OBJECTIVE: The aim of this study was to compare national estimates of self-reported and measured height and weight, BMI, and obesity prevalence among adults from US surveys. METHODS: Self-reported height and weight data came from the National Health and Nutrition Examination Survey (NHANES), the National Health Interview Survey, and the Behavioral Risk Factor Surveillance System for the years 1999 to 2016. Measured height and weight data were available from NHANES. BMI was calculated from height and weight; obesity was defined as BMI >/= 30. RESULTS: In all three surveys, mean self-reported height was higher than mean measured height in NHANES for both men and women. Mean BMI from self-reported data was lower than mean BMI from measured data across all surveys. For women, mean self-reported weight, BMI, and obesity prevalence in the National Health Interview Survey and Behavioral Risk Factor Surveillance System were lower than self-report in NHANES. The distribution of BMI was narrower for self-reported than for measured data, leading to lower estimates of obesity prevalence. CONCLUSIONS: Self-reported height, weight, BMI, and obesity prevalence were not identical across the three surveys, particularly for women. Patterns of misreporting of height and weight and their effects on BMI and obesity prevalence are complex.

Misclassification of body mass index (BMI) categories arising from self-reported weight and height can bias hazard ratios in studies of BMI and mortality. We examined the effects on hazard ratios of such misclassification using national US survey data for 1976 through 2010 that had both measured and self-reported weight and height along with mortality follow-up for 48,763 adults and a subset of 17,405 healthy never-smokers. BMI was categorized as <22.5 (low), 22.5-24.9 (referent), 25.0-29.9 (overweight), 30.0-34.9 (class I obesity), and >/=35.0 (class II-III obesity). Misreporting at higher BMI categories tended to bias hazard ratios upwards for those categories, but that effect was augmented, counterbalanced, or even reversed by misreporting in other BMI categories, in particular those that affected the reference category. For example, among healthy male never-smokers, misclassifications affecting the overweight and the reference categories changed the hazard ratio for overweight from 0.85 with measured data to 1.24 with self-reported data. Both the magnitude and direction of bias varied according to the underlying hazard ratios in measured data, showing that findings on bias from one study should not be extrapolated to a study with different underlying hazard ratios. Because of misclassification effects, self-reported weight and height cannot reliably indicate the lowest-risk BMI category.

The intent of our comments was not to dismiss projection analyses but rather to point out that it may be difficult to extrapolate accurately from past data to future trends in obesity. Changes in obesity prevalence over time have not followed a readily predictable pattern. Projections can vary according to the starting time point chosen and the statistical model used. Joinpoint analyses can identify past changes in slope but provide little guidance to predict future changes in slope.

IMPORTANCE: Between 1980 and 2000, the prevalence of obesity increased significantly among adult men and women in the United States; further significant increases were observed through 2003-2004 for men but not women. Subsequent comparisons of data from 2003-2004 with data through 2011-2012 showed no significant increases for men or women. OBJECTIVE: To examine obesity prevalence for 2013-2014 and trends over the decade from 2005 through 2014 adjusting for sex, age, race/Hispanic origin, smoking status, and education. DESIGN, SETTING, AND PARTICIPANTS: Analysis of data obtained from the National Health and Nutrition Examination Survey (NHANES), a cross-sectional, nationally representative health examination survey of the US civilian noninstitutionalized population that includes measured weight and height. EXPOSURES: Survey period. MAIN OUTCOMES AND MEASURES: Prevalence of obesity (body mass index ≥30) and class 3 obesity (body mass index ≥40). RESULTS: This report is based on data from 2638 adult men (mean age, 46.8 years) and 2817 women (mean age, 48.4 years) from the most recent 2 years (2013-2014) of NHANES and data from 21,013 participants in previous NHANES surveys from 2005 through 2012. For the years 2013-2014, the overall age-adjusted prevalence of obesity was 37.7% (95% CI, 35.8%-39.7%); among men, it was 35.0% (95% CI, 32.8%-37.3%); and among women, it was 40.4% (95% CI, 37.6%-43.3%). The corresponding prevalence of class 3 obesity overall was 7.7% (95% CI, 6.2%-9.3%); among men, it was 5.5% (95% CI, 4.0%-7.2%); and among women, it was 9.9% (95% CI, 7.5%-12.3%). Analyses of changes over the decade from 2005 through 2014, adjusted for age, race/Hispanic origin, smoking status, and education, showed significant increasing linear trends among women for overall obesity (P = .004) and for class 3 obesity (P = .01) but not among men (P = .30 for overall obesity; P = .14 for class 3 obesity). CONCLUSIONS AND RELEVANCE: In this nationally representative survey of adults in the United States, the age-adjusted prevalence of obesity in 2013-2014 was 35.0% among men and 40.4% among women. The corresponding values for class 3 obesity were 5.5% for men and 9.9% for women. For women, the prevalence of overall obesity and of class 3 obesity showed significant linear trends for increase between 2005 and 2014; there were no significant trends for men. Other studies are needed to determine the reasons for these trends.

IMPORTANCE: Previous analyses of obesity trends among children and adolescents showed an increase between 1988-1994 and 1999-2000, but no change between 2003-2004 and 2011-2012, except for a significant decline among children aged 2 to 5 years. OBJECTIVES: To provide estimates of obesity and extreme obesity prevalence for children and adolescents for 2011-2014 and investigate trends by age between 1988-1994 and 2013-2014. DESIGN, SETTING, AND PARTICIPANTS: Children and adolescents aged 2 to 19 years with measured weight and height in the 1988-1994 through 2013-2014 National Health and Nutrition Examination Surveys. EXPOSURES: Survey period. MAIN OUTCOMES AND MEASURES: Obesity was defined as a body mass index (BMI) at or above the sex-specific 95th percentile on the US Centers for Disease Control and Prevention (CDC) BMI-for-age growth charts. Extreme obesity was defined as a BMI at or above 120% of the sex-specific 95th percentile on the CDC BMI-for-age growth charts. Detailed estimates are presented for 2011-2014. The analyses of linear and quadratic trends in prevalence were conducted using 9 survey periods. Trend analyses between 2005-2006 and 2013-2014 also were conducted. RESULTS: Measurements from 40,780 children and adolescents (mean age, 11.0 years; 48.8% female) between 1988-1994 and 2013-2014 were analyzed. Among children and adolescents aged 2 to 19 years, the prevalence of obesity in 2011-2014 was 17.0% (95% CI, 15.5%-18.6%) and extreme obesity was 5.8% (95% CI, 4.9%-6.8%). Among children aged 2 to 5 years, obesity increased from 7.2% (95% CI, 5.8%-8.8%) in 1988-1994 to 13.9% (95% CI, 10.7%-17.7%) (P < .001) in 2003-2004 and then decreased to 9.4% (95% CI, 6.8%-12.6%) (P = .03) in 2013-2014. Among children aged 6 to 11 years, obesity increased from 11.3% (95% CI, 9.4%-13.4%) in 1988-1994 to 19.6% (95% CI, 17.1%-22.4%) (P < .001) in 2007-2008, and then did not change (2013-2014: 17.4% [95% CI, 13.8%-21.4%]; P = .44). Obesity increased among adolescents aged 12 to 19 years between 1988-1994 (10.5% [95% CI, 8.8%-12.5%]) and 2013-2014 (20.6% [95% CI, 16.2%-25.6%]; P < .001) as did extreme obesity among children aged 6 to 11 years (3.6% [95% CI, 2.5%-5.0%] in 1988-1994 to 4.3% [95% CI, 3.0%-6.1%] in 2013-2014; P = .02) and adolescents aged 12 to 19 years (2.6% [95% CI, 1.7%-3.9%] in 1988-1994 to 9.1% [95% CI, 7.0%-11.5%] in 2013-2014; P < .001). No significant trends were observed between 2005-2006 and 2013-2014 (P value range, .09-.87). CONCLUSIONS AND RELEVANCE: In this nationally representative study of US children and adolescents aged 2 to 19 years, the prevalence of obesity in 2011-2014 was 17.0% and extreme obesity was 5.8%. Between 1988-1994 and 2013-2014, the prevalence of obesity increased until 2003-2004 and then decreased in children aged 2 to 5 years, increased until 2007-2008 and then leveled off in children aged 6 to 11 years, and increased among adolescents aged 12 to 19 years.

Analyses of the Third National Health and Nutrition Examination Survey (NHANES III) in 1988 to 1994 found an association of increasing blood lead levels <10 mug/dL with a higher risk of cardiovascular disease (CVD) mortality. The potential need to correct blood lead for hematocrit/hemoglobin and adjust for biomarkers for other metals, for example, cadmium and iron, had not been addressed in the previous NHANES III-based studies on blood lead-CVD mortality association.We analyzed 1999 to 2010 NHANES data for 18,602 participants who had a blood lead measurement, were ≥40 years of age at the baseline examination and were followed for mortality through 2011. We calculated the relative risk for CVD mortality as a function of hemoglobin- or hematocrit-corrected log-transformed blood lead through Cox proportional hazard regression analysis with adjustment for serum iron, blood cadmium, serum C-reactive protein, serum calcium, smoking, alcohol intake, race/Hispanic origin, and sex.The adjusted relative risk for CVD mortality was 1.44 (95% confidence interval = 1.05, 1.98) per 10-fold increase in hematocrit-corrected blood lead with little evidence of nonlinearity. Similar results were obtained with hemoglobin-corrected blood lead. Not correcting blood lead for hematocrit/hemoglobin resulted in underestimation of the lead-CVD mortality association while not adjusting for iron status and blood cadmium resulted in overestimation of the lead-CVD mortality association.In a nationally representative sample of U.S. adults, log-transformed blood lead was linearly associated with increased CVD mortality. Correcting blood lead for hematocrit/hemoglobin and adjustments for some biomarkers affected the association.

PURPOSE: Obesity is a highly prevalent condition in the United States and elsewhere and is associated with increased mortality and morbidity. Here, we discuss some issues involved in quantifying the health burden of obesity using population attributable fraction (PAF) estimates and provide examples. METHODS: We searched PubMed for articles reporting attributable fraction estimates for obesity. We reviewed eligible articles to identify methodological concerns and tabulated illustrative examples of PAF estimates for obesity relative to cancer, diabetes, cardiovascular disease, and all-cause mortality. RESULTS: There is considerable variability among studies regarding the methods used for PAF calculation and the selection of appropriate counterfactuals. The reported estimates ranged from 5% to 15% for all-cause mortality, -0.2% to 8% for all-cancer incidence, 7% to 44% for cardiovascular disease incidence, and 3% to 83% for diabetes incidence. CONCLUSIONS: To evaluate a given estimate, it is important to consider whether the exposure and outcome were defined similarly for the PAF and for the relative risks, whether the relative risks were suitable for the population at hand, and whether PAF was calculated using correct methods. Strong causal assumptions are not necessarily warranted. In general, PAFs for obesity may be best considered as indicators of association.

We thank Dr. Stevens for her observations (1). We agree with Stevens that standardized body mass index (BMI; weight (kg)/height (m)2) cutpoints can aid comparisons among studies examining different populations. The standard BMI cutpoints of 18.5, 25, and 30, indicating underweight (<18.5), normal weight (18.5–<25), overweight (25–<30), and obesity (≥30), were developed by the National Heart, Lung, and Blood Institute (2) and the World Health Organization (WHO) (3) and were reiterated in the 2013 obesity guidelines (4). Since their inception, these standards have been widely used in studies of BMI and risk of death and appear to be well accepted. In a previous systematic search (5), we found well over 100 published studies of weight and mortality as of 2012 that had reported results using those standard categories, including approximately half of the studies published since 2000. Turner et al. (6) used those BMI standards as their example of an approach to categorization that used well-recognized, published boundaries. In our article (7), we did not suggest that a new or augmented set of standards was needed. Rather, we emphasized that use of the current standard BMI groupings in analysis would avoid issues arising from ad hoc and post hoc selection of BMI categories. Finer BMI categories are not clearly necessary and can create problems of interpretation.

Studies of weight and mortality sometimes state that the mortality relative risks for obesity from nonsmokers are valid estimates of the relative risks for obesity in both smokers and nonsmokers. Extending this idea, several influential articles have used relative risks for obesity from nonsmokers and attributable fraction methods for unadjusted risks to estimate attributable fractions of deaths in the entire population (smokers and nonsmokers combined). However, stratification by smoking is a form of adjustment for confounding. Simplified examples show that the use of relative risks from only 1 stratum to estimate attributable fractions, without incorporating data on the stratification variable, gives incorrect results for the entire population. Even if the mortality relative risks for obesity from nonsmokers are indeed valid in both smokers and nonsmokers, these relative risks nonetheless need to be treated as adjusted relative risks for the purpose of calculating attributable fractions for the whole sample.

The World Health Organization (Geneva, Switzerland) and the National Heart, Lung, and Blood Institute (Bethesda, Maryland) have developed standard categories of body mass index (BMI) (calculated as weight (kg)/height (m)(2)) of less than 18.5 (underweight), 18.5-24.9 (normal weight), 25.0-29.9 (overweight), and 30.0 or more (obesity). Nevertheless, studies of BMI and the risk of death sometimes use nonstandard BMI categories that vary across studies. In a meta-analysis of 8 large studies that used nonstandard BMI categories and were published between 1999 and 2014 and included 5.8 million participants, hazard ratios tended to be small throughout the range of overweight and normal weight. Risks were similar between subjects of high-normal weight (BMI of approximately 23.0-24.9) and those of low overweight (BMI of approximately 25.0-27.4). In an example using national survey data, minor variations in the reference category affected hazard ratios. For example, choosing high-normal weight (BMI of 23.0-24.9) instead of standard normal weight (BMI of 18.5-24.9) as the reference category produced higher nonsignificant hazard ratios (1.05 vs. 0.97 for men and 1.06 vs. 1.02 for women) for the standard overweight category (BMI of 25.0-29.9). Use of the standard BMI groupings avoids problems of ad hoc and post hoc category selection and facilitates between-study comparisons. The ways in which BMI data are categorized and reported may shape inferences about the degree of risk for various BMI categories.

An elevated body mass index (BMI) may make venipuncture more difficult, potentially impacting the use of home infusion (HI) and self-infusion (SI). We sought to determine whether above-normal BMI is associated with decreased use of HI treatment and SI of clotting factor concentrate among haemophilic persons. We analysed data from 10 814 male patients with haemophilia A and B (45% with severe disease) aged 6-79 years enrolled in the Centers for Disease Control and Prevention Universal Data Collection surveillance project between 1998 and 2008. Associations between the use of HI and SI and BMI were evaluated using logistic regression. Fifty per cent of haemophilic men were overweight or obese, similar to rates reported among the general US population by the 2007-2008 National Health and Nutrition Examination Survey [Flegal, KM et al., JAMA 2010;303:235-241;]. Twenty per cent of children and 22% of teens were obese, as were 28% of adults [Ogden, CL et al., JAMA 2010;303:235, 242]. Overall, 70% of the study sample used HI; 44% of those who used HI also used SI. Overweight and obese men were each less likely to use HI than those of normal weight [odds ratio (OR) 0.8; 95% confidence interval (CI) 0.7-1.0 and OR 0.7; 95% CI 0.6-0.8 respectively]. Obese teens and adult men were also less likely to practice SI than teens and adults of normal weight (OR 0.8; 95% CI 0.7-0.9 for each). We conclude that overweight and obese haemophilic men are less likely to use HI and obese men are less likely to use SI than their normal-weight counterparts.

IMPORTANCE: More than one-third of adults and 17% of youth in the United States are obese, although the prevalence remained stable between 2003-2004 and 2009-2010. OBJECTIVE: To provide the most recent national estimates of childhood obesity, analyze trends in childhood obesity between 2003 and 2012, and provide detailed obesity trend analyses among adults. DESIGN, SETTING, AND PARTICIPANTS: Weight and height or recumbent length were measured in 9120 participants in the 2011-2012 nationally representative National Health and Nutrition Examination Survey. MAIN OUTCOMES AND MEASURES: In infants and toddlers from birth to 2 years, high weight for recumbent length was defined as weight for length at or above the 95th percentile of the sex-specific Centers for Disease Control and Prevention (CDC) growth charts. In children and adolescents aged 2 to 19 years, obesity was defined as a body mass index (BMI) at or above the 95th percentile of the sex-specific CDC BMI-for-age growth charts. In adults, obesity was defined as a BMI greater than or equal to 30. Analyses of trends in high weight for recumbent length or obesity prevalence were conducted overall and separately by age across 5 periods (2003-2004, 2005-2006, 2007-2008, 2009-2010, and 2011-2012). RESULTS: In 2011-2012, 8.1% (95% CI, 5.8%-11.1%) of infants and toddlers had high weight for recumbent length, and 16.9% (95% CI, 14.9%-19.2%) of 2- to 19-year-olds and 34.9% (95% CI, 32.0%-37.9%) of adults (age-adjusted) aged 20 years or older were obese. Overall, there was no significant change from 2003-2004 through 2011-2012 in high weight for recumbent length among infants and toddlers, obesity in 2- to 19-year-olds, or obesity in adults. Tests for an interaction between survey period and age found an interaction in children (P = .03) and women (P = .02). There was a significant decrease in obesity among 2- to 5-year-old children (from 13.9% to 8.4%; P = .03) and a significant increase in obesity among women aged 60 years and older (from 31.5% to 38.1%; P = .006). CONCLUSIONS AND RELEVANCE: Overall, there have been no significant changes in obesity prevalence in youth or adults between 2003-2004 and 2011-2012. Obesity prevalence remains high and thus it is important to continue surveillance.

In the general population, obesity is associated with increased cardiovascular risk and decreased survival. In patients with end-stage renal disease (ESRD), however, an "obesity paradox" or "reverse epidemiology" (to include lipid and hypertension paradoxes) has been consistently reported, i.e. a higher body mass index (BMI) is paradoxically associated with better survival. This survival advantage of large body size is relatively consistent for hemodialysis patients across racial and regional differences, although published results are mixed for peritoneal dialysis patients. Recent data indicate that both higher skeletal muscle mass and increased total body fat are protective, although there are mixed data on visceral (intra-abdominal) fat. The obesity paradox in ESRD is unlikely to be due to residual confounding alone and has biologic plausibility. Possible causes of the obesity paradox include protein-energy wasting and inflammation, time discrepancy among competitive risk factors (undernutrition versus overnutrition), hemodynamic stability, alteration of circulatory cytokines, sequestration of uremic toxin in adipose tissue, and endotoxin-lipoprotein interaction. The obesity paradox may have significant clinical implications in the management of ESRD patients especially if obese dialysis patients are forced to lose weight upon transplant wait-listing. Well-designed studies exploring the causes and consequences of the reverse epidemiology of cardiovascular risk factors, including the obesity paradox, among ESRD patients could provide more information on mechanisms. These could include controlled trials of nutritional and pharmacologic interventions to examine whether gain in lean body mass or even body fat can improve survival and quality of life in these patients.

IMPORTANCE: Estimates of the relative mortality risks associated with normal weight, overweight, and obesity may help to inform decision making in the clinical setting. OBJECTIVE: To perform a systematic review of reported hazard ratios (HRs) of all-cause mortality for overweight and obesity relative to normal weight in the general population. DATA SOURCES: PubMed and EMBASE electronic databases were searched through September 30, 2012, without language restrictions. STUDY SELECTION: Articles that reported HRs for all-cause mortality using standard body mass index (BMI) categories from prospective studies of general populations of adults were selected by consensus among multiple reviewers. Studies were excluded that used nonstandard categories or that were limited to adolescents or to those with specific medical conditions or to those undergoing specific procedures. PubMed searches yielded 7034 articles, of which 141 (2.0%) were eligible. An EMBASE search yielded 2 additional articles. After eliminating overlap, 97 studies were retained for analysis, providing a combined sample size of more than 2.88 million individuals and more than 270,000 deaths. DATA EXTRACTION: Data were extracted by 1 reviewer and then reviewed by 3 independent reviewers. We selected the most complex model available for the full sample and used a variety of sensitivity analyses to address issues of possible overadjustment (adjusted for factors in causal pathway) or underadjustment (not adjusted for at least age, sex, and smoking). RESULTS: Random-effects summary all-cause mortality HRs for overweight (BMI of 25-<30), obesity (BMI of ≥30), grade 1 obesity (BMI of 30-<35), and grades 2 and 3 obesity (BMI of ≥35) were calculated relative to normal weight (BMI of 18.5-<25). The summary HRs were 0.94 (95% CI, 0.91-0.96) for overweight, 1.18 (95% CI, 1.12-1.25) for obesity (all grades combined), 0.95 (95% CI, 0.88-1.01) for grade 1 obesity, and 1.29 (95% CI, 1.18-1.41) for grades 2 and 3 obesity. These findings persisted when limited to studies with measured weight and height that were considered to be adequately adjusted. The HRs tended to be higher when weight and height were self-reported rather than measured. CONCLUSIONS AND RELEVANCE: Relative to normal weight, both obesity (all grades) and grades 2 and 3 obesity were associated with significantly higher all-cause mortality. Grade 1 obesity overall was not associated with higher mortality, and overweight was associated with significantly lower all-cause mortality. The use of predefined standard BMI groupings can facilitate between-study comparisons.

BACKGROUND: Before estimating smoothed percentiles of weight-for-height and BMI-for-age to construct the WHO growth charts, WHO excluded observations that were considered to represent unhealthy weights for height. OBJECTIVE: The objective was to estimate the effects of similar data trimming on empirical percentiles from the CDC growth-chart data set relative to the smoothed WHO percentiles for ages 24-59 mo. DESIGN: We used the nationally representative US weight and height data from 1971 to 1994, which was the source data for the 2000 CDC growth charts. Trimming cutoffs were calculated on the basis of weight-for-height for 9722 children aged 24-71 mo. Empirical percentiles for 7315 children aged 24-59 mo were compared with the corresponding smoothed WHO percentiles. RESULTS: Before trimming, the mean empirical percentiles for weight-for-height in the CDC data set were higher than the corresponding smoothed WHO percentiles. After trimming, the mean empirical 95th and 97th percentiles of weight-for-height were lower than the WHO percentiles, and the proportion of children in the CDC data set above the WHO 95th percentile decreased from 7% to 5%. The findings were similar for BMI-for-age. However, for weight-for-age, which had not been trimmed by the WHO, the empirical percentiles before trimming agreed closely with the upper percentiles from the WHO charts. CONCLUSION: WHO data-trimming procedures may account for some of the differences between the WHO growth charts and the 2000 CDC growth charts.

Tobacco use is the leading cause of preventable death in the United States, with smoking estimated to be responsible for over 440,000 deaths per year (King et al., 2011), and smoking is associated with myriad adverse health consequences (Office of the Surgeon General, 2004). Smoking rates continue to be high, with almost 1 out of 5 adults in the US being current smokers. The proportion of smokers declined from 20.9% in 2005 to 19.3% in 2010 (King et al., 2011). Smoking cessation is still an important public health goal. | Travier et al. (in press) provide some estimates of the weight gain associated with smoking and quitting smoking. Their general findings are consistent with a large body of evidence (Audrain-McGovern and Benowitz, 2011, Flegal et al., 1995, Williamson et al., 1991). Smokers tend to have a lower prevalence of obesity than non-smokers. When people quit smoking, they are likely to gain weight. After the initial weight gain upon cessation, the subsequent weight gain of quitters tends to be similar to that of non-smokers.

CONTEXT: Between 1980 and 1999, the prevalence of adult obesity (body mass index [BMI] ≥30) increased in the United States and the distribution of BMI changed. More recent data suggested a slowing or leveling off of these trends. OBJECTIVE: To estimate the prevalence of adult obesity from the 2009-2010 National Health and Nutrition Examination Survey (NHANES) and compare adult obesity and the distribution of BMI with data from 1999-2008. DESIGN, SETTING, AND PARTICIPANTS: NHANES includes measured heights and weights for 5926 adult men and women from a nationally representative sample of the civilian noninstitutionalized US population in 2009-2010 and for 22,847 men and women in 1999-2008. MAIN OUTCOME MEASURES: The prevalence of obesity and mean BMI. RESULTS: In 2009-2010 the age-adjusted mean BMI was 28.7 (95% CI, 28.3-29.1) for men and also 28.7 (95% CI, 28.4-29.0) for women. Median BMI was 27.8 (interquartile range [IQR], 24.7-31.7) for men and 27.3 (IQR, 23.3-32.7) for women. The age-adjusted prevalence of obesity was 35.5% (95% CI, 31.9%-39.2%) among adult men and 35.8% (95% CI, 34.0%-37.7%) among adult women. Over the 12-year period from 1999 through 2010, obesity showed no significant increase among women overall (age- and race-adjusted annual change in odds ratio [AOR], 1.01; 95% CI, 1.00-1.03; P=.07), but increases were statistically significant for non-Hispanic black women (P = .04) and Mexican American women (P=.046). For men, there was a significant linear trend (AOR, 1.04; 95% CI, 1.02-1.06; P<.001) over the 12-year period. For both men and women, the most recent 2 years (2009-2010) did not differ significantly (P=.08 for men and P=.24 for women) from the previous 6 years (2003-2008). Trends in BMI were similar to obesity trends. CONCLUSION: In 2009-2010, the prevalence of obesity was 35.5% among adult men and 35.8% among adult women, with no significant change compared with 2003-2008.

Terminology and measures used in studies of weight and adiposity in children can be complex and confusing. Differences arise in metrics, terminology, reference values, and reference levels. Most studies depend on body mass index (BMI) calculated from weight and height, rather than on more direct measures of body fatness. Definitions of overweight and obesity are generally statistical rather than risk-based and use a variety of different reference data sets for BMI. As a result, different definitions often do not give the same results. A basic problem is the lack of strong evidence for any one particular definition. Rather than formulate the question as being one of how to define obesity, it might be useful to consider what BMI cut-points best predict future health risks and how efficiently to screen for such risks. The answers may be different for different populations. In addition, rather than depending solely on BMI to make screening decisions, it is likely to be useful to also consider other factors, including not only race-ethnicity, sex and age, but also factors such as family history. Despite their limitations, BMI-based definitions of overweight and obesity provide working practical definitions that are valuable for general public health surveillance and screening.

CONTEXT: The prevalence of childhood obesity increased in the 1980s and 1990s but there were no significant changes in prevalence between 1999-2000 and 2007-2008 in the United States. OBJECTIVES: To present the most recent estimates of obesity prevalence in US children and adolescents for 2009-2010 and to investigate trends in obesity prevalence and body mass index (BMI) among children and adolescents between 1999-2000 and 2009-2010. DESIGN, SETTING, AND PARTICIPANTS: Cross-sectional analyses of a representative sample (N = 4111) of the US child and adolescent population (birth through 19 years of age) with measured heights and weights from the National Health and Nutrition Examination Survey 2009-2010. MAIN OUTCOME MEASURES: Prevalence of high weight-for-recumbent length (≥95th percentile on the growth charts) among infants and toddlers from birth to 2 years of age and obesity (BMI ≥95th percentile of the BMI-for-age growth charts) among children and adolescents aged 2 through 19 years. Analyses of trends in obesity by sex and race/ethnicity, and analyses of trends in BMI within sex-specific age groups for 6 survey periods (1999-2000, 2001-2002, 2003-2004, 2005-2006, 2007-2008, and 2009-2010) over 12 years. RESULTS: In 2009-2010, 9.7% (95% CI, 7.6%-12.3%) of infants and toddlers had a high weight-for-recumbent length and 16.9% (95% CI, 15.4%-18.4%) of children and adolescents from 2 through 19 years of age were obese. There was no difference in obesity prevalence among males (P = .62) or females (P = .65) between 2007-2008 and 2009-2010. However, trend analyses over a 12-year period indicated a significant increase in obesity prevalence between 1999-2000 and 2009-2010 in males aged 2 through 19 years (odds ratio, 1.05; 95% CI, 1.01-1.10) but not in females (odds ratio, 1.02; 95% CI, 0.98-1.07) per 2-year survey cycle. There was a significant increase in BMI among adolescent males aged 12 through 19 years (P = .04) but not among any other age group or among females. CONCLUSIONS: In 2009-2010, the prevalence of obesity in children and adolescents was 16.9%; this was not changed compared with 2007-2008.

BACKGROUND: The high prevalence of obesity (defined by body mass index) among children and adolescents in the United States and elsewhere has prompted increased attention to body fat in childhood and adolescence. OBJECTIVE: This report provides smoothed estimates of major percentiles of percentage body fat for boys and girls aged 8-19 years in the United States. METHODS: Percentage body fat was obtained from whole-body, dual-energy x-ray absorptiometry (DXA) scans conducted during the 1999-2004 National Health and Nutrition Examination Survey. A nonparametric double-kernel method was employed to smooth percentile curves for the DXA data. RESULTS: The pattern of body fat development differs between boys and girls aged 8-19 years. In most age groups, girls have a higher percentage of body fat than boys. Among boys, there is a drop in body fat percentage in early adolescence that is especially pronounced at the higher percentiles. Among girls this pattern is not seen; percentage body fat increases slightly with age. CONCLUSIONS: These results provide a smoothed reference distribution of percentage body fat for U.S. children and adolescents aged 8-19 years.

PURPOSE: We sought to describe differences between normal weight, overweight, and obese adults in use of specific prescription medication classes. METHODS: Cross-sectional analysis of prescription medication use among 9789 adults in the National Health and Nutrition Examination Survey, a nationally representative sample of the United States. RESULTS: In 2005-2008, 56.4% (95% confidence interval [CI], 54.6-58.3) of adults used 1+ prescription medication. Approximately one-quarter of adults used a hypertension medication (26.1%; 95% CI, 24.5%-27.8%). The use of hypertension medications increased with increasing weight status (normal weight: 17.2%; 95% CI, 15.6%-18.8%; overweight: 24.5%, 95% CI, 22.6%-26.4%; and obese: 35.1%, 95% CI, 32.8%-37.4%). Similarly, lipid-lowering, analgesic, antidepressant, proton pump inhibitors, thyroid, diabetes, and bronchodilator medication use was greater among obese compared with normal weight adults (each p < .01). Among adults 65+ years, 72% (95% CI, 68.2%-75.8%) of men and 67.7% (95% CI, 64.3%-71.2%) of women used a hypertension medication and a majority of men (51.2%, 95% CI, 48.4%-54%) and 40.3% (95% CI, 36.8%-43.8%) of women used lipid lowering medications; the use of both was greater among obese adults compared to normal weight adults (both p < .01). CONCLUSIONS: Obese adults in the United States use several prescription medication classes more frequently, than normal weight adults, including hypertension, lipid-lowering, and diabetes medications.

BACKGROUND: BMI is one factor that is used to determine a child's eligibility for lipid screening and treatment. BMI, which is an indirect measure of body fat, may inadequately represent the biological effect of body fat percentage on lipid concentrations. OBJECTIVE: We examined the relation between directly measured body fat percentage and lipid concentrations in a representative sample of US youths. DESIGN: Data from 7821 participants aged 8-19 y from the 1999-2004 NHANES were analyzed. Body fat percentage was measured by dual-energy X-ray absorptiometry. Total and HDL-cholesterol concentrations were measured in serum. Serum triglyceride and LDL-cholesterol concentrations were measured in a subsample of 2661 fasting NHANES participants aged 12-19 y. Prevalences of adverse total cholesterol (>200 mg/dL), LDL cholesterol (>130 mg/dL), triglycerides (>150 mg/dL), and HDL cholesterol (<35 mg/dL) were measured. RESULTS: Approximately 10.0% [+/-0.7% (SE)] of participants had high total cholesterol, 7.0 +/- 0.4% of participants had low HDL cholesterol, 9.7 +/- 1.0% of participants had high triglycerides, and 7.6 +/- 0.7% of participants had high LDL cholesterol. Prevalence of adverse total cholesterol, HDL cholesterol, triglycerides, and LDL cholesterol in US youths with high adiposity (greater than or equal to the age- and sex-specific 75th percentile of body fat percentage) was significantly greater (P < 0.01) than for participants without high adiposity. In multiple linear regressions adjusted for age, survey period, and race-ethnicity, the variance in lipid concentrations explained by body fat percentage was 2-20% (P < 0.001). CONCLUSION: Adverse lipid concentrations and high adiposity are significantly associated in youths.

Nightingale et al.1 used skinfold thicknesses and body fat from bioelectrical impedance as measures of adiposity and compared body size and composition among groups of UK children from three different ethnic origins: South Asian, black African–Caribbean and white European origins. The adiposity measures, along with body mass index (BMI) and a height-standardized weight-for-height index were all compared across the three race-ethnic groups. Nightingale et al. found that the results of such comparisons differed depending on whether they used adiposity measures or BMI or a height-standardized index. This finding led them to question the use of BMI to compare adiposity across race-ethnic groups of children. | Interest in the problem of comparing anthropometric data between different race-ethnic groups of children has a long history. In a study published in 1951, Greulich2 compared the growth data collected in 1947 from a representative sample of Guamanian (Chamorro) children from Guam with data from a slightly earlier Brush Foundation study of a sample of affluent white children from Cleveland Ohio. The Guamanian children, who had been subjected to hardships and deprivation during the war and the Japanese occupation of Guam, were both shorter and lighter than the Cleveland children of the same age. To put the weights of the children on a comparable scale, given the height difference, Greulich chose what he termed the ‘height–weight index’ (the ratio of weight to height). Using this index, he concluded that the Guamanian children were underweight for height relative to the Cleveland children, which he attributed in part to genetic differences between the Micronesian children of Guam and the children of European ancestry in Cleveland, as well as to the deprivations suffered by the Guamanians during the war. He also concluded that, although both boys and girls of Guam had suffered growth retardation in comparison with the Cleveland children, the relative effects had been greater for boys, ‘consistent with the view that the human male is less successful than the female in withstanding the rigors of an unfavourable environment’.

KEY FINDINGS: Data from the National Health and Nutrition Examination Survey, 2005-2008 Among men, obesity prevalence is generally similar at all income levels, however, among non-Hispanic black and Mexican-American men those with higher income are more likely to be obese than those with low income. Higher income women are less likely to be obese than low income women, but most obese women are not low income. There is no significant trend between obesity and education among men. Among women, however, there is a trend, those with college degrees are less likely to be obese compared with less educated women. Between 1988-1994 and 2007-2008 the prevalence of obesity increased in adults at all income and education levels.

KEY FINDINGS: Data from the National Health and Nutrition Examination Survey, 2005-2008 Low income children and adolescents are more likely to be obese than their higher income counterparts, but the relationship is not consistent across race and ethnicity groups. Most obese children and adolescents are not low income (below 130% of the poverty level). Children and adolescents living in households where the head of household has a college degree are less likely to be obese compared with those living in households where the household head has less education, but the relationship is not consistent across race and ethnicity groups. Between 1988-1994 and 2007-2008 the prevalence of childhood obesity increased at all income and education levels.

In studies of weight and mortality, the construct of reverse causation has come to be used to imply that the exposure-outcome relation is biased by weight loss due to preexisting illness. Observed weight-mortality associations are sometimes thought to result from this bias. Evidence for the occurrence of such bias is weak and inconsistent, suggesting that either the analytical methods used have been inadequate or else illness-related weight loss is not an important source of bias. Deleting participants has been the most frequent approach to control possible bias. As implemented, this can lead to deletion of almost 90% of all deaths in a sample and to deletion of more overweight and obese participants than participants with normal or below normal weight. Because it has not been demonstrated that the procedures used to adjust for reverse causation increase validity or have large or systematic effects on relative risks, it is premature to consider reverse causation as an important cause of bias. Further research would be useful to elucidate the potential effects and importance of reverse causation or illness-related weight loss as a source of bias in the observed associations between weight and mortality in cohort studies.

In 1999, dual-energy x-ray absorptiometry (DXA) scans were added to the National Health and Nutrition Examination Survey (NHANES) to provide information on soft tissue composition and bone mineral content. However, in 1999-2004, DXA data were missing in whole or in part for about 21 per cent of the NHANES participants eligible for the DXA examination; and the missingness is associated with important characteristics such as body mass index and age. To handle this missing-data problem, multiple imputation of the missing DXA data was performed. Several features made the project interesting and challenging statistically, including the relationship between missingness on the DXA measures and the values of other variables; the highly multivariate nature of the variables being imputed; the need to transform the DXA variables during the imputation process; the desire to use a large number of non-DXA predictors, many of which had small amounts of missing data themselves, in the imputation models; the use of lower bounds in the imputation procedure; and relationships between the DXA variables and other variables, which helped both in creating and evaluating the imputations. This paper describes the imputation models, methods, and evaluations for this publicly available data resource and demonstrates properties of the imputations via examples of analyses of the data. The analyses suggest that imputation helps to correct biases that occur in estimates based on the data without imputation, and that it helps to increase the precision of estimates as well. Moreover, multiple imputation usually yields larger estimated standard errors than those obtained with single imputation. Published in 2010 by John Wiley & Sons, Ltd.

Symonds’ article1 presents a table of standard weights for height for men that were derived from 1897 life insurance data from the USA and Canada. As has been the case with other life insurance height–weight tables,2–8 it is not completely clear how Symonds’ table was derived and exactly what his standards represent. As noted by Rothstein,9 the goals of life insurance companies tend to be somewhat different to those of health researchers and include finding accurately and inexpensively measured factors with good predictive values rather than looking for causal factors. Characteristics such as sex, age, occupation and possibly even body weight, may be good predictors without being in themselves causes of mortality. For purposes of assessing causality in health research, life insurance data tend to have many limitations, including that they represent policies, not individuals (one individual may have more than one policy), that the data on weight and height are collected unsystematically, show some digit preference and may be self-reported rather than measured, and that the sample is highly selected.

BACKGROUND: The prevalence of adult obesity has increased in recent decades. It is important to predict the long-term effect of body weight, and changes in body weight, in middle age on longevity and Medicare costs in older ages. METHODS: The relationships between individuals' characteristics in middle age and subsequent Medicare costs and mortality were estimated from the linkage of the National Health and Nutrition Examination Survey I Epidemiologic Follow-up Study to Medicare administrative records (1991-2000) and mortality information (1971-2000). We predicted longevity and lifetime Medicare costs via simulation for 45-year-old persons by body weight in 1973 and changes in body weight between 1973 and 1983. RESULTS: Obese 45-year-olds had a smaller chance of surviving to age 65 and, if they did, incurred significantly higher average lifetime Medicare costs than normal-weight 45-year-olds ($163,000 compared with $117,000). Those who remained obese between ages 45 and 55 in 1973 to 1983 incurred significantly higher lifetime Medicare costs than those who maintained normal weight. Other weight change categories did not differ significantly from those who maintained normal weight in terms of life expectancy at age 65, but overweight and obese people who lost weight had less chance of surviving to age 65 and the lowest estimated life expectancies thereafter. CONCLUSIONS: Chronic obesity in middle age increases lifetime Medicare costs relative to those who remained normal weight. As the survival of obese persons improves, it is possible that Medicare costs may rise substantially in the future to meet the health care needs of today's obese middle-aged population. Thus, active engagement by both the private and public sectors to prevent and to reduce obesity are critically needed.

BACKGROUND: Body mass index (BMI)-for-age has been recommended as a screening test for excess adiposity in children and adolescents. OBJECTIVE: We quantified the performance of standard categories of BMI-for-age relative to the population prevalence of high adiposity in children and adolescents overall and by race-ethnic group in a nationally representative US population sample by using definitions of high adiposity that are consistent with expert committee recommendations. DESIGN: Percentage body fat in 8821 children and adolescents aged 8-19 y was measured by using dual-energy X-ray absorptiometry in 1999-2004 as part of a health examination survey. RESULTS: With the use of several different cutoffs for percentage fat to define high adiposity, most children with high BMI-for-age (> or = 95th percentile of the growth charts) had high adiposity, and few children with normal BMI-for-age (<85th percentile) had high adiposity. The prevalence of high adiposity in intermediate BMI categories varied from 45% to 15% depending on the cutoff. The prevalence of a high BMI was significantly higher in non-Hispanic black girls than in non-Hispanic white girls, but the prevalence of high adiposity was not significantly different. CONCLUSIONS: Current BMI cutoffs can identify a high prevalence of high adiposity in children with high BMI-for-age and a low prevalence of high adiposity in children with normal BMI-for-age. By these adiposity measures, less than one-half of children with intermediate BMIs-for-age (85th to <95th percentile) have high adiposity. Differences in high BMI ranges between race-ethnic groups do not necessarily indicate differences in high adiposity.