Childhood obesity stats in preschoolers

On January 13, 2010, in Uncategorized, by Andrea

Obesity Prevalence Among Low-Income, Preschool-Aged Children—United States, 1998-2008

JAMA. 2010;303(1):28-30.

MMWR. 2009;58:769-773

1 figure, 1 table omitted

Childhood obesity continues to be a leading public health concern that disproportionately affects low-income and minority children.1 Children who are obese in their preschool years are more likely to be obese in adolescence and adulthood2 and to develop diabetes, hypertension, hyperlipidemia, asthma, and sleep apnea.3 One of the Healthy People 2010 objectives (19-3) is to reduce to 5% the proportion of children and adolescents who are obese.4 CDC’s Pediatric Nutrition Surveillance System (PedNSS) is the only source of nationally compiled obesity surveillance data obtained at the state and local level for low-income, preschool-aged children participating in federally funded health and nutrition programs. To describe progress in reducing childhood obesity, CDC examined trends and current prevalence in obesity using PedNSS data submitted by participating states, territories, and Indian tribal organizations during 1998-2008. The findings indicated that obesity prevalence among low-income, preschool-aged children increased steadily from 12.4% in 1998 to 14.5% in 2003, but subsequently remained essentially the same, with a 14.6% prevalence in 2008. Reducing childhood obesity will require effective prevention strategies that focus on environments and policies promoting physical activity and a healthy diet for families, child care centers, and communities.

PedNSS is a state-based surveillance system that monitors the nutritional status of children from birth through age 4 years enrolled in federally funded programs that serve low-income children. For all states except California and North Carolina, data come exclusively from the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC).* In California, data are exclusively from Medicaid-funded programs. North Carolina submits data from both WIC (95.5%) and non-WIC programs (4.5%).{dagger} For the states included in this analysis, 21.0% of children aged 2-4 years are covered by PedNSS. On average, children are seen twice a year by the program; height and weight are measured each time. Data are collected at the clinic level and submitted to CDC for analysis. Federally funded programs submit data on weight, height (measured by trained staff using a standard protocol during clinic visits), age, sex, and the race/ethnicity reported by the child’s parent or caregiver. CDC uses weight, height, and age data to calculate body mass index (BMI) (weight [kg]/ height [m2]). For children aged 2-4 years, obesity is defined as BMI-for-age ≥95th percentile based on the 2000 CDC sex-specific growth charts.5 CDC performs routine edits to assess data quality. An error flag is applied to height or weight data that are either missing, miscoded, or biologically implausible (e.g., height-for-age z-score <–5.0 or >3.0, body mass index [BMI]-for-age [children aged ≥2 years] z-score <–4.0 or >5.0, weight-for-age z-score <–4.0 or >5.0, or BMI-for-age [children aged ≥2 years] z-score <–4.0 or >5.0). All flagged data are excluded from PedNSS analyses.

CDC randomly selected one record per child per year to estimate obesity prevalence in 1998, 2003, and 2008. To assess the change in obesity prevalence in PedNSS overall and by race/ethnicity, prevalence was estimated using data only from the subset of federally funded programs that participated in 1998, 2003, and 2008 (N = 37). The average annual change in obesity prevalence during 1998-2003 and 2003-2008 was estimated for each PedNSS program. If data for a program were unavailable for a given year but were available for the preceding or subsequent year, CDC substituted the data for the adjacent year and calculated the annual change to account for the shorter or longer period. Chi-square tests for difference in proportions were conducted across each period, and tests were statistically significant (p<0.05) unless otherwise noted in this report.

During 1998-2008, the number of federally funded programs reporting data to PedNSS varied from 43 to 52. In 2008, records on approximately 8 million children were submitted from 43 states, the District of Columbia, Puerto Rico, the U.S. Virgin Islands, and six Indian tribal organizations. The overall prevalence of obesity among low-income, preschool-aged children increased from 12.4% (n = 1,999,970) in 1998 to 14.5% (n = 1,967,625) in 2003 and 14.6% (n = 2,222,410) in 2008. Obesity prevalence increased 0.43 percentage points annually during 1998-2003, but only 0.02 percentage points annually during 2003-2008. Obesity increased across all racial/ethnic groups during 1998-2003, with the exception of Asian/Pacific Islander (A/PI) children. However, during 2003-2008, obesity remained stable among all groups except American Indian/Alaska Native (AI/AN) children. In 2008, prevalence was highest among AI/AN (21.2%) and Hispanic (18.5%) children, and lowest among non-Hispanic white (12.6%), non-Hispanic black (11.8%), and A/PI (12.3%) children.

In 2008, only programs in Colorado and Hawaii had obesity prevalences ≤10%. The two federally funded programs with prevalence >20% were Indian tribal organizations. Of the 41 PedNSS programs supplying data for 1998-2003, a total of 38 (93%) reported an increase in obesity prevalence. In contrast, of the 44 programs supplying data for 2003-2008, 22 (50%) reported an increase in obesity, whereas 14 (32%) reported no change, and eight (18%) reported a decrease.

Reported by:


AJ Sharma, PhD, LM Grummer-Strawn, PhD, K Dalenius, MPH, D Galuska, PhD, M Anandappa, MS, E Borland, H Mackintosh, MSPH, R Smith, MS, Div of Nutrition, Physical Activity and Obesity, National Center for Chronic Disease Prevention and Health Promotion, CDC.


CDC Editorial Note:


Reduction of obesity among children and adolescents is a national priority in the United States.4 The results presented in this report indicate that among low-income, preschool-aged children participating in federally funded nutrition programs, the prevalence of obesity increased during 1998-2003, but stabilized during 2003-2008. In 2008, the national prevalence of obesity in this group remained highest among low-income Hispanic and AI/AN children and continued to increase among AI/AN children. These results suggest overall progress in stabilizing the prevalence of childhood obesity in a subset of low-income, preschool-aged children. However, these results should be confirmed through additional research using other data sets.

Children in preschool age groups are a priority for surveillance because obesity trends in this group can serve as a bellwether for trends in older children and adults.2 PedNSS currently serves as the only source of national obesity prevalence data compiled specifically on low-income, preschool-aged children. Because PedNSS nutritional data are dependent on enrollments in participating federally funded programs, PedNSS results are subject to variations in enrollment in these programs in each state. However, the effect of such variations on PedNSS results is difficult to determine. Conditions within a state that differentially affect the enrollment of children with varying prevalences of obesity could affect state or national results. In addition, changes in the proportion of children from each state might alter the results. For example, California, the largest data contributor to PedNSS, has one of the highest prevalences of obesity. The percentage of the total PedNSS sample provided by California decreased from 20.2% in 1998 to 13.6% in 2008. However, even deletion of all California data would not alter the overall results; an increase from 1998 to 2003 would still be observed, followed by stabilization through 2008. Furthermore, stabilization or declines were observed in half of the individual federally funded programs in PedNSS.

To maintain the consistency of PedNSS data, methods for data collection and recording are set nationally and are uniform across states and participating federal programs. The procedures for collecting height and weight data did not change during 1998-2008, with the exception of an increasing use of digital scales. Given the procedures within the WIC program for regular calibration of scales, this change should not affect rates of obesity. CDC has stringent requirements for data quality and uses standardized procedures for data cleaning; data files that do not meet these standards are rejected, as are records that do not meet standards for acceptable heights and weights.

The reason for the stabilization of overall obesity prevalence among these children during 2003-2008 is not known and likely is complex. One factor might be prevention efforts within state and local WIC programs targeting behaviors related to obesity in children. For example, certain initiatives in WIC{ddagger} have attempted to raise public awareness, acceptance, and support of breastfeeding, increased the percentage of low-fat or fat-free milk vouchers issued for children aged >2 years,§ and reduced television viewing.6 Recommendations such as those from the Institute of Medicine’s Preventing Childhood Obesity report also might have spurred greater attention to obesity prevention for all children.7

The National Health and Nutrition Examination Survey (NHANES) also has found a stabilization of obesity prevalence in U.S. children. NHANES found no significant increase in obesity prevalence during 1999-2006 in children aged 2-19 years.8 This apparent plateau remained even after adjusting for differences in prevalence by age group. Trends in the 2-5 year age group were not analyzed separately because of small sample size. For NHANES 2003-2006, the overall prevalence of obesity (BMI-for-age ≥95th percentile) for children aged 2-5 years was 12.4% (standard error = 1.0%), lower than the rates for both 2003 and 2008 described in this report.

The findings in this report are subject to at least three limitations. First, the proportion of children participating in federally funded nutrition programs increased during 1998-2008, as evidenced by the 11% increase in the number of children in these analyses (i.e., from 1,999,970 in 1998 to 2,222,410 in 2008). However, how the addition of these children might have affected the prevalence of obesity is unknown. Second, the percentage of the total PedNSS dataset that is made up of WIC records increased from 76% in 1998 to 85% in 2008. If the prevalence of obesity were lower in WIC than in non-WIC programs, this increase could partially explain the observed trends. However, when the analysis was conducted using only data from WIC, results were not substantially different. Finally, PedNSS data are not representative of all low-income, preschool-aged children in the United States because not all states participate in PedNSS and not all low-income children participate in federally funded programs.

Childhood obesity remains a serious public health problem even among this subset, particularly among AI/AN children. A sustained and effective public health response is necessary across the United States to reduce childhood obesity. Strategies should emphasize improving environments and policies that promote physical activity and a healthy diet.

REFERENCES

8 Available.

*Eligibility criteria for WIC includes a family income ≤185% of the poverty income threshold, based on U.S. Poverty Income Guidelines, available at http://aspe.os.dhhs.gov/poverty. A person who participates or has family members who participate in certain other benefit programs, such as the Medicaid or Aid to Families with Dependent Children/Temporary Assistance to Needy Families, automatically meets the income-eligibility requirement.

{dagger}Including the Early and Periodic Screening, Diagnosis, and Treatment Program, other Medicaid-funded child health programs, and Title V Maternal and Child Health Programs. Eligibility criteria includes a family income ≤200% of the poverty income threshold, based on U.S. Poverty Income Guidelines. The non-WIC records accounted for 24% of records in 1998, 19% in 2003, and 15% in 2008.

{ddagger}Additional information available at http://www.nal.usda.gov/wicworks/spotlight/bfweek_resources.html.

§Additional information available at http://www.health.state.ny.us/prevention/nutrition/resources/docs/2003-2006_ewph_community_intervention_projects.pdf.

Tired kids == diabetic kids?

On January 13, 2010, in Uncategorized, by Andrea

Uh-0h.  I’m in trouble then.  Cause my kids never seem to be IN bed.  I’m serious.  I say this as I’m drinking caffeine to try and chase away a migraine (aversion therapy, trying to ignore the elephants marching through my skull) and to try and stay awake since my son, my dear loving son, was awake at 2:30 this morning.  And didn’t go to sleep until about 4.  At least they are both in the 25th percentile for weight..

Of course, I also have read that metabolic syndrome numbers change quite frequently in kids, so I wouldn’t be surprised if this study were to be counterdicted, say, next week.

From Medscape:

By Joene Hendry

NEW YORK (Reuters Health) Jan 11 – Young children who average 8 hours or less of sleep a night may be at higher risk for developing diabetes, report Chinese and American researchers.

This risk may be even greater among obese youngsters, Dr. Zhijie Yu, at the Chinese Academy of Sciences in Shanghai and colleagues note in Archives of Pediatric and Adolescent Medicine.

Moreover, Dr. Yu said in an email to Reuters Health, shorter sleep seemed to influence blood glucose “independently of a large variety of risk factors,” such as age, gender, birth-related influences, early life feeding or later diet, recent illness, physical activity, body mass, and waist girth.

Dr. Yu’s team investigated sleep duration and blood glucose levels in 619 obese and 617 non-obese children who were 3 to 6 years old and free of diabetes.

Parental reports showed a greater percentage of the obese (47%) than the non-obese (37%) kids averaged 8 or fewer hours of sleep nightly. These reports also showed nightly averages of 9 or 10, or 11-plus, hours of sleep less common in obese (37% and 16%) versus non-obese (43% and 20%) kids, respectively.

High fasting glucose levels, defined as 100 mg/dL or greater, were about 1.35-fold and 2.15-fold more likely in the shorter-sleeping non-obese and obese kids, respectively. Overall, 11 children had levels above 126 mg/dL.

Among the children who slept less than 8 hours per night, elevated fasting glucose levels were documented in 23 of the 217 who were non-obese and in 49 of the 291 obese kids. By contrast, among children getting 9 or 10 hours of sleep each night, 21 of the 175 non-obese and 21 of 229 obese kids had high blood sugar.

These findings hint that, similar to adults, adequate sleep may help kids, maintain a healthy body weight and an optimal blood sugar level, Dr. Yu said.

However, Dr. Yu and co-authors emphasize the need for further studies to confirm these findings in both Chinese and other populations of youngsters.

Arch Pediatr Adolesc Med 2010.

Where America Stands: Obesity

On January 7, 2010, in Uncategorized, by Andrea

A CBS News segment from the Evening news on Thursday, 1/7/2010.

Very sad.  Very eye-opening.

60% of the people oppose a tax on junk food.


Watch CBS News Videos Online

Overdiagnosis of short, overweight kids.

On January 1, 2010, in Uncategorized, by Andrea

Kids that are both overweight AND short might be misdiagnosed when it comes to growth hormone deficiency.  Which is great — cause we want to screw up our kids even more, yes?

From Medscape:

Growth Hormone Deficiency May Be Overdiagnosed in Short Children With High BMI

NEW YORK (Reuters Health) Dec 30 – Body mass index (BMI) negatively affects peak stimulated growth hormone (GH) in children with short stature, which makes children with higher BMI standard deviation (SD) scores more vulnerable to overdiagnosis of GH deficiency, according to researchers from the Massachusetts General Hospital in Boston.

Led by Dr. Takara L. Stanley, they point out that while BMI is known to be inversely related to spontaneous GH secretion in children, there was little information on the impact of BMI on stimulated GH in younger patients.

In the December issue of the Journal of Clinical Endocrinology & Metabolism, the researchers report on the results of GH stimulation testing in 116 normal-weight children and adolescents (mean age, 10.3 years) with short stature.

BMI SD score was significantly and inversely associated with peak stimulated GH levels, regardless of whether SD score was based on bone age or chronological age, the authors report.

In contrast, height SD score was not significantly associated with peak GH levels.

In multivariate analyses, higher BMI was consistently and independently associated with lower peak growth hormone levels.

Ultimately, 36 children (31%) had a peak GH below 10 mcg/L, which is usually taken to indicate GH deficiency in the pediatric age group.

The authors found, however, that with this cutoff, GH deficiency would be diagnosed in 70% of children with a BMI SD score greater than 1, in 38% of those with a BMI SD score between 0 and 1, in 18% of children with a BMI SD score from 0 to -1, and in 29% with a BMI SD score less than -1.

Similar patterns were seen with cutoffs of 7 and 5 mcg/L.

“Our data highlight the need to consider BMI when interpreting the results of provocative GH stimulation testing in children,” the investigators say.

They conclude, “Although larger studies are clearly needed to determine the causative factors and metabolic consequences of reduced GH with increasing adiposity in the pediatric age group, our data demonstrate that even in a normal-weight cohort, children with higher BMI are disproportionately overdiagnosed with GH deficiency.”

J Clin Endocrinol Metab 2009;94:4875-4881.

Sadness.

On December 31, 2009, in Uncategorized, by Andrea

I’m all about the emotions today.

This is a study regarding adolescents undergoing RNY — specifically, it states that those with a lower BMI before surgery will have a lower BMI 1 year after surgery.  Those with a higher BMI prior to surgery (and they were looking at kids — and here’s where the sadness hits — with a BMI between 65 and 95) remain “extremely obese” one year post-op.

This is a limited study — only looking at a limited number of kids (61) and only follow them for one year.  We know that those that are super morbidly obese can continue to lose long past the year mark, so it is not as if the kids in the two groups with the larger BMIs (group 1 had BMIs between 40 and 54.9, group 2 had BMIs between 55 and 64.9, and group 3 had BMIs between 65 and 95) should be written off as “failures” by the medical community.  To do so would be extremely short-sighted and just plain incorrect.

For purposes of reading the study, nadir is defined as “the lowest point.”

From Medscape:

In Adolescents, Baseline BMI Predicts Nadir BMI After Gastric Bypass

NEW YORK (Reuters Health) Dec 29 – In adolescents undergoing gastric bypass surgery, baseline body mass index (BMI) predicts nadir BMI, a new study shows.

But regardless of baseline BMI, gastric bypass improves cardiovascular risk factors and brings BMI down by about 37% in all patients, the authors report in the January Journal of Pediatrics.

“This finding suggests that the timing of surgery for adolescent obesity is an important consideration, as ‘late’ referral for bariatric surgery at higher BMI values may preclude reversal of obesity or extreme obesity within the first post-operative year and may increase the risk of weight regain over the long term,” according to lead author Dr. Thomas H. Inge, of Cincinnati Children’s Hospital Medical Center, Ohio, and colleagues.

“The BMI spectrum for adolescents seeking surgery is broad, with values in the literature ranging from 35 to 95 kg/m squared, with average BMI values much higher than those seen in most adult surgical practices,” Dr. Inge and his co-authors point out.

To determine the effect of preoperative BMI status on outcomes in their younger patients, the investigators followed 61 adolescents for a year after laparoscopic Roux-en-Y gastric bypass. Nearly 70% were female, more than 80% were white, and their average age at surgery was 17.2 years.

In all cases, patients were left with a gastric pouch volume of 30 ml, and the jejunum was divided 15 to 20 cm from the ligament of Treitz.

Patients were stratified into three groups based on preoperative BMI (kg/m squared): Group 1, n = 23: BMI 40.0 to 54.9; Group 2, n = 21: BMI 55.0 to 64.9; and Group 3, n = 17: BMI 65.0 to 95.0.

The mean BMI in the overall cohort, which was 60.2 kg/m squared at baseline, fell by 37.4% at 1 year after surgery (p < 0.0001), with little variation in BMI reduction among the groups (37.2% in Group 1, 36.8% in Group 2, and 37.7% in Group 3).

The rate of change in absolute BMI units did vary significantly by group, however, with one-year nadir BMI (kg/m squared) reaching 31 in Group 1, 38 in Group 2, and 47 in Group 3.

Only 10 patients (17%) achieved a BMI of less than 30 kg/m squared at 1 year. Eight of these were from Group 1.

Systolic and diastolic blood pressures fell significantly after surgery by 8.8% and 13.5%, respectively, regardless of baseline BMI (P < 0.0001 for each). Surgery also reduced total cholesterol (by 16.8%; p = 0.0007), triglycerides (by 37.3%; p < 0.0001), and insulin (by 75.8%; p < 0.0001), no matter the baseline BMI.

Albumin levels did not change at 1 year despite the significant weight loss.

“In this investigation, we found that most adolescents within the highest ranges of baseline BMI…remained extremely obese…despite BMI reductions averaging nearly 40%,” the authors write.

Adolescents “who present at higher weights and BMI values lose more weight than those who present at lower weights but also plateau at a higher weight on average,” they add. “The biological and potentially behavioral reasons for this are unclear.”

J Pediatr 2010;156:103-108.

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