Skip to main content
NCBI home page
Childhood Obesity logoLink to Childhood Obesity
. 2023 Jun 27;19(5):341–352. doi: 10.1089/chi.2022.0012

Social Determinants of Health and Body Mass Index in American Indian/Alaska Native Children

Amber L Fyfe-Johnson 1,✉, Margaret M Reid 2, Luohua Jiang 3, Jenny J Chang 4, Kimberly R Huyser 5, Vanessa Y Hiratsuka 6, Michelle D Johnson-Jennings 7, Cheryl M Conway 8, Turner R Goins 9, Ka'imi A Sinclair 10, John F Steiner 11, Angela G Brega 12, Spero M Manson 12, Joan O'Connell 12
PMCID: PMC10316527  PMID: 36170116

Abstract

Objective:

To examine the associations between social determinants of health (SDOH) and prevalent overweight/obesity status and change in adiposity status among American Indian and Alaska Native (AI/AN) children.

Methods:

The study sample includes 23,950 AI/AN children 2–11 years of age, who used Indian Health Service (IHS) from 2010 to 2014. Multivariate generalized linear mixed models were used to examine the following: (1) cross-sectional associations between SDOH and prevalent overweight/obesity status and (2) longitudinal associations between SDOH and change in adiposity status over time.

Results:

Approximately 49% of children had prevalent overweight/obesity status; 18% had overweight status and 31% had obesity status. Prevalent severe obesity status was 20% in 6–11-year olds. In adjusted cross-sectional models, children living in counties with higher levels of poverty had 28% higher odds of prevalent overweight/obesity status. In adjusted longitudinal models, children 2–5 years old living in counties with more children eligible for free or reduced-priced lunch had 15% lower odds for transitioning from normal-weight status to overweight/obesity status.

Conclusions:

This work contributes to accumulating knowledge that economic instability, especially poverty, appears to play a large role in overweight/obesity status in AI/AN children. Research, clinical practice, and policy decisions should aim to address and eliminate economic instability in childhood.

Keywords: Alaska Native, American Indian, body mass index, childhood, obesity, overweight, social determinants

Introduction

Overweight/obesity status is an important health concern among American Indian/Alaska Native (AI/AN) peoples,1–5 with both conditions increasingly affecting youth. Approximately 19% of AI/AN of 9th–12th grade adolescents (mean: 16 years) have obesity status, double the percentage seen in non-Hispanic White adolescents,6 and nearly 50% of AI/AN youth 2–19 years of age have overweight/obesity status.7–9

Overweight/obesity status increases the risk of developing several chronic diseases, including type 2 diabetes mellitus (T2DM).10–12 AI/AN youth have a higher prevalence of T2DM than youth from all other racial and ethnic groups,13–15 with rates doubling from 9% in 1994 to 17% in 2004 in AIs younger than 35 years.16 Relative to other racial and ethnic groups, AI/ANs have younger ages of onset for both overweight and obesity status, thus predisposing them to cardiovascular disease, T2DM, and other chronic conditions earlier in life.17 Adolescents with obesity status have a 3.5 times higher risk of all-cause cardiovascular disease mortality compared to their normal-weight status counterparts.18,19

The foundation for life-limiting health conditions, such as overweight and obesity status, begins early in life.20–22 Social determinants of health (SDOH), defined as “the conditions in which people are born, grow, live, and work,”23 influence racial and ethnic health disparities.24–26 Neighborhood and built environment characteristics consistently account for racial and ethnic differences in prevalent overweight and obesity status, although AI/AN youth are not represented in most of these studies.21,22

One study reported that neighborhood socioeconomic status, physical activity, and healthy food opportunities explained all the differences in body mass index (BMI) z-scores for non-Hispanic White, Black, and Hispanic children.27 Trauma and stress in childhood are known risk factors for overweight and obesity status,28,29 particularly given the historical context of cultural oppression, discrimination, and colonial displacement that AI/AN peoples have faced for generations.30 Given the disproportionate burden of overweight and obesity status among AI/AN children and adolescents, prevention efforts would be informed by greater knowledge regarding SDOH that influence these health outcomes early in life.2,31,32

The objective of this project was to examine the cross-sectional and longitudinal associations of SDOH with overweight and obesity in AI/AN children 2–11 years of age. This analysis takes similar approaches to previously conducted work examining the association between SDOH and overweight/obesity status in childhood and adolescence,24,33 thus replicating results in a historically marginalized population that has not been thoroughly evaluated to date. There are two important contributions of this replication: (1) we cannot assume that results in non-Hispanic White populations are representative of historically marginalized communities such as AI/ANs and (2) replication work provides the opportunity to identify gaps in SDOH frameworks.

Little research has examined the longitudinal association between SDOH and incident overweight and obesity among AI/AN children, and cross-sectional associations were relevant to leverage the generalizability and larger sample size of the study sample. This work was guided by the Healthy People 2020 Social Determinants of Health Framework,23,24 and provides important insight into the drivers of, and targets for, interventions addressing overweight and obesity status in AI/AN youth.

Methods

Data

This study was conducted using data extracted from a longitudinal data infrastructure that houses health status, service utilization, and treatment cost data for over 640,000 AI/ANs who live throughout the United States and represent nearly 30% of AI/ANs using services through the Indian Health Service (IHS).34 The data infrastructure, created as part of the IHS Improving Health Care Delivery Data Project (“IHS Data Project”), is a synthesis of existing electronic health data from multiple IHS platforms and includes data for seven fiscal years (FYs; 2007–2013). IHS institutional review board and tribal review board approval was obtained from all relevant parties.

The IHS Data Project data infrastructure includes information for a purposeful sample of AI/ANs living in 15 IHS Service Units (hereafter referred to as project sites), which are IHS geographic classifications located throughout the United States.35 The IHS Data Project population was selected by geographic area, rather than by random sampling, to allow investigation of important community-level factors that may influence health outcomes (e.g., rural/urban, population counts, etc.). The IHS Data Project sample is comparable to the national IHS service population in terms of age and gender.36 More information about the data infrastructure may be found in prior reports.34

To examine SDOH, we extracted county-level measures from the 2010 to 2014 American Community Survey (ACS),37 Census Bureau,38 the United States Department of Agriculture (USDA) Food Environment Atlas,39 and the National Center for Health Statistics.40 These data were merged with the IHS Data Project data infrastructure by county.

Study Sample

The study sample includes AI/AN children 2–11 years of age, who were IHS active users at baseline year in FY2010 and had at least one biologically plausible BMI measurement between July 1, 2012, and December 31, 2012, the follow-up period of this study. An IHS active user is a patient who obtained services at least once during the FY or the preceding 2 years (i.e., FYs 2008–2009 for FY2010 active users). The follow-up period for the BMI measurements ranged from 18 to 24 months from baseline (the last day of year in 2010). This window was chosen to maximize the size of the analytic sample and to provide a minimum of 6 months between most SDOH and the follow-up BMI measurement.

We used the SAS program from CDC41 to calculate the percentiles and z-scores for a child's sex and age-specific BMI, weight, and height based on the CDC growth charts. Extreme or biologically implausible values are flagged by the program. Among 82,065 active users 2–11 years of age in FY2010, 24,351 had at least one BMI measurement during the follow-up period. We subsequently excluded 199 children who only had biologically implausible values of BMI during the follow-up period, and 202 children who had missing data in SDOH. Thus, the study sample for the cross-sectional analysis included 23,950 AI/AN children.

For the longitudinal analysis, after excluding biologically implausible BMI measurements, we included children who had a BMI measurement at baseline and at least one BMI measurement during the follow-up period. If there were multiple BMI measurements in 2010 (baseline year) or the follow-up period, the last BMI of each period was chosen. Among the 23,950 children included in the study sample for the cross-sectional analysis, a total of 17,115 children were included in the longitudinal analysis (n = 409 excluded due to having underweight status at baseline and n = 6426 excluded because they had missing or biologically implausible baseline BMI measurement).

Measures

Outcomes: BMI and weight category

We calculated BMI as weight in kilograms divided by height in meters squared10 and categorized weight category using age- and sex-specific BMI percentiles from the 2000 CDC growth charts.42,43 Underweight status was defined as BMI <5th percentile and normal-weight status was defined as ≥5th percentile to <85th percentile.44 For descriptive purposes (Table 1), we included the following categories for overweight and obesity status: (1) overweight status definition 1: BMI ≥85th percentile (includes overweight and obesity status); (2) overweight status definition 2: BMI ≥85th percentile, but <95th percentile; (3) obesity status definition: BMI ≥95th percentile (includes class II and class III obesity status); (4) class II obesity status definition: BMI ≥120% of the 95th percentile (includes class III obesity status); and (5) class III obesity status definition: BMI ≥140% of the 95th percentile.45,46 Multiple classes of obesity status were used because these BMI ranges are associated with early mortality in adults.47

Table 1.

Sample Characteristics Stratified by Age Group and Overweight/Obesity Status Prevalence in Study Sample

  Full sample, N (%) Ages 2–5, N (%) Ages 6–11, N (%) Overweight/obesity status at the follow-up period
Overweight status Definition 1,a % Overweight status Definition 2,b % Obesity Status,c % Class II obesity status,d % Class III obesity status,e %
Baseline all 23,950 (100) 10,100 (42.2) 13,850 (57.8) 48.7 18.1 30.6 12.0 3.9
Baseline gender
 Female 11,798 (49.3) 4933 (48.8) 6865 (49.6) 47.3 19.1 28.2 10.2 3.0
 Male 12,152 (50.7) 5167 (51.2) 6985 (50.4) 50.0 17.1 32.9 13.7 4.8
Baseline age (mean 6.8 ± 3.0 years)
 2–5 years (mean 3.8 ± 1.1 years) 10,100 (42.2) — — 41.6 17.4 24.3 8.0 2.4
 6–11 years (mean 9.0 ± 1.7 years) 13,850 (57.8) — — 53.9 18.6 35.3 14.9 5.0
  6–7 years 4293 (17.9) — — 52.0 17.9 34.1 14.6 4.4
  8–9 years 4678 (19.5) — — 55.4 18.6 36.8 15.5 5.4
  10–11 years 4879 (20.4) — — 54.1 19.2 34.8 14.7 5.1
Economic stability
 Percent of below 139% poverty level, 2010–2014
  Counties with lower levels of poverty (≤42.5%) 11,975 (50.0) 5156 (51.1) 6819 (49.2) 47.0 17.6 29.4 11.2 3.5
  Counties with higher levels of poverty (>42.5%) 11,975 (50.0) 4944 (49.0) 7031 (50.8) 50.4 18.6 31.8 12.8 4.3
 Percent eligible for free or reduced-price lunch, 2009
  Counties with fewer eligible children (≤49.3%) 9740 (40.7) 4185 (41.4) 5555 (40.1) 52.8 18.5 34.4 13.8 4.6
  Counties with more eligible children (>49.3%) 14,210 (59.3) 5915 (58.6) 8295 (59.9) 45.9 17.8 28.1 10.8 3.4
Educational attainment
 Percent of adults who did not complete high school, 2010–2014
  Counties below median (46.0%) 10,743 (44.9) 4593 (45.5) 6150 (44.4) 48.4 17.7 30.7 11.7 3.8
  Counties above median (46.0%) 13,207 (55.1) 5507 (54.5) 7700 (55.6) 48.9 18.4 30.5 12.2 3.9
Insurance coverage at baseline (2010)
 Medicaid
  No Medicaid 10,605 (44.3) 3805 (37.7) 6800 (49.1) 47.7 18.1 29.7 11.7 3.8
  Had Medicaid 13,345 (55.7) 6295 (62.3) 7050 (50.9) 49.5 18.1 31.4 12.2 4.0
 Private insurance
  No private insurance 19,224 (80.3) 8390 (83.1) 10834 (78.2) 48.9 18.2 30.7 12.0 3.9
  Had private insurance 4726 (19.7) 1710 (16.9) 3016 (21.8) 47.9 17.8 30.1 11.9 4.0
Neighborhood/built environment
 Rural/urban status, 2006
  Urban counties 14,955 (62.4) 6488 (64.2) 8467 (61.1) 49.5 18.0 31.5 12.7 4.2
  Rural counties 8995 (37.6) 3612 (35.8) 5383 (38.9) 47.3 18.2 29.1 10.9 3.4
Open in a new tab
a

Overweight status definition 1: BMI (kg/m2) ≥ 85th percentile, includes obesity.

b

Overweight status definition 2: BMI ≥85th, but <95th percentile.

c

Obesity status: BMI ≥95th percentile, includes class II and class III obesity.

d

Class II obesity status: BMI ≥120% of the 95th percentile, includes class III obesity.

e

Class III obesity status: BMI ≥140% of the 95th percentile.

Social determinants of health

As depicted in Figure 1, we examined variables addressing multiple categories of SDOH outlined in the Healthy People 2020 SDOH Conceptual Framework: economic stability, education, health and health care, and neighborhood and built environment. Median values were used to dichotomize all SDOH because this approach provided consistency across SDOH variables, and a priori standardized approaches were not found on extensive review of the literature. No available variable aligned with SDOH for social and community context.

Figure 1.

Figure 1.

Open in a new tab

Study conceptual framework, informed by Healthy People 2020 Social Determinants of Health.

Economic stability

County-level measures of AI/AN household income were derived from 2010 to 2014 ACS data from the US Census Bureau.37 We defined the percentage of households with a low income as the percentage with income below 139% of the federal poverty level. Dichotomous county-level economic stability variables were generated based on the percent of AI/AN households' low incomes (median = 42.5%) and the percent of AI/AN children eligible for free or reduced-price lunch (median = 49.3%).39 Youth were identified as living in counties with values above or below the median for each measure.

Education

County-level measures of AI/AN educational attainment were derived from 2010 to 2014 ACS data from the US Census Bureau.37 The educational attainment measure captured the percentage of adults 25 years of age and older, who did not complete high school. A dichotomous county-level variable was generated based on the percent of AI/AN adults who did not complete high school (median = 46.0%). Youth were identified as living in counties with values above or below the median.

Health and health care

All members of the sample were eligible for and received health care through the IHS. Some individuals also had health coverage, in addition to access to IHS services. Two individual-level health insurance coverage variables identified if AI/AN children had Medicaid and if they had private insurance.

Neighborhood and built environment

County-level neighborhood and built environment was approximated using the National Center for Health Statistics urban-rural classification for counties.40 Individuals were classified as living in an urban or rural county.

Demographic variables

The IHS Data Project data infrastructure included individual-level information on age (generated from month and year of birth) and sex for all IHS users.

Statistical Analyses

Descriptive statistics were generated for participants using mean and standard deviations (SDs) for continuous variables and count and percentage for categorical variables; results were stratified by age groups and BMI category (see measures for definitions). Multivariate generalized linear mixed models were used to examine the cross-sectional and longitudinal associations of SDOH with overweight and obesity status. First, multivariate generalized linear mixed models were used to examine the cross-sectional association between SDOH and childhood adiposity status with county-level random effects to account for clustering of observations within counties. The primary outcome was a binary variable indicating if the child had overweight or obesity status (overweight status definition 1) during the follow-up period. Second, we used multivariate generalized linear mixed models to examine the association between SDOH and a BMI category over time.

The primary outcome in this analysis was a binary variable indicating if a child changed BMI category, from having normal-weight status at baseline to overweight or obesity status (overweight status definition 1) during the follow-up period. For all regression models, we report age-stratified models (2–5 years; 6–11 years) based on clinically relevant stages of development.48 Adjusted odds ratios (ORs) and 95% confidence intervals (CIs) were calculated for all regression models after adjusting for age, gender, and months between baseline and follow-up BMI measures. Statistical significance was defined as p < 0.05. Data were analyzed using SAS 9.4 (SAS Institute, Cary, NC). All appropriate regulatory approvals were granted (see Acknowledgments section).

Results

Table 1 shows sample characteristics presented separately by age groups and by adiposity status. Mean age at baseline was 6.8 ± 3.0 years. Approximately 48.7% of children had prevalent overweight or obesity status during the follow-up period (BMI ≥85th percentile); 18.1% had overweight status (BMI ≥85th percentile to <95th percentile), and 30.6% had obesity status (BMI ≥95th percentile). Although the prevalence of overweight status was similar across age groups (17.4% 2–5 years; 18.6% 6–11 years), prevalent obesity status was higher as children aged (24.3% in 2–5-year olds to 35.3% in 6–11-year olds).

Severe obesity status was nearly twice as common among 6–11-year olds (19.9%), compared to 2–5-year olds (10.4%). Participants with overweight or obesity status were more likely to be male, older, and reside in a county with higher levels of poverty and fewer children eligible for free or reduced-price lunch. Table 2 confirms that BMI increased from normal status to overweight or obesity status over the course of the follow-up period.

Table 2.

Distribution of BMI Categories in Study Population at Baseline and End of Follow-Up in Longitudinal Study (n = 17,115)

  N BMI change from normal at baseline to overweight or obese status at the end of follow-up (%) Baseline
End of follow-up
Underweight or normal-weight status (row %)a Overweight status (row %)b Obese status (row %)c Underweight or normal-weight status (row %)a Overweight status (row %)b Obese status (row %)c
All 17115 9.9 53.0 18.5 28.5 51.6 18.2 31.2
Gender
 Female 8443 9.7 54.3 18.9 26.9 51.7 19.5 28.9
 Male 8672 10.2 51.8 18.1 30.1 49.5 17.0 33.5
Age, years
 2–5 7784 11.6 58.4 18.8 22.9 57.2 18.0 24.9
 6–11 9331 8.5 48.6 18.2 33.2 45.1 18.5 36.5
 6–7 2942 11.2 54.1 17.4 28.5 47.6 17.3 35.1
 8–9 3052 8.8 47.9 17.6 34.5 42.8 18.6 38.6
 10–11 3337 5.9 44.3 19.5 36.2 45.0 19.3 35.8
Economic stability
 Percent of below 139% poverty level
  Counties below median 8570 10.6 56.0 18.1 25.9 52.1 17.8 30.1
  Counties above median 8545 9.3 50.1 18.8 31.1 49.0 18.7 32.3
 Percent eligible for free or reduced-price lunch
  Counties below median 6866 10.8 50.8 19.4 29.8 47.3 18.3 34.4
  Counties above median 10,249 9.4 54.5 17.8 27.6 52.8 18.2 29.1
Educational attainment
 Percent of adults who did not complete high school
  Counties below median 7658 10.7 54.6 18.4 27.1 50.9 18.0 31.1
  Counties above median 9457 9.3 51.8 18.5 29.7 50.3 18.4 31.3
Insurance coverage at baseline
 Medicaid
  No Medicaid 7349 9.8 54.1 17.9 28.0 50.7 18.7 30.6
  Had Medicaid 9766 10.1 52.2 18.9 28.9 50.5 17.8 31.7
 Private Insurance
  No private insurance 13,642 9.8 52.5 18.6 28.9 50.5 18.2 31.3
  Had private insurance 3473 10.6 55.0 17.9 27.1 50.8 18.2 31.0
Neighborhood/built environment
 Rural/urban status, 2006
  Urban counties 10,977 10.4 53.5 18.1 28.4 50.2 18.0 31.7
  Rural counties 6138 9.1 52.1 19.2 28.7 51.2 18.6 30.3
Open in a new tab
a

Underweight or normal-weight status: BMI <85th percentile.

b

Overweight status: BMI ≥85th, but <95th percentile.

c

Obesity status: BMI ≥95th percentile.

Table 3 presents cross-sectional analyses examining the association of SDOH with overweight or obesity status during the follow-up period. Results showed that female participants had lower odds of overweight or obesity status than males (OR = 0.89; 95% CI = 0.85–0.94), although this association was limited to the 6–11-year age group (OR = 0.85; 95% CI = 0.80–0.91) than the 2–5-year age group (OR = 0.95; 95% CI = 0.88–1.03).

Table 3.

Cross-Sectional Adjusted Odds Ratios and 95% Confidence Intervals for Prevalent Overweight or Obesity Status (Body Mass Index ≥85th Percentile), Stratified by Age Group

  All sample (n = 23,950), OR and 95% CI Ages 2–5 years (n = 10,100), OR and 95% CI Ages 6–11 years (n = 13,850), OR and 95% CI
Follow-up time, months 1.02 (1.002–1.03)* 1.02 (0.99–1.04) 1.02 (0.99–1.04)
Female 0.89 (0.85–0.94)*** 0.95 (0.88–1.03) 0.85 (0.80–0.91)***
Age 1.09 (1.08–1.10)*** 1.08 (1.04–1.11)*** 1.03 (1.01–1.05)**
Economic stability
 Percent of below 139% poverty level
  Counties with higher levels of poverty (% below 139% FPL >42.5%) 1.28 (1.05–1.57)* 1.14 (0.84–1.55) 1.39 (1.12–1.72)**
 Percent eligible for free or reduced-price lunch, 2009
  Counties with more eligible children (% eligible >49.3%) 0.89 (0.73–1.09) 0.81 (0.60–1.10) 0.91 (0.74–1.13)
Educational attainment
 Percent of adults who did not complete high school
  Counties above median (46.0%) 1.09 (0.89–1.33) 1.11 (0.83–1.50) 1.11 (0.90–1.36)
Health and health care access
 Insurance coverage at baseline
  Medicaid 1.05 (0.99–1.11) 1.12 (1.02–1.22)* 1.03 (0.95–1.10)
  Private coverage 0.98 (0.92–1.05) 1.01 (0.90–1.13) 0.96 (0.88–1.04)
Neighborhood/built environment
 Rural/urban status, 2006
  Rural counties 0.96 (0.78–1.17) 1.10 (0.81–1.49) 0.90 (0.73–1.12)
Open in a new tab
***

p < 0.001, **p < 0.01, *p < 0.05.

95% CI, 95% confidence interval; FPL, federal poverty level; OR, odds ratio.

Living in counties with higher levels of poverty was associated with higher odds of overweight or obesity status. Although this association was not statistically significant among the 2- to 5-year-old children (OR = 1.14, 95% CI = 0.84–1.55), it was significant among 6–11-year olds (OR = 1.39; 95% CI = 1.12–1.72). Compared to individuals with no health coverage other than access to IHS services, Medicaid coverage was weakly, but positively associated with prevalent overweight and obesity status (OR = 1.05; 95% CI = 0.99–1.11), particularly in 2- to 5-year-old children (OR = 1.12; 95% CI = 1.02–1.22).

Table 4 presents longitudinal analyses examining the association of baseline SDOH with change from normal-weight status to overweight status or obesity status over the course of follow-up. Over a mean follow-up of 21.2 months (SD = 1.7), 10% of the sample (n = 1700) transitioned from normal weight to having overweight or obesity status. After adjustment for demographics and follow-up time, older baseline age was found to be protective (OR = 0.91; 95% CI = 0.90–0.93). Living in counties with more children eligible for free or reduced-price lunch was protective against change from normal weight to overweight or obesity BMI category (OR = 0.89; 95% CI = 0.78–1.02); the association was stronger among 2–5-year olds (OR = 0.85; 95% CI = 0.72–0.99) than 6–11–year olds (OR = 0.89; 95% CI = 0.75–1.06).

Table 4.

Longitudinal Adjusted Odds Ratios and 95% Confidence Intervals for Change in BMI Category from Normal-Weight Status at Baseline to Overweight or Obesity Status (Body Mass Index ≥85th Percentile) at Follow-Up, Stratified by Age Group

  All sample (n = 17,115), OR and 95% CI Ages 2–5 years (n = 7784), OR and 95% CI Ages 6–11 years (n = 9331), OR and 95% CI
Follow-up time, months 1.04 (1.01–1.07)* 1.03 (0.98–1.07) 1.05 (1.003–1.10)*
Female 0.95 (0.86–1.05) 0.88 (0.76–1.01) 1.04 (0.90–1.21)
Age at baseline 0.91 (0.90–0.93)*** 0.89 (0.84–0.95)*** 0.85 (0.81–0.88)***
Economic stability
 Percent of below 139% poverty level
  Counties with higher levels of poverty (% below 139% FPL >42.5%) 0.95 (0.82–1.11) 0.89 (0.74–1.06) 0.97 (0.80–1.17)
 Percent eligible for free or reduced-price lunch      
  Counties with more eligible children (% eligible >49.3%) 0.89 (0.78–1.02) 0.85 (0.72–0.99)* 0.89 (0.75–1.06)
Educational attainment
 Percent of adults who did not complete high school
  Counties above median (46.0%) 0.94 (0.83–1.06) 0.92 (0.78–1.09) 0.98 (0.82–1.17)
Health and health care access
 Insurance coverage at baseline
  Medicaid 0.98 (0.88–1.09) 0.99 (0.85–1.16) 1.00 (0.86–1.17)
  Private coverage 1.12 (0.99–1.27) 1.14 (0.95–1.37) 1.11 (0.93–1.32)
Neighborhood/built environment
 Rural/urban status, 2006
  Rural counties 0.96 (0.84–1.09) 1.06 (0.89–1.27) 0.92 (0.76–1.11)
Open in a new tab
***

p < 0.001, *p < 0.05.

Discussion

This article builds on accelerating work evaluating the influence of SDOH on health in youth. Important additions include addressing a historically marginalized population of AI/AN children and adolescents, a sample that is nationally representative of AI/AN IHS users, including a longitudinal approach, and identifying future areas of focus—such as systematic racism—which are likely important to include in broader SDOH frameworks. Although the negative consequences of poverty on childhood health are emphasized in the broader SDOH literature,49–52 to the best of our knowledge, this is the first analysis evaluating the longitudinal association between multiple SDOH and overweight and obesity status in a large sample of AI/AN children from multiple sites across the United States.

Our cross-sectional findings are consistent with previously reported risk factors (male sex and older age) and prevalence estimates for overweight and obesity status in AI/AN youth.9 While not novel in concept, the cross-sectional results were conducted to leverage the larger sample size and because, to the best of our knowledge, the IHS Data Project study sample is the largest and most generalizable for AI/AN children to date. In addition, we found that economic instability (measured by county-level poverty) was positively associated with overweight and obesity status in cross-sectional analyses (2–11 years).

It is unclear why a weak positive association was found between Medicaid coverage and prevalent overweight or obesity status, or why it was more pronounced in 2- to 5-year-old children. One possible explanation is that many tribal communities are extremely rural and lack medical facilities for routine medical care. Transportation and travel time barriers may further prohibit accessing and utilizing regular medical care, regardless of Medicaid status. Our cross-sectional results align with previous studies in AI/AN children reporting socioeconomic status as one of the most significant contributors to childhood overweight and obesity status,20 and another that found AI/AN children experiencing food insecurity were more likely to develop overweight and obesity status.53,54

No longitudinal association was present between economic instability (percentage of people living in poverty) and transition from normal-weight status to overweight status or obesity status in longitudinal analyses. Given that AI/ANs using IHS services are a group known to be lower income than other AI/AN populations,55 a wider distribution of economic stability (i.e., more high income users) may be needed to detect an effect. It is also possible that county-level variables do not adequately capture individual-level effects that contribute to change in adiposity status. Living in a county with more children eligible for free or reduced-priced lunch was protective of change in adiposity status from normal-weight status to overweight status or obesity status in 2- to 5-year-old children and was promising for 6- to 11-year-old children.

Programs such as the Supplementary Nutrition Assistance Program (SNAP)—an eligibility criterion for free and reduced-price lunch programs56,57—have been shown to be protective for change in BMI.58 IHS users in counties with more children eligible for free or reduced-price lunch may be concurrently receiving food assistance from SNAP or other programs that improve diet quality and contribute to overweight and obesity status prevention, although we cannot be certain as the data infrastructure did not include SNAP participation variables. Tribal interventions designed to improve access to SNAP authorized stores have been shown to increase access for healthy food options for youth, especially in rural locations.59 Thus, future interventions targeting program-level obesity status prevention strategies may be warranted.

The American Academy of Pediatrics' (AAP) policy statement on poverty and health emphasizes that poverty in childhood has lifelong effects on developmental, physical, and mental health.49 Data from the 2018 US Census Bureau suggest that 25% of AI/ANs live in poverty, the highest poverty rate compared to all other racial and ethnic groups.60 Although IHS facilities are available to AI/AN communities at no cost, staffing and funding for IHS services are sparse, limiting access to care.55

Transportation and travel time barriers may further prohibit access.61 Because inequities for childhood obesity are pervasive, multifactorial, and exist on individual,62 community,52,63 population,9,20 and built environment levels,27 it is critical to consider broader sources of health disparities for children in marginalized communities.64 Systematic racism, “a system of structuring opportunity and assigning value based on the social interpretation of how one looks (page 1),”65 is a known SDOH66 that has negative consequences for childhood health.66–70 Nonetheless, racism is not currently incorporated in conventionally used SDOH frameworks—including the Healthy People 2020 framework.

Although systematic racism is difficult to measure, it is important to consider when evaluating SDOH and should be given careful attention when working with historically marginalized communities. A recent AAP policy statement calls for clinicians and scientists to engage with communities, advocate for policy changes, and optimize research for childhood obesity.64 The results of this study further support these recommendations, given we found that economic instability was associated with overweight and obesity status in AI/AN children. Broadly speaking, poverty is addressable through policy changes and should be featured in the national policy agenda.71,72 Increased funding for evidence-based programs such as the Special Diabetes Program for Indians,73–75 tribal home visiting programs,76 and other community-based interventions may offer culturally grounded and feasible avenues for overweight and obesity status intervention.77,78

This study has notable strengths and limitations. Strengths include a large sample that represents AI/AN children across multiples tribes and geographic regions in the United States, and objectively measured height and weight data from clinical facilities, which provide estimates of BMI that are stable on a population level and align with other nationally representative samples.79 Some limitations warrant consideration. First, results are only generalizable to AI/AN IHS users, which does not include children who are not members of federally recognized tribes and who do not receive care from IHS facilities.80

Second, county-level SDOH variables are prone to variation based on size and diversity of the county and may not represent individual-level characteristics relevant to overweight and obesity status. Third, we lack access to SDOH variables representing social/community context (Fig. 1), which is relevant to minority communities where cultural norms and traditions are prioritized.81 Fourth, the data infrastructure did not include risk factors or relevant lifestyle variables (e.g., diet, physical activity, pubertal status).82 Fifth, for the longitudinal analysis, our limited follow-up time (21.2 months; SD = 1.7) may have compromised our ability to detect changes in adiposity status or capture the effects of SDOH that may have longer term consequences. Finally, BMI is an indirect measure of body fat; while a commonly used “field method” and reasonable in most scenarios, it is an imperfect measure.83

Conclusions

This work contributes to accumulating knowledge that SDOH play a fundamental role in prevalent and incident overweight and obesity status early in life. Specifically, economic instability appears to play a large role for AI/AN children. Our results build on previously published work by using a nationally representative study sample of AI/AN IHS users, thus improving generalizability of results, including longitudinal results in a historically marginalized population, and identifying areas of future work such as including systematic racism in broader SDOH frameworks. In particular, careful attention is needed to optimize research, clinical practice, and policy decisions to address and eliminate economic instability, and to increase funding for evidence-based approaches to address the consequential health inequities in childhood.

Acknowledgments

The data used in this secondary analysis stem from project, known as the Indian Health Service (IHS) Health Care Delivery Data Project. The data set includes information for many American Indian and Alaska Native communities. This work was conducted with the guidance and advice of IHS and Tribal health program colleagues, as well as members of the project's Steering, Project Site, and Patient Committees. Members of Tribal and IHS institutional review boards, Tribal Councils, and Tribal Authorities educate us about the health concerns they have for their Tribal members and how they hope this project will inform their work. This project relies on their support and approval. The authors wouldalso like to express their gratitude to Sara Mumby for her editorial assistance.

Impact Statement

This work contributes to accumulating knowledge that SDOH play a fundamental role in overweight and obesity early in life. Specifically, economic instability appears to play a large role for AI/AN children. Careful attention is needed to optimize research, clinical practice, and policy decisions to eliminate economic instability in childhood.

Authors' Contributions

All authors approved the final article as submitted and agree to be accountable for all aspects of the work.

Dr. Fyfe-Johnson conceptualized and designed the study, contributed methodological and statistical analysis decisions, drafted the initial manuscript, and reviewed and revised the article.

Ms. Reid, Dr. Jiang, and Ms. Chang contributed methodological and statistical analysis decisions, performed statistical analyses, and reviewed and revised the article.

Dr. Jiang contributed to conceptualizing the study and statistical analysis, performed statistical analyses, and reviewed and revised the article.

Drs. Huyser, Hiratsuka, Johnson-Jennings, Conway, Goins, Sinclair, Brega, Manson, and Steiner contributed to conceptualizing the study, and reviewed and revised the article.

Dr. O'Connell contributed to conceptualizing and designing the study, provided methodological and statistical analysis contributions, drafted the initial manuscript, and reviewed and revised the article.

Author Disclosure Statement

No competing financial interests exist.

Funding Information

The research reported in this publication was supported by the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) of the National Institutes of Health under Award Numbers P30DK092923 (Spero M. Manson) and R18DK114757 (Joan O'Connell and Spero M. Manson). Funding for the development of the data infrastructure, utilized in the reported analyses, was supported by the Agency for Health care Research and Quality (290-2006-00020-I, TO #11, Joan O'Connell) and the Patient-Centered Outcomes Research Institute (AD-1304-6451, Joan O'Connell). The content of this report is solely the responsibility of the authors and does not necessarily represent the official views of these organizations.

References

  • 1. Story M, Evans M, Fabsitz RR, et al. The epidemic of obesity in American Indian communities and the need for childhood obesity-prevention programs. Am J Clin Nutr 1999;69(4 Suppl):747S–754S. [DOI] [PubMed] [Google Scholar]
  • 2. Hutchinson RN, Shin S. Systematic review of health disparities for cardiovascular diseases and associated factors among American Indian and Alaska Native populations. PLoS One 2014;9:e80973. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3. U.S. Civil Rights Commission. Broken Promises: Evaluating the Native American Health Care System. 2004. CreateSpace Independent Publishing Platform. Scotts Valley, CA. [Google Scholar]
  • 4. Gohdes D, Harris MI, Cowie CC, et al. (Eds.). Diabetes in North American Indians and Alaska Natives. In: Diabetes in America. 2nd ed. National Institute of Diabetes and Diegestive and Kidney Diseases; 1995:683–702. Bethesda, MD. [Google Scholar]
  • 5. Lee ET, Howard BV, Savage PJ, et al. Diabetes and impaired glucose tolerance in three American Indian populations aged 45–74 years. The Strong Heart Study. Diabetes Care 1995;18:599–610. [DOI] [PubMed] [Google Scholar]
  • 6. Rutman S, Park A, Castor M, et al. Urban American Indian and Alaska Native youth: Youth risk behavior survey 1997–2003. Matern Child Health J 2008;12(Suppl 1):76–81. [DOI] [PubMed] [Google Scholar]
  • 7. Caballero B, Himes JH, Lohman T, et al. Body composition and overweight prevalence in 1704 schoolchildren from 7 American Indian communities. Am J Clin Nutr 2003;78:308–312. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8. Zephier E, Himes JH, Story M, et al. Increasing prevalences of overweight and obesity in Northern Plains American Indian children. Arch Pediatr Adolesc Med 2006;160:34–39. [DOI] [PubMed] [Google Scholar]
  • 9. Bullock A, Sheff K, Moore K, et al. Obesity and Overweight in American Indian and Alaska Native Children, 2006–2015. Am J Public Health 2017;107:1502–1507. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10. Virani SS, Alonso A, Benjamin EJ, et al. Heart disease and stroke statistics—2020 update: A report from the American Heart Association. Circulation 2020;141:E139–E596. [DOI] [PubMed] [Google Scholar]
  • 11. Eckel RH, Kahn SE, Ferrannini E, et al. Obesity and type 2 diabetes: What can be unified and what needs to be individualized? J Clin Endocrinol Metab 2011;96:1654–1663. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12. Sattar N, Gill JMR. Type 2 diabetes as a disease of ectopic fat? BMC Med 2014;12:123. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13. Dabelea D, Hanson RL, Bennett PH, et al. Increasing prevalence of type II diabetes in American Indian children. Diabetologia 1998;41:904–910. [DOI] [PubMed] [Google Scholar]
  • 14. Dean H. NIDDM-Y in First Nation children in Canada. Clin Pediatr (Phila) 1998;37:89–96. [DOI] [PubMed] [Google Scholar]
  • 15. Neufeld ND, Raffel LJ, Landon C, et al. Early presentation of type 2 diabetes in Mexican-American youth. Diabetes Care 1998;21:80–86. [DOI] [PubMed] [Google Scholar]
  • 16. Centers for Disease Control and Prevention. Diagnosed diabetes among American Indians and Alaska Natives aged <35 years—United States, 1994–2004. MMWR Morb Mortal Wkly Rep 2006;55:1201–1203. [PubMed] [Google Scholar]
  • 17. Schell LM, Gallo MV. Overweight and obesity among North American Indian infants, children, and youth. Am J Hum Biol 2012;24:302–313. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18. Twig G, Yaniv G, Levine H, et al. Body-mass index in 2.3 million adolescents and cardiovascular death in adulthood. N Engl J Med 2016;374:2430–2440. [DOI] [PubMed] [Google Scholar]
  • 19. Vivante A, Golan E, Tzur D, et al. Body mass index in 1.2 million adolescents and risk for end-stage renal disease. Arch Intern Med 2012;172:1644–1650. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20. Isong IA, Rao SR, Bind MA, et al. Racial and ethnic disparities in early childhood obesity. Pediatrics 2018;141:e20170865. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21. Rossen LM. Neighbourhood economic deprivation explains racial/ethnic disparities in overweight and obesity among children and adolescents in the USA. J Epidemiol Community Health 2014;68:123–129. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22. Rossen LM, Talih M. Social determinants of disparities in weight among US children and adolescents. Ann Epidemiol 2014;24:705–713.e2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23. WHO. Social Determinants of Health. WHO: Geneva, 2020. [Google Scholar]
  • 24. Office of Disease Prevention and Health Promotion. Healthy People 2030. U.S. Department of Health and Human Services. https://health.gov/healthypeople/objectives-and-data/browse-objectives/diabetes. Last accessed July 2022.
  • 25. Solar O, Irwin A. A conceptual framework for action on the social determinants of health. In: Social Determinants of Health Discussion Paper 2 (Policy and Practice). World Health Organization: Geneva, 2010. [Google Scholar]
  • 26. Link BG, Phelan J. Social conditions as fundamental causes of disease. J Health Soc Behav 1995;Spec No:80–94. [PubMed] [Google Scholar]
  • 27. Sharifi M, Sequist TD, Rifas-Shiman SL, et al. The role of neighborhood characteristics and the built environment in understanding racial/ethnic disparities in childhood obesity. Prev Med (Baltim) 2016;91:103–109. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28. Felitti VJ, Anda RF, Nordenberg D, et al. Relationship of childhood abuse and household dysfunction to many of the leading causes of death in adults: The adverse childhood experiences (ACE) study. Am J Prev Med 1998;14:245–258. [DOI] [PubMed] [Google Scholar]
  • 29. Thomas C, Hyppönen E, Power C. Obesity and type 2 diabetes risk in midadult life: The role of childhood adversity. Pediatrics 2008;121:e1240-9. [DOI] [PubMed] [Google Scholar]
  • 30. Manson SM, Beals J, Klein SA, et al. Social epidemiology of trauma among 2 American Indian reservation populations. Am J Public Health 2005;95:851–859. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31. Thomas B. Health and health care disparities: The effect of social and environmental factors on individual and population health. Int J Environ Res Public Health 2014;11:7492–7507. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32. Ridgeway JL, Wang Z, Finney Rutten LJ, et al. Conceptualising paediatric health disparities: a metanarrative systematic review and unified conceptual framework. BMJ Open 2017;7:e015456. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33. Federico SG, Cull W, Olson L, et al. United States pediatricians' attitudes regarding public policies for low-income children and their profession's advocacy priorities. Acad Pediatr 2018;18:783–788. [DOI] [PubMed] [Google Scholar]
  • 34. O'Connell JM, Guh S, Ouellet J, et al. ARRA ACTION: Comparative Effectiveness of Health Care Delivery Systems for American Indians and Alaska Natives Using Enhanced Data Infrastructure: Final Report. Agency for Healthcare Research and Quality: Rockville, MD, 2014. [Google Scholar]
  • 35. Espey DK, Jim MA, Cobb N, et al. Leading causes of death and all-cause mortality in American Indians and Alaska Natives. Am J Public Health 2014;104(Suppl 3):S303–S311. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36. U.S. Department of Health and Human Services. Age and Gender of the IHS User Population. Fiscal Year 2010. Unpublished Report. 2010. Indian Health Services, Rockville, MD. [Google Scholar]
  • 37. U.S. Census Bureau. American Community Survey. 2014. https://www.census.gov/programs-surveys/acs. Last accessed July 2022.
  • 38. US Census Bureau. The American Indian and Alaska Native Population: 2010. US Census Bureau: Washington DC, 2012. [Google Scholar]
  • 39. U.S. Department of Agriculture Economic Research Institute. Food Environment Atlas. 2015. https://www.ers.usda.gov/data-products/food-environment-atlas/. Last accessed July 2022.
  • 40. U.S. Centers for Disease Control and Prevention. Urban Rural Classification Scheme for Counties. 2013. https://www.cdc.gov/nchs/data_access/urban_rural.htm. Last accessed July 2022.
  • 41. Allison PD. Survival Analysis Using SAS: A Practical Guide, 12th ed. SAS Publishing: Cary, NC, 2008. [Google Scholar]
  • 42. Centers for Disease Control and Prevention, National Center for Health Statistics. Clinical Growth Charts. 2000. https://www.cdc.gov/growthcharts/clinical_charts.htm. Last accessed July 2022.
  • 43. Kuczmarski RJ, Ogden CL, Guo SS, et al. 2000 CDC Growth Charts for the United States: methods and development. Vital Health Stat 11 2002;246:1–190. [PubMed] [Google Scholar]
  • 44. Cole TJ, Bellizzi MC, Flegal KM, et al. Establishing a standard definition for child overweight and obesity worldwide: international survey. BMJ 2000;320:1240–1243. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45. Flegal KM, Wei R, Ogden CL, et al. Characterizing extreme values of body mass index-for-age by using the 2000 Centers for Disease Control and Prevention growth charts. Am J Clin Nutr 2009;90:1314–1320. [DOI] [PubMed] [Google Scholar]
  • 46. Kelly AS, Barlow SE, Rao G, et al. Severe obesity in children and adolescents: identification, associated health risks, and treatment approaches: a scientific statement from the American Heart Association. Circulation 2013;128:1689–1712. [DOI] [PubMed] [Google Scholar]
  • 47. Berrington de Gonzalez A, Hartge P, Cerhan JR, et al. Body-mass index and mortality among 1.46 million white adults. N Engl J Med 2010;363:2211–2219. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48. American Academy of Pediatrics. Ages & Stages. 2022. HealthyChildren.org. Last accessed July 2022.
  • 49. Council on Community Pediatrics. Poverty and child health in the United States. Pediatrics 2016;137:e20160339. [DOI] [PubMed] [Google Scholar]
  • 50. Pascoe JM, Wood DL, Duffee JH, et al. Mediators and adverse effects of child poverty in the United States. Pediatrics 2016;137:e20160340. [DOI] [PubMed] [Google Scholar]
  • 51. Yoshikawa H, Weiland C, Brooks-Gunn J, et al. Investing in Our Future: The Evidence Base for Preschool Education. Policy Brief, Society for Research in Child Development and the Foundation for Child Development. Society for Research in Child Development and the Foundation for Child Development, Washington, DC, 2013. [Google Scholar]
  • 52. Johnson TJ. Intersection of bias, structural racism, and social determinants with health care inequities. Pediatrics 2020;146:e2020003657. [DOI] [PubMed] [Google Scholar]
  • 53. Adams AK, Tomayko EJ, A. Cronin K, et al. Predictors of overweight and obesity in American Indian families with young children. J Nutr Educ Behav 2019;51:190–198. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 54. Ness M, Barradas DT, Irving J, et al. Correlates of overweight and obesity among American Indian/Alaska Native and Non-Hispanic White children and adolescents: National Survey of Children's Health, 2007. Matern Child Health J 2012;16(Suppl 2):268–277. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 55. Indian Health Service. Fact Sheets. 2020. https://www.ihs.gov/newsroom/factsheets/ihsprofile/. Last accessed July 2022.
  • 56. United States Department of Agriculture Food and Nutrition Service. National School Lunch Program. 2021. https://www.fns.usda.gov/data-research. Last accessed July 2022.
  • 57. Supplemental Nutrition Assistance Program. https://www.benefits.gov/benefit/361. 2020. Last accessed July 2022.
  • 58. Reynolds MM, Fox AM, Wen M, et al. Is less more? Examining the relationship between food assistance benefit levels and childhood weight. SSM Popul Health 2020;11:100573. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 59. Davis J, Jossefides M, Lane T, et al. A spatial evaluation of healthy food access: Special supplemental nutrition program for women, infants, and children (WIC) participants. J Public Health Manag Pract 2019;25:S91–S96. [DOI] [PubMed] [Google Scholar]
  • 60. Poverty USA. https://www.povertyusa.org/. 2022. Last accessed July 2022.
  • 61. Syed ST, Gerber BS, Sharp LK. Traveling towards disease: Transportation barriers to health care access. J Community Health 2013;38:976–993. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 62. Taveras EM, Gillman MW, Kleinman K, et al. Racial/ethnic differences in early-life risk factors for childhood obesity. Pediatrics 2010;125:686–695. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 63. Byrd AS, Toth AT, Stanford FC. Racial disparities in obesity treatment. Curr Obes Rep 2018;7:130–138. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 64. Trent M, Dooley DG, Dougé J, et al. The impact of racism on child and adolescent health. Pediatrics 2019;144:e20191765. [DOI] [PubMed] [Google Scholar]
  • 65. Jones CP, Truman BI, Elam-Evans LD, et al. Using “socially assigned race” to prove white advantages in health status. Ethn Dis 2008;18:496–504. [PubMed] [Google Scholar]
  • 66. Paradies Y, Ben J, Denson N, et al. Racism as a determinant of health: A systematic review and meta-analysis. PLoS One 2015;10:e0138511. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 67. Elias A, Paradies Y. Estimating the mental health costs of racial discrimination. BMC Public Health 2016;16:1205. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 68. Heard-Garris NJ, Cale M, Camaj L, et al. Transmitting trauma: A systematic review of vicarious racism and child health. Soc Sci Med 2018;199:230–240. [DOI] [PubMed] [Google Scholar]
  • 69. Pachter LM, García Coll C. Racism and child health: A review of the literature and future directions. J Dev Behav Pediatr 2009;30:255–263. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 70. Pachter LM, Bernstein BA, Szalacha LA, et al. Perceived racism and discrimination in children and youths: An exploratory study. Heal Soc Work 2010;35:61–69. [DOI] [PubMed] [Google Scholar]
  • 71. National Poverty Research Center—Institute for Research on Poverty—University of Wisconsin, Madison. https://www.irp.wisc.edu/. 2022. Last accessed July 2022.
  • 72. US Collaborative of Poverty Centers—Institute for Research on Poverty—University of Wisconsin, Madison. https://www.irp.wisc.edu/u-s-collaborative-of-poverty-centers-cpc/. 2022. Last accessed July 2022.
  • 73. Bullock A, Sheff K, Hora I, et al. Prevalence of diagnosed diabetes in American Indian and Alaska Native adults, 2006–2017. BMJ Open Diabetes Res Care 2020;8:e001218. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 74. Jiang L, Manson S, Beals J, et al. Special Diabetes Program for Indians Diabetes Prevention Demonstration Project: Translating the Diabetes Prevention Program into American Indian and Alaska Native Communities: Results from the Special Diabetes Program for Indians Diabetes Prevention demonstration project. Diabetes Care 2013;36:2027–2034. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 75. Special Diabetes Program for Indians, Indian Health Service. https://www.ihs.gov/sdpi/. 2022. Last accessed July 2022. [PubMed]
  • 76. Condon EM. Maternal, infant, and early childhood home visiting: A call for a paradigm shift in states' approaches to funding. Policy, Polit Nurs Pract 2019;20:28–40. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 77. Ingalls A, Rosenstock S, Foy Cuddy R, et al. Family Spirit Nurture (FSN)—A randomized controlled trial to prevent early childhood obesity in American Indian populations: Trial rationale and study protocol. BMC Obes 2019;6:18. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 78. Tomayko EJ, Prince RJ, Cronin KA, et al. The Healthy Children, Strong Families 2 (HCSF2) randomized controlled trial improved healthy behaviors in American Indian families with young children. Curr Dev Nutr 2019;3(Suppl 2):53–62. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 79. Bailey LC, Milov DE, Kelleher K, et al. Multi-institutional sharing of electronic health record data to assess childhood obesity. PLoS One 2013;8:66192. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 80. Norris T, Vines PL, Hoeffel EM. The American Indian and Alaska Native Population: 2010. United States Census Bureau, United States Department of Commerce, Economics and Statistics Administration, 2012. https://www.census.gov/history/pdf/c2010br-10.pdf
  • 81. Peña MM, Dixon B, Taveras EM. Are you talking to me? the importance of ethnicity and culture in childhood obesity prevention and management. Child Obes 2012;8:23–27. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 82. Zamora-Kapoor A, Sinclair K, Nelson L, et al. Obesity risk factors in American Indians and Alaska Natives: a systematic review. Public Health 2019;174:85–96. [DOI] [PubMed] [Google Scholar]
  • 83. Flegal KM, Shepherd JA, Looker AC, et al. Comparisons of percentage body fat, body mass index, waist circumference, and waist-stature ratio in adults. Am J Clin Nutr 2009;89:500–508. [DOI] [PMC free article] [PubMed] [Google Scholar]

Articles from Childhood Obesity are provided here courtesy of SAGE Publications

RESOURCES