Impact of a brief training on motivational interviewing and the 5A's approach on weight-related counseling practices of pediatricians.

Background: Pediatric primary healthcare providers are well-positioned to provide healthy lifestyle and weight management related counseling but many lack training in evidence-based messages and methods. Objectives: We assessed the impact of a brief, easy-to-access training for pediatric healthcare providers, (the Strong4Life Provider Training), designed to introduce/review current evidence-based messages and methods and improve healthy weight-related assessment and counseling practices. Methods: Following their well-child visit, a convenience sample of children 12-17 years and parents of children 6-11 years (N = 121) of randomly selected Strong4Life trained (N = 15) and untrained (N = 15) pediatricians were administered a survey designed to assess the frequency, content, and patient satisfaction with weight management-related counseling provided. Results from parents and patients of trained pediatricians (N = 62) compared to those from parents and patients of untrained pediatricians (n = 59) were compared using chi-square tests. Generalized estimating equations were used to account for lack of independence among respondents from the same practice. P-values less than 0.05 were considered to be statistically significant. Results: Parents/patients of trained pediatricians were more likely than those of an untrained pediatrician to report having been asked about child's consumption of fruit/vegetables, 57 (92%) versus 44 (75%), p = 0.04 and sugary drinks, 50 (81%) versus 29 (49%), p = 0.005, and their readiness for behavior change, 47 (76%) versus 29 (49%), p = 0.005, and to set a behavior change goal, 36 (59%) versus. 23 (40%), p = 0.005. Regardless of training status, physical activity, screen time, and weight status were assessed for most patients, and most were satisfied with the discussion. Few (21%) were asked about barriers to behavior change.

Measuring BMI change among children and adolescents.

BACKGROUND: Weight control programs for children monitor BMI changes using BMI z-scores that adjust BMI for the sex and age of the child. It is, however, uncertain if BMIz is the best metric for assessing BMI change. OBJECTIVE: To identify which of 6 BMI metrics is optimal for assessing change. We considered a metric to be optimal if its short-term variability was consistent across the entire BMI distribution. SUBJECTS: 285 643 2- to 17-year-olds with BMI measured 3 times over a 10- to 14-month period. METHODS: We summarized each metric's variability using the within-child standard deviation. RESULTS: Most metrics' initial or mean value correlated with short-term variability (|r| ~ 0.3 to 0.5). The metric for which the within-child variability was largely independent (r = 0.13) of the metric's initial or mean value was the percentage of the 50th expressed on a log scale. However, changes in this metric between the first and last visits were highly (r ≥ 0.97) correlated with changes in %95th and %50th. CONCLUSIONS: Log %50 was the metric for which the short-term variability was largely independent of a child's BMI. Changes in log %50th, %95th, and %50th are strongly correlated.

Interrelationships among age at adiposity rebound, BMI during childhood, and BMI after age 14 years in an electronic health record database.

OBJECTIVE: This study compared the importance of age at adiposity rebound versus childhood BMI to subsequent BMI levels in a longitudinal analysis. METHODS: From the electronic health records of 4.35 million children, a total of 12,228 children were selected who were examined at least once each year between ages 2 and 7 years and reexamined after age 14 years. The minimum number of examinations per child was six. Each child's rebound age was estimated using locally weighted regression (lowess), a smoothing technique. RESULTS: Children who had a rebound age < 3 years were, on average, 7 kg/m2 heavier after age 14 years than were children with a rebound age ≥ 7 years. However, BMI after age 14 years was more strongly associated with BMI at the rebound than with rebound age (r = 0.57 vs. -0.44). Furthermore, a child's BMI at age 3 years provided more information on BMI after age 14 years than did rebound age. In addition, rebound age provided no information on subsequent BMI if a child's BMI at age 6 years was known. CONCLUSIONS: Although rebound age is related to BMI after age 14 years, a child's BMI at age 3 years provides more information and is easier to obtain.

Longitudinal Trends in Body Mass Index Before and During the COVID-19 Pandemic Among Persons Aged 2-19 Years - United States, 2018-2020.

Obesity is a serious health concern in the United States, affecting more than one in six children (1) and putting their long-term health and quality of life at risk.* During the COVID-19 pandemic, children and adolescents spent more time than usual away from structured school settings, and families who were already disproportionally affected by obesity risk factors might have had additional disruptions in income, food, and other social determinants of health.† As a result, children and adolescents might have experienced circumstances that accelerated weight gain, including increased stress, irregular mealtimes, less access to nutritious foods, increased screen time, and fewer opportunities for physical activity (e.g., no recreational sports) (2,3). CDC used data from IQVIA's Ambulatory Electronic Medical Records database to compare longitudinal trends in body mass index (BMI, kg/m2) among a cohort of 432,302 persons aged 2-19 years before and during the COVID-19 pandemic (January 1, 2018-February 29, 2020 and March 1, 2020-November 30, 2020, respectively). Between the prepandemic and pandemic periods, the rate of BMI increase approximately doubled, from 0.052 (95% confidence interval [CI] = 0.051-0.052 to 0.100 (95% CI = 0.098-0.101) kg/m2/month (ratio = 1.93 [95% CI = 1.90-1.96]). Persons aged 2-19 years with overweight or obesity during the prepandemic period experienced significantly higher rates of BMI increase during the pandemic period than did those with healthy weight. These findings underscore the importance of efforts to prevent excess weight gain during and following the COVID-19 pandemic, as well as during future public health emergencies, including increased access to efforts that promote healthy behaviors. These efforts could include screening by health care providers for BMI, food security, and social determinants of health, increased access to evidence-based pediatric weight management programs and food assistance resources, and state, community, and school resources to facilitate healthy eating, physical activity, and chronic disease prevention.

Body Mass Index and Risk for COVID-19-Related Hospitalization, Intensive Care Unit Admission, Invasive Mechanical Ventilation, and Death - United States, March-December 2020.

Obesity* is a recognized risk factor for severe COVID-19 (1,2), possibly related to chronic inflammation that disrupts immune and thrombogenic responses to pathogens (3) as well as to impaired lung function from excess weight (4). Obesity is a common metabolic disease, affecting 42.4% of U.S. adults (5), and is a risk factor for other chronic diseases, including type 2 diabetes, heart disease, and some cancers.† The Advisory Committee on Immunization Practices considers obesity to be a high-risk medical condition for COVID-19 vaccine prioritization (6). Using data from the Premier Healthcare Database Special COVID-19 Release (PHD-SR),§ CDC assessed the association between body mass index (BMI) and risk for severe COVID-19 outcomes (i.e., hospitalization, intensive care unit [ICU] or stepdown unit admission, invasive mechanical ventilation, and death). Among 148,494 adults who received a COVID-19 diagnosis during an emergency department (ED) or inpatient visit at 238 U.S. hospitals during March-December 2020, 28.3% had overweight and 50.8% had obesity. Overweight and obesity were risk factors for invasive mechanical ventilation, and obesity was a risk factor for hospitalization and death, particularly among adults aged <65 years. Risks for hospitalization, ICU admission, and death were lowest among patients with BMIs of 24.2 kg/m2, 25.9 kg/m2, and 23.7 kg/m2, respectively, and then increased sharply with higher BMIs. Risk for invasive mechanical ventilation increased over the full range of BMIs, from 15 kg/m2 to 60 kg/m2. As clinicians develop care plans for COVID-19 patients, they should consider the risk for severe outcomes in patients with higher BMIs, especially for those with severe obesity. These findings highlight the clinical and public health implications of higher BMIs, including the need for intensive COVID-19 illness management as obesity severity increases, promotion of COVID-19 prevention strategies including continued vaccine prioritization (6) and masking, and policies to ensure community access to nutrition and physical activities that promote and support a healthy BMI.

A Longitudinal Comparison of Alternatives to Body Mass Index Z-Scores for Children with Very High Body Mass Indexes.

OBJECTIVE: The current Centers for Disease Control and Prevention (CDC) body mass index (BMI) z-scores are inaccurate for BMIs of ≥97th percentile. We, therefore, considered 5 alternatives that can be used across the entire BMI distribution: modified BMI-for-age z-score (BMIz), BMI expressed as a percentage of the 95th percentile (%CDC95th percentile), extended BMIz, BMI expressed as a percentage of the median (%median), and %median adjusted for the dispersion of BMIs. STUDY DESIGN: We illustrate the behavior of the metrics among children of different ages and BMIs. We then compared the longitudinal tracking of the BMI metrics in electronic health record data from 1.17 million children in PEDSnet using the intraclass correlation coefficient to determine if 1 metric was superior. RESULTS: Our examples show that using CDC BMIz for high BMIs can result in nonsensical results. All alternative metrics showed higher tracking than CDC BMIz among children with obesity. Of the alternatives, modified BMIz performed poorly among children with severe obesity, and %median performed poorly among children who did not have obesity at their first visit. The highest intraclass correlation coefficients were generally seen for extended BMIz, adjusted %median, and %CDC95th percentile. CONCLUSIONS: Based on the examples of differences in the BMI metrics, the longitudinal tracking results and current familiarity BMI z-scores and percentiles. Both extended BMIz and extended BMI percentiles may be suitable replacements for the current z-scores and percentiles. These metrics are identical to those in the CDC growth charts for BMIs of <95th percentile and are superior for very high BMIs. Researchers' familiarity with the current CDC z-scores and clinicians with the CDC percentiles may ease the transition to the extended BMI scale.

Associations between Food Policy Councils and Policies That Support Healthy Food Access: A National Survey of Community Policy Supports.

Food policy councils (FPCs) are one form of community coalition that aims to address challenges to local food systems and enhance availability, accessibility, and affordability of healthy foods for local residents. We used data from the 2014 National Survey of Community-Based Policy and Environmental Supports for Healthy Eating and Active Living, a nationally representative survey of US municipalities (n = 2029), to examine the prevalence of FPCs and cross-sectional associations between FPCs and four types of supports for healthy food access (approaches to help food stores, practices to support farmers markets, transportation-related supports, and community planning documents). Overall, 7.7% of municipalities reported having a local or regional FPC. FPCs were more commonly reported among larger municipalities with ≥50,000 people (29.2%, 95% Confidence Interval (CI): 21.6, 36.8) and western region municipalities (13.2%, 95% CI: 9.6, 16.8). After multivariable adjustment, municipalities with FPCs had significantly higher odds of having all four types of supports, compared to those without FPCs (adjusted odds ratio (aOR) range: 2.4-3.4). Among municipalities with FPCs (n = 156), 41% reported having a local government employee or elected official as a member, and 46% had a designated health or public health representative. Although FPCs were uncommon, municipalities that reported having a local or regional FPC were more likely to report having supports for healthy food access for their residents.

Percentage of Adolescents Meeting Federal Fruit and Vegetable Intake Recommendations - Youth Risk Behavior Surveillance System, United States, 2017.

According to the 2020-2025 Dietary Guidelines for Americans, persons should consume fruits and vegetables as part of a healthy eating pattern to reduce their risk for diet-related chronic diseases, such as cardiovascular disease, type 2 diabetes, some cancers, and obesity.* A healthy diet is important for healthy growth in adolescence, especially because adolescent health behaviors might continue into adulthood (1). The U.S. Department of Agriculture (USDA) recommends minimum daily intake of 1.5 cups of fruit and 2.5 cups of vegetables for females aged 14-18 years and 2 cups of fruit and 3 cups of vegetables for males aged 14-18 years.† Despite the benefits of fruit and vegetable consumption, few adolescents consume these recommended amounts (2-4). In 2013, only 8.5% of high school students met the recommendation for fruit consumption, and only 2.1% met the recommendation for vegetable consumption (2). To update the 2013 data, CDC analyzed data from the 2017 national and state Youth Risk Behavior Surveys (YRBSs) to describe the percentage of students who met intake recommendations, overall and by sex, school grade, and race/ethnicity. The median frequencies of fruit and vegetable consumption nationally were 0.9 and 1.1 times per day, respectively. Nationally, 7.1% of students met USDA intake recommendations for fruits (95% confidence interval [CI] = 4.0-10.3) and 2.0% for vegetables (upper 95% confidence limit = 7.9) using previously established scoring algorithms. State-specific estimates of the percentage of students meeting fruit intake recommendations ranged from 4.0% (Connecticut) to 9.3% (Louisiana), and the percentage meeting vegetable intake recommendations ranged from 0.6% (Kansas) to 3.7% (New Mexico). Additional efforts to expand the reach of existing school and community programs or to identify new effective strategies, such as social media approaches, might help address barriers and improve adolescent fruit and vegetable consumption.

Race/Ethnicity, Underlying Medical Conditions, Homelessness, and Hospitalization Status of Adult Patients with COVID-19 at an Urban Safety-Net Medical Center - Boston, Massachusetts, 2020.

As of July 5, 2020, approximately 2.8 million coronavirus disease 2019 (COVID-19) cases and 130,000 COVID-19-associated deaths had been reported in the United States (1). Populations historically affected by health disparities, including certain racial and ethnic minority populations, have been disproportionally affected by and hospitalized with COVID-19 (2-4). Data also suggest a higher prevalence of infection with SARS-CoV-2, the virus that causes COVID-19, among persons experiencing homelessness (5). Safety-net hospitals,† such as Boston Medical Center (BMC), which provide health care to persons regardless of their insurance status or ability to pay, treat higher proportions of these populations and might experience challenges during the COVID-19 pandemic. This report describes the characteristics and clinical outcomes of adult patients with laboratory-confirmed COVID-19 treated at BMC during March 1-May 18, 2020. During this time, 2,729 patients with SARS-CoV-2 infection were treated at BMC and categorized into one of the following mutually exclusive clinical severity designations: exclusive outpatient management (1,543; 56.5%), non-intensive care unit (ICU) hospitalization (900; 33.0%), ICU hospitalization without invasive mechanical ventilation (69; 2.5%), ICU hospitalization with mechanical ventilation (119; 4.4%), and death (98; 3.6%). The cohort comprised 44.6% non-Hispanic black (black) patients and 30.1% Hispanic or Latino (Hispanic) patients. Persons experiencing homelessness accounted for 16.4% of patients. Most patients who died were aged ≥60 years (81.6%). Clinical severity differed by age, race/ethnicity, underlying medical conditions, and homelessness. A higher proportion of Hispanic patients were hospitalized (46.5%) than were black (39.5%) or non-Hispanic white (white) (34.4%) patients, a finding most pronounced among those aged <60 years. A higher proportion of non-ICU inpatients were experiencing homelessness (24.3%), compared with homeless patients who were admitted to the ICU without mechanical ventilation (15.9%), with mechanical ventilation (15.1%), or who died (15.3%). Patient characteristics associated with illness and clinical severity, such as age, race/ethnicity, homelessness, and underlying medical conditions can inform tailored strategies that might improve outcomes and mitigate strain on the health care system from COVID-19.

Potential Indirect Effects of the COVID-19 Pandemic on Use of Emergency Departments for Acute Life-Threatening Conditions - United States, January-May 2020.

On March 13, 2020, the United States declared a national emergency in response to the coronavirus disease 2019 (COVID-19) pandemic. Subsequently, states enacted stay-at-home orders to slow the spread of SARS-CoV-2, the virus that causes COVID-19, and reduce the burden on the U.S. health care system. CDC* and the Centers for Medicare & Medicaid Services (CMS)† recommended that health care systems prioritize urgent visits and delay elective care to mitigate the spread of COVID-19 in health care settings. By May 2020, national syndromic surveillance data found that emergency department (ED) visits had declined 42% during the early months of the pandemic (1). This report describes trends in ED visits for three acute life-threatening health conditions (myocardial infarction [MI, also known as heart attack], stroke, and hyperglycemic crisis), immediately before and after declaration of the COVID-19 pandemic as a national emergency. These conditions represent acute events that always necessitate immediate emergency care, even during a public health emergency such as the COVID-19 pandemic. In the 10 weeks following the emergency declaration (March 15-May 23, 2020), ED visits declined 23% for MI, 20% for stroke, and 10% for hyperglycemic crisis, compared with the preceding 10-week period (January 5-March 14, 2020). EDs play a critical role in diagnosing and treating life-threatening conditions that might result in serious disability or death. Persons experiencing signs or symptoms of serious illness, such as severe chest pain, sudden or partial loss of motor function, altered mental state, signs of extreme hyperglycemia, or other life-threatening issues, should seek immediate emergency care, regardless of the pandemic. Clear, frequent, highly visible communication from public health and health care professionals is needed to reinforce the importance of timely care for medical emergencies and to assure the public that EDs are implementing infection prevention and control guidelines that help ensure the safety of their patients and health care personnel.

Data for Decision-Making: Exploring the Division of Nutrition, Physical Activity, and Obesity's Data, Trends, and Maps.

Public health practitioners need quick and easy access to reliable surveillance data to monitor states' progress over time, compare benchmarks nationally or among states, and make strategic decisions about priorities and resources. Data, Trends, and Maps (DTM) at https://www.cdc.gov/nccdphp/dnpao/data-trends-maps/index.html is a free, online interactive database that houses and displays data on nutrition, physical activity, breastfeeding, and obesity that practitioners can use for public health action. Created in 2015 by the Centers for Disease Control and Prevention's (CDC) Division of Nutrition, Physical Activity, and Obesity, DTM was updated and relaunched in April 2017 with the capability to customize and download data sets directly; DTM also has other user-friendly features, such as visualization options. Since its relaunch, DTM has received more than 386,000 page views from approximately 110,000 unique visitors. However, the potential exists for more widespread use of DTM if more public health practitioners understood what the site offered and how others have used it in the field. Here, we explain how public health practitioners can explore the most recent state-level data on nutrition, physical activity, breastfeeding, and obesity and use this data to inform programmatic and policy efforts to prevent and control chronic diseases. We demonstrate 3 different ways practitioners can visualize data (ie, Explore by Location, Explore by Topic, and the Open Data Portal) and present 3 real-world examples to highlight DTM's utility as a public health tool.

Local government retail incentives for healthier food retailers in the USA, 2014.

OBJECTIVE: National public health organizations recommend that local governments improve access to healthy foods. One way is by offering incentives for food retailer development and operation, but little is known about incentive use nationwide. We aimed to describe the national prevalence of local government reported incentives to increase access to healthy food options in three major food retail settings (farmers' markets, supermarkets, and convenience or corner (smaller) stores) overall and by municipality characteristics. DESIGN: Cross-sectional study using data from the 2014 National Survey of Community-Based Policy and Environmental Supports for Healthy Eating and Active Living. SETTING: USA, nationally representative survey of 2029 municipalities. PARTICIPANTS: Municipal officials (e.g. city/town managers or planners; n 1853). RESULTS: Overall, 67 % of municipalities reported incentives to support farmers' markets, 34 % reported incentives to encourage opening new supermarkets, and 14 % reported incentives to help existing convenience or corner stores. Municipality characteristics significantly associated with incentive use were larger population size (all settings), location in Midwest v. West (supermarkets, smaller stores), higher poverty level (farmers' markets) and ≤50 % of the population non-Hispanic White (supermarkets, smaller stores). The most commonly reported individual incentives were permission of sales on city property for farmers' markets, tax credits for supermarkets and linkage to revitalization projects for smaller stores. CONCLUSIONS: Most municipalities offered food retail incentives for farmers' markets, but fewer used incentives to open new supermarkets or assist existing smaller stores. National data can set benchmarks, provide relative comparisons for communities and identify areas for improvement.