Prevalence of diabetes in the USA from the perspective of demographic characteristics, physical indicators and living habits based on NHANES 2009-2018
Open accessAbstract
Objective: To determine differences in DM in the U.S. population according to demographic characteristics, physical indicators and living habits. Methods: 23 546 participants in the 2009 to 2018 National Health and Nutrition Examination Survey (NHANES) who were 20 year of age or older and not pregnant. All analyses used weighted samples and considered the stratification and clustering of the design. Specific indicators include length of leg (cm), BMI (kg/cm 2), TCHOL (mg/dL), fasting plasma glucose (mg/dL) and comparison of means and the proportion of participants with DM. Results: The prevalence of DM in the USA has been rising modestly in the past decade, and were consistent and robust for the observed differences in age, sex, and ethnicity. Compared with white participants, black participants and Mexican-American were both more likely (P<0.001) to have diabetes: 14.6% (CI, 13.6% to 15.6%) among black participants, 10.6% (CI, 9.9% to 11.3%) among white participants, and 13.5% (CI, 11.9% to 15.2%) among Mexican-American participants. The prevalence of diabetes is increasing with age, males peaked around the 60s, and women around the 70s. The overall mean leg length and TCHOL was lower in diabetics than in non-diabetics (1.07 cm, 18.67 mg/dL, respectively), while mean BMI were higher in diabetics than in non-diabetics (4.27 kg/cm2). DM had the greatest effect on decline of TCHOL in white participants (23.6 mg/dL), less of an effect in black participants (9.67 mg/dL), and the least effect in Mexican-American participants (8.25 mg/dL). Notably, smoking had great effect on percent increment of DM in whites (0.2%), and have little effect on black and Mexican-Americans. Conclusions: DM is more common in the general population than might be clinically recognized, and the prevalence of DM was associated to varying degrees with many indicators of demographic characteristics, physical indicators, and living habits. These indicators should be linked with medical resource allocation and scientific treatment methods to comprehensively implement the treatment of DM.
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Black and Mexican Americans have an increased prevalence of diabetes compared to their white counterparts.
“As Black and Mexican Americans have an increased prevalence of diabetes compared to their white counterparts, the omission of certain demographics in training models could perpetuate existing health inequities for already at-risk populations and introduce a high probability of bias14.”
From AI-based diabetes care: risk prediction models and implementation concerns (2024) · cites this paper as [14]
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- Authors
- Fang, L., Sheng, H., Tan, Y., & Zhang, Q.
- Affiliations
- Shaanxi University of Chinese Medicine (CN)
- Corresponding author
- Zhang, Qi
- Venue
- Frontiers in Endocrinology
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- Frontiers Media SA
- Published
- 2023-03-07
- Volume
- 14
- Pages
- 1088882-1088882
- ISSN
- 1664-2392 (ISSN-L)
- PMC
- PMC10028205
- OpenAlex
- W4323364247
- Cited by
- 52
- References
- 41
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- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Natural Science Foundation of China (81470543, 81774295, 81873296)
- Natural Science Basic Research Program of Shaanxi Province
- Topics
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes, Cardiovascular Risks, and Lipoproteins
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Obesity, Physical Activity, Diet
- Keywords
- diabetes prevalence, diabetes mellitus, demographic differences, racial and ethnic disparities, age and sex differences, total cholesterol, leg length, body mass index, smoking, United States population
- MeSH terms
- White, Diabetes Mellitus, Female, Habits, Humans, Male, Nutrition Surveys, Pregnancy, United States, Prevalence
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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