Evolving Trends and Disparities in Chronic Kidney Disease and Sepsis-Related Mortality in the United States from 1999 to 2023

Evolving Trends and Disparities in Chronic Kidney Disease and Sepsis-Related Mortality in the United States from 1999 to 2023 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Evolving Trends and Disparities in Chronic Kidney Disease and Sepsis-Related Mortality in the United States from 1999 to 2023 Laiba Jalal, Turba Naz, Areeba Aamir Ali Basaria, Maria Qadri, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7407919/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: Chronic kidney disease (CKD) predisposes patients to infections, and when sepsis occurs concurrently, outcomes are particularly severe. Despite this clinical importance, nationwide mortality trends for CKD–sepsis have not been comprehensively evaluated. Methods: We extracted Multiple Cause of Death data from the CDC WONDER database to analyze age-adjusted mortality rates (AAMRs) for sepsis-related deaths among patients with CKD in the United States from 1999 to 2023. Subgroup analyses were conducted by age, sex, race/ethnicity, and urban–rural status and mortality trends were assessed across the study period. Results: CKD–sepsis mortality increased steadily over the 25-year period with AAMR increasing from 4.53 per 100,000 in 1999 to 5.59 per 100,000 in 2023 (APC: 0.88*; 95% CI: 0.26 to 1.52; p = 0.0078). Men exhibited higher mortality than women. Race-stratified analysis revealed declining mortality among non-Hispanic Black, Hispanic, and American Indian/Alaska Native populations, while non-Hispanic White individuals experienced a notable rise. Mortality also significantly rose in the rural population compared to urban population. Conclusion: Mortality from CKD–sepsis has escalated steadily in the United States, with notable disparities across demographic groups. The rise amongst White and rural populations highlights the intersection of chronic illness, structural healthcare barriers, and potential impacts of the opioid crisis. Addressing these disparities will require targeted preventive strategies, improved infection control, and equitable access to nephrology and critical care services. Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Chronic kidney disease (CKD) is a serious global public health problem that now ranks as the seventh leading cause of mortality worldwide and it has an estimated prevalence of 850 million people in the world [ 1 ]. In the United States, approximately 37 million adults have CKD [ 2 ], and prevalence will continue to increase with an aging population and increasing burden of diabetes and hypertension [ 3 ]. Sepsis, is a life-threatening disorder caused by multiple organ failure due to an inappropriate response to an infection in the host’s body [ 4 ]. The inappropriate immune response results in an excessive release of multiple inflammatory regulators which increase vascular permeability and cause dilation of vessels. This allows pathogens to further disturb the immune regulatory mechanisms in the host body and cause multiple organ dysfunction which leads to septic shock and death [ 5 ]. The interaction between CKD and sepsis presents a very concerning clinical picture, as patients with pre-existing kidney disease are at much higher risk of death when they have sepsis on top of their pre-existing illness vs. those who do not have any prior history of kidney disease [ 3 ]. It is known that there exists a bidirectional relationship between CKD and sepsis. CKD patients suffer greater rates of infections as a result of uremia-induced immunosuppression, which is associated with disturbed pro-inflammatory responses and the frequent hospital exposure in this population [ 6 ]. On the other hand, sepsis-induced AKI can be an accelerator of CKD progression with a consequent increased risk of long-term mortality. Previous investigations have reported that the 90-day mortality rate is > 25% among individuals with chronic kidney disease (CKD) who are hospitalized for sepsis, whereas this rate is around 17% in patients with CKD without sepsis [ 7 ]. Although the importance of these comorbidities in clinical care is well recognized, few population-based studies have analyzed trends in sepsis-related mortality among patients with CKD disaggregated by age, sex, race/ethnicity and region over time. The main objective of this study is to fill this knowledge gap by describing population trends in CKD and sepsis-related mortality in the US from 1999 through 2023, with detailed stratification by sex, race/ethnicity, and urbanization level. Through the use of 24 years of national mortality data, this analysis will offer valuable information on changes in temporal patterns as well as which populations are most at risk and assist in the development of evidence-based strategies to mitigate the burden associated with these lethal comorbidities. Methodology Study setting and population: In this observational study, mortality related to chronic kidney disease and sepsis in the United States (US) from 1999 to 2023 was systematically analyzed using data from the CDC (Centers for Disease Control and Prevention) database. This database gathered information documented from publicly declared death certificates from all 50 states of the US and the District of Columbia. The multiple and underlying cause of deaths are listed on death certificate. Data pertaining to chronic kidney disease and sepsis for adults ≥ 25 years of age was abstracted using International Statistical Classification of Diseases and Related Health Problems, 10th Revision (ICD-10) codes: sepsis (A02.1-Salmonella septicaemia; A20.7-Septicaemic plague; A26.7-Erysipelothrix septicaemia; A32.7-Listerial septicaemia; A40.0-Septicaemia due to streptococcus, group A; A40.1-Septicaemia due to streptococcus, group B; A40.2-Septicaemia due to streptococcus, group D; A40.3-Septicaemia due to Streptococcus pneumoniae; A40.8-Other streptococcal septicaemia; A40.9- Streptococcal septicaemia, unspecified; A41.0 Septicaemia due to Staphylococcus aureus; A41.1-Septicaemia due to other specified staphylococcus; A41.2-Septicaemia due to unspecified staphylococcus;A41.3-Septicaemia due to Haemophilus influenzae; A41.4-Septicaemia due to anaerobes; A41.5-Septicaemia due to other Gram-negative organisms;A41.8-Other specified septicaemia; A41.9-Septicaemia, unspecified; A42.7-Actinomycotic septicaemia; B37.7-Candidal septicaemia) and chronic kidney disease (N18.0 -End-stage renal disease; N18.1-Chronic kidney disease, stage 1; N18.2-Chronic kidney disease stage 2; N18.3-Chronic kidney disease, stage 3; N18.4-Chronic kidney disease, stage 4; N18.5-Chronic kidney disease, stage 5; N18.8- Other chronic renal failure; N18.9-Chronic renal failure, unspecified). Since the study utilized de-identified, publicly available data, it was exempt from institutional review board (IRB) approval. Data Abstraction: Information on demographics, year, geographic location, and population estimates was abstracted from the dataset. Demographic variables include age, race/ethnicity, sex and urbanization. Race/ethnicity included American Indian or Alaska Native, Black or African American, White, and Hispanic in accordance with standards set by the Office of Management and Budget. National Center of Health Statistics 2013 Urban-Rural Classification Scheme which classifies population as metropolitan/urban (large central metro, large fringe metro, medium metro, and small metro) or nonmetropolitan/rural (micropolitan and noncore) was used. Statistical Analysis: We stratified data from the CDC WONDER database according to year, sex, race/ethnicity and urbanization to assess trends in sepsis and chronic kidney disease. AAMRs were standardized using the 2000 U.S. standard population as a reference. To evaluate temporal patterns in sepsis and chronic kidney disease-related mortality, we employed the Joinpoint Regression Program (version 5.5.0) to compute the Annual Percentage Change (APC) along with 95% confidence intervals (CIs). A statistically significant APC, as determined by a two-sided test, indicated a meaningful increasing or decreasing trend. The software was configured to identify up to four joinpoints—points at which significant changes in trend occurred—by selecting the best-fitting model based on statistical criteria. The APC and its corresponding 95% CIs were estimated using the Grid Search method (parameters: 2, 2, 0), permutation tests, and parametric methods. Additionally, the Average Annual Percent Change (AAPC) over the 24 years was calculated as a weighted average of the slope coefficients from each segment, with weights determined by the relative duration of each segment. AAPC values were considered statistically significant if their slope significantly differed from zero, based on a two-tailed t-test with a p-value threshold of < 0.05. Subgroup analyses were conducted based on sex, race/ethnicity and urbanization where applicable. Results A total of 281,784 deaths occurred due to sepsis and chronic kidney disease (CKD) as multiple causes of death from 1999 to 2023 in adults aged 25 or above in the United States. Amongst these, 136,808 deaths occurred in females and 144,976 occurred in males. 240,075 deaths were recorded in non-Hispanics, and 30,383 were recorded in Hispanic individuals. 196,391 deaths occurred in the United States urban population from 1999 to 2020, whereas 38,628 deaths occurred in the rural population from 1999 to 2020 (Table 1). From 1999 to 2023, the age-adjusted mortality rate (AAMR) for chronic kidney disease (CKD) and sepsis increased significantly, rising from 4.53 per 100,000 in 1999 to 5.59 per 100,000 in 2023. This corresponds to a statistically significant annual percent change (APC) of 0.88* (95% CI: 0.26 to 1.52; p = 0.0078) (Table 2,3; Figure 1). Sex: When stratified by sex, the data revealed persistent gender-based differences in CKD and sepsis-related mortality trends. Among females , the AAMR increased modestly from 4.15 per 100,000 in 1999 to 4.62 per 100,000 in 2023, with a non-significant APC of 0.44 (95% CI: -0.15 to 1.04; p = 0.137). In contrast, males experienced a more pronounced increase in mortality, with the AAMR rising from 5.12 to 6.91 per 100,000 over the same period. A statistically significant upward trend was observed with an APC of 1.26* (95% CI: 0.67 to 1.85; p = 0.00017), indicating a consistently higher burden in males (Table 2,3; Figure 2). Race: Racial and ethnic disparities in CKD and sepsis-related mortality were evident (Table 2,4; Figure 3). NH American Indian or Alaska Native individuals experienced a non-significant decline in AAMR, from 9.95 per 100,000 in 1999 to 8.56 per 100,000 in 2023 (APC: -0.62; 95% CI: -1.60 to 0.36; p = 0.2031). Among NH Black or African American individuals, AAMR declined from 17.43 per 100,000 in 1999 to 11.85 per 100,000 in 2023 (APC: -1.60*; 95% CI: -2.18 to -1.01; p = 0.00001). Hispanic or Latino individuals also demonstrated a significant decline in AAMR, from 7.52 in 1999 to 5.99 in 2023 per 100,000 (APC: -0.94*; 95% CI: -1.73 to -0.15; p = 0.0224). Conversely, among NH White individuals, the AAMR increased significantly from 2.83 per 100,000 in 1999 to 4.69 per 100,000 in 2023 (APC: 2.13*; 95% CI: 1.53 to 2.73; p < 0.000001). Urbanization: Differences in trends were also observed across urbanization levels (Table 2,5; Figure 4). In urban areas , AAMR increased slightly from 4.75 per 100,000 in 1999 to 5.23 per 100,000 in 2020, though this was not statistically significant (APC: 0.46; 95% CI: -0.31 to 1.24; p = 0.2281). In rural areas , however, a significant increase was noted, with AAMR rising from 3.72 per 100,000 in 1999 to 5.74 per 100,000 in 2020 (APC: 2.09*; 95% CI: 1.34 to 2.84; p = 0.00001), indicating widening rural-urban disparities over time. Discussion This study offers a thorough understanding of the trends in CKD and sepsis-related mortality across different regions and demographic groups in the United States from 1999 to 2023 in adults aged 25 or above. Across the study duration, we found an overall increase in AAMR where CKD and sepsis were mentioned together as contributors to mortality. Further analysis demonstrated that CKD and sepsis-related mortality was significantly higher in males compared to females. CKD and sepsis-related deaths were reported to be increasing in White individuals, with a decline in mortality in Black, Hispanic, and American Indian/Alaska Native groups throughout the study period. Rural areas also demonstrated a significant increase in mortality rates compared to urban areas. Patients with CKD have an increased risk of developing sepsis due to factors such as advanced age, uremia-associated immunodeficiency state, and other comorbid conditions like diabetes, all of which decrease the immunity of the patient [ 8 ]. Human-based studies have indicated that chronic kidney disease lead to substantial endothelial damage, associated with augmented oxidative stress, degradation of glycocalyx, increased vascular permeability also called endothelial dysfunction (ED), and a pro-coagulant condition. ED precedes organ dysfunction in sepsis which can lead to compromise of vital organ perfusion thereby, leading to death. These aberrations reflect the key pathological processes due to CKD which can lead to sepsis and death [ 9 ]. Hemodynamic instability due to sepsis affects the renal microcirculation, causing acute-tubular necrosis and renal damage. Studies have shown that inflammatory cytokines released during sepsis are related to worsening of renal function [ 5 , 10 ]. This bidirectional association between CKD and sepsis, where one condition leads to increased predisposition and worsening of the other condition, may partly explain the noted increasing mortality trend. The increasing trend in CKD and sepsis-related mortality may be partly explained by advances in the treatment of acute sepsis and chronic kidney disease, which allow patients to survive initial illness. However, surviving longer can lead to the development of multiple simultaneous health conditions, ultimately increasing the risk of death over time. A mortality trend analysis of CKD-related deaths amongst older adults by Hamza et al. demonstrated that the mortality trend increased significantly from 2009 and 2012, followed by a decline from 2012 to 2015 and an increase again from 2015–2020. The study concluded that the increasing mortality trend due to CKD can likely be attributed to the increasing prevalence of risk factors such as obesity, hypertension and cardiovascular diseases. These findings are consistent with the increasing trend observed in our analysis, suggesting that the increasing mortality trend may, in part, be driven by the heightened prevalence of obesity, hypertension, and cardiovascular diseases [ 11 ]. While our analysis demonstrated a linear increase in CKD and sepsis-related mortality from 1999 to 2023, the upsurge observed after 2020 may have been further amplified by the COVID-19 pandemic, which disrupted routine care and delayed necessary interventions [ 12 ]. In contrast, sepsis-related mortality stayed relatively steady from 2005 to 2018, though subclass differences existed. Rates were found to be higher in men, Native Americans, Blacks, and Hispanics compared to Whites, with Asians having the lowest rates. Notably, mortality decreased in Blacks, Hispanics, and Asians, but increased among Whites and Native Americans [ 13 ]. When CKD and sepsis co-occur, their risks are boosted, as CKD leads to infection and sepsis escalated kidney injury, leading to significantly worse outcomes than either condition alone. Moreover, the aging population and high frequency of multi-morbidity among CKD patients further accentuate vulnerability, as proven in a UK cohort study where around 86.6% CKD patients had multi-morbidity, with their BMI, age and reduced GFR as crucial drivers [ 14 ]. This accounts for the increasing disease complexities and contributes to the increasing mortality trends. Alongside, antimicrobial resistance has aggravated the increment in sepsis mortality, as delayed and inappropriate antibiotic therapy in resistant microbes substantially raises the risk of mortality, making resistant pathogens a major universal driver of sepsis-related mortality [ 15 ]. Further subgroup trend analysis illustrated that sex is an important transformer of outcomes where CKD and sepsis overlay. Studies have highlighted that while women have a higher prevalence of CKD, men are more likely to advance to ESRD and develop higher mortality, with infection and sepsis representing major contributors [ 16 ]. In severe septic conditions, women have demonstrated lower in-hospital mortality than men (27% vs. 36%), likely ascribed to hormonal and immune response discrepancies [ 17 ]. These results suggest that sex-based biological and behavioral differences not only impact CKD and sepsis outcomes individually but also amplify the mortality risks when both conditions co-occur. This explains the increasing mortality trend observed in males compared to females in our study. These subgroup trend explorations also expand to racial and ethnic divergences, where different mortality courses likely contemplate both designed interventions and varying subjection to risk factors. Our study also demonstrated a decreasing mortality trend amongst NH American Indians, NH Blacks, and Hispanic individuals which can likely be attributed to improved alertness, screening, and management. A study conducted by Chi D Chu et al. identified that races other than the White population demonstrated better adherence to guideline-recommended care for CKD, which in turn can reduce the development of pathophysiological mechanisms that drive disease progression to sepsis and adverse outcomes such as death [ 18 ]. Contrarily, the increase in mortality trend observed among White population may reflect the accelerating burden of chronic diseases such as diabetes and cardiovascular disorders in the United States, which worsen the outcome of CKD and sepsis [ 19 ]. Growing opioid use amongst White population has been actively linked to injection-related infections and sepsis, with studies showing a disproportionate surge in sepsis-related hospitalizations among young White adults in zones heavily impacted by opioid misuse [ 20 ]. The increasing CKD and sepsis-related mortality trend in rural population compared to urban population in our study can be explained due to healthcare discrepancies in the timely detection and management of diseases, particularly among rural and underprivileged populations. For instance, a Taiwanese community-based study proved that CKD frequency was almost three times higher in rural (29.2%) compared to urban (10.8%) populations, pointing out that despite global health insurance, these structural barriers hamper early diagnosis and management [ 21 ]. Similarly, a U.S cohort found significantly higher odds of in-hospital death from sepsis among rural patients compared to their urban counterparts [ 22 ]. These death trends underscore an immediate need for comprehensive public health strategies focusing on CKD and sepsis. Community-based holistic care models have proven potent in slowing CKD advancement, as shown in a Thai cohort where collective hospital and community-level actions, including home visits, helped halt decline in kidney function among stage 3–4 CKD patients [ 23 ]. Broadening rural healthcare facilities and telemedicine is equally vital: tele-nephrology programs in underprivileged hospitals have upgraded access to specialist management and outcomes in CKD [ 24 ], while tele-health assistance in rural emergency departments has improved sepsis guideline compliance and reduced mortality in the most remote zones [ 25 ]. Moreover, antimicrobial stewardship is important, as CKD patients are disproportionately exposed to resistant infections, necessitating cautious antibiotic use [ 26 ]. Equivalent to this, vaccination strategies against influenza and pneumococcal disease not only decrease infection risk but also decline antibiotic requirement, thereby causing resistance control [ 27 ]. Successful implementation of these strategies would offer a broad and coordinated approach in reducing the dual burden of CKD and sepsis Limitations: Although our analysis yielded strong findings, certain limitations must also be acknowledged. Cause of death was chosen from death certificates, which results in the likelihood of misclassification bias, particularly in distinguishing between primary and contributing factors. Secondly, the urban–rural population data could only be assessed uptill 2020, as data beyond 2020 was not available for this variable. Although all variables were extracted separately for 1999–2020 and 2018–2023 to ensure inclusion of the most recent data, minor discrepancies arose only in the race-stratified mortality counts for the overlapping years 2018–2020. To address this, we used the values from the 2018–2023 extraction for those three years, while all other variables remained unaffected. We also excluded the 'Asian or Pacific Islander' category because a sampling change introduced in the CDC WONDER database in 2021 limited comparability of this group across the study period. Conclusion In conclusion, our study demonstrates a persistent and rising trend in CKD–sepsis mortality in the United States from 1999 to 2023, with marked differences by age, sex, race/ethnicity, and geography. While mortality declined among some racial/ethnic minorities, non-Hispanic White and rural populations experienced a substantial increase, highlighting an evolving disparity. Factors such as the growing burden of diabetes and cardiovascular disease, the opioid epidemic, and disruptions to care during the COVID-19 pandemic may partly explain these patterns. These results emphasize the dual challenge of CKD and sepsis as a significant public health concern and call for comprehensive strategies to improve prevention, early recognition, and equitable management across all populations to reverse these trends. Declarations Funding: No funding was received for this study. Acknowledgements: None Conflict of Interest: None Ethical Approval: This study utilized publicly available, de-identified data from the CDC WONDER database. As no human participants were involved, ethical approval and informed consent were not required. The study was conducted in accordance with relevant ethical guidelines and regulations. Data Availability: The datasets generated and/or analysed during the current study are publicly available in the CDC WONDER database, which can be accessed via the following link: https://wonder.cdc.gov/ . Author Contribution L.J. performed the statistical analysis and supervised the work on the manuscript. T.N. , A.A.A.B. , and A.N. wrote the main manuscript text. Z.T. prepared the central illustration. M.Q. and U.O.A. edited the manuscript. All authors reviewed and approved the manuscript. References Jager KJ, Kovesdy C, Langham R, Rosenberg M, Jha V, Zoccali C. 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Effectiveness of integrated care on delaying chronic kidney disease progression in rural communities of Thailand (ESCORT-2) trials. Nephrology (Carlton). 2021 Apr;26(4):333-340. doi: 10.1111/nep.13849. Epub 2021 Feb 2. PMID: 33442912; PMCID: PMC7986192. Lea JP, Tannenbaum J. The Role of Telemedicine in Providing Nephrology Care in Rural Hospitals. Kidney360. 2020 Apr 22;1(6):553-556. doi: 10.34067/KID.0001122019. PMID: 35368600; PMCID: PMC8809317. Mohr NM, Merchant KAS, Fuller BM, Faine B, Mack L, Bell A, DeJong K, Parker EA, Mueller K, Chrischilles E, Carpenter CR, Jones MP, Simpson SQ, Ward MM. The role of telehealth in sepsis care in rural emergency departments: A qualitative study of emergency department sepsis telehealth user perspectives. PLoS One. 2025 Apr 23;20(4):e0321299. doi: 10.1371/journal.pone.0321299. PMID: 40267097; PMCID: PMC12017570. Wang TZ, Kodiyanplakkal RPL, Calfee DP. Antimicrobial resistance in nephrology. Nat Rev Nephrol. 2019 Aug;15(8):463-481. doi: 10.1038/s41581-019-0150-7. PMID: 31086308; PMCID: PMC7269065. Jansen KU, Anderson AS. The role of vaccines in fighting antimicrobial resistance (AMR). Hum Vaccin Immunother. 2018;14(9):2142-2149. doi: 10.1080/21645515.2018.1476814. Epub 2018 Jul 9. PMID: 29787323; PMCID: PMC6183139. Tables Table 1. CKD and sepsis-related deaths, stratified by overall, sex, race/ethnicity and urban-rural classification in adults above 25 years of age in the United States from 1999 to 2023. Year Overall Female Male NH American Indian or Alaska Native NH Black or African American NH White Hispanic or Latino Urban Areas Rural Areas Population 1999 7692 4025 3667 89 2639 4031 670 6493 1199 180408769 2000 8028 4170 3858 80 2713 4277 704 6783 1245 181984640 2001 8444 4437 4007 91 2805 4508 775 7074 1370 184305128 2002 8875 4593 4282 105 2885 4789 803 7514 1361 186208028 2003 9202 4705 4497 119 2903 4966 911 7776 1426 188090429 2004 9625 4923 4702 110 3045 5224 934 8119 1506 190205384 2005 9738 4949 4789 97 2944 5343 1026 8238 1500 192551384 2006 9608 4801 4807 80 2916 5234 1052 8146 1462 195019359 2007 9443 4619 4824 98 2759 5221 1012 7952 1491 197403777 2008 9195 4606 4589 95 2677 5116 973 7746 1449 199795090 2009 9216 4614 4602 102 2634 5081 1041 7693 1523 202107016 2010 9186 4383 4803 117 2521 5144 1071 7705 1481 203891983 2011 13668 6767 6901 141 3611 7799 1594 11419 2249 206592936 2012 13882 6807 7075 147 3594 8010 1554 11639 2243 208826037 2013 9905 4785 5120 80 2595 5846 1003 8206 1699 211085314 2014 10416 5043 5373 86 2599 6297 1012 8701 1715 213809280 2015 11669 5511 6158 112 2756 7134 1211 9571 2098 216553817 2016 12289 5715 6574 133 2922 7440 1333 10189 2100 218641417 2017 12722 6021 6701 138 2997 7661 1369 10540 2182 221447331 2018 13302 6228 7074 129 3049 8133 1436 10977 2325 223311190 2019 13338 6279 7059 115 3058 7983 1592 11006 2332 224981167 2020 15576 7227 8349 158 3550 9233 1898 12904 2672 226635013 2021 16132 7369 8763 164 3675 9668 1882 - - 228238412 2022 15608 7249 8359 159 3550 9307 1779 - - 229508599 2023 15025 6982 8043 130 3362 8996 1748 - - 231529762 Total 281784 136808 144976 2875 74759 162441 30383 196391 38628 5163131262 NH= Non-Hispanic Table 2. Annual percent change of CKD and sepsis-related AAMR per 100,000 in adults aged 25 or above in the United States from 1999 to 2023. Year Interval APC (95% CI) Overall 1999-2023 0.88* (0.26 to 1.52) Females 1999-2023 0.44 (-0.15 to 1.04) Males 1999-2023 1.26* (0.67 to 1.85) NH American Indian or Alaska Native 1999-2023 -0.62 (-1.60 to 0.36) NH Black or African American 1999-2023 -1.60* (-2.18 to -1.01) NH White 1999-2023 2.13* (1.53 to 2.73) Hispanic or Latino 1999-2023 -0.94* (-1.73 to -0.15) Urban population 1999-2023 0.46 (-0.31 to 1.24) Rural population 1999-2023 2.09* (1.34 to 2.84) APC= Annual Percent Change ; NH= Non-Hispanic; AAMR= Age Adjusted Mortality Rate *Indicates that the annual percentage change (APC) is significantly different from zero at α = 0.05. Table 3. Overall and Sex‐Stratified CKD and sepsis-related Age-Adjusted Mortality Rates per 100,000 in adults aged 25 or above in the United States from 1999 to 2023. Age Adjusted Mortality Rate (95% CI) Year Overall Male Female 1999 4.34 (4.24–4.44) 4.96 (4.79–5.12) 4.02 (3.89–4.14) 2000 4.47 (4.37–4.56) 5.11 (4.95–5.28) 4.13 (4.00–4.25) 2001 4.64 (4.54–4.74) 5.21 (5.04–5.37) 4.31 (4.18–4.43) 2002 4.77 (4.68–4.87) 5.47 (5.31–5.64) 4.35 (4.22–4.48) 2003 4.87 (4.77–4.97) 5.63 (5.46–5.80) 4.41 (4.28–4.53) 2004 5.00 (4.90–5.10) 5.79 (5.62–5.96) 4.56 (4.43–4.69) 2005 4.97 (4.87–5.07) 5.82 (5.65–5.98) 4.49 (4.36–4.61) 2006 4.83 (4.73–4.93) 5.69 (5.53–5.85) 4.28 (4.16–4.41) 2007 4.68 (4.59–4.78) 5.61 (5.45–5.77) 4.06 (3.94–4.17) 2008 4.42 (4.33–4.51) 5.19 (5.03–5.34) 3.98 (3.86–4.09) 2009 4.38 (4.29–4.47) 5.08 (4.93–5.23) 3.92 (3.80–4.03) 2010 4.28 (4.19–4.37) 5.21 (5.06–5.36) 3.66 (3.55–3.77) 2011 6.24 (6.13–6.34) 7.34 (7.16–7.52) 5.49 (5.36–5.62) 2012 6.18 (6.07–6.28) 7.33 (7.16–7.50) 5.37 (5.24–5.50) 2013 4.31 (4.22–4.40) 5.20 (5.05–5.34) 3.71 (3.60–3.81) 2014 4.42 (4.34–4.51) 5.28 (5.14–5.43) 3.81 (3.71–3.92) 2015 4.84 (4.75–4.93) 5.93 (5.77–6.08) 4.10 (3.99–4.21) 2016 4.98 (4.89–5.07) 6.18 (6.03–6.33) 4.16 (4.05–4.27) 2017 5.02 (4.93–5.11) 6.10 (5.95–6.25) 4.26 (4.15–4.37) 2018 5.15 (5.06–5.24) 6.29 (6.14–6.44) 4.34 (4.23–4.45) 2019 5.08 (4.99–5.16) 6.15 (6.01–6.30) 4.29 (4.18–4.40) 2020 5.84 (5.75–5.93) 7.11 (6.95–7.26) 4.86 (4.74–4.97) 2021 6.12 (6.03–6.22) 7.53 (7.37–7.70) 5.09 (4.97–5.21) 2022 5.73 (5.64–5.82) 7.02 (6.87–7.17) 4.85 (4.74–4.97) 2023 5.45 (5.36–5.54) 6.60 (6.46–6.75) 4.59 (4.48–4.70) Total 5.56 (5.52-5.60) 6.78 (6.71-6.84) 4.67 (4.62-4.71) Table 4. CKD and sepsis related Age-Adjusted Mortality Rates per 100,000, stratified by race/ethnicity in adults aged 25 or above in the United States from 1999 to 2023. Year NH American Indian or Alaska Native NH Black or African American NH White Hispanic or Latino 1999 10.78 (8.53–13.43) 16.71 (16.06–17.36) 2.75 (2.66–2.83) 6.90 (6.35–7.45) 2000 8.32 (6.51–10.48) 17.05 (16.40–17.70) 2.88 (2.79–2.97) 6.88 (6.34–7.41) 2001 10.33 (8.21–12.82) 17.12 (16.47–17.76) 3.01 (2.92–3.10) 7.05 (6.53–7.57) 2002 11.50 (9.17–13.84) 17.37 (16.72–18.01) 3.16 (3.07–3.25) 7.09 (6.58–7.61) 2003 12.40 (10.02–14.78) 17.10 (16.47–17.73) 3.23 (3.14–3.32) 7.55 (7.03–8.06) 2004 10.20 (8.17–12.22) 17.48 (16.85–18.12) 3.36 (3.27–3.45) 7.55 (7.04–8.06) 2005 9.07 (7.25–11.20) 16.57 (15.96–17.18) 3.39 (3.30–3.48) 7.84 (7.33–8.34) 2006 6.76 (5.27–8.54) 15.97 (15.38–16.57) 3.27 (3.19–3.36) 7.60 (7.12–8.08) 2007 8.27 (6.63–10.21) 14.72 (14.16–15.28) 3.23 (3.14–3.32) 7.02 (6.57–7.47) 2008 8.64 (6.89–10.70) 13.91 (13.37–14.46) 3.12 (3.03–3.20) 6.23 (5.82–6.64) 2009 8.86 (7.03–10.69) 13.42 (12.89–13.95) 3.05 (2.97–3.14) 6.46 (6.05–6.87) 2010 9.53 (7.68–11.37) 12.57 (12.06–13.07) 3.06 (2.97–3.14) 6.45 (6.05–6.86) 2011 11.52 (9.50–13.55) 17.48 (16.89–18.07) 4.51 (4.41–4.61) 9.05 (8.58–9.51) 2012 10.94 (9.07–12.81) 16.96 (16.39–17.54) 4.52 (4.42–4.62) 8.42 (7.98–8.85) 2013 5.77 (4.52–7.27) 11.92 (11.45–12.39) 3.25 (3.17–3.34) 5.18 (4.84–5.51) 2014 6.17 (4.89–7.69) 11.50 (11.04–11.95) 3.45 (3.36–3.53) 4.93 (4.61–5.24) 2015 7.66 (6.18–9.14) 11.72 (11.27–12.17) 3.86 (3.77–3.95) 5.59 (5.26–5.92) 2016 8.94 (7.35–10.53) 12.06 (11.61–12.51) 3.95 (3.86–4.04) 5.90 (5.57–6.23) 2017 8.93 (7.38–10.48) 12.06 (11.61–12.50) 3.95 (3.86–4.04) 5.77 (5.45–6.09) 2018 8.74 (7.18–10.30) 12.14 (11.70–12.58) 4.20 (4.10–4.29) 5.69 (5.39–6.00) 2019 7.81 (6.34–9.28) 11.83 (11.40–12.26) 4.05 (3.96–4.14) 6.16 (5.85–6.48) 2020 9.89 (8.29–11.48) 13.40 (12.95–13.86) 4.63 (4.53–4.72) 6.94 (6.62–7.26) 2021 9.91 (8.35–11.48) 13.82 (13.36–14.28) 5.01 (4.90–5.11) 6.67 (6.35–6.98) 2022 9.69 (8.15–11.23) 13.02 (12.58–13.46) 4.62 (4.52–4.72) 6.13 (5.84–6.43) 2023 7.89 (6.51–9.28) 12.09 (11.67–12.51) 4.47 (4.38–4.57) 5.81 (5.52–6.09) Total 8.95 (8.34–9.57) 12.68 (12.50–12.86) 4.49 (4.45–4.53) 6.21 (6.09–6.33) NH= Non-Hispanic Table 5. CKD and sepsis related Age-Adjusted Mortality Rates per 100,000, Stratified by Urban-Rural Classification in adults aged 25 or above in the United States from 1999 to 2023. Age Adjusted Mortality Rate (95% CI) Year Urban Areas Rural Areas 1999 4.49 (4.38–4.60) 3.68 (3.47–3.89) 2000 4.64 (4.53–4.75) 3.78 (3.57–3.99) 2001 4.77 (4.66–4.88) 4.16 (3.94–4.38) 2002 4.94 (4.83–5.05) 4.04 (3.83–4.26) 2003 5.00 (4.89–5.11) 4.16 (3.94–4.38) 2004 5.17 (5.06–5.28) 4.34 (4.12–4.56) 2005 5.13 (5.02–5.24) 4.28 (4.06–4.50) 2006 5.00 (4.89–5.11) 4.08 (3.87–4.29) 2007 4.79 (4.69–4.90) 4.12 (3.91–4.33) 2008 4.54 (4.44–4.64) 3.99 (3.78–4.20) 2009 4.45 (4.35–4.55) 4.12 (3.91–4.33) 2010 4.36 (4.26–4.46) 3.95 (3.75–4.15) 2011 6.29 (6.17–6.41) 5.92 (5.67–6.16) 2012 6.26 (6.14–6.37) 5.81 (5.56–6.05) 2013 4.31 (4.22–4.41) 4.30 (4.09–4.50) 2014 4.44 (4.34–4.53) 4.29 (4.08–4.50) 2015 4.79 (4.69–4.88) 5.22 (4.99–5.45) 2016 4.95 (4.85–5.04) 5.19 (4.96–5.41) 2017 5.00 (4.90–5.10) 5.26 (5.03–5.49) 2018 5.11 (5.01–5.21) 5.52 (5.29–5.75) 2019 5.00 (4.90–5.09) 5.44 (5.22–5.67) 2020 5.75 (5.65–5.86) 6.24 (5.99–6.48) Total 4.95 (4.93-4.98) 4.68 (4.63-4.73) Additional Declarations No competing interests reported. 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16:38:06","extension":"html","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":150882,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7407919/v1/285bca1f9626b929f2657a88.html"},{"id":93062636,"identity":"7186364e-bba4-4650-b134-5a5974983e31","added_by":"auto","created_at":"2025-10-08 16:38:01","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":43735,"visible":true,"origin":"","legend":"\u003cp\u003eObserved overall age-adjusted mortality rates for chronic kidney disease and sepsis in adults aged 25 or above inthe United States from 1999 to 2023.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7407919/v1/c29bae1a5c0e7e283e91f463.jpg"},{"id":93062480,"identity":"54a0aba4-758c-4c85-847f-1d932c4c080f","added_by":"auto","created_at":"2025-10-08 16:37:57","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":52898,"visible":true,"origin":"","legend":"\u003cp\u003eObserved age-adjusted mortality rates for chronic kidney disease and sepsis in the United States in adults aged 25 or above from 1999 to 2023, stratified by sex.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7407919/v1/7060be360d8108c5e2683300.jpg"},{"id":93062829,"identity":"bbb12b9e-5cd5-427c-ae21-fb5ef6ffbf2a","added_by":"auto","created_at":"2025-10-08 16:38:04","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":71218,"visible":true,"origin":"","legend":"\u003cp\u003eObserved age-adjusted mortality rates for chronic kidney disease and sepsis in the United States in adults aged 25 or above from 1999 to 2023, stratified by race/ethnicity.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7407919/v1/266268e84858b89aa2185c33.jpg"},{"id":93063123,"identity":"cdeb8fc6-c575-4e32-b54d-db58b98b7955","added_by":"auto","created_at":"2025-10-08 16:38:08","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":47462,"visible":true,"origin":"","legend":"\u003cp\u003eObserved age-adjusted mortality rates for chronic kidney disease and sepsis in the United States in adults aged 25 or above from 1999 to 2020, stratified by urban–rural classification.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7407919/v1/d0ffc8ac31fc0c3c62a6e34f.jpg"},{"id":94032206,"identity":"eac233ad-f422-45f5-830e-8744f410d4af","added_by":"auto","created_at":"2025-10-21 15:01:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1456146,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7407919/v1/95d3762a-4f9d-47da-8610-9ef46be627f8.pdf"},{"id":93063046,"identity":"bab94a49-9f5b-45d2-b36d-ac00ed06e7af","added_by":"auto","created_at":"2025-10-08 16:38:07","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":287543,"visible":true,"origin":"","legend":"","description":"","filename":"CI.png","url":"https://assets-eu.researchsquare.com/files/rs-7407919/v1/6a5f53305b96e875e17f49e3.png"}],"financialInterests":"No competing interests reported.","formattedTitle":"Evolving Trends and Disparities in Chronic Kidney Disease and Sepsis-Related Mortality in the United States from 1999 to 2023","fulltext":[{"header":"Introduction","content":"\u003cp\u003eChronic kidney disease (CKD) is a serious global public health problem that now ranks as the seventh leading cause of mortality worldwide and it has an estimated prevalence of 850\u0026nbsp;million people in the world [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In the United States, approximately 37\u0026nbsp;million adults have CKD [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], and prevalence will continue to increase with an aging population and increasing burden of diabetes and hypertension [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Sepsis, is a life-threatening disorder caused by multiple organ failure due to an inappropriate response to an infection in the host\u0026rsquo;s body [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The inappropriate immune response results in an excessive release of multiple inflammatory regulators which increase vascular permeability and cause dilation of vessels. This allows pathogens to further disturb the immune regulatory mechanisms in the host body and cause multiple organ dysfunction which leads to septic shock and death [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The interaction between CKD and sepsis presents a very concerning clinical picture, as patients with pre-existing kidney disease are at much higher risk of death when they have sepsis on top of their pre-existing illness vs. those who do not have any prior history of kidney disease [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIt is known that there exists a bidirectional relationship between CKD and sepsis. CKD patients suffer greater rates of infections as a result of uremia-induced immunosuppression, which is associated with disturbed pro-inflammatory responses and the frequent hospital exposure in this population [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. On the other hand, sepsis-induced AKI can be an accelerator of CKD progression with a consequent increased risk of long-term mortality. Previous investigations have reported that the 90-day mortality rate is \u0026gt;\u0026thinsp;25% among individuals with chronic kidney disease (CKD) who are hospitalized for sepsis, whereas this rate is around 17% in patients with CKD without sepsis [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAlthough the importance of these comorbidities in clinical care is well recognized, few population-based studies have analyzed trends in sepsis-related mortality among patients with CKD disaggregated by age, sex, race/ethnicity and region over time. The main objective of this study is to fill this knowledge gap by describing population trends in CKD and sepsis-related mortality in the US from 1999 through 2023, with detailed stratification by sex, race/ethnicity, and urbanization level. Through the use of 24 years of national mortality data, this analysis will offer valuable information on changes in temporal patterns as well as which populations are most at risk and assist in the development of evidence-based strategies to mitigate the burden associated with these lethal comorbidities.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cp\u003e\u003cstrong\u003e\u003cu\u003eStudy setting and population:\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this observational study, mortality related to chronic kidney disease and sepsis in the United States (US) from 1999 to 2023 was systematically analyzed using data from the CDC (Centers for Disease Control and Prevention) database. This database gathered information documented from publicly declared death certificates from all 50 states of the US and the District of Columbia.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe multiple and underlying cause of deaths are listed on death certificate. Data pertaining to chronic kidney disease and sepsis for adults \u0026ge; 25 years of age was abstracted using International Statistical Classification of Diseases and Related Health Problems, 10th Revision (ICD-10) codes: sepsis (A02.1-Salmonella septicaemia; A20.7-Septicaemic plague; A26.7-Erysipelothrix septicaemia; A32.7-Listerial septicaemia; A40.0-Septicaemia due to streptococcus, group A; A40.1-Septicaemia due to streptococcus, group B; A40.2-Septicaemia due to streptococcus, group D; A40.3-Septicaemia due to Streptococcus pneumoniae; A40.8-Other streptococcal septicaemia; A40.9- Streptococcal septicaemia, unspecified; A41.0 Septicaemia due to Staphylococcus aureus; A41.1-Septicaemia due to other specified staphylococcus; A41.2-Septicaemia due to unspecified staphylococcus;A41.3-Septicaemia due to Haemophilus influenzae; A41.4-Septicaemia due to anaerobes; A41.5-Septicaemia due to other Gram-negative organisms;A41.8-Other specified septicaemia; A41.9-Septicaemia, unspecified; A42.7-Actinomycotic septicaemia; B37.7-Candidal septicaemia) and chronic kidney disease (N18.0 -End-stage renal disease; N18.1-Chronic kidney disease, stage 1; N18.2-Chronic kidney disease stage 2; N18.3-Chronic kidney disease, stage 3; N18.4-Chronic kidney disease, stage 4; N18.5-Chronic kidney disease, stage 5; N18.8- Other chronic renal failure; N18.9-Chronic renal failure, unspecified). Since the study utilized de-identified, publicly available data, it was exempt from institutional review board (IRB) approval.\u0026nbsp;\u003c/p\u003e\n\u003ch4\u003e\u003cstrong\u003e\u003cu\u003eData Abstraction:\u003c/u\u003e\u003c/strong\u003e\u003c/h4\u003e\n\u003cp\u003eInformation on demographics, year, geographic location, and population estimates was abstracted from the dataset. Demographic variables include age, race/ethnicity, sex and urbanization. Race/ethnicity included American Indian or Alaska Native, Black or African American, White, and Hispanic in accordance with standards set by the Office of Management and Budget. National Center of Health Statistics 2013 Urban-Rural Classification Scheme which classifies population as metropolitan/urban (large central metro, large fringe metro, medium metro, and small metro) or nonmetropolitan/rural (micropolitan and noncore) was used.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cu\u003eStatistical Analysis:\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe stratified data from the CDC WONDER database according to year, sex, race/ethnicity and urbanization to assess trends in sepsis and chronic kidney disease. AAMRs were standardized using the 2000 U.S. standard population as a reference. To evaluate temporal patterns in sepsis and chronic kidney disease-related mortality, we employed the Joinpoint Regression Program (version 5.5.0) to compute the Annual Percentage Change (APC) along with 95% confidence intervals (CIs). A statistically significant APC, as determined by a two-sided test, indicated a meaningful increasing or decreasing trend. The software was configured to identify up to four joinpoints\u0026mdash;points at which significant changes in trend occurred\u0026mdash;by selecting the best-fitting model based on statistical criteria.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eThe APC and its corresponding 95% CIs were estimated using the Grid Search method (parameters: 2, 2, 0), permutation tests, and parametric methods. Additionally, the Average Annual Percent Change (AAPC) over the 24 years was calculated as a weighted average of the slope coefficients from each segment, with weights determined by the relative duration of each segment. AAPC values were considered statistically significant if their slope significantly differed from zero, based on a two-tailed t-test with a p-value threshold of \u0026lt; 0.05. Subgroup analyses were conducted based on sex, race/ethnicity and urbanization where applicable.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 281,784 deaths occurred due to sepsis and chronic kidney disease (CKD) as multiple causes of death from 1999 to 2023 in adults aged 25 or above in the United States. Amongst these, 136,808 deaths occurred in females and 144,976 occurred in males. 240,075 deaths were recorded in non-Hispanics, and 30,383 were recorded in Hispanic individuals. 196,391 deaths occurred in the United States urban population from 1999 to 2020, whereas 38,628 deaths occurred in the rural population from 1999 to 2020 (Table 1).\u003c/p\u003e\n\u003cp\u003eFrom 1999 to 2023, the age-adjusted mortality rate (AAMR) for chronic kidney disease (CKD) and sepsis increased significantly, rising from 4.53 per 100,000 in 1999 to 5.59 per 100,000 in 2023. This corresponds to a statistically significant annual percent change (APC) of 0.88* (95% CI: 0.26 to 1.52; \u003cem\u003ep\u003c/em\u003e = 0.0078) (Table 2,3; Figure 1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cu\u003eSex:\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhen stratified by sex, the data revealed persistent gender-based differences in CKD and sepsis-related mortality trends. Among \u003cstrong\u003efemales\u003c/strong\u003e, the AAMR increased modestly from 4.15 per 100,000 in 1999 to 4.62 per 100,000 in 2023, with a non-significant APC of 0.44 (95% CI: -0.15 to 1.04; \u003cem\u003ep\u003c/em\u003e = 0.137). In contrast, \u003cstrong\u003emales\u003c/strong\u003e experienced a more pronounced increase in mortality, with the AAMR rising from 5.12 to 6.91 per 100,000 over the same period. A statistically significant upward trend was observed with an APC of 1.26* (95% CI: 0.67 to 1.85; \u003cem\u003ep\u003c/em\u003e = 0.00017), indicating a consistently higher burden in males (Table 2,3; Figure 2).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cu\u003eRace:\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRacial and ethnic disparities in CKD and sepsis-related mortality were evident (Table 2,4; Figure 3).\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eNH American Indian or Alaska Native\u003c/strong\u003e individuals experienced a non-significant decline in AAMR, from 9.95 per 100,000 in 1999 to 8.56 per 100,000 in 2023 (APC: -0.62; 95% CI: -1.60 to 0.36; \u003cem\u003ep\u003c/em\u003e = 0.2031).\u003c/li\u003e\n \u003cli\u003eAmong \u003cstrong\u003eNH\u003c/strong\u003e \u003cstrong\u003eBlack or African American\u003c/strong\u003e individuals, AAMR declined from 17.43 per 100,000 in 1999 to 11.85 per 100,000 in 2023 (APC: -1.60*; 95% CI: -2.18 to -1.01; \u003cem\u003ep\u003c/em\u003e = 0.00001).\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eHispanic or Latino\u003c/strong\u003e individuals also demonstrated a significant decline in AAMR, from 7.52 in 1999 to 5.99 in 2023 per 100,000 (APC: -0.94*; 95% CI: -1.73 to -0.15; \u003cem\u003ep\u003c/em\u003e = 0.0224).\u003c/li\u003e\n \u003cli\u003eConversely, among \u003cstrong\u003eNH\u003c/strong\u003e \u003cstrong\u003eWhite\u003c/strong\u003e individuals, the AAMR increased significantly from 2.83 per 100,000 in 1999 to 4.69 per 100,000 in 2023 (APC: 2.13*; 95% CI: 1.53 to 2.73; \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.000001).\u0026nbsp;\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cu\u003eUrbanization:\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDifferences in trends were also observed across urbanization levels (Table 2,5; Figure 4).\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eIn \u003cstrong\u003eurban areas\u003c/strong\u003e, AAMR increased slightly from 4.75 per 100,000 in 1999 to 5.23 per 100,000 in 2020, though this was not statistically significant (APC: 0.46; 95% CI: -0.31 to 1.24; \u003cem\u003ep\u003c/em\u003e = 0.2281).\u003c/li\u003e\n \u003cli\u003eIn \u003cstrong\u003erural areas\u003c/strong\u003e, however, a significant increase was noted, with AAMR rising from 3.72 per 100,000 in 1999 to 5.74 per 100,000 in 2020 (APC: 2.09*; 95% CI: 1.34 to 2.84; \u003cem\u003ep\u003c/em\u003e = 0.00001), indicating widening rural-urban disparities over time.\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study offers a thorough understanding of the trends in CKD and sepsis-related mortality across different regions and demographic groups in the United States from 1999 to 2023 in adults aged 25 or above. Across the study duration, we found an overall increase in AAMR where CKD and sepsis were mentioned together as contributors to mortality. Further analysis demonstrated that CKD and sepsis-related mortality was significantly higher in males compared to females. CKD and sepsis-related deaths were reported to be increasing in White individuals, with a decline in mortality in Black, Hispanic, and American Indian/Alaska Native groups throughout the study period. Rural areas also demonstrated a significant increase in mortality rates compared to urban areas.\u003c/p\u003e\u003cp\u003ePatients with CKD have an increased risk of developing sepsis due to factors such as advanced age, uremia-associated immunodeficiency state, and other comorbid conditions like diabetes, all of which decrease the immunity of the patient [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Human-based studies have indicated that chronic kidney disease lead to substantial endothelial damage, associated with augmented oxidative stress, degradation of glycocalyx, increased vascular permeability also called endothelial dysfunction (ED), and a pro-coagulant condition. ED precedes organ dysfunction in sepsis which can lead to compromise of vital organ perfusion thereby, leading to death. These aberrations reflect the key pathological processes due to CKD which can lead to sepsis and death [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eHemodynamic instability due to sepsis affects the renal microcirculation, causing acute-tubular necrosis and renal damage. Studies have shown that inflammatory cytokines released during sepsis are related to worsening of renal function [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. This bidirectional association between CKD and sepsis, where one condition leads to increased predisposition and worsening of the other condition, may partly explain the noted increasing mortality trend.\u003c/p\u003e\u003cp\u003eThe increasing trend in CKD and sepsis-related mortality may be partly explained by advances in the treatment of acute sepsis and chronic kidney disease, which allow patients to survive initial illness. However, surviving longer can lead to the development of multiple simultaneous health conditions, ultimately increasing the risk of death over time. A mortality trend analysis of CKD-related deaths amongst older adults by Hamza et al. demonstrated that the mortality trend increased significantly from 2009 and 2012, followed by a decline from 2012 to 2015 and an increase again from 2015\u0026ndash;2020. The study concluded that the increasing mortality trend due to CKD can likely be attributed to the increasing prevalence of risk factors such as obesity, hypertension and cardiovascular diseases. These findings are consistent with the increasing trend observed in our analysis, suggesting that the increasing mortality trend may, in part, be driven by the heightened prevalence of obesity, hypertension, and cardiovascular diseases [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. While our analysis demonstrated a linear increase in CKD and sepsis-related mortality from 1999 to 2023, the upsurge observed after 2020 may have been further amplified by the COVID-19 pandemic, which disrupted routine care and delayed necessary interventions [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. In contrast, sepsis-related mortality stayed relatively steady from 2005 to 2018, though subclass differences existed. Rates were found to be higher in men, Native Americans, Blacks, and Hispanics compared to Whites, with Asians having the lowest rates. Notably, mortality decreased in Blacks, Hispanics, and Asians, but increased among Whites and Native Americans [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. When CKD and sepsis co-occur, their risks are boosted, as CKD leads to infection and sepsis escalated kidney injury, leading to significantly worse outcomes than either condition alone. Moreover, the aging population and high frequency of multi-morbidity among CKD patients further accentuate vulnerability, as proven in a UK cohort study where around 86.6% CKD patients had multi-morbidity, with their BMI, age and reduced GFR as crucial drivers [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. This accounts for the increasing disease complexities and contributes to the increasing mortality trends. Alongside, antimicrobial resistance has aggravated the increment in sepsis mortality, as delayed and inappropriate antibiotic therapy in resistant microbes substantially raises the risk of mortality, making resistant pathogens a major universal driver of sepsis-related mortality [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eFurther subgroup trend analysis illustrated that sex is an important transformer of outcomes where CKD and sepsis overlay. Studies have highlighted that while women have a higher prevalence of CKD, men are more likely to advance to ESRD and develop higher mortality, with infection and sepsis representing major contributors [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. In severe septic conditions, women have demonstrated lower in-hospital mortality than men (27% vs. 36%), likely ascribed to hormonal and immune response discrepancies [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. These results suggest that sex-based biological and behavioral differences not only impact CKD and sepsis outcomes individually but also amplify the mortality risks when both conditions co-occur. This explains the increasing mortality trend observed in males compared to females in our study.\u003c/p\u003e\u003cp\u003eThese subgroup trend explorations also expand to racial and ethnic divergences, where different mortality courses likely contemplate both designed interventions and varying subjection to risk factors. Our study also demonstrated a decreasing mortality trend amongst NH American Indians, NH Blacks, and Hispanic individuals which can likely be attributed to improved alertness, screening, and management. A study conducted by Chi D Chu et al. identified that races other than the White population demonstrated better adherence to guideline-recommended care for CKD, which in turn can reduce the development of pathophysiological mechanisms that drive disease progression to sepsis and adverse outcomes such as death [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Contrarily, the increase in mortality trend observed among White population may reflect the accelerating burden of chronic diseases such as diabetes and cardiovascular disorders in the United States, which worsen the outcome of CKD and sepsis [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Growing opioid use amongst White population has been actively linked to injection-related infections and sepsis, with studies showing a disproportionate surge in sepsis-related hospitalizations among young White adults in zones heavily impacted by opioid misuse [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe increasing CKD and sepsis-related mortality trend in rural population compared to urban population in our study can be explained due to healthcare discrepancies in the timely detection and management of diseases, particularly among rural and underprivileged populations. For instance, a Taiwanese community-based study proved that CKD frequency was almost three times higher in rural (29.2%) compared to urban (10.8%) populations, pointing out that despite global health insurance, these structural barriers hamper early diagnosis and management [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Similarly, a U.S cohort found significantly higher odds of in-hospital death from sepsis among rural patients compared to their urban counterparts [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThese death trends underscore an immediate need for comprehensive public health strategies focusing on CKD and sepsis. Community-based holistic care models have proven potent in slowing CKD advancement, as shown in a Thai cohort where collective hospital and community-level actions, including home visits, helped halt decline in kidney function among stage 3\u0026ndash;4 CKD patients [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Broadening rural healthcare facilities and telemedicine is equally vital: tele-nephrology programs in underprivileged hospitals have upgraded access to specialist management and outcomes in CKD [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], while tele-health assistance in rural emergency departments has improved sepsis guideline compliance and reduced mortality in the most remote zones [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Moreover, antimicrobial stewardship is important, as CKD patients are disproportionately exposed to resistant infections, necessitating cautious antibiotic use [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Equivalent to this, vaccination strategies against influenza and pneumococcal disease not only decrease infection risk but also decline antibiotic requirement, thereby causing resistance control [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Successful implementation of these strategies would offer a broad and coordinated approach in reducing the dual burden of CKD and sepsis\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eLimitations:\u003c/h2\u003e\u003cp\u003eAlthough our analysis yielded strong findings, certain limitations must also be acknowledged. Cause of death was chosen from death certificates, which results in the likelihood of misclassification bias, particularly in distinguishing between primary and contributing factors. Secondly, the urban\u0026ndash;rural population data could only be assessed uptill 2020, as data beyond 2020 was not available for this variable. Although all variables were extracted separately for 1999\u0026ndash;2020 and 2018\u0026ndash;2023 to ensure inclusion of the most recent data, minor discrepancies arose only in the race-stratified mortality counts for the overlapping years 2018\u0026ndash;2020. To address this, we used the values from the 2018\u0026ndash;2023 extraction for those three years, while all other variables remained unaffected. We also excluded the 'Asian or Pacific Islander' category because a sampling change introduced in the CDC WONDER database in 2021 limited comparability of this group across the study period.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, our study demonstrates a persistent and rising trend in CKD\u0026ndash;sepsis mortality in the United States from 1999 to 2023, with marked differences by age, sex, race/ethnicity, and geography. While mortality declined among some racial/ethnic minorities, non-Hispanic White and rural populations experienced a substantial increase, highlighting an evolving disparity. Factors such as the growing burden of diabetes and cardiovascular disease, the opioid epidemic, and disruptions to care during the COVID-19 pandemic may partly explain these patterns. These results emphasize the dual challenge of CKD and sepsis as a significant public health concern and call for comprehensive strategies to improve prevention, early recognition, and equitable management across all populations to reverse these trends.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding was received for this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study utilized publicly available, de-identified data from the CDC WONDER database. As no human participants were involved, ethical approval and informed consent were not required. The study was conducted in accordance with relevant ethical guidelines and regulations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analysed during the current study are publicly available in the CDC WONDER database, which can be accessed via the following link: https://wonder.cdc.gov/ \u0026nbsp;.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eL.J. performed the statistical analysis and supervised the work on the manuscript. T.N. , A.A.A.B. , and A.N. wrote the main manuscript text. Z.T. prepared the central illustration. M.Q. and U.O.A. edited the manuscript. All authors reviewed and approved the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col start=\"1\" type=\"1\"\u003e\n \u003cli\u003eJager KJ, Kovesdy C, Langham R, Rosenberg M, Jha V, Zoccali C. A single number for advocacy and communication-worldwide more than 850\u0026nbsp;million individuals have kidney diseases. Kidney Int. 2019 Nov;96(5):1048-1050. doi: 10.1016/j.kint.2019.07.012. Epub 2019 Sep 30. PMID: 31582227.\u003c/li\u003e\n \u003cli\u003eCenters for Disease Control and Prevention (US). Chronic Kidney Disease in the United States, 2023. Atlanta (GA): US Department of Health and Human Services, Centers for Disease Control and Prevention; 2023 [cited 2025 Mar 18]. Available from: https://www.cdc.gov/kidneydisease/publications-resources/ckd-national-facts.html\u003c/li\u003e\n \u003cli\u003eMansur A, Mulwande E, Steinau M, Bergmann I, Popov AF, Ghadimi M, Beissbarth T, Bauer M, Hinz J. Chronic kidney disease is associated with a higher 90-day mortality than other chronic medical conditions in patients with sepsis. Sci Rep. 2015 May 21;5:10539. doi: 10.1038/srep10539. PMID: 25995131; PMCID: PMC4650757.\u003c/li\u003e\n \u003cli\u003eSrzić I, Nesek Adam V, Tunjić Pejak D. SEPSIS DEFINITION: WHAT\u0026apos;S NEW\u0026nbsp;\u2028IN THE TREATMENT GUIDELINES. Acta Clin Croat. 2022 Jun;61(Suppl 1):67-72. doi: 10.20471/acc.2022.61.s1.11. PMID: 36304809; PMCID: PMC9536156.\u003c/li\u003e\n\u003c/ol\u003e\n\u003col start=\"5\"\u003e\n \u003cli\u003eSchulte W, Bernhagen J, Bucala R. Cytokines in sepsis: potent immunoregulators and potential therapeutic targets\u0026mdash;an updated view. Mediators of inflammation. 2013;2013(1):165974.\u003c/li\u003e\n\u003c/ol\u003e\n\u003col start=\"6\" type=\"1\"\u003e\n \u003cli\u003eSarnak MJ, Jaber BL. Mortality caused by sepsis in patients with end-stage renal disease compared with the general population. Kidney Int. 2000 Oct;58(4):1758-64. doi: 10.1111/j.1523-1755.2000.00337.x. PMID: 11012910.\u003c/li\u003e\n \u003cli\u003ePoston JT, Koyner JL. Sepsis associated acute kidney injury. BMJ. 2019 Jan 9;364:k4891. doi: 10.1136/bmj.k4891. PMID: 30626586; PMCID: PMC6890472.\u003c/li\u003e\n \u003cli\u003eDalrymple LS, Go AS. Epidemiology of acute infections among patients with chronic kidney disease. Clin J Am Soc Nephrol. 2008 Sep;3(5):1487-93. doi: 10.2215/CJN.01290308. Epub 2008 Jul 23. PMID: 18650409; PMCID: PMC4571152.\u003c/li\u003e\n \u003cli\u003eBermejo-Martin JF, Mart\u0026iacute;n-Fernandez M, L\u0026oacute;pez-Mestanza C, Duque P, Almansa R. Shared Features of Endothelial Dysfunction between Sepsis and Its Preceding Risk Factors (Aging and Chronic Disease). J Clin Med. 2018 Oct 30;7(11):400. doi: 10.3390/jcm7110400. PMID: 30380785; PMCID: PMC6262336.\u003c/li\u003e\n \u003cli\u003eBlackwell TS, Christman JW. Sepsis and cytokines: current status. Br J Anaesth. 1996 Jul;77(1):110-7. doi: 10.1093/bja/77.1.110. PMID: 8703620.\u003c/li\u003e\n \u003cli\u003eEhtesham H, Siddiqi AK, Mirza MO, Ahmad M, Shakil R. Trends in chronic kidney disease-related mortality among older adults in the United States from 1999-2020. Arch Gerontol Geriatr Plus. 2025;2(3):100161. doi:10.1016/j.aggp.2025.100161.\u003c/li\u003e\n \u003cli\u003eHuggins A, Husaini M, Wang F, Waken RJ, Epstein AM, Orav EJ, Joynt Maddox KE. Care Disruption During COVID-19: a National Survey of Hospital Leaders. J Gen Intern Med. 2023 Apr;38(5):1232-1238. doi: 10.1007/s11606-022-08002-5. Epub 2023 Jan 17. PMID: 36650332; PMCID: PMC9845025.\u003c/li\u003e\n \u003cli\u003ePrest J, Sathananthan M, Jeganathan N. Current Trends in Sepsis-Related Mortality in the United States. Crit Care Med. 2021 Aug 1;49(8):1276-1284. doi: 10.1097/CCM.0000000000005017. PMID: 34261926.\u003c/li\u003e\n \u003cli\u003eFraccaro P, Kontopantelis E, Sperrin M, Peek N, Mallen C, Urban P, Buchan IE, Mamas MA. Predicting mortality from change-over-time in the Charlson Comorbidity Index: A retrospective cohort study in a data-intensive UK health system. Medicine (Baltimore). 2016 Oct;95(43):e4973. doi: 10.1097/MD.0000000000004973. PMID: 27787358; PMCID: PMC5089087.\u003c/li\u003e\n \u003cli\u003eDe Waele JJ, Schouten J, Beovic B, Tabah A, Leone M. Antimicrobial de-escalation as part of antimicrobial stewardship in intensive care: no simple answers to simple questions-a viewpoint of experts. Intensive Care Med. 2020 Feb;46(2):236-244. doi: 10.1007/s00134-019-05871-z. Epub 2020 Feb 5. PMID: 32025778; PMCID: PMC7224113.\u003c/li\u003e\n \u003cli\u003eCarrero JJ, Hecking M, Chesnaye NC, Jager KJ. Sex and gender disparities in the epidemiology and outcomes of chronic kidney disease. Nat Rev Nephrol. 2018 Mar;14(3):151-164. doi: 10.1038/nrneph.2017.181. Epub 2018 Jan 22. PMID: 29355169.\u003c/li\u003e\n \u003cli\u003eAdrie C, Azoulay E, Francais A, Clec\u0026apos;h C, Darques L, Schwebel C, Nakache D, Jamali S, Goldgran-Toledano D, Garrouste-Orgeas M, Timsit JF; OutcomeRea Study Group. Influence of gender on the outcome of severe sepsis: a reappraisal. Chest. 2007 Dec;132(6):1786-93. doi: 10.1378/chest.07-0420. Epub 2007 Sep 21. PMID: 17890473.\u003c/li\u003e\n \u003cli\u003eChu CD, Powe NR, McCulloch CE, Crews DC, Han Y, Bragg-Gresham JL, Saran R, Koyama A, Burrows NR, Tuot DS; Centers for Disease Control and Prevention Chronic Kidney Disease Surveillance Team. Trends in Chronic Kidney Disease Care in the US by Race and Ethnicity, 2012-2019. JAMA Netw Open. 2021 Sep 1;4(9):e2127014. doi: 10.1001/jamanetworkopen.2021.27014. PMID: 34570204; PMCID: PMC8477264.\u003c/li\u003e\n \u003cli\u003eAxios. U.S. diabetes burden grew since 2000. Axios. 2024 Nov 7. Available from: https://www.axios.com/2024/11/07/us-diabetes-burden-grew-since-2000\u003c/li\u003e\n \u003cli\u003eWurcel AG, Anderson JE, Chui KK, Skinner S, Knox TA, Snydman DR, Stopka TJ. Increasing Infectious Endocarditis Admissions Among Young People Who Inject Drugs. Open Forum Infect Dis. 2016 Jul 26;3(3):ofw157. doi: 10.1093/ofid/ofw157. PMID: 27800528; PMCID: PMC5084714.\u003c/li\u003e\n \u003cli\u003eWu YL, Wu YC, Akhmetzhanov AR, Wu MY, Lin YF, Lin CC. Urban-rural health disparity among patients with chronic kidney disease: a cross-sectional community-based study from 2012 to 2019. BMJ Open. 2024 Jul 30;14(7):e082959. doi: 10.1136/bmjopen-2023-082959. PMID: 39079922; PMCID: PMC11293390.\u003c/li\u003e\n \u003cli\u003eChang J, Medina M, Kim SJ. Is patients\u0026apos; rurality associated with in-hospital sepsis death in US hospitals? Front Public Health. 2023 Jun 13;11:1169209. doi: 10.3389/fpubh.2023.1169209. PMID: 37383255; PMCID: PMC10294422.\u003c/li\u003e\n \u003cli\u003eThanachayanont T, Chanpitakkul M, Hengtrakulvenit J, Watcharakanon P, Wisansak W, Tancharoensukjit T, Kaewsringam P, Leesmidt V, Pongpirul K, Lekagul S, Tungsanga K; ESCORT-2 Research Team. Effectiveness of integrated care on delaying chronic kidney disease progression in rural communities of Thailand (ESCORT-2) trials. Nephrology (Carlton). 2021 Apr;26(4):333-340. doi: 10.1111/nep.13849. Epub 2021 Feb 2. PMID: 33442912; PMCID: PMC7986192.\u003c/li\u003e\n \u003cli\u003eLea JP, Tannenbaum J. The Role of Telemedicine in Providing Nephrology Care in Rural Hospitals. Kidney360. 2020 Apr 22;1(6):553-556. doi: 10.34067/KID.0001122019. PMID: 35368600; PMCID: PMC8809317.\u003c/li\u003e\n \u003cli\u003eMohr NM, Merchant KAS, Fuller BM, Faine B, Mack L, Bell A, DeJong K, Parker EA, Mueller K, Chrischilles E, Carpenter CR, Jones MP, Simpson SQ, Ward MM. The role of telehealth in sepsis care in rural emergency departments: A qualitative study of emergency department sepsis telehealth user perspectives. PLoS One. 2025 Apr 23;20(4):e0321299. doi: 10.1371/journal.pone.0321299. PMID: 40267097; PMCID: PMC12017570.\u003c/li\u003e\n \u003cli\u003eWang TZ, Kodiyanplakkal RPL, Calfee DP. Antimicrobial resistance in nephrology. Nat Rev Nephrol. 2019 Aug;15(8):463-481. doi: 10.1038/s41581-019-0150-7. PMID: 31086308; PMCID: PMC7269065.\u003c/li\u003e\n \u003cli\u003eJansen KU, Anderson AS. The role of vaccines in fighting antimicrobial resistance (AMR). Hum Vaccin Immunother. 2018;14(9):2142-2149. doi: 10.1080/21645515.2018.1476814. Epub 2018 Jul 9. PMID: 29787323; PMCID: PMC6183139.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1.\u0026nbsp;\u003c/strong\u003eCKD and sepsis-related deaths, stratified by overall, sex, race/ethnicity and urban-rural classification in adults above 25 years of age in the United States from 1999 to 2023.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"814\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYear\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverall\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNH American Indian or Alaska Native\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNH Black or African American\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNH White\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHispanic or Latino\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUrban Areas\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRural Areas\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePopulation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e1999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e7692\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e4025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e3667\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e2639\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e4031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e670\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e6493\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e1199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e180408769\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e2000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e8028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e4170\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e3858\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e2713\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e4277\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e704\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e6783\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e1245\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e181984640\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e2001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e8444\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e4437\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e4007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e2805\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e4508\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e775\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e7074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e1370\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e184305128\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e2002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e8875\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e4593\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e4282\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e2885\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e4789\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e803\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e7514\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e1361\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e186208028\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e2003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e9202\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e4705\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e4497\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e2903\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e4966\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n 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64px;\"\u003e\n \u003cp\u003e5343\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e1026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e8238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e1500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e192551384\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e2006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e9608\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e4801\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e4807\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n 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style=\"width: 91px;\"\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e2677\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e5116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e973\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e7746\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e1449\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e199795090\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e2009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e9216\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e4614\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n 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style=\"width: 73px;\"\u003e\n \u003cp\u003e6767\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e6901\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e3611\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e7799\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e1594\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e11419\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e2249\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e206592936\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e2012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n 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52px;\"\u003e\n \u003cp\u003e2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e10416\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e5043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e5373\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e2599\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e6297\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e1012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e8701\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e1715\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e213809280\u003c/p\u003e\n \u003c/td\u003e\n 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style=\"width: 61px;\"\u003e\n \u003cp\u003e2182\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e221447331\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e13302\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e6228\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e7074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e3049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e8133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e1436\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n 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style=\"width: 82px;\"\u003e\n \u003cp\u003e1898\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e12904\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e2672\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e226635013\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e16132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e7369\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e8763\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e3675\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n 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91px;\"\u003e\n \u003cp\u003e3362\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e8996\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e1748\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e231529762\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e281784\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e136808\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e144976\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2875\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e74759\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e162441\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e30383\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e196391\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e38628\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e5163131262\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNH= Non-Hispanic\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eTable 2.\u0026nbsp;\u003c/strong\u003eAnnual percent change of CKD and sepsis-related AAMR per 100,000 in adults aged 25 or above in the United States from 1999 to 2023.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 326px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYear Interval\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 326px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAPC (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 652px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverall\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 326px;\"\u003e\n \u003cp\u003e1999-2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 326px;\"\u003e\n \u003cp\u003e0.88* (0.26 to 1.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 652px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemales\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 326px;\"\u003e\n \u003cp\u003e1999-2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 326px;\"\u003e\n \u003cp\u003e0.44 (-0.15 to 1.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 652px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMales\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 326px;\"\u003e\n \u003cp\u003e1999-2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 326px;\"\u003e\n \u003cp\u003e1.26* (0.67 to 1.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 652px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNH American Indian or Alaska Native\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 326px;\"\u003e\n \u003cp\u003e1999-2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 326px;\"\u003e\n \u003cp\u003e-0.62 (-1.60 to 0.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 652px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNH Black or African American\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 326px;\"\u003e\n \u003cp\u003e1999-2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 326px;\"\u003e\n \u003cp\u003e-1.60* (-2.18 to -1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 652px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNH White\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 326px;\"\u003e\n \u003cp\u003e1999-2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 326px;\"\u003e\n \u003cp\u003e2.13* (1.53 to 2.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 652px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHispanic or Latino\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 326px;\"\u003e\n \u003cp\u003e1999-2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 326px;\"\u003e\n \u003cp\u003e-0.94* (-1.73 to -0.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 652px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUrban population\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 326px;\"\u003e\n \u003cp\u003e1999-2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 326px;\"\u003e\n \u003cp\u003e0.46 (-0.31 to 1.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 652px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRural population\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 326px;\"\u003e\n \u003cp\u003e1999-2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 326px;\"\u003e\n \u003cp\u003e2.09* (1.34 to 2.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;APC= Annual Percent Change ; NH= Non-Hispanic; AAMR= Age Adjusted Mortality Rate\u003c/p\u003e\n\u003cp\u003e*Indicates that the annual percentage change (APC) is significantly different from zero at \u0026alpha; = 0.05.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003e\u0026nbsp;Table\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e3.\u003c/strong\u003e Overall and Sex‐Stratified CKD and sepsis-related Age-Adjusted Mortality Rates per 100,000 in adults aged 25 or above in the United States from 1999 to 2023.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" style=\"width: 680px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge Adjusted Mortality Rate (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYear\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverall\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e1999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e4.34 (4.24\u0026ndash;4.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e4.96 (4.79\u0026ndash;5.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e4.02 (3.89\u0026ndash;4.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e2000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e4.47 (4.37\u0026ndash;4.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e5.11 (4.95\u0026ndash;5.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e4.13 (4.00\u0026ndash;4.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e2001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e4.64 (4.54\u0026ndash;4.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e5.21 (5.04\u0026ndash;5.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e4.31 (4.18\u0026ndash;4.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e2002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e4.77 (4.68\u0026ndash;4.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e5.47 (5.31\u0026ndash;5.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e4.35 (4.22\u0026ndash;4.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e2003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e4.87 (4.77\u0026ndash;4.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e5.63 (5.46\u0026ndash;5.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e4.41 (4.28\u0026ndash;4.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e2004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e5.00 (4.90\u0026ndash;5.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e5.79 (5.62\u0026ndash;5.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e4.56 (4.43\u0026ndash;4.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e2005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e4.97 (4.87\u0026ndash;5.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e5.82 (5.65\u0026ndash;5.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e4.49 (4.36\u0026ndash;4.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e2006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e4.83 (4.73\u0026ndash;4.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e5.69 (5.53\u0026ndash;5.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e4.28 (4.16\u0026ndash;4.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e2007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e4.68 (4.59\u0026ndash;4.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e5.61 (5.45\u0026ndash;5.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e4.06 (3.94\u0026ndash;4.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e2008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e4.42 (4.33\u0026ndash;4.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e5.19 (5.03\u0026ndash;5.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e3.98 (3.86\u0026ndash;4.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e2009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e4.38 (4.29\u0026ndash;4.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e5.08 (4.93\u0026ndash;5.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e3.92 (3.80\u0026ndash;4.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e2010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e4.28 (4.19\u0026ndash;4.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e5.21 (5.06\u0026ndash;5.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e3.66 (3.55\u0026ndash;3.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e2011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e6.24 (6.13\u0026ndash;6.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e7.34 (7.16\u0026ndash;7.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e5.49 (5.36\u0026ndash;5.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e2012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e6.18 (6.07\u0026ndash;6.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e7.33 (7.16\u0026ndash;7.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e5.37 (5.24\u0026ndash;5.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e2013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e4.31 (4.22\u0026ndash;4.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e5.20 (5.05\u0026ndash;5.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e3.71 (3.60\u0026ndash;3.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e4.42 (4.34\u0026ndash;4.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e5.28 (5.14\u0026ndash;5.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e3.81 (3.71\u0026ndash;3.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e4.84 (4.75\u0026ndash;4.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e5.93 (5.77\u0026ndash;6.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e4.10 (3.99\u0026ndash;4.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e2016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e4.98 (4.89\u0026ndash;5.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e6.18 (6.03\u0026ndash;6.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e4.16 (4.05\u0026ndash;4.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e5.02 (4.93\u0026ndash;5.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e6.10 (5.95\u0026ndash;6.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e4.26 (4.15\u0026ndash;4.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e5.15 (5.06\u0026ndash;5.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e6.29 (6.14\u0026ndash;6.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e4.34 (4.23\u0026ndash;4.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e5.08 (4.99\u0026ndash;5.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e6.15 (6.01\u0026ndash;6.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e4.29 (4.18\u0026ndash;4.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e5.84 (5.75\u0026ndash;5.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e7.11 (6.95\u0026ndash;7.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e4.86 (4.74\u0026ndash;4.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e6.12 (6.03\u0026ndash;6.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e7.53 (7.37\u0026ndash;7.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e5.09 (4.97\u0026ndash;5.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e5.73 (5.64\u0026ndash;5.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e7.02 (6.87\u0026ndash;7.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e4.85 (4.74\u0026ndash;4.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e5.45 (5.36\u0026ndash;5.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e6.60 (6.46\u0026ndash;6.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e4.59 (4.48\u0026ndash;4.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e5.56 (5.52-5.60)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e6.78 (6.71-6.84)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4.67 (4.62-4.71)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4.\u0026nbsp;\u003c/strong\u003eCKD and sepsis related\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eAge-Adjusted Mortality Rates per 100,000, stratified by race/ethnicity in adults aged 25 or above in the United States from 1999 to 2023.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"881\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYear\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNH American Indian or Alaska Native\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNH Black or African American\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNH White\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHispanic or Latino\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e1999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e10.78 (8.53\u0026ndash;13.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e16.71 (16.06\u0026ndash;17.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e2.75 (2.66\u0026ndash;2.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e6.90 (6.35\u0026ndash;7.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e2000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e8.32 (6.51\u0026ndash;10.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e17.05 (16.40\u0026ndash;17.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e2.88 (2.79\u0026ndash;2.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e6.88 (6.34\u0026ndash;7.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e2001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e10.33 (8.21\u0026ndash;12.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e17.12 (16.47\u0026ndash;17.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e3.01 (2.92\u0026ndash;3.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e7.05 (6.53\u0026ndash;7.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e2002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e11.50 (9.17\u0026ndash;13.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e17.37 (16.72\u0026ndash;18.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e3.16 (3.07\u0026ndash;3.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e7.09 (6.58\u0026ndash;7.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e2003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e12.40 (10.02\u0026ndash;14.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e17.10 (16.47\u0026ndash;17.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e3.23 (3.14\u0026ndash;3.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e7.55 (7.03\u0026ndash;8.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e2004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e10.20 (8.17\u0026ndash;12.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e17.48 (16.85\u0026ndash;18.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e3.36 (3.27\u0026ndash;3.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e7.55 (7.04\u0026ndash;8.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e2005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e9.07 (7.25\u0026ndash;11.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e16.57 (15.96\u0026ndash;17.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e3.39 (3.30\u0026ndash;3.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e7.84 (7.33\u0026ndash;8.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e2006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e6.76 (5.27\u0026ndash;8.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e15.97 (15.38\u0026ndash;16.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e3.27 (3.19\u0026ndash;3.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e7.60 (7.12\u0026ndash;8.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e2007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e8.27 (6.63\u0026ndash;10.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e14.72 (14.16\u0026ndash;15.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e3.23 (3.14\u0026ndash;3.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e7.02 (6.57\u0026ndash;7.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e2008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e8.64 (6.89\u0026ndash;10.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e13.91 (13.37\u0026ndash;14.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e3.12 (3.03\u0026ndash;3.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e6.23 (5.82\u0026ndash;6.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e2009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e8.86 (7.03\u0026ndash;10.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e13.42 (12.89\u0026ndash;13.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e3.05 (2.97\u0026ndash;3.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e6.46 (6.05\u0026ndash;6.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e2010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e9.53 (7.68\u0026ndash;11.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e12.57 (12.06\u0026ndash;13.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e3.06 (2.97\u0026ndash;3.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e6.45 (6.05\u0026ndash;6.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e2011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e11.52 (9.50\u0026ndash;13.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e17.48 (16.89\u0026ndash;18.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e4.51 (4.41\u0026ndash;4.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e9.05 (8.58\u0026ndash;9.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e2012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e10.94 (9.07\u0026ndash;12.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e16.96 (16.39\u0026ndash;17.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e4.52 (4.42\u0026ndash;4.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e8.42 (7.98\u0026ndash;8.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e2013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e5.77 (4.52\u0026ndash;7.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e11.92 (11.45\u0026ndash;12.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e3.25 (3.17\u0026ndash;3.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e5.18 (4.84\u0026ndash;5.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e6.17 (4.89\u0026ndash;7.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e11.50 (11.04\u0026ndash;11.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e3.45 (3.36\u0026ndash;3.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e4.93 (4.61\u0026ndash;5.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e7.66 (6.18\u0026ndash;9.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e11.72 (11.27\u0026ndash;12.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e3.86 (3.77\u0026ndash;3.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e5.59 (5.26\u0026ndash;5.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e2016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e8.94 (7.35\u0026ndash;10.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e12.06 (11.61\u0026ndash;12.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e3.95 (3.86\u0026ndash;4.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e5.90 (5.57\u0026ndash;6.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e8.93 (7.38\u0026ndash;10.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e12.06 (11.61\u0026ndash;12.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e3.95 (3.86\u0026ndash;4.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e5.77 (5.45\u0026ndash;6.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e8.74 (7.18\u0026ndash;10.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e12.14 (11.70\u0026ndash;12.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e4.20 (4.10\u0026ndash;4.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e5.69 (5.39\u0026ndash;6.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e7.81 (6.34\u0026ndash;9.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e11.83 (11.40\u0026ndash;12.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e4.05 (3.96\u0026ndash;4.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e6.16 (5.85\u0026ndash;6.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e9.89 (8.29\u0026ndash;11.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e13.40 (12.95\u0026ndash;13.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e4.63 (4.53\u0026ndash;4.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e6.94 (6.62\u0026ndash;7.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e9.91 (8.35\u0026ndash;11.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e13.82 (13.36\u0026ndash;14.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e5.01 (4.90\u0026ndash;5.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e6.67 (6.35\u0026ndash;6.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e9.69 (8.15\u0026ndash;11.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e13.02 (12.58\u0026ndash;13.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e4.62 (4.52\u0026ndash;4.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e6.13 (5.84\u0026ndash;6.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e7.89 (6.51\u0026ndash;9.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e12.09 (11.67\u0026ndash;12.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e4.47 (4.38\u0026ndash;4.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e5.81 (5.52\u0026ndash;6.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e8.95 (8.34\u0026ndash;9.57)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e12.68 (12.50\u0026ndash;12.86)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4.49 (4.45\u0026ndash;4.53)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e6.21 (6.09\u0026ndash;6.33)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;NH= Non-Hispanic\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eTable 5.\u0026nbsp;\u003c/strong\u003eCKD and sepsis related Age-Adjusted Mortality Rates per 100,000, Stratified by Urban-Rural Classification in adults aged 25 or above in the United States from 1999 to 2023.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 771px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge Adjusted Mortality Rate (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYear\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUrban Areas\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRural Areas\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e1999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e4.49 (4.38\u0026ndash;4.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e3.68 (3.47\u0026ndash;3.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e2000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e4.64 (4.53\u0026ndash;4.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e3.78 (3.57\u0026ndash;3.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e2001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e4.77 (4.66\u0026ndash;4.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e4.16 (3.94\u0026ndash;4.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e2002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e4.94 (4.83\u0026ndash;5.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e4.04 (3.83\u0026ndash;4.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e2003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e5.00 (4.89\u0026ndash;5.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e4.16 (3.94\u0026ndash;4.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e2004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e5.17 (5.06\u0026ndash;5.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e4.34 (4.12\u0026ndash;4.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e2005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e5.13 (5.02\u0026ndash;5.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e4.28 (4.06\u0026ndash;4.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e2006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e5.00 (4.89\u0026ndash;5.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e4.08 (3.87\u0026ndash;4.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e2007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e4.79 (4.69\u0026ndash;4.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e4.12 (3.91\u0026ndash;4.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e2008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e4.54 (4.44\u0026ndash;4.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e3.99 (3.78\u0026ndash;4.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e2009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e4.45 (4.35\u0026ndash;4.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e4.12 (3.91\u0026ndash;4.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e2010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e4.36 (4.26\u0026ndash;4.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e3.95 (3.75\u0026ndash;4.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e2011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e6.29 (6.17\u0026ndash;6.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e5.92 (5.67\u0026ndash;6.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e2012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e6.26 (6.14\u0026ndash;6.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e5.81 (5.56\u0026ndash;6.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e2013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e4.31 (4.22\u0026ndash;4.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e4.30 (4.09\u0026ndash;4.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e4.44 (4.34\u0026ndash;4.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e4.29 (4.08\u0026ndash;4.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e4.79 (4.69\u0026ndash;4.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e5.22 (4.99\u0026ndash;5.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e2016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e4.95 (4.85\u0026ndash;5.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e5.19 (4.96\u0026ndash;5.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e5.00 (4.90\u0026ndash;5.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e5.26 (5.03\u0026ndash;5.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e5.11 (5.01\u0026ndash;5.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e5.52 (5.29\u0026ndash;5.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e5.00 (4.90\u0026ndash;5.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e5.44 (5.22\u0026ndash;5.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e5.75 (5.65\u0026ndash;5.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e6.24 (5.99\u0026ndash;6.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4.95 (4.93-4.98)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4.68 (4.63-4.73)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7407919/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7407919/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground:\u003c/h2\u003e\u003cp\u003eChronic kidney disease (CKD) predisposes patients to infections, and when sepsis occurs concurrently, outcomes are particularly severe. Despite this clinical importance, nationwide mortality trends for CKD\u0026ndash;sepsis have not been comprehensively evaluated.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e\u003cp\u003eWe extracted Multiple Cause of Death data from the CDC WONDER database to analyze age-adjusted mortality rates (AAMRs) for sepsis-related deaths among patients with CKD in the United States from 1999 to 2023. Subgroup analyses were conducted by age, sex, race/ethnicity, and urban\u0026ndash;rural status and mortality trends were assessed across the study period.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e\u003cp\u003eCKD\u0026ndash;sepsis mortality increased steadily over the 25-year period with AAMR increasing from 4.53 per 100,000 in 1999 to 5.59 per 100,000 in 2023 (APC: 0.88*; 95% CI: 0.26 to 1.52; p\u0026thinsp;=\u0026thinsp;0.0078). Men exhibited higher mortality than women. Race-stratified analysis revealed declining mortality among non-Hispanic Black, Hispanic, and American Indian/Alaska Native populations, while non-Hispanic White individuals experienced a notable rise. Mortality also significantly rose in the rural population compared to urban population.\u003c/p\u003e\u003ch2\u003eConclusion:\u003c/h2\u003e\u003cp\u003eMortality from CKD\u0026ndash;sepsis has escalated steadily in the United States, with notable disparities across demographic groups. The rise amongst White and rural populations highlights the intersection of chronic illness, structural healthcare barriers, and potential impacts of the opioid crisis. Addressing these disparities will require targeted preventive strategies, improved infection control, and equitable access to nephrology and critical care services.\u003c/p\u003e","manuscriptTitle":"Evolving Trends and Disparities in Chronic Kidney Disease and Sepsis-Related Mortality in the United States from 1999 to 2023","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-08 15:59:29","doi":"10.21203/rs.3.rs-7407919/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":"f7442c18-6b8e-4c37-9c43-7d6824d0556f","owner":[],"postedDate":"October 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-21T14:53:21+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-08 15:59:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7407919","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7407919","identity":"rs-7407919","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}