Trends in Mortality from Chronic Obstructive Pulmonary Disease Among Adults with Chronic Kidney Disease in the United States: A CDC WONDER Analysis, 1999-2020

Trends in Mortality from Chronic Obstructive Pulmonary Disease Among Adults with Chronic Kidney Disease in the United States: A CDC WONDER Analysis, 1999-2020 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Trends in Mortality from Chronic Obstructive Pulmonary Disease Among Adults with Chronic Kidney Disease in the United States: A CDC WONDER Analysis, 1999-2020 Guoxin Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7826341/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Background Chronic obstructive pulmonary disease (COPD) and chronic kidney disease (CKD) frequently coexist, yet mortality trends in patients with both conditions remain poorly characterized. Methods Using the CDC WONDER Multiple Cause of Death database, we analyzed death certificates from 1999 to 2020 for adults aged ≥ 25 years with COPD (ICD-10: J40-J44) as the underlying cause and CKD (N18) as a contributing cause. Age-adjusted mortality rates (AAMRs) per 100,000 were calculated using 2000 US standard population. Trends were assessed via Joinpoint regression, with stratifications by sex, race/ethnicity, age, urbanization, census regions, and state. Results Deaths increased from 1,405 in 1999 to 5,277 in 2020. The AAMR increased from 0.79 in 1999 to 1.96 in 2020. The overall AAPC was + 4.71% (95% CI: 3.06–6.38). Mortality rates were higher in males (AAMR: 2.41) than females (AAMR: 1.65), though females had faster increases (AAPC + 5.86% vs + 3.42%). The 85 + age group had the highest rates (27.98 per 100,000) and fastest growth (APC + 6.43%). NH White individuals exhibited the steepest increase (AAPC + 5.25%), while NH Black individuals had no significant trend (AAPC − 0.22%). Nonmetropolitan areas had higher AAMRs (2.68) compared to metropolitan areas (1.82). Regionally, the Midwest recorded the highest AAPC (+ 5.32%) and 2020 AAMR (2.42). State-level AAPCs ranged from 1.5% to 7.0%. Conclusion Rising COPD-related mortality among adults with CKD highlights a need for integrated pulmonary-renal care, targeted interventions for high-risk populations, and policies addressing rural healthcare access and environmental risk factors. chronic obstructive pulmonary disease CDC WONDER database chronic kidney disease mortality trends health disparities Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1 INTRODUCTION Chronic obstructive pulmonary disease (COPD) affects over 15 million US adults and ranks as the fourth leading cause of death nationally [ 1 , 2 ] , with mortality rates continuing to rise despite declining smoking prevalence [ 3 ] . Chronic kidney disease (CKD) affects approximately 37 million Americans, with prevalence increasing due to aging populations and rising rates of diabetes and hypertension [ 4 , 5 ] . Both conditions impose substantial morbidity, healthcare costs, and premature mortality [ 6 , 7 ] . The coexistence of COPD and CKD is common yet underrecognized. Epidemiologic studies suggest 20%-30% of COPD patients have comorbid CKD [ 8 , 9 ] , and conversely, CKD patients have higher COPD prevalence than the general population [ 10 ] . This comorbidity reflects shared risk factors—smoking, aging, hypertension, diabetes—and pathophysiological links including systemic inflammation, oxidative stress, and endothelial dysfunction [ 11 , 12 ] . Chronic hypoxemia from COPD activates neurohormonal pathways that accelerate renal injury [ 13 ] , while CKD-associated anemia, fluid overload, and metabolic derangements worsen respiratory function [ 14 , 15 ] . Patients with COPD-CKD comorbidity face worse outcomes than those with either condition alone. Observational studies report higher hospitalization rates, more frequent acute exacerbations, longer hospital stays, and increased mortality [ 16 , 17 ] . CKD modifies COPD treatment responses—impaired drug clearance necessitates dose adjustments, while concerns about nephrotoxicity may limit antibiotic and nonsteroidal anti-inflammatory drug use [ 18 ]. Conversely, COPD complicates CKD management through increased infection risk, medication interactions, and diagnostic challenges (respiratory symptoms may mask uremic manifestations) [ 19 ] . Despite growing recognition of COPD-CKD interactions, national mortality trends in this population remain incompletely understood. Previous studies examined COPD and CKD mortality independently [ 20 , 21 ] , but few investigated trends specifically among patients with both conditions. Understanding temporal patterns and identifying high-risk subgroups is essential for resource allocation, intervention development, and policy formulation. Using the CDC Wide-Ranging Online Data for Epidemiologic Research (WONDER) system, we conducted a comprehensive 22-year analysis of COPD-related mortality among US adults with CKD. We aimed to: (1) characterize overall temporal trends from 1999 to 2020; (2) identify disparities by sex, age, race/ethnicity, urbanization, and geography; and (3) inform clinical and public health strategies to reduce mortality in this high-risk population. 2 MATERIALS AND METHODS 2.1 Study Setting and Population This population-based mortality study analyzed death certificate data from the CDC Wide-Ranging Online Data for Epidemiologic Research (WONDER) Multiple Cause of Death database [ 22 ] for adults aged ≥ 25 years during 1999–2020. We identified deaths with chronic obstructive pulmonary disease (ICD-10 codes J40-J44) as the underlying cause and chronic kidney disease (N18) listed as a contributing cause, following standard CDC mortality surveillance definitions [ 23 , 24 ] . As this study used deidentified public data, institutional review board approval was not required. Reporting followed STROBE guidelines [ 25 ] . 2.2 Data Abstraction Deaths were stratified by sex (male/female), race/ethnicity (Hispanic, Non-Hispanic White, Non-Hispanic Black, Non-Hispanic Other), age groups (25–34, 35–44, 45–54, 55–64, 65–74, 75–84, 85 + years), urbanization (metropolitan/nonmetropolitan per 2013 NCHS classification [ 26 , 27 ] ), census region (Northeast, Midwest, South, West [ 28 ] ), and state. 2.3 Statistical Analysis Crude mortality rates and age-adjusted mortality rates (AAMRs) per 100,000 population were calculated using the 2000 US standard population. For age-specific analyses, crude rates were used as they were already age-stratified. Temporal trends were assessed via Joinpoint regression (Version 5.2.0) [ 29 ] , yielding annual percent changes (APCs) and average annual percent changes (AAPCs) with 95% confidence intervals. Statistical significance was set at p < 0.05. State-level variations were visualized using heat maps displaying total deaths, AAMRs, percent change (1999–2020), and AAPCs. 3 RESULTS 3.1 Annual Trends for COPD and CKD-Related Mortality Deaths increased from 1,405 in 1999 to 5,277 in 2020 (Table 1 ). The AAMR for COPD and CKD-related deaths in adults increased from 0.79 per 100,000 (95% CI: 0.75–0.83) in 1999 to 1.96 per 100,000 (95% CI: 1.91–2.02) in 2020. The overall AAMR rose consistently from 1999 to 2020 (AAPC: +4.71%; 95% CI: 3.06–6.38), highlighting a substantial rise over the study period (Fig. 2 , Table 1 ). Table 1 Mortality trends stratified by demographics and geography for COPD-related deaths among adults with chronic kidney disease in the United States, 1999–2020 Measure Deaths_1999 Deaths_2020 percent.change AAMR_1999 AAMR_2020 AAPC (95% CI Both 1405 5277 275.59 0.79 (0.75 to 0.83) 1.96 (1.91 to 2.02) 4.71 (3.06 to 6.38)* Female 523 2557 388.91 0.50 (0.46 to 0.55) 1.65 (1.58 to 1.71) 5.86 (4.07 to 7.69)* Male 882 2720 208.39 1.32 (1.23 to 1.41) 2.41 (2.32 to 2.50) 3.42 (1.85 to 5.02)* Midwest 336 1413 320.54 0.78 (0.70 to 0.87) 2.42 (2.30 to 2.55) 5.32 (3.73 to 6.94)* Northeast 316 750 137.34 0.85 (0.75 to 0.94) 1.49 (1.39 to 1.60) 3.87 (2.51 to 5.26)* South 503 2068 311.13 0.80 (0.73 to 0.88) 2.03 (1.94 to 2.12) 4.65 (2.98 to 6.36)* West 250 1046 318.4 0.71 (0.62 to 0.80) 1.74 (1.63 to 1.84) 4.59 (2.55 to 6.67)* Hispanic 43 219 409.3 0.57 (0.40 to 0.77) 0.94 (0.82 to 1.07) 2.36 (0.46 to 4.30)* NH Black 28 109 289.29 0.66 (0.42 to 0.97) 0.76 (0.61 to 0.90) -0.22 (-7.79 to 7.98) NH Other 159 533 235.22 1.09 (0.92 to 1.26) 2.09 (1.91 to 2.28) 3.30 (1.85 to 4.77)* NH White 1172 4407 276.02 0.78 (0.73 to 0.82) 2.14 (2.08 to 2.21) 5.25 (3.60 to 6.92)* Metropolitan 1107 4072 267.84 0.77 (0.73 to 0.82) 1.82 (1.76 to 1.87) 4.47 (2.84 to 6.13)* Nonmetropolitan 298 1205 304.36 0.87 (0.77 to 0.97) 2.68 (2.53 to 2.84) 5.71 (3.99 to 7.47)* 25–34 years 0 - - 35–44 years 12 - - 45–54 years 34 73 114.71 0.09 (0.09 to 0.09) 0.18 (0.18 to 0.18) 3.93 (2.63 to 5.24)* 55–64 years 107 385 259.81 0.45 (0.45 to 0.45) 0.91 (0.91 to 0.91) 3.47 (1.93 to 5.04)* 65–74 years 330 1065 222.73 1.79 (1.79 to 1.79) 3.27 (3.27 to 3.27) 3.04 (1.43 to 4.68)* 75–84 years 573 1879 227.92 4.69 (4.69 to 4.69) 11.42 (11.42 to 11.42) 4.50 (2.88 to 6.15)* 85 + years 352 1863 429.26 8.47 (8.47 to 8.47) 27.98 (27.98 to 27.98) 6.43 (4.52 to 8.37)* 3.2 COPD and CKD-Related Mortality Trends Stratified by Sex Trends in mortalities differed by sex. The overall AAMR was 2.41 (95% CI: 2.32–2.50) for males and 1.65 (95% CI: 1.58–1.71) for females (Table 1 ). For males, the AAMR increased from 1.32 in 1999 to 2.41 in 2020, showing an AAPC of + 3.42% (95% CI: 1.85–5.02). For females, the AAMR rose from 0.50 in 1999 to 1.65 in 2020, with an AAPC of + 5.86% (95% CI: 4.07–7.69) (Fig. 2 , Table 1 ). 3.3 COPD and CKD-Related Mortality Trends Stratified by Age No deaths occurred in the 25–34 age group; only 12 deaths were recorded in the 35–44 age group in 2020. Figure 1 displays crude death rates for five age groups from 45 years onward. Deaths increased across all age groups: 45–54 years (34→73, APC + 3.93%), 55–64 years (107→385, APC + 3.47%), 65–74 years (330→1,065, APC + 3.04%), 75–84 years (573→1,879, APC + 4.50%), and 85 + years (352→1,863, APC + 6.43%). The 85 + age group had the highest crude mortality rate (27.98 per 100,000 in 2020) and fastest growth (Fig. 1 , Table 1 ). 3.4 COPD and CKD-Related Mortality Trends Stratified by Race The overall AAMR was highest among NH White adults (2.14; 95% CI: 2.08–2.21), followed by NH Other (2.09; 95% CI: 1.91–2.28), Hispanic adults (0.94; 95% CI: 0.82–1.07), and NH Black adults (0.76; 95% CI: 0.61–0.90) (Table 1 ). NH White adults experienced a steady increase from 1999 to 2020 (AAPC: +5.25%; 95% CI: 3.60–6.92). NH Other showed an AAPC of + 3.30% (95% CI: 1.85–4.77). Hispanic adults had an AAPC of + 2.36% (95% CI: 0.46–4.30). NH Black individuals showed non-linear trends: declining 1999–2008 (APC − 1.27%, NS), volatility 2008–2011 (APC + 22.77%, NS), and significant decline 2011–2020 (APC − 5.89%, p < 0.05). Overall AAPC was − 0.22% (NS), the only group without significant increase (Fig. 3 , Table 1 ). 3.5 Geographic Mortality Patterns 3.5.1 Census Regions The Midwest had the highest AAPC (+ 5.32%) and 2020 AAMR (2.42 per 100,000), followed by South (+ 4.65%, 2.03 per 100,000), West (+ 4.59%, 1.74 per 100,000), and Northeast (+ 3.87%, 1.49 per 100,000) (Fig. 4 , Table 1 ). 3.5.2 State-Level Variation Figure 6 A shows total deaths ranged from < 25 in small states to 238–468 in populous states. Panel B reveals AAMR variation from 1.16–1.70 per 100,000 (Southwest/West) to 3.15–4.40 per 100,000 (Midwest/Appalachia). Panel C shows most states had 95–155% increases, with Nevada and South Carolina exceeding 155%. Panel D demonstrates AAPC variation from 1.5–3.5% (Southwest) to 6.0–7.0% (Maine, Vermont, Wisconsin, Michigan, Kentucky, West Virginia, Florida, Oregon, Utah). 3.6 | COPD and CKD-Related Mortality Trends Stratified by Urbanization Nonmetropolitan areas exhibited a higher AAMR than metropolitan areas (Table 1 ). The AAMR was 2.68 (95% CI: 2.53–2.84) for nonmetropolitan areas and 1.82 (95% CI: 1.76–1.87) for metropolitan areas. Metropolitan areas had an AAPC of + 4.47% (95% CI: 2.84–6.13), while nonmetropolitan areas had an AAPC of + 5.71% (95% CI: 3.99–7.47) (Fig. 5 , Table 1 ). 4 DISCUSSION This 22-year analysis of CDC WONDER data reveals a substantial burden of COPD-related mortality among adults with CKD in the United States. Between 1999 and 2020, deaths increased 276% with an AAPC of + 4.71%, demonstrating persistent growth across all demographic subgroups. Notable disparities emerged by sex, race/ethnicity, age, and geography, with females, older adults, NH White individuals, and Midwest/Appalachian states experiencing disproportionate increases. Cardiopulmonary-Renal Interactions The co-occurrence of COPD and CKD reflects shared pathophysiological mechanisms. Chronic hypoxemia from COPD impairs renal perfusion and activates the renin-angiotensin-aldosterone system, accelerating CKD progression [ 30 ] . Conversely, CKD-associated fluid overload, anemia, and uremia exacerbate respiratory dysfunction and increase susceptibility to respiratory infections [ 31 ] . Systemic inflammation, oxidative stress, and endothelial dysfunction are common to both conditions [ 32 , 33 ] , creating a self-reinforcing cycle of organ damage. This multiorgan involvement likely explains the elevated mortality observed in our cohort, where patients face compounded risks from both pulmonary and renal failure. Rising Mortality Trends The sustained 4.71% annual increase in COPD-CKD mortality parallels trends in both conditions independently. COPD mortality has risen in recent decades despite declining smoking rates, attributed to aging populations, increasing biomass fuel exposure, and air pollution [ 34 , 35 ] . Similarly, CKD prevalence has grown due to rising diabetes and hypertension rates [ 36 ] . Our findings suggest these epidemics intersect with multiplicative rather than additive effects on mortality. The acceleration from 2018–2020 warrants attention. While broader trends reflect demographic aging and disease prevalence increases, the 2020 spike likely reflects COVID-19 impacts. Patients with COPD-CKD comorbidity faced heightened vulnerability to severe COVID-19 complications [ 37 ] , and pandemic-related healthcare disruptions may have delayed routine disease management. Additionally, increased medical scrutiny during the pandemic may have improved death certificate coding accuracy [ 38 ] . Sex Disparities Females demonstrated faster mortality increases (AAPC + 5.86%) despite lower absolute rates than males. This paradox reflects changing smoking patterns—while male smoking rates declined earlier, female smoking prevalence remained elevated through the 1990s-2000s, creating a cohort effect now manifesting in mortality [ 39 , 40 ] . Additionally, females may have greater susceptibility to COPD at equivalent smoking exposures due to smaller airway dimensions and hormonal influences on lung development [ 41 ] . Diagnostic delays in females, where COPD symptoms are often attributed to aging or anxiety, may contribute to more advanced disease at diagnosis [ 42 ] . The loss of estrogen's renoprotective effects post-menopause may also accelerate CKD progression in females with COPD [ 43 ] . Age-Specific Patterns The exponential increase in mortality with age—from APC + 3.04% in 65–74 year-olds to + 6.43% in those 85+—reflects cumulative disease burden, declining physiologic reserve, and reduced tolerance for acute decompensations [ 44 ] . Older adults with COPD-CKD comorbidity face challenges from polypharmacy, frailty, and limited rehabilitation potential [ 45 ] . The near-absence of deaths in adults < 45 years suggests COPD-CKD mortality primarily affects those with decades of exposure to risk factors, though early-onset cases may represent genetic predispositions requiring targeted screening. Racial and Ethnic Disparities NH White individuals had the highest mortality rates and fastest increases (AAPC + 5.25%), consistent with higher COPD prevalence in this population [ 46 , 47 ] . Genetic factors may contribute [ 48 ] . Higher historical smoking rates and greater occupational exposures (mining, agriculture) in predominantly White rural areas likely play roles [ 49 ] . The unique pattern in NH Black individuals—with non-linear trends and an overall non-significant AAPC—merits discussion. While this population has lower COPD prevalence despite higher smoking rates [ 50 ] , possible explanations include: (1) survival bias, where NH Black individuals with severe COPD may die before developing advanced CKD; (2) differences in COPD phenotypes [ 51 ] ; (3) improved CKD management in this population through initiatives targeting diabetic nephropathy [ 52 ] ; or (4) coding inconsistencies on death certificates. The decline from 2011–2020 (APC − 5.89%) coincides with increased nephrology care access following the Affordable Care Act, suggesting healthcare policy impacts. Hispanic individuals maintained the lowest mortality rates, potentially reflecting the "Hispanic paradox," healthier lifestyle factors, or younger population demographics [ 53 ] . However, their significant AAPC (+ 2.36%) indicates rising burden requiring attention. Geographic Variations The Midwest's highest mortality rates and fastest increases (AAPC + 5.32%, 2020 AAMR 2.42 per 100,000) align with regional characteristics: high smoking prevalence, coal mining and agricultural occupational exposures, industrial air pollution, and limited healthcare access in rural areas [ 54 , 55 ] . States like Kentucky, West Virginia, and Ohio—with historically coal-dependent economies—bear disproportionate burdens. A significant difference in AAMRs was observed across states, with rates ranging from 1.16–1.70 per 100,000 (Southwest/West) to 3.15–4.40 per 100,000 (Midwest/Appalachia) (Fig. 6 ). States in the top 90th percentile—Maine, Vermont, Wisconsin, Michigan, Kentucky, West Virginia, Florida, Oregon, and Utah—had almost triple the AAMRs compared with states in the lower 10th percentile, namely Nevada, Arizona, California, and Texas. The Southwest's lower rates may reflect migration of healthier individuals, warmer climates reducing COPD exacerbations, and lower smoking rates [ 56 ] . However, rapid growth in Nevada and Arizona suggests emerging problems requiring intervention. State-level variation (AAPC range 1.5-7.0%) indicates modifiable factors beyond demographic composition. States with robust tobacco control, clean air policies, and integrated pulmonary-renal care models may serve as blueprints for high-burden regions [ 57 ] . Rural-Urban Disparities Although nonmetropolitan areas had a higher growth rate (AAPC + 5.71% vs + 4.47% for metropolitan areas), they maintained 47% higher 2020 mortality than metropolitan areas. Rural populations face multiple disadvantages: higher smoking and obesity rates, occupational exposures (farming, mining), provider shortages (particularly pulmonologists and nephrologists), delayed diagnoses, and barriers to specialty care [ 58 , 59 ] . Rural patients are less likely to receive guideline-concordant COPD therapies (inhaled corticosteroids, long-acting bronchodilators) or CKD management (renin-angiotensin system inhibitors, sodium-glucose cotransporter-2 inhibitors) [ 60 , 61 ] . Telemedicine expansion and mobile specialty clinics may help bridge these gaps. Clinical and Policy Implications These findings have several implications. First, integrated care models coordinating pulmonology and nephrology are needed for COPD-CKD patients, who often receive fragmented care [ 62 ] . Second, given the age-specific burden, geriatric assessment including frailty screening should guide treatment intensity in older adults [ 63 ] . Third, addressing modifiable risk factors—smoking cessation, air quality improvement, vaccination against respiratory pathogens, optimal management of hypertension and diabetes—remains paramount [ 64 , 65 ] . Fourth, rural health infrastructure investment is critical to reducing geographic disparities. Clinicians should maintain high suspicion for CKD in COPD patients and vice versa, with routine screening using creatinine, estimated glomerular filtration rate, and urinary albumin [ 66 ] . Early identification enables intervention before irreversible organ damage occurs. Medication reconciliation is essential, as drugs commonly used in one condition may worsen the other (e.g., nonsteroidal anti-inflammatory drugs exacerbating CKD, beta-blockers worsening COPD) [ 67 ] . Public health efforts should target high-risk populations identified in this study: females, older adults, NH White individuals, and residents of Midwest/Appalachian states and rural areas. Tailored interventions addressing region-specific risk factors (occupational exposures in mining states, agricultural dust in rural areas) may yield benefits. Limitations This study has limitations. First, death certificate data may underreport or misclassify CKD as a contributing cause, particularly if not clinically recognized or deemed irrelevant by certifying physicians [ 68 ] . Second, we could not assess disease severity, treatment received, or socioeconomic factors beyond urbanization. Third, we lacked data on smoking history, occupational exposures, or comorbidities beyond COPD and CKD. Fourth, ecological analysis of state-level data cannot establish individual-level causation. Fifth, ICD-10 coding changes over time may introduce temporal artifacts, though our use of consistent codes (J40-J44, N18) throughout minimizes this concern. Finally, the 2020 data reflect COVID-19 pandemic effects, complicating interpretation of recent trends. Future Directions Future research should examine: (1) mechanisms linking COPD and CKD progression using longitudinal cohorts with biomarker assessments; (2) effectiveness of integrated care models on mortality and quality of life; (3) optimal medication regimens for COPD-CKD comorbidity, particularly novel agents (triple inhaled therapy for COPD, SGLT2 inhibitors for CKD) [ 69 , 70 ] ; (4) implementation strategies for telemedicine-based specialty care in rural areas; and (5) policy evaluations of tobacco control and air quality regulations on COPD-CKD mortality. 5 CONCLUSION COPD-related mortality among adults with CKD increased substantially from 1999 to 2020, with significant disparities by sex, age, race/ethnicity, and geography. Females, older adults, NH White individuals, Midwest/Appalachian states, and rural areas experienced disproportionate burdens. These findings underscore the need for integrated pulmonary-renal care, targeted interventions for high-risk populations, and policies addressing tobacco use, air quality, and rural healthcare access. As the US population ages and chronic disease prevalence rises, the intersection of COPD and CKD will increasingly challenge healthcare systems, necessitating proactive strategies to mitigate mortality. Declarations ACKNOWLEDGMENTS The authors thank the Centers for Disease Control and Prevention for maintaining the CDC WONDER database and making these vital statistics publicly available for research purposes. CLINICAL TRIAL NUMBER Not applicable. FUNDING DECLARATION This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. No financial support was provided for the conduct of this study or preparation of this manuscript. ETHICS STATEMENT Institutional Review Board approval was not required for this study as it utilized deidentified, publicly available data from the CDC WONDER database. The study was conducted in accordance with the Declaration of Helsinki and followed the STROBE reporting guidelines for observational studies. CONFLICTS OF INTEREST All authors declare that they have no financial or non-financial conflicts of interest related to this work. The authors have no relevant financial or non-financial interests to disclose. DATA AVAILABILITY STATEMENT The data that support the findings of this study are available in National Center for Health Statistics at https://wonder.cdc.gov/mcd.html. These data were derived from the following resources available in the public domain: CDC WONDER, https://wonder.cdc.gov/. References Adeloye D, Song P, Zhu Y, et al. 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Frailty is a predictive factor of readmission within 90 days of hospitalization for acute exacerbations of chronic obstructive pulmonary disease: a longitudinal study. Ther Adv Respir Dis. 2017;11(10):383–92. Global Initiative for Chronic Obstructive Lung Disease. Global Strategy for Prevention, Diagnosis and Management of COPD: 2024 Report. Available from: https://goldcopd.org Kidney Disease: Improving Global Outcomes (KDIGO) CKD Work Group. KDIGO 2024 Clinical Practice Guideline for the Evaluation and Management of Chronic Kidney Disease. Kidney Int. 2024;105(3S):S1–163. Gansevoort RT, Correa-Rotter R, Hemmelgarn BR, et al. Chronic kidney disease and cardiovascular risk: epidemiology, mechanisms, and prevention. Lancet. 2013;382(9889):339–52. Hörl WH. Nonsteroidal anti-inflammatory drugs and the kidney. Pharmaceuticals (Basel). 2010;3(7):2291–321. Lloyd-Jones DM, Martin DO, Larson MG, et al. Accuracy of death certificates for coding coronary heart disease as the cause of death. Ann Intern Med. 1998;129(12):1020–6. Rabe KF, Martinez FJ, Ferguson GT, et al. Triple inhaled therapy at two glucocorticoid doses in moderate-to-very-severe COPD. N Engl J Med. 2020;383(1):35–48. Perkovic V, Jardine MJ, Neal B, et al. Canagliflozin and renal outcomes in type 2 diabetes and nephropathy. N Engl J Med. 2019;380(24):2295–306. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 16 Nov, 2025 Reviewers agreed at journal 14 Nov, 2025 Reviewers invited by journal 14 Nov, 2025 Editor assigned by journal 12 Nov, 2025 Editor invited by journal 24 Oct, 2025 Submission checks completed at journal 23 Oct, 2025 First submitted to journal 23 Oct, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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1","display":"","copyAsset":false,"role":"figure","size":137577,"visible":true,"origin":"","legend":"\u003cp\u003eAge-stratified crude death rates per 100,000 population for COPD-related mortality among adults with chronic kidney disease in the United States from 1999 to 2020\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7826341/v1/692fcd922fb24282bd4b9fbb.png"},{"id":96915697,"identity":"bc10c128-e339-43fa-9bcd-cb41b6ecd0ae","added_by":"auto","created_at":"2025-11-27 14:07:32","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":152373,"visible":true,"origin":"","legend":"\u003cp\u003eOverall and sex-stratified COPD-related AAMRs per 100,000 among adults with chronic kidney disease in the United States from 1999 to 2020\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7826341/v1/97db4a924904c54d043fadf1.png"},{"id":96917781,"identity":"693434e3-a87b-49f3-b316-e445d04feb7d","added_by":"auto","created_at":"2025-11-27 14:10:34","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":190525,"visible":true,"origin":"","legend":"\u003cp\u003eRace/ethnicity-stratified COPD-related AAMRs per 100,000 among adults with chronic kidney disease in the United States from 1999 to 2020\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7826341/v1/2c9c0b2d94e07be681fab640.png"},{"id":96803871,"identity":"f9a900d8-3d76-4094-85b7-663d5d4ee43b","added_by":"auto","created_at":"2025-11-26 09:03:59","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":207849,"visible":true,"origin":"","legend":"\u003cp\u003eCensus region-stratified COPD-related AAMRs per 100,000 among adults with chronic kidney disease in the United States from 1999 to 2020\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7826341/v1/6a7906d100f14cd0aadb3623.png"},{"id":96803878,"identity":"e621357f-7963-4601-af09-e9fe9eee8386","added_by":"auto","created_at":"2025-11-26 09:03:59","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":139426,"visible":true,"origin":"","legend":"\u003cp\u003eUrbanization-stratified COPD-related AAMRs per 100,000 among adults with chronic kidney disease in the United States from 1999 to 2020\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7826341/v1/8d9929603f8adf2d2eb9e41d.png"},{"id":96803877,"identity":"f2e99007-13a7-4eb0-8fec-1631092e2cd8","added_by":"auto","created_at":"2025-11-26 09:03:59","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":382952,"visible":true,"origin":"","legend":"\u003cp\u003eGeographic distribution of COPD-related mortality among adults with chronic kidney disease in the United States, 1999-2020. (A) Total death counts by state. (B) Age-adjusted mortality rates (AAMRs) per 100,000 population by state. (C) Percent change in death counts from 1999 to 2020 by state. (D) Average annual percent change (AAPC) in mortality rates by state.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7826341/v1/92b37192340f52cf6402fa5b.png"},{"id":97144031,"identity":"75d16ab0-d29c-44e0-8621-f2d29ab10f59","added_by":"auto","created_at":"2025-12-01 10:10:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1910608,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7826341/v1/38e0af7d-4915-4e51-9dec-ec09bab01f39.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eTrends in Mortality from Chronic Obstructive Pulmonary Disease Among Adults with Chronic Kidney Disease in the United States: A CDC WONDER Analysis, 1999-2020\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1 INTRODUCTION","content":"\u003cp\u003eChronic obstructive pulmonary disease (COPD) affects over 15\u0026nbsp;million US adults and ranks as the fourth leading cause of death nationally \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e, with mortality rates continuing to rise despite declining smoking prevalence \u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Chronic kidney disease (CKD) affects approximately 37\u0026nbsp;million Americans, with prevalence increasing due to aging populations and rising rates of diabetes and hypertension \u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. Both conditions impose substantial morbidity, healthcare costs, and premature mortality \u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe coexistence of COPD and CKD is common yet underrecognized. Epidemiologic studies suggest 20%-30% of COPD patients have comorbid CKD \u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e, and conversely, CKD patients have higher COPD prevalence than the general population \u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. This comorbidity reflects shared risk factors\u0026mdash;smoking, aging, hypertension, diabetes\u0026mdash;and pathophysiological links including systemic inflammation, oxidative stress, and endothelial dysfunction \u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. Chronic hypoxemia from COPD activates neurohormonal pathways that accelerate renal injury \u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e, while CKD-associated anemia, fluid overload, and metabolic derangements worsen respiratory function \u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003ePatients with COPD-CKD comorbidity face worse outcomes than those with either condition alone. Observational studies report higher hospitalization rates, more frequent acute exacerbations, longer hospital stays, and increased mortality \u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. CKD modifies COPD treatment responses\u0026mdash;impaired drug clearance necessitates dose adjustments, while concerns about nephrotoxicity may limit antibiotic and nonsteroidal anti-inflammatory drug use \u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e]. Conversely, COPD complicates CKD management through increased infection risk, medication interactions, and diagnostic challenges (respiratory symptoms may mask uremic manifestations) \u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eDespite growing recognition of COPD-CKD interactions, national mortality trends in this population remain incompletely understood. Previous studies examined COPD and CKD mortality independently \u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e, but few investigated trends specifically among patients with both conditions. Understanding temporal patterns and identifying high-risk subgroups is essential for resource allocation, intervention development, and policy formulation.\u003c/p\u003e\u003cp\u003eUsing the CDC Wide-Ranging Online Data for Epidemiologic Research (WONDER) system, we conducted a comprehensive 22-year analysis of COPD-related mortality among US adults with CKD. We aimed to: (1) characterize overall temporal trends from 1999 to 2020; (2) identify disparities by sex, age, race/ethnicity, urbanization, and geography; and (3) inform clinical and public health strategies to reduce mortality in this high-risk population.\u003c/p\u003e"},{"header":"2 MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Study Setting and Population\u003c/h2\u003e\u003cp\u003eThis population-based mortality study analyzed death certificate data from the CDC Wide-Ranging Online Data for Epidemiologic Research (WONDER) Multiple Cause of Death database \u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e for adults aged\u0026thinsp;\u0026ge;\u0026thinsp;25 years during 1999\u0026ndash;2020. We identified deaths with chronic obstructive pulmonary disease (ICD-10 codes J40-J44) as the underlying cause and chronic kidney disease (N18) listed as a contributing cause, following standard CDC mortality surveillance definitions \u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. As this study used deidentified public data, institutional review board approval was not required. Reporting followed STROBE guidelines \u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Data Abstraction\u003c/h2\u003e\u003cp\u003eDeaths were stratified by sex (male/female), race/ethnicity (Hispanic, Non-Hispanic White, Non-Hispanic Black, Non-Hispanic Other), age groups (25\u0026ndash;34, 35\u0026ndash;44, 45\u0026ndash;54, 55\u0026ndash;64, 65\u0026ndash;74, 75\u0026ndash;84, 85\u0026thinsp;+\u0026thinsp;years), urbanization (metropolitan/nonmetropolitan per 2013 NCHS classification \u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e), census region (Northeast, Midwest, South, West \u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e), and state.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Statistical Analysis\u003c/h2\u003e\u003cp\u003eCrude mortality rates and age-adjusted mortality rates (AAMRs) per 100,000 population were calculated using the 2000 US standard population. For age-specific analyses, crude rates were used as they were already age-stratified. Temporal trends were assessed via Joinpoint regression (Version 5.2.0) \u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e, yielding annual percent changes (APCs) and average annual percent changes (AAPCs) with 95% confidence intervals. Statistical significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. State-level variations were visualized using heat maps displaying total deaths, AAMRs, percent change (1999\u0026ndash;2020), and AAPCs.\u003c/p\u003e\u003c/div\u003e"},{"header":"3 RESULTS","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Annual Trends for COPD and CKD-Related Mortality\u003c/h2\u003e\u003cp\u003eDeaths increased from 1,405 in 1999 to 5,277 in 2020 (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The AAMR for COPD and CKD-related deaths in adults increased from 0.79 per 100,000 (95% CI: 0.75\u0026ndash;0.83) in 1999 to 1.96 per 100,000 (95% CI: 1.91\u0026ndash;2.02) in 2020. The overall AAMR rose consistently from 1999 to 2020 (AAPC: +4.71%; 95% CI: 3.06\u0026ndash;6.38), highlighting a substantial rise over the study period (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMortality trends stratified by demographics and geography for COPD-related deaths among adults with chronic kidney disease 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=\"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=\"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\u003eMeasure\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDeaths_1999\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDeaths_2020\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003epercent.change\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAAMR_1999\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAAMR_2020\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eAAPC (95% CI\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBoth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1405\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5277\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e275.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.79 (0.75 to 0.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.96 (1.91 to 2.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4.71 (3.06 to 6.38)*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e523\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2557\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e388.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.50 (0.46 to 0.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.65 (1.58 to 1.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e5.86 (4.07 to 7.69)*\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=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e882\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2720\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e208.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.32 (1.23 to 1.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.41 (2.32 to 2.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3.42 (1.85 to 5.02)*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMidwest\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e336\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1413\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e320.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.78 (0.70 to 0.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.42 (2.30 to 2.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e5.32 (3.73 to 6.94)*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNortheast\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e316\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e750\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e137.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.85 (0.75 to 0.94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.49 (1.39 to 1.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3.87 (2.51 to 5.26)*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSouth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e503\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2068\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e311.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.80 (0.73 to 0.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.03 (1.94 to 2.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4.65 (2.98 to 6.36)*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWest\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e250\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1046\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e318.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.71 (0.62 to 0.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.74 (1.63 to 1.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4.59 (2.55 to 6.67)*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHispanic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e219\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e409.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.57 (0.40 to 0.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.94 (0.82 to 1.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.36 (0.46 to 4.30)*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNH Black\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e109\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e289.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.66 (0.42 to 0.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.76 (0.61 to 0.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.22 (-7.79 to 7.98)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNH Other\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e159\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e533\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e235.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.09 (0.92 to 1.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.09 (1.91 to 2.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3.30 (1.85 to 4.77)*\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=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1172\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4407\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e276.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.78 (0.73 to 0.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.14 (2.08 to 2.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e5.25 (3.60 to 6.92)*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMetropolitan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1107\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4072\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e267.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.77 (0.73 to 0.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.82 (1.76 to 1.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4.47 (2.84 to 6.13)*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNonmetropolitan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e298\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1205\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e304.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.87 (0.77 to 0.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.68 (2.53 to 2.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e5.71 (3.99 to 7.47)*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e25\u0026ndash;34 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\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\u003e35\u0026ndash;44 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\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\u003e45\u0026ndash;54 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e114.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.09 (0.09 to 0.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.18 (0.18 to 0.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3.93 (2.63 to 5.24)*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e55\u0026ndash;64 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e107\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e385\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e259.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.45 (0.45 to 0.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.91 (0.91 to 0.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3.47 (1.93 to 5.04)*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e65\u0026ndash;74 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e330\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1065\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e222.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.79 (1.79 to 1.79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.27 (3.27 to 3.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3.04 (1.43 to 4.68)*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e75\u0026ndash;84 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e573\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1879\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e227.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.69 (4.69 to 4.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e11.42 (11.42 to 11.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4.50 (2.88 to 6.15)*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e85\u0026thinsp;+\u0026thinsp;years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e352\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1863\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e429.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8.47 (8.47 to 8.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e27.98 (27.98 to 27.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e6.43 (4.52 to 8.37)*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.2 COPD and CKD-Related Mortality Trends Stratified by Sex\u003c/h2\u003e\u003cp\u003eTrends in mortalities differed by sex. The overall AAMR was 2.41 (95% CI: 2.32\u0026ndash;2.50) for males and 1.65 (95% CI: 1.58\u0026ndash;1.71) for females (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). For males, the AAMR increased from 1.32 in 1999 to 2.41 in 2020, showing an AAPC of +\u0026thinsp;3.42% (95% CI: 1.85\u0026ndash;5.02). For females, the AAMR rose from 0.50 in 1999 to 1.65 in 2020, with an AAPC of +\u0026thinsp;5.86% (95% CI: 4.07\u0026ndash;7.69) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.3 COPD and CKD-Related Mortality Trends Stratified by Age\u003c/h2\u003e\u003cp\u003eNo deaths occurred in the 25\u0026ndash;34 age group; only 12 deaths were recorded in the 35\u0026ndash;44 age group in 2020. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e displays crude death rates for five age groups from 45 years onward.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eDeaths increased across all age groups: 45\u0026ndash;54 years (34\u0026rarr;73, APC\u0026thinsp;+\u0026thinsp;3.93%), 55\u0026ndash;64 years (107\u0026rarr;385, APC\u0026thinsp;+\u0026thinsp;3.47%), 65\u0026ndash;74 years (330\u0026rarr;1,065, APC\u0026thinsp;+\u0026thinsp;3.04%), 75\u0026ndash;84 years (573\u0026rarr;1,879, APC\u0026thinsp;+\u0026thinsp;4.50%), and 85\u0026thinsp;+\u0026thinsp;years (352\u0026rarr;1,863, APC\u0026thinsp;+\u0026thinsp;6.43%). The 85\u0026thinsp;+\u0026thinsp;age group had the highest crude mortality rate (27.98 per 100,000 in 2020) and fastest growth (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.4 COPD and CKD-Related Mortality Trends Stratified by Race\u003c/h2\u003e\u003cp\u003eThe overall AAMR was highest among NH White adults (2.14; 95% CI: 2.08\u0026ndash;2.21), followed by NH Other (2.09; 95% CI: 1.91\u0026ndash;2.28), Hispanic adults (0.94; 95% CI: 0.82\u0026ndash;1.07), and NH Black adults (0.76; 95% CI: 0.61\u0026ndash;0.90) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eNH White adults experienced a steady increase from 1999 to 2020 (AAPC: +5.25%; 95% CI: 3.60\u0026ndash;6.92). NH Other showed an AAPC of +\u0026thinsp;3.30% (95% CI: 1.85\u0026ndash;4.77). Hispanic adults had an AAPC of +\u0026thinsp;2.36% (95% CI: 0.46\u0026ndash;4.30). NH Black individuals showed non-linear trends: declining 1999\u0026ndash;2008 (APC \u0026minus;\u0026thinsp;1.27%, NS), volatility 2008\u0026ndash;2011 (APC\u0026thinsp;+\u0026thinsp;22.77%, NS), and significant decline 2011\u0026ndash;2020 (APC \u0026minus;\u0026thinsp;5.89%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Overall AAPC was \u0026minus;\u0026thinsp;0.22% (NS), the only group without significant increase (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Geographic Mortality Patterns\u003c/h2\u003e\u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\u003ch2\u003e3.5.1 Census Regions\u003c/h2\u003e\u003cp\u003eThe Midwest had the highest AAPC (+\u0026thinsp;5.32%) and 2020 AAMR (2.42 per 100,000), followed by South (+\u0026thinsp;4.65%, 2.03 per 100,000), West (+\u0026thinsp;4.59%, 1.74 per 100,000), and Northeast (+\u0026thinsp;3.87%, 1.49 per 100,000) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section3\"\u003e\u003ch2\u003e3.5.2 State-Level Variation\u003c/h2\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003eA shows total deaths ranged from \u0026lt;\u0026thinsp;25 in small states to 238\u0026ndash;468 in populous states. Panel B reveals AAMR variation from 1.16\u0026ndash;1.70 per 100,000 (Southwest/West) to 3.15\u0026ndash;4.40 per 100,000 (Midwest/Appalachia). Panel C shows most states had 95\u0026ndash;155% increases, with Nevada and South Carolina exceeding 155%. Panel D demonstrates AAPC variation from 1.5\u0026ndash;3.5% (Southwest) to 6.0\u0026ndash;7.0% (Maine, Vermont, Wisconsin, Michigan, Kentucky, West Virginia, Florida, Oregon, Utah).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.6 | COPD and CKD-Related Mortality Trends Stratified by Urbanization\u003c/h2\u003e\u003cp\u003eNonmetropolitan areas exhibited a higher AAMR than metropolitan areas (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The AAMR was 2.68 (95% CI: 2.53\u0026ndash;2.84) for nonmetropolitan areas and 1.82 (95% CI: 1.76\u0026ndash;1.87) for metropolitan areas. Metropolitan areas had an AAPC of +\u0026thinsp;4.47% (95% CI: 2.84\u0026ndash;6.13), while nonmetropolitan areas had an AAPC of +\u0026thinsp;5.71% (95% CI: 3.99\u0026ndash;7.47) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4 DISCUSSION","content":"\u003cp\u003eThis 22-year analysis of CDC WONDER data reveals a substantial burden of COPD-related mortality among adults with CKD in the United States. Between 1999 and 2020, deaths increased 276% with an AAPC of +\u0026thinsp;4.71%, demonstrating persistent growth across all demographic subgroups. Notable disparities emerged by sex, race/ethnicity, age, and geography, with females, older adults, NH White individuals, and Midwest/Appalachian states experiencing disproportionate increases.\u003c/p\u003e\u003cp\u003e\u003cb\u003eCardiopulmonary-Renal Interactions\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe co-occurrence of COPD and CKD reflects shared pathophysiological mechanisms. Chronic hypoxemia from COPD impairs renal perfusion and activates the renin-angiotensin-aldosterone system, accelerating CKD progression \u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e. Conversely, CKD-associated fluid overload, anemia, and uremia exacerbate respiratory dysfunction and increase susceptibility to respiratory infections \u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e. Systemic inflammation, oxidative stress, and endothelial dysfunction are common to both conditions \u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e, creating a self-reinforcing cycle of organ damage. This multiorgan involvement likely explains the elevated mortality observed in our cohort, where patients face compounded risks from both pulmonary and renal failure.\u003c/p\u003e\u003cp\u003e\u003cb\u003eRising Mortality Trends\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe sustained 4.71% annual increase in COPD-CKD mortality parallels trends in both conditions independently. COPD mortality has risen in recent decades despite declining smoking rates, attributed to aging populations, increasing biomass fuel exposure, and air pollution \u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e. Similarly, CKD prevalence has grown due to rising diabetes and hypertension rates \u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e. Our findings suggest these epidemics intersect with multiplicative rather than additive effects on mortality.\u003c/p\u003e\u003cp\u003eThe acceleration from 2018\u0026ndash;2020 warrants attention. While broader trends reflect demographic aging and disease prevalence increases, the 2020 spike likely reflects COVID-19 impacts. Patients with COPD-CKD comorbidity faced heightened vulnerability to severe COVID-19 complications \u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e, and pandemic-related healthcare disruptions may have delayed routine disease management. Additionally, increased medical scrutiny during the pandemic may have improved death certificate coding accuracy \u003csup\u003e[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSex Disparities\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFemales demonstrated faster mortality increases (AAPC\u0026thinsp;+\u0026thinsp;5.86%) despite lower absolute rates than males. This paradox reflects changing smoking patterns\u0026mdash;while male smoking rates declined earlier, female smoking prevalence remained elevated through the 1990s-2000s, creating a cohort effect now manifesting in mortality \u003csup\u003e[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/sup\u003e. Additionally, females may have greater susceptibility to COPD at equivalent smoking exposures due to smaller airway dimensions and hormonal influences on lung development \u003csup\u003e[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/sup\u003e. Diagnostic delays in females, where COPD symptoms are often attributed to aging or anxiety, may contribute to more advanced disease at diagnosis \u003csup\u003e[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]\u003c/sup\u003e. The loss of estrogen's renoprotective effects post-menopause may also accelerate CKD progression in females with COPD \u003csup\u003e[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003cb\u003eAge-Specific Patterns\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe exponential increase in mortality with age\u0026mdash;from APC\u0026thinsp;+\u0026thinsp;3.04% in 65\u0026ndash;74 year-olds to +\u0026thinsp;6.43% in those 85+\u0026mdash;reflects cumulative disease burden, declining physiologic reserve, and reduced tolerance for acute decompensations \u003csup\u003e[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]\u003c/sup\u003e. Older adults with COPD-CKD comorbidity face challenges from polypharmacy, frailty, and limited rehabilitation potential \u003csup\u003e[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]\u003c/sup\u003e. The near-absence of deaths in adults\u0026thinsp;\u0026lt;\u0026thinsp;45 years suggests COPD-CKD mortality primarily affects those with decades of exposure to risk factors, though early-onset cases may represent genetic predispositions requiring targeted screening.\u003c/p\u003e\u003cp\u003e\u003cb\u003eRacial and Ethnic Disparities\u003c/b\u003e\u003c/p\u003e\u003cp\u003eNH White individuals had the highest mortality rates and fastest increases (AAPC\u0026thinsp;+\u0026thinsp;5.25%), consistent with higher COPD prevalence in this population \u003csup\u003e[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]\u003c/sup\u003e. Genetic factors may contribute \u003csup\u003e[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]\u003c/sup\u003e. Higher historical smoking rates and greater occupational exposures (mining, agriculture) in predominantly White rural areas likely play roles \u003csup\u003e[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe unique pattern in NH Black individuals\u0026mdash;with non-linear trends and an overall non-significant AAPC\u0026mdash;merits discussion. While this population has lower COPD prevalence despite higher smoking rates \u003csup\u003e[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]\u003c/sup\u003e, possible explanations include: (1) survival bias, where NH Black individuals with severe COPD may die before developing advanced CKD; (2) differences in COPD phenotypes \u003csup\u003e[\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]\u003c/sup\u003e; (3) improved CKD management in this population through initiatives targeting diabetic nephropathy \u003csup\u003e[\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]\u003c/sup\u003e; or (4) coding inconsistencies on death certificates. The decline from 2011\u0026ndash;2020 (APC \u0026minus;\u0026thinsp;5.89%) coincides with increased nephrology care access following the Affordable Care Act, suggesting healthcare policy impacts.\u003c/p\u003e\u003cp\u003eHispanic individuals maintained the lowest mortality rates, potentially reflecting the \"Hispanic paradox,\" healthier lifestyle factors, or younger population demographics \u003csup\u003e[\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]\u003c/sup\u003e. However, their significant AAPC (+\u0026thinsp;2.36%) indicates rising burden requiring attention.\u003c/p\u003e\u003cp\u003e\u003cb\u003eGeographic Variations\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe Midwest's highest mortality rates and fastest increases (AAPC\u0026thinsp;+\u0026thinsp;5.32%, 2020 AAMR 2.42 per 100,000) align with regional characteristics: high smoking prevalence, coal mining and agricultural occupational exposures, industrial air pollution, and limited healthcare access in rural areas \u003csup\u003e[\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]\u003c/sup\u003e. States like Kentucky, West Virginia, and Ohio\u0026mdash;with historically coal-dependent economies\u0026mdash;bear disproportionate burdens.\u003c/p\u003e\u003cp\u003eA significant difference in AAMRs was observed across states, with rates ranging from 1.16\u0026ndash;1.70 per 100,000 (Southwest/West) to 3.15\u0026ndash;4.40 per 100,000 (Midwest/Appalachia) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003e). States in the top 90th percentile\u0026mdash;Maine, Vermont, Wisconsin, Michigan, Kentucky, West Virginia, Florida, Oregon, and Utah\u0026mdash;had almost triple the AAMRs compared with states in the lower 10th percentile, namely Nevada, Arizona, California, and Texas. The Southwest's lower rates may reflect migration of healthier individuals, warmer climates reducing COPD exacerbations, and lower smoking rates \u003csup\u003e[\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]\u003c/sup\u003e. However, rapid growth in Nevada and Arizona suggests emerging problems requiring intervention.\u003c/p\u003e\u003cp\u003eState-level variation (AAPC range 1.5-7.0%) indicates modifiable factors beyond demographic composition. States with robust tobacco control, clean air policies, and integrated pulmonary-renal care models may serve as blueprints for high-burden regions \u003csup\u003e[\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003cb\u003eRural-Urban Disparities\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAlthough nonmetropolitan areas had a higher growth rate (AAPC\u0026thinsp;+\u0026thinsp;5.71% vs\u0026thinsp;+\u0026thinsp;4.47% for metropolitan areas), they maintained 47% higher 2020 mortality than metropolitan areas. Rural populations face multiple disadvantages: higher smoking and obesity rates, occupational exposures (farming, mining), provider shortages (particularly pulmonologists and nephrologists), delayed diagnoses, and barriers to specialty care \u003csup\u003e[\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]\u003c/sup\u003e. Rural patients are less likely to receive guideline-concordant COPD therapies (inhaled corticosteroids, long-acting bronchodilators) or CKD management (renin-angiotensin system inhibitors, sodium-glucose cotransporter-2 inhibitors) \u003csup\u003e[\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]\u003c/sup\u003e. Telemedicine expansion and mobile specialty clinics may help bridge these gaps.\u003c/p\u003e\u003cp\u003e\u003cb\u003eClinical and Policy Implications\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThese findings have several implications. First, integrated care models coordinating pulmonology and nephrology are needed for COPD-CKD patients, who often receive fragmented care \u003csup\u003e[\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]\u003c/sup\u003e. Second, given the age-specific burden, geriatric assessment including frailty screening should guide treatment intensity in older adults \u003csup\u003e[\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]\u003c/sup\u003e. Third, addressing modifiable risk factors\u0026mdash;smoking cessation, air quality improvement, vaccination against respiratory pathogens, optimal management of hypertension and diabetes\u0026mdash;remains paramount \u003csup\u003e[\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]\u003c/sup\u003e. Fourth, rural health infrastructure investment is critical to reducing geographic disparities.\u003c/p\u003e\u003cp\u003eClinicians should maintain high suspicion for CKD in COPD patients and vice versa, with routine screening using creatinine, estimated glomerular filtration rate, and urinary albumin \u003csup\u003e[\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]\u003c/sup\u003e. Early identification enables intervention before irreversible organ damage occurs. Medication reconciliation is essential, as drugs commonly used in one condition may worsen the other (e.g., nonsteroidal anti-inflammatory drugs exacerbating CKD, beta-blockers worsening COPD) \u003csup\u003e[\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003ePublic health efforts should target high-risk populations identified in this study: females, older adults, NH White individuals, and residents of Midwest/Appalachian states and rural areas. Tailored interventions addressing region-specific risk factors (occupational exposures in mining states, agricultural dust in rural areas) may yield benefits.\u003c/p\u003e\u003cp\u003e\u003cb\u003eLimitations\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis study has limitations. First, death certificate data may underreport or misclassify CKD as a contributing cause, particularly if not clinically recognized or deemed irrelevant by certifying physicians \u003csup\u003e[\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]\u003c/sup\u003e. Second, we could not assess disease severity, treatment received, or socioeconomic factors beyond urbanization. Third, we lacked data on smoking history, occupational exposures, or comorbidities beyond COPD and CKD. Fourth, ecological analysis of state-level data cannot establish individual-level causation. Fifth, ICD-10 coding changes over time may introduce temporal artifacts, though our use of consistent codes (J40-J44, N18) throughout minimizes this concern. Finally, the 2020 data reflect COVID-19 pandemic effects, complicating interpretation of recent trends.\u003c/p\u003e\u003cp\u003e\u003cb\u003eFuture Directions\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFuture research should examine: (1) mechanisms linking COPD and CKD progression using longitudinal cohorts with biomarker assessments; (2) effectiveness of integrated care models on mortality and quality of life; (3) optimal medication regimens for COPD-CKD comorbidity, particularly novel agents (triple inhaled therapy for COPD, SGLT2 inhibitors for CKD) \u003csup\u003e[\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]\u003c/sup\u003e; (4) implementation strategies for telemedicine-based specialty care in rural areas; and (5) policy evaluations of tobacco control and air quality regulations on COPD-CKD mortality.\u003c/p\u003e"},{"header":"5 CONCLUSION","content":"\u003cp\u003eCOPD-related mortality among adults with CKD increased substantially from 1999 to 2020, with significant disparities by sex, age, race/ethnicity, and geography. Females, older adults, NH White individuals, Midwest/Appalachian states, and rural areas experienced disproportionate burdens. These findings underscore the need for integrated pulmonary-renal care, targeted interventions for high-risk populations, and policies addressing tobacco use, air quality, and rural healthcare access. As the US population ages and chronic disease prevalence rises, the intersection of COPD and CKD will increasingly challenge healthcare systems, necessitating proactive strategies to mitigate mortality.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eACKNOWLEDGMENTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank the Centers for Disease Control and Prevention for maintaining the CDC WONDER database and making these vital statistics publicly available for research purposes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCLINICAL TRIAL NUMBER\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFUNDING DECLARATION\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. No financial support was provided for the conduct of this study or preparation of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eETHICS STATEMENT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInstitutional Review Board approval was not required for this study as it utilized deidentified, publicly available data from the CDC WONDER database. The study was conducted in accordance with the Declaration of Helsinki and followed the STROBE reporting guidelines for observational studies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCONFLICTS OF INTEREST\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors declare that they have no financial or non-financial conflicts of interest related to this work. The authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDATA AVAILABILITY STATEMENT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available in National Center for Health Statistics at https://wonder.cdc.gov/mcd.html. These data were derived from the following resources available in the public domain: CDC WONDER, https://wonder.cdc.gov/.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAdeloye D, Song P, Zhu Y, et al. Global, regional, and national prevalence of, and risk factors for, chronic obstructive pulmonary disease (COPD) in 2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet Respir Med. 2022;10(5):447\u0026ndash;58.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRabe KF, Watz H. Chronic obstructive pulmonary disease. 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N Engl J Med. 2019;380(24):2295\u0026ndash;306.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-pulmonary-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pulm","sideBox":"Learn more about [BMC Pulmonary Medicine](http://bmcpulmmed.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pulm/default.aspx","title":"BMC Pulmonary Medicine","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"chronic obstructive pulmonary disease, CDC WONDER database, chronic kidney disease, mortality trends, health disparities","lastPublishedDoi":"10.21203/rs.3.rs-7826341/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7826341/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eChronic obstructive pulmonary disease (COPD) and chronic kidney disease (CKD) frequently coexist, yet mortality trends in patients with both conditions remain poorly characterized.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eUsing the CDC WONDER Multiple Cause of Death database, we analyzed death certificates from 1999 to 2020 for adults aged\u0026thinsp;\u0026ge;\u0026thinsp;25 years with COPD (ICD-10: J40-J44) as the underlying cause and CKD (N18) as a contributing cause. Age-adjusted mortality rates (AAMRs) per 100,000 were calculated using 2000 US standard population. Trends were assessed via Joinpoint regression, with stratifications by sex, race/ethnicity, age, urbanization, census regions, and state.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eDeaths increased from 1,405 in 1999 to 5,277 in 2020. The AAMR increased from 0.79 in 1999 to 1.96 in 2020. The overall AAPC was +\u0026thinsp;4.71% (95% CI: 3.06\u0026ndash;6.38). Mortality rates were higher in males (AAMR: 2.41) than females (AAMR: 1.65), though females had faster increases (AAPC\u0026thinsp;+\u0026thinsp;5.86% vs\u0026thinsp;+\u0026thinsp;3.42%). The 85\u0026thinsp;+\u0026thinsp;age group had the highest rates (27.98 per 100,000) and fastest growth (APC\u0026thinsp;+\u0026thinsp;6.43%). NH White individuals exhibited the steepest increase (AAPC\u0026thinsp;+\u0026thinsp;5.25%), while NH Black individuals had no significant trend (AAPC \u0026minus;\u0026thinsp;0.22%). Nonmetropolitan areas had higher AAMRs (2.68) compared to metropolitan areas (1.82). Regionally, the Midwest recorded the highest AAPC (+\u0026thinsp;5.32%) and 2020 AAMR (2.42). State-level AAPCs ranged from 1.5% to 7.0%.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eRising COPD-related mortality among adults with CKD highlights a need for integrated pulmonary-renal care, targeted interventions for high-risk populations, and policies addressing rural healthcare access and environmental risk factors.\u003c/p\u003e","manuscriptTitle":"Trends in Mortality from Chronic Obstructive Pulmonary Disease Among Adults with Chronic Kidney Disease in the United States: A CDC WONDER Analysis, 1999-2020","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-26 09:03:54","doi":"10.21203/rs.3.rs-7826341/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2025-11-16T12:10:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"237325818405121462301427000748001990880","date":"2025-11-14T16:11:02+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-14T15:22:20+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-12T19:29:14+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-10-24T18:17:58+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-24T02:18:50+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Pulmonary Medicine","date":"2025-10-24T02:15:59+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-pulmonary-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pulm","sideBox":"Learn more about [BMC Pulmonary Medicine](http://bmcpulmmed.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pulm/default.aspx","title":"BMC Pulmonary Medicine","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"79ce7e1f-8de1-4db8-aefe-aa09b9a94bc2","owner":[],"postedDate":"November 26th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-11-26T09:03:54+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-26 09:03:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7826341","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7826341","identity":"rs-7826341","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}