Addressing Disparities in Osteomyelitis: The WONDER Project

Addressing Disparities in Osteomyelitis: The WONDER Project | 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 Addressing Disparities in Osteomyelitis: The WONDER Project John Paul Braun, Cinthiya Chander, Vikram Murugan, Erica J. Stohs, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7844039/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract Introduction Osteomyelitis, an infection of the bone and bone marrow, causes substantial morbidity and mortality, particularly in older adults and those with comorbidities such as diabetes and peripheral vascular disease. This study examines national trends in osteomyelitis mortality from 1999–2023 using CDC WONDER. Despite therapeutic advances, U.S. mortality appears to be rising, with significant demographic and geographic disparities. Methods Using CDC WONDER Multiple Cause-of-Death data, we identified U.S. adults ≥ 35 years with osteomyelitis (ICD-10 M86) as a contributing cause of death. Age-adjusted mortality rates (AAMRs) were calculated using the 2000 U.S. standard population, and trends analyzed with Joinpoint regression to estimate annual and average annual percent change (APC, AAPC) with 95% CIs. Results From 1999–2023, 111,115 osteomyelitis-related deaths occurred. AAMR rose from 1.7 to 4.1 (AAPC: 3.6%), peaking in 2018–2021 (APC: 13.2%). Males had higher rates than females (2023 AAMR: 5.7 vs 2.9), but both increased proportionally. American Indian/Alaska Native individuals had the highest 2023 rate (8.0) and largest post‑2010 rise, followed by Black individuals (7.1). Mortality increased with age, reaching 33.6 in those ≥ 85 years. Rural areas saw faster increases than urban areas, surpassing them by 2020. Conclusions Osteomyelitis mortality in U.S. adults more than doubled since 1999, accelerating sharply in the last decade and during the COVID‑19 pandemic. Disproportionate burdens among males, AI/AN and Black populations, the oldest adults, and rural residents highlight the need for targeted, equity‑focused interventions and improved access to preventive and specialty care. Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Osteomyelitis, an infection of bone and bone marrow, remains a significant clinical challenge with substantial morbidity and mortality, particularly among older adults and those with chronic comorbidities such as diabetes mellitus and peripheral vascular disease [ 1 , 2 ]. Despite advances in antimicrobial therapy and surgical management, osteomyelitis continues to contribute to substantial incidence and mortality, especially in the context of an aging population, rising prevalence of diabetes, and increasing rates of antimicrobial resistance [ 3 , 4 ]. Recent epidemiological studies suggest that osteomyelitis disproportionately affects certain demographic groups, with notable demographic disparities seen in age, sex, race, and geography. However, comprehensive national-level data on long-term trends in osteomyelitis mortality in the United States remain limited. This study utilizes data from the Centers for Disease Control and Prevention Wide-ranging Online Data for Epidemiologic Research (CDC WONDER) database to provide a detailed analysis of osteomyelitis-related mortality trends in the United States from 1999 to 2023. By elucidating these temporal patterns across demographic and geographic groups, we aim to inform future public health strategies and clinical interventions to reduce the burden of osteomyelitis and address persistent health disparities. Methods Study Design and Database CDC WONDER was used to identify osteomyelitis-related deaths occurring within the United States [ 5 ]. Death certificate data from the Multiple Cause-of-Death Public Use Record were analyzed to determine osteomyelitis-related cause of death as a contributing cause on nationwide death certificate records. This database has previously been used in several other studies to analyze nationwide trends in mortality of emphysema [ 6 ]. Osteomyelitis-related mortality was identified using the International Classification of Diseases, 10th Revision, Clinical Modification codes M86 in patients ≥ 35 years. Patients under 35 years of age were excluded due to the low incidence of osteomyelitis in this population. This study was exempt from institutional review board approval because of the CDC WONDER database containing anonymized, publicly available data. Demographic and geographical study groups We extracted osteomyelitis-related mortality and population data from 1999 to 2023. Data on demographic and regional groups were extracted, including gender, race/ethnicity, age, urban-rural classification, region, and states. Racial/ethnicity groups were defined as non-Hispanic (NH) White, NH Black, NH American Indian/Alaskan Native, NH Asian/Pacific Islander, and Hispanic people as identified on death certificates. Age groups were defined as 25–39, 40–54, 55–69, 70–84, 85 + years of age. Urban-rural classification followed the 2013 National Center for Health Statistics Urban-Rural Classification Scheme to divide the population into urban (large metropolitan area [population ≥ 1 million], medium/small metropolitan area [population 50,000 to 999,999]) and rural (population < 50,000) counties per the 2013 United States census classification.(citation) Regions were classified into Northeast, Midwest, South, and West according to the Census Bureau definitions [ 5 ]. Statistical analysis Osteomyelitis-related crude and age-adjusted mortality rates (AAMR) per 100,000 US adults above the age of 35 were calculated. Crude mortality rates were calculated by dividing the number of osteomyelitis-related deaths by the corresponding United States population. AAMR were standardized using the 2000 United States standard population as previously described. Temporal trends from 1999 to 2023 were assessed using Joinpoint Regression Program (version 5.4.0National Cancer Institute) [ 7 ]. This program identifies significant changes in mortality trends by fitting linear segments to data where significant variation occurred. Annual percentage change (APC) with 95% confidence intervals (CIs) for the AAMRs were calculated for each segment using the Monte Carlo permutation test. The average annual percent change (AAPC) was computed as a weighted average of APCs over the full study period. APC and AAPC values were considered statistically significant if their 95% CIs did not include zero using a 2-tailed t-test, and a p-value ≤ 0.05 was used for significance testing. Results Osteomyelitis mortality overall Table 1 Osteomyelitis-Related mortality AAPC values of investigated demographic factors. Demographic Factor Cohort Lower Endpoint Upper Endpoint AAPC (95% CI) P-Value Overall Full Range 1999 2023 3.6114* (2.6006, 4.632) < 0.000001 Female 1999 2023 2.6845* (1.2444, 4.1451) 0.000237 Male 1999 2023 4.1932* (3.2228, 5.1726) < 0.000001 Region Northeast 1999 2023 3.0286* (1.7372, 4.3365) 0.000004 Midwest 1999 2023 3.6649* (2.0683, 5.2865) 0.000005 South 1999 2023 3.8198* (2.7532, 4.8975) < 0.000001 Race Asian or Pacific Islander 1999 2023 1.4127 (-0.307, 3.1621) 0.107922 Black or African American 1999 2023 2.9537* (2.1407, 3.7732) < 0.000001 White 1999 2023 4.0420* (2.8797, 5.2175) < 0.000001 Urban/Rural Urban 1999 2020 2.8802* (1.8659, 3.9045) < 0.000001 Rural 1999 2020 5.1110* (3.4264, 6.8229) < 0.000001 Age Group 35–44 years 1999 2023 5.9408* (3.8902, 8.0318) < 0.000001 45–54 years 1999 2023 5.3222* (4.4385, 6.2134) < 0.000001 55–64 years 1999 2023 5.3882* (4.5425, 6.2407) < 0.000001 65–74 years 1999 2023 4.8154* (3.0677, 6.5929) < 0.000001 75–84 years 1999 2023 3.4124* (2.7768, 4.0519) < 0.000001 85 + years 1999 2023 1.8740* (1.1206, 2.6331) 0.000001 Between 1999 and 2023 there were 111,115 deaths in U.S. adults aged 35 years or older diagnosed with osteomyelitis (Fig. 1 ). Overall age-adjusted mortality rates (AAMR) increased during this period from 1.7* (95% CI 1.6 to 1.8) in 1999 to 4.1* (95% CI 4.0 to 4.2) in 2023, with an average annual percentage change (AAPC) of 3.6* (95% CI 2.6 to 4.6) (Table 1 ). The annual percent change (APC) in AAMR was 3.2* from 1999 to 2004 (95% CI 1.0 to 5.4), which decreased to -2.7* (95% CI -5.3 to -0.1) from 2004 to 2009, increased to 5.0* (95% CI 4.2 to 5.8) from 2009 to 2018, increased again to 13.2* (95% CI 7.1 to 19.7) from 2018 to 2021, and decreased to 1.2 (95% CI -3.2 to 5.8) from 2021 to 2023. Osteomyelitis mortality by gender Between 1999 and 2023 there were 62,519 deaths in males aged 35 years or older diagnosed with osteomyelitis in the United States (Fig. 1 ). Overall AAMR increased during this period from 2.1* (95% CI 2.0 to 2.2) in 1999 to 5.7* (95% CI 5.6 to 5.9) in 2023, with an AAPC of 4.2* (95% CI 3.2 to 5.2) (Table 1 ). The APC in AAMR was 3.1* from 1999 to 2004 (95% CI 0.9 to 5.4), which decreased to -0.2 (95% CI -1.5 to 1.3) from 2004 to 2011, increased to 6.4* (95% CI 5.2 to 7.7) from 2011 to 2018, increased again to 12.7* (95% CI 6.8 to 18.9) from 2018 to 2021, and decreased to 2.5 (95% CI -2.3 to 7.6) from 2021 to 2023. Between 1999 and 2023 there were 48,596 deaths in females aged 35 years or older diagnosed with osteomyelitis in the United States (Fig. 1 ). Overall AAMR increased during this period from 1.5* (95% CI 1.4 to 1.6) in 1999 to 2.9* (95% CI 2.8 to 3.0) in 2023, with an AAPC of 2.7* (95% CI 1.2 to 4.1) (Table 1 ). The APC in AAMR was 2.5 (95% CI 0.0 to 5.1) from 1999 to 2004, which decreased to -4.0* (95% CI -7.4 to -0.5) from 2004 to 2009, increased to 3.9* (95% CI 2.7 to 5.1) from 2009 to 2018, increased again to 14.0* (95% CI 4.8 to 24.0) from 2018 to 2021, and decreased to -1.0 (95% CI -8.2 to 6.8) from 2021 to 2023. Osteomyelitis mortality by race American Indian or Alaska Native (AI/AN) individuals had the highest AAMR in 2007 with a value of 4.0* (95% CI 2.7 to 5.9), and the highest AAMR in 2023 with a value of 8.0* (95% CI 6.4 to 9.6) (Fig. 2 ). The APC was − 10.2 from 2007 to 2010 and subsequently increased to 9.4* from 2010 to 2023. Black or African American individuals had the highest AAMR in 1999 with a value of 3.5* (95% CI 3.1 to 3.8) and the second-highest AAMR in 2023 with a value of 7.1* (95% CI 6.7 to 7.4) (Fig. 2 ). The APC was − 0.2 from 1999 to 2014 and subsequently increased to 8.5* from 2010 to 2023. White individuals had the second-lowest AAMR for every year studied, with 1.6* (95% CI 1.5 to 1.6) in 1999 and 4.1* (95% CI 4.0 to 4.2) in 2023 (Fig. 2 ). The APC was 4.7* from 1999 to 2004, decreased to -1.3 from 2003 to 2010, increased to 5.7* from 2010 to 2018, increased to 13.4*, and tapered off to 1.8 from 2021 to 2023. Asian or Pacific Islander individuals had the lowest AAMR for every year studied (and experienced the smallest increase in AAMR), with 1.3* (95% CI 0.9 to 1.8) in 1999 and 1.3* (95% CI 1.1 to 1.5) in 2023 and AAPC of 1.4 (Fig. 2 , Table 1 ). The APC was − 2.4 from 1999 to 2011 and 5.4* from 2011 to 2023. Osteomyelitis mortality by age (≥ 35): The 85 + year category had the highest crude mortality rate for every year studied, with a value of 19.4 in 1999 and 33.6 in 2023 (Fig. 3 ). They had an AAPC of 1.9* (Table 1 ). Their APC was − 1.7* from 1999 to 2012 and increased to 6.2* from 2012 to 2023. The 75–84 year age category had a crude mortality rate of 6.5 in 1999, 14.4 in 2023, and an AAPC of 3.4* (Fig. 3 , Table 1 ). Their APC was 0.8* from 1999 to 2014 and increased to 7.8* from 2014 to 2023. The 65–74 year age category had a crude mortality rate of 2.2 in 1999, 6.7 in 2023, and an AAPC of 4.8* (Fig. 3 , Table 1 ). Their APC was 16.8 from 1999 to 2001, decreased to -1.2 from 2001 to 2010, increased to 7.0*, increased again to 14.1* from 2018 to 2021, and decreased to -0.5 from 2021 to 2023. The 55–64 year age category had a crude mortality rate of 0.9 in 1999, 3.0 in 2023, and an AAPC of 5.4* (Fig. 3 , Table 1 ). Their APC was 3.0* from 1999 to 2012 and increased to 8.3* from 2012 to 2023. The 45–54 year age category had a crude mortality rate of 0.4 in 1999, 1.3 in 2023, and an AAPC of 2.0* (Fig. 3 , Table 1 ). Their APC was 2.1* from 1999 to 2012 and increased to 8.3* from 2012 to 2023. The 35–44 year age category had the lowest crude mortality rate for every year studied, with a value of 0.1 in 1999, 0.4 in 2023, and an AAPC of 3.4* (Fig. 3 , Table 1 ). Their APC was 3.5* from 1999 to 2013 and increased to 9.5* from 2013 to 2023. Osteomyelitis mortality by urban vs rural Urban regions initially had an AAMR of 1.8 in 1999, which was higher than rural regions (Fig. 4 ). However, urban regions experienced a smaller overall increase and had a smaller AAMR in 2020 with a value of 3.3. The APC in urban regions was 2.9* from 1999 to 2004, decreased to -3.2 from 2004 to 2008, increased to 2.0 from 2008 to 2012, increased to 5.4* from 2012 to 2018, and increased again to 10.2* from 2018 to 2020. The AAPC in urban regions was 2.9* (Table 1 ). In contrast, rural regions had a smaller AAMR in 1999 with a value of 1.3, but increased to a larger AAMR in 2020 with a value of 4.3 (Fig. 4 ). The APC was 8.1* from 1999 to 2003, decreased to -1.4 from 2003 to 2009, and increased to 7.8* from 2009 to 2020. The AAPC in rural regions was 5.1* (Table 1 ). Discussion Overall Mortality Previous research on osteomyelitis mortality trends in the United States has been limited, with most studies focusing on specific populations or subtypes rather than national surveillance data [ 1 , 2 ]. The observed increase in osteomyelitis mortality rates can be attributed to several converging factors. First, the aging population in the United States has created a larger cohort of individuals with predisposing conditions for osteomyelitis. Older adults are particularly vulnerable to osteomyelitis due to increased incidence of associated disorders such as peripheral vascular disease, diabetes mellitus, malnutrition, and compromised immune function [ 8 , 9 , 10 , 11 ]. Second, the diabetes epidemic has played a crucial role in the rising osteomyelitis mortality rates. Diabetes-related osteomyelitis has shown particularly concerning trends, with age-adjusted mortality rates increasing from 2.63 per 1,000,000 person-years in 1999 to 4.25 per 1,000,000 person-years in 2017 [ 10 ]. Patients with diabetes may have a 15–25% lifetime risk of developing a foot ulcer, with 20% of infections progressing to osteomyelitis. The mortality associated with diabetic foot ulcers, which frequently develop osteomyelitis, is substantial, with 5-year mortality rates ranging from 50–70% [ 12 ]. Third, the emergence of antibiotic-resistant organisms has significantly complicated treatment outcomes. Studies have shown that chronic osteomyelitis is increasingly caused by multidrug-resistant organisms, with some antibiotics promoting the formation of small colony variants that enhance bacterial persistence and treatment resistance [ 13 ]. The proportion of methicillin-resistant Staphylococcus aureus (MRSA) infections has increased substantially, with some populations experiencing rates over 75% [ 14 ]. Fourth, the COVID-19 pandemic likely contributed to the recent mortality increase. The pandemic disrupted routine healthcare services, delayed diagnoses, and may have worsened outcomes for patients with chronic conditions like osteomyelitis [ 15 ]. Healthcare system strain during this period may have impacted the quality and timeliness of care for complex infections. Mortality by Gender This study’s finding that males have consistently higher osteomyelitis mortality rates (AAPC 4.2% vs 2.7% in females) aligns with established patterns in infectious disease mortality (Fig. 1 ). Males accounted for 62,519 of the total 111,115 deaths (56.3%) despite similar population sizes. This gender disparity is consistent with broader infectious disease patterns, where males typically experience higher mortality rates [ 16 ]. The higher male mortality may be explained by several factors. Males have higher rates of certain risk factors for osteomyelitis, including higher prevalence of diabetes, smoking, and occupational exposures that predispose to bone infections. Additionally, males may delay seeking medical care, leading to more advanced disease at presentation. A study of spinal infections found that although males had higher incidence rates, females presented with more severe disease characteristics, suggesting different care-seeking behaviors between genders [ 16 ]. Mortality by Race American Indian/Alaska Native (AI/AN) individuals had the highest osteomyelitis mortality rates by 2023 (8.0 per 100,000), representing a concerning health disparity (Fig. 2 ). Prior to 2010, AI/AN populations had lower rates than Black populations, but experienced the largest increases over the study period (APC of 9.4% from 2010–2023). This pattern reflects broader health disparities experienced by Native American populations, who have significantly higher rates of diabetes, invasive bacterial infections, and other conditions predisposing to osteomyelitis [ 17 ]. Additionally, healthcare access barriers, including geographic isolation and underfunding of Indian Health Service facilities, may contribute to delayed diagnosis and treatment [ 17 ]. Black individuals consistently maintained high osteomyelitis mortality rates throughout the study period, with rates of 7.1 per 100,000 in 2023 (Fig. 2 ). These disparities reflect well-documented racial inequities in healthcare access and outcomes including higher rates of diabetes, limited healthcare access, and structural racism affecting quality of care. Rural Black patients face particularly severe disparities, with significantly reduced access to specialty care for diabetes-related complications that can progress to osteomyelitis [ 18 ]. Asian/Pacific Islander individuals had the lowest osteomyelitis mortality rates throughout the study period and experienced the smallest increases (Fig. 2 ). This pattern likely reflects multiple protective factors, including lower rates of diabetes, different genetic susceptibilities, and potentially better healthcare access in some Asian communities. However, recent research has highlighted that Pacific Islander populations, when disaggregated from Asian populations, actually experience significantly higher mortality rates across multiple conditions [ 19 ]. This suggests that combining these diverse populations may mask important health disparities within Pacific Islander communities. Mortality by Age The stepwise increase in osteomyelitis mortality rates with advancing age reflects well-established patterns in infectious disease outcomes (Fig. 3 ). The finding that patients aged 85 + years had the highest crude mortality rates (33.6 per 100,000 in 2023) is consistent with age-related declines in immune function and increasing comorbidity burden [ 3 ]. The elderly population (85 + years) experienced substantial increases in mortality rates, particularly after 2012 (APC of 6.2% from 2012–2023) (Fig. 3 ). This trend reflects several concerning factors. First, the growing elderly population with multiple comorbidities creates a larger pool of individuals at high risk for osteomyelitis. Second, elderly patients with osteomyelitis have significantly higher rates of complications and treatment failures [ 8 ]. The accelerated mortality increases in recent years among elderly patients may also reflect the impact of prosthetic joint infections, which are increasingly common in this age group. Studies have shown that prosthetic joint infections carry mortality rates of 5.5% at one year and 7.3% at two years, with higher rates in elderly patients [ 20 ]. The growing number of joint replacement surgeries in elderly patients creates an expanding population at risk for these serious infections. Mortality by Urban vs Rural Rural populations experienced larger increases in osteomyelitis-related mortality and ultimately surpassed urban population rates, a finding consistent with research on other diseases (Fig. 4 ). Studies of diabetes-related complications, which frequently lead to osteomyelitis, have shown that rural populations experience higher rates of complications and poorer outcomes [ 21 ]. Rural patients also face significant barriers including limited access to specialty care, longer travel distances to healthcare facilities, and reduced availability of multidisciplinary care teams necessary for complex infections [ 22 ]. Strengths & Limitations This study has several strengths. It utilizes a large, nationwide dataset from the CDC WONDER Multiple Cause-of-Death database, providing substantial statistical power and a comprehensive overview of osteomyelitis-related mortality in the United States over a 24-year period. The breadth of the dataset allows for in-depth temporal trend analysis and facilitates examination of disparities across numerous subgroups, and the use of Joinpoint regression further strengthens this analysis by identifying statistically significant inflection points in mortality trends. However, there are notable limitations. As a retrospective, population-level analysis, the study relies on mortality data derived from death certificates, which may contain inaccuracies due to misclassification or reporting errors. The use of aggregate data precludes access to individual-level clinical variables, including comorbidities, treatment history, infection severity, or socioeconomic status, which limits the ability to control for important confounding factors. Furthermore, the study design does not allow for causal inference. The findings are not generalizable to younger individuals, as the analysis focused on patients aged 35 and older, in whom osteomyelitis is more commonly observed. Finally, while the study highlights significant trends and disparities, future research should incorporate prospective cohort studies and clinical datasets to evaluate the influence of healthcare access, intervention strategies, and disease management on osteomyelitis outcomes. Conclusion This national temporal analysis reveals a marked rise in osteomyelitis-related mortality among U.S. adults aged ≥35 from 1999 to 2023, with the age-adjusted mortality rate more than doubling–particularly accelerating between 2018 and 2021, coinciding with the COVID-19 pandemic. The data highlight widespread increases across racial, gender, age, and geographic groups, with American Indian/Alaska Native and Black populations, males, older adults, and rural residents disproportionately affected, which is likely due to structural health inequities, comorbidity burdens, and healthcare access challenges. Contributing factors include an aging population, rising diabetes rates, antimicrobial resistance, and pandemic-related care disruptions. These findings underscore the urgent need for equity-focused, multidisciplinary public health strategies, enhanced infection surveillance, and improved access to preventative and specialty care in underserved communities. Declarations Data Availability Statement Publicly available datasets were utilized for the present study. This data can be accessed online: https://wonder.cdc.gov/mcd.html. Acknowledgements We would like to thank Creighton University School of Medicine, Hospital Research Interest Group, Department of Medicine, and Department of Infectious Diseases for their assistance in developing and finalizing this project. Funding The author(s) declare that no financial support was received for the research and/or publication of this article. Author Affiliations Creighton University School of Medicine, Omaha, NE, United States, Omaha, NE, United States John Paul Braun, Cinthiya Chander, and Vikram Murugan Creighton University School of Medicine, Department of Infectious Disease, Omaha, NE, United States Erica J. Stohs Creighton University School of Medicine, Department of Internal Medicine, Omaha, NE, United States Abubakar Tauseef Contributions All authors have approved the final version of the manuscript and agree with the order of authorship. The corresponding authors, J.P.B. and C.C., confirm that all data, figures, and materials comply with field and journal standards for transparency and reproducibility. Original data and figures are preserved in accordance with best practices to ensure retrievability for reanalysis. J.P.B. and C.C. wrote the majority of the manuscript. J.P.B. developed the Methods and Results sections and created all figures and tables, while C.C. wrote the Introduction, Discussion, and Conclusion sections and coordinated the manuscript revision process. V.M. conducted the statistical analysis, including Joinpoint regression modeling and verification of age-adjusted mortality calculations. E.S. peer-reviewed the manuscript for clinical accuracy and provided editorial feedback, and A.T. peer-reviewed the final version, supervised data interpretation, and served as the faculty mentor throughout the project. No professional medical writers or editorial services were involved in the preparation of this manuscript. All authors have reviewed and approved the submission, are aware of their responsibilities, and confirm the accuracy of the author contributions listed above. Ethics Declarations Ethics approval and consent to participate Not applicable Consent for publication Not applicable Competing Interests Not applicable References Lew DP, Waldvogel FA. Osteomyelitis. The Lancet . 2004;364(9431):369-379. doi:10.1016/s0140-6736(04)16727-5 Kremers HM, Nwojo ME, Ransom JE, Wood-Wentz CM, Melton LJ, Huddleston PM. Trends in the epidemiology of osteomyelitis. Journal of Bone and Joint Surgery . 2015;97(10):837-845. doi:10.2106/jbjs.n.01350 Huang CC, Tsai KT, Weng SF, et al. Chronic osteomyelitis increases long-term mortality risk in the elderly: a nationwide population-based cohort study. BMC Geriatrics . 2016;16(1). doi:10.1186/s12877-016-0248-8 Tong SYC, Davis JS, Eichenberger E, Holland TL, Fowler VG. Staphylococcus aureus Infections: Epidemiology, Pathophysiology, Clinical Manifestations, and Management. Clinical Microbiology Reviews . 2015;28(3):603-661. doi:10.1128/cmr.00134-14 Centers for Disease Control and Prevention, National Center for Health Statistics. National Vital Statistics System, Mortality 1999-2020 on CDC WONDER Online Database, released in 2021. Data are from the Multiple Cause of Death Files, 1999-2020, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/mcd-icd10.html on Feb 6, 2024 4:56:31 AM Brown A, Karl A, Murugan V, Billion T, Jabbar ABA, Mirza M. Emphysema-related mortality rates in the U.S. from 1999 to 2022. Frontiers in Medicine . 2025;12. doi:10.3389/fmed.2025.1579177 Kim HJ, Fay MP, Feuer EJ, Midthune DN. Permutation tests for joinpoint regression with applications to cancer rates. Stat Med 2000;19:335-51 (correction: 2001;20:655). Romanò CL, Logoluso N, Elia A, Romanò D. Osteomyelitis in elderly patients. BMC Geriatrics . 2010;10(S1). doi:10.1186/1471-2318-10-s1-l15 Hill SL, Holtzman GI, Buse R. The effects of peripheral vascular disease with osteomyelitis in the diabetic foot. The American Journal of Surgery . 1999;177(4):282-286. doi:10.1016/s0002-9610(99)00050-1 Huang JF, Wu QN, Zheng XQ, et al. The characteristics and mortality of osteoporosis, osteomyelitis, or rheumatoid arthritis in the diabetes population: a retrospective study. International Journal of Endocrinology . 2020;2020:1-13. doi:10.1155/2020/8821978 Siddique N, O’Donoghue M, Casey MC, Walsh JB. Malnutrition in the elderly and its effects on bone health – A review. Clinical Nutrition ESPEN . 2017;21:31-39. doi:10.1016/j.clnesp.2017.06.001 McDermott K, Fang M, Boulton AJM, Selvin E, Hicks CW. Etiology, epidemiology, and disparities in the burden of diabetic foot ulcers. Diabetes Care . 2022;46(1):209-221. doi:10.2337/dci22-0043 Tuchscherr L, Kreis CA, Hoerr V, et al. Staphylococcus aureusdevelops increased resistance to antibiotics by forming dynamic small colony variants during chronic osteomyelitis. Journal of Antimicrobial Chemotherapy . 2015;71(2):438-448. doi:10.1093/jac/dkv371 Sutcliffe CG, Grant LR, Reid A, et al. High Burden of Staphylococcus aureus Among Native American Individuals on the White Mountain Apache Tribal Lands. Open Forum Infectious Diseases . 2020;7(3). doi:10.1093/ofid/ofaa061 Dehghani Tafti A, Fatehpanah A, Salmani I, Bahrami MA, Tavangar H, Fallahzadeh H, Tehrani AA, Bahariniya S, Tehrani GA. COVID-19 pandemic has disrupted the continuity of care for chronic patients: evidence from a cross-sectional retrospective study in a developing country. BMC Prim Care. 2023 Jul 1;24(1):137. doi: 10.1186/s12875-023-02086-6. Lener S, Wipplinger C, Hartmann S, Rietzler A, Thomé C, Tschugg A. Gender-Specific differences in presentation and management of spinal infection: a Single-Center retrospective study of 159 cases. Global Spine Journal . 2020;11(4):430-436. doi:10.1177/2192568220905804 Disparities | Fact sheets. Newsroom. October 2019. https://www.ihs.gov/newsroom/factsheets/disparities/ Taylor L, Gangnon R, Powell WR, et al. Association of rurality and identifying as black with receipt of specialty care among patients hospitalized with a diabetic foot ulcer: a Medicare cohort study. BMJ Open Diabetes Research & Care . 2023;11(2):e003185. doi:10.1136/bmjdrc-2022-003185 Costello M. Pacific Islander Adults Experience Significantly Higher Overall and Leading-Cause Death Rates than A. National Cancer Institute . Published January 8, 2025. https://dceg.cancer.gov/news-events/news/2025/pacific-islander-mortality-disparities Fischbacher A, Borens O. Prosthetic-joint infections: mortality over the last 10 years. Journal of Bone and Joint Infection . 2019;4(4):198-202. doi:10.7150/jbji.35428 Steiger K, Herrin J, Swarna KS, Davis EM, McCoy RG. Disparities in acute and chronic complications of diabetes along the U.S. Rural-Urban continuum. Diabetes Care . 2024;47(5):818-825. doi:10.2337/dc23-1552 Campbell JI, Shanahan KH, Bartick M, et al. Racial and ethnic differences in length of stay for US children hospitalized for acute osteomyelitis. The Journal of Pediatrics . 2023;259:113424. doi:10.1016/j.jpeds.2023.113424 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 25 Nov, 2025 Reviews received at journal 24 Nov, 2025 Reviewers agreed at journal 21 Nov, 2025 Reviews received at journal 19 Nov, 2025 Reviews received at journal 15 Nov, 2025 Reviewers agreed at journal 27 Oct, 2025 Reviewers agreed at journal 22 Oct, 2025 Reviewers invited by journal 22 Oct, 2025 Editor assigned by journal 22 Oct, 2025 Editor invited by journal 20 Oct, 2025 Submission checks completed at journal 19 Oct, 2025 First submitted to journal 19 Oct, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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-7844039","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":538566605,"identity":"31d65097-96c6-42ae-bc16-dcd7fa9f28af","order_by":0,"name":"John Paul Braun","email":"","orcid":"","institution":"Creighton University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"John","middleName":"Paul","lastName":"Braun","suffix":""},{"id":538566606,"identity":"3745e58a-0d33-4468-8814-b7620018458e","order_by":1,"name":"Cinthiya Chander","email":"data:image/png;base64,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","orcid":"","institution":"Creighton University School of Medicine","correspondingAuthor":true,"prefix":"","firstName":"Cinthiya","middleName":"","lastName":"Chander","suffix":""},{"id":538566607,"identity":"15c50a94-4c57-4d01-a304-43eadd730fe1","order_by":2,"name":"Vikram Murugan","email":"","orcid":"","institution":"Creighton University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Vikram","middleName":"","lastName":"Murugan","suffix":""},{"id":538566608,"identity":"6eef8186-021f-4225-8645-a9ef99b63dac","order_by":3,"name":"Erica J. Stohs","email":"","orcid":"","institution":"Creighton University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Erica","middleName":"J.","lastName":"Stohs","suffix":""},{"id":538566609,"identity":"83d63313-813a-40b6-b26a-13539e25f6ea","order_by":4,"name":"Abubakar Tauseef","email":"","orcid":"","institution":"Creighton University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Abubakar","middleName":"","lastName":"Tauseef","suffix":""}],"badges":[],"createdAt":"2025-10-13 03:23:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7844039/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7844039/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":95002592,"identity":"1ac331a9-66a0-4161-8e48-0d51c886fb4b","added_by":"auto","created_at":"2025-11-03 09:06:09","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1067385,"visible":true,"origin":"","legend":"","description":"","filename":"OsteomyelitisWONDERProjectBraunChander1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7844039/v1/44773991bbabdaba30340e71.docx"},{"id":95002589,"identity":"d93be102-dfdd-4dbb-836d-fff0e6dae149","added_by":"auto","created_at":"2025-11-03 09:06:09","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":8177,"visible":true,"origin":"","legend":"","description":"","filename":"efaf67aca22b4b3896f47d25f62e3809.json","url":"https://assets-eu.researchsquare.com/files/rs-7844039/v1/b143ee5fe91798c5563e10ef.json"},{"id":95221151,"identity":"127e59e6-b2f6-464e-80d4-b8dbb51c39bf","added_by":"auto","created_at":"2025-11-05 16:18:28","extension":"xml","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":82054,"visible":true,"origin":"","legend":"","description":"","filename":"efaf67aca22b4b3896f47d25f62e38091enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7844039/v1/cbf330313547e2b3c3804121.xml"},{"id":95002591,"identity":"46163beb-fe0d-4a87-84c1-c103fc47fe3d","added_by":"auto","created_at":"2025-11-03 09:06:09","extension":"png","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":282276,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7844039/v1/67519b46ca60addc1acfaa45.png"},{"id":95002595,"identity":"3cc791e0-db0e-4af1-9144-c5678063f18c","added_by":"auto","created_at":"2025-11-03 09:06:09","extension":"png","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":287067,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7844039/v1/34e09a4ddbf195d259b8f683.png"},{"id":95220826,"identity":"eeeebef4-7c8b-4b1d-9b32-c7d118f6d647","added_by":"auto","created_at":"2025-11-05 16:15:04","extension":"png","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":306669,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7844039/v1/3c9e52ec46095e5dcd904a53.png"},{"id":95002597,"identity":"691a1416-7612-45e9-a21a-400d9f1bb61f","added_by":"auto","created_at":"2025-11-03 09:06:09","extension":"png","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":217789,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7844039/v1/8e7f8b8cc4c4a6a8a52eb451.png"},{"id":95002600,"identity":"6811c5c9-c06f-49a9-b413-4465da416476","added_by":"auto","created_at":"2025-11-03 09:06:09","extension":"png","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":59671,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7844039/v1/6a079ea22d068bd2c8451fa0.png"},{"id":95002599,"identity":"ca3a90f9-9788-41ef-b443-695dce0d90c8","added_by":"auto","created_at":"2025-11-03 09:06:09","extension":"png","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":52210,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7844039/v1/decb44f20f30373c362cc882.png"},{"id":95221030,"identity":"1a5764e4-18c5-4249-bc27-4e60a5be6ec9","added_by":"auto","created_at":"2025-11-05 16:17:58","extension":"png","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":56579,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7844039/v1/8315fef2e1c1f939c1c00bb4.png"},{"id":95220915,"identity":"71679196-24b0-42a3-b275-54b175bd7e6f","added_by":"auto","created_at":"2025-11-05 16:17:03","extension":"png","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":42748,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7844039/v1/858ee2f032194e1c86255833.png"},{"id":95002601,"identity":"d7398068-9250-4f2a-8747-b71ddbfbe62a","added_by":"auto","created_at":"2025-11-03 09:06:09","extension":"xml","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":79293,"visible":true,"origin":"","legend":"","description":"","filename":"efaf67aca22b4b3896f47d25f62e38091structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7844039/v1/237f82679b1ab65b8c622520.xml"},{"id":95002602,"identity":"b963fcd8-1349-4b2c-b7f3-438c6bb343ca","added_by":"auto","created_at":"2025-11-03 09:06:09","extension":"html","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":87653,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7844039/v1/0c2c5c28695c2f201772500a.html"},{"id":95002588,"identity":"789e5cbb-10a2-47f2-ab85-e062ee3eb28e","added_by":"auto","created_at":"2025-11-03 09:06:08","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":81314,"visible":true,"origin":"","legend":"\u003cp\u003eJoinpoint Model of Osteomyelitis-Related AAMR per 100,000 U.S Adults 35+ by Sex, 1999-2023 (*APC Significant).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7844039/v1/4e916bf3b5a5a9c3a78b2b75.png"},{"id":95002586,"identity":"8cb08237-d64c-417d-9762-aa9dbef94e42","added_by":"auto","created_at":"2025-11-03 09:06:08","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":106363,"visible":true,"origin":"","legend":"\u003cp\u003eJoinpoint Model of Osteomyelitis-Related AAMR per 100,000 U.S Adults 35+ by Race, 1999-2023 (*APC Significant).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7844039/v1/bafeccd51f81195e3b8bcf22.png"},{"id":95221481,"identity":"606697da-cf68-419b-8f49-428eded98cd3","added_by":"auto","created_at":"2025-11-05 16:19:07","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":92189,"visible":true,"origin":"","legend":"\u003cp\u003eJoinpoint Model of Osteomyelitis-Related Crude Mortality Rate per 100,000 U.S Adults 35+ by Age, 1999-2023 (*APC Significant).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7844039/v1/66a8eca3bd5292e2ef59bce3.png"},{"id":95220831,"identity":"f242921f-4146-48e2-9c2f-4d2945094d55","added_by":"auto","created_at":"2025-11-05 16:15:13","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":55928,"visible":true,"origin":"","legend":"\u003cp\u003eJoinpoint Model of Osteomyelitis-Related AAMR per 100,000 U.S Adults 35+ by Urban-Rural Regions, 1999-2023 (*APC Significant).\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7844039/v1/30107367d4f9049f3d8ab173.png"},{"id":95312026,"identity":"db60d5db-6f7f-4f36-be80-a5873f8d706e","added_by":"auto","created_at":"2025-11-06 15:44:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1062790,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7844039/v1/587826be-bbcc-4109-b708-612db9ffd1cb.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Addressing Disparities in Osteomyelitis: The WONDER Project","fulltext":[{"header":"Introduction","content":"\u003cp\u003eOsteomyelitis, an infection of bone and bone marrow, remains a significant clinical challenge with substantial morbidity and mortality, particularly among older adults and those with chronic comorbidities such as diabetes mellitus and peripheral vascular disease [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Despite advances in antimicrobial therapy and surgical management, osteomyelitis continues to contribute to substantial incidence and mortality, especially in the context of an aging population, rising prevalence of diabetes, and increasing rates of antimicrobial resistance [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eRecent epidemiological studies suggest that osteomyelitis disproportionately affects certain demographic groups, with notable demographic disparities seen in age, sex, race, and geography. However, comprehensive national-level data on long-term trends in osteomyelitis mortality in the United States remain limited.\u003c/p\u003e\u003cp\u003eThis study utilizes data from the Centers for Disease Control and Prevention Wide-ranging Online Data for Epidemiologic Research (CDC WONDER) database to provide a detailed analysis of osteomyelitis-related mortality trends in the United States from 1999 to 2023. By elucidating these temporal patterns across demographic and geographic groups, we aim to inform future public health strategies and clinical interventions to reduce the burden of osteomyelitis and address persistent health disparities.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy Design and Database\u003c/h2\u003e\u003cp\u003eCDC WONDER was used to identify osteomyelitis-related deaths occurring within the United States [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Death certificate data from the Multiple Cause-of-Death Public Use Record were analyzed to determine osteomyelitis-related cause of death as a contributing cause on nationwide death certificate records. This database has previously been used in several other studies to analyze nationwide trends in mortality of emphysema [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Osteomyelitis-related mortality was identified using the International Classification of Diseases, 10th Revision, Clinical Modification codes M86 in patients\u0026thinsp;\u0026ge;\u0026thinsp;35 years. Patients under 35 years of age were excluded due to the low incidence of osteomyelitis in this population. This study was exempt from institutional review board approval because of the CDC WONDER database containing anonymized, publicly available data.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eDemographic and geographical study groups\u003c/h3\u003e\n\u003cp\u003eWe extracted osteomyelitis-related mortality and population data from 1999 to 2023. Data on demographic and regional groups were extracted, including gender, race/ethnicity, age, urban-rural classification, region, and states. Racial/ethnicity groups were defined as non-Hispanic (NH) White, NH Black, NH American Indian/Alaskan Native, NH Asian/Pacific Islander, and Hispanic people as identified on death certificates. Age groups were defined as 25\u0026ndash;39, 40\u0026ndash;54, 55\u0026ndash;69, 70\u0026ndash;84, 85\u0026thinsp;+\u0026thinsp;years of age. Urban-rural classification followed the 2013 National Center for Health Statistics Urban-Rural Classification Scheme to divide the population into urban (large metropolitan area [population\u0026thinsp;\u0026ge;\u0026thinsp;1 million], medium/small metropolitan area [population 50,000 to 999,999]) and rural (population\u0026thinsp;\u0026lt;\u0026thinsp;50,000) counties per the 2013 United States census classification.(citation) Regions were classified into Northeast, Midwest, South, and West according to the Census Bureau definitions [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eOsteomyelitis-related crude and age-adjusted mortality rates (AAMR) per 100,000 US adults above the age of 35 were calculated. Crude mortality rates were calculated by dividing the number of osteomyelitis-related deaths by the corresponding United States population. AAMR were standardized using the 2000 United States standard population as previously described. Temporal trends from 1999 to 2023 were assessed using Joinpoint Regression Program (version 5.4.0National Cancer Institute) [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. This program identifies significant changes in mortality trends by fitting linear segments to data where significant variation occurred. Annual percentage change (APC) with 95% confidence intervals (CIs) for the AAMRs were calculated for each segment using the Monte Carlo permutation test. The average annual percent change (AAPC) was computed as a weighted average of APCs over the full study period. APC and AAPC values were considered statistically significant if their 95% CIs did not include zero using a 2-tailed t-test, and a p-value\u0026thinsp;\u0026le;\u0026thinsp;0.05 was used for significance testing.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003eOsteomyelitis mortality overall\u003c/h2\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\u003eOsteomyelitis-Related mortality AAPC values of investigated demographic factors.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDemographic Factor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCohort\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLower Endpoint\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eUpper Endpoint\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAAPC (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP-Value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eOverall\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFull Range\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1999\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.6114* (2.6006, 4.632)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.000001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1999\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.6845* (1.2444, 4.1451)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.000237\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1999\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4.1932* (3.2228, 5.1726)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.000001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eRegion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNortheast\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1999\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.0286* (1.7372, 4.3365)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.000004\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMidwest\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1999\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.6649* (2.0683, 5.2865)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.000005\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSouth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1999\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.8198* (2.7532, 4.8975)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.000001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eRace\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAsian or Pacific Islander\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1999\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.4127 (-0.307, 3.1621)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.107922\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBlack or African American\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1999\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.9537* (2.1407, 3.7732)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.000001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWhite\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1999\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4.0420* (2.8797, 5.2175)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.000001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eUrban/Rural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1999\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.8802* (1.8659, 3.9045)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.000001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1999\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5.1110* (3.4264, 6.8229)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.000001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003eAge Group\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e35\u0026ndash;44 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1999\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5.9408* (3.8902, 8.0318)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.000001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e45\u0026ndash;54 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1999\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5.3222* (4.4385, 6.2134)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.000001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e55\u0026ndash;64 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1999\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5.3882* (4.5425, 6.2407)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.000001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e65\u0026ndash;74 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1999\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4.8154* (3.0677, 6.5929)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.000001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e75\u0026ndash;84 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1999\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.4124* (2.7768, 4.0519)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.000001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e85\u0026thinsp;+\u0026thinsp;years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1999\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.8740* (1.1206, 2.6331)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.000001\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\u003eBetween 1999 and 2023 there were 111,115 deaths in U.S. adults aged 35 years or older diagnosed with osteomyelitis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Overall age-adjusted mortality rates (AAMR) increased during this period from 1.7* (95% CI 1.6 to 1.8) in 1999 to 4.1* (95% CI 4.0 to 4.2) in 2023, with an average annual percentage change (AAPC) of 3.6* (95% CI 2.6 to 4.6) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The annual percent change (APC) in AAMR was 3.2* from 1999 to 2004 (95% CI 1.0 to 5.4), which decreased to -2.7* (95% CI -5.3 to -0.1) from 2004 to 2009, increased to 5.0* (95% CI 4.2 to 5.8) from 2009 to 2018, increased again to 13.2* (95% CI 7.1 to 19.7) from 2018 to 2021, and decreased to 1.2 (95% CI -3.2 to 5.8) from 2021 to 2023.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eOsteomyelitis mortality by gender\u003c/h2\u003e\u003cp\u003eBetween 1999 and 2023 there were 62,519 deaths in males aged 35 years or older diagnosed with osteomyelitis in the United States (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Overall AAMR increased during this period from 2.1* (95% CI 2.0 to 2.2) in 1999 to 5.7* (95% CI 5.6 to 5.9) in 2023, with an AAPC of 4.2* (95% CI 3.2 to 5.2) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The APC in AAMR was 3.1* from 1999 to 2004 (95% CI 0.9 to 5.4), which decreased to -0.2 (95% CI -1.5 to 1.3) from 2004 to 2011, increased to 6.4* (95% CI 5.2 to 7.7) from 2011 to 2018, increased again to 12.7* (95% CI 6.8 to 18.9) from 2018 to 2021, and decreased to 2.5 (95% CI -2.3 to 7.6) from 2021 to 2023.\u003c/p\u003e\u003cp\u003eBetween 1999 and 2023 there were 48,596 deaths in females aged 35 years or older diagnosed with osteomyelitis in the United States (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Overall AAMR increased during this period from 1.5* (95% CI 1.4 to 1.6) in 1999 to 2.9* (95% CI 2.8 to 3.0) in 2023, with an AAPC of 2.7* (95% CI 1.2 to 4.1) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The APC in AAMR was 2.5 (95% CI 0.0 to 5.1) from 1999 to 2004, which decreased to -4.0* (95% CI -7.4 to -0.5) from 2004 to 2009, increased to 3.9* (95% CI 2.7 to 5.1) from 2009 to 2018, increased again to 14.0* (95% CI 4.8 to 24.0) from 2018 to 2021, and decreased to -1.0 (95% CI -8.2 to 6.8) from 2021 to 2023.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eOsteomyelitis mortality by race\u003c/h3\u003e\n\u003cp\u003e\u003c/p\u003e\u003cp\u003eAmerican Indian or Alaska Native (AI/AN) individuals had the highest AAMR in 2007 with a value of 4.0* (95% CI 2.7 to 5.9), and the highest AAMR in 2023 with a value of 8.0* (95% CI 6.4 to 9.6) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The APC was \u0026minus;\u0026thinsp;10.2 from 2007 to 2010 and subsequently increased to 9.4* from 2010 to 2023.\u003c/p\u003e\u003cp\u003eBlack or African American individuals had the highest AAMR in 1999 with a value of 3.5* (95% CI 3.1 to 3.8) and the second-highest AAMR in 2023 with a value of 7.1* (95% CI 6.7 to 7.4) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The APC was \u0026minus;\u0026thinsp;0.2 from 1999 to 2014 and subsequently increased to 8.5* from 2010 to 2023.\u003c/p\u003e\u003cp\u003eWhite individuals had the second-lowest AAMR for every year studied, with 1.6* (95% CI 1.5 to 1.6) in 1999 and 4.1* (95% CI 4.0 to 4.2) in 2023 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The APC was 4.7* from 1999 to 2004, decreased to -1.3 from 2003 to 2010, increased to 5.7* from 2010 to 2018, increased to 13.4*, and tapered off to 1.8 from 2021 to 2023.\u003c/p\u003e\u003cp\u003eAsian or Pacific Islander individuals had the lowest AAMR for every year studied (and experienced the smallest increase in AAMR), with 1.3* (95% CI 0.9 to 1.8) in 1999 and 1.3* (95% CI 1.1 to 1.5) in 2023 and AAPC of 1.4 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The APC was \u0026minus;\u0026thinsp;2.4 from 1999 to 2011 and 5.4* from 2011 to 2023.\u003c/p\u003e\n\u003ch3\u003eOsteomyelitis mortality by age (≥ 35):\u003c/h3\u003e\n\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe 85\u0026thinsp;+\u0026thinsp;year category had the highest crude mortality rate for every year studied, with a value of 19.4 in 1999 and 33.6 in 2023 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). They had an AAPC of 1.9* (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Their APC was \u0026minus;\u0026thinsp;1.7* from 1999 to 2012 and increased to 6.2* from 2012 to 2023.\u003c/p\u003e\u003cp\u003eThe 75\u0026ndash;84 year age category had a crude mortality rate of 6.5 in 1999, 14.4 in 2023, and an AAPC of 3.4* (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Their APC was 0.8* from 1999 to 2014 and increased to 7.8* from 2014 to 2023.\u003c/p\u003e\u003cp\u003eThe 65\u0026ndash;74 year age category had a crude mortality rate of 2.2 in 1999, 6.7 in 2023, and an AAPC of 4.8* (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Their APC was 16.8 from 1999 to 2001, decreased to -1.2 from 2001 to 2010, increased to 7.0*, increased again to 14.1* from 2018 to 2021, and decreased to -0.5 from 2021 to 2023.\u003c/p\u003e\u003cp\u003eThe 55\u0026ndash;64 year age category had a crude mortality rate of 0.9 in 1999, 3.0 in 2023, and an AAPC of 5.4* (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Their APC was 3.0* from 1999 to 2012 and increased to 8.3* from 2012 to 2023.\u003c/p\u003e\u003cp\u003eThe 45\u0026ndash;54 year age category had a crude mortality rate of 0.4 in 1999, 1.3 in 2023, and an AAPC of 2.0* (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Their APC was 2.1* from 1999 to 2012 and increased to 8.3* from 2012 to 2023.\u003c/p\u003e\u003cp\u003eThe 35\u0026ndash;44 year age category had the lowest crude mortality rate for every year studied, with a value of 0.1 in 1999, 0.4 in 2023, and an AAPC of 3.4* (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Their APC was 3.5* from 1999 to 2013 and increased to 9.5* from 2013 to 2023.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eOsteomyelitis mortality by urban vs rural\u003c/h2\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eUrban regions initially had an AAMR of 1.8 in 1999, which was higher than rural regions (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). However, urban regions experienced a smaller overall increase and had a smaller AAMR in 2020 with a value of 3.3. The APC in urban regions was 2.9* from 1999 to 2004, decreased to -3.2 from 2004 to 2008, increased to 2.0 from 2008 to 2012, increased to 5.4* from 2012 to 2018, and increased again to 10.2* from 2018 to 2020. The AAPC in urban regions was 2.9* (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn contrast, rural regions had a smaller AAMR in 1999 with a value of 1.3, but increased to a larger AAMR in 2020 with a value of 4.3 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The APC was 8.1* from 1999 to 2003, decreased to -1.4 from 2003 to 2009, and increased to 7.8* from 2009 to 2020. The AAPC in rural regions was 5.1* (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eOverall Mortality\u003c/h2\u003e\u003cp\u003ePrevious research on osteomyelitis mortality trends in the United States has been limited, with most studies focusing on specific populations or subtypes rather than national surveillance data [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The observed increase in osteomyelitis mortality rates can be attributed to several converging factors.\u003c/p\u003e\u003cp\u003eFirst, the aging population in the United States has created a larger cohort of individuals with predisposing conditions for osteomyelitis. Older adults are particularly vulnerable to osteomyelitis due to increased incidence of associated disorders such as peripheral vascular disease, diabetes mellitus, malnutrition, and compromised immune function [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSecond, the diabetes epidemic has played a crucial role in the rising osteomyelitis mortality rates. Diabetes-related osteomyelitis has shown particularly concerning trends, with age-adjusted mortality rates increasing from 2.63 per 1,000,000 person-years in 1999 to 4.25 per 1,000,000 person-years in 2017 [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Patients with diabetes may have a 15\u0026ndash;25% lifetime risk of developing a foot ulcer, with 20% of infections progressing to osteomyelitis. The mortality associated with diabetic foot ulcers, which frequently develop osteomyelitis, is substantial, with 5-year mortality rates ranging from 50\u0026ndash;70% [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThird, the emergence of antibiotic-resistant organisms has significantly complicated treatment outcomes. Studies have shown that chronic osteomyelitis is increasingly caused by multidrug-resistant organisms, with some antibiotics promoting the formation of small colony variants that enhance bacterial persistence and treatment resistance [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The proportion of methicillin-resistant Staphylococcus aureus (MRSA) infections has increased substantially, with some populations experiencing rates over 75% [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eFourth, the COVID-19 pandemic likely contributed to the recent mortality increase. The pandemic disrupted routine healthcare services, delayed diagnoses, and may have worsened outcomes for patients with chronic conditions like osteomyelitis [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Healthcare system strain during this period may have impacted the quality and timeliness of care for complex infections.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eMortality by Gender\u003c/h2\u003e\u003cp\u003eThis study\u0026rsquo;s finding that males have consistently higher osteomyelitis mortality rates (AAPC 4.2% vs 2.7% in females) aligns with established patterns in infectious disease mortality (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Males accounted for 62,519 of the total 111,115 deaths (56.3%) despite similar population sizes. This gender disparity is consistent with broader infectious disease patterns, where males typically experience higher mortality rates [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe higher male mortality may be explained by several factors. Males have higher rates of certain risk factors for osteomyelitis, including higher prevalence of diabetes, smoking, and occupational exposures that predispose to bone infections. Additionally, males may delay seeking medical care, leading to more advanced disease at presentation. A study of spinal infections found that although males had higher incidence rates, females presented with more severe disease characteristics, suggesting different care-seeking behaviors between genders [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eMortality by Race\u003c/h2\u003e\u003cp\u003eAmerican Indian/Alaska Native (AI/AN) individuals had the highest osteomyelitis mortality rates by 2023 (8.0 per 100,000), representing a concerning health disparity (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Prior to 2010, AI/AN populations had lower rates than Black populations, but experienced the largest increases over the study period (APC of 9.4% from 2010\u0026ndash;2023). This pattern reflects broader health disparities experienced by Native American populations, who have significantly higher rates of diabetes, invasive bacterial infections, and other conditions predisposing to osteomyelitis [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Additionally, healthcare access barriers, including geographic isolation and underfunding of Indian Health Service facilities, may contribute to delayed diagnosis and treatment [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eBlack individuals consistently maintained high osteomyelitis mortality rates throughout the study period, with rates of 7.1 per 100,000 in 2023 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). These disparities reflect well-documented racial inequities in healthcare access and outcomes including higher rates of diabetes, limited healthcare access, and structural racism affecting quality of care. Rural Black patients face particularly severe disparities, with significantly reduced access to specialty care for diabetes-related complications that can progress to osteomyelitis [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAsian/Pacific Islander individuals had the lowest osteomyelitis mortality rates throughout the study period and experienced the smallest increases (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This pattern likely reflects multiple protective factors, including lower rates of diabetes, different genetic susceptibilities, and potentially better healthcare access in some Asian communities. However, recent research has highlighted that Pacific Islander populations, when disaggregated from Asian populations, actually experience significantly higher mortality rates across multiple conditions [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. This suggests that combining these diverse populations may mask important health disparities within Pacific Islander communities.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eMortality by Age\u003c/h2\u003e\u003cp\u003eThe stepwise increase in osteomyelitis mortality rates with advancing age reflects well-established patterns in infectious disease outcomes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The finding that patients aged 85\u0026thinsp;+\u0026thinsp;years had the highest crude mortality rates (33.6 per 100,000 in 2023) is consistent with age-related declines in immune function and increasing comorbidity burden [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe elderly population (85\u0026thinsp;+\u0026thinsp;years) experienced substantial increases in mortality rates, particularly after 2012 (APC of 6.2% from 2012\u0026ndash;2023) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). This trend reflects several concerning factors. First, the growing elderly population with multiple comorbidities creates a larger pool of individuals at high risk for osteomyelitis. Second, elderly patients with osteomyelitis have significantly higher rates of complications and treatment failures [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe accelerated mortality increases in recent years among elderly patients may also reflect the impact of prosthetic joint infections, which are increasingly common in this age group. Studies have shown that prosthetic joint infections carry mortality rates of 5.5% at one year and 7.3% at two years, with higher rates in elderly patients [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The growing number of joint replacement surgeries in elderly patients creates an expanding population at risk for these serious infections.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eMortality by Urban vs Rural\u003c/h2\u003e\u003cp\u003eRural populations experienced larger increases in osteomyelitis-related mortality and ultimately surpassed urban population rates, a finding consistent with research on other diseases (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Studies of diabetes-related complications, which frequently lead to osteomyelitis, have shown that rural populations experience higher rates of complications and poorer outcomes [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Rural patients also face significant barriers including limited access to specialty care, longer travel distances to healthcare facilities, and reduced availability of multidisciplinary care teams necessary for complex infections [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eStrengths \u0026amp; Limitations\u003c/h2\u003e\u003cp\u003eThis study has several strengths. It utilizes a large, nationwide dataset from the CDC WONDER Multiple Cause-of-Death database, providing substantial statistical power and a comprehensive overview of osteomyelitis-related mortality in the United States over a 24-year period. The breadth of the dataset allows for in-depth temporal trend analysis and facilitates examination of disparities across numerous subgroups, and the use of Joinpoint regression further strengthens this analysis by identifying statistically significant inflection points in mortality trends.\u003c/p\u003e\u003cp\u003eHowever, there are notable limitations. As a retrospective, population-level analysis, the study relies on mortality data derived from death certificates, which may contain inaccuracies due to misclassification or reporting errors. The use of aggregate data precludes access to individual-level clinical variables, including comorbidities, treatment history, infection severity, or socioeconomic status, which limits the ability to control for important confounding factors. Furthermore, the study design does not allow for causal inference. The findings are not generalizable to younger individuals, as the analysis focused on patients aged 35 and older, in whom osteomyelitis is more commonly observed. Finally, while the study highlights significant trends and disparities, future research should incorporate prospective cohort studies and clinical datasets to evaluate the influence of healthcare access, intervention strategies, and disease management on osteomyelitis outcomes.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis national temporal analysis reveals a marked rise in osteomyelitis-related mortality among U.S. adults aged ≥35 from 1999 to 2023, with the age-adjusted mortality rate more than doubling–particularly accelerating between 2018 and 2021, coinciding with the COVID-19 pandemic. The data highlight widespread increases across racial, gender, age, and geographic groups, with American Indian/Alaska Native and Black populations, males, older adults, and rural residents disproportionately affected, which is likely due to structural health inequities, comorbidity burdens, and healthcare access challenges. Contributing factors include an aging population, rising diabetes rates, antimicrobial resistance, and pandemic-related care disruptions. These findings underscore the urgent need for equity-focused, multidisciplinary public health strategies, enhanced infection surveillance, and improved access to preventative and specialty care in underserved communities.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eData Availability Statement\u003c/p\u003e\n\u003cp\u003ePublicly available datasets were utilized for the present study. This data can be accessed online: https://wonder.cdc.gov/mcd.html.\u003c/p\u003e\n\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eWe would like to thank Creighton University School of Medicine, Hospital Research Interest Group, Department of Medicine, and Department of Infectious Diseases for their assistance in developing and finalizing this project.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThe author(s) declare that no financial support was received for the research and/or publication of this article.\u003c/p\u003e\n\u003cp\u003eAuthor Affiliations\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCreighton University School of Medicine, Omaha, NE, United States, Omaha, NE, United States\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJohn Paul Braun, Cinthiya Chander, and Vikram Murugan\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCreighton University School of Medicine, Department of Infectious Disease, Omaha, NE, United States\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eErica J. Stohs\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCreighton University School of Medicine, Department of Internal Medicine, Omaha, NE, United States\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAbubakar Tauseef\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors have approved the final version of the manuscript and agree with the order of authorship. The corresponding authors, J.P.B. and C.C., confirm that all data, figures, and materials comply with field and journal standards for transparency and reproducibility. Original data and figures are preserved in accordance with best practices to ensure retrievability for reanalysis. J.P.B. and C.C. wrote the majority of the manuscript. J.P.B. developed the Methods and Results sections and created all figures and tables, while C.C. wrote the Introduction, Discussion, and Conclusion sections and coordinated the manuscript revision process. V.M. conducted the statistical analysis, including Joinpoint regression modeling and verification of age-adjusted mortality calculations. E.S. peer-reviewed the manuscript for clinical accuracy and provided editorial feedback, and A.T. peer-reviewed the final version, supervised data interpretation, and served as the faculty mentor throughout the project. No professional medical writers or editorial services were involved in the preparation of this manuscript. All authors have reviewed and approved the submission, are aware of their responsibilities, and confirm the accuracy of the author contributions listed above.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col start=\"1\" type=\"1\"\u003e\n \u003cli\u003eLew DP, Waldvogel FA. Osteomyelitis. \u003cem\u003eThe Lancet\u003c/em\u003e. 2004;364(9431):369-379. doi:10.1016/s0140-6736(04)16727-5\u003c/li\u003e\n \u003cli\u003eKremers HM, Nwojo ME, Ransom JE, Wood-Wentz CM, Melton LJ, Huddleston PM. Trends in the epidemiology of osteomyelitis. \u003cem\u003eJournal of Bone and Joint Surgery\u003c/em\u003e. 2015;97(10):837-845. doi:10.2106/jbjs.n.01350\u003c/li\u003e\n \u003cli\u003eHuang CC, Tsai KT, Weng SF, et al. Chronic osteomyelitis increases long-term mortality risk in the elderly: a nationwide population-based cohort study. \u003cem\u003eBMC Geriatrics\u003c/em\u003e. 2016;16(1). doi:10.1186/s12877-016-0248-8\u003c/li\u003e\n \u003cli\u003eTong SYC, Davis JS, Eichenberger E, Holland TL, Fowler VG. Staphylococcus aureus Infections: Epidemiology, Pathophysiology, Clinical Manifestations, and Management. \u003cem\u003eClinical Microbiology Reviews\u003c/em\u003e. 2015;28(3):603-661. doi:10.1128/cmr.00134-14\u003c/li\u003e\n \u003cli\u003eCenters for Disease Control and Prevention, National Center for Health Statistics. National Vital Statistics System, Mortality 1999-2020 on CDC WONDER Online Database, released in 2021. Data are from the Multiple Cause of Death Files, 1999-2020, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/mcd-icd10.html on Feb 6, 2024 4:56:31 AM\u003c/li\u003e\n \u003cli\u003eBrown A, Karl A, Murugan V, Billion T, Jabbar ABA, Mirza M. Emphysema-related mortality rates in the U.S. from 1999 to 2022. \u003cem\u003eFrontiers in Medicine\u003c/em\u003e. 2025;12. doi:10.3389/fmed.2025.1579177\u003c/li\u003e\n \u003cli\u003eKim HJ, Fay MP, Feuer EJ, Midthune DN. Permutation tests for joinpoint regression with applications to cancer rates. Stat Med 2000;19:335-51 (correction: 2001;20:655).\u003c/li\u003e\n \u003cli\u003eRomanò CL, Logoluso N, Elia A, Romanò D. Osteomyelitis in elderly patients. \u003cem\u003eBMC Geriatrics\u003c/em\u003e. 2010;10(S1). doi:10.1186/1471-2318-10-s1-l15\u003c/li\u003e\n \u003cli\u003eHill SL, Holtzman GI, Buse R. The effects of peripheral vascular disease with osteomyelitis in the diabetic foot. \u003cem\u003eThe American Journal of Surgery\u003c/em\u003e. 1999;177(4):282-286. doi:10.1016/s0002-9610(99)00050-1\u003c/li\u003e\n \u003cli\u003eHuang JF, Wu QN, Zheng XQ, et al. The characteristics and mortality of osteoporosis, osteomyelitis, or rheumatoid arthritis in the diabetes population: a retrospective study. \u003cem\u003eInternational Journal of Endocrinology\u003c/em\u003e. 2020;2020:1-13. doi:10.1155/2020/8821978\u003c/li\u003e\n \u003cli\u003eSiddique N, O’Donoghue M, Casey MC, Walsh JB. Malnutrition in the elderly and its effects on bone health – A review. \u003cem\u003eClinical Nutrition ESPEN\u003c/em\u003e. 2017;21:31-39. doi:10.1016/j.clnesp.2017.06.001\u003c/li\u003e\n \u003cli\u003eMcDermott K, Fang M, Boulton AJM, Selvin E, Hicks CW. Etiology, epidemiology, and disparities in the burden of diabetic foot ulcers. \u003cem\u003eDiabetes Care\u003c/em\u003e. 2022;46(1):209-221. doi:10.2337/dci22-0043\u003c/li\u003e\n \u003cli\u003eTuchscherr L, Kreis CA, Hoerr V, et al. Staphylococcus aureusdevelops increased resistance to antibiotics by forming dynamic small colony variants during chronic osteomyelitis. \u003cem\u003eJournal of Antimicrobial Chemotherapy\u003c/em\u003e. 2015;71(2):438-448. doi:10.1093/jac/dkv371\u003c/li\u003e\n \u003cli\u003eSutcliffe CG, Grant LR, Reid A, et al. High Burden of Staphylococcus aureus Among Native American Individuals on the White Mountain Apache Tribal Lands. \u003cem\u003eOpen Forum Infectious Diseases\u003c/em\u003e. 2020;7(3). doi:10.1093/ofid/ofaa061\u003c/li\u003e\n \u003cli\u003eDehghani Tafti A, Fatehpanah A, Salmani I, Bahrami MA, Tavangar H, Fallahzadeh H, Tehrani AA, Bahariniya S, Tehrani GA. COVID-19 pandemic has disrupted the continuity of care for chronic patients: evidence from a cross-sectional retrospective study in a developing country. BMC Prim Care. 2023 Jul 1;24(1):137. doi: 10.1186/s12875-023-02086-6.\u003c/li\u003e\n \u003cli\u003eLener S, Wipplinger C, Hartmann S, Rietzler A, Thomé C, Tschugg A. Gender-Specific differences in presentation and management of spinal infection: a Single-Center retrospective study of 159 cases. \u003cem\u003eGlobal Spine Journal\u003c/em\u003e. 2020;11(4):430-436. doi:10.1177/2192568220905804\u003c/li\u003e\n \u003cli\u003eDisparities | Fact sheets. Newsroom. October 2019. https://www.ihs.gov/newsroom/factsheets/disparities/\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eTaylor L, Gangnon R, Powell WR, et al. Association of rurality and identifying as black with receipt of specialty care among patients hospitalized with a diabetic foot ulcer: a Medicare cohort study. \u003cem\u003eBMJ Open Diabetes Research \u0026amp; Care\u003c/em\u003e. 2023;11(2):e003185. doi:10.1136/bmjdrc-2022-003185\u003c/li\u003e\n \u003cli\u003eCostello M. Pacific Islander Adults Experience Significantly Higher Overall and Leading-Cause Death Rates than A. \u003cem\u003eNational Cancer Institute\u003c/em\u003e. Published January 8, 2025. https://dceg.cancer.gov/news-events/news/2025/pacific-islander-mortality-disparities\u003c/li\u003e\n \u003cli\u003eFischbacher A, Borens O. Prosthetic-joint infections: mortality over the last 10 years. \u003cem\u003eJournal of Bone and Joint Infection\u003c/em\u003e. 2019;4(4):198-202. doi:10.7150/jbji.35428\u003c/li\u003e\n \u003cli\u003eSteiger K, Herrin J, Swarna KS, Davis EM, McCoy RG. Disparities in acute and chronic complications of diabetes along the U.S. Rural-Urban continuum. \u003cem\u003eDiabetes Care\u003c/em\u003e. 2024;47(5):818-825. doi:10.2337/dc23-1552\u003c/li\u003e\n \u003cli\u003eCampbell JI, Shanahan KH, Bartick M, et al. Racial and ethnic differences in length of stay for US children hospitalized for acute osteomyelitis. \u003cem\u003eThe Journal of Pediatrics\u003c/em\u003e. 2023;259:113424. doi:10.1016/j.jpeds.2023.113424\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7844039/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7844039/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eIntroduction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOsteomyelitis, an infection of the bone and bone marrow, causes substantial morbidity and mortality, particularly in older adults and those with comorbidities such as diabetes and peripheral vascular disease. This study examines national trends in osteomyelitis mortality from 1999–2023 using CDC WONDER. Despite therapeutic advances, U.S. mortality appears to be rising, with significant demographic and geographic disparities.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUsing CDC WONDER Multiple Cause-of-Death data, we identified U.S. adults ≥ 35 years with osteomyelitis (ICD-10 M86) as a contributing cause of death. Age-adjusted mortality rates (AAMRs) were calculated using the 2000 U.S. standard population, and trends analyzed with Joinpoint regression to estimate annual and average annual percent change (APC, AAPC) with 95% CIs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFrom 1999–2023, 111,115 osteomyelitis-related deaths occurred. AAMR rose from 1.7 to 4.1 (AAPC: 3.6%), peaking in 2018–2021 (APC: 13.2%). Males had higher rates than females (2023 AAMR: 5.7 vs 2.9), but both increased proportionally. American Indian/Alaska Native individuals had the highest 2023 rate (8.0) and largest post‑2010 rise, followed by Black individuals (7.1). Mortality increased with age, reaching 33.6 in those ≥ 85 years. Rural areas saw faster increases than urban areas, surpassing them by 2020.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOsteomyelitis mortality in U.S. adults more than doubled since 1999, accelerating sharply in the last decade and during the COVID‑19 pandemic. Disproportionate burdens among males, AI/AN and Black populations, the oldest adults, and rural residents highlight the need for targeted, equity‑focused interventions and improved access to preventive and specialty care.\u003c/p\u003e","manuscriptTitle":"Addressing Disparities in Osteomyelitis: The WONDER Project","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-03 09:06:04","doi":"10.21203/rs.3.rs-7844039/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-25T07:42:31+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-24T21:28:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"266715351822624178588362866986523494445","date":"2025-11-21T14:21:05+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-20T02:16:33+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-16T02:33:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"13219384568409622312771270304594112586","date":"2025-10-27T14:07:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"250809815735646968053571602759835266251","date":"2025-10-23T03:16:43+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-22T23:44:32+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-22T23:35:45+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-10-20T07:33:52+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-19T21:34:40+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Infectious Diseases","date":"2025-10-19T21:31:49+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ae189838-51a9-40a6-b753-b900a89ad3a6","owner":[],"postedDate":"November 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-26T06:24:25+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-03 09:06:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7844039","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7844039","identity":"rs-7844039","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}