Prevalence of Diabetes by BMI: China Nutrition and Health Surveillance (2015-2017) and U.S. National Health and Nutrition Examination Survey (2015-2018).

Introduction: The risk of diabetes begins at a lower BMI among Asian adults. This study compares the prevalence of diabetes between the U.S. and China by BMI. Methods: Data from the 2015-2017 China Nutrition and Health Surveillance (n=176,223) and the 2015-2018 U.S. National Health and Nutrition Examination Survey (n=4,464) were used. Diagnosed diabetes was self-reported. Undiagnosed diabetes was no report of diagnosed diabetes and fasting plasma glucose ≥126 mg/dL or HbA1c ≥6.5%. Predicted age-adjusted prevalence estimates by BMI were produced using sex- and country-specific logistic regression models. Results: In China, the age-adjusted prevalence of total diabetes was 7.8% (95% CI=7.4%, 8.3%), lower than the 14.6% (95% CI=13.1%, 16.3%) in the U.S. The prevalence of diagnosed diabetes was also lower in China than in the U.S. There were no statistically significant differences in the prevalence of undiagnosed diabetes between China and the U.S. The distribution of BMI in China was lower than in the U.S., and the predicted prevalence of total diabetes was similar between China and the U.S. when comparing adults with the same BMI. The predicted prevalence of undiagnosed diabetes was higher in China than in the U.S. for both men and women, and this disparity increased with BMI. When comparing adults at the same BMI, there was little difference in the prevalence of total diabetes, but diagnosed diabetes was lower in China than in the U.S., and undiagnosed was higher. Conclusions: Although differences in BMI appear to explain nearly all of the differences in total diabetes prevalence in the 2 countries, not all factors that are associated with diabetes risk have been investigated.

Body fat differences among US youth aged 8-19 by race and Hispanic origin, 2011-2018.

BACKGROUND: The association between body mass index (BMI) and adiposity differs by race/ethnicity. OBJECTIVE: To examine differences in adiposity by race/Hispanic origin among US youth and explore how those differences relate to differences in BMI using the most recent national data, including non-Hispanic Asian youth. METHODS: Weight, height and DXA-derived fat mass index (FMI) and percentage body fat (%BF) from 6923 youth 8-19 years in the National Health and Nutrition Examination Survey (NHANES) 2011-2018 were examined. Age-adjusted mean BMI, FMI and %BF were reported. Sex-specific linear regression models predicting %BF and FMI were adjusted for age, BMI category and BMI category*race/Hispanic origin interaction. RESULTS: %BF was highest among Hispanic males (28.2%) and females (35.7%). %BF was lower among non-Hispanic Black (23.9%) compared with non-Hispanic White (26.0%) and non-Hispanic Asian (26.6%) males. There was no difference between non-Hispanic Black females (32.7%) and non-Hispanic White (33.2%) or non-Hispanic Asian (32.7%) females. FMI was higher among Hispanic youth compared with non-Hispanic White youth. Among youth with underweight/healthy weight, predicted %BF and FMI were lower among non-Hispanic Black males (-2.8%; -0.5) and females (-2.0%; -0.3), compared with non-Hispanic White youth, and higher among Hispanic males (0.9%; 0.2) and females (2.0%; 0.5), while %BF but not FMI was higher among non-Hispanic Asian males (1.3%) and females (1.4%). Among females with obesity, non-Hispanic Asian females had lower %BF (-2.3%) and FMI (-1.7) than non-Hispanic White females. CONCLUSIONS: Differences in %BF and FMI by race/Hispanic origin were not consistent by BMI category among US youth in 2011-2018.

Changes in adiposity among children and adolescents in the United States, 1999-2006 to 2011-2018.

BACKGROUND: Data from the NHANES indicate that BMI has increased in some subgroups of children and adolescents in the United States over the past 20 y; however, BMI is an indirect measure of body fatness. OBJECTIVES: We assessed changes in DXA-derived measures of adiposity in a nationally representative population of US children and adolescents aged 8-19 y from 1999-2006 to 2011-2018. METHODS: Using data from the NHANES, we compared the means and distributions of DXA-derived percentage body fat (%BF) and fat mass index (FMI; fat mass/height2 in kg/m2) between 1999-2006 (n = 10,231) and 2011-2018 (n = 6923) among males and females by age group, race and Hispanic origin, and BMI categories. Estimates were standardized by age and race and Hispanic origin. RESULTS: From 1999-2006 to 2011-2018, mean %BF increased from 25.6% to 26.3% (change in %BF: 0.7%; 95% CI: 0.2%, 1.2%; P < 0.01) among all males, whereas mean %BF increased from 33.0% to 33.7% (change in %BF: 0.7%; 95% CI: 0.2%, 1.2%; P = 0.01) and mean FMI increased from 7.7 to 8.0 fat mass kg/m2 (change in FMI: 0.3 fat mass kg/m2; 95% CI: 0.0, 0.6 fat mass kg/m2; P = 0.02) among all females. Changes were not consistent across all age, race and Hispanic origin, and BMI categories. Both %BF and FMI increased among Mexican-American children and adolescents, but not other race and Hispanic origin groups. CONCLUSIONS: Among US children and adolescents, DXA-derived measures of adiposity increased from 1999-2006 to 2011-2018, albeit not consistently in every age, race and Hispanic origin, and BMI subgroup. These data reinforce the need to consider other measures, besides BMI categories, when studying adiposity in children and adolescents.

Prevalence of Prescription Pain Medication Use Among Adults: United States, 2015-2018.

Prescription pain medications are used to treat pain due to injury, surgery, and health conditions, such as arthritis and cancer. While opioids may be prescribed together with nonopioid pain medications, nonpharmacologic and nonopioid-containing pharmacologic therapies are preferred for management of chronic pain, where appropriate (1). This report shows the percentage of U.S. adults who, in the past 30 days, used one or more prescription pain medications, used prescription opioid medications, or used nonopioid prescription pain medications (without prescription opioids) in 2015-2018.

Prevalence and Trends in Hepatitis B Virus Infection in the United States, 2015-2018.

Hepatitis B virus (HBV) is a type of viral hepatitis transmitted through sexual contact, contaminated blood, or from an infected mother to her newborn (1). HBV may cause a liver infection that is acute or short-term, but may also cause chronic or long-term infection. Vaccination was targeted to high-risk groups in 1982, and universal vaccination of newborns was recommended beginning in 1991 in the United States (2). This report provides 2015-2018 prevalence estimates of past or present HBV infection and evidence of hepatitis B vaccination, based on blood collected in the National Health and Nutrition Examination Survey (NHANES).

An Investigation of Nonresponse Bias and Survey Location Variability in the 2017-2018 National Health and Nutrition Examination Survey.

Over the past two decades, a steady decline in response rates on national face-to-face surveys has been documented, with steeper declines observed in recent years. The impact of nonresponse on survey estimates is inconsistent and depends on the correlation between response propensity and the survey estimates. To better understand the impact of declining response rates on the 2017-2018 National Health and Nutrition Examination Survey (NHANES), potential nonresponse bias (NRB) was investigated. NRB was assessed using three approaches: (a) studying variation within the respondent set; (b) benchmarking and comparisons to external data; and (c) comparing alternative weighting adjustments. Because NHANES only samples 30 counties in every 2-year cycle, the sample of counties in any given cycle may be an outlier on some characteristics. Such sampling variability may compound the effects of NRB. For this reason, the representativeness of the 2017-2018 NHANES counties was examined by comparing: (a) the characteristics of the 2017-2018 sampled counties with those from prior cycles; (b) each sampled county with the average of all the counties in the sampling stratum from which that county was selected; and (c) the 2017-2018 counties with 5,000 other samples that could have been drawn under the same sample design using a simulation study. The NRB analyses showed that the 2017-2018 NHANES sample had a lower proportion of college graduates and higher-income individuals compared with prior cycles. Additionally, the 2017-2018 NHANES counties had lower proportions of college graduates and lower mean incomes compared with counties from prior cycles and counties not selected in 2017-2018, which exacerbated the effects of NRB. Weighting adjustments used in prior cycles were not sufficient to address the bias in the 2017-2018 NHANES. Instead, enhanced weighting adjustments for education and income reduced the bias resulting from nonresponse and location sampling variability.

Prescription Drug Use Among Adults Aged 40-79 in the United States and Canada.

Patterns of prescription drug use may reflect underlying patterns of health conditions and health care access in the population. Polypharmacy, often defined as the simultaneous use of five or more prescription drugs, is more common in an aging population where multiple coexisting chronic conditions often occur; however, safety concerns may arise (1). The United States and Canada share many cultural and historical ties, but with different models for health care delivery (2). This report describes the use of one or more and five or more prescription drugs among adults aged 40-79 in the United States and Canada.

Attempts to Lose Weight Among Adults in the United States, 2013-2016.

Almost 40% of adults in the United States had obesity in 2015-2016 (1). Obesity is associated with a range of serious health risks (2). Individuals may have multiple motivations for trying to losing weight, including health and appearance reasons (3). This report describes the percentage of U.S. adults who tried to lose weight in the past year by sex, age, race and Hispanic origin, family income, and weight status, based on data collected in the National Health and Nutrition Examination Survey (NHANES) from 2013 -2016.