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  • Measure Summary
  • NQMC:010189
  • Apr 2015

High body mass index (BMI) follow-up: percentage of children who are either 1) obese or 2) overweight with a comorbid condition who have an outpatient care visit where weight is addressed subsequent to their initial diagnosis.

Quality Measurement, Evaluation, Testing, Review, and Implementation Consortium (Q-METRIC). Basic measure information: follow-up visits for children who are obese or overweight with a weight-related comorbidity. Ann Arbor (MI): Quality Measurement, Evaluation, Testing, Review, and Implementation Consortium (Q-METRIC); 2015 Apr. 47 p.

View the original measure documentation External Web Site Policy

This is the current release of the measure.

The measure developer reaffirmed the currency of this measure in January 2016.

Primary Measure Domain

Clinical Quality Measures: Process

Secondary Measure Domain

Does not apply to this measure

Description

This measure is used to assess the percentage of children who are either 1) obese or 2) overweight with a comorbid condition who have an outpatient care visit where weight is addressed subsequent to their initial diagnosis.

Eligible children are ages 2 through 17 years with either a body mass index (BMI) greater than or equal to 95th percentile (obese) or a BMI greater than or equal to 85th percentile (overweight) who are also diagnosed with diabetes, hypertension, or hyperlipidemia. The event is a second outpatient visit in which weight is addressed during the measurement year. A higher proportion indicates better performance.

Rationale

Obesity in children is associated with a broad spectrum of serious health issues, including obstructive sleep apnea, asthma, nonalcoholic fatty liver disease, type 2 diabetes mellitus, depression, orthopedic problems, and skin conditions (Barlow, 2007). While childhood obesity rates have stabilized over the past decade, the percentage of young children and adolescents who are overweight or obese remains high (Ogden et al., 2014). For the 2011–2012 period, nearly 32% of children in the United States were reported to have a body mass index (BMI) greater than or equal to 85th percentile; of these, 17% were obese (Ogden et al., 2014).

Health risks and body fat levels are proportionate. Using BMI as an initial screen of adiposity, providers can identify pediatric patients who, because of their excess weight, have health risks that need to be addressed (Barlow, 2007; Speiser et al., 2005). In this population of overweight and obese children, treatment involves addressing both the energy imbalance between diet and exercise and any weight-related health problems. This work is ongoing and requires regular visits subsequent to the initial assessment, as well as continued monitoring to track progress (Barlow, 2007; US Preventive Services Task Force [USPSTF], 2010). Children with a BMI greater than or equal to 85th percentile should receive regular lifestyle counseling addressing diet and exercise. Children with a BMI above the 95th percentile require specialist pediatric care, and those with comorbidities or severe obesity should receive care in a multidisciplinary specialist service (Speiser et al., 2005). Excess weight can be a challenging problem to treat, given the ubiquity of processed foods and sugary drinks and decreasing opportunities for physical activity. However, addressing weight problems early and persistently reduces the risk of serious chronic health issues and sets children on course for a healthy adulthood (Barlow, 2007).

Evidence for Rationale

Barlow SE, Expert Committee. Expert committee recommendations regarding the prevention, assessment, and treatment of child and adolescent overweight and obesity: summary report. Pediatrics. 2007 Dec;120(Suppl):S164-92. PubMed External Web Site Policy

Ogden CL, Carroll MD, Kit BK, Flegal KM. Prevalence of childhood and adult obesity in the United States, 2011-2012. JAMA. 2014 Feb 26;311(8):806-14. PubMed External Web Site Policy

Quality Measurement, Evaluation, Testing, Review, and Implementation Consortium (Q-METRIC). Basic measure information: follow-up visits for children who are obese or overweight with a weight-related comorbidity. Ann Arbor (MI): Quality Measurement, Evaluation, Testing, Review, and Implementation Consortium (Q-METRIC); 2015 Apr. 47 p.

Speiser PW, Rudolf MC, Anhalt H, Camacho-Hubner C, Chiarelli F, Eliakim A, Freemark M, Gruters A, Hershkovitz E, Iughetti L, Krude H, Latzer Y, Lustig RH, Pescovitz OH, Pinhas-Hamiel O, Rogol AD, Shalitin S, Sultan C, Stein D, Vardi P, Werther GA, Zadik Z, Zuckerman-Levin N, Hochberg Z, Obesity Consensus Working Group. Childhood obesity. J Clin Endocrinol Metab. 2005 Mar;90(3):1871-87. PubMed External Web Site Policy

US Preventive Services Task Force. Screening for obesity in children and adolescents: US Preventive Services Task Force recommendation statement. Pediatrics. 2010 Feb;125(2):361-7. [19 references] PubMed External Web Site Policy

Primary Health Components

High body mass index; obese; overweight; comorbid condition; diabetes; hypertension; hyperlipidemia; children

Denominator Description

The eligible population for the denominator is the number of children ages 2 through 17 years with a body mass index (BMI) greater than or equal to 95th percentile or a BMI greater than or equal to 85th percentile and a weight-related comorbidity who had an outpatient care visit during the measurement year. See the related "Denominator Inclusions/Exclusions" field.

Numerator Description

The eligible population for the numerator is the number of children ages 2 through 17 years with either a body mass index (BMI) greater than or equal to 95th percentile (obese) or a BMI greater than or equal to 85th percentile (overweight) and a weight-related comorbidity who have two outpatient care visits during the measurement year; weight is addressed in the subsequent visit. See the related "Numerator Inclusions/Exclusions" field.

Type of Evidence Supporting the Criterion of Quality for the Measure

  • A clinical practice guideline or other peer-reviewed synthesis of the clinical research evidence
  • A formal consensus procedure, involving experts in relevant clinical, methodological, public health and organizational sciences
  • A systematic review of the clinical research literature (e.g., Cochrane Review)
  • One or more research studies published in a National Library of Medicine (NLM) indexed, peer-reviewed journal

Additional Information Supporting Need for the Measure

Importance
Childhood overweight and obesity are recognized as major medical and public health problems associated with serious medical complications over the life course, including conditions such as type 2 diabetes, metabolic syndrome, and hypertension (Speiser et al., 2005). As a result, early screening and identification of weight status in children is critical for both prevention and treatment of childhood overweight and obesity. Primary care providers measure weight and height at yearly visits throughout childhood and calculate body mass index (BMI) by dividing weight by height squared. Overweight is defined as a BMI of the 85th to 94th percentile, and obesity is defined as a BMI greater than or equal to 95th percentile (Barlow, 2007). Guidelines suggest that patients in the overweight category who show no evidence of health risk receive counseling about prevention; those who are obese are quite likely to have obesity-related health risks and should be encouraged to work on weight control practices (Barlow, 2007). These levels of intervention require treatment subsequent to the initial screening visit.

Prevalence of Obesity and Unhealthy Weight in Children
Significant increases in the prevalence of childhood obesity in the United States (U.S.) across both sexes were seen in the 1980s and 1990s (Ogden et al., 2012). For the 2011–2012 period, nearly 32% of children in the U.S. were reported to be overweight or obese and at least 17% were obese (Ogden et al., 2014). At the population level, this increase in prevalence is too rapid to be a genetic shift. Rather, changes in eating and physical activity behaviors are affecting the intake and expenditure of energy, resulting in overweight and obesity (Barlow, 2007).

Cost of Obesity and Unhealthy Weight in Children
Excess weight in young people creates great economic burden. Children who are obese are approximately three times more expensive for the health care system than the average insured child, and children diagnosed with obesity are two to three times more likely to be hospitalized (Marder & Chang, 2006). In a study by Wang et al. (2008), the authors used projected overweight/obesity prevalence and national estimates of per capita excess health care costs of overweight/obesity to estimate that health care costs attributable to overweight/obesity in the entire U.S. population would reach between $861 and $957 billion by 2030, accounting for 16% to 18% of U.S. health care costs.

Pathology and Severity of Obesity and Unhealthy Weight in Children
Medical issues associated with obesity affect almost every organ of the body, though some conditions are without symptoms and signs (Barlow, 2007). Obese children are more likely to suffer from respiratory issues such as disordered breathing (Wing et al., 2003), which can lead to right ventricular hypertrophy and pulmonary hypertension, as well as inattention, poor academic performance, and enuresis (Barlow, 2007). Asthma also occurs more frequently among children who are obese (Barlow, 2007). Gastrointestinal problems include nonalcoholic fatty liver disease (NAFLD), which is related to both obesity and diabetes (Barlow, 2007); gallstones (Kaechele et al., 2006); and gastroesophageal reflux disease and constipation, which are worsened by obesity (Barlow, 2007). Obese children are more likely to have endocrine disorders such as abnormal glucose metabolism (sometimes called pre-diabetes), which indicates higher risk for the development of diabetes (Li et al., 2009); type 2 diabetes mellitus; polycystic ovary syndrome; and hypothyroidism (Barlow, 2007). Cardiovascular problems for overweight/obese children include dyslipidemia (Lamb et al., 2011) and hypertension (Barlow, 2007). Orthopedic problems include Blount disease (a visible bowing of the lower extremities), slipped capital femoral epiphysis, and an increased risk of fractures and musculoskeletal pain and orthopedic problems (Dietz, Gross, & Kirkpatrick, 1982; Manoff, Banffy, & Winell, 2005). Skin conditions include acanthosis nigricans, a chronic irritation and infection in the folds of the skin (Nguyen et al., 2001). Metabolic syndrome, a cluster of concurrent conditions (abnormal triglycerides, large waist circumference, and high blood pressure) that increase the risk of heart disease, stroke, and diabetes is not yet defined in children (Speiser et al., 2005). However, among severely obese children, the risk of developing metabolic syndrome has been estimated at 50% (Weiss et al., 2004).

Children who are obese also contend with psychiatric problems including depression, anxiety, and eating disorders (Barlow, 2007). One study found that among female adolescents who were obese, patterns of observation showed more adverse social, educational, and psychological correlates (Falkner et al., 2001). Children who are obese may also be at risk for academic difficulties, alcohol and tobacco use, premature sexual behavior, inappropriate dieting practices, and physical inactivity (Daniels et al., 2009). Increasing weight is associated with decreasing health-related quality of life, lower body satisfaction, and low self-esteem. Children who are overweight experience more teasing and are vulnerable to bullying (Daniels et al., 2009). Children share society's negative opinions about those who are overweight or obese, regardless of their own weight status or sex (Speiser et al., 2005). Their perceptions of obesity emphasize laziness, selfishness, lower intelligence, social isolation, poor social functioning, as well as low levels of perceived health, healthy eating, and activity. Children as young as 5 years of age are aware of their own levels of overweight, which affects their perceptions of appearance, athletic ability, social competence, and self-worth (Speiser et al., 2005). Research has also shown that children diagnosed with obesity are much more likely to be diagnosed with mental health disorders or bone and joint disorders than children who are not obese; they are also two-to-three times more likely to be hospitalized (Marder & Chang, 2006).

Being overweight or obese in early life also has implications for a child's future health. First, for a child with a BMI above the 85th percentile, medical risks include future or persistent obesity (Barlow, 2007; Daniels et al., 2009). Being overweight or obese in childhood and adolescence is associated with increased risk of premature mortality and comorbidities in adulthood. A 2011 systematic review reports a significant association between child and adolescent overweight/obesity and premature mortality, with hazard ratios ranging from 1.4 to 2.9 (Reilly & Kelly, 2011). In addition, being overweight or obese as a child or adolescent is significantly associated with increased risk of cardiometabolic morbidity (including diabetes, hypertension, heart disease, and stroke) in later life, with hazard ratios ranging from 1.1 to 5.1, as well as increased risk of asthma in adulthood and polycystic ovary syndrome in adult women (Reilly & Kelly, 2011). Obesity in adolescence is associated with negative self-image that persists into adulthood (Dietz, 1998). These children are also at long-term higher risk for chronic conditions such as breast, colon, and kidney cancer; musculoskeletal disorders; and gall bladder disease (Daniels et al., 2009). Childhood obesity contributes to a significant and increasing burden of chronic disease, rising health care costs, disability, and premature death.

Performance Gap
Relative to the frequency of obesity among children, the level of assessment, referrals, and follow-up is low (Huang et al., 2011). Lazorick et al. (2011) found that documentation of counseling regarding nutrition and physical activity was rare: 16% for children ages 3 to 5 years old and 7% for ages 13 to 16 years old. Many of the overweight adolescents in this study already had comorbidities seen more frequently in adults. Patel et al. (2010) reported a somewhat better rate of 51% for frequency of diet, exercise, and weight reduction counseling but noted the rate was still inadequate and did not address the depth or quality of counseling. Research has further shown that overweight parents of overweight, but not obese, children reported receiving too little advice on nutrition and physical activity, compared with parents of obese children, and they rated the quality of the advice as poor or fair (Taveras et al., 2008). Identifying barriers to providing care and developing strategies to help clinicians improve care is critical (Huang et al., 2011).

The intensity of what is needed from primary care providers to address obesity in children has been identified as a problem (Barlow, 2007; Klein et al., 2010; US Preventive Services Task Force [USPSTF], 2010). Practicing pediatricians have reported lacking time to counsel patients about weight, finding counseling alone to provide poor results, and needing simple diet and exercise recommendations. That clinicians feel at a loss is unsurprising. Science has lagged behind the obesity epidemic, leaving many gaps in evidence-based recommendations (Barlow, 2007). Well-defined, validated preventive and therapeutic interventions for children and families are simply lacking (Daniels et al., 2009), and public policy has not kept up (Speiser et al., 2005).

See the original measure documentation for additional evidence supporting the measure.

Evidence for Additional Information Supporting Need for the Measure

Barlow SE, Expert Committee. Expert committee recommendations regarding the prevention, assessment, and treatment of child and adolescent overweight and obesity: summary report. Pediatrics. 2007 Dec;120(Suppl):S164-92. PubMed External Web Site Policy

Daniels SR, Jacobson MS, McCrindle BW, Eckel RH, Sanner BM. American Heart Association Childhood Obesity Research Summit: executive summary. Circulation. 2009 Apr 21;119(15):2114-23. PubMed External Web Site Policy

Dietz WH, Gross WL, Kirkpatrick JA. Blount disease (tibia vara): another skeletal disorder associated with childhood obesity. J Pediatr. 1982 Nov;101(5):735-7. PubMed External Web Site Policy

Dietz WH. Health consequences of obesity in youth: childhood predictors of adult disease. Pediatrics. 1998 Mar;101(3 Pt 2):518-25. PubMed External Web Site Policy

Falkner NH, Neumark-Sztainer D, Story M, Jeffery RW, Beuhring T, Resnick MD. Social, educational, and psychological correlates of weight status in adolescents. Obesity Res. 2001 Jan;9(1):32-42. PubMed External Web Site Policy

Huang TT, Borowski LA, Liu B, Galuska DA, Ballard-Barbash R, Yanovski SZ, Olster DH, Atienza AA, Smith AW. Pediatricians' and family physicians' weight-related care of children in the U.S. Am J Prev Med. 2011 Jul;41(1):24-32. PubMed External Web Site Policy

Kaechele V, Wabitsch M, Thiere D, Kessler AL, Haenle MM, Mayer H, Kratzer W. Prevalence of gallbladder stone disease in obese children and adolescents: influence of the degree of obesity, sex, and pubertal development. J Pediatr Gastroenterol Nutr. 2006 Jan;42(1):66-70. PubMed External Web Site Policy

Klein JD, Sesselberg TS, Johnson MS, O'Connor KG, Cook S, Coon M, Homer C, Krebs N, Washington R. Adoption of body mass index guidelines for screening and counseling in pediatric practice. Pediatrics. 2010 Feb;125(2):265-72. PubMed External Web Site Policy

Lamb MM, Ogden CL, Carroll MD, Lacher DA, Flegal KM. Association of body fat percentage with lipid concentrations in children and adolescents: United States, 1999-2004. Am J Clin Nutr. 2011 Sep;94(3):877-83. PubMed External Web Site Policy

Lazorick S, Peaker B, Perrin EM, Schmid D, Pennington T, Yow A, DuBard CA. Prevention and treatment of childhood obesity: care received by a state Medicaid population. Clin Pediatr. 2011 Sep;50(9):816-26. PubMed External Web Site Policy

Li C, Ford ES, Zhao G, Mokdad AH. Prevalence of pre-diabetes and its association with clustering of cardiometabolic risk factors and hyperinsulinemia among U.S. adolescents: National Health and Nutrition Examination Survey 2005-2006. Diabetes Care. 2009 Feb;32(2):342-7. PubMed External Web Site Policy

Manoff EM, Banffy MB, Winell JJ. Relationship between Body Mass Index and slipped capital femoral epiphysis. J Pediatr Orthop. 2005 Nov-Dec;25(6):744-6. PubMed External Web Site Policy

Marder W, Chang S. Childhood obesity: costs, treatment patterns, disparities in care, and prevalent medical conditions. Thomson Medstat Research Brief; 2006.

Nguyen TT, Keil MF, Russell DL, Pathomvanich A, Uwaifo GI, Sebring NG, Reynolds JC, Yanovski JA. Relation of acanthosis nigricans to hyperinsulinemia and insulin sensitivity in overweight African American and white children. J Pediatr. 2001 Apr;138(4):474-80. PubMed External Web Site Policy

Ogden CL, Carroll MD, Kit BK, Flegal KM. Prevalence of childhood and adult obesity in the United States, 2011-2012. JAMA. 2014 Feb 26;311(8):806-14. PubMed External Web Site Policy

Ogden CL, Carroll MD, Kit BK, Flegal KM. Prevalence of obesity and trends in body mass index among US children and adolescents, 1999-2010. JAMA. 2012 Feb 1;307(5):483-90. PubMed External Web Site Policy

Patel AI, Madsen KA, Maselli JH, Cabana MD, Stafford RS, Hersh AL. Underdiagnosis of pediatric obesity during outpatient preventive care visits. Acad Pediatr. 2010 Nov-Dec;10(6):405-9. PubMed External Web Site Policy

Quality Measurement, Evaluation, Testing, Review, and Implementation Consortium (Q-METRIC). Basic measure information: follow-up visits for children who are obese or overweight with a weight-related comorbidity. Ann Arbor (MI): Quality Measurement, Evaluation, Testing, Review, and Implementation Consortium (Q-METRIC); 2015 Apr. 47 p.

Reilly JJ, Kelly J. Long-term impact of overweight and obesity in childhood and adolescence on morbidity and premature mortality in adulthood: systematic review. Int J Obes (Lond). 2011 Jul;35(7):891-8. PubMed External Web Site Policy

Speiser PW, Rudolf MC, Anhalt H, Camacho-Hubner C, Chiarelli F, Eliakim A, Freemark M, Gruters A, Hershkovitz E, Iughetti L, Krude H, Latzer Y, Lustig RH, Pescovitz OH, Pinhas-Hamiel O, Rogol AD, Shalitin S, Sultan C, Stein D, Vardi P, Werther GA, Zadik Z, Zuckerman-Levin N, Hochberg Z, Obesity Consensus Working Group. Childhood obesity. J Clin Endocrinol Metab. 2005 Mar;90(3):1871-87. PubMed External Web Site Policy

Taveras EM, Gortmaker SL, Mitchell KF, Gillman MW. Parental perceptions of overweight counseling in primary care: the roles of race/ethnicity and parent overweight. Obesity (Silver Spring). 2008 Aug;16(8):1794-801. PubMed External Web Site Policy

US Preventive Services Task Force. Screening for obesity in children and adolescents: US Preventive Services Task Force recommendation statement. Pediatrics. 2010 Feb;125(2):361-7. [19 references] PubMed External Web Site Policy

Wang Y, Beydoun MA, Liang L, Caballero B, Kumanyika SK. Will all Americans become overweight or obese? Estimating the progression and cost of the US obesity epidemic. Obesity (Silver Spring). 2008 Oct;16(10):2323-30. PubMed External Web Site Policy

Weiss R, Dziura J, Burgert TS, Tamborlane WV, Taksali SE, Yeckel CW, Allen K, Lopes M, Savoye M, Morrison J, Sherwin RS, Caprio S. Obesity and the metabolic syndrome in children and adolescents. N Engl J Med. 2004 Jun 3;350(23):2362-74. PubMed External Web Site Policy

Wing YK, Hui SH, Pak WM, Ho CK, Cheung A, Li AM, Fok TF. A controlled study of sleep related disordered breathing in obese children. Arch Dis Child. 2003 Dec;88(12):1043-7. PubMed External Web Site Policy

Extent of Measure Testing

Reliability
Data and Methods. Testing data were obtained through an audit of medical records maintained by HealthCore, Inc. HealthCore is an independent subsidiary of Anthem, Inc., the largest health benefits company/insurer in the United States. HealthCore owns and operates the HealthCore Integrated Research Database (HIRD), a longitudinal database of medical and pharmacy claims and enrollment information for members from 14 geographically diverse Blue Cross and/or Blue Shield Health Plans in the Northeast, South, West, and Central regions of the United States with members living in all 50 states. In total, the HIRD includes approximately 59 million individuals between January 2006 and June 2014.

More than 12 million members were enrolled at some point during the 2013 measurement year for this study, among which 2.3 million were aged 2 to 18 years old. There were 637,100 children aged 2 to 18 years with a routine outpatient encounter in 2013, who were currently enrolled and were fully insured. This group was narrowed to a subset who had a provider with a specialty of pediatric medicine or general practice/family practice (451,003). One child per family was then randomly selected, resulting in 293,741 eligible children from all 50 states, as well as the District of Columbia and territories such as Puerto Rico and the Virgin Islands.

A simple random sample (SRS) was used to select 27,000 candidates for a parent survey, of which 26,569 (98%) had valid contact information. From this group, a total of 1,580 parent surveys were completed, of which 402 had a body mass index (BMI) greater than or equal to 85th percentile according to parent-reported height and weight for their eligible child. Additionally, an independent SRS of 750 candidates was selected to provide additional cases for medical record abstraction to ensure the study goal for abstracted charts would be achieved; 722 children from this group had valid contact information. Combining these two groups, medical records were requested for review for 1,124 (402+722) children. In total, 600 medical records were reviewed and abstracted.

Once subjects were identified, patient medical records were requested from provider offices and health care facilities; these records were sent to a centralized location for data abstraction. Trained nurse or pharmacist medical record abstractors collected and entered information from paper copies of the medical records into a password-protected database. To help ensure consistency of data collection, the medical record abstractors were trained on the study's design and presented with a standardized data collection form designed to minimize the need to make subjective judgments during the abstraction process. In addition, data entered onto a scanner form and subsequently scanned was reviewed through a series of quality checks.

Reliability of medical record data was determined through re-abstraction of patient record data to calculate the inter-rater reliability (IRR). Broadly, IRR is the extent to which the abstracted information is collected in a consistent manner. Low IRR may be a sign of poorly executed abstraction procedures, such as ambiguous wording in the data collection tool, inadequate abstractor training, or abstractor fatigue. For this measure, the medical record data collected by two abstractors was compared with the data obtained by a senior abstractor to gauge IRR for each abstractor individually. Any differences were remedied by review of the chart. IRR was determined by calculating both percent agreement and Cohen's Kappa statistic.

Results. Data were abstracted from 600 medical records; 48 children (8.0%) met denominator criteria for having a recorded BMI greater than or equal to 85th percentile (overweight, based on a BMI percentile recorded by the provider) and a weight-related comorbidity (diabetes, hypertension, or hyperlipidemia), who had an outpatient care visit during the measurement year. Of these, four to six records (8.3% to 12.5%) from the two abstractors were reviewed for IRR. Agreement was assessed for four measure variables: documentation of BMI greater than or equal to 95th percentile, documentation of BMI greater than or equal to 85th percentile, and documentation of both height and weight (necessary to calculate BMI).

Table 5 in the original measure documentation shows the percent agreement and Kappa statistic for each variable. Abstractor agreement for all variables (documentation of BMI greater than or equal to 95th percentile, documentation of BMI greater than or equal to 85th percentile, and documentation of height and weight) was 100% with a Kappa statistic of 1. These results indicate a perfect level of IRR was achieved for all measure variables.

Validity
Face Validity. Face validity is the degree to which the measure construct characterizes the concept being assessed. The face validity of this measure was established by a national panel of experts and advocates for families of children with high BMI convened by the Quality Measurement, Evaluation, Testing, Review, and Implementation Consortium (Q-METRIC). The Q-METRIC expert panel included nationally recognized experts in childhood obesity, representing pediatrics, nephrology, nutrition and dietetics, endocrinology, gastroenterology, health behavior/education, and family advocacy. In addition, measure validity was considered by experts in state Medicaid program operations, health plan quality measurement, health informatics, and health care quality measurement. In total, the Q-METRIC High BMI Follow-Up panel included 17 experts, providing a comprehensive perspective on childhood obesity and the measurement of quality metrics for states and health plans.

The Q-METRIC expert panel concluded that this measure has a high degree of face validity through a detailed review of concepts and metrics considered to be essential to effective management and treatment of childhood obesity. Concepts and draft measures were rated by this group for their relative importance. This measure was very highly rated, receiving an average score of 8.5 (with 9 as the highest possible score).

Abstracted Medical Record Data. This measure was tested using medical record data. This source is considered the gold standard for clinical information; our findings indicate that these data have a high degree of face validity and reliability. In total, 600 charts were reviewed.

The eligible population for the denominator is the number of children ages 2 through 17 years with a BMI greater than or equal to 95th percentile or a BMI greater than or equal to 85th percentile and a weight-related comorbidity, who had an outpatient care visit during the measurement year (January 1–December 31). This measure was tested using two methods for determining the denominator:

  1. Calculated BMI percentile; based on BMI calculated from height and weight recorded in the medical record.
  2. Recorded BMI percentile; based on a BMI percentile recorded in the medical record.

Calculated BMI. There were 140 charts (23.3%) that met denominator criteria for having a calculated BMI (based on height and weight from the medical record) either greater than or equal to 95th percentile (obese) or greater than or equal to 85th percentile (overweight) with a weight-related comorbidity, who had an outpatient care visit during the measurement year. Overall, 22.9% (n=32) children ages 2 through 17 years old with a calculated BMI greater than or equal to 95th percentile or greater than or equal to 85th percentile with a weight-related comorbidity, had a subsequent outpatient visit where weight was addressed (refer to Table 6 in the original measure documentation).

Recorded BMI. There were 48 charts (8.0%) that met denominator criteria for having a recorded BMI (based on a BMI percentile recorded by the provider) either greater than or equal to 95th percentile (obese) or greater than or equal to 85th percentile (overweight) with a weight-related comorbidity, who had an outpatient care visit during the measurement year. Overall, 10.4% (n=5) children ages 2 through 17 years old with a recorded BMI greater than or equal to 95th percentile or greater than or equal to 85th percentile with a weight-related comorbidity, had a subsequent outpatient visit where weight was addressed (refer to Table 6 in the original measure documentation).

Evidence for Extent of Measure Testing

Quality Measurement, Evaluation, Testing, Review, and Implementation Consortium (Q-METRIC). Basic measure information: follow-up visits for children who are obese or overweight with a weight-related comorbidity. Ann Arbor (MI): Quality Measurement, Evaluation, Testing, Review, and Implementation Consortium (Q-METRIC); 2015 Apr. 47 p.

State of Use

Current routine use

Current Use

Internal quality improvement

Measurement Setting

Ambulatory/Office-based Care

Professionals Involved in Delivery of Health Services

Advanced Practice Nurses

Nurses

Physician Assistants

Physicians

Least Aggregated Level of Services Delivery Addressed

Individual Clinicians or Public Health Professionals

Statement of Acceptable Minimum Sample Size

Specified

Target Population Age

Age 2 through 17 years

Target Population Gender

Either male or female

National Quality Strategy Aim

Better Care

National Quality Strategy Priority

Person- and Family-centered Care
Prevention and Treatment of Leading Causes of Mortality

IOM Care Need

Getting Better

Living with Illness

IOM Domain

Effectiveness

Patient-centeredness

Case Finding Period

January 1 of the measurement year to December 31 of the year following the measurement year

Denominator Sampling Frame

Patients associated with provider

Denominator (Index) Event or Characteristic

Clinical Condition

Encounter

Patient/Individual (Consumer) Characteristic

Denominator Time Window

Does not apply to this measure

Denominator Inclusions/Exclusions

Inclusions
The eligible population for the denominator is the number of children ages 2 through 17 years with a body mass index (BMI) greater than or equal to 95th percentile or a BMI greater than or equal to 85th percentile and a weight-related comorbidity who had an outpatient care visit during the measurement year.

Note: Refer to Table 3 in the original measure documentation for codes used to identify outpatient care visits.

Exclusions

  • Inpatient stays, emergency department visits, and urgent care visits are excluded from the calculation.
  • A diagnosis of pregnancy during the measurement year excludes the patient from the calculation.

Exclusions/Exceptions

Medical factors addressed

Numerator Inclusions/Exclusions

Inclusions
The eligible population for the numerator is the number of children ages 2 through 17 years with either a body mass index (BMI) greater than or equal to 95th percentile (obese) or a BMI greater than or equal to 85th percentile (overweight) and a weight-related comorbidity who have two outpatient care visits during the measurement year; weight is addressed* in the subsequent visit.

An additional outpatient visit where weight is addressed is defined as an encounter with a health care provider within the measurement year, which is subsequent to the yearly primary care visit. This visit in which weight was addressed could occur in the primary care or mental health setting or with a dietician or subspecialist. Documentation that weight was addressed must include a note indicating the date and at least one of the descriptions provided below and in Table 1 in the original measure documentation. Comorbidities are defined as a note in the medical record that indicates the presence of comorbidity or an international Classification of Diseases, 9th Revision (ICD-9) code from Table 2 in the original measure documentation.

Note: Refer to Table 3 in the original measure documentation for codes used to identify outpatient care visits.

*Weight is addressed:

Medical record: Documentation must include a note indicating the date and at least one of the following:

  • Discussion of current nutrition behaviors (e.g., eating habits, dieting behaviors)
  • Checklist indicating nutrition was addressed
  • Counseling or referral for nutrition education
  • Member received educational materials on nutrition
  • Anticipatory guidance for nutrition
  • Discussion of current physical activity behaviors (e.g., exercise routine, participation in sports activities, exam for sports participation)
  • Checklist indicating physical activity was addressed
  • Counseling or referral for physical activity
  • Member received educational materials on physical activity
  • Anticipatory guidance for physical activity

    OR

  • Visit includes documentation that provider addressed comorbidities of overweight including hypertension, hyperlipidemia, and/or type 2 diabetes.

Exclusions
Unspecified

Numerator Search Strategy

Fixed time period or point in time

Data Source

Electronic health/medical record

Paper medical record

Type of Health State

Does not apply to this measure

Instruments Used and/or Associated with the Measure

Unspecified

Measure Specifies Disaggregation

Does not apply to this measure

Scoring

Rate/Proportion

Interpretation of Score

Desired value is a higher score

Allowance for Patient or Population Factors

Unspecified

Standard of Comparison

External comparison at a point in, or interval of, time

External comparison of time trends

Internal time comparison

Original Title

Follow-up visits for children who are obese or overweight with a weight-related comorbidity.

Measure Collection Name

High Body Mass Index (BMI) in Children Follow-up Measures

Submitter

Quality Measurement, Evaluation, Testing, Review, and Implementation Consortium (Q-METRIC) - Academic Affiliated Research Institute

Developer

Quality Measurement, Evaluation, Testing, Review, and Implementation Consortium (Q-METRIC) - Academic Affiliated Research Institute

Funding Source(s)

This work was funded by the Agency for Healthcare Research and Quality (AHRQ) and the Centers for Medicare & Medicaid Services (CMS) under the Children's Health Insurance Program Reauthorization Act (CHIPRA) Pediatric Quality Measures Program Centers of Excellence grant number U18 HS020516.

Composition of the Group that Developed the Measure

High BMI in Children Follow-Up Expert Panels

Representative Panel

  • Adam Becker, PhD, MPH, Executive Director, Consortium to Lower Obesity in Chicago Children (CLOCC), Chicago, IL
  • Craig Belsha, MD, Professor of Pediatrics, St. Louis University, Director of the Pediatric Hypertension Program, SSM Cardinal Glennon Children's Medical Center, St. Louis, MO
  • Nancy Butte, PhD, MPH, RD, Professor of Pediatrics, USDA/ARS Children's Nutrition Research Center, Department of Pediatrics, Baylor College of Medicine, Houston, TX
  • Elena Fuentes-Afflick, MD, MPH, Chief of Pediatrics, San Francisco General Hospital, Vice Dean for Academic Affairs and Faculty Development, Vice Chair and Professor of Pediatrics, Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA
  • Suzanne Lazorick, MD, MPH, Assistant Professor of Pediatrics and Public Health, Brody School of Medicine, East Carolina University, Greenville, NC
  • Esther F. Myers, PhD, RD, Chief Science Officer, Academy of Nutrition and Dietetics, St. Louis, MO
  • Stephen Pont, MD, MPH, FAAP, Assistant Professor of Pediatrics, University of Texas Southwestern Medical Center, Austin, TX
  • Dennis Styne, MD, Professor of Pediatrics, Director of Pediatric Endocrine Fellowship Program, University of California Davis School of Medicine, Davis, CA
  • Miriam Vos, MD, MSPH, Assistant Professor of Pediatrics, Division of GI, Hepatology and Nutrition, Emory University School of Medicine, Research Program Director, Child Wellness, Children's Healthcare of Atlanta, Atlanta, GA
  • Nora Wells, Med, Director of Programs, Co-Director of National Center for Family/Professional Partnerships, Family Voices, Boston, MA

Feasibility Panel

  • Cathy Call, BSN, MSEd, MSN, Senior Policy Analyst and Director for Health Quality Research, Altarum Institute, Alexandria, VA
  • J. Mitchell Harris, PhD, Director of Research and Statistics, Children's Hospital Association, (formerly NACHRI), Alexandria, VA
  • Don Lighter, MD, MBA, FAAP, FACHE, Director, The Institute for Health Quality Research and Education, Knoxville, TN
  • Paula Lozano, MD, MPH, Assistant Director Preventive Care, Group Health Cooperative, Associate Investigator, Group Health Research Institute, Group Health Physician, Seattle, WA
  • Sue Moran, BSN, MPH, Director of the Bureau of Medicaid Program Operations and Quality Assurance, Michigan Department of Community Health, Lansing, MI
  • Joseph Singer, MD, Vice President Clinical Affairs, HealthCore, Inc., Wilmington, DE
  • Stuart Weinberg, MD, Assistant Professor of Biomedical Informatics, Assistant Professor of Pediatrics, Vanderbilt University, Nashville, TN

Quality Measurement, Evaluation, Testing, Review, and Implementation Consortium (Q-METRIC) Investigators

  • Joyce M. Lee, MD, MPH, Associate Professor, Department of Pediatrics and Communicable Diseases, School of Medicine, University of Michigan, Ann Arbor, MI
  • Gary L. Freed, MD, MPH, Professor of Pediatrics, School of Medicine, Professor of Health Management and Policy, School of Public Health, University of Michigan, Ann Arbor, MI (principal investigator)
  • Kevin J. Dombkowski, DrPH, MS, Research Associate Professor of Pediatrics, School of Medicine, University of Michigan, Ann Arbor, MI

Financial Disclosures/Other Potential Conflicts of Interest

Unspecified

Adaptation

This measure was not adapted from another source.

Date of Most Current Version in NQMC

2015 Apr

Measure Maintenance

Unspecified

Date of Next Anticipated Revision

Unspecified

Measure Status

This is the current release of the measure.

The measure developer reaffirmed the currency of this measure in January 2016.

Source(s)

Quality Measurement, Evaluation, Testing, Review, and Implementation Consortium (Q-METRIC). Basic measure information: follow-up visits for children who are obese or overweight with a weight-related comorbidity. Ann Arbor (MI): Quality Measurement, Evaluation, Testing, Review, and Implementation Consortium (Q-METRIC); 2015 Apr. 47 p.

Measure Availability

Source available from the Quality Measurement, Evaluation, Testing, Review, and Implementation Consortium (Q-METRIC) Web site External Web Site Policy. Support documents External Web Site Policy are also available.

For more information, contact Q-METRIC at 300 North Ingalls Street, Room 6C08, SPC 5456, Ann Arbor, MI 48109-5456; Phone: 734-232-0657; Fax: 734-764-2599.

NQMC Status

This NQMC summary was completed by ECRI Institute on September 29, 2015. The information was verified by the measure developer on November 2, 2015.

The information was reaffirmed by the measure developer on January 7, 2016.

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Inform Quality Measurement, Evaluation, Testing, Review, and Implementation Consortium (Q-METRIC) if users implement the measures in their health care settings.

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