Published online January 2, 2007
PEDIATRICS Vol. 119 No. 1 January 2007, pp. e148-e155 (doi:10.1542/peds.2005-2867)
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ARTICLE

Identification of Overweight Status Is Associated With Higher Rates of Screening for Comorbidities of Overweight in Pediatric Primary Care Practice

Kimberley J. Dilley, MD, MPHa,b, Lisa A. Martin, MD, MPHa,b, Christine Sullivan, MBA, MSc, Roopa Seshadri, PhDa,b,c, Helen J. Binns, MD, MPHa,b,c for the Pediatric Practice Research Group

a Department of Pediatrics, Children's Memorial Hospital, Chicago, Illinois
b Feinberg School of Medicine, Northwestern University, Chicago, Illinois
c Mary Ann and J. Milburn Smith Child Health Research Program, Children's Memorial Research Center, Chicago, Illinois


   ABSTRACT
TOP
 ABSTRACT
METHODS
 RESULTS
 DISCUSSION
 CONCLUSIONS
 REFERENCES
 
OBJECTIVES. The goals were to determine whether primary care provider identification of children as overweight was associated with additional screening or referrals and whether the types and numbers of visits to primary care differed for overweight and nonoverweight children.

METHODS. Sequential parents/guardians at 13 diverse pediatric practices completed an in-office survey addressing health habits and demographic features. Medical records of each child from a sample of families were reviewed. Data were abstracted from the first visit and from all visits in the 14-month period before study enrollment. Analyses were limited to children ≥2 years of age for whom BMI percentile could be calculated.

RESULTS. The analytic sample included 1216 children (mean age: 7.9 years; 51% male) from 777 families (parents were 43% white, 18% black, 34% Hispanic, and 5% other; 49% of families had a child receiving Medicaid/uninsured). Among overweight children (BMI of ≥95th percentile; n = 248), 28% had been identified as such in the record. Screening or referral for evaluation of comorbidities was more likely among overweight children who were identified in the record (54%) than among overweight children who were not identified (17%). Among children at risk of overweight (BMI of 85th to 94th percentile; n = 186), 5% had been identified as such in the record and overall 15% were screened/referred. In logistic regression modeling, the children identified as overweight/at risk of overweight had 6 times greater odds of receiving any management for overweight.

CONCLUSIONS. Low rates of identification of overweight status and evaluation or referrals for comorbidities were found. Identification of overweight status was associated with a greatly increased rate of screening for comorbidities.

Key Words: children • overweight • practice-based research • screening

Abbreviations: AROW—at risk of overweight • ICC—intracluster correlation • LR—logistic regression • OR—odds ratio • HFHC—Healthy Families, Healthy Children: The Pediatrician's Role • CI—confidence interval

Obesity in children is an increasing problem in the United States, mirroring similar increases in obesity rates for adults in the United States and for populations in other nations. An estimated 16.0% of US children and adolescents 6 to 19 years of age are overweight (BMI of ≥95th percentile), and 15.0% are at risk of overweight (AROW) (BMI of 85th to 94th percentile).1 Estimates are slightly lower for children 2 to 5 years of age (10.3% overweight and 12.3% AROW).1 Not only is the prevalence of childhood obesity increasing, but also there is a shift in weight distribution; obese children are further from their ideal body weights than ever before.2

Health care expenditures related to obesity are also increasing. In the inpatient setting, 1.7% of all hospital costs for children between 1997 and 1999 were related to obesity.3 This number was >3 times the 0.43% found between 1979 and 1981.4 Costs of outpatient care for obese children have not been reported, but we do know that reimbursement rates for outpatient treatment of obese children are very low.5 Furthermore, although many studies have documented higher rates of health care utilization and higher health care expenditures for obese adults, compared with nonobese adults,4,6–11 little is known about whether health care utilization patterns in primary care settings differ for overweight children, compared with their nonoverweight counterparts.

Recommendations for the prevention,12 evaluation, and treatment13 of childhood obesity include a call for repeated evaluations of a child's nutritional status (ie, an interpretation of weight in relation to height) by using BMI calculations and percentile ranks for children that are based on age and gender. The extent to which physicians evaluate a child's nutritional status annually in this manner is not well known. Cross-sectional medical record reviews in an academic primary care setting14 and an urban community health center that had introduced a type 2 diabetes mellitus screening protocol15 reported identification of obesity in ~50% of health maintenance visits in which the patient met objective criteria for overweight. Lower rates of identification among AROW children (8%) were reported.15 Rates of documented diagnosis of overweight are lower (~20%) when AROW and overweight children are grouped together.16

It seems likely that identifying overweight and AROW children appropriately would foster evaluations, counseling, and treatments regarding health habits and comorbid conditions. At the urban community health center, children identified accurately as overweight were more likely to receive counseling and type 2 diabetes screening.15 Analysis of data from the National Ambulatory Medical Care Survey and the National Hospital Ambulatory Medical Care Survey indicated that obesity was diagnosed in only 0.93% of all well-child visits, a much lower rate than would be expected on the basis of overweight prevalence.17 However, counseling regarding diet and physical activity was more likely at visits at which obesity was diagnosed.17 Another study that examined data from both pediatric primary care and subspecialty practices showed that the majority of children, especially younger children and those in the AROW category, were not diagnosed as overweight and did not receive recommended screening.18 No studies to date have examined health care utilization patterns for overweight children in primary care settings. This study used a medical record review of visits to diverse primary care pediatric practices to examine the patterns of visits and counseling for prevention or management of overweight and the patterns of additional evaluations provided to those who met the criteria for overweight or AROW nutritional status classification.


   METHODS
TOP
 ABSTRACT
 METHODS
RESULTS
 DISCUSSION
 CONCLUSIONS
 REFERENCES
 
Participants
Thirteen practices, which were members of the Pediatric Practice Research Group, a practice-based, primary care research network,19 participated in study processes. These practices, including 6 private practices and 7 health center/public health sites serving inner-city, low-income families, were recruited for participation in the study titled Healthy Families, Healthy Children: The Pediatrician's Role (HFHC). The aims of HFHC were to examine dissemination methods for practice systems changes related to queries and counseling about tobacco usage and to evaluate family health habits and practice systems related to general preventive evaluation and counseling. Because HFHC included extensive parent-reported data both on family health habits and on parental perceptions of the pediatrician's role in health promotion counseling, we chose to examine the pediatric providers' rates of evaluation of health status related to the weight of their pediatric patients. No specific hypothesis regarding the link between smoking households and pediatric obesity was examined.

Individual study subjects entered the study in a 2-stage process. First, a consecutive sample of parents/guardians of children who received care at the practice completed a baseline survey and provided signed consent for telephone follow-up contact and review of the practice's medical records for their children. Recruitment was accomplished between June 2002 and July 2003 and occurred on 25 days at each practice. Of the 6617 eligible subjects approached, 4218 (64%) signed consent forms and completed a baseline survey. Of those who consented, a sample was selected for follow-up evaluation, including all families that reported household tobacco use, those with missing information on household tobacco use, and 1 randomly selected, nonsmoking household per practice per recruitment day. At 11 practices, the subjects for the current study included those who completed a first follow-up telephone call; at 2 practices, all subjects selected for follow-up evaluation (regardless of success with a first follow-up telephone call) were included. This study was approved by the institutional review boards of the Children's Memorial Hospital and the Chicago Department of Public Health.

Data Instruments
The baseline survey collected data on demographic features, diet and activity habits of the respondent and an index child (the oldest child in the office on the day of the survey), and household and personal smoking patterns. Demographic data included age and health insurance type of the index child and age, race/ethnicity, and education level of the respondent. Additional questions addressed the relationship between parent and child health habits and pediatricians' role in counseling about parental health habits.

The retrospective medical record review began with an examination of the child's initial visit and initial intake form (if available) for any family history of overweight-related diagnoses (obesity, hypertension, heart disease, or high cholesterol levels), asthma, or tobacco use. Data were then collected by reviewing each visit for a 14-month period before and including visits occurring on the enrollment date. Overweight-related data collected from the provider notes and from laboratory results sections of the chart are shown in Table 1. Correspondence sections of the records were also reviewed, to facilitate assessment for specialist referrals. Chronic conditions expected to cause overweight, such as Prader-Willi syndrome, were noted if they appeared in any section of the record that was reviewed. Data on respiratory-related symptoms, diagnoses, and treatments were also collected but are not reported here.


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  		TABLE 1 Data Collected in Medical Record Review

 
At each practice, a sample of ~10% of medical records were reviewed by Dr Dilley and either Dr Martin or another reviewer. Interrater reliability for individual items within the medical record reviews had a {kappa} of 0.687, with 95.7% agreement, for Drs Dilley and Martin and a {kappa} of 0.852, with 98.0% agreement, for Dr Dilley and the other reviewer.

Analysis
Only children ≥2 years of age within the medical record review period were included in analyses. Respondents' reports of race/ethnicity and the medical insurance status for the index child were assigned to all children in the family. Height and weight data linked to age at the visit and gender were used to calculate BMI, BMI percentile, and BMI z score, based on national reference standards, by using Epi Info (Centers for Disease Control and Prevention, Atlanta, GA). In analyses, overweight was defined as ≥95th BMI percentile and AROW was defined as 85th to 94th BMI percentile.20 Each subject child was assigned to a BMI percentile group on the basis of the highest BMI obtained during the 14-month study period. However, documentation of diagnoses, counseling, laboratory tests, or referrals from any visit in the review period was considered for reports of whether or not these were provided. Bivariate analyses included {chi}2, analysis of variance, and nonparametric tests, as appropriate.

The first series of analyses described the prevalence of overweight and rates of physician documentation for children who were overweight or AROW. Factors from the record reviews (family history characteristics, child age group, and visit patterns) and parental surveys (eg, parent age group and race/ethnicity and child insurance type) were examined for association with physician documentation of an overweight diagnosis or concern about overweight and provision of counseling or other assessments or referrals related to overweight. Multivariate logistic regression (LR) models were developed to examine the relative importance of the various factors associated with overweight identification and evaluation/referral for comorbid conditions. These models included 4 categorical demographic variables (household tobacco use, insurance status, race/ethnicity, and parent education) and 2 continuous variables (BMI percentile and child age).

Next, multivariate LR modeling was used to examine what factors contributed to a high visit frequency (>3 visits during the 14-month study period), to assess whether overweight and AROW children would seek more medical care, as has been shown for adults.4,6–11 The model included 4 categorical demographic variables (household tobacco use, insurance status, race/ethnicity, and parent education), 2 continuous variables (BMI percentile and child age), and whether AROW/overweight was identified by the provider.

To account for clustering of children within families and practices, multivariate LR models used the generalized linear model with random effects approach,21 with SAS Proc Glimmix (SAS Institute, Cary, NC). Failure to account for clustering would mean that observed associations could be attributable simply to nonrandom differences between included clusters.


   RESULTS
TOP
 ABSTRACT
 METHODS
 RESULTS
DISCUSSION
 CONCLUSIONS
 REFERENCES
 
Demographic Features and Visit Content
Among the families selected for medical record reviews, 96% (1153 of 1198 families) had a record reviewed for ≥1 child (n = 2089 children), with 1940 children having a visit in the review period. No subjects were found to have chronic conditions expected to cause overweight; therefore, no exclusions for medical history were made. Seventy-five percent of children (n = 1453) with a visit in the period were ≥2 years of age at a visit, and 84% (n = 1216 children in 777 families) of those had a visit in the review period at which height and weight were recorded. Most respondents (46%) had 1 child in the analysis (32% had 2 children and 22% had ≥3 children).

Twenty percent of children were overweight (15% AROW and 64% not overweight). Greater proportions of overweight/AROW children were in nonwhite racial/ethnic groups and among those receiving Medicaid/uninsured (Table 2). The median number of visits per child was 3 (range: 1–43 visits; 1 visit: 19%; 2 visits: 17%; 3 visits: 18%; 4 visits: 14%; ≥5 visits: 32%), and 91% had a health maintenance visit in the review period; these values did not vary according to BMI percentile group. Among children with simultaneous height and weight measurements, 18 AROW (10%) and 33 overweight (13%) children did not have a comprehensive health maintenance visit. Most children (70%) lived with a smoker, according to the study design. A greater percentage of nonoverweight children (34%) lived in a nonsmoking household, compared with AROW (21%) and overweight (25%) children (P = .0007).


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  		TABLE 2 Child Subject Characteristics (n = 1216)

 
Influence of BMI Percentile Group on Content of Care
Documentation of diagnosis of or concern about overweight (defined as identification), as well as any management related to overweight, was most commonly found for the overweight group of children (Table 3). Approximately one fourth of overweight and 15% of AROW children had any management (defined as diagnosis of a comorbid condition, any referral, or any testing related to overweight) for overweight. However, documentation of assessment or counseling provided at the visit related to diet and physical activity did not differ according to BMI percentile group status (Table 3).


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  		TABLE 3 Medical Record Documentation of Assessment and Counseling

 
A total of 13% of overweight children had any diagnosis of a comorbid condition (<1% diabetes mellitus, <1% hypertension, 2% hyperlipidemia, 5% sleep-related disorders, 6% orthopedic diagnoses, and 2% skin changes). A comorbidity was more likely to be noted for those for whom a diagnosis of overweight had been documented (21% vs 10%; P = .012) (Table 4). For children in all BMI categories combined, diagnosis of a comorbid condition was more likely if an overweight diagnosis was documented (22% vs 5%; P < .0001).


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  		TABLE 4 Management of Overweight and AROW Children

 
Among overweight children, 4% had glucose or insulin levels checked, 9% had lipid levels checked, 3% had thyroid function checked, and 12% received other overweight-related laboratory tests. Overweight children identified as such in the medical records were more likely to have had laboratory testing than were overweight children not identified (Table 4). Among the overweight children, 7% were referred to a nutritionist, 1% to a medical specialist such as an endocrinologist, and 5% to a surgical specialist such as orthopedist or otolaryngologist. Of the overweight children identified as such in the medical record, 54% had any management, compared with only 17% of those not identified (Table 4).

Only 27 AROW children (15%) had management that could be classified as possibly related to their abnormal weight, and 7% had a diagnosis of a comorbid condition. Two children with a BMI of <85th percentile were identified in the record as being overweight. Neither of those children underwent laboratory testing or had a specialist referral, and both medical records had documentation of dietary but not physical activity counseling.

Among overweight and AROW children, older children and children in the highest BMI percentiles were the most likely to be identified. A multivariate LR model indicated increasing odds of identification for each advancing 1 year of age (adjusted odds ratio [OR]: 1.10; 95% confidence interval [CI]: 1.02–1.19) and increasing odds for each 1-point increase in BMI percentile (adjusted OR: 1.47; 95% CI: 1.30–1.65). Additional variables found to be significant in the model included race/ethnicity and parent education. Among these overweight/AROW children, black children were less likely to be identified, compared with white children (adjusted OR: 0.28; 95% CI: 0.087–0.89). Contrasts between white children and the other race/ethnicity groups were not significant. Children of high school graduates had similar rates of identification as did children of parents with college or postgraduate education (adjusted OR: 0.81; 95% CI: 0.27–2.50). However, children of parents with college or postgraduate education were less likely to be identified than were those with less than high school education (adjusted OR: 0.28; 95% CI: 0.086–0.94) or with only some college (adjusted OR: 0.33; 95% CI: 0.12–0.93). The intracluster correlation (ICC) for practice was 10.1%, and that for family was 4.7%.

Among overweight and AROW children, identification of the child's overweight or AROW nutritional status increased the odds of any management (adjusted OR: 5.96; 95% CI: 2.95–12.03), even after adjustment for BMI percentile and child age. In this LR model, the odds of any management increased with each 1-year increase in age (adjusted OR: 1.14; 95% CI: 1.07–1.22) and each 1-point increase in BMI percentile (adjusted OR: 1.10; 95% CI: 1.02–1.19). No other variables were significant in the model. The ICC for practice was 8.4%, and that for family was 28.0%.

Influence of BMI Percentile Group on Number of Visits
The likelihood of having >3 visits (ie, a high number of visits) in the 14-month study period was examined to determine whether overweight and AROW children sought care more frequently. The odds of having a high number of visits were not affected by being in the AROW group (adjusted OR: 1.11; 95% CI: 0.77–1.60) or overweight group (adjusted OR: 0.99; 95% CI: 0.68–1.42), compared with the normal-weight group. Younger children were slightly more likely to have >3 visits (adjusted OR: 1.11; 95% CI: 1.08–1.15), and those identified as overweight were marginally more likely to have >3 visits (adjusted OR: 1.77; 95% CI: 1.00–3.14). Additional variables found to be significant in the model included race/ethnicity and parent education. Children of black parents, compared with white parents, were less likely to have >3 visits (adjusted OR: 0.55; 95% CI: 0.45–0.67). Children of parents with less than high school education (OR: 0.46; 95% CI: 0.28–0.76) or high school graduates (OR: 0.60; 95% CI: 0.39–0.93), but not those with only some college (OR: 0.86; 95% CI: 0.59–1.26), were less likely than children of parents with college or postgraduate education to have >3 visits. The ICC for practice was 1.2%, and that for family was 21.7%.


   DISCUSSION
TOP
 ABSTRACT
 METHODS
 RESULTS
 DISCUSSION
CONCLUSIONS
 REFERENCES
 
This article demonstrates the significant relationship between documentation of a diagnosis of or concern about overweight in a child's medical record and management decisions related to comorbid conditions. The results lend support to the presumption that improved identification would lead to more comprehensive medical evaluations of secondary effects in overweight children. Furthermore, it demonstrated that existing methods of overweight identification used at these practices at the time of this study favored older and more overweight children, which points to the need for routine evaluation of all children. The strengths of the study include the large number of subjects from diverse practices, objective calculation of BMI percentile values (to allow for assessment of missed diagnoses of overweight or AROW), and the analytic method to evaluate the effects of the practice and family on identification and management of overweight. In addition, the examination of all visits in a defined time period of >1 year and limiting of the sample to children with both weight and height measurements available in that period allowed us to draw conclusions based on complete data for children who all had the opportunity for BMI interpretation and additional management as indicated.

Use of objective data from the records to calculate BMI percentiles for age and gender yielded rates in this sample of 20% overweight and 15% AROW. Only 28% of the overweight children were identified as such by their providers, but identified children were more likely to receive screening or referrals for comorbid conditions. Very few children in the AROW category were identified or had any management. Providers were more likely to identify weight as a problem for older children and those in the highest BMI percentiles, potentially missing important opportunities for intervention or prevention at an early age and lower BMI.

Rates of identification of comorbid diagnoses that might be related to overweight were quite low for the overweight and AROW children. This indicates either low incidence of complications in the community sample or underidentification of children with comorbidities actually occurring at expected rates.

Although rates of laboratory testing among the overweight and AROW children were low, the expected rate is unclear, because it would not be expected that all overweight children would necessarily have a laboratory test in a specific 14-month period. Published recommendations for testing include (1) fasting glucose and insulin measurements for all overweight children and for AROW children with a family history of diabetes mellitus or with other evidence of obesity complications,13 (2) lipid profiles for any child >2 years of age with a family history of dyslipidemia or early cardiovascular disease or children with other risk factors, including obesity,22 and (3) evaluation for any medical causes of obesity as indicated by history and physical examination.13 No specific consensus guidelines exist covering screening for nonalcoholic steatohepatitis. Notably absent from the guidelines is comment on how often to retest children with past normal laboratory results who remain overweight.

The children in this sample at ≥85th BMI percentile (overweight or AROW) were not more likely than their nonoverweight counterparts to have >3 visits in a 14-month period. The fact that, unlike adults, overweight and AROW children are not presenting more frequently for care suggests either that they are not experiencing complaints as a result of comorbid complications of obesity or that clinicians are not recommending or patients are not completing more frequent follow-up evaluations for their weight. An alternative explanation could be that not enough time elapsed between the identification of patients as overweight and the end of the study period for assessment of the true impact. Because children would be expected to have a health maintenance visit annually, each subject should have a BMI measurement and a health maintenance visit simultaneously. However, 10% of AROW and 13% of overweight patients who did have adequate data to allow BMI calculation never had a health maintenance visit, which means that height and weight were instead measured simultaneously at a focused visit, with the focus being weight-related in some but not all cases.

ICCs are used to account for sample designs in which study subjects relate in some manner to other study subjects. In this study, random-effects modeling was necessary, and reported ICCs provide information on the strength of care delivery patterns according to practice and family. We found practice decision-making outcomes to have ICCs as expected23 (10% for identification of overweight and 10% for additional evaluation), whereas having a high number of visits had little practice-related influence (ICC: 1%). Family influences were strong for both frequent visits (ICC: 22%) and children having an additional evaluation (ICC: 28%). Family relationship had only a modest influence on identification of children as overweight (ICC: 5%).

Potential limitations of this study include the use of medical record review data, which may not reflect all services rendered or discussions held with patients, although record reviews do provide an improvement over administrative data. In addition, only visits to the primary care office were captured; therefore, it is possible that overweight children have increased or decreased contact with medical providers when specialty visits and acute care outside of the office are considered. Although rates of counseling regarding diet and physical activity were high even for nonoverweight patients, the quality and amount of such counseling could not be assessed accurately from this record review. The 14-month study period might not be optimal to capture all changes in management that occur after a patient is identified by the provider as overweight. BMI is only one of many possible ways to assess nutritional status, and this study was performed at a time when neither BMI nor any other method of relating weight to height was used routinely in pediatric primary care. Because evaluation for a comorbid condition was measured and likely occurred separately from provider identification of a patient as overweight or AROW, these data may falsely inflate the impact that identification of overweight has on additional evaluation for comorbidities. The inclusion of a sample weighted to have high smoking rates could have introduced unmeasured bias. Finally, although recommendations for screening for overweight patients exist, the need for and frequency of rescreening that would bring overweight patients back to the office without existing complications is not clear.


   CONCLUSIONS
TOP
 ABSTRACT
 METHODS
 RESULTS
 DISCUSSION
 CONCLUSIONS
REFERENCES
 
Overweight children were frequently not identified as such by their primary care providers. However, the children who were identified were far more likely to receive counseling and additional evaluations, possibly only because they were in the highest range of BMI and were considered at risk for complications by the provider without calculation of BMI. These findings indicate a need for office systems incorporating routine growth interpretation for identification of overweight children, to initiate prompt and early intervention with the potential to prevent comorbidities and persistence of overweight into later life.


   ACKNOWLEDGMENTS
 
Funding for the Healthy Families, Healthy Children: The Pediatrician's Role study was provided by the Robert Wood Johnson Foundation, Substance Abuse Policy Research Program Grant 45645. Support was also provided by Health Resources and Services Administration Faculty Development Grant D55HP00069 for Dr Dilley's fellowship and Dr Binns' supervision of the fellowship program.

We thank the numerous research staff members, in particular Robert S. Greenberg, MD, who provided help with medical record reviews and the many subjects who participated in this project. In particular, we acknowledge the dedicated efforts of physicians and staff members of the following practices and the physicians who guided study participation at their offices: Dianna Brogan, MD, Associated Pediatricians (Portage, IN); Kathy Shepherd, MD, Lisa McKenna, MD, Near North Health Services (Chicago, IL); David Dobkin, MD, North Arlington Pediatrics (Arlington Heights, IL); Sara Naureckas, MD, Erie Family Health Center (Chicago, IL); David Claus, MD, Pedios (Oak Park, IL); Carl Toren, MD, MPH, Ramon Cabe, MD, Chicago Family Health Center (Chicago, IL): Barbara Bayldon, MD, Children's Memorial Pediatrics-Uptown (Chicago, IL); Judith Brown, MD, Pediatric Associates of Barrington (Barrington, IL); Bruce Rowell, MD, Lawndale Christian Health Center (Chicago, IL); George Harris, MD, Southwest Pediatrics (Palos Park, IL); Kamala Ghaey, MD, Kidz Health (Chicago, IL); W. Daniel Perez, MD, Abdul Bhurgri, MD, Alivio Medical Center (Chicago, IL); Judith Neafsey MD, Chicago Department of Public Health (Chicago, IL).


   FOOTNOTES
 
Accepted Jul 17, 2006.

Address correspondence to Kimberley J. Dilley, MD, MPH, Children's Memorial Hospital, 2300 Children's Plaza, Box 30, Chicago, IL 60614. E-mail: kdilley{at}childrensmemorial.org

The authors have indicated they have no financial relationships relevant to this article to disclose.

Dr Martin's current address is Loyola University, Stritch School of Medicine, Department of Pediatrics, 2160 S First Ave, Maywood, IL 60153.


   REFERENCES
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 ABSTRACT
 METHODS
 RESULTS
 DISCUSSION
 CONCLUSIONS
 REFERENCES
 

  1. Hedley AA, Ogden CL, Johnson CL, Carroll MD, Curtin LR, Flegal KM. Prevalence of overweight and obesity among US children, adolescents, and adults, 1999–2002. JAMA. 2004;291 :2847 –2850[Abstract/Free Full Text]
  2. Serdula MK, Ivery D, Coates RJ, Freedman DS, Williamson DF, Byers T. Do obese children become obese adults? A review of the literature. Prev Med. 1993;22 :167 –177[CrossRef][ISI][Medline]
  3. Wang G, Dietz WH. Economic burden of obesity in youths aged 6 to 17 years: 1979–1999. Pediatrics. 2002;109(5) . Available at: www.pediatrics.org/cgi/content/full/109/5/e81
  4. Sansone RA, Sansone LA, Wiederman MW. The relationship between obesity and medical utilization among women in a primary care setting. Int J Eat Disord. 1998;23 :161 –167[CrossRef][ISI][Medline]
  5. Tershakovec AM, Watson MH, Wenner WJ, Marx AL. Insurance reimbursement for the treatment of obesity in children. J Pediatr. 1999;134 :573 –578[CrossRef][ISI][Medline]
  6. Tucker LA, Clegg AG. Differences in health care costs and utilization among adults with selected lifestyle-related risk factors. Am J Health Promot. 2002;16 :225 –233[ISI][Medline]
  7. Sturm R. The effects of obesity, smoking, and drinking on medical problems and costs. Health Aff (Millwood). 2002;21 :245 –253[Abstract/Free Full Text]
  8. Guallar-Castillon P, Lopez Garcia E, Lozano Palacios L, et al. The relationship of overweight and obesity with subjective health and use of health-care services among Spanish women. Int J Obes Metab Disord. 2002;26 :247 –252[CrossRef][ISI][Medline]
  9. Pronk NP, Tan AWH, O'Connor P. Obesity, fitness, willingness to communicate and health care costs. Med Sci Sports Exerc. 1999;31 :1535 –1543
  10. Heithoff KA, Cuffel BJ, Kennedy S, Peters J. The association between body mass and health care expenditures. Clin Ther. 1997;19 :811 –820[CrossRef][ISI][Medline]
  11. Raebel MA, Malone DC, Conner DA, Xu S, Porter JA, Lanty FL. Health services use and health care costs of obese and nonobese individuals. Arch Intern Med. 2004;164 :2135 –2140[Abstract/Free Full Text]
  12. American Academy of Pediatrics, Committee on Nutrition. Prevention of pediatric overweight and obesity. Pediatrics. 2003;112 :424 –430[Abstract/Free Full Text]
  13. Barlow SE, Dietz WH. Obesity evaluation and treatment: expert committee recommendations. Pediatrics. 1998;102(3) . Available at: www.pediatrics.org/cgi/content/full/102/3/e29
  14. O'Brien SH, Holubkov R, Reis EC. Identification, evaluation, and management of obesity in an academic primary care center. Pediatrics. 2004;114(2) . Available at: www.pediatrics.org/cgi/content/full/114/2/e154
  15. Drobac S, Brickman W, Smith T, Binns HJ. Evaluation of a type 2 diabetes screening protocol in an urban pediatric clinic. Pediatrics. 2004;114 :141 –148[Abstract/Free Full Text]
  16. Dorsey KB, Wells C, Krumholz HM, Concato JC. Diagnosis, evaluation, and treatment of childhood obesity in pediatric practice. Arch Pediatr Adolesc Med. 2005;159 :632 –638[Abstract/Free Full Text]
  17. Cook S, Weitzman M, Auinger P, Barlow SE. Screening and counseling associated with obesity diagnosis in a national survey of ambulatory pediatric visits. Pediatrics. 2005;116 :112 –116[Abstract/Free Full Text]
  18. Riley MR, Bass NM, Rosenthal P, Merriman RB. Underdiagnosis of pediatric obesity and underscreening for fatty liver disease and metabolic syndrome by pediatricians and pediatric subspecialists. J Pediatr. 2005;147 :839 –842[CrossRef][ISI][Medline]
  19. LeBailly S, Ariza A, Bayldon B, Binns HJ, Pediatric Practice Research Group. The origin and evolution of a regional pediatric practice-based research network: practical and methodological lessons from the Pediatric Practice Research Group. Curr Probl Pediatr Adolesc Health Care. 2003;33 :124 –134[CrossRef][Medline]
  20. Himes JH, Dietz WH. Guidelines for overweight in adolescent preventive services: recommendations from an expert committee. Am J Clin Nutr. 1994;59 :307 –316[Abstract/Free Full Text]
  21. Wolfinger R, O'Connell M. Generalized linear mixed models: a pseudo-likelihood approach. J Stat Comput Simul. 1993;48 :233 –243
  22. Kavey RE, Daniels SR, Lauer RM, Atkins DL, Hayman LL, Taubert K. American Heart Association guidelines for primary prevention of atherosclerotic cardiovascular diseases beginning in childhood. Circulation. 2003;107 :1562 –1566[Free Full Text]
  23. Davis P, Gribben B, Lay-Yee R, Scott A. How much variation in clinical activity is there between general practitioners? A multi-level analysis of decision-making in primary care. J Health Serv Res Policy. 2002;7 :202 –208[Abstract/Free Full Text]
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D. M. Janicke, J. S. Harman, K. J. Kelleher, and J. Zhang
The Association of Psychiatric Diagnoses, Health Service Use, and Expenditures in Children with Obesity-related Health Conditions
J. Pediatr. Psychol., June 3, 2008; (2008) jsn051v1.
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