Assessing Prevalence and Trends in Obesity Navigating the Evidence (2016) / Chapter Skim
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Appendix D: Presentation of Findings
Appendix D: Presentation of Findings
Pages 215-278
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From page 215... ...
Table D-8 presents a simulation demonstrating that the current FitnessGram's®1 "Needs Improvement-Health Risk" cut points correspond to the 95th percentile on the 2000 Centers for Disease Control and Prevention (CDC) sex-specific body mass index (BMI)
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216 ASSESSING PREVALENCE AND TRENDS IN OBESITY LIST OF TABLES • Table D-1 Examples of Protocols for Directly Measuring Height, 217 • Table D-2 Examples of Protocols for Directly Measuring Weight, 222 • Table D-3 Examples of Protocols for Data Collectors, 226 • Table D-4 Race and Ethnicity Categories, as Presented in a Collec tion of Recent Published Reports, 229 • Table D-5 Individual and Community-Level Socioeconomic Status (SES) Categories, as Presented in a Collection of Recent Published Reports, 233 • Table D-6 Variables and Categories Related to Age, as Presented in a Collection of Recent Published Reports, 237 • Table D-7 Summary of Statistical Approaches Taken in a Collection of Recent Published Reports, 242 • Table D-8 2000 CDC Body Mass Index-for-Age Percentiles Corre sponding to the 2015 FitnessGram's® Needs Improvement-Health Risk (NI-HR)
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TABLE D-1 Examples of Protocols for Directly Measuring Height Precision of Number Measured Study or Data Stadiometer Recorded Number of of Contact Without Sourcea Reference Type Height Repetitions Pointsb Shoes Special Instructions Add Health Entzel et al., Portablec 0.50 cm 4 X No hat, hair 2009 ornament, or other accessories that would affect measurement Measurement taken at the end of a normal exhalation Bogalusa Heart BioLINCC, Wall-mountedd 0.10 cm 3 X In socks Study 2008 Flat hairstyles California FitnessGram, Wall-mounted 1.00 in FitnessGram® 2016 CARDIAC Lilly et al., Wall-mounted X 2014 CAYPOS Kolbo et al., Wall-mounted 1.00 in X No belts 2012 No jackets No heavy jewelry Child Health Brown et al., Wall-mounted 0.25 in X No excess clothing Measures Study 2010 Cincinnati Crowley et al., Wall-mounted X Children's Hospital 2011 Medical Center Echocardiography Database 217 continued
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TABLE D-1 Continued 218 Precision of Number Measured Study or Data Stadiometer Recorded Number of of Contact Without Sourcea Reference Type Height Repetitions Pointsb Shoes Special Instructions Community Alliance Kallem et al., Wall-mounted for Research and 2013e Engagement Creating Tovar et al., Portable 0.30 cm 3 Healthy, Active 2012e (1/8 in) and Nurturing Growing-up Environments Early Childhood Najarian et al., Portable 2 X Light clothing Longitudinal Survey- 2010 Birth Cohort EAT-I, EAT-2010 Larson et al., Portable 0.10 cm 2013f Fels Longitudinal Sun et al., Portable 0.10 cm 2 Study 2012g HEALTH-KIDS Wang et al., Portable 0.10 cm 2 X Light clothing 2009 Louisiana Health Williamson et Portable X Normal clothing Control Participants al., 2011 No socks Mississippi Delta Gamble et al., 1.00 cm Study 2012 New York City New York City 0.30 cm X No hats or hairpieces FitnessGram® Departmemt (1/8 in)
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NHANES CDC, 2013a Wall-mounted 4 X No hair ornaments, or Portable jewelry, buns, or braids on top of head Measurement taken on exhale Head aligned in horizontal planeh Ohio Schools Ohio Wall-mounted 0.30 cm X No hats Department of or Portable (1/8 in) No bulky clothing Health, 2010 Looking straight ahead Penn State Child Bixler et Portable 0.10 cm X Light clothing Cohort al., 2008; Rodríguez Colón et al., 2011 Philadelphia Schools Pennsylvania Wall-mounted 0.25 in or 4 if X No hats or hairpieces Department of 0.10 cm possible, No bulky clothing Health, 2011 minimum 2 Feet flat on the floor Head aligned in horizontal planeh Philadelphia Schools Lawman et al., Portable 0.10 cm 2i X No bulky clothing 2015 No items in pockets Pine Ridge Hearst et al., Portable 0.10 cm Reservation School- 2011 Based Assessment, 1998-2002 219 continued
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From page 220... ...
TABLE D-1 Continued 220 Precision of Number Measured Study or Data Stadiometer Recorded Number of of Contact Without Sourcea Reference Type Height Repetitions Pointsb Shoes Special Instructions South Dakota Hearst et al., Wall-mounted 0.10 cm School-Based BMI 2013 Assessment Special Olympics Special Wall-mounted 0.01 cm 4 X Feet flat on the floor International Olympics Looking straight Healthy Athletes International, aheadh,j Database 2007 Texas SPAN Texas School Portable 1.00 cm 2 X Study Physical Activity and Nutrition (SPAN) Study, 2016 Not Specified Acharya et al., Wall-mounted 0.10 cm 2011k Not Specified Huh et al., Portable 0.10 cm 2 2 X Head aligned in 2012 horizontal planeh Not Specified Nafiu et al., Wall-mounted 0.10 cm X Head aligned in 2014 horizontal planeh Not Specified Rogozinski et Wall-mountedl al., 2007 Not Specified Taylor et al., Wall-mounted 0.25 in 2014
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a The manuals and measurement protocolsin this table are from the data sources included in the committee's review of recent reports. If the publication did not specifically name the data source that was used, but provided details about data collection protocol, it is labeled as "Not Specified" in the table.
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From page 222... ...
TABLE D-2 Examples of Protocols for Directly Measuring Weight 222 Precision of Recorded Number of Clothing Status of Special Study or Data Sourcea Reference Scale Type Weight Repetitions Participant Instructions Add Health Entzel et al., 2009 Digitalb 0.10 kg No shoes No change, wallets, keys in pockets Bogalusa Heart Study BioLINCC, 2008 Digital 2 Short sleeve hospital gown Underpants Socks No shoes CARDIAC Project Lilly et al., 2014 Digital No shoes CAYPOS Kolbo et al., 2012 Digitalc 1.00 lb No belts No heavy jewelry No jackets No shoes Child Health Measures Brown et al., 2010 Digitald 0.10 lb No shoes Study No excess clothing Cincinnati Children's Crowley et al, 2011 Not specifiedd Light street clothing Hospital Medical Center No shoes Echocardiography Database Community Alliance for Kallem et al., 2013 Digital 0.10 kge Research and Engagement
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From page 223... ...
Creating Healthy, Tovar et al., 2012f Digital 0.50 lb 3 Light clothing Active and Nurturing No shoes Growing-up Environments EAT-I, EAT-2010 Larson et al., 2013g Beam or 0.10 kg Electronicd ECLS-B Najarian et al. 2010 Digital 2 Light clothing No shoes Fels Longitudinal Study Sun et al., 2012h Beam 0.10 kg 2 HEALTH-KIDS Wang et al., 2009 Electronic 0.10 kg 2 Light clothing No shoes Louisiana Health Control Williamson et al., Digital Normal school Participants 2011 clothing No shoes No socks Mississippi Delta Study Gamble et al., 2012 Portable New York City New York City Digital Beam No shoes FitnessGram® Department of No heavy jackets Education, 2016 NHANES CDC, 2013a Digital, Standard Small children Portablei examination Casts or gown, including prosthesis slippers Wearing street No shoes clothes Exceeding scale's capacity Ohio Schools Ohio Department of Digital 0.20 lb 2 No shoes 223 Health, 2010 No bulky clothing continued
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TABLE D-2 Continued 224 Precision of Recorded Number of Clothing Status of Special Study or Data Sourcea Reference Scale Type Weight Repetitions Participant Instructions Penn State Child Cohort Bixler et al., 2008; Digitalj 0.01 lb Light clothing Rodríguez-Colón et No shoes al., 2011 Philadelphia Schools Robbins et al., 2012 Digital, Beam, 0.25 lb Light clothing Note special Diald No shoes devices No jackets worn (e.g., Empty pockets prosthesis) Philadelphia Schools Lawman et al., 2015 Digital 0.20 kg 2k No shoes No excess clothing Empty pockets Pine Ridge Reservation Hearst et al., 2011 Balance 0.10 lb School-Based Assessment South Dakota School- Hearst et al., 2013 Beam 0.10 lb Based BMI Assessment Special Olympics Special Olympics Digital, 0.10 kg No shoes Weighing International Healthy International, 2007 Beamd No sports packs individuals in Athletes Database No jackets or other wheelchairs bulky items Texas SPAN Study SPAN Study, 2016 Digitald 0.25 lb 2 No shoes No jacket No heavy clothing Empty pockets
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a The manuals and measurement protocols in this table are from the data sources included in the committee's review of recent reports. If the publication did not specifically name the data source that was used, but provided details about data collection protocol, it is labeled as "Not Specified" in the table.
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From page 226... ...
226 ASSESSING PREVALENCE AND TRENDS IN OBESITY TABLE D-3 Examples of Protocols for Data Collectors Data Collector Study or Data Source Position of the Received Data Entry Namea Reference Data Collector Training Method Add Health, Wave IV Entzel et al., Interviewer Hand-written 2009 values on Post-it note; later entered into a computer Anchorage and CDC, 2013b School nurse Matanuska-Susitna Borough School Districts, Alaska California Madsen et al., Physical Xb FitnessGram® 2010 Fitness Test coordinators, teachers, and other local educational agency staff CAYPOS Kolbo et al., School nurse Direct entry to a 2012 secure website Chicago school-based, Wang et al., Research staff X environmental obesity 2009 prevention program in low-income African American adolescents Child Health Brown et al., Staff X Measures Study 2010 Community Alliance Kallem et al., Research X for Research and 2013 assistants Engagement Control Group from Carlson et al., Staff X the MOVE Projectc 2012 Creating Healthy, Tovar et al., Staff X Active and Nurturing 2012 Growing-up Environments ECLS-B Najarian et Interviewer X Hand-written in al. 2010 Child Assessment Booklet and entered into computer-based system
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APPENDIX D 227 TABLE D-3 Continued Data Collector Study or Data Source Position of the Received Data Entry Namea Reference Data Collector Training Method Fels Longitudinal Sun et al., Researchers Study 2012 Head Start Simmons et Teachers and X al., 2012 assistants Health e-Tools for Lohrmann, School nurse Schools, Pennsylvania 2014; YoussefAgha et al., 2013 New York City Rundle et al., Physical Xd Hand-written; FitnessGram® 2012 education later entry into teachers a Web-based systeme NHANES CDC, 2013a Examiner and Direct entry into recorder ISISf Ohio Schools Ohio Volunteer X Department health care of Health, professionals 2010 Penn State Child Rodríguez- Research staff Cohort Colón et al., 2011 Philadelphia Schools Lawman et Research X al., 2015 assistants Philadelphia Schools Pennsylvania School nurse Direct entry to Department a secure school of Health, district database 2011 Pine Ridge Hearst et al., Research staff Xg Reservation School- 2011 Based Assessment South Dakota School- Hearst et al., Staff including X Based BMI Assessment 2013 school nurses and physical education or health teachers continued
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Not Specified Acharya et Interviewer al., 2011 Not Specified Huh et al., Research 2012 assistants Not Specified Nafiu et al., Research X 2014 assistants Not Specified Taylor et al., Nursing X 2014 students enrolled in community health course NOTE: All study and dataset acronyms are listed in Appendix A a The manuals and measurement protocols in this table are from the data sources included in the committee's review of recent reports.
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From page 229... ...
Brown et al., 2010 American Indian; Non-Hispanic White Hearst et al., 2013 White/Caucasian; Non-White Adams et al., 2008; Kolbo et al., 2008; Rodríguez-Colón et al., 2011 Non-Hispanic White; Non-White Nader et al., 2014 White/Non-Whitea Bailey-Davis et al., 2012 Three Categories American Indian/Alaskan Native; Non-Hispanic CDC, 2013b White; All Other Mexicans; Mexican immigrants; Mexican Hernandez-Valero et al., 2012 Americans Mexican American (U.S. born)
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; Non-Hispanic Black; Non-Hispanic White Mexican American; Non-Hispanic Black; Non- Skelton et al., 2009b; Wang and Hispanic White; Other Zhang, 2006b; Wang et al., 2012b Mexican American; Non-Hispanic Black; Non- Rossen and Schoendorf, 2012b Hispanic White; Otherd Mexican Americand; Non-Hispanic Black; Non- Murasko, 2011b Hispanic White; Other Hispanicd Hispanic (includes Mexican American) ; Ogden et al., 2012b Mexican American; Non-Hispanic Black; Non Hispanic White Hispanicd; Non-Hispanic Black; Non-Hispanic Lee et al., 2011 White; Otherd Hispanic/Mexican Americane; Non-Hispanic Skinner and Skelton, 2014b Black; Non-Hispanic White; Other Hispanic; Non-Hispanic Black; Non-Hispanic Oza-Frank et al., 2013; Taber et al., White; Non-Hispanic Other 2012 Hispanic; Non-Hispanic Black; Non-Hispanic Sekhobo et al., 2010f White; Other Five Categories African American; Asian; Caucasian; Hispanic; Shustak et al., 2012 Unknownd African American; Black Caribbean; Black Saab et al., 2011 Hispanic; Hispanic White; White African American; Asian; Hispanic; Non- Lawman et al., 2015; Robbins et al., Hispanic White; Other 2012 American Indian/Alaska Native; Asian/Pacific CDC, 2009f; Hinkle et al., 2012; Pan Islander; Hispanic; Non-Hispanic Black; Non- et al., 2012f Hispanic White Asian; Black; Hispanic; White; Other Rundle et al., 2012; Wen et al., 2012 Asian/Pacific Islander; Black; Hispanic; White; Lo et al., 2014 Other/Unknownd
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; Missing Hispanic-Mexican American; Hispanic-Other; Khoury et al., 2013b; Trasande et al., Non-Hispanic Black; Non-Hispanic White; 2012b Other Hispanic; Native American; Non-Hispanic Harris et al., 2006 Asian; Non-Hispanic Black; Non-Hispanic White Hispanic; Non-Hispanic Asian; Non-Hispanic Ogden et al., 2014b Black; Non-Hispanic White; Non-Hispanic Other/Multipled Six or More Categories African American; American Indian; Asian; Babey et al., 2010 Latino; White; Mixed Races or Other African American; Asian/Pacific Islander; Huh et al., 2012 European American; Latinah; Native American; Other/Mixedd African American; American Indian/Alaska Jin and Jones-Smith, 2015 Native; Asian; Filipino; Hispanic/Latino; Pacific Islander/Native Hawaiian; White; Two or More Races African American; American Indian/Alaskan Madsen et al., 2010 Native; Asiani; Filipinoi; Hispanic/Latino; Pacific Islanderi; Non-Hispanic White African American; American Indian/Alaskan Weedn et al., 2014 Native; Asiand; Hispanic; Multiraciald; Native Hawaiian/Pacific Islanderd; White African/African American; American Native/ Aryana et al., 2012 Alaska Native; Asian/Asian American; Filipino/ Filipino American; Hispanic; Pacific Islander; White African-American; Asian; Latino; Dominican; Stingone et al., 2011 Mexican; Puerto Rican; Other Latino; White; Other American Indian/Native Alaskan; Asian/Pacific Hruby et al., 2015 Islander; Hispanic; Non-Hispanic Black; Non Hispanic White; Other/Unknown continued
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d Presented in aggregate estimates, but did not have estimate separate from other racial/ ethnic groups. e Claims to have categorized "Mexican American" and "other Hispanics" in separate groups in the methods section, but results are presented as "Hispanic." f Primary dataset was PedNSS.
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From page 233... ...
Kim, 2012 0-100%, 100-300%, 300%+ Babey et al., 2010; Wang et al., 2012 0-130%, 130-350%, 350%+ Fakhouri et al., 2013 0-130%, 130-300%, 300%+ Ver Ploeg et al., 2008c 0-185%, 185-300%, 300%+ Ver Ploeg et al., 2008c Household Study sample divided into tertiles based on Wang and Zhang, Income, Poverty- PIR distribution 2006a to-Income Ratio Study sample divided into quartiles based on Trasande et al., 2012a (PIR) PIR distribution PIR <1, PIR >1 Lalwani et al., 2013 PIR <1, PIR >4 Murasko, 2011 PIR <1, 1 to 3, >3 Skelton et al., 2009a PIR <1 to <2, 2 to <4, >4 Li et al., 2012 PIR calculated, no further grouping Sekhobo et al., 2014 continued
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234 ASSESSING PREVALENCE AND TRENDS IN OBESITY TABLE D-5 Continued Measure of SES Categories Presented in the Report Reference Highest Less than high school, High school, Greater Kim, 2012a Education Level than high school Attained by Less than high school, High school graduate, Suglia et al., 2014 Either Parent/ Greater than high school, College or greater Caregiver Less than high school, Some high school, Trasande et al., 2012d High school graduate or GED, Some college, College graduate or greater Grade school graduate, Some high school, Huh et al., 2012 High school graduate, Some college, Advanced degree Maternal Less than high school graduate, High school Kim et al., 2011 Education Level graduate or greater Less than high school, Some high school, Lemay et al., 2008 High school graduate Less than high school, High school graduate, Tovar et al., 2012a Some college, College/graduate school Categorized as high, average, or low based Halloran et al., 2012 on expected years of education at reported age Parent or No college degree, College degree or more Carlson et al., 2012 Caregiver's Less than high school, High school diploma/ Stingone et al., 2011 Education Level GED, Some college, College degree (not specified further) Eligibility for Eligible for free or reduced lunch (yes/no)
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APPENDIX D 235 TABLE D-5 Continued Measure of SES Categories Presented in the Report Reference Participitation Head Start enrollment Acharya et al., 2011g in an Assistance Head Start enrollment and SNAP Simmons et al., 2012g Program participation WIC participation CDC, 2009g; Davis et al., 2014g; Hinkle et al., 2012g; Sekhobo et al., 2010,g 2014g; Weedn et al., 2014g WIC or SNAP enrollment Ver Ploeg et al., 2008 Participation in any assistance program CDC, 2013cg; Murasko, 2011g Eligibility for any assistance program Reed et al., 2013; Tovar et al., 2012 Perception of Neighborhood perceived as safe (yes/no) Kim et al., 2011 Neighborhood as Safe Measure of Community-Level SES Status Eligibility for Percentage of students eligible for free or Sanchez-Vaznaugh et Free or Reduced- reduced-price school meals al., 2015 Price School Percentage of students receiving free or CDC, 2013ba,h, OzaMeals reduced-price lunch Frank et al., 2013a; Rundle et al., 2012 Racial/Ethnic Greater or less than 70% black, greater or Rundle et al., 2012 Population less than 70% Hispanic students in schools Percentage black and percentage white in Gamble et al., 2012 county Percentage of non-white population in Bailey-Davis et al., school district 2012a Mean <$15,000, $15,000-$34,999, $35,000- Black et al., 2012 Neighborhood $49,999, $50,000-$74,999, $75,000Income, Gross $99,999, $100,000-$149,999, $150,000+ Cutoffs Mean Percent of households living below federal Day et al., 2014i, Neighborhood poverty line Gamble et al., 2012; Income, Percent Taylor et al., 2014; of FPL Warner et al., 2013 Median Study sample grouped into income tertiles Sanchez-Vaznaugh et Neighborhood based on annual median income in the al., 2015a Income census tract continued
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a Obesity prevalence or trend estimate was reported for these subgroup, rather than only being a demographic characteristic. b Actual cutoffs for this study were 0-100% FPL, 101-200% FPL, 201-400% FPL, >400% FPL.
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2, 3, 4 CDC, 2013c; Pan et al., 2012b 3, 4, 5 Lo et al., 2014b 7 Warner et al., 2013 10, 11, 12 Sanchez-Vaznaugh et al., 2015 12, 13, 14, 15, 16, 17, 18, 19, 20, Lee et al., 2011b 21, 22, 23, 24, 25, 26 <14, 15, 16, 17+ Nickelson et al, 2012 14, 15, 16, 17, 18, 19 Adams et al., 2008b 18 Hsu et al., 2007b Age 0-5 Holtby et al., 2015 Groups (years) 0-<0.5, 0.5-<1, 1-<2, 2-<3, 3-<6 Wen et al., 2012 1-16 Saland et al., 2010 1-18, 19+ Song et al., 2012b 2-4 CDC, 2009; Davis et al., 2014; Weedn et al., 2014 2-4, 5-9, 10-14, 15-19 Robinson et al., 2013b,c 2-4, 5-19 Ver Ploeg et al., 2008 2 to <5 Sekhobo et al., 2010 2-5 Simmons et al., 2012 2-5, 6-11, 12-17, 18+ Eilerman et al., 2014b 2-5, 6-11, 12-17 Freedman et al., 2006 2-5, 6-11, 12-18 Skelton et al., 2009b; Skinner and Skelton, 2014 2-5, 6-11, 12-19 Gee et al., 2013; Ogden et al., 2006, 2012,b 2014; Shustak et al., 2012; Wang et al., 2012b 2-9, 10-18 Wang and Zhang, 2006b 2-9, 10-19, 20-29, 30-39, 40-49, Lee et al., 2010b 50-59, 60-69, 70-79 2-10 (2-3, 4-5, 6-10)
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238 ASSESSING PREVALENCE AND TRENDS IN OBESITY TABLE D-6 Continued Measure of Age Categories Presented in the Report Reference 2-19 Crowley et al., 2011 3-4 Sekhobo et al., 2014 3-5 Acharya et al., 2011 3-5, 6-8, 9-11, 12-14, 15-19 Hearst et al., 2011b 3-18 von Hippel and Nahhas, 2013 3-19 (3-5, 6-11, 12-19) Skinner et al., 2015b 5-6, 7-8, 9-10, 11-12, 13-14, 15-16, Staiano et al., 2013 17-18 5-6, 7-10, 11-14 Day et al., 2014b 5-8, 9-11, 12-14, 15+ Brown et al., 2010b 5-8.9, 9-11.9, 12-14.9, 15-19.9 Hearst et al., 2013 5-9, 10-14, 15-19 Hernandez-Valero, 2012b 5-9, 10-14 Broyles et al., 2010b 5-12 Bailey-Davis et al., 2012; Calhoun et al., 2011 5-17 Broyles et al., 2010b; Freedman et al., 2012 5-18 Khoury et al., 2013 6-8, 9-11 Fakhouri et al., 2013 6-9 Carlson et al., 2012 6-10, 11-14, 15-19 Black et al., 2012 6-11 Archbold et al., 2012; Gamble et al., 2012; Tovar et al., 2012 6-11, 12-18 Li et al., 2012b 6-11, 12-19 Trasande et al., 2012b 6-12 Lasserre et al., 2007 6-17 Kim et al., 2011 6-18 Huang et al., 2013b; Nafiu et al., 2014 6-25 Koebnick et al., 2009 <8, 8-10, 10+ Rogozinski et al., 2007 8-<19 Foley et al., 2014b 8-11, 12-14, 15-17 Din-Dzietham et al., 2007d 8-11, 12-17 Madsen et al., 2010b 8-11, 16-19 Spilsbury et al., 2015 8-17 Rosner et al., 2013; Zachariah et al., 2014 8-18 Sun et al., 2012 8-20 Joyce et al., 2015
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From page 239... ...
APPENDIX D 239 TABLE D-6 Continued Measure of Age Categories Presented in the Report Reference 9-15 Kallem et al., 2013 10-12 Reed et al., 2013b 10-14 Chen and Wang, 2012 11-17 Halloran et al., 2012; Kim, 2012b 12-13, 14-15, 16-17, 18-19 May et al., 2012 12-14, 15-17 Babey et al., 2010b 12-15, 16-17, 18-21 Gordon-Larsen et al., 2010b,e 12-15, 16-19 Lalwani et al., 2013 12-17 Okosun et al., 2010 12-19 Harris et al., 2006b,f 13-14, 15-16, 17-18 Blank et al., 2015 13-19 Huh et al., 2012 14-19 Lemay et al., 2008b <15, 16-17, 18-19, 20-24 Salihu et al., 2010b 15-19 Christensen et al., 2013 15-34, 35-44 George et al., 2011 <18, 18-24, 25-34, 35-44, 45-54, Crawford et al., 2010g 55-59, 60-64, 65-67, 70+ 18-20, 21-23, 24-28, 29-54 Hinkle et al., 2012 <20, 20-<30, 30-<40, 40+ Hruby et al., 2015 <20, 20-39, 40-59, 60+ Crawford et al., 2010h <20 and >20 Ng et al., 2014 Mean Age 5.8 Arcan et al., 2012 of Study Sample (years) 8.8 Taylor et al., 2014 10.0 Choumenkovitch et al., 2013 12.2 Wilson et al., 2011 13 Jin and Jones-Smith, 2015 15.6 Saab et al., 2011 16 (adolescents)
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a Assessed the same child at each age given. b Estimate of obesity prevalence or trend reported by age groupings given.
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APPENDIX D 241 TABLE D-6 Continued c Ages based on cohort birth year. d This age categorization is clinical and not data based; it was used because the Tanner index of sexual maturation was not measured at all periods.
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From page 242... ...
2001-2002 Add Health Suglia et al., 2014 1994-2001 Percent (standard error) Anchorage and CDC, 2013b 2003-2004 Weighted percentages; Matanuska-Susitna through unadjusted obesity Borough School 2010-2011 prevalence; 95 percent Districts, Alaska confidence interval Bogalusa Heart Study Broyles et al., 2010 1973-2008b Percent Bright Start Study Acran et al., 2012 2005-2006 Number of participants California FitnessGram® Madsen et al., 2001-2008 Percent (standard 2010 error; most considered negligible)
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Regression models NR NR (difference between sexes) Pearson Chi-square test Relative percent change Multivariate logistic regression Stratified by socioeconomic model; linear term for time, status, gender, race and Unadjusted weighted ethnicity, grade grouping prevalence (95 percent CIs)
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CHAMACOS Study Warner et al., 2013 1999-2008 Number of participants (%) Child Health Measures Brown et al., 2010 2007-2008 n (%)
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NR NR Generalized estimating equation model NR NR Graphed the prevalence for each year; approach not described Proc Crosstab NR NR Considered nonoverlapping 95 percent CI significant Proc Crosstab NR Compared values across the Considered non- 3 years overlapping 95 percent CI significant Proc Crosstab NR Logistic regression used to Considered non- assess linearity of longitudinal overlapping 95 percent CI trends; linear coefficients and significant quadratic coefficients Proc Crosstab NR Logistic regression used to Considered non- assess linearity of longitudinal overlapping 95 percent CI trends; linear coefficients and significant quadratic coefficients were assigned Stratified analyses Relative change Multivariable logistic regression models during two Compared to NHANES, Absolute change 5-year periods separately PedNSS Adjusted obesity risk per year Logistic regression NR Generalized estimating equation model NR NR NR Chi-square analyses NR Trajectory modeling Multivariate analyses NR NR NR continued
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Sleep and Health Study 2015 Community Alliance Kallem et al., 2013 NR n (%) for Research and Engagement – Baseline Creating Healthy, Tovar et al., 2012 NR Number of participants Active and Nurturing Growing-up Environments Creating Healthy, Choumenkovitch 2008 Percent Active and Nurturing et al., 2013 Growing-up Environments ECLS-B Castetbon and 2005-2006, Percent Andreyeva, 2012 2006-2007 Fels Longitudinal Study Johnson et al., 1930-2008d Mean (standard 2012 deviation)
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Generalize linear model NR NR Multiple logistic regression Stratified by sex, presented NR NR by age groups (4, 5-6 years) Presented by birth cohort NR Mixed effects growth modesl Presented by birth cohort NR Sex-specific mixed-effect repeated measure analysis of variance model (BMI not percentile)
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From page 248... ...
248 ASSESSING PREVALENCE AND TRENDS IN OBESITY TABLE D-7 Continued Statistical Approach Data Study or Data Source Collection Namea Reference Years Prevalence Hawaiian HMO Stark et al., 2011 2003 n (%) Health Behavior in Iannotti and Wang, 2001-2002, Percent (SE)
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From page 249... ...
Wald Chi-square analysis NR NR Logistic regression Multinomial logistic Multinomial logistic Multinomial logistic regression analysis regression analysis regression analysis Presented by school level Least-squares method, a for each year simple linear regression Compared prevalence by formula HS grades to YRBS (just percent, no CI) Pearson chi-square NR Least-squares method, a simple linear regression formula Stratified by site NR NR Stratified logistic regression Absolute and relative NR models, combination of age change presented and race categories Logistic regression Compared to NHANES, California FitnessGram® Chi-square test NR NR Cochrane-Armitage test Chi-square test NR NR Multiple logistic regression models Presented by asthma status NR NR chi-square Chi-square NR NR Logistic regression Performed; presumably Generalized estimating through the generalized equations for logistic estimating equation regression with autoregressive correlation structure continued
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250 ASSESSING PREVALENCE AND TRENDS IN OBESITY TABLE D-7 Continued Statistical Approach Data Study or Data Source Collection Namea Reference Years Prevalence MetroHealth System, Benson et al., 2011 1999-2008 Number of participants EpicCare – Northeast Ohio Miami-Dade County Saab et al., 2011 1999-2005 Percent Schools Health Screenings Military Health System Eilerman et al., 2009-2012 Crude and age-adjusted 2014 prevalence Entire population; no standard error or CIs presented Monitoring the Future Slater et al., 2013 2010 Percent MOVE Projectf Carlson et al., 2007-2010 n (%) 2012 Multiple datasetsg Lee et al., 2011 1959-2002 Plotted on a graph Multiple datasetsh Ng et al., 2014 1984-2012 Age-standardized prevalence rates Multiple datasetsj Hernández-Valero 2001-2007 Percent et al., 2012 Multiple datasetsk Lasserre et al., Varied by Percent 2007 dataset National Comorbidity Blank et al., 2015 2001-2002 Number of participants Survey – Adolescent Supplement National Hospital Koebnick et al., 1986-2006 Calculated per 100,000 Discharge Survey 2009 population for 3-year periods
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From page 251... ...
NR NR NR Presented by school year NR Logistic regression analyses Analyses stratified, by sex Stratified by sex, active NR Presented prevalence for each duty status year Compared calculated prevalence to NHANES 95 percent confidence interval Multivariable logistic NR NR regression NR Absolute change in NR BMI z-score (standard deviation) ; paired t-test Presented by dataset used Linear regression Plotted on a graph Stratified by sex, race, and coefficients for time periods Linear regression analysis sex*
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From page 252... ...
2014 versus 2008-2010 New York State PedNSS Sekhobo et al., 2002-2007 Percent 2010 NHANES Din-Dzietham et 1963-2002 Prevalence with Taylor al., 2007 series linearization for variance estimation Unadjusted weighted prevalence (standard error) NHANES Freedman et al., 1971-1974, Percent (standard error)
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From page 253... ...
prevalence and mean (SE) by race and NHANES of participants' selected cycles characteristics by race/ ethnicity over time Presented by race/ethnicity Present absolute change Logistic regression models groups (*
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From page 254... ...
population NHANES Ver Ploeg et al., 1976-2002 NR 2008 NHANES Zachariah et al., 1976-2008 Percent 2014 NHANES Rosner et al., 2013 1988-2008 Mean ± standard error BMI NHANES Ogden et al., 2006 1999-2000, Weighted prevalence 2001-2002, estimates (95 percent 2003-2004 CI) ; Taylor series linearization for variance estimation NHANES May et al., 2012 1999-2008 Weighted prevalence, n (%)
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From page 255... ...
Models presented by age Average annual changes Average annual changes groups and race/ethnicity estimated by regression estimated by regression models models Logistic regression models Logistic regression models also fitted Differences in the slope of trends tested (per/post 1999) Chi-square tests NR Cochran-Armitage trend test Bonferroni correction for multiple comparisons Multiple regression analysis Multiple regression analysis Multiple regression analysis Logit models Logit models Logit models NR NR Present percent by NHANES cycle Stratified by NHANES NR NR cycle, sex Sex-specific multiple logistic NR Sex-specific logistic regression; regression models survey years was used as a T-tests ordinal variable Chi-square tests NR Presented prevalence Bonferroni correction for (standard error)
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From page 256... ...
; Taylor series linearization for variance estimation NHANES Okosun et al., 2003-2004 Number of participants 2010 NHANES Ogden et al., 2014 2003-2004, Weighted prevalence 2005-2006, estimates (95 percent 2007-2008, CI) ; Taylor series 2009-2010, linearization for 2011-2012 variance estimation NHANES Trasande et al., 2003-2008 Number of participants 2012 Percent ± standard error NHANES Lalwani et al., 2005-2006 Number of participants 2013 NHANES Li et al., 2012 2005-2008 Percent ± standard error NHANES Fakhouri et al., 2009-2010 Number of participants 2013
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From page 257... ...
Sex-specific multiple logistic "Regression models using Sex-specific multiple logistic regression models survey period as a discrete regression models; linear T-tests (difference by sex variable with appropriate trends tested with survey cycle overall, between race/ contrast matrices" as discrete and continuous ethnicity groups) variable Stratified by age category NR NR Adjusted Wald tests Adjusted Wald test Logistic regression; regressed of differences by NHANES years as an ordinal demographics variable on the binary outcome; coefficient and standard errors represent a test for a linear trend Presented by race, ethnicity Presented estimates by cycle NR groups, and socioeconomic year status by cycle year One-way ANOVA NR NR Pearson chi-square tests T-tests (sex difference)
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From page 258... ...
et al., 2011 Penn State Child Cohort Calhoun et al., NR Average BMI percentile 2011 presented Pennsylvania Public Bailey-Davis et al., 2006-2007, Proportion School BMI Surveillance 2012 2007-2008, Presented by school 2008-2009 years and as 3-year mean
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From page 259... ...
Logistic regression; odds of NR NR obesity by school physical education requirements Stratified by sex NR NR Regression analyses used to assess adjusted odd ratios by ADHD and medication status Presented prevalence by NR Logistic regression; survey age, sex, race/ethnicity, year included as ordinal National School Lunch variable Program participation, county type Sex-stratified multivariable NR Sex-specific regression models; logistic regression with year included as continuous interaction terms variable Presented by state for three Chi-square tests for Graphed the prevalence for different years difference in proportions the entire sample over the Figure present prevalence 3-year period over time by race/ethnicity Average absolute change categories T-tests with Bonferroni NR Joinpoint regression adjustments Piecewise logistic regression Logistic regression Absolute change presented Logistic regression models Crude and adjusted odds ratios are presented NR NR NR Average BMI percentile NR NR presented by groups Bivariate comparisons; NR Box-plots presented by school ANOVA year Multivariate models with adjusted pairwise Linear models used and F test comparisons and model coefficients continued
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From page 260... ...
International Healthy Athletes Database Texas SPAN Study Ezendam et al., 2000- Percent 2011 2002 to 2004-2005 Total Army Injury Hruby et al., 2015 1989-2012 Percent and Health Outcomes Database Truven Health Analytics Joyce et al., 2015 2004-2010 n (%) MarketScan Database The Tucson Children's Archbold et al., 1999-2004 n (%)
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From page 261... ...
Prevalence presented Relative percent change Testing a linear variable for by grade groups, sex, presented school year in multivariable sex* race/ethnicity group, models by eligibility for free/ P-value for trend was reduced-priced lunch calculated using Wald Chi square test Stratified by sex, race, and Present absolute change Ordinal regression mixed grade model Multinomial logistic regressions Chi-square Presented by age groups, NR NR sex, percent Indian heritage Wald Chi-square test Presented by participating Average annual percentage Cochran-Armitage test for locations, by demographic point changes trend characteristics by year 1 degree of freedom Wald 1 degree of freedom Wald chi-square test chi-square test Chi-squared tests NR Chi-squared tests comparing results to Presented percent (95 percent NHANES CI)
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From page 262... ...
YRBS (national, state, Kann et al., 2014 1999-2013 Percent (95 percent CI) large urban school districts)
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From page 263... ...
potential confounders T-test T-test Logistic regression analyses; simultaneously assessed linear and quadratic time effects T-test T-test Logistic regression analyses; simultaneously assessed linear and quadratic time effects Joinpoint analysis T-test T-test Logistic regression analyses; simultaneously assessed linear and quadratic time effects T-test T-test Logistic regression analyses; simultaneously assessed linear and quadratic time effects NR NR NR Present by school level Simply report apercentage NR for each year Linear mixed models NR Sex-stratified linear model and (between state included a quadratic term for comparisons) time Chi-square, by race, NR NR ethnicity Logistic regression NR NR adjusting for covariates Stratified by "era" (1986- Likelihood ratio NR 1989, 2008)
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From page 264... ...
a Study or data source name provided in text of the publication. If the publication did not specifically name the data source that was used, but provided details about data collection protocol, it is labeled as "Not Specified" in the table.
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From page 265... ...
Chi-square tests Reported the percent at NR each time point Univariate and multivariate Chi-square test Cochran–Armitage test for logistic regression models trend NR NR NR ANOVA NR NR Chi-square Compared estimates to NR Chi-square test for trend NHANES (graph) Multiple logistic regression analysis Chi-square for trend NR NR Chi-square test Wilcoxon signed-rank test NR NR NR NR NRn NR NR Pearson product-moment NR NR correlation coefficients (partial correlations)
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From page 266... ...
TABLE D-8 2000 CDC Body Mass Index-for-Age Percentiles Corresponding to the 2015 FitnessGram's® Needs 266 Improvement-Health Risk (NI-HR) Cut Points, by Age and Sex Hypothetical Information Used to Calculate a BMI Corresponding to the NI-HR BMI Cut Point Date of Height, Weight, NI-HR BMI Corresponding BMI-forAge, years Sex Date of Birth Measurement inchesa poundsb Cut Pointc Age Percentiled 5.0 F 01/01/1999 01/01/2004 48 60.6 18.5 95.8 5.5 F 01/01/1999 07/01/2004 48 60.6 18.5 95.0 6.0 F 01/01/1999 01/01/2005 48 62.9 19.2 96.0 6.5 F 01/01/1999 07/01/2005 48 62.9 19.2 95.0 7.0 F 01/01/1999 01/01/2006 48 66.2 20.2 96.1 7.5 F 01/01/1999 07/01/2006 48 66.2 20.2 95.1 8.0 F 01/01/1999 01/01/2007 48 69.5 21.2 96.0 8.5 F 01/01/1999 07/01/2007 48 69.5 21.2 95.0 9.0 F 01/01/1999 01/01/2008 48 73.4 22.4 96.0 9.5 F 01/01/1999 07/01/2008 48 73.4 22.4 95.0 10.0 F 01/01/1999 01/01/2009 48 77.3 23.6 95.9 10.5 F 01/01/1999 07/01/2009 48 77.3 23.6 95.1 11.0 F 01/01/1999 01/01/2010 48 81.0 24.7 95.8 11.5 F 01/01/1999 07/01/2010 48 81.0 24.7 95.0 12.0 F 01/01/1999 01/01/2011 48 84.6 25.8 95.7 12.5 F 01/01/1999 07/01/2011 48 84.6 25.8 95.0 13.0 F 01/01/1999 01/01/2012 48 87.8 26.8 95.6 13.5 F 01/01/1999 07/01/2012 48 87.8 26.8 95.0 14.0 F 01/01/1999 01/01/2013 48 90.8 27.7 95.5 14.5 F 01/01/1999 07/01/2013 48 90.8 27.7 95.0 15.0 F 01/01/1999 01/01/2014 48 93.4 28.5 95.4 15.5 F 01/01/1999 07/01/2014 48 93.4 28.5 95.0 16.0 F 01/01/1999 01/01/2015 48 96.0 29.3 95.4 16.5 F 01/01/1999 07/01/2015 48 96.0 29.3 95.0 17.0 F 01/01/1999 01/01/2016 48 98.3 30.0 95.4 17.5 F 01/01/1999 07/01/2016 48 98.3 30.0 95.0
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From page 267... ...
b Values are weights in pounds that correspond to the NI-HR BMI cut point, based on a height of 48 inches. c BMI cut points correspond to the values used in the 2015-2016 California Physical Fitness Test (California Department of Education, 2015)
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From page 268... ...
2010. Body mass index measurement and obesity prevalence in ten U.S.
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From page 269... ...
2011. Cardio vascular impact of the pediatric obesity epidemic: Higher left ventricular mass is related to higher body mass index.
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From page 270... ...
2016. California physical fitness test: Body mass index.
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From page 271... ...
2013. Secular trends in the fat and fat-free components of body mass index in children aged 8-18 years born 1958-1995.
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From page 272... ...
2011. Trends in body mass index in adolescence and young adulthood in the United States: 1959-2002.
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From page 273... ...
2014. Trends in body mass index and prevalence of extreme high obesity among Pennsylvania children and adolescents, 2007 2011: Promising but cautionary.
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From page 274... ...
2010. Continuous metabolic syn drome risk score, body mass index percentile, and leisure time physical activity in Ameri can children.
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From page 275... ...
2011. Elevated body mass index and obesity among ethnically diverse adolescents.
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From page 276... ...
2009. Measured body mass index, body weight perception, dissatisfaction and control practices in urban, low-income African American adolescents.
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From page 277... ...
2013. Use of data mining to reveal body mass index (BMI)
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Key Terms
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