051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e17,750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e9,392\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e43,093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e3,280\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e431\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e1,530\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e252769942\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2013\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e49,794\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24,920\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24874\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8,022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12,690\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e19,070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e10,012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e45,379\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e3,625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e512\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e1,580\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e255,039,716\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2014\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50,306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7,952\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e13,071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e19,227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e10,056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e45,800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e3,662\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e533\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e311\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e1,620\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e257789101\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2015\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e55,457\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28,001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8,704\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e14,228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e21,352\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e11,173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e50,320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e4,172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e629\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e336\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e1,887\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e260,402,033\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2016\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e56,596\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27977\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28619\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8,498\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e14,175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e22,358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e11,565\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e51,277\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e4,380\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e583\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e356\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e1,968\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e262152444\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2017\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e61,075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30619\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9,116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e15,328\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e24,175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e12,456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e55,146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e4,858\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e681\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e390\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e2,175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e264697626\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2018\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e63,240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e32010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9,320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e15,688\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e25,533\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e12,699\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e56,966\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e5,149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e727\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e398\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e2,306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e266281990\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2019\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e65,133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e32886\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9,501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e16,082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e26,509\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e13,041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e58,573\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e5,392\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e754\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e414\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e2,389\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e267668677\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2020\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e71,805\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e35,153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e36,652\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10,366\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e18,130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e29,241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e14,068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e63,757\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e6,689\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e882\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e477\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e2,722\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e269,190,697\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\u003e1,054,847\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e529,795\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e525,052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e172,931\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e272,770\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e397,044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e212,102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e964,653\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e74,540\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e10,184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e5,470\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e32,021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e5,409,649,211\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 \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\u003eTrends in heart failure and COPD-related mortality among adults in the United States (1999\u0026ndash;2020)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eTrend 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eTrend 2\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAPC (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAPC (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1999\u0026ndash;12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.24 (-0.59 to 0.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2012\u0026ndash;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.80* (2.16 to 3.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\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\u003e1999\u0026ndash;12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.21 (-0.20 to 0.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2012\u0026ndash;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.78* (2.03 to 3.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\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\u003e1999\u0026ndash;12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.94* (-1.28 to -0.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2012\u0026ndash;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.57* (1.95 to 3.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace/Ethnicity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNH-AmericanIndian/AK Native\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1999\u0026ndash;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.08* (0.55 to 1.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNH-Asian/Pacific Islander\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1999\u0026ndash;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.80* (-1.27 to -0.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNH-Black/African American\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1999\u0026ndash;14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.58 (-0.04 to 1.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2014\u0026ndash;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.90* (3.99 to 7.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\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\u003e1999\u0026ndash;12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.15 (-0.48 to 0.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2012\u0026ndash;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.86* (2.26 to 3.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHispanic/Latino\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1999\u0026ndash;11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.73* (-2.51 to -0.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2011\u0026ndash;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.79* (0.88 to 2.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\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\u003eAPC: annual percent change, NH: non-Hispanic.\u003c/p\u003e \u003cp\u003eA total of 1,054,847 deaths occurred in patients aged 15\u0026thinsp;+\u0026thinsp;with concurrent heart failure and COPD from 1999 to 2020 (Supplementary Table\u0026nbsp;1). 39.28% occurred at medical facility, 25.63% took place in nursing homes/long-term care facilities, 26.88% happened at home, 4.50% in hospice facility and 3.49% at other locations and 0.22% population place of death was known (Supplementary Table\u0026nbsp;7).\u003c/p\u003e \u003cp\u003e \u003cb\u003eOverall Trends in Age-Adjusted Mortality Rates (1999\u0026ndash;2020).\u003c/b\u003e \u003c/p\u003e \u003cp\u003eFrom 1999 to 2020, a total of 1,054,847 deaths related to heart failure and obstructive sleep apnea were recorded in the United States. Over the study period, the AAMR followed a biphasic temporal pattern, characterized by an early attenuation from 17.64 in 1999 to 17.30 in 2012 (annual percent change [APC]: \u0026minus;0.24; 95% CI: \u0026minus;0.59 to 0.12; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.17), followed by a pronounced upward inflection, reaching 21.83 by 2020 (APC: 2.80; 95% CI: 2.16 to 3.44; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Across the entire study interval, the average annual percent change (AAPC) indicated an overall upward trajectory in mortality (AAPC: [insert AAPC value]; 95% CI: [insert CI]; \u003cem\u003ep\u003c/em\u003e [insert p-value]) (Supplementary Table\u0026nbsp;1; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAAMRs: age-adjusted mortality rates, AAPC: average annual percentage change, APC: annual percent change.\u003c/p\u003e \u003cp\u003e \u003cb\u003eGender-Based Stratification.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAcross the entire study period, males consistently experienced higher age-adjusted mortality rates (AAMRs) than females, indicating a sustained sex-based disparity in mortality burden (overall AAMR: males, 22.74 [95% CI: 22.68\u0026ndash;22.81] vs females, 15.34 [95% CI: 15.30\u0026ndash;15.39]). Among males, the AAMR exhibited an early attenuation, declining from 24.01 in 1999 to 21.22 in 2012 (annual percent change [APC]: \u0026minus;0.94; 95% CI: \u0026minus;1.28 to \u0026minus;\u0026thinsp;0.59; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), followed by a pronounced upward inflection, reaching 26.23 by 2020 (APC: 2.57; 95% CI: 1.95 to 3.20; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eFemales displayed a broadly parallel temporal configuration, though at substantially lower absolute rates throughout the study period. The AAMR among females changed modestly from 13.91 in 1999 to 14.68 in 2012 (APC: 0.21; 95% CI: \u0026minus;0.20 to 0.62; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.30), followed by a marked escalation to 18.59 in 2020 (APC: 2.78; 95% CI: 2.02 to 3.54; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001)..(supplementary table 2, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e )\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAAMRs: age-adjusted mortality rates, AAPC: average annual percentage change, APC: annual percent change.\u003c/p\u003e\n\u003ch3\u003eRace/Ethnicity–Based Stratification:\u003c/h3\u003e\n\u003cp\u003eWhen stratified by ethnicity, non-Hispanic populations consistently experienced markedly higher age-adjusted mortality rates (AAMRs) than Hispanic populations (overall AAMR: 18.97; 95% CI: 18.93\u0026ndash;19.01 vs 8.57; 95% CI: 8.47\u0026ndash;8.66). Across racial and ethnic groups, the greatest mortality burden was observed among White individuals, followed sequentially by American Indian/Alaska Native (AI/AN), Black, Hispanic, and Asian/Pacific Islander populations (overall AAMR: White, 19.18 [95% CI: 19.14\u0026ndash;19.21]; AI/AN, 15.26 [95% CI: 14.84\u0026ndash;15.68]; Black, 14.34 [95% CI: 14.24\u0026ndash;14.45]; Hispanic, 8.57 [95% CI: 8.47\u0026ndash;8.66]; Asian/Pacific Islander, 5.12 [95% CI: 5.02\u0026ndash;5.22]).\u003c/p\u003e \u003cp\u003eOver time, White individuals exhibited a modest early attenuation in AAMR from 18.35 in 1999 to 18.23 in 2012 (APC: \u0026minus;0.15; 95% CI: \u0026minus;0.48 to 0.19; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.36), followed by a pronounced upward inflection reaching 22.96 by 2020 (APC: 2.86; 95% CI: 2.26\u0026ndash;3.47; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In contrast, AI/AN populations showed a persistent upward trajectory across the study period, with AAMR increasing from 12.35 in 1999 to 17.14 in 2020 (APC: 1.08; 95% CI: 0.55\u0026ndash;1.61; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01).(supplementary table 3 ,Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eNH: non-Hispanic, AAMR: age-adjusted mortality rate, APC: annual percent change.\u003c/p\u003e \u003cp\u003eAmong Black individuals, AAMRs rose gradually from 12.89 in 1999 to 13.69 in 2014 (APC: 0.58; 95% CI: \u0026minus;0.04 to 1.21; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.07), followed by a steep acceleration to 20.25 in 2020 (APC: 5.90; 95% CI: 3.99\u0026ndash;7.86; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Hispanic populations demonstrated a biphasic pattern, with an early contraction from 9.53 in 1999 to 7.95 in 2011 (APC: \u0026minus;1.73; 95% CI: \u0026minus;2.51 to \u0026minus;\u0026thinsp;0.94; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), followed by a renewed upward shift to 9.48 in 2020 (APC: 1.79; 95% CI: 0.88\u0026ndash;2.70; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Conversely, Asian/Pacific Islander populations displayed a sustained downward trajectory, with AAMR declining from 6.15 in 1999 to 5.35 in 2020 (APC: \u0026minus;0.80; 95% CI: \u0026minus;1.27 to \u0026minus;\u0026thinsp;0.36; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003eUrbanization-Based Stratification\u003c/b\u003e:\u003c/h2\u003e \u003cp\u003eAcross the study period, non-metropolitan areas consistently experienced higher age-adjusted mortality rates (AAMRs) than metropolitan areas, reflecting a clear urban\u0026ndash;rural gradient in mortality burden. Within non-metropolitan regions, the highest AAMRs were observed in noncore areas, followed by micropolitan areas. Among metropolitan regions, mortality burden was greatest in small metropolitan areas, followed sequentially by medium metropolitan, large fringe metropolitan, and large central metropolitan areas (overall AAMR: noncore, 26.19 [95% CI: 26.04\u0026ndash;26.34]; micropolitan, 24.65 [95% CI: 24.52\u0026ndash;24.78]; small metropolitan, 21.52 [95% CI: 21.40\u0026ndash;21.64]; medium metropolitan, 19.27 [95% CI: 19.19\u0026ndash;19.35]; large fringe metropolitan, 15.68 [95% CI: 15.62\u0026ndash;15.75]; large central metropolitan, 14.02 [95% CI: 13.96\u0026ndash;14.08]).\u003c/p\u003e \u003cp\u003eNoncore areas exhibited an early period of relative stability, with AAMRs changing from 23.83 in 1999 to 25.69 in 2013 (annual percent change [APC]: 0.54; 95% CI: 0.25\u0026ndash;0.83; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), followed by a pronounced upward inflection to 32.23 by 2020 (APC: 3.29; 95% CI: 2.57\u0026ndash;4.02; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Micropolitan areas demonstrated a modest early attenuation, with AAMRs shifting from 22.84 in 1999 to 23.15 in 2012 (APC: \u0026minus;0.08; 95% CI: \u0026minus;0.50 to 0.34; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.69), followed by a steep rise to 31.51 in 2020 (APC: 3.57; 95% CI: 2.80\u0026ndash;4.34; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eAmong small metropolitan areas, AAMRs increased from 19.82 in 1999 to 20.89 in 2013 (APC: 0.24; 95% CI: \u0026minus;0.13 to 0.62; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.19), followed by a sharp escalation to 26.79 in 2020 (APC: 3.53; 95% CI: 2.63\u0026ndash;4.43; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Medium metropolitan areas experienced a slight early shift, with AAMRs changing from 17.51 in 1999 to 17.84 in 2011 (APC: \u0026minus;0.01; 95% CI: \u0026minus;0.39 to 0.38; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.96), followed by a pronounced increase to 23.62 by 2020 (APC: 2.94; 95% CI: 2.44\u0026ndash;3.44; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eLarge fringe metropolitan areas displayed an early contraction, with AAMRs declining from 15.70 in 1999 to 14.84 in 2011 (APC: \u0026minus;0.81; 95% CI: \u0026minus;1.29 to \u0026minus;\u0026thinsp;0.34; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), followed by a marked upward shift to 18.41 in 2020 (APC: 2.63; 95% CI: 2.01\u0026ndash;3.25; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Similarly, large central metropolitan areas demonstrated an early decline, with AAMRs decreasing from 14.66 in 1999 to 13.21 in 2012 (APC: \u0026minus;0.71; 95% CI: \u0026minus;1.17 to \u0026minus;\u0026thinsp;0.25; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), followed by a renewed rise to 15.75 by 2020 (APC: 1.82; 95% CI: 0.98\u0026ndash;2.68; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).(supplementary table 4, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAAMR: age-adjusted mortality rate, APC: annual percent change\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eGeographic Region–Based Stratification:\u003c/h3\u003e\n\u003cp\u003eAcross the study period, the highest age-adjusted mortality rates (AAMRs) were consistently observed in the Midwest, followed by the South, West, and Northeast, indicating marked regional variation in mortality burden (overall AAMR: Midwest, 20.75 [95% CI: 20.67\u0026ndash;20.83]; South, 19.03 [95% CI: 18.97\u0026ndash;19.09]; West, 17.39 [95% CI: 17.32\u0026ndash;17.47]; Northeast, 14.89 [95% CI: 14.82\u0026ndash;14.96]).(REFER TO FIGURE \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) At the state level, West Virginia, located in the South, recorded the highest AAMR (32.40), whereas Hawaii, in the West Census Region, exhibited the lowest (7.28).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAAMR: age-adjusted mortality rate, AAPC: average annual percentage change, APC: annual percent change.\u003c/p\u003e \u003cp\u003eWithin the Southern region, states in the upper decile of mortality included West Virginia (32.40), Oklahoma (31.49), Kentucky (31.06), and Mississippi (28.74)\u0026mdash;rates exceeding twofold those observed among states in the lowest decile, such as Florida (12.45) and the District of Columbia (10.07).\u003c/p\u003e \u003cp\u003eThe Midwest demonstrated comparatively narrower dispersion in AAMRs, with Indiana (24.74), Ohio (23.95), and Nebraska (23.14) among states with the highest mortality, while Wisconsin (18.82) and Illinois (16.31) occupied the lower end of the regional spectrum.\u003c/p\u003e \u003cp\u003eIn the Western region, the highest AAMRs were observed in Wyoming (25.97) and Oregon (24.17), whereas Arizona (11.00) and Hawaii (7.28) consistently reported the lowest rates.\u003c/p\u003e \u003cp\u003eWithin the Northeast, Vermont (23.67) and Rhode Island (23.33) exhibited the greatest mortality burden, in contrast to New York (12.81) and New Jersey (12.03), which reported the lowest AAMRs in the region.(supplementary table 5)\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this comprehensive national analysis spanning more than two decades, we identified a substantial and growing mortality burden attributable to the coexistence of heart failure (HF) and chronic obstructive pulmonary disease (COPD) among individuals aged 15 years and older in the United States. Our findings demonstrate a clear biphasic temporal pattern in age adjusted mortality rates (AAMRs), with a period of relative stability or modest decline from 1999 through approximately 2012, followed by a pronounced and statistically significant increase through 2020. This inflection was consistently observed across sex, race and ethnicity, urbanization status, and geographic region, underscoring the pervasive and widening impact of HF and COPD comorbidity on population level mortality.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe early attenuation in mortality observed during the first decade of the study period likely reflects improvements in cardiovascular and pulmonary care, including advancements in early detection, increased use of guideline directed medical therapy for HF, improved management of COPD exacerbations, and declining smoking prevalence during the late twentieth and early twenty first centuries (\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). However, the subsequent reversal and acceleration in mortality after 2012 suggest that these gains have not been sustained. Several factors may account for this shift, including population aging, increasing multimorbidity, rising prevalence of obesity and metabolic disease, and the growing recognition of HF with preserved ejection fraction, a condition for which effective mortality reducing therapies have historically been limited.\u003c/p\u003e \u003cp\u003eThe sharp rise in mortality observed in the latter years of the study, particularly approaching 2020, may also reflect the direct and indirect effects of the COVID 19 pandemic, including disruptions in routine outpatient care, delayed presentation for acute decompensation, and heightened vulnerability among patients with chronic cardiopulmonary disease (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Although CDC WONDER data do not permit causal attribution, the temporal concordance suggests that the pandemic may have amplified pre existing mortality trends in this high risk population.\u003c/p\u003e \u003cp\u003eConsistent with prior epidemiologic studies, males experienced substantially higher AAMRs than females throughout the study period (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). While both sexes demonstrated similar biphasic trajectories, mortality rates among males remained persistently elevated, likely reflecting a higher cumulative burden of smoking exposure, occupational risk factors, and cardiometabolic disease. Socioeconomic factors also influence healthcare seeking behavior and access to preventive treatments, which may contribute to observed sex based differences in mortality outcomes (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Notably, females exhibited a comparable relative acceleration in mortality after 2012, narrowing the sex gap in recent years. This pattern may reflect evolving risk factor profiles among women, including increased smoking prevalence in prior decades, as well as under recognition or undertreatment of HF and COPD in female patients (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMarked racial and ethnic disparities were evident in both absolute mortality burden and temporal trends. White individuals exhibited the highest overall AAMRs, likely reflecting the older age structure of this population and a higher prevalence of diagnosed HF and COPD (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). However, the most concerning trend was observed among Black individuals, who experienced a dramatic acceleration in mortality after 2014, culminating in one of the steepest annual increases among all racial and ethnic groups. This pattern likely reflects structural inequities, including disparities in access to preventive care, disease modifying therapies, and timely management of acute cardiopulmonary exacerbations (\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAmerican Indian and Alaska Native populations demonstrated a persistently rising mortality trajectory throughout the study period, highlighting a long standing and inadequately addressed public health crisis (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). In contrast, Hispanic populations exhibited a classic Hispanic paradox, with lower overall mortality rates and an initial decline, although this advantage diminished in later years (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Asian and Pacific Islander populations were the only group to demonstrate a sustained decline in mortality, suggesting potential protective sociocultural factors, lower smoking prevalence, or differences in disease phenotype that warrant further investigation.\u003c/p\u003e \u003cp\u003eA pronounced urban rural gradient in mortality was observed, with the highest AAMRs consistently documented in noncore and micropolitan areas. Rural populations experienced both higher baseline mortality and steeper increases in recent years. These disparities likely reflect reduced access to specialty care, higher prevalence of smoking and occupational lung disease, socioeconomic disadvantage, elevated cardiovascular risk factors, and limited availability of advanced HF therapies in rural settings (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Policy initiatives aimed at improving healthcare access, workforce distribution, and chronic disease management in underserved areas are essential to mitigating these disparities.\u003c/p\u003e \u003cp\u003eGeographically, the Midwest and South bore the greatest mortality burden, with particularly high rates observed in Appalachian and Southern states such as West Virginia, Kentucky, and Oklahoma. These regional patterns parallel known distributions of smoking prevalence, poverty, environmental exposures, and healthcare access inequities (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Conversely, states in the West and Northeast, particularly Hawaii, Arizona, New York, and New Jersey, consistently demonstrated lower mortality rates, potentially reflecting differences in public health infrastructure, healthcare delivery systems, and population level health behaviors.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eClinical and Public Health Implications\u003c/h2\u003e \u003cp\u003eThe rising mortality associated with HF and COPD comorbidity has important clinical and public health implications. Individuals with coexisting HF and COPD represent a uniquely vulnerable population characterized by complex pathophysiology, diagnostic overlap, and therapeutic tradeoffs. Fragmented care models that address these conditions in isolation may be insufficient to reduce mortality risk. Integrated cardiopulmonary care strategies, improved transitional care, and targeted interventions in high risk populations, particularly rural communities and racial and ethnic minorities, are urgently needed.\u003c/p\u003e \u003cp\u003eAdditionally, the substantial proportion of deaths occurring outside acute care hospital settings, including at home and in long term care facilities, underscores the importance of timely palliative care integration, advance care planning, and enhanced outpatient disease management for patients with advanced cardiopulmonary disease.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eThis study has several limitations. Mortality data derived from death certificates are subject to misclassification and coding variability, particularly for comorbid conditions such as HF and COPD. The CDC WONDER database lacks granular clinical information, including disease severity, treatment patterns, smoking history, and socioeconomic status, limiting causal inference. Additionally, observed trends may be influenced by changes in diagnostic practices, reporting accuracy, and ICD coding over time. Despite these limitations, the large nationally representative sample and extended study period strengthen the robustness and generalizability of our findings.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, mortality associated with the coexistence of heart failure and chronic obstructive pulmonary disease in the United States has increased substantially over the past decade following an earlier period of relative stability. This rise has disproportionately affected males, Black and American Indian and Alaska Native populations, rural communities, and residents of the Midwest and South. These findings highlight an urgent need for renewed public health efforts, integrated care models, and equity focused interventions to address the growing mortality burden associated with cardiopulmonary multimorbidity.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e The authors did not receive support from any organization for the submitted work.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eConflict of Interest:\u003c/strong\u003e All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eEthics Approval:\u003c/strong\u003e This study analyzed publicly available, de-identified data from the CDC WONDER database. As such, it was exempt from Institutional Review Board approval.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eConsent to Participate:\u003c/strong\u003e Not applicable.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eConsent for Publication:\u003c/strong\u003e Not applicable.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eData Availability:\u003c/strong\u003e The datasets generated and analyzed during the current study are publicly available in the CDC WONDER repository: https://wonder.cdc.gov/mcd.html\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eCode Availability:\u003c/strong\u003e Not applicable.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eAuthor Contributions:\u003c/strong\u003e\u0026nbsp;\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eJaveria Gul:\u003c/strong\u003e Methodology, Formal analysis, Data curation, Writing \u0026ndash; original draft, Writing \u0026ndash; review \u0026amp; editing, Visualization.\u003cbr\u003e\u003cstrong\u003eEishal Khan:\u003c/strong\u003e Data curation, Formal analysis, Writing \u0026ndash; review \u0026amp; editing.\u003cbr\u003e\u003cstrong\u003eMaria Campwala:\u003c/strong\u003e Conceptualization, Methodology, Validation, Writing \u0026ndash; review \u0026amp; editing.\u003cbr\u003e\u003cstrong\u003eIbadullah Khan:\u003c/strong\u003e Data curation, Writing \u0026ndash; review \u0026amp; editing.\u003cbr\u003e\u003cstrong\u003eAsad Ullah Farooq:\u003c/strong\u003e Data curation, Writing \u0026ndash; review \u0026amp; editing.\u003cbr\u003e\u003cstrong\u003eAnid Hassan, M.D.:\u003c/strong\u003e Conceptualization, Project administration, Visualization, Writing \u0026ndash; review \u0026amp; editing.\u003cbr\u003e\u003cstrong\u003eRumman Javed:\u003c/strong\u003e Methodology, Data curation, Writing \u0026ndash; review \u0026amp; editing.\u003cbr\u003e\u003cstrong\u003eAreeba Zia:\u003c/strong\u003e Data curation, Writing \u0026ndash; review \u0026amp; editing.\u003cbr\u003e\u003cstrong\u003eMahnoor Khuram:\u003c/strong\u003e Data curation, Writing \u0026ndash; review \u0026amp; editing.\u003cbr\u003e\u003cstrong\u003eSana Mehreen:\u003c/strong\u003e Data curation, Writing \u0026ndash; review \u0026amp; editing.\u003cbr\u003e\u003cstrong\u003eSadia Haleema:\u003c/strong\u003e Data curation, Writing \u0026ndash; review \u0026amp; editing.\u003cbr\u003e\u003cstrong\u003eHira Zeb:\u003c/strong\u003e Data curation, Writing \u0026ndash; review \u0026amp; editing.\u003cbr\u003e\u003cstrong\u003eHiba Noor:\u003c/strong\u003e Data curation, Writing \u0026ndash; review \u0026amp; editing.\u003cbr\u003e\u003cstrong\u003eIftekhar Khan:\u003c/strong\u003e Data curation, Writing \u0026ndash; review \u0026amp; editing.\u003cbr\u003e\u003cstrong\u003eHamid Shams:\u003c/strong\u003e Data curation, Writing \u0026ndash; review \u0026amp; editing.\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003eAcknowledgments:\u003c/strong\u003e The authors thank the CDC for maintaining and providing access to the WONDER database.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMartin SS, Aday AW, Almarzooq ZI, Anderson CAM, Arora P, Avery CL, American Heart Association Council on Epidemiology and Prevention Statistics Committee and Stroke Statistics Subcommittee, et al. 2024 Heart Disease and Stroke Statistics: A Report of US and Global Data From the American Heart Association. 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Their coexistence poses major clinical challenges and complicates management. This study quantified and analyzed mortality trends highlighting disparities by sex, race/ethnicity, age, region, state, and urban-rural status.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThe CDC WONDER multiple-cause mortality database was used (1999\u0026ndash;2020), including deaths in which both HF and COPD were recorded on death certificates. Age-adjusted mortality rates (AAMRs) per 100,000, with 95% confidence intervals (CIs), were calculated across demographic, geographic, and temporal variables using ICD-10 codes. Joinpoint regression identified statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) trend changes and annual percent changes (APCs).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total o f 1,054,847 deaths occurred in adults\u0026thinsp;\u0026ge;\u0026thinsp;15 years with both HF and COPD. In males, AAMR declined from 24.01 in 1999 to 21.22 in 2012, followed by an increase to 26.23 in 2020; in females, from 13.91 in 1999 to 14.68 in 2012 and to 18.59 in 2020. White individuals had the highest racial AAMR (19.18). The Midwest reported the highest regional AAMR (20.75), while West Virginia ranked highest among states (32.40). Noncore and micropolitan areas showed higher AAMRs compared with metropolitan areas.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eMortality associated with the coexistence of heart failure and chronic obstructive pulmonary disease in the United States has increased substantially over the past decade Persistent disparities were observed across sex, race/ethnicity, geography, and urban-rural status. These findings underscore the need for targeted public health strategies to reduce mortality and narrow disparities in high risk groups.\u003c/p\u003e","manuscriptTitle":"Mortality Trends in Heart Disease and COPD from 1999-2020: A CDC Wonder Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-10 11:37:58","doi":"10.21203/rs.3.rs-8822335/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a76ba01b-21ee-423a-a801-059e36b5b960","owner":[],"postedDate":"February 10th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-19T19:53:52+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-10 11:37:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8822335","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8822335","identity":"rs-8822335","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}