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Background and objectivesWe recently reported the short-term results of this trial revealing that higher-calorie refeeding (HCR) restored medical stability earlier, with no increase in safety events and significant savings associated with shorter length of stay, in comparison with lower-calorie refeeding (LCR) in hospitalized adolescents with anorexia nervosa. Here, we report the 1-year outcomes, including rates of clinical remission and rehospitalizations.MethodsIn this multicenter, randomized controlled trial, eligible patients admitted for medical instability to 2 tertiary care eating disorder programs were randomly assigned to HCR (2000 kcals per day, increasing by 200 kcals per day) or LCR (1400 kcals per day, increasing by 200 kcals every other day) within 24 hours of admission and followed-up at 10 days and 1, 3, 6, and 12 months post discharge. Clinical remission at 12 months post discharge was defined as weight restoration (≥95% median BMI) plus psychological recovery. With generalized linear mixed effect models, we examined differences in clinical remission over time.ResultsOf 120 enrollees, 111 were included in modified intent-to-treat analyses, 60 received HCR, and 51 received LCR. Clinical remission rates changed over time in both groups, with no evidence of significant group differences (P = .42). Medical rehospitalization rates within 1-year post discharge (32.8% [19 of 58] vs 35.4% [17 of 48], P = .84), number of rehospitalizations (2.4 [SD: 2.2] vs 2.0 [SD: 1.6]; P = .52), and total number of days rehospitalized (6.0 [SD: 14.8] vs 5.1 [SD: 10.3] days; P = .81) did not differ by HCR versus LCR.ConclusionsThe finding that clinical remission and medical rehospitalization did not differ over 1-year, in conjunction with the end-of-treatment outcomes, support the superior efficacy of HCR as compared with LCR.

BACKGROUND: Lower weight has historically been equated with more severe illness in anorexia nervosa (AN). Reliance on admission weight to guide clinical concern is challenged by the rise in patients with atypical anorexia nervosa (AAN) requiring hospitalization at normal weight. METHODS: We examined weight history and illness severity in 12- to 24-year-olds with AN (n = 66) and AAN (n = 50) in a randomized clinical trial, the Study of Refeeding to Optimize Inpatient Gains (www.clinicaltrials.gov; NCT02488109). Amount of weight loss was the difference between the highest historical percentage median BMI and admission; rate was the amount divided by duration (months). Unpaired t tests compared AAN and AN; multiple variable regressions examined associations between weight history variables and markers of illness severity at admission. Stepwise regression examined the explanatory value of weight and menstrual history on selected markers. RESULTS: Participants were 16.5 ± 2.6 years old, and 91% were of female sex. Groups did not differ by weight history or admission heart rate (HR). Eating Disorder Examination Questionnaire global scores were higher in AAN (mean 3.80 [SD 1.66] vs mean 3.00 [SD 1.66]; P = .02). Independent of admission weight, lower HR (β = -0.492 [confidence interval (CI) -0.883 to -0.100]; P = .01) was associated with faster loss; lower serum phosphorus was associated with a greater amount (β = -0.005 [CI -0.010 to 0.000]; P = .04) and longer duration (β = -0.011 [CI -0.017 to 0.005]; P = .001). Weight and menstrual history explained 28% of the variance in HR and 36% of the variance in serum phosphorus. CONCLUSIONS: Weight history was independently associated with markers of malnutrition in inpatients with restrictive eating disorders across a range of body weights and should be considered when assessing illness severity on hospital admission.

BACKGROUND:Lower weight has historically been equated with more severe illness in anorexia nervosa (AN). Reliance on admission weight to guide clinical concern is challenged by the rise in patients with atypical anorexia nervosa (AAN) requiring hospitalization at normal weight. METHODS:We examined weight history and illness severity in 12- to 24-year-olds with AN (n = 66) and AAN (n = 50) in a randomized clinical trial, the Study of Refeeding to Optimize Inpatient Gains (www.clinicaltrials.gov; NCT02488109). Amount of weight loss was the difference between the highest historical percentage median BMI and admission; rate was the amount divided by duration (months). Unpaired t tests compared AAN and AN; multiple variable regressions examined associations between weight history variables and markers of illness severity at admission. Stepwise regression examined the explanatory value of weight and menstrual history on selected markers. RESULTS:Participants were 16.5 ± 2.6 years old, and 91% were of female sex. Groups did not differ by weight history or admission heart rate (HR). Eating Disorder Examination Questionnaire global scores were higher in AAN (mean 3.80 [SD 1.66] vs mean 3.00 [SD 1.66]; P = .02). Independent of admission weight, lower HR (β = -0.492 [confidence interval (CI) -0.883 to -0.100]; P = .01) was associated with faster loss; lower serum phosphorus was associated with a greater amount (β = -0.005 [CI -0.010 to 0.000]; P = .04) and longer duration (β = -0.011 [CI -0.017 to 0.005]; P = .001). Weight and menstrual history explained 28% of the variance in HR and 36% of the variance in serum phosphorus. CONCLUSIONS:Weight history was independently associated with markers of malnutrition in inpatients with restrictive eating disorders across a range of body weights and should be considered when assessing illness severity on hospital admission.

ABBREVIATIONS. GAPS, Guidelines for Preventive Services; HMO, health maintenance organization; AROV, Adolescent Report of the Visit; ANCOVA, analysis of covariance. The majority of adolescent morbidity and mortality is preventable and [...], Objective. To determine whether a systems intervention for primary care providers resulted in increased preventive screening and counseling of adolescent patients, compared with the usual standard of care. Methods. The intervention was conducted in 2 outpatient pediatric clinics; 2 other pediatric clinics in the same health maintenance organization served as comparison sites. The intervention was implemented in 2 phases: first, pediatric primary care providers attended a training workshop (N = 37) to increase screening and counseling of adolescents in the areas of tobacco, alcohol, drugs, sexual behavior, and safety (seatbelt and helmet use). Second, screening and charting tools were integrated into the intervention clinics. Providers in the comparison sites (N = 39) continued to provide the usual standard of care to their adolescent patients. Adolescent reports were used to assess changes in provider behavior. After a well visit, 13- to 17-year olds (N = 2628) completed surveys reporting on whether their provider screened and counseled them for risky behavior. Results. Screening and counseling rates increased significantly in each of the 6 areas in the intervention sites, compared with rates of delivery using the usual standard of care. Across the 6 areas combined, the average screening rate increased from 58% to 83%; counseling rates increased from 52% to 78%. There were no significant increases in the comparison sites during the same period. The training component seems to account for most of this increase, with the tools sustaining the effects of the training. Conclusions. The study offers strong support for an intervention to increase clinicians' delivery of preventive services to a wide age range of adolescent patients. Pediatrics 2005;115:960-968; primary care, adolescents, risk behavior, preventive services, quality improvement, managed care.

OBJECTIVE. Young adults have the lowest rate of insurance coverage of any age group. Little is known about insurance patterns from adolescence through the early 30s. The objective of this study was to assess patterns and disparities in health insurance from adolescence through the early 30s. DESIGN. We analyzed data from the 2002 and 2003 National Health Interview Survey (ages 13-32; N = 48 827). We examined public and private insurance coverage and conducted logistic regression to evaluate racial/ ethnic and income disparities in coverage. Outcomes were insurance coverage at ages 13 to 32. RESULTS. Insurance patterns follow a U-shaped curve across the age categories. Rates are highest at ages 13 to 14, lowest at ages 23 to 24, and then increase gradually. Private rate patterns are similar; however, public coverage decreases across ages. In bivariate analyses, black and Hispanic groups had lower coverage rates than the white group, and the low- and middle-income groups had lower rates than the high-income group. After adjustment for confounding variables, all disparities remained significant except for differences between the black and white groups. CONCLUSIONS. After age 18, all groups are vulnerable to lack of insurance. Rate increases beyond age 25 to 26 years are attributable to increases in private coverage, whereas decreases in public coverage account for the lack of a full recovery to the higher rates seen in adolescence. The safety net of public programs that cover adolescents disappears in young adulthood, leaving young adults vulnerable, a problem that persists into the 30s for those who are in poverty and those who are of Hispanic origin. KEY WORDS. health insurance coverage gaps, young adults., URL: [...]

Objective: To examine the relationship between BMI status (normal, overweight, and obese) and preventive screening among adolescents at their last checkup., Methods: We used population-based data from the 2003-2007 California Health Interview Surveys, telephone interviews of adolescents aged 12 to 17 years with a checkup in the past 12 months (n = 9220). Respondents were asked whether they received screening for nutrition, physical activity, and emotional distress. BMI was calculated from self-reported height and weight: (1) normal weight or underweight (<85th percentile); (2) overweight (85th-94th percentile); and (3) obese (>95th percentile). Multivariate logistic regression models tested how screening by topic differed according to BMI status, adjusting for age, gender, income, race/ethnicity, and survey year., Results: Screening percentages in the pooled sample (all 3 years) were higher for obese, but not overweight, adolescents for physical activity (odds ratio: 1.4; P < .01) and nutrition (odds ratio: 1.6; screening did not differ P < .01). Stratified analysis by year revealed higher screening for obese (versus normal-weight) adolescents for nutrition and physical activity in 2003 and for all 3 topics in 2005. However, by 2007, screening did not differ according to BMI status. Overall screening between 2003 and 2007 declined for nutrition (75%-59%; P < .01), physical activity (74%-60%; P < .01), and emotional distress (31%-24%; P < .01)., Conclusions: Obese adolescents receive more preventive screening versus their normal-weight peers. Overweight adolescents do not report more screening, but standards of care dictate increased attention for this group. These results are discouraging amid a rise in pediatric obesity and new guidelines that recommend screening by BMI status.