Examination of Prediabetes and Diabetes Testing Among US Pediatric Patients With Overweight or Obesity Using an Electronic Health Record.

Background: Youth with excess weight are at risk of developing type 2 diabetes (T2DM). Guidelines recommend screening for prediabetes and/or T2DM after 10 years of age or after puberty in youth with excess weight who have ≥1 risk factor(s) for T2DM. Electronic health records (EHRs) offer an opportunity to study the use of tests to detect diabetes in youth. Methods: We examined the frequency of (1) diabetes testing and (2) elevated test results among youth aged 10-19 years with at least one BMI measurement in an EHR from 2019 to 2021. We examined the presence of hemoglobin A1C (A1C), fasting plasma glucose (FPG), or oral glucose tolerance test (2-hour plasma glucose [2-hrPG]) results and, among those tested, the frequency of elevated values (A1C ≥6.5%, FPG ≥126 mg/dL, or 2-hrPG ≥200 mg/dL). Patients with pre-existing diabetes (n = 6793) were excluded. Results: Among 1,024,743 patients, 17% had overweight, 21% had obesity, including 8% with severe obesity. Among patients with excess weight, 10% had ≥1 glucose test result. Among those tested, elevated values were more common in patients with severe obesity (27%) and obesity (22%) than in those with healthy weight (8%), and among Black youth (30%) than White youth (13%). Among patients with excess weight, >80% of elevated values fell in the prediabetes range. Conclusions: In youth with excess weight, the use of laboratory tests for prediabetes and T2DM was infrequent. Among youth with test results, elevated FPG, 2hrPG, or A1C levels were most common in those with severe obesity and Black youth.

Probability of 5% or Greater Weight Loss or BMI Reduction to Healthy Weight Among Adults With Overweight or Obesity.

Importance: Information on the probability of weight loss among US adults with overweight or obesity is limited. Objective: To assess the probability of 5% or greater weight loss, 10% or greater weight loss, body mass index (BMI) reduction to a lower BMI category, and BMI reduction to the healthy weight category among US adults with initial overweight or obesity overall and by sex and race. Design, Setting, and Participants: This cohort study obtained data from the IQVIA ambulatory electronic medical records database. The sample consists of US ambulatory patients 17 years or older with at least 3 years of BMI information from January 1, 2009, to February 28, 2022. Minimum age was set at 17 years to allow for the change in BMI or weight starting at 18 years. Maximum age was censored at 70 years. Exposures: Initial BMI (calculated as weight in kilograms divided by height in meters squared) category was the independent variable of interest, and the categories were as follows: lower than 18.5 (underweight), 18.5 to 24.9 (healthy weight), 25.0 to 29.9 (overweight), 30.0 to 34.9 (class 1 obesity), 35.0 to 39.9 (class 2 obesity), and 40.0 to 44.9 and 45.0 or higher (class 3 or severe obesity). Main Outcomes and Measures: The 2 main outcomes were 5% or greater weight loss (ie, a ≥5% reduction in initial weight) and BMI reduction to the healthy weight category (ie, BMI of 18.5-24.9). Results: The 18 461 623 individuals in the sample had a median (IQR) age of 54 (40-66) years and included 10 464 598 females (56.7%) as well as 7.7% Black and 72.3% White patients. Overall, 72.5% of patients had overweight or obesity at the initial visit. Among adults with overweight and obesity, the annual probability of 5% or greater weight loss was low (1 in 10) but increased with higher initial BMI (from 1 in 12 individuals with initial overweight to 1 in 6 individuals with initial BMI of 45 or higher). Annual probability of BMI reduction to the healthy weight category ranged from 1 in 19 individuals with initial overweight to 1 in 1667 individuals with initial BMI of 45 or higher. Both outcomes were generally more likely among females than males and were highest among White females. Over the 3 to 14 years of follow-up, 33.4% of persons with overweight and 41.8% of persons with obesity lost 5% or greater of their initial weight. At the same time, 23.2% of persons with overweight and 2.0% of persons with obesity reduced BMI to the healthy weight category. Conclusions and Relevance: Results of this cohort study indicate that the annual probability of 5% or greater weight loss was low (1 in 10) despite the known benefits of clinically meaningful weight loss, but 5% or greater weight loss was more likely than BMI reduction to the healthy weight category, especially for patients with the highest initial BMIs. Clinicians and public health efforts can focus on messaging and referrals to interventions that are aimed at clinically meaningful weight loss (ie, ≥5%) for adults at any level of excess weight.

Body Mass Index and Associated Medical Expenditures in the US Among Privately Insured Individuals Aged 2 to 19 Years in 2018.

Importance: Nearly 40% of US youth aged 2 to 19 years do not have a body mass index (BMI) in the healthy weight category. However, there are no recent estimates for BMI-associated expenditures using clinical or claims data. Objective: To estimate medical expenditures among US youth across all BMI categories along with sex and age groups. Design, Setting, Participants: This cross-sectional study used IQVIA's ambulatory electronic medical records (AEMR) data set linked with IQVIA's PharMetrics Plus Claims database from January 2018 through December 2018. Analysis was performed from March 25, 2022, through June 20, 2022. It included a convenience sample of a geographically diverse patient population from AEMR and PharMetrics Plus. The study sample included privately insured individuals with a BMI measurement in 2018 and excluded patients with pregnancy-related visits. Exposure: BMI categories. Main Outcomes and Measures: Total medical expenditures were estimated using generalized linear model regression with γ distribution and log-link function. For out-of-pocket (OOP) expenditures, a 2-part model was used that included logistic regression to estimate the probability of positive expenditures followed by generalized linear model. Estimates were shown with and without accounting for sex, race and ethnicity, payer type, geographic region, age interacted with sex and BMI categories, and confounding conditions. Results: The sample included 205 876 individuals aged 2 to 19 years; 104 066 were male (50.5%) and the median age was 12 years. Compared with those with healthy weight, total and OOP expenditures were higher for all other BMI categories. Differences in total expenditures were highest for those with severe obesity ($909; 95% CI, $600-$1218) followed by underweight ($671; 95% CI, $286-$1055) compared with healthy weight. Differences in OOP expenditures were highest for those with severe obesity ($121; 95% CI, $86-$155) followed by underweight ($117; 95% CI, $78-$157) compared with healthy weight. Having underweight was associated with higher total expenditures at ages 2 to 5 years and 6 to 11 years by $679 (95% CI, $228-$1129) and $1166 (95% CI, $632-$1700), respectively; having severe obesity was associated with higher total expenditures at ages 2 to 5 years, 6 to 11 years, and 12 to 17 years by $1035 (95% CI, $208-$1863), $821 (95% CI, $414-$1227), and $1088 (95% CI, $594-$1582), respectively. Conclusions and Relevance: The study team found medical expenditures to be higher for all BMI categories when compared with those with healthy weight. These findings may indicate potential economic value of interventions or treatments aimed at reducing BMI-associated health risks.

Associations between comfort eating and weight change during the COVID-19 pandemic among U.S. adults.

Objective: To examine associations between comfort eating in response to loneliness or stress and weight change during the COVID-19 pandemic among U.S. adults. Design: Quantitative, cross-sectional study. Setting: The 2021 SummerStyles survey data. Subjects: U.S. adults (≥18 years; N = 4068). Measures: The outcome was reported weight changes since the start of the COVID-19 pandemic with four responses: lost weight, weight remained the same, gained weight, and don't know. The exposure variable was frequency of comfort eating in response to loneliness or stress during the past year with three responses: never/ rarely, sometimes, or often/always. Analysis: We used chi-square analysis to examine the independence of survey variables related to weight changes, and comfort eating in response to loneliness or stress during the COVID-19 pandemic. Next, we used a multinomial logistic regression to estimate adjusted odds ratios for weight changes by comfort eating in response to loneliness or stress frequency. Results: Overall, 20.1% of adults reported losing weight, 39.9% remained about the same weight, 30.4% gained weight, and 9.4% did not know about their weight change during the COVID-19 pandemic. Taking comfort by eating in response to loneliness or stress was reported by over 33% of participants (often/always = 8.3%; sometimes = 25.3%). Weight change and comfort eating during the COVID-19 pandemic significantly varied by sociodemographic factors. Respondents that sometimes or often/always reported taking comfort by eating in response to loneliness or stress were more likely to report losing weight (Adjusted Odds Ratio ranges: 1.62-2.99) or gaining weight (Adjusted Odds Ratio ranges: 3.10-4.61) than those who never/rarely took comfort by eating in response to loneliness or stress. Conclusions: Taking comfort by eating when stressed/lonely was significantly associated with reported weight changes during the COVID-19 pandemic. Weight changes may lead to additional health complications. Implementing evidence-based strategies to reduce loneliness or stress and support healthy eating during the COVID-19 pandemic may benefit weight management and future well-being.

Underlying Medical Conditions and Severe Illness Among 540,667 Adults Hospitalized With COVID-19, March 2020-March 2021.

INTRODUCTION: Severe COVID-19 illness in adults has been linked to underlying medical conditions. This study identified frequent underlying conditions and their attributable risk of severe COVID-19 illness. METHODS: We used data from more than 800 US hospitals in the Premier Healthcare Database Special COVID-19 Release (PHD-SR) to describe hospitalized patients aged 18 years or older with COVID-19 from March 2020 through March 2021. We used multivariable generalized linear models to estimate adjusted risk of intensive care unit admission, invasive mechanical ventilation, and death associated with frequent conditions and total number of conditions. RESULTS: Among 4,899,447 hospitalized adults in PHD-SR, 540,667 (11.0%) were patients with COVID-19, of whom 94.9% had at least 1 underlying medical condition. Essential hypertension (50.4%), disorders of lipid metabolism (49.4%), and obesity (33.0%) were the most common. The strongest risk factors for death were obesity (adjusted risk ratio [aRR] = 1.30; 95% CI, 1.27-1.33), anxiety and fear-related disorders (aRR = 1.28; 95% CI, 1.25-1.31), and diabetes with complication (aRR = 1.26; 95% CI, 1.24-1.28), as well as the total number of conditions, with aRRs of death ranging from 1.53 (95% CI, 1.41-1.67) for patients with 1 condition to 3.82 (95% CI, 3.45-4.23) for patients with more than 10 conditions (compared with patients with no conditions). CONCLUSION: Certain underlying conditions and the number of conditions were associated with severe COVID-19 illness. Hypertension and disorders of lipid metabolism were the most frequent, whereas obesity, diabetes with complication, and anxiety disorders were the strongest risk factors for severe COVID-19 illness. Careful evaluation and management of underlying conditions among patients with COVID-19 can help stratify risk for severe illness.

Underlying Medical Conditions Associated With Severe COVID-19 Illness Among Children.

Importance: Information on underlying conditions and severe COVID-19 illness among children is limited. Objective: To examine the risk of severe COVID-19 illness among children associated with underlying medical conditions and medical complexity. Design, Setting, and Participants: This cross-sectional study included patients aged 18 years and younger with International Statistical Classification of Diseases, Tenth Revision, Clinical Modification code U07.1 (COVID-19) or B97.29 (other coronavirus) during an emergency department or inpatient encounter from March 2020 through January 2021. Data were collected from the Premier Healthcare Database Special COVID-19 Release, which included data from more than 800 US hospitals. Multivariable generalized linear models, controlling for patient and hospital characteristics, were used to estimate adjusted risk of severe COVID-19 illness associated with underlying medical conditions and medical complexity. Exposures: Underlying medical conditions and medical complexity (ie, presence of complex or noncomplex chronic disease). Main Outcomes and Measures: Hospitalization and severe illness when hospitalized (ie, combined outcome of intensive care unit admission, invasive mechanical ventilation, or death). Results: Among 43 465 patients with COVID-19 aged 18 years or younger, the median (interquartile range) age was 12 (4-16) years, 22 943 (52.8%) were female patients, and 12 491 (28.7%) had underlying medical conditions. The most common diagnosed conditions were asthma (4416 [10.2%]), neurodevelopmental disorders (1690 [3.9%]), anxiety and fear-related disorders (1374 [3.2%]), depressive disorders (1209 [2.8%]), and obesity (1071 [2.5%]). The strongest risk factors for hospitalization were type 1 diabetes (adjusted risk ratio [aRR], 4.60; 95% CI, 3.91-5.42) and obesity (aRR, 3.07; 95% CI, 2.66-3.54), and the strongest risk factors for severe COVID-19 illness were type 1 diabetes (aRR, 2.38; 95% CI, 2.06-2.76) and cardiac and circulatory congenital anomalies (aRR, 1.72; 95% CI, 1.48-1.99). Prematurity was a risk factor for severe COVID-19 illness among children younger than 2 years (aRR, 1.83; 95% CI, 1.47-2.29). Chronic and complex chronic disease were risk factors for hospitalization, with aRRs of 2.91 (95% CI, 2.63-3.23) and 7.86 (95% CI, 6.91-8.95), respectively, as well as for severe COVID-19 illness, with aRRs of 1.95 (95% CI, 1.69-2.26) and 2.86 (95% CI, 2.47-3.32), respectively. Conclusions and Relevance: This cross-sectional study found a higher risk of severe COVID-19 illness among children with medical complexity and certain underlying conditions, such as type 1 diabetes, cardiac and circulatory congenital anomalies, and obesity. Health care practitioners could consider the potential need for close observation and cautious clinical management of children with these conditions and COVID-19.

Racial and Ethnic Disparities in COVID-19 Incidence by Age, Sex, and Period Among Persons Aged <25 Years - 16 U.S. Jurisdictions, January 1-December 31, 2020.

The COVID-19 pandemic has disproportionately affected racial and ethnic minority groups in the United States. Whereas racial and ethnic disparities in severe COVID-19-associated outcomes, including mortality, have been documented (1-3), less is known about population-based disparities in infection with SARS-CoV-2, the virus that causes COVID-19. In addition, although persons aged <30 years account for approximately one third of reported infections,§ there is limited information on racial and ethnic disparities in infection among young persons over time and by sex and age. Based on 689,672 U.S. COVID-19 cases reported to CDC's case-based surveillance system by jurisdictional health departments, racial and ethnic disparities in COVID-19 incidence among persons aged <25 years in 16 U.S. jurisdictions¶ were described by age group and sex and across three periods during January 1-December 31, 2020. During January-April, COVID-19 incidence was substantially higher among most racial and ethnic minority groups compared with that among non-Hispanic White (White) persons (rate ratio [RR] range = 1.09-4.62). During May-August, the RR increased from 2.49 to 4.57 among non-Hispanic Native Hawaiian and Pacific Islander (NH/PI) persons but decreased among other racial and ethnic minority groups (RR range = 0.52-2.82). Decreases in disparities were observed during September-December (RR range = 0.37-1.69); these decreases were largely because of a greater increase in incidence among White persons, rather than a decline in incidence among racial and ethnic minority groups. NH/PI, non-Hispanic American Indian or Alaska Native (AI/AN), and Hispanic or Latino (Hispanic) persons experienced the largest persistent disparities over the entire period. Ensuring equitable and timely access to preventive measures, including testing, safe work and education settings, and vaccination when eligible is important to address racial/ethnic disparities.

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.

Hospital Length of Stay, Charges, and Costs Associated With a Diagnosis of Obesity in US Children and Youth, 2006-2016.

BACKGROUND: Childhood obesity is linked with adverse health outcomes and associated costs. Current information on the relationship between childhood obesity and inpatient costs is limited. OBJECTIVE: The objective of this study was to describe trends and quantify the link between childhood obesity diagnosis and hospitalization length of stay (LOS), costs, and charges. RESEARCH DESIGN: We use the National Inpatient Sample data from 2006 to 2016. SUBJECTS: The sample includes hospitalizations among children aged 2-19 years. The treatment group of interest includes child hospitalizations with an obesity diagnosis. MEASURES: Hospital LOS, charges, and costs associated with a diagnosis of obesity. RESULTS: We find increases in obesity-coded hospitalizations and associated charges and costs during 2006-2016. Obesity as a primary diagnosis is associated with a shorter hospital LOS (by 1.8 d), but higher charges and costs (by $20,879 and $6049, respectively); obesity as a secondary diagnosis is associated with a longer LOS (by 0.8 d), and higher charges and costs of hospitalizations (by $3453 and $1359, respectively). The most common primary conditions occurring with a secondary diagnosis of obesity are pregnancy conditions, mood disorders, asthma, and diabetes; the effect of a secondary diagnosis of obesity on LOS, charges, and costs holds across these conditions. CONCLUSIONS: Childhood obesity diagnosis-related hospitalizations, charges, and costs increased substantially during 2006-2016, and obesity diagnosis is associated with higher hospitalization charges and costs. Our findings provide clinicians and policymakers with additional evidence of the economic burden of childhood obesity and further justify efforts to prevent and manage the disease.

Prevalence of Workplace Health Practices and Policies in Hospitals: Results From the Workplace Health in America Study.

PURPOSE: To provide a nationally representative description on the prevalences of policies, practices, programs, and supports relating to worksite wellness in US hospitals. DESIGN: Cross-sectional, self-report of hospitals participating in Workplace Health in America (WHA) survey from November 2016 through September 2017. SETTING: Hospitals across the United States. PARTICIPANTS: Random sample of 338 eligible hospitals participating in the WHA survey. MEASURES: We used previous items from the 2004 National Worksite Health Promotion survey. Key measures included presence of Worksite Health Promotion programs, evidence-based strategies, health screenings, disease management programs, incentives, work-life policies, barriers to health promotion program implementation, and occupational safety and health. ANALYSIS: Independent variables included hospital characteristics (eg, size). Dependent characteristics included worksite health promotion components. Descriptive statistics and χ2 analyses were used. RESULTS: Eighty-two percent of hospitals offered a wellness programs during the previous year with larger hospitals more likely than smaller hospitals to offer programs (P < .01). Among hospitals with wellness programs, 69% offered nutrition programs, 74% offered physical activity (PA) programs, and 84% had a policy to restrict all tobacco use. Among those with cafeterias or vending machines, 40% had a policy for healthier foods. Only 47% and 25% of hospitals offered lactation support or healthy sleep programs, respectively. CONCLUSION: Most hospitals offer wellness programs. However, there remain hospitals that do not offer wellness programs. Among those that have wellness programs, most offer supports for nutrition, PA, and tobacco control. Few hospitals offered programs on healthy sleep or lactation support.

US Pediatrician Practices on Early Nutrition, Feeding, and Growth.

OBJECTIVE: Assess pediatrician practices around growth and nutrition for children under 2 years. DESIGN: 2017 cross-sectional survey of a national random sample of the American Academy of Pediatrics members. SETTING: US. PARTICIPANTS: Practicing primary care pediatricians and residents (n = 698). MAIN OUTCOME MEASURES: World Health Organization growth chart use, solid food introduction recommendations, healthy behaviors discussion. ANALYSIS: Descriptive statistics were calculated for nutrition-related questions. McNemar tests compared recommendations on the introduction of different solid foods at <6 months; chi-square tests of independence examined outcomes by pediatrician and practice characteristics. RESULTS: Most respondents (82.2%) reported using the World Health Organization growth charts at all well visits. Nearly half (45.3%) recommended solid food introduction at 6 months; 48.2% recommended <6 months. Cereals were more frequently recommended at <6 months than fruits/vegetables or meats (P <.001). Topics most frequently discussed were limiting juice (92.3%), and sugar-sweetened beverages (92.0%), avoiding restrictive and permissive food practices (30.7%), and avoiding food as a reward (29.1%) were least discussed. Pediatricians in hospital/clinic settings discussed healthy behaviors less than group or solo/2-physician practices. CONCLUSIONS AND IMPLICATIONS: For children under 2 years, most pediatricians reported using recommended growth charts and discussing healthy behaviors. Fewer discussed responsive feeding topics. Results for guiding solid food introduction were mixed. Continued efforts to support pediatricians' work could improve the implementation of recommended practices.

Clinical Decision Supports in Electronic Health Records to Promote Childhood Obesity-Related Care: Results from a 2015 Survey of Healthcare Providers.

Obesity-related clinical decision support tools in electronic health records (EHRs) can improve pediatric care, but the degree of adoption of these tools is unknown. DocStyles 2015 survey data from US pediatric healthcare providers (n = 1,156) were analyzed. Multivariable logistic regression identified provider characteristics associated with three EHR functionalities: automatically calculating body mass index (BMI) percentile (AUTO), displaying BMI trajectory (DISPLAY), and flagging abnormal BMIs (FLAG). Most providers had EHRs (88%). Of those with EHRs, 90% reporting having AUTO, 62% DISPLAY, and 54% FLAG functionalities. Only provider age was associated with all three functionalities. Compared to providers aged > 54 years, providers < 40 years had greater odds for: AUTO (adjusted odds ratio [aOR], 3.0; 95% confidence interval [CI], 1.58-5.70), DISPLAY (aOR, 2.07; 95% CI, 1.38-3.12), and FLAG (aOR, 1.67; 95% CI, 1.14-2.44). Future investigations can elucidate causes of lower adoption of EHR functions that display growth trajectories and flag abnormal BMIs.

US Pediatrician Practices and Attitudes Concerning Childhood Obesity: 2006 and 2017.

OBJECTIVE: To compare primary care pediatricians' practices and attitudes regarding obesity assessment, prevention, and treatment in children 2 years and older in 2006 and 2017. STUDY DESIGN: National, random samples of American Academy of Pediatrics members were surveyed in 2006, 2010, and 2017 on practices and attitudes regarding overweight and obesity (analytic n = 655, 592, and 558, respectively). Using logistic regression models (controlling for pediatrician and practice characteristics), we examined survey year with predicted values (PVs), including body mass index (BMI) assessment across 2006, 2010, and 2017 and practices and attitudes in 2006 and 2017. RESULTS: Pediatrician respondents in 2017 were significantly more likely than in 2006 and 2010 to report calculating and plotting BMI at every well-child visit, with 96% of 2017 pediatricians reporting they do this. Compared with 2006, in 2017 pediatricians were more likely to discuss family behaviors related to screen time, sugar-sweetened beverages, and eating meals together, P < .001 for all. There were no observed differences in frequency of discussions on parental role modeling of nutrition and activity-related behaviors, roles in food selection, and frequency of eating fast foods or eating out. Pediatricians in 2017 were more likely to agree BMI adds new information relevant to medical care (PV = 69.8% and 78.1%), they have support staff for screening (PV = 45.3% and 60.5%), and there are effective means of treating obesity (PV = 36.3% and 56.2%), P < .001 for all. CONCLUSIONS: Results from cross-sectional surveys in 2006 and 2017 suggest nationwide, practicing pediatricians have increased discussions with families on several behaviors and their awareness and practices around obesity care.

Screening and Referral for Childhood Obesity: Adherence to the U.S. Preventive Services Task Force Recommendation.

INTRODUCTION: The U.S. Preventive Services Task Force recommends clinicians screen children aged 6 years or older for obesity and offer or refer children with obesity to intensive weight management programs. This study explores clinician awareness of weight management programs meeting the recommendation, adherence to the recommendation of screening and referral, and associations between provider and practice characteristics and weight management program referrals. METHODS: This cross-sectional study used data from the DocStyles survey 2017, a web-based panel survey, analyzed in 2017. Among 1,023 clinicians who see pediatric patients, this study examined clinician awareness of weight management programs in their communities that met the recommendation, practice of screening for childhood obesity, and referral to weight management programs. Multivariable logistic regression estimated associations between the demographic and practice characteristics of clinicians and weight management program referrals. RESULTS: Only 24.6% of surveyed clinicians were aware of a weight management program that met the U.S. Preventive Services Task Force recommendation in their community; of those aware, 88.9% referred patients to these weight management programs. Most (83.6%) clinicians screened children for obesity in ≥75% of visits. Overall, 53.5% of clinicians provided referrals to weight management programs. Referral was higher among female clinicians and clinicians serving mostly middle-income patients. Providers without teaching hospital privileges had lower odds of referral. CONCLUSIONS: Adherence to clinical recommendations is essential to curbing the childhood obesity epidemic. Only one in four surveyed clinicians were aware of weight management programs in their community meeting U.S. Preventive Services Task Force criteria. Half of clinicians referred pediatric patients with obesity to a weight management program. Results suggest efforts are needed to increase awareness of, and referral to, weight management programs meeting the recommendation.

Health Care Provider Counseling for Weight Loss Among Adults with Arthritis and Overweight or Obesity - United States, 2002-2014.

In the United States, 54.4 million adults report having doctor-diagnosed arthritis (1). Among adults with arthritis, 32.7% and 38.1% also have overweight and obesity, respectively (1), with obesity being more prevalent among persons with arthritis than among those who do not have arthritis (2). Furthermore, severe joint pain among adults with arthritis in 2014 was reported by 23.5% of adults with overweight and 31.7% of adults with obesity (3). The American College of Rheumatology recommends weight loss for adults with hip or knee osteoarthritis and overweight or obesity,* which can improve function and mobility while reducing pain and disability (4,5). The Healthy People 2020 target for health care provider (hereafter provider) counseling for weight loss among persons with arthritis and overweight or obesity is 45.3%.† Adults with overweight or obesity who receive weight-loss counseling from a provider are approximately four times more likely to attempt to lose weight than are those who do not receive counseling (6). To estimate changes in the prevalence of provider counseling for weight loss reported by adults with arthritis and overweight or obesity, CDC analyzed National Health Interview Survey (NHIS) data.§ Overall, age-standardized estimates of provider counseling for weight loss increased by 10.4 percentage points from 2002 (35.1%; 95% confidence interval [CI] = 33.0-37.3) to 2014 (45.5%; 95% CI = 42.9-48.1) (p<0.001). Providing comprehensive behavioral counseling (including nutrition, physical activity, and self-management education) and encouraging evidence-based weight-loss program participation can result in enhanced health benefits for this population.

Childhood Obesity Research Demonstration (CORD): the cross-site overview and opportunities for interventions addressing obesity community-wide.

BACKGROUND: This is the first of a set of articles in this issue on the Childhood Obesity Research Demonstration (CORD) project and provides an overview of the multisite approach and community-wide interventions. Innovative multisetting, multilevel approaches that integrate primary healthcare and public health interventions to improve outcomes for children with obesity need to be evaluated. The CORD project aims to improve BMI and obesity-related behaviors among underserved 2- to 12-year-old children by utilizing these approaches. METHODS: The CORD consortium, structure, model terminology and key components, and common measures were solidified in year 1 of the CORD project. Demonstration sites applied the CORD model across communities in years 2 and 3. Evaluation plans for year 4 include site-specific analyses as well as cross-site impact, process, and sustainability evaluations. RESULTS: The CORD approach resulted in commonalities and differences in participant, intervention, comparison, and outcome elements across sites. Products are to include analytic results as well as cost assessment, lessons learned, tools, and materials. DISCUSSION: Foreseen opportunities and challenges arise from the similarities and unique aspects across sites. Communities adapted interventions to fit their local context and build on strengths, but, in turn, this flexibility makes cross-site evaluation challenging. CONCLUSION: The CORD project represents an evidence-based approach that integrates primary care and public health strategies and evaluates multisetting multilevel interventions, thus adding to the limited research in this field. CORD products will be disseminated to a variety of stakeholders to aid the understanding, prevention, and management of childhood obesity.

The Childhood Obesity Research Demonstration project: a team approach for supporting a multisite, multisector intervention.

BACKGROUND: Comprehensive multisector, multilevel approaches are needed to address childhood obesity. This article introduces the structure of a multidisciplinary team approach used to support and guide the multisite, multisector interventions implemented as part of the Childhood Obesity Research Demonstration (CORD) project. This article will describe the function, roles, and lessons learned from the CDC-CORD approach to project management. METHODS: The CORD project works across multisectors and multilevels in three demonstration communities. Working with principal investigators and their research teams who are engaging multiple stakeholder groups, including community organizations, schools and child care centers, health departments, and healthcare providers, can be a complex endeavor. To best support the community-based research project, scientific and programmatic expertise in a wide range of areas was required. The team was configured based on the skill sets needed to interact with the various levels of staff working with the project. CONCLUSIONS: By thoughtful development of the team and processes, an efficient system for supporting the multisite, multisector intervention project sites was developed. The team approach will be formally evaluated at the end of the project period.