Skip to main content
NCBI home page
BMC Public Health logoLink to BMC Public Health
. 2026 Jul 30;26:2982. doi: 10.1186/s12889-026-28738-7

Mortality trends and disparities among older adults with coexisting diabetes and anemia from 1999 to 2023: a CDC WONDER database analysis

Yuxin Liao 1,2,#, Hongjie Lin 1,2,#, Zhiyi Wang 1,2, Xiangru Zhang 1,2, Pu Zhang 1,2,✉
PMCID: PMC13670701  PMID: 42881440

Abstract

Background

As the global population ages, diabetes and anemia increasingly coexist among older adults. This study examined mortality patterns involving both diabetes and anemia and associated demographic disparities among older adults in the United States from 1999 to 2023.

Methods

This study utilized national mortality data from 1999 to 2023 obtained from the Centers for Disease Control and Prevention (CDC) Wide-ranging Online Data for Epidemiologic Research (WONDER) database, focusing on deaths involving both diabetes and anemia among adults aged ≥ 65 years. Crude mortality rates (CMR) and age-adjusted mortality rates (AAMR) per 100,000 population and annual percent change (APC) were calculated by year, sex, race/ethnicity, and urban-rural status.

Results

A total of 181,430 deaths involving both diabetes and anemia among the elderly were recorded during the study period. Trend analysis revealed a significant increase from 1999 to 2006 (APC = 3.52%), followed by a significant decline from 2006 to 2018 (APC = -1.25%). Subsequently, a sharp surge occurred from 2018 to 2021 (APC = 14.17%), after which a decrease was observed from 2021 to 2023 (APC = -4.13%). Marked disparities were observed: males consistently exhibited higher AAMRs than females; the non-Hispanic (NH) Black population had the highest AAMR among all the racial/ethnic groups; and AAMRs were higher in rural areas compared to urban areas.

Conclusion

This study describes long-term mortality patterns involving both diabetes and anemia among older adults in the United States and highlights persistent demographic disparities. Further studies are warranted to clarify the mechanisms underlying these observed patterns.

Keywords: Diabetes mellitus, Anemia, Population ageing, CDC WONDER, Age-adjusted mortality rates

Introduction

Diabetes mellitus (DM) is one of the most common chronic metabolic diseases worldwide. Current epidemiological data indicate that over 40 million adults in the United States (U.S.) have DM, with an estimated 11 million cases remaining undiagnosed in 2023 [1] and the prevalence has nearly doubled in recent decades [2].

As a chronic metabolic disease, DM affects multiple organ systems and is frequently accompanied by a range of comorbidities, including anemia [3]. Mechanisms contributing to anemia in DM may include reduced erythropoietin (EPO) production due to diabetic nephropathy, functional iron deficiency secondary to chronic inflammation, and vitamin B12 deficiency associated with metformin use [4–6].

Evidence indicates that the prevalence of anemia in patients with DM is approximately 2–4 times higher than that of non-diabetic individuals [7, 8]. In patients with DM, anemia is associated with older age, longer diabetes duration, and an increased risk of adverse clinical outcomes, including cardiovascular and renal events, as well as increased mortality [8, 9]. Because both diabetes and anemia become increasingly prevalent with advancing age [10–12], the elderly (≥ 65 years) bear a disproportionately high burden of these coexisting conditions, resulting in substantially greater disease burden and mortality [13].

While studies have demonstrated that anemia is associated with poorer clinical outcomes in individuals with DM [14, 15], evidence regarding long-term national mortality trends involving both diabetes and anemia, particularly among older adults, remains limited. The Centers for Disease Control and Prevention (CDC) Wide-ranging Online Data for Epidemiologic Research (WONDER) Multiple Cause of Death database provides nationwide mortality patterns based on death certificate data. To address the knowledge gap, we used this database to characterize temporal trends and demographic disparities in deaths involving both diabetes and anemia among older adults in the United States from 1999 to 2023.

Methods

Study setting and population

This retrospective descriptive study utilized data from the CDC WONDER database. The database provides demographic information and multiple causes of death from U.S. death certificates, coded according to the International Classification of Diseases, 10th Revision (ICD-10). We analyzed mortality data from 1999 to 2023 to evaluate trends in deaths involving diabetes mellitus and anemia. Deaths were identified from death certificates listing both diabetes mellitus (ICD-10 E10–E14) and anemia (ICD-10 D50–D64). The CDC WONDER Multiple Cause of Death database was queried to identify deaths in which both conditions were listed on the same death certificate, regardless of whether either condition was recorded as the underlying or contributing cause of death. Because CDC WONDER is a de-identified public government dataset, this study did not require institutional review board (IRB) approval [16].

Data extraction

Data extracted from CDC WONDER included the number of deaths, year of death, sex, race/ethnicity, state, metropolitan status, and geographic region. The study population was limited to U.S. residents aged ≥ 65 years. Race/ethnicity was classified as non-Hispanic (NH) White, NH Black, and Hispanic. Metropolitan status was classified according to the 2013 National Center for Health Statistics (NCHS) Urban–Rural Classification Scheme, dividing counties into metropolitan and non-metropolitan areas [17].

Statistical analysis

Crude mortality rates (CMR) and age-adjusted mortality rates (AAMR) per 100,000 population for deaths involving both diabetes and anemia were calculated. CMR was determined by dividing the number of deaths by the corresponding population for each year. AAMR was age-standardized to the 2000 U.S. standard population to account for differences in age distribution. Temporal trends in AAMR were assessed using Joinpoint regression analysis (Joinpoint V 5.3.0.0, November 2025), estimating annual percent change (APC) and corresponding 95% confidence intervals (CIs). Joinpoint regression fits log-linear models to identify significant changes in mortality trends over time; trends were considered increasing or decreasing if the slope differed significantly from zero using a two-sided t-test (p < 0.05). Demographic and geographic characteristics were summarized using descriptive statistics, presented as counts and percentages. All data processing and statistical analyses were conducted in R software (version 4.2.3) following extraction from CDC WONDER.

Results

From 1999 to 2023, 181,430 deaths involving both diabetes and anemia occurred among older adults (aged ≥ 65 years). Among these, 40,685 deaths listed diabetes as the underlying cause of death, with anemia as a contributing cause, whereas 6,969 deaths listed anemia as the underlying cause of death, with diabetes as a contributing cause. The remaining 133,776 deaths listed neither diabetes nor anemia as the underlying cause of death. Regarding place of death, 38.9% occurred in a medical facility, 24.3% at the decedent’s home, 3.6% in a hospice facility, and 29.7% in a nursing home or long-term care setting; the remaining 3.5% occurred in other or unknown locations.

Annual trends

From 1999 to 2023, the AAMR showed an overall upward trend, increasing from 12.5 to 21.0 per 100,000 population. Specifically, between 1999 and 2006, AAMR increased significantly, with an APC of 3.52% (95% CI: 2.19 to 5.55). From 2006 to 2018, AAMR decreased significantly, with an APC of − 1.25% (95% CI: −2.00 to − 0.72). This was followed by a sharp rise from 2018 to 2021, with an APC of 14.17% (95% CI: 10.77 to 16.44), reaching its peak. Subsequently, AAMR declined from 2021 to 2023, with an APC of − 4.13% (95% CI: −7.83 to − 0.49), indicating a recent downward trend (Fig. 1).

Fig. 1.

Fig. 1

Open in a new tab

Annual and sex-stratified trends for AAMR involving both diabetes and anemia among older adults aged ≥ 65 years in the United States, 1999–2023

AAMR involving both diabetes and anemia stratified by sex

From 1999 to 2023, males consistently exhibited higher AAMRs than females (Fig. 1).

In males, the AAMR rose from 14.16 in 1999 to 18.74 in 2006 (APC 3.86%, 95% CI: 2.24 to 6.72; p = 0.0008), then declined slightly to 18.01 by 2018 (APC − 0.88%, 95% CI: −1.98 to − 0.27; p = 0.0096). From 2018 to 2021, AAMR increased sharply to a peak of 26.36 (APC 15.48%, 95% CI: 11.50 to 18.10; p = 0.0024), followed by a nonsignificant decrease to 24.28 in 2023 (APC − 4.36%, 95% CI: −9.04 to 0.17; p = 0.054).

Among females, the AAMR rose from 12.20 in 1999 to 15.67 in 2006 (APC 3.31%, 95% CI: 1.85 to 5.58; p = 0.0024), then declined steadily to 13.89 by 2018 (APC − 1.68%, 95% CI: −2.67 to − 1.09; p = 0.0024). A subsequent surge was observed from 2018 to 2021, reaching 19.21 (APC 12.86%, 95% CI: 8.76 to 15.44; p = 0.0040). After 2021, AAMR decreased slightly from 19.21 to 17.70 by 2023, but the change was not significant (APC − 4.09%, 95% CI: −8.55 to 0.57; p = 0.0716).

AAMR involving both diabetes and anemia stratified by race/ethnicity

During the study period, the highest AAMR was observed in the NH Black population, followed by the Hispanic population (Fig. 2).

Fig. 2.

Fig. 2

Open in a new tab

Annual and race/ethnicity-stratified trends for AAMR involving both diabetes and anemia among older adults aged ≥ 65 years in the United States, 1999–2023

Among Hispanic individuals, the AAMR remained stable from 1999 to 2018, then rose sharply from 21.04 in 2018 to 30.22 in 2021 (APC 13.19%, 95% CI: 6.69 to 17.00; p = 0.019), followed by a decline to 25.35 in 2023 (APC − 9.76%, 95% CI: −16.27 to − 1.50; p = 0.0312).

Among NH Black individuals, the AAMR increased from 27.05 in 1999 to 32.93 in 2005 (APC 3.35%, 95% CI: 0.62 to 9.47, p = 0.02), then decreased to 25.70 in 2018 (APC − 2.64%, 95% CI: −4.40 to − 1.90, p = 0.0064). A sharp rise occurred from 2018 to 2021, with the AAMR reaching 36.79 in 2021 (APC 16.30%, 95% CI: 9.80 to 20.16, p = 0.0232), followed by a slight decline to 34.31 in 2023 (APC − 5.02%, 95% CI: −11.13 to 2.13, p = 0.116).

Among NH White individuals, the AAMR increased from 11.17 in 1999 to 14.95 in 2006 (APC 3.71%, 95% CI: 2.31 to 5.80, p = 0.002), then decreased to 13.79 in 2018 (APC − 1.23%, 95% CI: −2.11 to − 0.68, p = 0.0044). A sharp rise occurred from 2018 to 2021, with AAMR increasing to 19.47 in 2021 (APC 13.59%, 95% CI: 9.67 to 16.00, p = 0.006), followed by a slight decline to 18.32 in 2023 (APC − 2.67%, 95% CI: −6.66 to 1.67, p = 0.1776).

AAMR involving both diabetes and anemia stratified by geographical variables

Throughout 1999–2020, AAMRs involving both anemia and diabetes remained consistently higher in rural areas than in urban areas (Fig. 3).

Fig. 3.

Fig. 3

Open in a new tab

Annual and geographical variables-stratified trends for AAMR involving both diabetes and anemia among older adults aged ≥ 65 years in the United States, 1999–2020

In rural areas, the AAMR increased markedly from 14.59 in 1999 to 20.98 in 2008 (APC 3.96%, 95% CI: 2.98 to 5.23; p < 0.001). This was followed by a moderate decline to 17.65 by 2016 (APC − 2.32%, 95% CI: −4.84 to − 1.21; p < 0.001), and then a sharp resurgence to 24.84 in 2020 (APC 7.19%, 95% CI: 4.32 to 13.57; p < 0.001).

In contrast, urban areas experienced a moderate increase in AAMR from 12.52 in 1999 to 15.97 in 2005 (APC 3.99%, 95% CI: 1.73 to 8.26; p = 0.0004), followed by a steady decline to 14.70 by 2018 (APC − 1.18%, 95% CI: −2.65 to − 0.58; p = 0.0004). However, from 2018 to 2020, AAMR rose abruptly and significantly to 19.18 (APC 14.56%, 95% CI: 6.13 to 19.16; p = 0.0004).

Discussion

Analysis revealed the AAMR increased significantly between 1999 and 2006, followed by a significant decline from 2006 to 2018. Thereafter, a sharp rise occurred from 2018 to 2021, before a decrease was observed from 2021 to 2023. Overall, the AAMR showed a fluctuating yet upward trend. Specifically, the AAMR was consistently higher among males than females. NH Black individuals exhibited substantially higher AAMR than Hispanic and NH White populations. Furthermore, rural areas experienced significantly higher AAMR than urban areas. These findings indicate substantial temporal changes and persistent demographic disparities in deaths involving both diabetes and anemia.

The significant increase in AAMR between 1999 and 2006 may reflect several factors. During this period, the global burden of non-communicable diseases (NCDs) increased substantially [18, 19], accompanied by rapid population ageing which has been associated with a higher burden of chronic diseases and increased mortality [19, 20]. WHO statistics indicate that between 2000 and 2019, the global proportion of deaths linked to NCDs increased from 61% (31 million) to 74% (41 million) [21, 22]. Another possible explanation for the increasing AAMR is the rising prevalence of obesity [23, 24], an established risk factor for diabetes [23] and a condition affecting nearly 10.5% of the world’s population [25]. Obesity has also been associated with anemia through elevated demand for iron, inadequate dietary iron intake and reduced physical activity [24]. However, because the CDC WONDER database does not include information on individual risk factors or disease severity, these explanations should be interpreted as plausible hypotheses rather than direct evidence from the present study.

The AAMR declined significantly from 2006 to 2018. This decline may reflect improvements in diabetes management and clinical care during this period [26], including the widespread use of metformin, insulin analogues, and statins, which have enabled better control of blood glucose, lipids, and blood pressure, thus lowering cardiovascular risk [27–29]. Furthermore, the revision of diabetes diagnostic criteria has facilitated earlier diagnosis and intervention [30]. Meanwhile, evidence linking Erythropoiesis-Stimulating Agents (ESAs) to a higher stroke incidence in patients with diabetes, chronic kidney disease, and anemia [9] has prompted the refinement of therapeutic strategies. These improvements in clinical practice may have contributed, at least in part, to the observed decline in AAMR. Nevertheless, the database does not contain treatment-level information, preventing us from directly evaluating the contribution of these improvements to the observed mortality trends.

A marked surge in the AAMR between 2018 and 2021 was observed. This abrupt increase in mortality coincides temporally with the emergence of the COVID-19 pandemic. In light of this observation, several factors may explain this pattern. Previous evidence indicates that DM is associated with an increased risk of severe COVID-19 [31], and SARS-CoV-2 infection may further compromise glycemic control, creating a bidirectional interaction [32]. Furthermore, the COVID-19 pandemic placed unprecedented pressure on healthcare systems. Consequently, many individuals with diabetes and anemia experienced interruptions in regular follow-up and pharmacological treatment, while concerns about SARS-CoV-2 transmission further reduced healthcare utilization and contributed to poorer chronic disease management [33, 34]. In addition, lifestyle changes including reduced physical activity and weight gain during the COVID-19 pandemic may have further impaired diabetes management [34]. Consistent with this, one report showed that 73% of diabetic patients had poor disease management during the COVID-19 period [33, 35]. Evidence indicates that DM patients infected with SARS-CoV-2 are more susceptible to anemia, which substantially increases the likelihood of critical illness, ICU admission, and mortality, thereby contributing to poorer clinical outcomes [36]. Another possible explanation is that temporal changes in the documentation and recording of coexisting diabetes and anemia on death certificates may also have contributed to the observed increase in mortality. Similarly, because the CDC WONDER database lacks information on COVID-19 infection status, treatment patterns, and disease severity, the relative contribution of these potential mechanisms to the observed trends could not be determined.

Mortality was consistently higher among men than women throughout the study period. Studies indicate that male patients with DM exhibit a 5.66-fold higher rate in anemia prevalence than females [37]. In addition, men generally bear a higher burden of cardiovascular disease than women [38], including peripheral artery disease (PAD), a manifestation of atherosclerotic cardiovascular disease (ASCVD) that disproportionately affects older adults with diabetes [39]. In this context, anemia may further aggravate myocardial and cerebral ischemia by reducing oxygen delivery, thereby increasing the risk of adverse cardiovascular events and deaths [40, 41]. Diabetic Foot Disease (DFD) is also associated with high mortality rates and exhibits a marked male predominance [42]. Taken together, these mechanisms may partly underlie the excess mortality observed among male participants in our study.

Among patients with concurrent DM and anemia, the AAMR is notably higher in the NH Black population compared to other racial groups [43]. Research has found that Black individuals experience a threefold greater likelihood of developing anemia than Whites, while Black populations also carry a higher risk for DM relative to the White population [23]. Persistent income inequality may contribute to limited access to healthy food and healthcare resources, wherein predominantly Black neighborhoods frequently face systemic barriers, including food deserts and under-resourced medical facilities, which exacerbate health disparities [44, 45]. Genetic susceptibility may also have played a role. For example, research indicates that Sickle Cell Disease (SCD) is overrepresented in Black individuals [46].

A persistent urban-rural disparity in AAMR is observed, with rural regions demonstrating higher rates, which may partly reflect differences in urbanization [19, 34]. Individuals living in less urbanized regions often have more limited access to education, sanitation, healthcare services, stable employment, and adequate housing [18, 44], which may lead to delayed detection of chronic diseases and reduced access to timely diagnosis and treatment. Moreover, poorer socioeconomic conditions in rural areas may further compromise diabetes management and long-term disease control [31].

Strengths and limitations

This study has several strengths. The use of a nationwide database spanning 25 years enabled the evaluation of long-term mortality trends involving both diabetes and anemia among older adults in the United States. In addition, stratified analyses by demographic characteristics enabled a comprehensive assessment of population disparities in mortality.

Several limitations should be acknowledged. First, the CDC WONDER database provides only aggregate mortality data and lacks individual-level clinical information, such as diabetes duration, glycemic control, medication use, renal function, and other comorbidities. We were unable to adjust for these potential confounders or explore the underlying mechanisms of the observed associations. Second, because this study relied on death certificate data, temporal changes in documentation and coding practices, as well as potential misclassification and incomplete documentation, particularly for deaths occurring in non-hospital settings such as nursing homes and community-based environments, may have affected the reported mortality estimates. Third, our analysis was limited to deaths in which both diabetes and anemia were recorded on death certificates. Therefore, the findings may not be generalizable to all individuals living with diabetes and anemia. Finally, the observational nature of this study allows the identification of associations but does not permit causal inference.

Conclusion

Using nationally representative death certificate data, this study provides a descriptive assessment of long-term mortality patterns involving diabetes and anemia among older adults in the United States. It contributes to a better understanding of temporal trends and population disparities in mortality involving these coexisting conditions and may inform future epidemiological research. Future studies incorporating individual-level clinical information are needed to further explore the mechanisms underlying these observed patterns.

Acknowledgements

Not applicable.

Authors’ contributions

YL and HL performed the analysis and wrote the manuscript. XZ and ZW completed data collection. PZ supervised the analysis and revised the manuscript. All authors have reviewed the final manuscript and agreed to its submission.

Funding

This work was supported by the XZHMU-QL Joint Research Fund (Grant No. QL-YB072).

Data availability

The data supporting the findings of this study are publicly available through the Centers for Disease Control and Prevention (CDC) Wide-ranging Online Data for Epidemiologic Research (WONDER) Multiple Cause of Death database (https://wonder.cdc.gov/mcd.html). This database provides access to de-identified, aggregated mortality data derived from U.S. death certificates. All analyses were performed using the publicly accessible online query system, and no individual-level identifiable data were used or generated in this study.

Declarations

Ethics approval and consent to participate

This study used publicly available de-identified CDC WONDER data; IRB approval and informed consent were not required.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Yuxin Liao and Hongjie Lin contributed equally to this work and should be regarded as co-first authors.

References

  • 1.Baumblatt J, Fryar C, Gu Q, Ashman J. Prevalence of Total, Diagnosed, and Undiagnosed Diabetes in Adults: United States, August 2021–August 2023. National Center for Health Statistics (U.S.). 2024. 10.15620/cdc/165794.  [DOI] [PMC free article] [PubMed]
  • 2.Fox CS, Pencina MJ, Meigs JB, Vasan RS, Levitzky YS, D’Agostino RB. Trends in the incidence of type 2 diabetes mellitus from the 1970s to the 1990s: the Framingham Heart Study. Circulation. 2006;113:2914–8. 10.1161/CIRCULATIONAHA.106.613828. [DOI] [PubMed] [Google Scholar]
  • 3.Thomas MC, Tsalamandris C, MacIsaac RJ, Jerums G. The Epidemiology of Hemoglobin Levels in Patients With Type 2 Diabetes. Am J Kidney Dis. 2006;48:537–45. 10.1053/j.ajkd.2006.06.011. [DOI] [PubMed] [Google Scholar]
  • 4.KDOQI. KDOQI Clinical Practice Guideline and Clinical Practice Recommendations for anemia in chronic kidney disease: 2007 update of hemoglobin target. Am J Kidney Dis. 2007;50:471–530. 10.1053/j.ajkd.2007.06.008 [DOI] [PubMed]
  • 5.Weiss G, Goodnough LT. Anemia of chronic disease. N Engl J Med. 2005;352:1011–23. 10.1056/NEJMra041809. [DOI] [PubMed] [Google Scholar]
  • 6.Chapman LE, Darling AL, Brown JE. Association between metformin and vitamin B12 deficiency in patients with type 2 diabetes: A systematic review and meta-analysis. Diabetes Metab. 2016;42:316–27. 10.1016/j.diabet.2016.03.008. [DOI] [PubMed] [Google Scholar]
  • 7.Wang D, Morton JI, Salim A, Shaw JE, Magliano DJ. Association Between Diabetes and Anemia: Evidence From NHANES and the UK Biobank. Diabetes Care. 2025;48:816–26. 10.2337/dc24-2535. [DOI] [PubMed] [Google Scholar]
  • 8.Trevest K, Treadway H, Hawkins-van der Cingel G, Bailey C, Abdelhafiz AH. Prevalence and Determinants of Anemia in Older People With Diabetes Attending an Outpatient Clinic: A Cross-Sectional Audit. Clin Diabetes. 2014;32:158–62. 10.2337/diaclin.32.4.158. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Pfeffer MA, Burdmann EA, Chen C-Y, Cooper ME, de Zeeuw D, Eckardt K-U, et al. A trial of darbepoetin alfa in type 2 diabetes and chronic kidney disease. N Engl J Med. 2009;361:2019–32. 10.1056/NEJMoa0907845. [DOI] [PubMed] [Google Scholar]
  • 10.Adane T, Getawa S. Anaemia and its associated factors among diabetes mellitus patients in Ethiopia: A systematic review and meta-analysis. Endocrinol Diabetes Metab. 2021;4:e00260. 10.1002/edm2.260. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.He BB, Xu M, Wei L, Gu YJ, Han JF, Liu YX, et al. Relationship between Anemia and Chronic Complications in Chinese Patients with Type 2 Diabetes Mellitus. Arch Iran Med. 2015;18:277–83. [PubMed] [Google Scholar]
  • 12.De Migone M, Poggiali E, Motta I, Minonzio F, Fabio G, Hu C, et al. Anemia in elderly hospitalized patients: prevalence and clinical impact. Intern Emerg Med. 2015;10:581–6. 10.1007/s11739-015-1197-5. [DOI] [PubMed] [Google Scholar]
  • 13.Wang Y, Gu S, Xie Z, Xu Z, He W, Chen Y et al. Trends and Disparities in the Burden of Chronic Kidney Disease due to Type 2 Diabetes in China From 1990 to 2021: A Population-Based Study. Journal of Diabetes. John Wiley & Sons, Ltd; 2025;17:e70084. 10.1111/1753-0407.70084 [DOI] [PMC free article] [PubMed]
  • 14.Koyama AK, Lundeen E, Bullard KM, Pavkov ME. Prevalence of Anemia and Associated All-Cause Mortality Among Adults With Diabetes: The Role of Chronic Kidney Disease. Diabetes Res Clin Pract. 2023;200:110695. 10.1016/j.diabres.2023.110695. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Ito K, Yokota S, Watanabe M, Inoue Y, Takahashi K, Himuro N, et al. Anemia in Diabetic Patients Reflects Severe Tubulointerstitial Injury and Aids in Clinically Predicting a Diagnosis of Diabetic Nephropathy. Intern Med. 2021;60:1349–57. 10.2169/internalmedicine.5455-20. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.von Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, Vandenbroucke JP, et al. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. J Clin Epidemiol. 2008;61:344–9. 10.1016/j.jclinepi.2007.11.008. [DOI] [PubMed] [Google Scholar]
  • 17.Ingram DD, Franco SJ. 2013 NCHS Urban-Rural Classification Scheme for Counties. Hyattsville: National Center for Health Statistics (U.S.); 2014:1–73. [PubMed]
  • 18.Lin X, Xu Y, Pan X, Xu J, Ding Y, Sun X, et al. Global, regional, and national burden and trend of diabetes in 195 countries and territories: an analysis from 1990 to 2025. Sci Rep Nat Publishing Group. 2020;10:14790. 10.1038/s41598-020-71908-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Safiri S, Kolahi A-A, Noori M, Nejadghaderi SA, Karamzad N, Bragazzi NL, et al. Burden of anemia and its underlying causes in 204 countries and territories, 1990–2019: results from the Global Burden of Disease Study 2019. J Hematol Oncol. 2021;14:185. 10.1186/s13045-021-01202-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Zuin M, Ferrucci L, Zuliani G. Iron deficiency anemia-related mortality trends in US older subjects, 1999 to 2019. Aging Clin Exp Res. 2025;37:99. 10.1007/s40520-025-02982-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Bennett JE, O’Driscoll ON, Stevens GA, Aldea-Ramos N, Bray F, Farzadfar F, et al. Benchmarking progress in non-communicable diseases: a global analysis of cause-specific mortality from 2001 to 2019. Lancet Elsevier. 2025;406:1255–82. 10.1016/S0140-6736(25)01388-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.World Health Organization. World health statistics 2023: monitoring health for the SDGs, sustainable development goals. Geneva: World Health Organization. 2023. https://www.who.int/publications/i/item/9789240074323. Accessed 30 Oct 2025.
  • 23.Fang L, Sheng H, Tan Y, Zhang Q. Prevalence of diabetes in the USA from the perspective of demographic characteristics, physical indicators and living habits based on NHANES 2009–2018. Front Endocrinol (Lausanne). 2023;14:1088882. 10.3389/fendo.2023.1088882. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Wang T, Gao Q, Yao Y, Luo G, Lv T, Xu G, et al. Causal relationship between obesity and iron deficiency anemia: a two-sample Mendelian randomization study. Front Public Health. 2023;11:1188246. 10.3389/fpubh.2023.1188246. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Ruze R, Liu T, Zou X, Song J, Chen Y, Xu R, et al. Obesity and type 2 diabetes mellitus: connections in epidemiology, pathogenesis, and treatments. Front Endocrinol Front. 2023;14. 10.3389/fendo.2023.1161521. Cited 1 Nov 2025. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Nassereldine H, Li Z, Compton K, Kendrick P, Kahn E, Kelly YO, et al. The Burden of Diabetes Mortality by County, Race, and Ethnicity in the U.S., 2000–2019. Diabetes Care. 2025;48:546–55. 10.2337/dc24-2259. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Knowler WC, Barrett-Connor E, Fowler SE, Hamman RF, Lachin JM, Walker EA, et al. Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. N Engl J Med. 2002;346:393–403. 10.1056/NEJMoa012512. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Al-Sadawi MA, Aslam FM, Tao M, Alsaiqali M, Almasry IO, Fan R, et al. Effects of GLP-1 Agonists on mortality and arrhythmias in patients with Type II diabetes. Int J Cardiol Heart Vasc. 2023;47:101218. 10.1016/j.ijcha.2023.101218. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Mensah GA, Wei GS, Sorlie PD, Fine LJ, Rosenberg Y, Kaufmann PG, et al. Decline in Cardiovascular Mortality: Possible Causes and Implications. Circ Res. 2017;120:366–80. 10.1161/CIRCRESAHA.116.309115. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.The Expert Committee on the Diagnosis and Classification of Diabetes Mellitus. Report of the Expert Committee on the Diagnosis and Classification of Diabetes Mellitus. Diabetes Care. 1997;20:1183–97. 10.2337/diacare.20.7.1183. [DOI] [PubMed] [Google Scholar]
  • 31.Cuadros DF, Li J, Musuka G, Awad SF. Spatial epidemiology of diabetes: Methods and insights. World J Diabetes. 2021;12:1042–56. 10.4239/wjd.v12.i7.1042. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Landstra CP, de Koning EJP. COVID-19 and Diabetes: Understanding the Interrelationship and Risks for a Severe Course. Front Endocrinol (Lausanne). 2021;12:649525. 10.3389/fendo.2021.649525. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Hassan ST, Faheem MSB, Sufi Y, Nadeem A, Siddiqui W, Sameen DE, et al. Trends in Diabetes Mellitus and Associated Mental Disorders-Related Mortality (1999–2023): A CDC WONDER Database Analysis. Brain Behav. 2025;15:e70723. 10.1002/brb3.70723. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.dos Anjos JRC, Correia IM, de Moraes CALC, Cordeiro JFC, Trapé AA, Mota J, et al. Sedentary Behavior, Physical Inactivity, and the Prevalence of Hypertension, Diabetes, and Obesity During COVID-19 in Brazil. Int J Environ Res Public Health. 2025;22:1367. 10.3390/ijerph22091367. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Mantovani A, Byrne CD, Zheng M-H, Targher G. Diabetes as a risk factor for greater COVID-19 severity and in-hospital death: A meta-analysis of observational studies. Nutr Metab Cardiovasc Dis. 2020;30:1236–48. 10.1016/j.numecd.2020.05.014. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Gembillo G, Peritore L, Spadaro G, Cuzzola F, Calderone M, Messina R, et al. Kidney involvement and anemia in COVID-19 infection. World J Nephrol. 2025;14:107582. 10.5527/wjn.v14.i3.107582. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Kim M, Lee S-H, Park KS, Kim E-J, Yeo S, Ha I-H. Association between diabetes mellitus and anemia among Korean adults according to sex: a cross-sectional analysis of data from the Korea National Health and Nutrition Examination Survey (2010–2016). BMC Endocr Disord. 2021;21:209. 10.1186/s12902-021-00873-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Martin SS, Aday AW, Almarzooq ZI, Anderson CAM, Arora P, Avery CL, et al. 2024 heart disease and stroke statistics: a report of us and global data from the american heart association. Circulation. 2024;149:e347–913. 10.1161/CIR.0000000000001209. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39.Selvin E, Erlinger TP. Prevalence of and Risk Factors for Peripheral Arterial Disease in the United States: Results From the National Health and Nutrition Examination Survey, 1999–2000. Circulation. 2004;110:738–43. 10.1161/01.CIR.0000137913.26087.F0. [DOI] [PubMed] [Google Scholar]
  • 40.Vlagopoulos PT, Tighiouart H, Weiner DE, Griffith J, Pettitt D, Salem DN, et al. Anemia as a Risk Factor for Cardiovascular Disease and All-Cause Mortality in Diabetes: The Impact of Chronic Kidney Disease. J Am Soc Nephrol. 2005;16:3403–10. 10.1681/ASN.2005030226. [DOI] [PubMed] [Google Scholar]
  • 41.Fiseha T, Adamu A, Tesfaye M, Gebreweld A. Prevalence of anemia in diabetic adult outpatients in Northeast Ethiopia. Hirst JA, editor. PLoS ONE. 2019;14:e0222111. 10.1371/journal.pone.0222111. [DOI] [PMC free article] [PubMed]
  • 42.Iacopi E, Pieruzzi L, Riitano N, Abbruzzese L, Goretti C, Piaggesi A. The Weakness of the Strong Sex: Differences Between Men and Women Affected by Diabetic Foot Disease. Int J Low Extrem Wounds. 2023;22:19–26. 10.1177/1534734620984604. [DOI] [PubMed] [Google Scholar]
  • 43.Le CHH. The Prevalence of Anemia and Moderate-Severe Anemia in the US Population (NHANES 2003–2012). PLoS ONE. 2016;11:e0166635. 10.1371/journal.pone.0166635. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44.Jain V, Minhas AMK, Ariss RW, Nazir S, Khan SU, Khan MS, et al. Demographic and Regional Trends of Cardiovascular Diseases and Diabetes Mellitus-Related Mortality in the United States From 1999 to 2019. Am J Med Elsevier. 2023;136:659–e6687. 10.1016/j.amjmed.2023.03.002. [DOI] [PubMed] [Google Scholar]
  • 45.Hwang Y, Ahuja KR, Haque SM, Jones GF, Naseer A, Shechter O, et al. Anemia prevalence time trends and disparities in the US population: examination of NHANES 1999–2020. J Investig Med. 2023;71:286–94. 10.1177/10815589221140597. [DOI] [PubMed] [Google Scholar]
  • 46.Boghossian NS, Greenberg LT, Saade GR, Rogowski J, Phibbs CS, Passarella M, et al. Association of Sickle Cell Disease With Racial Disparities and Severe Maternal Morbidities in Black Individuals. JAMA Pediatr. 2023;177:808–17. 10.1001/jamapediatrics.2023.1580. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The data supporting the findings of this study are publicly available through the Centers for Disease Control and Prevention (CDC) Wide-ranging Online Data for Epidemiologic Research (WONDER) Multiple Cause of Death database (https://wonder.cdc.gov/mcd.html). This database provides access to de-identified, aggregated mortality data derived from U.S. death certificates. All analyses were performed using the publicly accessible online query system, and no individual-level identifiable data were used or generated in this study.

Articles from BMC Public Health are provided here courtesy of BMC

RESOURCES