Trends in Mortality of Renal Tubulointerstitial Diseases in the United States from 1999 to 2020 Using CDC WONDER Data

Trends in Mortality of Renal Tubulointerstitial Diseases in the United States from 1999 to 2020 Using CDC WONDER Data | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Trends in Mortality of Renal Tubulointerstitial Diseases in the United States from 1999 to 2020 Using CDC WONDER Data Eisha Moazzam, Azka Ijaz, Haram Aftab, Sara Sohail, Umaima Cheema, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8595322/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 Purpose: Renal tubulointerstitial disease, also known as tubulointerstitial nephritis, involves primary injury to renal structures, leading to kidney inflammation. It may result from drug hypersensitivity, infections, or toxic exposures. This study examined mortality trends due to renal tubulointerstitial disease in the United States from 1999 to 2020 and evaluated disparities by demographic and geographic factors. Methods: Mortality data from 1999 to 2020 were obtained from the CDC WONDER database. Age-adjusted mortality rates (AAMRs) per 1,000,000 population and annual percent change (APC) with 95% confidence intervals were calculated. The Joinpoint Regression Program was used to assess temporal trends and demographic disparities. Results: A total of 7,341 deaths were recorded. AAMR increased from 15.228 in 1999 to 17.962 in 2020. APC trends showed an initial decline (1999–2011: −2.1150), followed by an increase (2011–2018: 3.8974) and a marked rise (2018–2020: 9.5021). Higher mortality occurred among Black individuals, males, those ≥ 85 years, rural residents, and individuals in the western US. Tests for parallelism revealed significant differences by sex (p = 0.000667), race (p = 0.00444), and region: Northeast vs. West (p = 0.009778), Northeast vs. South (p = 0.000222), Midwest vs. South (p = 0.014000), Midwest vs. West (p = 0.029111), and South vs. West (p = 0.000444). Significant differences were also observed between urban and rural populations (p = 0.028889). Conclusion: Mortality from renal tubulointerstitial disease has risen over the past two decades, with persistent demographic disparities. These findings underscore the need for targeted research and public health interventions to address these inequalities. Tubulointerstitial CDC Mortality Trend Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Renal tubulointerstitial diseases (TID) are an umbrella term for conditions that affect the kidney's tubular structures and interstitial tissues. It is also referred to as tubulointerstitial nephritis. The tubules comprise 80% of the total volume of the kidney and perform crucial functions. Due to their high energy demand, they are susceptible to various injuries. Abnormal glomerular filtration, inflammation, fibrogenesis, and hypoxia are common consequences that lead to tubulointerstitial injury [ 1 ]. TID can result from multiple causes and can progress from acute kidney injury to chronic kidney disease. Various genetic and environmental factors contribute to its development, with drug-induced causes being among the most common. Common medications, such as beta-lactam antibiotics and non-steroidal anti-inflammatory drugs (NSAIDs), often present classic TID [ 2 ]. The global prevalence of acute tubulointerstitial nephritis due to any cause is estimated to be 1%-3% in all kidney-related biopsies, rising to 15%-27% when only acute cases are considered [ 3 ]. TID is characterized by various levels of inflammation and edema, which lead to a decrease in glomerular filtration rate (GFR) due to affected renal blood flow. Any delay in addressing these conditions can lead to progression into chronic disease[ 2 ]. Differentiating between chronic and acute forms of the condition can be challenging. The following methods are useful for making a successful diagnosis: blood tests, imaging tests, urinary biomarkers, renal biopsy, urinalysis, and microscopy [ 3 ]. Treatment strategies often include corticosteroids, which are more effective when initiated during the early stages and continued for at least a month [ 4 ]. This study aims to contribute to existing knowledge by analyzing deaths registered in the Centers for Disease Control and Prevention - Wide-ranging Online Data for Epidemiologic Research (CDC-WONDER) database, an extensive repository of mortality data. Insights from this analysis can inform future treatment strategies for renal tubulointerstitial diseases. Materials and Methods Study Design: Data regarding deaths resulting from renal tubule-interstitial disease as an underlying cause were extracted from the Centers for Disease Control and Prevention Wide-ranging Online Data for Epidemiologic Research (CDC-WONDER) database [ 5 ]. The time range for data extraction covered two decades, from 1999 to 2020. The International Classification of Diseases, Tenth Revision (ICD-10) codes N10-N15 (renal tubulointerstitial diseases) were utilized to obtain information from death certificates in the CDC-WONDER database. This study adheres to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines [ 6 ]. Data Extraction: Data extraction was based on significant demographic categories such as states, census regions, race, age groups, urbanization, and gender. The analysis included all 50 states. Gender was categorized as male and female. Census regions were divided into the West, South, Northeast, and Midwest. Age groups were defined in 10-year intervals. Race categories included Black/African American, White, and Asian. Urbanization was classified into rural areas and urban areas. According to the U.S. Census Bureau definition (2013) [ 7 ], areas with a population of 50,000 or more were classified as urban, while areas with a population of less than 50,000 were considered non-metropolitan or rural. Statistical Analysis: The Age-Adjusted Mortality Rate (AAMR) was used to analyze data regarding deaths stratified by age, gender, race, and urban areas. The formula for AAMR is: Age-adjusted mortality rate = Sum of (Age-specific mortality rate × Standard population weight) × 100,000 , where the age-specific mortality rate is the number of deaths for a given age group divided by the population of that age group. The standard population weight is calculated by dividing the population for the age group by the sum of the populations for all age groups in the query [ 8 ]. The crude rate was calculated by dividing the renal tubule-interstitial disease-related deaths by the corresponding U.S. population for that year. The Joinpoint Regression Program was used to calculate the annual percent change (APC) in AAMR and 95% confidence intervals (CIs)[ 9 ]. Permutation tests and tests for parallelism were also conducted. IBM Corp. released 2023 (IBM SPSS Statistics for Windows, Version 29.0.2.0, Armonk, NY: IBM Corp.) and Stata Corp. 2023 (Stata Statistical Software: Release 18, College Station, TX: Stata Corp LLC) software were used for further statistical analysis. This study did not require Institutional Review Board approval because the CDC WONDER is a publicly available database that contains de-identified data. Results Overall Mortality Trends: Between 1999 and 2020, there were a total of 98,241 deaths associated with tubulointerstitial diseases. The Average Annual Mortality Rate (AAMR) was calculated at 13.42 per 100,000 individuals (95% CI = 4.66 to 4.76). In 1999, the AAMR stood at 15.22 (95% CI = 14.76 to 15.69), and by 2020, this increased to 17.96 (95% CI = 17.54 to 18.3). The Average Annual Percentage Change (AAPC) of 0.92% (95% CI = 0.39 to 1.45) was calculated. The initial decrease in AAMR, observed from 1999 to 2011, showed an Annual Percentage Change (APC) of -2.11% (95% CI =-2.46 to -1.76). After 2011, the rate grew at a slow pace, with an APC of 3.89% (95% CI = 2.93 to 4.86) until 2018. During 2018–2020, the AAMR showed a very rapid rise with an APC of 9.50% (95% CI = 4.54 to 14.69). Statistical analysis from Stata provided a z-value of 0.65 and a p-value of 0.516. (Fig. 1 ) Mortality Trends by Age: Focusing on individuals aged 85 years and older, this age group emerged as the most affected population in 2020. Tubulointerstitial diseases were found to be a significant cause of mortality in this demographic, leading to 2167 deaths in 2020 alone. Their crude rate (CR) was recorded at 325.45 in 2020, with a 95% Confidence Interval (CI) ranging from 311.75 to 339.15, making this the highest rate among all age groups. This CMR represents no significant change in rate from 335.097 in 1999. The AAPC for this period was 0.02% (95%CI =-0.69 to 0.74). The age group 75–84 years represents the second-highest mortality rate during 1999–2020. Although, unlike the 85 + year group, 75–84 years had a slight increase in mortality rate with AAPC of 0.39% (95%CI = 0.07 to 0.71). The 3rd highest rate was found in the 55–64 year age group, and it also showed an increase in mortality rate during 1999–2020 (AAPC = 1.41%, 95%CI = 0.66 to 2.16). This indicates that tubulointerstitial disease mostly causes mortality in the older population rather than the young. Analysis in Stata resulted in a z-value of 4.52 and a p-value of 0, confirming the statistical significance and describing the critical impact of tubulointerstitial diseases on this particular age group. (Fig. 2 ) Mortality Trends by Gender: From 1999 to 2020, males exhibited an Average Annual Mortality Rate (AAMR) of 16.901 (95% CI = 16.75 TO 17.052), while females had an AAMR of 11.498 (95% CI = 11.394 to 11.601). Between 1999 and 2010, the Average Percentage Change (APC) for males showed a significant decrease of -3.05% (95%CI =-3.50 to -2.61), followed by an upward trend of 2.76% (95%CI = 1.92 to 3.6) during 2010–2018. During 2018–2020, there was a sharp rise in AAMR with an APC of 10.18% (95%CI = 4.71 to 15.93). Similarly, females experienced a decrease in AAMR from 1999 to 2011 with APC at -1.69% (95%CI =-2.12 to -1.25), and from 2011 to 2017, the rate grew from 3.90% (95%CI = 2.27 to 5.55). However, post-2017, females saw a rapid increase in AAMR, with the APC at 7.90% (95% CI = 4.74 to 11.17) till 2020. We observed non-parallel trends between males and females after performing a pairwise analysis. A Stata analysis yielded a z-value of 5.49 with a p-value of 0, confirming statistical significance. (Fig. 3 ) Mortality Trends by Region and State: From 1999 to 2020, the West region exhibited the highest AAMR of 16.583 (95% CI = 16.381 to 16.786), followed by South (AAMR = 13.019; 95% CI = 12.881 to 13.157), Midwest (AAMR = 12.896; 95% CI = 12.722 to 13.07) and finally Northeast (AAMR = 11.287; 95% CI = 11.113 to 11.461). All four regions exhibited an initial downward curve (Northeast from 1999 to 2010: APC = -2.188; 95% CI = -3.192 to -1.174, Midwest from 1999 to 2001: APC = -9.216; 95% CI = -19.262 to 2.078, South from 1999 to 2011: APC = -2.792; 95% CI = -3.246 to -2.337 and West from 1999 to 2011: APC = -1.636; 95% CI = -2.342 to 0.924). This was followed by an upward rise in the case of Northeast from 2010 to 2020 (APC = 5.147; 95% CI = 4.064 to 6.241) and West from 2011 to 2020 (APC = 3.912; 95% CI = 2.956 to 4.876). In the case of South, the initial decline was followed by a gradual upward rise from 2011 to 2018 (APC = 3.335; 95% CI = 2.066 to 4.62) and finally a steeper upward slope from 2018 to 2020 (APC = 11.963; 95% CI = 5.419 to 18.914). The curve of Midwest region showed several fluctuations, the initial sharp decline followed by a much steadier downward slope from 2001 to 2007 (APC = -0.166; 95% CI = -2.843 to 2.591), a sharp downward trajectory again from 2007 to 2010 (APC = -5.602; 95% CI = -16.781 to 7.078) followed by a gradual upward rise from 2010 to 2017 (APC = 3.303; 95% CI = 1.233 to 5.414) and finally by a sharp upward climb from 2017 to 2020 (APC = 8.98; 95% CI = 3.68 to 14.549). Stata analysis reported a z-value of 6.67 and a p-value of 0, indicating a statistically significant difference. SPSS analysis using the Kruskal-Wallis test reported a statistic of 49.270 with 3 degrees of freedom. (Fig. 4 ) The mortality rates showed a noticeable difference among different states. States displaying the highest AAMR values were Alaska (AAMR = 25.304; 95% CI = 21.928 to 28.621), Vermont (AAMR = 21.529; 95% CI = 19.283 to 23.776) and Washington (AAMR = 21.132; 95% CI = 20.397 to 21.867) followed by Utah (AAMR = 19.847; 95% CI = 18.535 to 21.159), North Dakota (AAMR = 18.977; 95% CI = 16.979 to 20.975) and Tennessee (AAMR = 18.177; 95% CI = 17.483 to 18.871). On the other end of the spectrum were states showing considerably lower mortality rates, like Louisiana (AAMR = 9.442; 95% CI = 8.838 to 10.047), Florida (AAMR = 9.563; 95% CI = 9.303 to 9.823, and Massachusetts (AAMR = 9.706; 95% CI = 9.238 to 10.174). Stata analysis reported a z value of 0.7 with a p value of 0.48,2 highlighting the statistical significance. (Fig. 5 , 6 ) The AAMR was steadily higher in micropolitan non-metro rural areas (AAMR = 15.317; 95% CI = 15.027 to 15.606) than in large central metro urban areas (AAMR = 12.839; 95% CI = 12.683 to 12.996). Urban areas exhibited an initial downward trajectory from 1999 to 2011 (APC = -2.408; 95% CI = -2.982 to -1.831) followed by an upward rise from 2011 to 2020 (AAMR = 3.774; 95% CI = 2.926 to 4.629). The trend for rural areas was divided into 3 segments, a decline from 1999 to 2010 (APC = -2.861; 95% CI = -3.876 to -1.834) followed by a gradual rise from 2010 to 2017 (APC = 3.343; 95% CI = 0.728 to 6.025) and much steeper rise from 2017 to 2020 (APC = 11.641; 95% CI = 4.934 to 18.778). The Mann-Whitney test of spss analysis reported a U statistic of 409. Stata analysis revealed a z value of 3.92 and a p value of 0, highlighting statistical significance. (Fig. 7 ) Mortality Trends by Race and Ethnicity: From 1990 to 2020, the AAMR reported among NH Blacks (AAMR = 15.243; 95% CI = 14.946 to 15.541) and NH Whites (AAMR = 13.353; 95% CI = 13.262to 13.443) was considerably higher than that of Asians/Pacific Islanders (AAMR = 8.246; 95% CI = 7.905 to 8.587). From 1999 to 2020, Asians (APC = -0.616; 95% CI = -1.485 to 0.26) and NH Blacks (APC = -1.064; 95% CI = -1.942 to 0.178) showed a consistent downward trend, while NH Whites showed an upward trend (APC = 0.962; 95% CI = 0.163 to 1.768). SPSS analysis using the Kruskal-Wallis test revealed a statistic of 47.534 with 2 degrees of freedom. The z value reported by the Stata analysis was 4.48, and the p value was 0, which confirms statistical significance. (Fig. 8 ) Table 1 Demographic Characteristics of Deaths from Renal Tubulointerstitial Diseases in the USA from 1999 to 2020. Variable Renal tubulointerstitial disease Related Deaths (n) Age-Adjusted Mortality Rate (AAMR) per 1,000,000 Overall Population 98,241 13.428 AGE 25–34 years 1,082 1.166(Avg CR) 35–44 years 2,566 2.757(Avg CR) 45–54 years 5,809 6.279(Avg CR) 55–64 years 10,482 13.403(Avg CR) 65–74 years 16,642 31.918(Avg CR) 75–84 years 27,936 93.178(Avg CR) Gender Male 49,626 16.901 Female 48,615 11.498 US Census Region Northeast 16431 11.287 Midwest 21430 12.896 South 34355 13.019 West 26025 16.583 Race / Ethnicity NH Black or African American 10630 15.243 NH White Asian or Pacific Islander 84465 2355 13.353 8.246 Urban / Rural Urban 26089 12.839 Rural 10917 15.317 Table 2 Annual Percentage Changes (APCs) and Average Annual Percentage Changes (AAPCs) in Renal Tubulointerstitial Disease-Related Mortality Rate in the USA from 1999 to 2020. Variable Trend Segment Year APC (95% CI) AAPC (95% CI) Overall 1 1999–2011 -2.1150* (-2.4631 to -1.7657) 0.9216* (0.3920 to 1.4541) 2 2011–2018 3.8974* (2.9379 to 4.8658) 3 2018–2020 9.5021* (4.5471 to 14.6920) Gender Female 1 1999–2011 -1.6917 (-2.1287, -1.2527) 1.2128* (0.5950 to 1.8345) 2 2011–2017 3.9005 (2.2750, 5.5517) 3 2017–2020 7.9080 (4.7402, 11.1716) Male 1 1999–2011 -3.0576 (-3.5025, -2.6107) 0.3357 (-0.2365 to 0.9111) 2 2011–2018 2.7629 (1.9207, 3.6120) 3 2018–2020 10.1819 (4.7105, 15.9392) US Central Region Northeast 1 1999–2010 -2.188* (-3.192 to -1.174) 1.2387* (0.5532 to 1.9290) 2 2010–2020 5.147* (4.064 to 6.241) Midwest 1 1999–2001 -9.216 (-19.262 to 2.078 0.5209 (-1.5600 to 2.6458) 2 2001–2007 -0.166 (-2.843 to 2.591) 3 2007–2010 -5.602 (-16.781 to 7.078) 4 2010–2017 3.303* (1.233 to 5.414) 5 2017–2020 8.98* (3.68 to 14.549) South 1 1999–2011 -2.792* (-3.246 to -2.337 0.5529 (-0.1385 to 1.2491) 2 2011–2018 3.335* (2.066 to 4.62) 3 2018–2020 11.963* (5.419 to 18.914) West 1 1999–2011 -1.636* (-2.342 to 0.924) 0.7044* (0.1715 to 1.2402) 2 2011–2020 3.912* (2.956 to 4.876) Race / Ethnicity NH Black or African American 1 1999–2020 -1.064* (-1.942 to 0.178) -1.064* (-1.942 to 0.178) NH White 1 1999–2020 0.962* (0.163 to 1.768) 0.962* (0.163 to 1.768) Asian or Pacific Islanders 1 1999–2020 -0.616 (-1.485 to 0.26) -0.616 (-1.485 to 0.26) URBAN / RURAL Urban 1 1999–2011 -2.408* (-2.982 to -1.831) 0.1951 (-0.2573 to 0.6496) 2 2011–2020 3.774* (2.926 to 4.629) Rural 1 1999–2010 -2.861* (-3.876 to -1.834) 1.1557 (-0.0824 to 2.4091) 2 2010–2017 3.343* (0.728 to 6.025) 3 2017–2020 11.641* (4.934 to 18.778) Discussion Analysis of CDC WONDER data from 1999 to 2020 highlights significant trends in age, gender, geography, urbanization, and ethnicity related to RTID mortality. Overall, the AAMR for RTID showed a steady increase, initially decreasing slightly from 1999 to 2011, then rising from 2011 to 2018, with the most pronounced increase occurring between 2018 and 2020. Mortality rates were highest among individuals aged 85 and older, followed by those aged 75 to 84. Males exhibited higher AAMRs than females, while Non-Hispanic Blacks had higher rates than Non-Hispanic Whites and Asians/Pacific Islanders. Geographic disparities were evident, with the highest rates in the West, followed by the South, Midwest, and Northeast. Non-metropolitan areas had higher mortality rates than their metropolitan counterparts, underscoring the need for targeted interventions. This sharp rise in overall mortality trends in recent years could be attributed to the growing incidence of chronic conditions such as hypertension, diabetes, and obesity, which are known risk factors for renal diseases, especially among older populations[ 10 ]. Chronic kidney disease (CKD) is often secondary to these conditions, and as they rise, so does the incidence of RTID[ 11 ]. Studies indicate that poorly managed diabetes, which leads to diabetic nephropathy, contributes to tubulointerstitial damage[ 12 ]​. With an aging U.S. population, a multifaceted approach is necessary to address the rising mortality from RTID. Preventative measures focusing on managing risk factors and public health campaigns targeting lifestyle modifications can help reduce the incidence of kidney diseases​[ 13 ]. Age plays a crucial role in RTID mortality, with those aged 85 and above being disproportionately affected. This is consistent with findings in nephrology literature, which indicate that age-related structural and functional changes in the kidneys contribute to the development and progression of tubulointerstitial disorders[ 14 , 15 ]. The cumulative effect of long-term comorbid conditions and multiple medications for chronic diseases in older adults, especially nephrotoxic medications like NSAIDs, PPIs, and certain antibiotics, further worsens tubulointerstitial damage[ 16 , 17 ]. Renal diseases in older adults are often underdiagnosed due to the non-specific nature of early symptoms. Greater awareness of the early signs of kidney disease among older adults could lead to earlier diagnosis and intervention, reducing mortality in the long term[ 18 ]. Gender-based differences in mortality are evident, with males consistently exhibiting higher AAMRs than females, likely due to lifestyle factors such as higher rates of smoking, alcohol consumption, and exposure to occupational nephrotoxic agents like solvents and heavy metals among men[ 19 ]. Furthermore, estrogen has a protective effect against kidney diseases by reducing fibrosis and inflammation in women[ 20 ], while testosterone has been linked to increased susceptibility to renal injury[ 21 ]. Geographical disparities in RTID mortality reflect variations in socioeconomic status and healthcare access. The West had the highest AAMR, while the Northeast had the lowest. Access to healthcare, particularly specialized nephrology services, differs significantly between regions[ 22 ]. For example, rural areas in the South and Midwest, with fewer nephrologists and dialysis centers, face delayed diagnoses and treatments, while disparities in insurance coverage further widen healthcare inequality across regions[ 23 ]. In the West, higher exposure to environmental toxins may contribute to the higher mortality rates[ 24 ]. Agricultural areas with high nephrotoxic pesticide use, common in Western and Midwestern states, have a higher incidence of kidney diseases[ 24 ]. By focusing on early detection, improving healthcare access, and addressing the social determinants of health, the rising burden of renal diseases can be mitigated in the future. States with higher AAMRs, like Alaska, Vermont, and Washington, face challenges in accessing equitable nephrology care, with Alaska's remote areas particularly affected[ 25 ]. Environmental factors, such as pesticide use in agricultural states like Washington and Utah, contribute to increased RTID mortality due to nephrotoxicity[ 26 ]. Socioeconomic disparities also influence mortality trends, as states with lower incomes and higher poverty rates, like Tennessee, experience higher mortality, while wealthier states like Massachusetts and Florida have lower mortality rates[ 27 ]. The differences in CKD burden across states highlight the necessity to explore targeted policy measures and interventions aimed at reducing exposure to risk factors on a state level[ 22 ]. Non-metro rural areas exhibit higher AAMRs than urban areas, reflecting challenges including longer travel distances, fewer healthcare providers, and economic constraints[ 28 ]. Rural populations often face lower incomes and more uninsured individuals[ 27 ], leading to delayed diagnosis and limited access to medications and follow-up care for RTID. Expanding nephrology services in these areas is crucial, with telemedicine offering a potential solution to improve timely consultations and management for patients in remote areas [ 29 ]. Racial disparities in RTID mortality are evident, with NH Blacks showing higher rates than NH Whites and Asians/Pacific Islanders. This disparity is attributed to the higher prevalence of comorbidities such as diabetes and hypertension within Black populations, as well as socioeconomic challenges like lower income and limited access to healthcare[ 30 , 31 ]. Additionally, genetic variants, particularly certain variants of the APOL1 gene, which are more common in people of African descent, have been associated with a higher risk of kidney diseases[ 32 ]. Further research is essential to explore the biological factors involved and tailor personalized medicine strategies for high-risk populations. In conclusion, while progress has been made in reducing mortality from RTID in certain demographics, the recent upward trend, particularly in older adults, rural populations, and specific racial groups, indicates the need for ongoing public health efforts. Conclusion To summarize, this population-based analysis shows that mortality from renal tubulointerstitial diseases in the United States has increased over the past two decades, with a pronounced rise observed after 2018. Significant demographic and geographic disparities persist, disproportionately affecting older adults, males, non-Hispanic Black individuals, rural populations, and residents of certain regions. These findings underscore the growing public health burden of renal tubulointerstitial diseases and highlight the need for improved early detection, equitable access to nephrology care, and targeted interventions for high-risk populations. Abbreviations AAPC Average Annual Percent Change APC Annual Percent Change AAMR Age-Adjusted Mortality Rate CDC Centers for Disease Control and Prevention CI Confidence Interval CMR Crude Mortality Rate RTID Renal tubulo-interstitial diseases CKD: Chronic kidney disease ICD-10 International Classification of Diseases, 10th Revision NH Non-Hispanic STROBE Strengthening the Reporting of Observational Studies in Epidemiology US United States WONDER Wide-Ranging Online Data for Epidemiologic Research Declarations Funding: The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. Competing Interests: The authors have no relevant financial or non-financial interests to disclose. Author Contributions: Conceptualization: Eisha Moazzam Methodology: Eisha Moazzam, Azka Ijaz, Haram Aftab, Tanzeela Sameen Saeed Formal analysis and investigation: Eisha Moazzam, Sara Sohail, Umaima Cheema, Tanzeela Sameen Saeed, Muhammad Ramish Saeed, Luqman Munir, Mohammad Ammar Ur Rahman Writing – original draft preparation: Eisha Moazzam, Azka Ijaz, Haram Aftab, Sara Sohail, Umaima Cheema, Tanzeela Sameen Saeed, Muhammad Ramish Saeed, Luqman Munir, Mohammad Ammar Ur Rahman Writing – review and editing: Eisha Moazzam, Tanzeela Sameen Saeed, Muhammad Ramish Saeed, Luqman Munir, Mohammad Ammar Ur Rahman, Khizar Razzaq Supervision: Eisha Moazzam, Khizar Razzaq References Ks H, Hw S. Tubulointerstitial injury and the progression of chronic kidney disease. Pediatric nephrology (Berlin, Germany) [Internet]. 2012 Jun [cited 2024 Oct 4];27(6). Available from: https://pubmed.ncbi.nlm.nih.gov/21947270/ Joyce E, Glasner P, Ranganathan S, Swiatecka-Urban A. 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Available from: https://www.annualreviews.org/content/journals/10.1146/annurev-physiol-021119-034345 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8595322","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":592337803,"identity":"2289025e-2e78-4461-a921-2cd3220d3419","order_by":0,"name":"Eisha Moazzam","email":"","orcid":"","institution":"King Edward Medical University","correspondingAuthor":false,"prefix":"","firstName":"Eisha","middleName":"","lastName":"Moazzam","suffix":""},{"id":592337804,"identity":"424438f2-fe90-4517-a1a3-3d59cf724e30","order_by":1,"name":"Azka Ijaz","email":"","orcid":"","institution":"King Edward Medical 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2","display":"","copyAsset":false,"role":"figure","size":58497,"visible":true,"origin":"","legend":"\u003cp\u003eRenal Tubulointerstitial Disease -Related Crude Rate stratified by age groups per 1,000,000 in the United States, 1999 to 2020.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8595322/v1/11a3d6ec26651a66eb5517f0.png"},{"id":102963268,"identity":"ee114e8d-4047-466e-9246-f1be7326b817","added_by":"auto","created_at":"2026-02-19 04:14:59","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":43004,"visible":true,"origin":"","legend":"\u003cp\u003eRenal Tubulointerstitial Disease-Related AAMRs Stratified by Gender per 1,000,000 in the United States, 1999 to 2020.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8595322/v1/c289d5d55e3c7f12b1fda9c5.png"},{"id":102854125,"identity":"adaa17ed-4dbd-4bd9-ac91-b31140f18208","added_by":"auto","created_at":"2026-02-17 14:47:19","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":53959,"visible":true,"origin":"","legend":"\u003cp\u003eRenal Tubulointerstitial Disease-Related AAMRs stratified by Census Region per 1,000,000 in the United States, 1999 to 2020.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8595322/v1/60019edbeb6df3cfd96e6aa5.png"},{"id":102854133,"identity":"b9501f6a-95b1-41cd-9755-94dae00fefe9","added_by":"auto","created_at":"2026-02-17 14:47:19","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":54287,"visible":true,"origin":"","legend":"\u003cp\u003eRenal Tubulointerstitial Disease-Related AAMRs stratified by State per 1,000,000 in the United States, 1999 to 2020.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8595322/v1/4f5392627bd8ae811d64b860.png"},{"id":102854132,"identity":"22a44a15-697a-4683-b10c-7fd8986d11f2","added_by":"auto","created_at":"2026-02-17 14:47:19","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":69460,"visible":true,"origin":"","legend":"\u003cp\u003eRenal Tubulointerstitial Disease-Related AAMRs stratified by State per 1,000,000 in the United States, 1999 to 2020.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-8595322/v1/cb110db54003d8b7e6be8fde.png"},{"id":102854127,"identity":"51f04bb0-01b0-4494-b3f5-e45fcb2a9e05","added_by":"auto","created_at":"2026-02-17 14:47:19","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":43027,"visible":true,"origin":"","legend":"\u003cp\u003eRenal Tubulointerstitial Disease-Related AAMRs stratified by Urbanisation per 1,000,000 in the United States, 1999 to 2020.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-8595322/v1/54dea8f0fde4a134f9aa07b5.png"},{"id":102854130,"identity":"8a79fbd3-f49d-4869-8307-adab2c6a1a40","added_by":"auto","created_at":"2026-02-17 14:47:19","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":53462,"visible":true,"origin":"","legend":"\u003cp\u003eRenal Tubulointerstitial Disease-Related AAMRs stratified by Race per 1,000,000 in the United States, 1999 to 2020.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-8595322/v1/077b6970a2f4281b4b8cd884.png"},{"id":104741720,"identity":"d964f49c-df3f-4579-a21b-d1dc65b6ab51","added_by":"auto","created_at":"2026-03-16 16:26:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1246015,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8595322/v1/8e247d74-d087-4b1c-b8a0-32e711763886.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Trends in Mortality of Renal Tubulointerstitial Diseases in the United States from 1999 to 2020 Using CDC WONDER Data","fulltext":[{"header":"Introduction","content":"\u003cp\u003eRenal tubulointerstitial diseases (TID) are an umbrella term for conditions that affect the kidney's tubular structures and interstitial tissues. It is also referred to as tubulointerstitial nephritis. The tubules comprise 80% of the total volume of the kidney and perform crucial functions. Due to their high energy demand, they are susceptible to various injuries. Abnormal glomerular filtration, inflammation, fibrogenesis, and hypoxia are common consequences that lead to tubulointerstitial injury [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTID can result from multiple causes and can progress from acute kidney injury to chronic kidney disease. Various genetic and environmental factors contribute to its development, with drug-induced causes being among the most common. Common medications, such as beta-lactam antibiotics and non-steroidal anti-inflammatory drugs (NSAIDs), often present classic TID [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The global prevalence of acute tubulointerstitial nephritis due to any cause is estimated to be 1%-3% in all kidney-related biopsies, rising to 15%-27% when only acute cases are considered [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTID is characterized by various levels of inflammation and edema, which lead to a decrease in glomerular filtration rate (GFR) due to affected renal blood flow. Any delay in addressing these conditions can lead to progression into chronic disease[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Differentiating between chronic and acute forms of the condition can be challenging. The following methods are useful for making a successful diagnosis: blood tests, imaging tests, urinary biomarkers, renal biopsy, urinalysis, and microscopy [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Treatment strategies often include corticosteroids, which are more effective when initiated during the early stages and continued for at least a month [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study aims to contribute to existing knowledge by analyzing deaths registered in the Centers for Disease Control and Prevention - Wide-ranging Online Data for Epidemiologic Research (CDC-WONDER) database, an extensive repository of mortality data. Insights from this analysis can inform future treatment strategies for renal tubulointerstitial diseases.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design:\u003c/h2\u003e \u003cp\u003eData regarding deaths resulting from renal tubule-interstitial disease as an underlying cause were extracted from the Centers for Disease Control and Prevention Wide-ranging Online Data for Epidemiologic Research (CDC-WONDER) database [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The time range for data extraction covered two decades, from 1999 to 2020. The International Classification of Diseases, Tenth Revision (ICD-10) codes N10-N15 (renal tubulointerstitial diseases) were utilized to obtain information from death certificates in the CDC-WONDER database. This study adheres to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData Extraction:\u003c/h3\u003e\n\u003cp\u003eData extraction was based on significant demographic categories such as states, census regions, race, age groups, urbanization, and gender. The analysis included all 50 states. Gender was categorized as male and female. Census regions were divided into the West, South, Northeast, and Midwest. Age groups were defined in 10-year intervals. Race categories included Black/African American, White, and Asian. Urbanization was classified into rural areas and urban areas. According to the U.S. Census Bureau definition (2013) [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], areas with a population of 50,000 or more were classified as urban, while areas with a population of less than 50,000 were considered non-metropolitan or rural.\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis:\u003c/h2\u003e \u003cp\u003eThe Age-Adjusted Mortality Rate (AAMR) was used to analyze data regarding deaths stratified by age, gender, race, and urban areas. The formula for AAMR is:\u003c/p\u003e \u003cp\u003e \u003cb\u003eAge-adjusted mortality rate\u0026thinsp;=\u0026thinsp;Sum of (Age-specific mortality rate \u0026times; Standard population weight) \u0026times; 100,000\u003c/b\u003e, where the age-specific mortality rate is the number of deaths for a given age group divided by the population of that age group. The standard population weight is calculated by dividing the population for the age group by the sum of the populations for all age groups in the query [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe crude rate was calculated by dividing the renal tubule-interstitial disease-related deaths by the corresponding U.S. population for that year. The Joinpoint Regression Program was used to calculate the annual percent change (APC) in AAMR and 95% confidence intervals (CIs)[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Permutation tests and tests for parallelism were also conducted. IBM Corp. released 2023 (IBM SPSS Statistics for Windows, Version 29.0.2.0, Armonk, NY: IBM Corp.) and Stata Corp. 2023 (Stata Statistical Software: Release 18, College Station, TX: Stata Corp LLC) software were used for further statistical analysis. This study did not require Institutional Review Board approval because the CDC WONDER is a publicly available database that contains de-identified data.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eOverall Mortality Trends:\u003c/h2\u003e \u003cp\u003eBetween 1999 and 2020, there were a total of 98,241 deaths associated with tubulointerstitial diseases. The Average Annual Mortality Rate (AAMR) was calculated at 13.42 per 100,000 individuals (95% CI\u0026thinsp;=\u0026thinsp;4.66 to 4.76). In 1999, the AAMR stood at 15.22 (95% CI\u0026thinsp;=\u0026thinsp;14.76 to 15.69), and by 2020, this increased to 17.96 (95% CI\u0026thinsp;=\u0026thinsp;17.54 to 18.3). The Average Annual Percentage Change (AAPC) of 0.92% (95% CI\u0026thinsp;=\u0026thinsp;0.39 to 1.45) was calculated. The initial decrease in AAMR, observed from 1999 to 2011, showed an Annual Percentage Change (APC) of -2.11% (95% CI =-2.46 to -1.76). After 2011, the rate grew at a slow pace, with an APC of 3.89% (95% CI\u0026thinsp;=\u0026thinsp;2.93 to 4.86) until 2018. During 2018\u0026ndash;2020, the AAMR showed a very rapid rise with an APC of 9.50% (95% CI\u0026thinsp;=\u0026thinsp;4.54 to 14.69). Statistical analysis from Stata provided a z-value of 0.65 and a p-value of 0.516. (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eMortality Trends by Age:\u003c/h2\u003e \u003cp\u003eFocusing on individuals aged 85 years and older, this age group emerged as the most affected population in 2020. Tubulointerstitial diseases were found to be a significant cause of mortality in this demographic, leading to 2167 deaths in 2020 alone. Their crude rate (CR) was recorded at 325.45 in 2020, with a 95% Confidence Interval (CI) ranging from 311.75 to 339.15, making this the highest rate among all age groups. This CMR represents no significant change in rate from 335.097 in 1999. The AAPC for this period was 0.02% (95%CI =-0.69 to 0.74). The age group 75\u0026ndash;84 years represents the second-highest mortality rate during 1999\u0026ndash;2020. Although, unlike the 85\u0026thinsp;+\u0026thinsp;year group, 75\u0026ndash;84 years had a slight increase in mortality rate with AAPC of 0.39% (95%CI\u0026thinsp;=\u0026thinsp;0.07 to 0.71). The 3rd highest rate was found in the 55\u0026ndash;64 year age group, and it also showed an increase in mortality rate during 1999\u0026ndash;2020 (AAPC\u0026thinsp;=\u0026thinsp;1.41%, 95%CI\u0026thinsp;=\u0026thinsp;0.66 to 2.16). This indicates that tubulointerstitial disease mostly causes mortality in the older population rather than the young. Analysis in Stata resulted in a z-value of 4.52 and a p-value of 0, confirming the statistical significance and describing the critical impact of tubulointerstitial diseases on this particular age group. (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMortality Trends by Gender:\u003c/h3\u003e\n\u003cp\u003eFrom 1999 to 2020, males exhibited an Average Annual Mortality Rate (AAMR) of 16.901 (95% CI\u0026thinsp;=\u0026thinsp;16.75 TO 17.052), while females had an AAMR of 11.498 (95% CI\u0026thinsp;=\u0026thinsp;11.394 to 11.601). Between 1999 and 2010, the Average Percentage Change (APC) for males showed a significant decrease of -3.05% (95%CI =-3.50 to -2.61), followed by an upward trend of 2.76% (95%CI\u0026thinsp;=\u0026thinsp;1.92 to 3.6) during 2010\u0026ndash;2018. During 2018\u0026ndash;2020, there was a sharp rise in AAMR with an APC of 10.18% (95%CI\u0026thinsp;=\u0026thinsp;4.71 to 15.93). Similarly, females experienced a decrease in AAMR from 1999 to 2011 with APC at -1.69% (95%CI =-2.12 to -1.25), and from 2011 to 2017, the rate grew from 3.90% (95%CI\u0026thinsp;=\u0026thinsp;2.27 to 5.55). However, post-2017, females saw a rapid increase in AAMR, with the APC at 7.90% (95% CI\u0026thinsp;=\u0026thinsp;4.74 to 11.17) till 2020. We observed non-parallel trends between males and females after performing a pairwise analysis. A Stata analysis yielded a z-value of 5.49 with a p-value of 0, confirming statistical significance. (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eMortality Trends by Region and State:\u003c/h3\u003e\n\u003cp\u003eFrom 1999 to 2020, the West region exhibited the highest AAMR of 16.583 (95% CI\u0026thinsp;=\u0026thinsp;16.381 to 16.786), followed by South (AAMR\u0026thinsp;=\u0026thinsp;13.019; 95% CI\u0026thinsp;=\u0026thinsp;12.881 to 13.157), Midwest (AAMR\u0026thinsp;=\u0026thinsp;12.896; 95% CI\u0026thinsp;=\u0026thinsp;12.722 to 13.07) and finally Northeast (AAMR\u0026thinsp;=\u0026thinsp;11.287; 95% CI\u0026thinsp;=\u0026thinsp;11.113 to 11.461). All four regions exhibited an initial downward curve (Northeast from 1999 to 2010: APC = -2.188; 95% CI = -3.192 to -1.174, Midwest from 1999 to 2001: APC = -9.216; 95% CI = -19.262 to 2.078, South from 1999 to 2011: APC = -2.792; 95% CI = -3.246 to -2.337 and West from 1999 to 2011: APC = -1.636; 95% CI = -2.342 to 0.924). This was followed by an upward rise in the case of Northeast from 2010 to 2020 (APC\u0026thinsp;=\u0026thinsp;5.147; 95% CI\u0026thinsp;=\u0026thinsp;4.064 to 6.241) and West from 2011 to 2020 (APC\u0026thinsp;=\u0026thinsp;3.912; 95% CI\u0026thinsp;=\u0026thinsp;2.956 to 4.876). In the case of South, the initial decline was followed by a gradual upward rise from 2011 to 2018 (APC\u0026thinsp;=\u0026thinsp;3.335; 95% CI\u0026thinsp;=\u0026thinsp;2.066 to 4.62) and finally a steeper upward slope from 2018 to 2020 (APC\u0026thinsp;=\u0026thinsp;11.963; 95% CI\u0026thinsp;=\u0026thinsp;5.419 to 18.914).\u003c/p\u003e \u003cp\u003eThe curve of Midwest region showed several fluctuations, the initial sharp decline followed by a much steadier downward slope from 2001 to 2007 (APC = -0.166; 95% CI = -2.843 to 2.591), a sharp downward trajectory again from 2007 to 2010 (APC = -5.602; 95% CI = -16.781 to 7.078) followed by a gradual upward rise from 2010 to 2017 (APC\u0026thinsp;=\u0026thinsp;3.303; 95% CI\u0026thinsp;=\u0026thinsp;1.233 to 5.414) and finally by a sharp upward climb from 2017 to 2020 (APC\u0026thinsp;=\u0026thinsp;8.98; 95% CI\u0026thinsp;=\u0026thinsp;3.68 to 14.549). Stata analysis reported a z-value of 6.67 and a p-value of 0, indicating a statistically significant difference. SPSS analysis using the Kruskal-Wallis test reported a statistic of 49.270 with 3 degrees of freedom. (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe mortality rates showed a noticeable difference among different states. States displaying the highest AAMR values were Alaska (AAMR\u0026thinsp;=\u0026thinsp;25.304; 95% CI\u0026thinsp;=\u0026thinsp;21.928 to 28.621), Vermont (AAMR\u0026thinsp;=\u0026thinsp;21.529; 95% CI\u0026thinsp;=\u0026thinsp;19.283 to 23.776) and Washington (AAMR\u0026thinsp;=\u0026thinsp;21.132; 95% CI\u0026thinsp;=\u0026thinsp;20.397 to 21.867) followed by Utah (AAMR\u0026thinsp;=\u0026thinsp;19.847; 95% CI\u0026thinsp;=\u0026thinsp;18.535 to 21.159), North Dakota (AAMR\u0026thinsp;=\u0026thinsp;18.977; 95% CI\u0026thinsp;=\u0026thinsp;16.979 to 20.975) and Tennessee (AAMR\u0026thinsp;=\u0026thinsp;18.177; 95% CI\u0026thinsp;=\u0026thinsp;17.483 to 18.871). On the other end of the spectrum were states showing considerably lower mortality rates, like Louisiana (AAMR\u0026thinsp;=\u0026thinsp;9.442; 95% CI\u0026thinsp;=\u0026thinsp;8.838 to 10.047), Florida (AAMR\u0026thinsp;=\u0026thinsp;9.563; 95% CI\u0026thinsp;=\u0026thinsp;9.303 to 9.823, and Massachusetts (AAMR\u0026thinsp;=\u0026thinsp;9.706; 95% CI\u0026thinsp;=\u0026thinsp;9.238 to 10.174). Stata analysis reported a z value of 0.7 with a p value of 0.48,2 highlighting the statistical significance. (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e5\u003c/span\u003e,\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e6\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe AAMR was steadily higher in micropolitan non-metro rural areas (AAMR\u0026thinsp;=\u0026thinsp;15.317; 95% CI\u0026thinsp;=\u0026thinsp;15.027 to 15.606) than in large central metro urban areas (AAMR\u0026thinsp;=\u0026thinsp;12.839; 95% CI\u0026thinsp;=\u0026thinsp;12.683 to 12.996). Urban areas exhibited an initial downward trajectory from 1999 to 2011 (APC = -2.408; 95% CI = -2.982 to -1.831) followed by an upward rise from 2011 to 2020 (AAMR\u0026thinsp;=\u0026thinsp;3.774; 95% CI\u0026thinsp;=\u0026thinsp;2.926 to 4.629). The trend for rural areas was divided into 3 segments, a decline from 1999 to 2010 (APC = -2.861; 95% CI = -3.876 to -1.834) followed by a gradual rise from 2010 to 2017 (APC\u0026thinsp;=\u0026thinsp;3.343; 95% CI\u0026thinsp;=\u0026thinsp;0.728 to 6.025) and much steeper rise from 2017 to 2020 (APC\u0026thinsp;=\u0026thinsp;11.641; 95% CI\u0026thinsp;=\u0026thinsp;4.934 to 18.778). The Mann-Whitney test of spss analysis reported a U statistic of 409. Stata analysis revealed a z value of 3.92 and a p value of 0, highlighting statistical significance. (Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e7\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eMortality Trends by Race and Ethnicity:\u003c/h2\u003e \u003cp\u003eFrom 1990 to 2020, the AAMR reported among NH Blacks (AAMR\u0026thinsp;=\u0026thinsp;15.243; 95% CI\u0026thinsp;=\u0026thinsp;14.946 to 15.541) and NH Whites (AAMR\u0026thinsp;=\u0026thinsp;13.353; 95% CI\u0026thinsp;=\u0026thinsp;13.262to 13.443) was considerably higher than that of Asians/Pacific Islanders (AAMR\u0026thinsp;=\u0026thinsp;8.246; 95% CI\u0026thinsp;=\u0026thinsp;7.905 to 8.587). From 1999 to 2020, Asians (APC = -0.616; 95% CI = -1.485 to 0.26) and NH Blacks (APC = -1.064; 95% CI = -1.942 to 0.178) showed a consistent downward trend, while NH Whites showed an upward trend (APC\u0026thinsp;=\u0026thinsp;0.962; 95% CI\u0026thinsp;=\u0026thinsp;0.163 to 1.768). SPSS analysis using the Kruskal-Wallis test revealed a statistic of 47.534 with 2 degrees of freedom. The z value reported by the Stata analysis was 4.48, and the p value was 0, which confirms statistical significance. (Fig.\u0026nbsp;\u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003e8\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic Characteristics of Deaths from Renal Tubulointerstitial Diseases in the USA from 1999 to 2020.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRenal tubulointerstitial disease Related Deaths (n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAge-Adjusted Mortality Rate (AAMR) per 1,000,000\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOverall Population\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e98,241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.428\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAGE\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u0026ndash;34 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.166(Avg CR)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35\u0026ndash;44 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,566\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.757(Avg CR)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e45\u0026ndash;54 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5,809\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.279(Avg CR)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e55\u0026ndash;64 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10,482\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.403(Avg CR)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e65\u0026ndash;74 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16,642\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.918(Avg CR)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e75\u0026ndash;84 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27,936\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e93.178(Avg CR)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49,626\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.901\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48,615\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.498\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUS Census Region\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNortheast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16431\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.287\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMidwest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21430\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.896\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.583\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRace / Ethnicity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNH Black or African American\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10630\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.243\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNH White\u003c/p\u003e \u003cp\u003eAsian or Pacific Islander\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e84465\u003c/p\u003e \u003cp\u003e2355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.353\u003c/p\u003e \u003cp\u003e8.246\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUrban / Rural\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.839\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10917\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.317\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAnnual Percentage Changes (APCs) and Average Annual Percentage Changes (AAPCs) in Renal Tubulointerstitial Disease-Related Mortality Rate in the USA from 1999 to 2020.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTrend Segment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAPC (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAAPC (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1999\u0026ndash;2011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.1150* (-2.4631 to -1.7657)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.9216* (0.3920 to 1.4541)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2011\u0026ndash;2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.8974* (2.9379 to 4.8658)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2018\u0026ndash;2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.5021* (4.5471 to 14.6920)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1999\u0026ndash;2011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.6917 (-2.1287, -1.2527)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.2128* (0.5950 to 1.8345)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2011\u0026ndash;2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.9005 (2.2750, 5.5517)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2017\u0026ndash;2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.9080 (4.7402, 11.1716)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1999\u0026ndash;2011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-3.0576 (-3.5025, -2.6107)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.3357 (-0.2365 to 0.9111)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2011\u0026ndash;2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.7629 (1.9207, 3.6120)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2018\u0026ndash;2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.1819 (4.7105, 15.9392)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUS Central Region\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNortheast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1999\u0026ndash;2010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.188* (-3.192 to -1.174)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1.2387* (0.5532 to 1.9290)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2010\u0026ndash;2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.147* (4.064 to 6.241)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eMidwest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1999\u0026ndash;2001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-9.216 (-19.262 to 2.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.5209 (-1.5600 to 2.6458)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2001\u0026ndash;2007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.166 (-2.843 to 2.591)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2007\u0026ndash;2010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-5.602 (-16.781 to 7.078)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2010\u0026ndash;2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.303* (1.233 to 5.414)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2017\u0026ndash;2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.98* (3.68 to 14.549)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eSouth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1999\u0026ndash;2011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.792* (-3.246 to -2.337\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.5529 (-0.1385 to 1.2491)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2011\u0026ndash;2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.335* (2.066 to 4.62)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2018\u0026ndash;2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.963* (5.419 to 18.914)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eWest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1999\u0026ndash;2011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.636* (-2.342 to 0.924)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.7044* (0.1715 to 1.2402)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2011\u0026ndash;2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.912* (2.956 to 4.876)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRace / Ethnicity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNH Black or African American\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1999\u0026ndash;2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.064* (-1.942 to 0.178)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.064* (-1.942 to 0.178)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNH White\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1999\u0026ndash;2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.962* (0.163 to 1.768)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.962* (0.163 to 1.768)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsian or Pacific Islanders\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1999\u0026ndash;2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.616 (-1.485 to 0.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.616 (-1.485 to 0.26)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eURBAN / RURAL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1999\u0026ndash;2011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.408* (-2.982 to -1.831)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.1951 (-0.2573 to 0.6496)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2011\u0026ndash;2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.774* (2.926 to 4.629)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1999\u0026ndash;2010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.861* (-3.876 to -1.834)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e1.1557 (-0.0824 to 2.4091)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2010\u0026ndash;2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.343* (0.728 to 6.025)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2017\u0026ndash;2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.641* (4.934 to 18.778)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eAnalysis of CDC WONDER data from 1999 to 2020 highlights significant trends in age, gender, geography, urbanization, and ethnicity related to RTID mortality. Overall, the AAMR for RTID showed a steady increase, initially decreasing slightly from 1999 to 2011, then rising from 2011 to 2018, with the most pronounced increase occurring between 2018 and 2020. Mortality rates were highest among individuals aged 85 and older, followed by those aged 75 to 84. Males exhibited higher AAMRs than females, while Non-Hispanic Blacks had higher rates than Non-Hispanic Whites and Asians/Pacific Islanders. Geographic disparities were evident, with the highest rates in the West, followed by the South, Midwest, and Northeast. Non-metropolitan areas had higher mortality rates than their metropolitan counterparts, underscoring the need for targeted interventions.\u003c/p\u003e \u003cp\u003eThis sharp rise in overall mortality trends in recent years could be attributed to the growing incidence of chronic conditions such as hypertension, diabetes, and obesity, which are known risk factors for renal diseases, especially among older populations[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Chronic kidney disease (CKD) is often secondary to these conditions, and as they rise, so does the incidence of RTID[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Studies indicate that poorly managed diabetes, which leads to diabetic nephropathy, contributes to tubulointerstitial damage[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]​. With an aging U.S. population, a multifaceted approach is necessary to address the rising mortality from RTID. Preventative measures focusing on managing risk factors and public health campaigns targeting lifestyle modifications can help reduce the incidence of kidney diseases​[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAge plays a crucial role in RTID mortality, with those aged 85 and above being disproportionately affected. This is consistent with findings in nephrology literature, which indicate that age-related structural and functional changes in the kidneys contribute to the development and progression of tubulointerstitial disorders[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The cumulative effect of long-term comorbid conditions and multiple medications for chronic diseases in older adults, especially nephrotoxic medications like NSAIDs, PPIs, and certain antibiotics, further worsens tubulointerstitial damage[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Renal diseases in older adults are often underdiagnosed due to the non-specific nature of early symptoms. Greater awareness of the early signs of kidney disease among older adults could lead to earlier diagnosis and intervention, reducing mortality in the long term[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGender-based differences in mortality are evident, with males consistently exhibiting higher AAMRs than females, likely due to lifestyle factors such as higher rates of smoking, alcohol consumption, and exposure to occupational nephrotoxic agents like solvents and heavy metals among men[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Furthermore, estrogen has a protective effect against kidney diseases by reducing fibrosis and inflammation in women[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], while testosterone has been linked to increased susceptibility to renal injury[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGeographical disparities in RTID mortality reflect variations in socioeconomic status and healthcare access. The West had the highest AAMR, while the Northeast had the lowest. Access to healthcare, particularly specialized nephrology services, differs significantly between regions[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. For example, rural areas in the South and Midwest, with fewer nephrologists and dialysis centers, face delayed diagnoses and treatments, while disparities in insurance coverage further widen healthcare inequality across regions[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. In the West, higher exposure to environmental toxins may contribute to the higher mortality rates[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Agricultural areas with high nephrotoxic pesticide use, common in Western and Midwestern states, have a higher incidence of kidney diseases[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. By focusing on early detection, improving healthcare access, and addressing the social determinants of health, the rising burden of renal diseases can be mitigated in the future.\u003c/p\u003e \u003cp\u003eStates with higher AAMRs, like Alaska, Vermont, and Washington, face challenges in accessing equitable nephrology care, with Alaska's remote areas particularly affected[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Environmental factors, such as pesticide use in agricultural states like Washington and Utah, contribute to increased RTID mortality due to nephrotoxicity[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Socioeconomic disparities also influence mortality trends, as states with lower incomes and higher poverty rates, like Tennessee, experience higher mortality, while wealthier states like Massachusetts and Florida have lower mortality rates[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The differences in CKD burden across states highlight the necessity to explore targeted policy measures and interventions aimed at reducing exposure to risk factors on a state level[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNon-metro rural areas exhibit higher AAMRs than urban areas, reflecting challenges including longer travel distances, fewer healthcare providers, and economic constraints[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Rural populations often face lower incomes and more uninsured individuals[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], leading to delayed diagnosis and limited access to medications and follow-up care for RTID. Expanding nephrology services in these areas is crucial, with telemedicine offering a potential solution to improve timely consultations and management for patients in remote areas [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRacial disparities in RTID mortality are evident, with NH Blacks showing higher rates than NH Whites and Asians/Pacific Islanders. This disparity is attributed to the higher prevalence of comorbidities such as diabetes and hypertension within Black populations, as well as socioeconomic challenges like lower income and limited access to healthcare[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Additionally, genetic variants, particularly certain variants of the APOL1 gene, which are more common in people of African descent, have been associated with a higher risk of kidney diseases[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Further research is essential to explore the biological factors involved and tailor personalized medicine strategies for high-risk populations.\u003c/p\u003e \u003cp\u003eIn conclusion, while progress has been made in reducing mortality from RTID in certain demographics, the recent upward trend, particularly in older adults, rural populations, and specific racial groups, indicates the need for ongoing public health efforts.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eTo summarize, this population-based analysis shows that mortality from renal tubulointerstitial diseases in the United States has increased over the past two decades, with a pronounced rise observed after 2018. Significant demographic and geographic disparities persist, disproportionately affecting older adults, males, non-Hispanic Black individuals, rural populations, and residents of certain regions. These findings underscore the growing public health burden of renal tubulointerstitial diseases and highlight the need for improved early detection, equitable access to nephrology care, and targeted interventions for high-risk populations.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u0026nbsp;AAPC \u0026nbsp;Average Annual Percent Change\u003c/p\u003e\n\u003cp\u003eAPC \u0026nbsp;Annual Percent Change\u003c/p\u003e\n\u003cp\u003eAAMR \u0026nbsp;Age-Adjusted Mortality Rate\u003c/p\u003e\n\u003cp\u003eCDC \u0026nbsp;Centers for Disease Control and Prevention\u003c/p\u003e\n\u003cp\u003eCI \u0026nbsp;Confidence Interval\u003c/p\u003e\n\u003cp\u003eCMR \u0026nbsp;Crude Mortality Rate\u003c/p\u003e\n\u003cp\u003eRTID \u0026nbsp;Renal tubulo-interstitial diseases\u003c/p\u003e\n\u003cp\u003eCKD: Chronic kidney disease\u003c/p\u003e\n\u003cp\u003eICD-10 \u0026nbsp;International Classification of Diseases, 10th Revision\u003c/p\u003e\n\u003cp\u003eNH \u0026nbsp;Non-Hispanic\u003c/p\u003e\n\u003cp\u003eSTROBE \u0026nbsp;Strengthening the Reporting of Observational Studies in Epidemiology\u003c/p\u003e\n\u003cp\u003eUS \u0026nbsp;United States\u003c/p\u003e\n\u003cp\u003eWONDER \u0026nbsp;Wide-Ranging Online Data for Epidemiologic Research\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that no funds, grants, or other support were received during the preparation of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConceptualization:\u003c/strong\u003e Eisha Moazzam\u003cbr\u003e\u003cstrong\u003eMethodology:\u003c/strong\u003e Eisha Moazzam, Azka Ijaz, Haram Aftab, Tanzeela Sameen Saeed\u003cbr\u003e\u003cstrong\u003eFormal analysis and investigation:\u003c/strong\u003e Eisha Moazzam, Sara Sohail, Umaima Cheema, Tanzeela Sameen Saeed, Muhammad Ramish Saeed, Luqman Munir, Mohammad Ammar Ur Rahman\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWriting \u0026ndash; original draft preparation:\u003c/strong\u003e Eisha Moazzam, Azka Ijaz, Haram Aftab, Sara Sohail, Umaima Cheema, Tanzeela Sameen Saeed, Muhammad Ramish Saeed, Luqman Munir, Mohammad Ammar Ur Rahman\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWriting \u0026ndash; review and editing:\u003c/strong\u003e Eisha Moazzam, Tanzeela Sameen Saeed, Muhammad Ramish Saeed, Luqman Munir, Mohammad Ammar Ur Rahman, \u0026nbsp;Khizar Razzaq\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupervision:\u003c/strong\u003e Eisha Moazzam, Khizar Razzaq\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eKs H, Hw S. 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Available from: https://www.annualreviews.org/content/journals/10.1146/annurev-physiol-021119-034345\u003c/li\u003e\n\u003c/ol\u003e"}],"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":"Tubulointerstitial, CDC, Mortality, Trend","lastPublishedDoi":"10.21203/rs.3.rs-8595322/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8595322/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose:\u003c/h2\u003e \u003cp\u003eRenal tubulointerstitial disease, also known as tubulointerstitial nephritis, involves primary injury to renal structures, leading to kidney inflammation. It may result from drug hypersensitivity, infections, or toxic exposures. This study examined mortality trends due to renal tubulointerstitial disease in the United States from 1999 to 2020 and evaluated disparities by demographic and geographic factors.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e \u003cp\u003eMortality data from 1999 to 2020 were obtained from the CDC WONDER database. Age-adjusted mortality rates (AAMRs) per 1,000,000 population and annual percent change (APC) with 95% confidence intervals were calculated. The Joinpoint Regression Program was used to assess temporal trends and demographic disparities.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e \u003cp\u003eA total of 7,341 deaths were recorded. AAMR increased from 15.228 in 1999 to 17.962 in 2020. APC trends showed an initial decline (1999\u0026ndash;2011: \u0026minus;2.1150), followed by an increase (2011\u0026ndash;2018: 3.8974) and a marked rise (2018\u0026ndash;2020: 9.5021). Higher mortality occurred among Black individuals, males, those\u0026thinsp;\u0026ge;\u0026thinsp;85 years, rural residents, and individuals in the western US. Tests for parallelism revealed significant differences by sex (p\u0026thinsp;=\u0026thinsp;0.000667), race (p\u0026thinsp;=\u0026thinsp;0.00444), and region: Northeast vs. West (p\u0026thinsp;=\u0026thinsp;0.009778), Northeast vs. South (p\u0026thinsp;=\u0026thinsp;0.000222), Midwest vs. South (p\u0026thinsp;=\u0026thinsp;0.014000), Midwest vs. West (p\u0026thinsp;=\u0026thinsp;0.029111), and South vs. West (p\u0026thinsp;=\u0026thinsp;0.000444). Significant differences were also observed between urban and rural populations (p\u0026thinsp;=\u0026thinsp;0.028889).\u003c/p\u003e\u003ch2\u003eConclusion:\u003c/h2\u003e \u003cp\u003eMortality from renal tubulointerstitial disease has risen over the past two decades, with persistent demographic disparities. These findings underscore the need for targeted research and public health interventions to address these inequalities.\u003c/p\u003e","manuscriptTitle":"Trends in Mortality of Renal Tubulointerstitial Diseases in the United States from 1999 to 2020 Using CDC WONDER Data","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-17 14:47:14","doi":"10.21203/rs.3.rs-8595322/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":"8abef4be-0c0c-4c69-9822-4df1f8ca5c5a","owner":[],"postedDate":"February 17th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-16T16:26:37+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-17 14:47:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8595322","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8595322","identity":"rs-8595322","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}