Systematic Review of Prevalence of Young Child Overweight And

SYSTEMATIC REVIEW

Systematic Review of Prevalence of Young Child Overweight and Obesity in the United States–Afﬁliated Paciﬁc Region Compared With the 48 Contiguous States: The Children’s Healthy Living Program

We estimated overweight Rachel Novotny, PhD, Marie Kainoa Fialkowski, PhD, Fenfang Li, PhD, Yvette Paulino, PhD, Donald Vargo, PhD, and obesity (OWOB) prev- Rally Jim, MO, Patricia Coleman, BS, Andrea Bersamin, PhD, Claudio R. Nigg, PhD, Rachael T. Leon Guerrero, alence of children in US- PhD, Jonathan Deenik, PhD, Jang Ho Kim, PhD, and Lynne R. Wilkens, DrPH Affiliated Pacific jurisdictions (USAP) of the Children’s THERE ARE FEW DATA ON Hawaii was 33% (13% over- Study Selection Healthy Living Program com- overweight and obesity (OWOB) weight and 20% obese) and the Peer-reviewed literature. For our pared with the contiguous fi United States. of children in the US-Af liated risk for OWOB varied by eth- primary data sources, we searched fi We searched peer-reviewed Paci c Islands, Hawaii, and nicity,from2-foldinAsiansto electronic databases (PubMed, literature and government Alaska, collectively referred to as 17-fold in Samoans, compared US National Library of Medicine; 8,9 reports (January 2001–April the US-Affiliated Pacific region with Whites. Data from the EBSCO Publishing; and Web of 2014) for OWOB prevalence (USAP) in this article (Figure A, Commonwealth of the Northern Science) for articles published be- of children aged 2 to 8 years available as a supplement to the Mariana Islands (CNMI) showed tween January 2001 and April in the USAP and found 24 online version of this article at similar OWOB prevalence.10 2014 with the following search sources. We used 3 articles http://www.ajph.org). The USAP Aggregating prevalence esti- terms: child, obesity, overweight, from National Health and has not been included in the Na- mates for the region and by juris- Pacific, Alaska, Samoa, Micronesia, Nutrition Examination Sur- tional Health and Nutrition Exam- diction will allow programs to Hawaii, Marshall Islands, Mariana, veys for comparison. Mixed ination Survey (NHANES) or other target their activities and poli- Palau, Guam. We found 323 models regressed OWOB prevalence on an age poly- national surveillance systems with cies. The purpose of this article articles; 223 were unique and 1,2 nomial to compare trends measured anthropometric data. is to (1) estimate prevalence of we reviewed these for other (n = 246 data points). Native ethnic populations (Native OWOB of children aged 2 to 8 inclusion criteria. In the USAP, OWOB prev- Hawaiians, Pacific Islanders, Alaska years living in the USAP and (2) Publicly available government alence estimates increased Natives) of the USAP have not determine how that prevalence agency data. For our secondary with age, from 21% at age 2 been reported on in national sur- compares with children aged 2 to data sources, we located other re- years to 39% at age 8 years, veillance,3 yet Native Hawaiians 8 years living in the 48 contigu- ports on OWOB from the USAP increasing markedly at age 5 and other Pacific Islanders consti- ous states. by Internet search engine (Google) years; the proportion obese tute 1.2 million people (0.4% of with the same search terms. We increased from 10% at age 2 the total US population) and have METHODS further limited search hits in ex- years to 23% at age 8 years. increased 40% in the past decade,4 cess of 1 million to government The highest prevalence was in American Samoa and and Native Alaskans constitute Investigators from the Chil- agencies that focused on the 2- to 5 ’ Guam. (Am J Public Health. another quarter million people. dren s Healthy Living for the 8-year-old age group (e.g., Head Published online ahead of The USAP has political ties to the Remote Underserved Minority Start, Department of Education, print November 13, 2014: United States (Table A, available as Populations of the Pacific Program Department of Health and Human e1–e14. doi:10.2105/AJPH. a supplement to the online version searched peer-reviewed literature Services, Special Supplemental 2014.302283) of this article at http://www.ajph. and publicly available agency Feeding Program for Women, org).4 data for OWOB prevalence rates Infants, and Children [WIC]). In The high prevalence of obe- in the USAP relative to the Centers addition, we contacted child obesity and noncommunicable for Disease Control and Preven- sity experts in the Pacific region diseases in USAP adult population (CDC) body mass index (BMI; for the relevant government tions6 and consequent state of definedasweightinkilograms agency reports. We found 14 emergency declared7 underpins divided by the square of height reports and reviewed these for the urgency of obesity preven- in meters) reference, as is re- other inclusion criteria. tion, starting with children. The ported by NHANES and has The other inclusion criteria in- mean OWOB prevalence for been used in past reports for cluded (1) English language, the children aged 5 to 8 years in the USAP.9,11,12 main language used for business in

Published online ahead of print November 13, 2014 | American Journal of Public Health Novotny et al. | Peer Reviewed | Systematic Review | e1 SYSTEMATIC REVIEW

the region; (2) children aged 2 to 8 the best estimate available for any overall estimates were not overly bootstrap analysis performed years were included in the report; particular single age; for instance, influenced by jurisdictions with 500 iterations in which a random and (3) OWOB prevalence (%) if the prevalence was 10% for more publications. The weights selection of data sources with re- in the USAP defined with CDC children aged 2 to 4 years, the best were adjusted so that the total placement was made within juris- body mass index criteria13---15 estimate of the probability of obe- sample size n, defined as the sum diction maintaining the number (‡ 85th percentile and < 95th sity for a 2-year-old child is 10%. of the weights, equaled the num- of data sources per jurisdiction percentile for age and sex was Therefore, a record was created ber of children included in the at each iteration. We performed labeled “overweight”; ‡ 95th per- for each single age in the age model to maintain the correct further subgroup analyses (based centile was labeled “obese”16). group with the age group---specific type I and II errors. Thus, for on jurisdiction, year, source of prevalence and an equal propor- estimation of the overall USAP data, and type of sampling) as Data Extraction and Synthesis tion of the sample size (e.g., a prevalence, each jurisdiction was sensitivity analyses. To test disag-

One experienced reviewer prevalence estimate for the age assigned a sample size of pJ n, gregation of published estimates 17 (M. K. F.) independently identi- group aged 2 to 4 years would where pJ is the number of children of age groups into single ages, we fied eligible data sets and recorded lead to 3 records). One investiga- younger than 10 years in the did analysis of variance modelling study year, authors, publication tor (L. R. W.) entered data into 2010 census of jurisdiction (J) of prevalence by age group, using year, location, racial/ethnic group(s), a spreadsheet and a separate in- divided by the total number of the same weighting scheme as ages, sample size, OWOB vestigator (F. L.) reviewed the children younger than 10 years described previously and assign- prevalence, and notes on OWOB data. We performed an inverse across jurisdictions included in ing each data source to age group criteria (list of eligible data sets variance---weighted, fixed-effect the model.42,43 This poststratifi- 2 to 5 years or 6 to 8 years; we available on request). A second meta-regression39 to produce cation weighting44 allows for the assigned estimates to one of these reviewer (F. L.) confirmed the curves for OWOB prevalence overall USAP estimate to reflect categories. data. by single ages. A mixed model the distribution of children across We identified 11 primary and regressed the OWOB prevalence jurisdictions as in a simple random RESULTS 14 secondary data sources from on a polynomial of age (years) sample. 2001 to 2014 from Alaska, accounting for the variance of We used one set of models to Two hundred forty-six single- American Samoa, CNMI, Feder- the prevalence estimates,40 with predict prevalence by single ages year data points resulted from ated States of Micronesia (FSM; polynomials up to the fifth power. for each jurisdiction within the 27 data sources: 3 from the Yap, Kosrae, Pohnpei, and Chuuk), As the power functions were corre- USAP and to test for differences contiguous states (27 data points Guam, and Hawaii (Table 1). Be- lated, we used orthogonal polyno- between jurisdictions using for single ages),11,37,38 3 from cause 2 primary data sources18,19 mials41 to determine the signifi- a global F test of all age power Alaska,25---27 5 from American reported on the same data set, we cance of each independent power components. We used another Samoa,28---31 2 from CNMI,10,18 dropped 1, yielding 10. We found component (linear, squared, cubic, model to predict prevalence by 2 from Guam,33,34 10 from no data sources for the Marshall etc.) and the maximum degree single ages for the USAP region Hawaii,8,9,20---23,35---37,45and 2 Islands or Palau. We used data needed to fit the curve. We also overall and to compare the overall from the FSM.24,32 The 24 USAP from NHANES from 2009 to performed random models and USAP and contiguous US curves sources contributed 219 data 2010, 2007 to 2008, and 2003 the results were similar to the across ages with a global F test. points of prevalence for single to 200611,37,38 as a reference data fixed-effect model results; how- We computed separate models for ages. The adjusted sample size per set from the 48 contiguous states. ever, the random effects models overweight, obesity, and OWOB age group for each USAP jurisdic- were not as stable, so only the combined. tion is presented in Table B Analytic Methods fixed-effects models are presented. To ensure that the published (available as a supplement to the We used regression to estimate In addition to the inverse vari- prevalence data that were being online version of this article at OWOB prevalence for each single ance weighting that accounted aggregated within each USAP ju- http://www.ajph.org). When we age by jurisdiction (USAP state for precision of the individual risdiction were homogeneous, we added NHANES reference data, or territory or contiguous United prevalence estimates, in the anal- performed models with and with- the total sample size was 230 515 States). The prevalence estimates ysis for the overall USAP region, out inclusion of a random effect children with OWOB data. Sample in the 24 USAP sources and 3 further weighting was performed for manuscript number for each sizes per jurisdiction and for the NHANES sources were given for so that the contribution of data jurisdiction with 3 or more data contiguous states are presented in age groups, rather than for single from a single jurisdiction to the sources. There was no evidence of Table 2. ages. For the regression to provide overall estimate was equal to the heterogeneity for any jurisdiction Most data sets targeted children a smooth curve of estimates across proportion of children younger (all Ps > .1). We used a bootstrap aged 2 to 5 years. Only data for all ages from 2 to 8 years, we than 10 years from the USAP analysis to determine the effect this age group were available for needed estimates for single ages. (based on census data) that it of the variability of the included Guam and for the states of Chuuk, The estimate for the age group is contributes. This ensures that manuscripts on the results. The Kosrae, and Pohnpei of the FSM.

e2 | Systematic Review | Peer Reviewed | Novotny et al. American Journal of Public Health | Published online ahead of print November 13, 2014 ulse nieaedo rn oebr1,2014 13, November print of ahead online Published TABLE 1—Sources of Overweight, Obesity, or Both With Prevalence (%) Data Used in the Meta-analysis for the US-Afﬁliated Paciﬁc Region and the 48 Contiguous United States, in Literature Published January 2001–April 2014

Study Source and Cohort Ref. Cutoff, CDC Author Region Age, y Sexa No. of children Racial/Ethnic Group(s) (Year of Data Collection) Sampling Frame Criteriab Age Group, %

Peer-reviewed literature (n = 10c) Bruss et al.10 CNMI 8–9 Both sexes 407 Paciﬁc Islander Project Familia Giya Caregivers from 12 public ‡ 85th percentile 47 (Chamorro, Marianas—Children from schools were invited to ‡ 95th percentile 32 Carolinian, 12 public schools in the participate Micronesian), Asian CNMI (2005) Paulino et al.18 CNMI 1–10 Both sexes 393 Paciﬁc Islander Children randomly selected Random cluster survey ‡ 85th percentile 2–3 y = 25 (Chamorro, from 16 villages on Rota, sampling proportionate to 4–6 y = 26

| Carolinian), mixed Saipan, and Tinian (2005) 2000 US Census population 7–10 y = 45 mrcnJunlo ulcHealth Public of Journal American or other, Asian estimate 85th–94th percentile 2–3 y = 12 2–3 y = 83 4–6 y = 13 4–6 y = 127 7–10 y = 18 7–10 y = 136 ‡ 95th percentile 2–3 y = 13 2–10 y = 346 4–6 y = 13 7–10 y = 27 ‡ 85th percentile 34 85th–94th percentile 15 ‡ 95th percentile 19 Novotny et al.19 Hawaii 2–10 Both sexes 554 Native Hawaiian, Children who accessed one Stratified random 85th–94th percentile 13 (all) Pacific Islander, of the health maintenance sample 15 males, 10 females Asian, White, other organization’s (Kaiser 2–3 y = 10 Permanente’s) 10 Oahu 4–5 y = 14 clinics for a physical 6–10 y = 13 oon tal. et Novotny examination (2003) Native Hawaiian = 11 Pacific Islander = 18 Filipino = 11 ‡ 95th percentile 19 (all) | erReviewed Peer 22 males, 15 females 2–3 y = 7 4–5 y = 20 6–10 y = 29

| Native Hawaiian = 19 ytmtcReview Systematic Paciﬁc Islander = 40 Filipino = 19

Continued | e3 e4

| TABLE 1—Continued ytmtcReview Systematic

Barufﬁ et al.8 Hawaii 2–4 Both sexes 12 709 Asian, Black, White, WIC (1997–1998) Data with complete date, ‡ 95th percentile 2–4 y = 11.4 Filipino, Native age, sex, weight, and height Native Hawaiian = 11.3 Hawaiian, Hispanic, information Samoan = 27 Samoan, other Filipino = 12.4

| Pobutsky et al.20 Hawaii 4–6 Both sexes 10 199 Multiple racial/ethnic Public elementary schools All student health records ‡ 85th percentile 28.5 erReviewed Peer groups (children who (2002–2003) with complete age, sex, 85th–94th percentile 14.1 entered kindergarten weight, and height information ‡ 95th percentile 14.4 in the Hawaii public and plausible anthropometric school system) values | 9 oon tal. et Novotny Novotny et al. Hawaii 5–8 Both sexes 4608 (with ethnic White, Asian, Filipino, Health maintenance Cross-sectional study design of ‡ 85th percentile 32.6 (with ethnic information) information) Native Hawaiian, organization (Kaiser electronic medical record data with 29.4 (with and without ethnic Native Hawaiian- Permanente; 2010) complete weight and height information) Asian, Samoan, other information 85th–94th percentile 12.9 (with ethnic information) mixed, other ‡ 95th percentile 19.7 (with ethnic information) Chai et al.21 Hawaii 6–17 Males only 1437 Native Hawaiian, Asian, Public school students in Five years of semilongitudinal data ‡ 95th percentile males = Native Hawaiian (6–11 y), 29.3 and females Filipino, Portuguese, a Hawaii district with from a cohort of students females = Native Hawaiian (6–11 y), 23.7 only White, other a higher population of in grades 1–12 both = Native Hawaiian (6–11 y), 26.5 residents of Native males = non-Native Hawaiian (6–11 y), 25.1 mrcnJunlo ulcHealth Public of Journal American Hawaiian ancestry (1992– females = non-Native Hawaiian (6–11 y), 16.3 1996) both = non-Native Hawaiian (6–11 y), 20.7 Okihiro et al.22 Hawaii 4–5 Both sexes 389 Native Hawaiian, Children from 2 rural and Retrospective study of children: ‡ 95th percentile 22.7 Samoan, Filipino impoverished communities (1) Native Hawaiian, Samoan 85th–94th percentile 20.1 who utilized Hawaii’s or Filipino; (2) lived in the ‡ 85th percentile 42.8 largest federally qualiﬁed CHC zip code; (3) born during CHC 1 of 4 periods: 1981–1983, 1986–1988, 1991–1993, and 1996–1998; (4) attended the

| CHC for their well-child care; ulse nieaedo rn oebr1,2014 13, November print of ahead online Published and (5) had a prekindergarten physical examination at the CHC Manea23 Hawaii 4–5 Both sexes 586 Unspeciﬁed Kauai public elementary Student health records of all Kauai ‡ 85th percentile 4 y = 41.6 males, 27.9 females school 1st graders (2003) children enrolled in the ﬁrst grade 5 y = 38.8 males, 37.3 females 4 y = 316 (144 during the period from August to 4–5 y = 36 males, 172 December 85th–94th percentile 4 y = 19.4 males, 8.7 females females) 5 y = 15.9 males, 15.1 females 5 y = 270 (144 4–5 y = 14.5 males, 126 ‡ 95th percentile 4 y = 22.2 males, 19.2 females females) 5 y = 22.9 males, 22.2 females 4–5 y = 21.5

Continued ulse nieaedo rn oebr1,2014 13, November print of ahead online Published TABLE 1—Continued

Ichiho et al.24 Federated 2–14 Both sexes 1948 Unspeciﬁed Yap State Cancer Household survey 85th–94th percentile 15.6 States of Prevention and Control ‡ 85th percentile 33.8 Micronesia, Program—Outer Island ‡ 95th percentile 18.2 Yap Household Survey Agency literature (2008–2009) (n = 14) Boles et al.25 Alaska 3–19 Both sexes 5902 White, Alaska Native/ Kenai Peninsula All student records with valid ‡ 85th percentile 3–6 y = 32.9 American Indian, Borough school height and weight measures 7–10 y = 34.2 Asian, Black/African district (2011) from the electronic student 85th–94th percentile 3–6 y = 19.7 American, Paciﬁc information system 7–10 y = 17.8 Islander/Native ‡ 95th percentile 3–6 y = 13.1

| Hawaiian, Hispanic/ 7–10 y = 16.4 mrcnJunlo ulcHealth Public of Journal American Latino, mixed Alaska Special Alaska 2–5 Both sexes 16 525 (2012) Unspecified Alaska WIC (2005–2012) Data with complete age, ‡ 85th percentile 22 (2012) Supplemental 16 192 (2011) sex, weight, and height 21.42 (2011) Nutrition Program 17 273 (2010) information 21.52 (2010) for Women, 16 462 (2009) 21.69 (2009) Infants, and 15 662 (2008) 21.54 (2008) Children26 15 579 (2007) 21.60 (2007) 15 667 (2006) 21.7 (2006) 17 128 (2005) 22.1 (2005) Eberling27 Alaska 5–8 Males only, 650 (334 males, White, American Representative sample A consent form and ‡ 85th percentile 35 both, 37 males, 32 females females 316 females) Indian/Alaska of kindergarten questionnaire was sent 85th-94th percentile 19 both, 20 males, 17 females only, and Native, Black/African students at selected home to parents and ‡ 95th percentile 16 both, 17 males, 15 females both sexes American, Hispanic/ elementary schools guardians Latino, Asian, Native (2010–2011) oon tal. et Novotny Hawaiian/ Pacific Islander, mixed, unknown Vargo28 American 4–20 Males only 5390 Unspecified A sample of public and School-based measurement ‡ 85th percentile males = 46 (kindergarten) |

erReviewed Peer Samoa and females K–5: 1015 (524 private school children males = 46 (3rd grade) only males, 491 attending grades K, 3, 6, females = 42 (kindergarten) females) 9, and 12 (2006–2007) females = 45 (3rd grade) 3rd grade: 1189 85th–94th percentile males = 21.4 (kindergarten)

| (615 males, 574 males = 17.4 (3rd grade) ytmtcReview Systematic females) females = 21.4 (kindergarten) females = 17.8 (3rd grade)

Continued | e5 e6

| TABLE 1—Continued ytmtcReview Systematic

‡ 95th percentile males = 25.4 (kindergarten) males = 29.4 (3rd grade) females = 20.8 (kindergarten) females = 27.4 (3rd grade)

| Vargo29 American 5–18 Males only 4214 Unspeciﬁed Students in grades K School-based measurement ‡ 85th percentile 5 y = 36.05 males, 41.4 females erReviewed Peer Samoa and females 5 y = 213 (114 through 12 at public and 6 y = 38.4 males, 34.9 females only males, 99 private schools (2007– 7 y = 51.1 males, 47.6 females females) 2008) 8 y = 50 males, 39 females 6 y = 264 (138 9 y = 42 males, 51.4 females | oon tal. et Novotny males, 126 10 y = 54.2 males, 54.7 females females) 85th–94th percentile 5 y = 21.1 males, 18.2 females 7 y = 263 (135 6 y = 18.1 males, 22.2 females males, 128 7 y = 18.5 males, 14.8 females females) 8 y = 19.1 males, 15.8 females 8 y = 256 (136 9 y = 16.2 males, 15.5 females males, 120 10 y = 16.1 males, 25.0 females females) ‡ 95th percentile 34.4 males, 36.3 females 9 y = 284 (142 5 y = 14.9 males, 23.2 females mrcnJunlo ulcHealth Public of Journal American males, 142 6 y = 20.3 males, 12.7 females females) 7 y = 32.6 males, 32.8 females 10 y = 246 (118 8 y = 30.9 males, 23.4 females males, 128 9 y = 26.1 males, 35.9 females 10 y = 38.1 males, 29.7 females females) Vargo30 American 2–20 Males only 3478 Unspeciﬁed Students in grades 2, 5, and School-based ‡ 85th percentile 20 males, 23.5 females Samoa and females 8 from each of the 23 measurement 6–11 y = 47.7 males, 46.8 only public elementary schools females (2008–2009) and juniors from each of 48.5 males, 47.4 females | ulse nieaedo rn oebr1,2014 13, November print of ahead online Published the 6 public high schools (2007–2008) (2008–2009) 52.4 males, 50.1 females (2006–2007) ‡ 95th percentile 33.6 males, 34.3 females 6–11 y = 29.2 males, 25.1 females (2008–2009) 30.2 males, 27.8 females (2007–2008) 33.5 males, 31.3 females (2006–2007)

Continued ulse nieaedo rn oebr1,2014 13, November print of ahead online Published TABLE 1—Continued

American Samoa American 2–5 Both sexes 4225 Unspecified American Samoa Data with complete date, ‡ 85th percentile 33.7 Maternal and Child Samoa WIC (2009) age, sex, weight, and height 85th–94th percentile 19.1 Health Program31 information ‡ 95th percentile 14.6 American Samoa American 2–4 Both sexes 576 Unspecified Tafuna Health Center and Data with complete date, ‡ 85th percentile 35.1 Maternal and Child Samoa Leone Health Center age, sex, weight, and height 85th–94th percentile 19.1 Health Program31 (2010) information ‡ 95th percentile 16 Federated States of Federated 2–5 Both sexes 800 (Chk) Unspecified Maternal and Child Health School-based measurement ‡ 85th percentile 3 (Chuuk) Micronesia States of 812 (Kos) data collection in Chuuk, 2.5 (Kosrae) Maternal and Child Micronesia 580 (Poh) Kosrae, Pohnpei, and Yap 1.5 (Pohnpei) Health Program32 205 (Yap) (2010) 14 (Yap) Department of Guam 3–5 Both sexes 576 Unspecified Guam Headstart School-based 85th–94th percentile 13 33 ‡

| Education Program (2011–2012) measurement 95th percentile 14.2 mrcnJunlo ulcHealth Public of Journal American Department of Guam 2–5 Both sexes 4410 Unspecified WICd Data with complete date, ‡ 85th percentile 52.7 (2009) Public Health and 4029 (2006–2009) age, sex, weight, and height 34.9 (2008) Social Services34 2653 information 26.8 (2007) 3369 31.8 (2006) Hawaii Special Hawaii 2–5 Both sexes 1766 (2010) Unspecified Hilo WICd (2008–2010) Data with complete date, 85th–94th percentile 15.7 (2010) Supplemental 1748 (2009) age, sex, weight, and height 14.9 (2009) Nutrition Program 1531 (2008) information 15.7 (2008) for Women, ‡ 95th percentile 10.6 (2010) Infants, and 11.6 (2009) Children35 10.3 (2008) Hawaii Special Hawaii 2–5 Both sexes 731 (2010) Unspecified Kona WICd (2008–2010) Data with complete date, 85th–95th percentile 13.3 (2010) Supplemental 650 (2009) age, sex, weight, and height 13.8 (2009) Nutrition Program 650 (2008) information 15.5 (2008) for Women, ‡ 95th percentile 10.8 (2010) oon tal. et Novotny Infants, and 8.5 (2009) Children35 8.8 (2008) Centers for Disease Hawaii 2–5 Both sexes 17 879 Unspecified Pediatric Nutrition WIC data with complete ‡ 85th percentile 21.5 Control and Surveillance date, age, sex, weight, and 85th–94th percentile 12.3

| 36 erReviewed Peer Prevention System data (2011) height information ‡ 95th percentile 9.2 Reference data (n = 3) Ogden et al.11 Contiguous 0–19 Both sexes 4111 White, Hispanic, NHANES 2009–2010 Cross-sectional analyses of all ‡ 85th percentile 2–5 y = 26.7 US 0 to < 2 y = 703; Black, Mexican children and adolescents with 6–11 y = 32.6 ‡ | 2–5 y = 903; American measured heights and weights 95th percentile 2–5 y = 12.1 ytmtcReview Systematic 6–11 y = 1213; from NHANES 6–11 y = 18.0 12–19 y = 1292

Continued | e7 Sample sizes varied slightly be- size of the USAP. The trend of tween models. Two of the 24 OWOB was also unchanged by sources from the USAP only the removal of American Samoa reported obesity prevalence8,21 where prevalence estimates are leaving 22 sources that contrib- much higher, also because of the 6–11 y = 19.6 6–11 y = 17.0 6–11 y = 35.5 6–11 y = 33.3 uted 206 data points for the small population sizes of American overweight plus obesity analyses. Samoa. Removal of Hawaii, the Four sources did not provide jurisdiction contributing the larg- a separate prevalence of obe- est population of children, led to sity26,32,34,35; thus, the model pre- a similar prevalence curve, with egories. 95th percentile 2–5 y = 10.4 95th percentile 2–5 y = 12.4 85th percentile 2–5 y = 21.2 85th percentile 2–5 y = 24.4 dicting obesity included 143 data a steeper increase at age 5 years. ‡ ‡ ‡ ‡ points from 20 USAP sources. Six Separate analyses of data collected sources did not separate prevalence in 2007 and earlier and collected of overweight from overweight plus in 2008 and later showed a simi- obesity8,21,26,32,34,35; thus, 18 USAP lar trend in OWOB for the USAP. sources contributed 130 data The prevalence curve was un- ndergarten; Kos = Kosrae; Poh = Pohnpei; NHANES = National Health and Nutrition Examination points for the model predicting changed when the data were lim- 13 overall overweight prevalence. ited to WIC and school sources The vast majority of the data only.

of all children and adolescents with measured heights and weights from NHANES of all children and adolescents with measured heights and weights from NHANES sources for the USAP were from The trend was mirrored in the census or near censuses of chil- obesity prevalence data, in which dren who were members of gov- the estimates did not change in ernment organizations, such as early ages, but increased signifi- schools, health care organizations, cantly in later ages (5---8 years), and WIC programs. For 2- to from 10% at age 2 years to 23% 5-year-old children, WIC was the at age 8 years. None of the age NHANES 2007–2008 Cross-sectional analyses NHANES 2003–2006 Cross-sectional analyses major contributor to data, and for terms reached significance in 6- to 8-year-old children, schools the overweight model and the were major contributors to data overall prevalence was stable (Table 1). from ages 2 years (13%) to 8 years (15%). 95 percentile defined as obese (previously defined as overweight). Mexican American (beginning in 2007, all Hispanics were oversampled while allowing for a sufficient no. of Mexican Americans) American, and other (in 2005–2006 survey, Mexican Americans were oversampled) Predicted Overweight and Individual jurisdictions within the ‡ Obesity Prevalence US-Affiliated Pacific jurisdictions. Overall US-Affiliated Pacific For Alaska, American Samoa, jurisdictions. Predicted curves are CNMI, Hawaii, and Yap, preva- given in Figure 1, and regression lence of OWOB was predicted 12–19 y = 1199 12–19 y = 4300 0to<2y=719 2–5 y = 885 6–11 y = 1197 2–5 y = 1770 6–11 y = 2095 coefficients for age for each of the for ages 2 to 8 years. For Guam models in Table C (available as and the FSM states of Chuuk, a supplement to the online version Kosrae, and Pohnpei, prevalence of this article at http://www.ajph. of OWOB was only predicted for org). The OWOB estimate in- ages 2 to 5 years because of lack creased by year of age from 21% of data in children aged 5 to 8

0–19 Both sexes 4000 White, Hispanic, Black, 2–19 Both sexes 8165 White, Black, Mexican at age 2 years to 39% at age 8 years. Separate prevalence esti-

presented prevalence data for the same study population. For analysis, onlyyears Paulino et al. data are included because data areand presented in more age cat the trend of OWOB mates of overweight and obesity 19 increased sharply at age 5 years. were not reported because of

US US After the removal of the data from small sample sizes by jurisdiction. Contiguous Contiguous FSM states, where the prevalence Figure 2 shows the prevalence estimates are much lower and curves for each USAP jurisdiction; and Novotny et al. 37 38

18 ﬁ

Continued generally only data through age regression coef cients for models 4 years are available, the pattern are given in Table D (available as CDC = Centers for Disease Control and Prevention; CHC = community health center; Chk = Chuuk; CNMI = Commonwealth of the Northern Mariana Islands; K = ki was unchanged, because of the a supplement to the online version Ogden et al. Ogden et al. 85th–95th percentile deﬁned as overweight (previously deﬁned as at risk for overweight), Males only, females only, or both sexes combined. Paulino et al. c Note. Survey; US = United States; WIC = Supplemental Nutrition Program for Women, Infants, and Children. a b TABLE 1— small contribution of the FSM of this article at http://www.ajph. states to the overall population org). Based on jurisdictions with

e8 | Systematic Review | Peer Reviewed | Novotny et al. American Journal of Public Health | Published online ahead of print November 13, 2014 SYSTEMATIC REVIEW

pattern of stable prevalence in TABLE 2—Sample Size per Jurisdiction of the US–Affiliated Pacific Region and the 48 Contiguous United early ages and increase thereafter States in Literature on Prevalence of Young Child Overweight and Obesity, Published January 2001–April was displayed for the contiguous 2014 United States as well. However, No. Aged 0–9 Years No. Articles or No. Children Measured Aged No. With Proportional an increase occurred at age 4 Jurisdiction in 2010 Census Reports Included 2–10 Years Since 2000 Weighting years and the prevalence pla- teaued at age 7 years. The OWOB Alaska 104 883 3 137 040.0 594.7 prevalence increased from 24% American Samoa 13 146 5 11 429.3 73.8 at age 2 years to 35% at 8 years. Chuuk 11 733 1a 800 66.8 We compared the contiguous CNMI 9440 2 753.0 53.0 US curve with a USAP curve ag- Contiguous US 40 223 509 3 7312.2 228 474.6 gregated across jurisdictions. We Guam 28 273 2 15 037.0 161.4 included only jurisdictions with Hawaii 170 768 10 55 197.5 970.5 data from children aged 2 to 8 Kosrae 1587 1a 812.0 9.2 years (Alaska, American Samoa, Pohnpei 8298 1a 580.0 46.1 CNMI, and Hawaii) in the overall Yap 11 376 2a 1553.6 64.5 USAP comparison curve, created Sum 40 583 013 27 230 514.6 230 514.6 within the model as a weighted Note. CNMI = Commonwealth of the Northern Mariana Islands; US = United States. The sample sizes were adjusted to be proportional to the average across jurisdictions, where jurisdiction census counts of children aged 10 years and younger and so that the total sample size equals the number of children across the weights were proportional to publications included in the model. The jurisdiction adjusted counts were divided into ages proportionally based on the observed counts. aYap had a total of 2 data sources, of which one is a shared data source with Chuuk, Kosrae, and Pohnpei. the size of the jurisdiction population and adjusted to sum to the overall sample size (Table B, data beyond age 5 years, the across the jurisdictions. One pat- Samoa, the OWOB prevalence in- available as a supplement to the OWOB prevalence estimates were tern was defined by high OWOB creased from 34% at age 2 years online version of this article at relatively flat until that time and prevalence. The prevalence was to 47% at 8 years. In another http://www.ajph.org). The esti- then increased yearly up to age 8 highest in Guam and American pattern, Alaska, Hawaii, and CNMI mated prevalence curve of the years. The overall curves were Samoa, followed by Yap. For had lower, although still substan- USAP differed from that of the found to differ across jurisdictions Guam, OWOB prevalence was tial, OWOB prevalence estimates. contiguous states (P <.001;F test). (P < .001 for the global F test). 39% at age 2 years and 38% from From ages 5 to 8 years, CNMI The prevalence at younger ages There were 3 general patterns age 3 to 5 years. For American showed a much steeper increase. was lower in the USAP than the For Alaska, OWOB prevalence contiguous United States, and the 50 increased from 22% at age 2 increase in OWOB was steeper % overweight and obese years to 35% at 8 years, for after age 5 years in the USAP. % overweight 40 Hawaii from 20% at 2 years to Significant differences between % obese 34% at 8 years, and for CNMI USAP and the contiguous United 30 from 25% at 2 years to 47% at 8 States persisted when we removed years. In the third pattern, 3 states the jurisdictions of American 20 of the FSM showed low OWOB Samoa or Hawaii from the USAP — 10 prevalence up to age 5 years estimate. Also, each of the USAP Prevalence Rate, % Chuuk, Kosrae, and Pohnpei. With jurisdiction-specific curves for 0 only 1 data source, the prevalence Alaska, American Samoa, CNMI, 2345678 was constant across ages and was and Hawaii differed significantly Age, y 2% for Pohnpei, and 3% for from the contiguous US curve Note. Prevalence estimates are predicted values from a meta-regression of overweight or Chuuk and Kosrae. For Yap, an (all Ps < .001). obesity prevalence on an age quintic polynomial curve. Overweight defined as ‡ 85th to FSM state with more available £ 94th percentile; obesity as ‡ 95th percentile, and overweight or obesity as ‡ 85th data, the estimated OWOB preva- Sensitivity Analysis percentile. lence was higher and remained We performed several sensitiv- FIGURE 1—Predicted prevalence of overweight, obesity, and similar across ages, from 30% at ity analyses to determine the overweight or obesity for ages 2–8 years among all US-Affiliated age 2 years to 34% at age 8 years. robustness of our estimation pro- Pacific jurisdictions combined, in literature published January The 48 contiguous states. Figure cedure. The prevalence curve was 2001–April 2014. 2 displays the prevalence curve similar when we removed the for the 48 contiguous states. The jurisdiction with the highest

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BMI. If this confounding of age 50 and data source introduces a bias Alaska (3 articles) 45 Hawaii (8 articles) in the present study, it would be American Samoa (5 articles) expected that the prevalence at 40 CNMI (2 articles) younger ages would be overesti- 35 Guam (2 articles) Chuuk (1 article) mated. 30 Kosrae (1 article) Pohnpei (1 article) The increase in obesity in the 25 Yap (2 articles) older age group could also reﬂect 20 Pacific region (all jurisdictions, 22 articles) Contiguous US (3 articles) the lifestyle transition to attending 15 elementary school, and the food

Prevalence Rate, % Rate, Prevalence 10 and physical activity environment 5 at elementary schools warrants further exploration. Early life influ- 0 234567 8 ences, as early as fetal life and in- Age, y fancy, could also have set a growth trajectory from malnutrition at- Note. CNMI = Commonwealth of the Northern Mariana Islands; US = United States. Prevalence estimates are predicted values from a meta- tributable to either lack of energy regression of overweight and obesity prevalence on an age quintic polynomial curve. Overweight and obesity defined as ‡ 85th percentile. and nutrients, or excess.52 Fetal FIGURE 2—Predicted prevalence of overweight and obesity, ages 2–8 years, for the US-Affiliated Pacific programming and metabolic region (USAP) combined, individual USAP jurisdictions, and the 48 contiguous United States, in literature changes can optimize for energy published January 2001–April 2014. storage, and in a mismatched postnatal obesogenic environment can lead to childhood obesity.53 prevalence (American Samoa) or contiguous United States, and the the USAP, and at age 4 years in The states of Chuuk, Pohnpei, with the largest contribution to prevalence was higher at ages 6 to the 48 contiguous states. The start and Kosrae have lower income, sample size (Hawaii), or when 8 years in the USAP (P < .001). of the abrupt increase in OWOB according to the World Bank,54 stratified by data collection year. at age 4 or 5 years in the USAP and may have more undernutri- We found a significant difference, DISCUSSION and the US contiguous states could tion than obesity in children aged at P < .001, between the curve for be an artifact of the 0-to-5---year 0 to 5 years. Public health mes- the US Pacific region and that from At first contact with Europeans, and 6-to-10---year age grouping of sages should focus on sustainable the contiguous United States for Pacific people were described as samples that were available. Also, diets, healthy eating, and physical each iteration of the bootstrap strong, muscular, slim, and fewer data were available for activity,55 rather than on weight analysis. Also, the 95% confidence healthy.46 Traditional foods were children aged 6 to 8 years, and reduction, particularly where un- intervals for the prevalence curves nutritionally adequate.47 Global- this age group could be biased by dernutrition and obesity coexist.56 predicted from the bootstrap anal- ization48 and trade policies placed the inclusion of 9- to 10-year-old We defined OWOB for this ysis were similar in shape to that in pressures on food security.48,49 children in only some of the sam- study on the basis of CDC criteria. Figure 1. The prevalence curve The introduction of a cash econ- ples. Also, older age groups were Definitions of OWOB vary across pattern was maintained when we omy50 and other social, economic, sampled from schools with rela- countries.57 In the 48 contiguous limited the data to WIC and to and political changes contributed tively comprehensive sampling US states, including in NHANES, school sources, which represent to a nutritional and epidemiologi- whereas younger age groups were CDC growth charts are used for census or near census data. Thus, cal transition that resulted in an sampled from agencies that could children aged 2 to 19 years.58,59 the prevalence curve is quite ro- increase in chronic diseases. represent lower socioeconomic These charts were developed from bust to the variation in the data The estimated prevalence of subsets of populations (e.g., WIC). the noninstitutionalized popula- sources. OWOB in the USAP varied in the However, these sources of data tion of the contiguous states.13 We found the same pattern in present study from 21% at age 2 are likely comparable as USAP There is a need for global com- the analysis of variance (Figure B, years to 39% at age 8 years and in populations are generally in lower parisons. The International Obesity available as a supplement to the the contiguous United States from income brackets. Also, the OWOB Task Force used 6 large nationally online version of this article at 24% at age 2 years to 35% at age 8 prevalence of children was found representative samples (Brazil, http://www.ajph.org)—the preva- years. The prevalence of OWOB to be similar between WIC par- Great Britain, Hong Kong, the lence of overweight and obesity was lower at younger ages in the ticipants and nonparticipants in Netherlands, and Singapore) to rose between the group aged 2 to USAP but increased at a faster NHANES data,51 although non- develop global centile curves for 5 years and the group aged 6 to 8 rate. The prevalence of OWOB participants in the highest income children aged 2 to18 years that are years for both the USAP and the increased sharply at age 5 years in level were found to have lower linked to the adult BMI cut-points

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of 25 kg/m2 and 30 kg/m2.15 estimates are substantially higher populations of the Pacificishigher The present data show that the World Health Organization among young children in the than the estimates in the current American Samoan children al- (WHO) reference data for children USAP. At 21% at age 2 years, analysis. Interestingly, mixed ethnic ready display higher BMI than aged 0 to 5 years are from samples the overall OWOB prevalence in children have shown higher risk children of the contiguous states of healthy children in Brazil, the USAP was already in excess of of OWOB than is expected from at age 2 years. American Samoan Ghana, India, Norway, Oman, and the 15% expected in a normal a mean of the 2 component ethnic- parents have attributed the high the United States,60 and define distribution (> 85th percentile). ities.9 Reasons for this are not prevalence of OWOB in children overweight as greater than 1 stan- Furthermore, American Samoa known, but might be related to and adolescents to high bone tis- dard deviation and obesity as and Guam rates were an addi- retaining favored (energy-dense) sue and lean tissue, rather than greater than 2 standard deviations tional 10 to 15 percentile points foods of component cultures. In the high body fat tissue.76 Still, above the mean.60,61 The WHO higher. Thus, factors before age nutrition transition continuum, American Samoan adolescent reference is an international multi- 2 years, during infancy and the the USAP jurisdictions with mean BMIs increased between ethnic standard for how children intrauterine period, may contrib- higher OWOB are further along 1978 and 2007.77 The high should grow, rather than how chil- ute to excess OWOB in the USAP, in the transition. Some features prevalence of obesity-driven non- dren are growing, in an environ- especially in American Samoa of this transition include US mil- communicable diseases among ment where they may or may not and Guam, and deserve further itary presence, imported US food, American Samoan adults implies be healthy. study. and presence of US fast-food that population-level BMI is higher Further examination of USAP Ethnicity varies substantially restaurants. than is healthy.78 data with global references should across the USAP, with Alaska As each of the USAP jurisdic- At age 2 years, CNMI children be pursued where data are avail- Natives, the natives of Hawaii and tions has a different environment showed a relatively high preva- able to do so, to help interpret American Samoa of Polynesian and a different pattern of OWOB lence of OWOB (25%) with health implications of body size ancestry, and natives of the FSM, for children and adults, the results a rapid increase to 47% at 8 years, among the region’s diverse ethnic Guam, and CNMI of Micronesian are discussed separately by juris- the most rapid increase in OWOB groups. For example, evidence ancestry. Body attributes vary, diction. The estimated prevalence prevalence between age groups suggests that definitions of OWOB with Polynesians historically of OWOB among young children among all jurisdictions studied. On do not adequately correspond showing especially large heights in Alaska was high. Although data Guam, the prevalence of OWOB to body fat levels in children of and weights.67 The proportion were drawn from many regions among children was high and diverse ethnic backgrounds,62 of native ethnic population also and subpopulations in Alaska, remained stable in the younger especially among Pacific people.63 varies within each jurisdiction, and they cannot be considered repre- ages, from ages 2 years (39%) to 5 Several studies have devel- among individuals in many cases, sentative of Alaska Natives, as they years (38%). oped ethnic-specificBMIcut- with a history of colonization by were not specifically sampled.74 The Hawaii OWOB prevalence points,64,65 including for Pacific and immigration from a number Alaska Natives constitute approxi- increased from about one fifth of Islanders from the South Pacific,63 of Asian and non-Hispanic White mately 15% of the population in 2-year-old children to about one though these studies have focused populations.68 Mixing and migra- Alaska and are disproportionately third of 8-year-old children, re- on adults. Such cut-points may tion of ethnic populations in the young.73 Regions within Alaska sembling rates of Alaska and the provide additional insight into the Pacific is very high,69 and in- also differ by a number of socio- contiguous states, but lower than interpretation of the data. How- creasing elsewhere in the world economic, cultural, and geo- Guam, CNMI, and American ever, with the high prevalence of as well.70 graphic factors that affect risk for Samoa. Because of the ethnic di- mixed ethnicity (e.g., Pacific Is- It must be noted that we do OWOB. Data were collected as versity in Hawaii, which includes lander and Asian groups) in the not have ethnic identifiers in our part of health screenings and substantial proportions of Asians Pacific,9 the application of differ- data sets and proportion of the a standardized protocol was not and non-Hispanic Whites in ent cut-points or reference data population that is native in each followed. Despite these limitations, addition to Native Hawaiians (e.g., WHO, International Obesity jurisdiction varies substantially findings highlight the importance and other Pacific Islanders,42 Task Force, or CDC) for different from 19% Alaska Native or of identifying effective preventive disaggregating the Hawaii data ethnic groups would not allow Native American71 to 28% Native interventions that address the would likely show different comparison across these popula- Hawaiian in Hawaii71 to 89% root causes of OWOB. OWOB prevalence among the tion groups. Samoan in American Samoa.72 As early as 1952, American ethnic groups in Hawaii, as has Although the prevalence of In Hawaii and Alaska, less than half Samoan (Polynesian) infants been shown before,79 and OWOB in this study cannot be of the jurisdiction’spopulationsis showed high weight-for-age in which has implications for re- directly compared with the global native, and native ethnic groups the first year of life, trending near source allocation targeting at-risk prevalence of 7% for preschool show higher rates of OWOB com- the 75th percentile of US children groups.9 children,66 based on WHO refer- pared with jurisdiction means,9,73 regardless of feeding pattern. Data from the FSM Maternal ence data and cut-points, the implying that OWOB among native High birth weight was common.75 and Child Health (MCH) Program

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report32 present a different view patterns of OWOB in the Freely Pago Pago. Patricia Coleman and Jang Ho Human Participant Protection of OWOB prevalence. Chuuk, Associated States of Micronesia Kim are with the Cooperative Research, This study used secondary data sources Extension, and Education Service Department with no individual identifiers and, thus, Kosrae, and Pohnpei were low at will require additional data Northern Marianas College, Saipan. Andrea human participant protection was not 2% to 3%, and Yap was higher, from FSM, and inclusion of Palau Bersamin is with the Department of Alaska needed. closer to the expected normal dis- and the Republic of the Marshall Native Health at the University of Alaska, Fairbanks. tribution of 15%. Generally, these Islands. Correspondence should be sent to Rachel References data were from children who vis- Despite containing among the Novotny, PhD, 1955 East West Road, 1. Murphy SP. Collection and analysis of intake data from the integrated survey. ited MCH centers in urban centers fastest growing racial/ethnic Agricultural Science 216, University of Hawaii at Manoa, Honolulu, HI 96822 J Nutr. 2003;133(2):585S---589S. and may not be representative of groups in the United States in (e-mail: [email protected]). Reprints can 2. Peterkin BB. Nationwide food con- 4 all children in FSM. Sick children 2000 to 2010, the USAP is un- be ordered at http://www.ajph.org by clicking sumption survey, 1977---1978. Prog Clin “ ” may be more likely to attend MCH derrepresented and not reported the Reprints link. Biol Res. 1981;67:59---69. This article was accepted July 28, centers. Furthermore, the data in US health surveillance reports. 2014. 3. Centers for Disease Control and may not represent unique chil- The 2-fold increase in obesity Prevention, National Center for Health Statistics. National Health and Nutrition dren, because children may attend from ages 2 to 8 years in USAP Contributors Examination Survey: note on 2007--- MCH centers more than once, children shown here is a public R. Novotny led the study concept, 2010 sampling methodology. 2011. especially if sick. The second pa- health concern. Disparities in interpretation of data, and writing of the Available at: http://www.cdc.gov/nchs/ article, and oversaw and had primary nhanes/nhanes2007-2008/sampling_ per from Yap was from an outer health status, including by race responsibility for the final article. M. K. 0708.htm. Accessed April 12, 2013. 80 Fialkowski led data abstraction, drafted island and also may not be re- and ethnicity, have widened in 4. Hixson L, Hepler BB, Kim MO. The discussions on nutrition and epidemiological presentative of Yap. Yap was not the USAP. Availability of data on Native Hawaiian and Other Pacific Islander transition and overweight and obesity Population: 2010. Washington, DC: US expected to show such a large USAP populations are limited or global definitions, and compiled the Census Bureau; 2012. difference in comparison with the scattered at best (Table 1). Addi- article. F. Li conducted statistical 5. Department of Labor and Work- 3 other states of FSM. It is difficult tional data are needed among analysis, reported results, and had full access to all the data in the study, and force Development. Alaska 2010 census to gauge the quality of the an- young children, especially in Palau takes responsibility for the integrity of demographic profiles. 2010. Available at: thropometric data and there were and the Marshall Islands. Institu- thedataandtheaccuracyofthedata http://live.laborstats.alaska.gov/cen/dp. cfm. Accessed January 5, 2014. no quality control assurances. tions that collect infant and child analysis. Y. Paulino participated in design and interpretation of Guam data, 6. Secretariat of the Pacific Community. The small sample also limits the BMI data are encouraged to pub- and drafted the data systems discussion. NCD statistics for the Pacific Islands ability to draw robust conclusions. lish their findings as this would D.Vargoparticipatedindesignandled countries and territories. 2010. Available Nonetheless, the FSM data suggest add to the sparse pool of published interpretation of American Samoa data. at: http://www.spc.int/hpl/index.php? R. Jim and J. Deenik participated in that the prevalence of OWOB data available on children in the option=com_docman&task=doc_ design and interpretation of Federated details&gid=67&Itemid=99999999. children in 3 states of the FSM region. Policymakers, public States (Yap, Kosrae, Pohnpei, and Accessed June 26, 2013. may be much lower than in health workers, and the USAP Chuuk) of Micronesia data. P. Coleman and J. H. Kim participated in design and 7. Board resolution #48-01: the bur- fi other jurisdictions of the USAP, community are encouraged to interpretation of Commonwealth of the den of NCDs. Honolulu, HI: Paci c Islands fi despite high levels of OWOB and generate and use available data to Northern Marianas data. A. Bersamin Health Of cers Association; 2010. fi obesity-related diseases among develop monitoring systems and participated in design and interpretation 8. Baruf G, Hardy CJ, Waslien CI, 6 of Alaska data. C.R. Nigg participated in Uyehara SJ, Krupitsky D. Ethnic differ- adults in these populations. For- formulate policies that will im- design and interpretation of Hawaii data. ences in the prevalence of overweight mal studies utilizing standard prove the health status of USAP R. T. L. Guerrero participated in design among young children in Hawaii. JAm measuring protocols should be children and adults. j and interpretation of Guam data. L. R. Diet Assoc. 2004;104(11):1701---1707. Wilkens led the meta-regression and had fi 9. Novotny R, Oshiro CE, Wilkens LR. implemented to con rm these full access to all the data in the study and Prevalence of childhood obesity among fi takes responsibility for the integrity of ndings. young multiethnic children from a health About the Authors thedataandtheaccuracyofthedata The results reflect all data maintenance organization in Hawaii. Rachel Novotny, Marie Kainoa Fialkowski, analysis. All authors critically reviewed Child Obes. 2013;9(1):35---42. available on OWOB prevalence in Fenfang Li, and Rally Jim are with the and approved the final article. the USAP, and results are likely Department of Human Nutrition, Food, and 10. Bruss MB, Michael TJ, Morris JR, Animal Sciences, University of Hawaii at et al. Childhood obesity prevention: an relatively generalizable to children Manoa, Honolulu. Claudio R. Nigg is with Acknowledgments intervention targeting primary caregivers in the USAP. The jurisdiction- the Department of Public Health Sciences, This study was supported by US De- of school children. Obesity (Silver Spring). specific estimates are aggregates of University of Hawaii at Manoa. Jonathan partment of Agriculture/Agriculture 2010;18(1):99---107. Deenik is with the Department of Tropical and Food Research Initiative/National 11. Ogden CL, Carroll MD, Kit BK, homogeneous data sources and Plants and Soil Science, University of Institute of Food and Agriculture grant Flegal KM. Prevalence of obesity and also likely represent their jurisdic- Hawaii at Manoa. Lynne R. Wilkens is 2011-68001-30335, Children’s trends in body mass index among US with Cancer Center, University of Hawaii Healthy Living Program for Remote tions, apart from the concerns children and adolescents, 1999---2010. at Manoa. Yvette Paulino is with the Underserved Minority Populations of JAMA. 2012;307(5):483---490. raised previously. However, the Department of Nursing and Health Sciences, the Pacific (P. Novotny, PI). prevalence estimates will not be University of Guam, Mangilao. Rachael T. An abstract of this study was presented 12. Novotny R. Nutrition and Health representative of native popula- Leon Guerrero is with the College of at Experimental Biology 2014; April 26--- Status of Children in the CNMI. Honolulu, HI: Agriculture, University of Guam. Donald 30, 2014; San Diego, CA. University of Hawaii, Northern Marianas tions as described previously. A Vargo is with the Land Grant Program, Jodi Leslie contributed to the writing of College, Department of Public Health; fuller understanding of the American Samoa Community College, the abstract and article. 2006.

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