Assessing Prevalence and Trends in Obesity Navigating the Evidence (2016) / Chapter Skim
Currently Skimming:
3 Methodological Approaches to Data Collection
3 Methodological Approaches to Data Collection
Pages 57-88
The Chapter Skim interface presents what we've algorithmically identified as the most significant single chunk of text within every page in the chapter.
Select key terms on the right to highlight them within pages of the chapter.
From page 57... ...
Common physical settings in which height and weight data are being collected on children and adolescents include schools, medical facilities, and public health programs. • Sampling techniques are used to arrive at the group of indi viduals included in a dataset.
|
From page 58... ...
. STUDY DESIGNS USED IN DATA SOURCES Published reports presenting estimates of obesity prevalence or trends have been based on analyses of cross-sectional, repeated cross-sectional, and, to a lesser extent, longitudinal data.
|
From page 59... ...
The validity and reliability of the obesity prevalence estimates obtained through the various study designs depend to a large extent on the sampling design used. The committee acknowledges that data from intervention studies also are pervasive in the published TABLE 3-1 Potential Advantages and Disadvantages of Using CrossSectional, Repeated Cross-Sectional, and Longitudinal Study Designs to Assess Obesity Prevalence and Trends Study Design Potential Advantagesa Potential Disadvantagesa Cross-Sectional Can be used to assess obesity Cannot be used to determine change prevalence at a defined time or trend in prevalence of obesity.
|
From page 60... ...
. SETTINGS OF DATA COLLECTION Common settings in which data on obesity status are currently being collected on children, adolescents, and young adults include schools, medical facilities and public health settings, and other research and surveillance settings.
|
From page 61... ...
2014 School Health Policies and Practices Study, which sampled a nationally representative collection of public and private schools throughout the country, reported that slightly more than half of sampled schools obtain and maintain information regarding students' weight status in the students' records (overall: 54.1 percent [95 percent confidence interval [CI] : 47.2-60.9 percent]
|
From page 62... ...
" uses a tally sheet to summarize the weight status classification of the students; the tally sheet is transmitted to the "School District Reporter," the sole person responsible for entering data for the entire district, who then submits the aggregate numbers to the state using a secure reporting system portal (New York State Center for School Health, 2015)
|
From page 63... ...
In some instances, a child may be identified based on extremes in weight status, especially when small sample sizes exist. Beyond FERPA, states and school districts can have additional regu lations regarding student privacy, which may further limit the ability to use data collected in the school setting.
|
From page 64... ...
Other Research and Surveillance Settings Other settings for capturing data related to obesity exist beyond schools, medical facilities, and public health programs. The settings for these evaluations are typically specific to the data source, and encompass both in-person and remote data collection.
|
From page 65... ...
Key considerations when reviewing information about the study population and sample include the source of the data and associated sampling approach, size of the study sample, demographic characteristics of the study sample, as well as the extent to which the study population was stable during the time period when data were collected for trends analyses. The committee identified three key features related to the individuals included in a data source: sampling approach, sample size, and stability of the population over time.
|
From page 66... ...
Calculation of response rates and comparisons of the sampled population to the total target population can provide insight into the representativeness of the data and facilitate adjustment for potential sources of bias. Intentional Oversampling Intentional oversampling is a technique used across different sampling approaches.
|
From page 67... ...
a The potential advantages and disadvantages are contingent on the population assessed, the methodology employed, the analytic approach, and the end user seeking to apply such information. Population and methodologic considerations are discussed throughout this chapter.
|
From page 68... ...
. These evaluations provide insight into the obesity status of all individuals seen at a particular prac tice or within a specific health system within the defined inclusion criteria.
|
From page 69... ...
. As described in Box 3-4, protocols for capturing directly measured height and weight vary in terms of the equipment used, participant procedures, and data collector procedures.
|
From page 70... ...
Proxy- or Convenient, easily captured. Prevalence estimates are not comparable to self-report Can provide insight into estimates generated from directly measured trends over time.
|
From page 71... ...
C Differences in Data Collector Procedures • raining and oversight of data collectors T • taff position of the data collector (e.g., research staff, school nurse, physical S education teacher, other staff) • recision of the data recorded (e.g., to the nearest pound, 1/10th of a pound)
|
From page 72... ...
72 TABLE 3-5 Illustrative Examples Demonstrating Differences in Height or Weight That Categorically Change Weight Status from Normal to Obese at Two Different Ages Characteristics of the Height Weight Body Mass Index Weight Status Difference That Changes Weight Individual (centimeters) (kilograms)
|
From page 73... ...
Fill in the shaded blank boxes. Fill in the matching oval below each the matching oval below each number." number." "How much do you weigh "How tall are you without your (Healthy Youth without your shoes on?
|
From page 74... ...
In young children, relatively small differences between proxy-reported and directly measured values can cause a significant shift in the child's weight status classification (see Table 3-5) , with estimates of obesity prevalence notably affected by errors in proxy-reported heights (Akinbami and Ogden, 2009; Weden et al., 2013)
|
From page 75... ...
Estimatea 2-3 Male 3.1b –0.3b Overestimate Female 3.2b –0.2b 4-5 Male 4.1b 0.7b Female 5.2b 0.5b 6-7 Male 5.6b 0.3 Female 5.3b 0.3 8-9c Male 6.1b 1.2b Female 6.9b 2.5b 10-11c Male 4.7b 0.5 Female 6.3b 4.1b 12-13d Male 2.3b –0.1 Similar estimate Female 2.9b 3.0b 14-15e Male 2.6b 0.5 Underestimate Female 1.3b 2.5b 16-17e Male 0.0 0.7 Female 0.4 2.6b NOTES: Absolute difference values were obtained from mean height and weight data from two nationally representative surveys, NHANES (directly measured) and NHIS (proxy- reported)
|
From page 76... ...
, some investigators have suggested that such data have limited utility for estimating obesity prevalence in adolescent populations or may be used cautiously when measured data are not available (Morrissey et al., TABLE 3-8 Bias in Self-Reported Heights and Weights Compared to Directly Measured Data and Associated Effect on Obesity Prevalence Among Children and Adolescents Age Effect on Obesity (years) Height Weight Prevalencea Reference ~6-11 Underestimate Underestimate Overestimateb Beck et al., 2012 10-16 Not Significantly Underestimate Underestimate Morrissey et al., Different 2006c 12-18 Overestimate Underestimate Underestimate Himes et al., 2005d ~12-18 NR Underestimate Underestimatee Goodman et al., 2000 ~12-18 Overestimated Underestimated Underestimate Pérez et al., 2015f ~14-18 Overestimate Underestimate Underestimate Brener et al, 2003g; Jayawardene et al., 2014h NOTES: Only studies evaluating U.S.
|
From page 77... ...
. Although adolescent self-reported height and weight data generally do not lead to obesity estimates comparable to those generated from directly measured data, analyses of national YRBS data suggest adolescent selfreported data may provide insight into the directionality of the overall trend.
|
From page 78... ...
. Additionally, a 2015 American Academy of Pediatrics Policy Statement highlights the need for consideration of both biological and social mechanisms of action of race, ethnicity, and socioeconomic status and makes specific recom mendations for measurement of these constructs (Cheng and Goodman, 2015)
|
From page 79... ...
. Socioeconomic Status Measures of socioeconomic status (SES)
|
From page 80... ...
80 ASSESSING PREVALENCE AND TRENDS IN OBESITY At the individual-level, household income can be used to produce different variables of SES. These include the income-to-poverty ratio; the child or family's eligibility for and/or participation in assistance programs (e.g., WIC)
|
From page 81... ...
Other data sources, for example WIC administrative data or school-based BMI assessments, are defined because their collection at the state or local level is required by law. Some data sources, however, are not representative of a geographic location but rather a physical location.
|
From page 82... ...
Estimates of obesity prevalence calculated from these data collection approaches are generally not interchangeable. Obesity trends based on self-reported heights and weights from nationally representative samples of high school students suggest such data may provide insight into the general directionality of obesity trends over time, similar to those calculated from directly measured data.
|
From page 83... ...
2014b. A guide to conducting your own Youth Risk Behavior Survey.
|
From page 84... ...
2005. Ethnic variation in validity of classification of over weight and obesity using self-reported weight and height in American women and men: The third National Health and Nutrition Examination Survey.
|
From page 85... ...
2001. Effects of age on validity of self reported height, weight, and body mass index: Findings from the third National Health and Nutrition Examination Survey, 1988-1994.
|
From page 86... ...
2015. Measuring the bias, precision, accuracy, and validity of self-reported height and weight in assessing overweight and obesity status among adolescents using a surveillance system.
|
From page 87... ...
2016. What's new in the 2013-2014 California Health Interview Survey http://healthpolicy.ucla.edu/ chis/design/Documents/Whats-New-CHIS-2013-2014.pdf (accessed March 14, 2016)
|
Key Terms
This material may be derived from roughly machine-read images, and so is provided only to facilitate research.
More
information on Chapter Skim is available.