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.

Differences in Intakes of Select Nutrients by Urbanization Level in the United States Population 2 Years and Older, NHANES 2013-2018.

BACKGROUND: Differences in nutrient intakes by urbanization level in the Unites States is not well understood. OBJECTIVE: Describe, by urbanization level: 1) intake of protein, fiber, percent of energy from added sugars (AS) and saturated fat (SF), calcium, iron, potassium, sodium, and vitamin D; 2) the percent of the population meeting nutrient recommendations. METHODS: Twenty-four-hour dietary recalls from 23,107 participants aged 2 y and over from the 2013-2018 National Health and Nutrition Examination Surveys were analyzed. Usual intakes were estimated, and linear regression models adjusted for age, sex, race and Hispanic origin, and whether family income met the 130% threshold examined intake differences by urbanization levels-large urban areas (LUA), small to medium metro areas (SMMA), and rural areas (RA). RESULTS: A small percentage of the population met the nutrient recommendations, except for protein (92.8%) and iron (70.5%). A higher percentage of the population met recommendations than SMMA and RA for fiber (11.8% compared with 8.1% and 5.3%, P < 0.001), AS (40.2% compared with 33.4% and 31.3%, P < 0.001), SF (26.8% compared with 18.2% and 20.1%, P < 0.001), and potassium (31.5% compared with 25.5% and 22.0%, P < 0.001). Mean protein intake were also higher in LUA than RA (80.0 g compared with 77.7 g, P = 0.003) and fiber intake higher in LUA than SMMA (16.5 g compared with 15.9 g, P = 0.01) and RA (16.5 g compared with 15.2 g, P = 0.001). In addition, contributions to energy intake were lower in LUA than SMMA for AS (11.3% compared with 12.0%, P < 0.001) and SF (11.5% compared with 11.7%, P < 0.001), and for LUA than RA for AS (11.3% compared with 12.9%, P < 0.001) and SF (11.5% compared with 11.8%, P < 0.001). CONCLUSIONS: RA had some markers of poorer diet quality-lower protein and fiber intake and higher AS intake-compared with LUA, and these differences persisted in adjusted regression models. These results may inform public health efforts to address health disparities by urbanization levels in the Unites States.

Federal Housing Assistance and Chronic Disease Among US Adults, 2005-2018.

Introduction: Housing insecurity is associated with poor health outcomes. Characterization of chronic disease outcomes among adults with and without housing assistance would enable housing programs to better understand their population's health care needs. Methods: We used National Health and Nutrition Examination Survey (NHANES) data from 2005 through 2018 linked to US Department of Housing and Urban Development (HUD) administrative records to estimate the prevalence of obesity, diabetes, and hypertension and to assess the independent associations between housing assistance and chronic conditions among adults receiving HUD assistance and HUD-assistance-eligible adults not receiving HUD assistance at the time of their NHANES examination. We estimated propensity scores to adjust for potential confounders among linkage-eligible adults who had an income-to-poverty ratio less than 2 and were not receiving HUD assistance. Sensitivity analysis used 2013-2018 NHANES cycles to account for disability status. Results: Adults not receiving HUD assistance had a significantly lower adjusted prevalence of obesity (42.1%; 95% CI, 40.4%-43.8%) compared with adults receiving HUD assistance (47.5%; 95% CI, 44.8%-50.3%), but we found no differences for diabetes and hypertension. We found significant associations between housing assistance and obesity (adjusted odds ratio = 1.29; 95% CI, 1.12-1.47), but these were not significant in the sensitivity analysis with and without controlling for disability status. We found no significant associations between housing assistance and diabetes or hypertension. Conclusion: Based on data from a cross-sectional survey, we observed a higher prevalence of obesity among adults with HUD assistance compared with HUD-assistance-eligible adults without HUD assistance. Results from this study can help inform research on understanding the prevalence of chronic disease among adults with HUD assistance.

Dietary contributions of food outlets by urbanization level in the US population aged 2 years and older-NHANES 2013-2018.

BACKGROUND: Differences in food access, availability, affordability, and dietary intake are influenced by the food environment, which includes outlets where foods are obtained. These differences between food outlets within rural and urban food environments in the United States are not well understood. OBJECTIVES: The aim of this analysis is to describe the contribution of foods and beverages from 6 outlets-grocery stores, convenience stores, full-service restaurants, quick-service restaurants, schools, and other outlets-to the total energy intake and Healthy Eating Index (HEI)-2015 scores in the United States population, by urbanization level (nonmetropolitan statistical areas [MSAs], small-to-medium MSAs, and large MSAs). METHODS: Data from the National Health and Nutrition Examination Survey 2013-2018 were used. Dietary intake from one 24-h dietary recall was analyzed by the outlet where a food or beverage was obtained and by urbanization. Linear regression, adjusted for sex, age, race and Hispanic origin, and family income, was used to predict the contribution of each food outlet to the total energy intake and HEI-2015 total and component scores by urbanization level. RESULTS: During 2013-2018, foods and beverages from grocery stores and quick-service and full-service restaurants contributed to 62.1%, 15.1%, and 8.5% of the energy intake, respectively. The percentage of energy intake from full- and quick-service restaurants increased with increasing urbanization level. HEI-2015 total scores increased with the increasing urbanization level overall (48.1 non-MSAs, 49.2 small-to-medium MSAs, and 51.3 large MSAs) for grocery stores (46.7 non-MSAs, 48.0 small-to-medium MSAs, and 50.6 large MSAs) and for quick-service restaurants (35.8 non-MSAs, 36.3 small-to-medium MSAs, and 37.5 large MSAs). CONCLUSIONS: Grocery stores and restaurants were the largest contributors of energy intake in urban and rural areas. Diet quality improved with increasing urbanization overall and for grocery stores and quick-service restaurants.

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.

Evaluation of Alternative Body Mass Index (BMI) Metrics to Monitor Weight Status in Children and Adolescents With Extremely High BMI Using CDC BMI-for-age Growth Charts.

In the United States, obesity and severe obesity in children and adolescents are defined using threshold values from the 2000 Centers for Disease Control and Prevention (CDC) sex-specific body mass index (BMI)- for-age growth charts. BMI z-scores and percentiles from the 2000 CDC BMI-for-age growth charts are also used to monitor children's weight status over time and to evaluate obesity treatments. Parameters to calculate percentiles and corresponding z-scores (BMIz) were derived from selected percentiles between the 3rd and 97th. Use of the BMI-for-age growth charts for children and adolescents with extremely high BMI requires extrapolation beyond the 97th percentile, which leads to compression of BMIz values into a very narrow range and is not recommended. This report evaluates eight alternative BMI metrics for monitoring weight status in children and adolescents with extremely high BMI.

Use of prescription medications associated with weight gain among US adults, 1999-2018: A nationally representative survey.

OBJECTIVE: This study aimed to evaluate trends in the use of obesogenic medications among adults. METHODS: Cross-sectional data on adults aged ≥20 years are from the 1999 to 2018 National Health and Nutrition Examination Survey (n = 52,340). Obesogenic medications were defined according to the 2015 Endocrine Society guidelines on the pharmacological management of obesity. Weight status was categorized according to BMI. Trends in prior 30-day use were evaluated. RESULTS: In NHANES 2017-2018, 20.3% of US adults used an obesogenic medication. Beta-blockers (9.8%) and antidiabetics (5.7%) were the most common; antipsychotics (1.0%) were the least common. Most common indications were disorders of glucose metabolism, hypertension, neuralgia or neuritis, heart disease, and musculoskeletal pain and/or inflammation. From 1999 to 2018, the proportional use of obesogenic medications increased for anticonvulsants (34.4% to 55.0%) but decreased for antidepressants (32.1% to 18.8%), antidiabetics (82.9% to 52.5%), and beta-blockers (83.9% to 80.7%). The proportional use of obesogenic medications was not associated with weight status, except for antipsychotics. CONCLUSIONS: Use of obesogenic medications was common. Differences in the proportional use of obesogenic medication may reflect changing availability of obesogenic versus nonobesogenic medications over time. The decision to prescribe a nonobesogenic alternative, if one exists, is guided by weighing the risks and benefits of available treatments.

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.

Prescription medication use among Canadian children and youth, 2012 to 2017.

BACKGROUND: Prescription medications are used throughout the life course, including among children and youth. Prescribing practices may be influenced by emerging medical conditions, the availability of new medications, changing clinical practices, and evolving knowledge of the safety and effectiveness of medications. The Canadian Health Measures Survey (CHMS) provides national-level information to help monitor the use of prescribed medications in the population. DATA AND METHODS: Based on data from the CHMS (2012 to 2017), this article describes prescription medication use in the past month among those aged 3 to 19 years. Information on up to 45 prescription medications was recorded and classified according to Health Canada's Anatomical Therapeutic Chemical classification. Frequencies and bivariate analyses examined medication use by sociodemographic and health-related factors. The most common medication classes were identified for each age group. RESULTS: An estimated 23% of Canadian children and youth (1.5 million) had used at least one prescription medication in the past month and 9% had used two or more prescription medications.Prescription medication use was more common among those who reported lower levels of general and mental health, as well as among those with asthma (51%), a mood disorder (71%), attention deficit disorder (60%) or a learning disability (43%). Medications for the respiratory and nervous systems were among those most commonly prescribed. Of youth aged 14 years or older, 4% had misused prescription medications for non-medicinal purposes, for the experience, for the feeling they cause or to get high. DISCUSSION: Prescription medication use among children and youth is common in Canada. It is associated with lower levels of self-reported health and the presence of chronic conditions. The estimates provide a benchmark to help monitor prescription drug use in Canada.

Special Diets Among Adults: United States, 2015-2018.

To help achieve and maintain a healthy body weight, support nutrient adequacy, and reduce the risk of chronic disease, the 2015-2020 Dietary Guidelines for Americans recommend following a healthy eating pattern across the lifespan (1). Some people adhere to specific eating patterns, otherwise known as special diets, for the purposes of weight loss or other health-related reasons. This report shows the percentage of U.S. adults who, on a given day, were on any special diet and specific types of special diets in 2015-2018 and trends from 2007-2008 through 2017-2018.

Dietary Supplement Use in Children and Adolescents Aged ≤19 Years - United States, 2017-2018.

Dietary supplement use is common among children and adolescents. During 2013-2014, approximately one third of children and adolescents (persons aged ≤19 years) in the United States were reported to use a dietary supplement in the past 30 days, and use varied by demographic characteristics (1,2). Dietary supplements can contribute substantially to overall nutrient intake, having the potential to both mitigate nutrient shortfalls as well as to lead to nutrient intake above recommended upper limits (3). However, because nutritional needs should generally be met through food consumption according to the 2015-2020 Dietary Guidelines for Americans, only a few dietary supplements are specifically recommended for use among children and adolescents and only under particular conditions (4). The most recently released data from the National Health and Nutrition Examination Survey (NHANES) during 2017-2018 were used to estimate the prevalence of use among U.S. children and adolescents of any dietary supplement, two or more dietary supplements, and specific dietary supplement product types. Trends were calculated for dietary supplement use from 2009-2010 to 2017-2018. During 2017-2018, 34.0% of children and adolescents used any dietary supplement in the past 30 days, with no significant change since 2009-2010. Use of two or more dietary supplements increased from 4.3% during 2009-2010 to 7.1% during 2017-2018. Multivitamin-mineral products were used by 23.8% of children and adolescents, making these the products most commonly used. Because dietary supplement use is common, surveillance of dietary supplement use, combined with nutrient intake from diet, will remain an important component of monitoring nutritional intake in children and adolescents to inform clinical practice and dietary recommendations.

A method for calculating BMI z-scores and percentiles above the 95th percentile of the CDC growth charts.

BACKGROUND: The 2000 CDC growth charts are based on national data collected between 1963 and 1994 and include a set of selected percentiles between the 3rd and 97th and LMS parameters that can be used to obtain other percentiles and associated z-scores. Obesity is defined as a sex- and age-specific body mass index (BMI) at or above the 95th percentile. Extrapolating beyond the 97th percentile is not recommended and leads to compressed z-score values. AIM: This study attempts to overcome this limitation by constructing a new method for calculating BMI distributions above the 95th percentile using an extended reference population. SUBJECTS AND METHODS: Data from youth at or above the 95th percentile of BMI-for-age in national surveys between 1963 and 2016 were modelled as half-normal distributions. Scale parameters for these distributions were estimated at each sex-specific 6-month age-interval, from 24 to 239 months, and then smoothed as a function of age using regression procedures. RESULTS: The modelled distributions above the 95th percentile can be used to calculate percentiles and non-compressed z-scores for extreme BMI values among youth. CONCLUSION: This method can be used, in conjunction with the current CDC BMI-for-age growth charts, to track extreme values of BMI among youth.

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 of Obesity and Severe Obesity Among Adults: United States, 2017-2018.

Obesity is associated with serious health risks (1). Severe obesity further increases the risk of obesity-related complications, such as coronary heart disease and end-stage renal disease (2,3). From 1999-2000 through 2015-2016, a significantly increasing trend in obesity was observed (4). This report provides the most recent national data for 2017-2018 on obesity and severe obesity prevalence among adults by sex, age, and race and Hispanic origin. Trends from 1999-2000 through 2017-2018 for adults aged 20 and over are also presented.

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.