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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.

Approximately 30 million persons in the United States have diabetes.* Persons with diabetes are at risk for vision loss from diabetic retinopathy and other eye diseases (1). Diabetic retinopathy, the most common diabetes-related eye disease, affects 29% of U.S. adults aged ≥40 years with diabetes (2) and is the leading cause of incident blindness among working-age adults (1). It is caused by chronically high blood glucose damaging blood vessels in the retina.† Annual dilated eye exams are recommended for persons with diabetes because early detection and timely treatment of diabetic eye diseases can prevent irreversible vision loss§,¶ (3,4). Studies have documented prevalence of annual eye exams among U.S. adults with diabetes (5,6); however, a lack of recent state-level data limits identification of geographic disparities in adherence to this recommendation. Medicare claims from the 50 states, the District of Columbia (DC), Puerto Rico, and U.S. Virgin Islands (USVI) were examined to assess the prevalence of eye exams in 2017 among beneficiaries with diabetes who were continuously enrolled in Part B fee-for-service insurance, which covers annual eye exams for beneficiaries with diabetes.** This report also examines disparities, by state and race/ethnicity, in receipt of eye exams. Nationally, 54.1% of beneficiaries with diabetes had an eye exam in 2017. Prevalence ranged from 43.9% in Puerto Rico to 64.8% in Rhode Island. Fewer than 50% of beneficiaries received an eye exam in seven states (Alabama, Alaska, Kentucky, Louisiana, Nevada, West Virginia, and Wyoming) and Puerto Rico. Non-Hispanic white (white) beneficiaries had a higher prevalence of receiving an eye exam (55.6%) than did non-Hispanic blacks (blacks) (48.9%) and Hispanics (48.2%). Barriers to receiving eye care (e.g., suboptimal clinical care coordination and referral, low health literacy, and lack of perceived need for care) might limit Medicare beneficiaries' ability to follow this preventive care recommendation. Understanding and addressing these barriers might prevent irreversible vision loss among persons with diabetes.

Purpose: To examine associations of adolescent sugar-sweetened beverage (SSB) intake with parent SSB intake and parent and adolescent attitudes about limiting SSB and junk food (SSB/JF) intake. Design: Quantitative, cross-sectional study. Setting: The 2014 Family Life, Activity, Sun, Health, and Eating study. Sample: Parent–adolescent dyads (N = 1555). Measures: The outcome was adolescent SSB intake. Exposure variables were parent SSB intake, sociodemographics, and parent and adolescent attitudes about SSB/JF intake (responses: agree, neither, or disagree). Analysis: Multinomial logistic regressions estimated adjusted odds ratios (aOR) and 95% confidence intervals (CIs). Results: Half (49.5%) of adolescents and 33.7% of parents consumed SSB ≥1 time/day. Parent daily SSB intake was associated with adolescent daily SSB intake (aOR = 8.9; CI = 4.6-17.3) [referent: no consumption]. Adolescents who disagreed on having confidence to limit SSB/JF intake had higher odds of daily SSB intake (aOR = 3.5; CI = 1.8-6.8), as did those who disagreed they felt bad about themselves if they did not limit SSB/JF intake (aOR = 1.9; CI=1.1-3.3), compared to adolescents who agreed with these attitudes. No parental attitudes were significant. Conclusion: Higher odds of daily SSB intake among adolescents was associated with parent SSB intake and adolescent attitudes about confidence in, and feeling bad about, limiting SSB/JF intake. Parent attitudes were not associated with daily adolescent SSB intake. Efforts to reduce adolescent SSB intake could consider strategies geared toward improving adolescent attitudes and dietary behaviors and parental SSB intake.

Key Points Question How many people in the US are living with uncorrectable visual acuity loss or blindness? Findings This bayesian meta-analysis generated an estimate that, in 2017, there were 7.08 million people living with visual acuity loss, of whom 1.08 million were living with blindness. Meaning Per this study, uncorrectable visual acuity loss and blindness are even larger drivers of health burden in the US than was previously known., This meta-analysis combines population-based surveys and data from the US Centers for Disease Control and Prevention’s Vision and Eye Health Surveillance System to produce estimates of visual acuity loss and blindness by age, sex, race/ethnicity, and US state., Importance Globally, more than 250 million people live with visual acuity loss or blindness, and people in the US fear losing vision more than memory, hearing, or speech. But it appears there are no recent empirical estimates of visual acuity loss or blindness for the US. Objective To produce estimates of visual acuity loss and blindness by age, sex, race/ethnicity, and US state. Data Sources Data from the American Community Survey (2017), National Health and Nutrition Examination Survey (1999-2008), and National Survey of Children’s Health (2017), as well as population-based studies (2000-2013), were included. Study Selection All relevant data from the US Centers for Disease Control and Prevention’s Vision and Eye Health Surveillance System were included. Data Extraction and Synthesis The prevalence of visual acuity loss or blindness was estimated, stratified when possible by factors including US state, age group, sex, race/ethnicity, and community-dwelling or group-quarters status. Data analysis occurred from March 2018 to March 2020. Main Outcomes or Measures The prevalence of visual acuity loss (defined as a best-corrected visual acuity greater than or equal to 0.3 logMAR) and blindness (defined as a logMAR of 1.0 or greater) in the better-seeing eye. Results For 2017, this meta-analysis generated an estimated US prevalence of 7.08 (95% uncertainty interval, 6.32-7.89) million people living with visual acuity loss, of whom 1.08 (95% uncertainty interval, 0.82-1.30) million people were living with blindness. Of this, 1.62 (95% uncertainty interval, 1.32-1.92) million persons with visual acuity loss are younger than 40 years, and 141 000 (95% uncertainty interval, 95 000-187 000) persons with blindness are younger than 40 years. Conclusions and Relevance This analysis of all available data with modern methods produced estimates substantially higher than those previously published.

Congregate work and residential locations are at increased risk for infectious disease transmission including respiratory illness outbreaks. SARS-CoV-2, the virus that causes coronavirus disease 2019 (COVID-19), is primarily spread person to person through respiratory droplets. Nationwide, the meat and poultry processing industry, an essential component of the U.S. food infrastructure, employs approximately 500,000 persons, many of whom work in proximity to other workers (1). Because of reports of initial cases of COVID-19, in some meat processing facilities, states were asked to provide aggregated data concerning the number of meat and poultry processing facilities affected by COVID-19 and the number of workers with COVID-19 in these facilities, including COVID-19-related deaths. Qualitative data gathered by CDC during on-site and remote assessments were analyzed and summarized. During April 9-27, aggregate data on COVID-19 cases among 115 meat or poultry processing facilities in 19 states were reported to CDC. Among these facilities, COVID-19 was diagnosed in 4,913 (approximately 3%) workers, and 20 COVID-19-related deaths were reported. Facility barriers to effective prevention and control of COVID-19 included difficulty distancing workers at least 6 feet (2 meters) from one another (2) and in implementing COVID-19-specific disinfection guidelines.* Among workers, socioeconomic challenges might contribute to working while feeling ill, particularly if there are management practices such as bonuses that incentivize attendance. Methods to decrease transmission within the facility include worker symptom screening programs, policies to discourage working while experiencing symptoms compatible with COVID-19, and social distancing by workers. Source control measures (e.g., the use of cloth face covers) as well as increased disinfection of high-touch surfaces are also important means of preventing SARS-CoV-2 exposure. Mitigation efforts to reduce transmission in the community should also be considered. Many of these measures might also reduce asymptomatic and presymptomatic transmission (3). Implementation of these public health strategies will help protect workers from COVID-19 in this industry and assist in preserving the critical meat and poultry production infrastructure (4).

Frequent sugar-sweetened beverage (SSB) intake is associated with weight gain in women, and pre-pregnancy overweight and excessive gestational weight gain are linked to adverse pregnancy outcomes. SSB intake information for women of reproductive age (WRA) is limited. We described SSB intake among non-pregnant and pregnant WRA and identified correlates of daily intake. Using 2017 Behavioral Risk Factor Surveillance System data, we analyzed SSB intake (regular soda, fruit drinks, sweet tea, sports/energy drinks) for 11,321 non-pregnant and 392 pregnant WRA (18–49 years) in 12 states and D.C. Adjusted odds ratios (aOR) for daily (≥ 1 time/day) SSB intake (reference

In 2009, the Ministry of Health of Kyrgyzstan launched a national Infant and Young Child Nutrition (IYCN) program which included point-of-use fortification of foods with micronutrient powders (MNP) containing iron, vitamin A, and other micronutrients. Caretakers of children aged 6–23 months were given 30 sachets of MNP every 2 months. Micronutrient surveys were conducted in 2009 and 2013. The objective of the study was to compare the prevalence of anemia and deficiencies of iron and vitamin A among children aged 6–29 months prior to the MNP program (2009) with those after full implementation (2013). Cross-sectional national surveys were conducted in 2009 (n = 666) and 2013 (n = 2150). Capillary blood samples were collected to measure hemoglobin, iron (ferritin and soluble transferrin receptor [sTfR]) and vitamin A (retinol binding protein [RBP]) status, and inflammation status (C-reactive protein [CRP] and α-1-acid glycoprotein [AGP]). Ferritin, sTfR, and RBP were adjusted for inflammation; hemoglobin was adjusted for altitude. The prevalence of anemia was non-significantly lower in 2013 compared to 2009 (32.7% vs. 39.0%, p = 0.076). Prevalence of inflammation-adjusted iron deficiency (54.8% vs. 74.2%, p

Approximately 46 million persons (14%) in the United States live in nonmetropolitan counties.* Compared with metropolitan residents, nonmetropolitan residents have a higher prevalence of obesity-associated chronic diseases such as diabetes (1), coronary heart disease (1), and arthritis (2). The 2005-2008 National Health and Nutrition Examination Survey (NHANES) found a significantly higher obesity prevalence among adults in nonmetropolitan (39.6%) than in metropolitan (33.4%) counties (3). However, this difference has not been examined by state. Therefore, CDC examined state-level 2016 Behavioral Risk Factor Surveillance System (BRFSS) data and found that the prevalence of obesity (body mass index [BMI] ≥30 kg/m2) was 34.2% among U.S. adults living in nonmetropolitan counties and 28.7% among those living in metropolitan counties (p

Purpose: To compare the performance of a 1-question survey screener measuring total sugar-sweetened beverage (SSB) intake to a screener measuring SSB types separately using 4 questions. Design: Cross-sectional. Setting: Web-based 2014 SummerStyles survey. Participants: A total of 4167 US adults (≥18 years). Measures: Frequency of SSB intake measured using a 1-question screener was compared to frequency using a 4-question screener (regular soda, fruit drinks, sports/energy drinks, sweetened coffee/tea). SSB intake (number of time/day) was categorized as 0, >0 to Analysis: Paired t tests were used, and agreement was evaluated using weighted κ and Lin’s concordance correlation coefficient (CCC). Results: Mean SSB intake was 1.7 (95% confidence interval [CI]: 1.65-1.79) times/day using 4 questions and 0.6 (95% CI: 0.56-0.62) times/day using 1 question ( P < .001). Intake frequency based on 4 questions versus 1 was 16.0% versus 38.5% for 0 time/day, 15.6% versus 42.5% for >0 to Conclusion: Daily SSB intake was significantly lower using a 1-question screener versus a 4-question screener. Researchers should assess SSB types separately or consider that daily SSB intake is likely underestimated with 1 question.

The objective of this study was to describe the prevalence of sugar-sweetened beverage (SSB) intake among US adults (n = 68,896) residing in metropolitan and nonmetropolitan counties, by state, using data from the Behavioral Risk Factor Surveillance System. We used multinomial logistic regression to calculate adjusted prevalence ratios for daily (≥1 time per day) SSB intake. Overall, 26.0% of respondents reported daily SSB intake, with significantly higher prevalence in nonmetropolitan counties (30.9%) than in metropolitan counties (24.8%) (adjusted prevalence ratio = 1.32, 95% confidence interval, 1.26-1.39). This same pattern was significant in 5 of 11 states with metropolitan and nonmetropolitan counties. These findings could inform efforts to reduce frequent SSB intake in nonmetropolitan areas.

Objective Although the Centers for Disease Control and Prevention (CDC) growth charts are widely used, BMI-for-age z-Scores (BMIz) are known to be uninformative above the 97th percentile. This study compared the relations of BMIz and other BMI metrics (%BMIp95, percent of 95th percentile, and ΔBMIp95, BMI minus 95th percentile) to circumferences, skinfolds, and fat mass. We were particularly interested in the differences among children with severe obesity (%BMIp95 ≥ 120). Methods Data was used from 30,003 2- to 19-year-olds who were examined from 1999-2000 through 2013-2014 in the National Health and Nutrition Examination Survey (NHANES). Results The theoretical maximum BMIz based on the growth charts varied by more than threefold across ages. The BMI metrics were strongly intercorrelated, but BMIz was less strongly related to the adiposity measures than were ΔBMIp95 and %BMIp95. Among children with severe obesity, circumferences and triceps skinfold showed almost no association with BMIz (r ≤ 0.10), whereas associations with %BMIp95 and ΔBMIp95 ranged from r = 0.32 to 0.79. Corresponding associations with fat mass ÷ height2 ranged from r = 0.40 (BMIz) to r =0.82 (%BMIp95) among 8- to 19-year-olds. Conclusions Among children with severe obesity, BMIz is only weakly associated with other measures of body fatness. Very high BMIs should be expressed relative to the CDC 95th percentile, particularly in studies that evaluate obesity interventions.

Background: Height and adiposity track over childhood, but few studies, to our knowledge, have longitudinally examined the mediating relation of the timing and progression of puberty. Objective: We assessed interrelations between prepubertal height and body mass index, the progression through puberty, and young adult height and adiposity. Methods: We analyzed data from the Birth to Twenty Plus study (females, n = 823; males, n = 765). Serial measures of anthropometry and pubertal development were obtained between ages 9 and 16 y. We used latent class growth analysis to categorize pubertal development with respect to pubic hair (females and males), breasts (females), and genitalia (males) development. Adult height and weight were obtained at ages 18 to 20 y. Results: Among females, higher latent class (earlier initiation and faster progression through puberty) was associated with an increased risk of obesity [pubic hair class 3 compared with class 1: RR, 3.41 (95% CI: 1.57, 7.44)] and inconsistent associations with height. Among males, higher latent class was associated with increased adult height [pubic hair development class 3 compared with class 1: 2.43 cm (95% CI: 0.88, 4.00)] and increased risk of overweight/obesity [pubic hair development class 3 compared with class 1: OR, 3.44 (95% CI: 1.44, 8.20)]. In females, the association with adult height became inverse after adjusting for prepubertal height [pubic hair development class 3 compared with class 1: females, 21.31 cm (95% CI: 22.32, 20.31)]; in males, the association with height was attenuated with this adjustment [20.56 cm (95% CI: 21.63, 0.52)]. Associations with adiposity were attenuated after adjusting for prepubertal adiposity. Conclusions: Progression throughpubertymodifies the relation between prepubertal and adult anthropometry. Screening for early or rapid progression of puberty might identify children at an increased risk of becoming overweight or obese adults. J Nutr doi: 10.3945/jn.116.231076.

Background: Childhood obesity care management options can be delivered in community-, clinic-, and hospital-settings. The referral practices of clinicians to these various settings have not previously been characterized beyond the local level. This study describes the management strategies and referral practices of clinicians caring for pediatric patients with obesity and associated clinician characteristics in a geographically diverse sample. Methods: This cross-sectional study used data from the DocStyles 2017 panel-based survey of 891 clinicians who see pediatric patients. We used multivariable logistic regression to estimate associations between the demographic and practice characteristics of clinicians and types of referrals for the purposes of pediatric weight management. Results: About half of surveyed clinicians (54%) referred

We examined associations between sugar-sweetened beverage (SSB) intake - a chronic disease risk factor - and characteristics of 75,029 adults (≥18 y) in 9 states by using 2016 Behavioral Risk Factor Surveillance System (BRFSS) data. We used multinomial logistic regression to estimate adjusted odds ratios for SSB intake categorized as none (reference), fewer than 1 time per day, and 1 or more times per day, by sociodemographic and behavioral characteristics. Overall, 32.1% of respondents drank SSBs 1 or more times per day. We found higher odds for 1 or more times per day among younger respondents, men, Hispanic and non-Hispanic black respondents, current smokers, respondents residing in nonmetropolitan counties, employed respondents, and those with less than high school education, obesity, and no physical activity. Our findings can inform the targeting of efforts to reduce SSB consumption.

BACKGROUND: The role that childhood stunting plays in the development of overweight and obesity later in life is not well understood, particularly in adolescence and young adulthood, because most studies have only followed up through midchildhood. OBJECTIVE: The objective of this study was to examine the relation between stunting and age-specific patterns of overweight and obesity incidence from early childhood to young adulthood in the context of a country in the process of the nutrition transition while these children were growing up. METHODS: We analyzed data from 895 participants in the Birth-to-Twenty Plus Cohort (Bt20(+)), an urban South African birth cohort initiated in 1990. Anthropometric data were collected at multiple ages and participants were included if they provided height at age 24 mo and ≥1 measure of body mass index [BMI; weight (kg)/height (m)(2)] in each of the following time periods: 4–8 y, 11–12 y, 13–15 y, 16–18 y, and 22–24 y. We defined stunting at age 24 mo as height-for-age z score 1 in childhood (4–8 y) and adolescence (11–12 y, 13–15 y, and 16–18 y) and BMI ≥25 in young adulthood (22–24 y). We compared BMI, BMIZ, and the prevalence of overweight by stunting status, stratified by sex. RESULTS: Our sample was 93% black and 51% female. The prevalence of stunting at 24 mo was 26% in males and 19% in females. In young adulthood, the prevalence of overweight and obesity was 15.5% (men) and 47.5% (women). Among both males and females, neither mean BMI nor a combined measure of overweight and obesity in any subsequent period differed by stunting status at 24 mo (P ≥ 0.05). CONCLUSION: Stunting at 24 mo was not related to the risk of overweight or obesity in this cohort. Stunting may not be an important contributor to the increasing obesity rates in urban South Africa.

We analyzed data from the Birth-to-Twenty study, a birth cohort initiated in 1990 in Soweto, the largest urban township in South Africa, adjacent to Johannesburg. Detailed information on this cohort has been published elsewhere (29). The study was designed to track the growth, health, and well-being of children. The cohort enrolled 3273 children who were born during a 6-wk period in early 1990. Participants were predominantly Black, and from families with a low socioeconomic status. Ethical approval was obtained from the University of the Witwatersrand Committee for Research on Human Subjects (approval identification no. M010556), and participants or their caregivers provided written informed consent throughout the study. Weight at birth, and weight and length/height at subsequent annual rounds were measured with the use of standard procedures (30). BMI was calculated as weight (kilograms) divided by height squared (meters squared). Height and BMI measures were converted to height-for-age z scores (HAZs)8 and BMI-for-age z scores (BMIZs) with the use of the WHO standards (31, 32). At birth or soon thereafter, data on demographic and socioeconomic variables, including the child’s ethnicity and maternal monthly income, were collected. Income was categorized into quintiles. At 9–16 y of age, pubertal development was assessed with the Tanner sexual maturation scale (SMS), a commonly used standard for evaluating pubertal stage in both clinical and research settings (33). The SMS consists of drawings of progressive stages in the development of secondary sexual characteristics—breasts in girls, genitals in boys, and pubic hair in both sexs (34, 35). The drawings are accompanied by written descriptions of each stage. The stages range from 1 (prepubertal) to 5 (postpubertal), with stage 2 considered to be the onset of puberty. From 9 to 11 y of age, a trained health care provider administered the SMS in a subset of Birth-to-Twenty participants. In a comparable group of South African adolescents, self-assessment was validated against expert assessment, both with the use of the SMS, and a substantial degree of concordance was found between the 2 ratings (36). Based on these findings, pubertal self-rating with the use of SMS was carried out at 12–16 y of age in the entire cohort. Participants were included in the present analysis if they had at least one SMS measurement, which resulted in an analytical sample of 2195 participants. Missing data were accounted for with the use of the Full Information Maximum Likelihood technique (37). Data management. We used latent class growth analysis (LCGA) in order to group participants into distinct classes based on common developmental trajectories for the SMS indicators of pubertal stage (Supplemental Figure 1). LCGA is an extension of latent growth curve modeling, which estimates a latent intercept (initial level of the outcome variable) and latent slope (rate of outcome change over time) for the entire population. However, instead of assuming that one growth trajectory adequately describes the entire population, LCGA identifies multiple growth trajectories and groups observations into distinct classes based on similarities in their growth patterns. We conducted separate analyses for girls (breast and pubic hair development), and for boys (genital and pubic hair development). We determined the optimal model in terms of the number of classes based on model fit statistics, with a focus on minimizing the value for the Bayesian Information Criteria, as well as parsimony of the model and identifying classes that had distinct differences in their developmental trajectories (37). We used ANOVA to assess the significance of differences in HAZ and BMIZ at ages 5 and 8 y between classes. We used multinomial logistic regression to determine whether HAZ and BMIZ at ages 5 and 8 y were associated with class membership. To address the strong correlation between repeated measures of HAZ and BMIZ in the same individuals, we computed sex-specific conditional measures of HAZ and BMIZ at age 8 y, as described elsewhere (38). These conditional measures represent the change in each of these measures that is not explained by the prior measure. A positive value represents a child who is growing faster than expected and a negative value characterizes a child growing slower than expected from their measure at age 5 y and the mean pattern in the sample. The results of the multinomial regression analyses are presented as relative risk ratios (RRRs), which are interpretable as ORs. To facilitate more direct comparisons with the existing literature, we created an indicator to represent the timing of pubertal development by comparing the children’s level of development in early adolescence to that of their peers. SMS scores at age 12 y were used, because the full cohort began providing pubertal data at this wave. Children who were at the median score at 12 y of age for pubic hair, breast, and genital development were considered to be the reference category. Those with a lower score were classified as slower developers, and those with a higher score were considered to be more rapid developers. With these 3 categories, we used multinomial logistic regression to assess the association between HAZ and BMIZ at ages 5 and 8 y with stage of pubertal development at age 12 y. We adjusted all models for ethnicity (Black, White, Indian, and Coloured) and quintiles of income. We used Mplus 7.3 (Muthen & Muthen) to perform the LGCA and to handle the missing data, and STATA 13.0 for all other analyses. P < 0.05 was considered to be statistically significant.

Background Prospective data spanning childhood and adolescence are needed to better understand obesity incidence among children and to identify important periods for intervention.

The Centers for Disease Control and Prevention recommends that schools adopt 10 safeguards before launching a body mass index (BMI) screening program; however, little is known about schools’ safeguard adoption. Authors identified questions from the 2014 School Health Policies and Practices Study that aligned with 4 of the 10 safeguards to estimate safeguard prevalence among schools that screened students for BMI (40.7%, N = 223). Among these schools, 3.1% had all four safeguards and 56.5% had none or one. The most prevalent safeguard was having reliable and accurate equipment (54.1%, 95% confidence interval [CI] = [46.1, 62.1]). Providing staff with appropriate expertise and training was the least prevalent; respondents in 26.4% (95% CI [17.1, 35.6]) of schools received recent training on weight status assessment, weight management, and eating disorder identification. School-based BMI screening is common, but adopting multiple recommended safeguards is not. Absent these safeguards, BMI screening programs may fall short of intended outcomes and potentially incur unintended consequences.

Introduction More than 42 million people in the United States are food insecure. Although some health care entities are addressing food insecurity among patients because of associations with disease risk and management, little is known about the components of these initiatives. Methods The Systematic Screening and Assessment Method was used to conduct a landscape assessment of US health care entity-based programs that screen patients for food insecurity and connect them with food resources. A network of food insecurity researchers, experts, and practitioners identified 57 programs, 22 of which met the inclusion criteria of being health care entities that 1) screen patients for food insecurity, 2) link patients to food resources, and 3) target patients including adults aged 50 years or older (a focus of this assessment). Data on key features of each program were abstracted from documentation and telephone interviews. Results Most programs (n = 13) focus on patients with chronic disease, and most (n = 12) partner with food banks. Common interventions include referrals to or a list of food resources (n = 19), case managers who navigate patients to resources (n = 15), assistance with federal benefit applications (n = 14), patient education and skill building (n = 13), and distribution of fruit and vegetable vouchers redeemable at farmers markets (n = 8). Most programs (n = 14) routinely screen all patients. Conclusion The programs reviewed use various strategies to screen patients, including older adults, for food insecurity and to connect them to food resources. Research is needed on program effectiveness in improving patient outcomes. Such evidence can be used to inform the investments of potential stakeholders, including health care entities, community organizations, and insurers.

Chronic disease, which is linked to unhealthy nutrition environments, is highly prevalent in Guam. The nutrition environment was assessed in 114 stores and 63 restaurants in Guam. Stores had limited availability of some healthier foods such as lean ground meat (7.5%) and 100% whole-wheat bread (11.4%), while fruits (81.0%) and vegetables (94.8%) were more commonly available; 43.7% of restaurants offered a healthy entrée or main dish salad, 4.1% provided calorie information, and 15.7% denoted healthier choices on menus. Improving the nutrition environment could help customers make healthier choices.

Introduction Non-medical use of prescription drugs (NMUPD) has reached epidemic proportions in the U.S. With approximately one in five high school students engaging in NMUPD, it is important to understand behavioral correlates. Methods Data were combined from the 2011 and 2013 cycles of the Youth Risk Behavior Survey, a nationally representative, cross-sectional survey. After restricting the analytic sample to students who reported a weight loss goal of either staying the same weight or losing weight, logistic regression models were used to estimate adjusted prevalence ratios and 95% CIs for associations between unhealthy weight management practices (UWMPs) and lifetime NMUPD. Individual UWMPs—fasting; taking diet pills, powders, or liquids without a doctor’s advice; and vomiting or taking laxatives—and total number of UWMPs were examined. Data were analyzed in 2016. Results UWMPs were more prevalent among female students (21.1% vs 10.7% for fasting; 7.5% vs 5.2% for taking diet pills, powders, or liquids; and 7.6% vs 3.2% for vomiting or taking laxatives). Significant associations between individual UWMPs and NMUPD and between the number of UWMPs and NMUPD were observed. Discussion UWMPs were associated with NMUPD. Health educators in the school setting, as well as other health professionals who provide services to an adolescent population, can focus on healthy weight management strategies, and other substance-specific messages. Conclusions The association between UWMPs and NMUPD may reflect a constellation of problem behaviors exhibited among some adolescents.

IMPORTANCE: The impact of adolescent pregnancy on offspring birth outcomes has been widely studied but less is known about its impact on the growth of the young mother herself. OBJECTIVE: To determine the association between adolescent pregnancy and attained height. DESIGN: Prospective birth cohort study. SETTING: Cohort members followed from birth to age 20 y in Soweto South Africa. PARTICIPANT: From among 840 Black females with sufficient data we identified 54 matched pairs in which a girl who became pregnant before the age of 17 years was matched with a girl who did not have a pregnancy by age 20 y. Pairs were matched on age at menarche and height-for-age z scores in the year before the case became pregnant (mean 15.0 y). MAIN OUTCOME MEASURES: The two groups were compared with respect to attained height measured at mean age 18.5 y. RESULTS: Mean age at conception was 15.9 years (range: 13.7 to 16.9 y). Mean height at matching was 159.4 cm in the adolescent pregnancy group and 159.3 cm in the comparison group (p = 0.3). Mean attained height was 160.4 cm in the adolescent pregnancy group and 160.3 cm in the comparison group (p = 0.7). CONCLUSIONS: Among Black females in Soweto South Africa adolescent pregnancy was not associated with attained height.

Background: Early life growth failure and resulting cognitive deficits are often assumed to be very difficult to reverse after infancy. Objective: We used data from Young Lives, which is an observational cohort of 8062 children in Ethiopia, India, Peru, and Vietnam, to determine whether changes in growth after infancy are associated with schooling and cognitive achievement at age 8 y. Design: We represented the growth by height-for-age z score at 1 y [HAZ(1)] and height-for-age z score at 8 y that was not predicted by the HAZ(1). We also characterized growth as recovered (stunted at age 1 y and not at age 8 y), faltered (not stunted at age 1 y and stunted at age 8 y), persistently stunted (stunted at ages 1 and 8 y), or never stunted (not stunted at ages 1 and 8 y). Outcome measures were assessed at age 8 y. Results: The HAZ(1) was inversely associated with overage for grade and positively associated with mathematics achievement, reading comprehension, and receptive vocabulary. Unpredicted growth from 1 to 8 y of age was also inversely associated with overage for grade (OR range across countries: 0.80–0.84) and positively associated with mathematics achievement (effect-size range: 0.05–0.10), reading comprehension (0.02–0.10), and receptive vocabulary (0.04–0.08). Children who recovered in linear growth had better outcomes than did children who were persistently stunted but were not generally different from children who experienced growth faltering. Conclusions: Improvements in child growth after early faltering might have significant benefits on schooling and cognitive achievement. Hence, although early interventions remain critical, interventions to improve the nutrition of preprimary and early primary school–age children also merit consideration.

Recent research has demonstrated some growth recovery among children stunted in infancy. Less is known about key age ranges for such growth recovery, and what factors are correlates with this growth. This study characterized child growth up to age 1 year, and from ages 1 to 5 and 5 to 8 years controlling for initial height-for-age z-score (HAZ), and identified key distal household and community factors associated with these growth measures using longitudinal data on 7266 children in the Young Lives (YL) study in Ethiopia, India, Peru and Vietnam. HAZ at about age 1 year and age in months predicted much of the variation in HAZ at age 5 years, but 40–71% was not predicted. Similarly, HAZ at age 5 years and age in months did not predict 26–47% of variation in HAZ at 8 years. Multiple regression analysis suggests that parental schooling, consumption, and mothers' height are key correlates of HAZ at about age 1 and also are associated with unpredicted change in HAZ from ages 1 to 5 and 5 to 8 years, given initial HAZ. These results underline the importance of a child's starting point in infancy in determining his or her growth, point to key distal household and community factors that may determine early growth in early life and subsequent growth recovery and growth failure, and indicate that these factors vary some by country, urban/rural designation, and child sex.