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. Author manuscript; available in PMC: 2025 Jan 1.
Published in final edited form as: Pediatrics. 2024 Jan 1;153(1):e2023062461. doi: 10.1542/peds.2023-062461

Trends in Severe Obesity Among Children Aged 2 to 4 Years in WIC: 2010 to 2020

Lixia Zhao 1, David S Freedman 1, Heidi M Blanck 1, Sohyun Park 1
PMCID: PMC10863498  NIHMSID: NIHMS1965065  PMID: 38105679

Abstract

OBJECTIVES:

To examine the prevalence and trends in severe obesity among 16.6 million children aged 2 to 4 years enrolled in the Special Supplemental Nutrition Program for Women, Infants, and Children from 2010 to 2020.

METHODS:

Severe obesity was defined as a sex-specific BMI for age ≥120% of the 95th percentile on the Centers for Disease Control and Prevention growth charts or BMI ≥35kg/m2. Joinpoint regression was used to identify when changes occurred in the overall trend. Logistic regression was used to compute the adjusted prevalence differences between years controlling for sex, age, and race and ethnicity.

RESULTS:

The prevalence of severe obesity significantly decreased from 2.1% in 2010 to 1.8% in 2016 and then increased to 2.0% in 2020. From 2010 to 2016, the prevalence decreased significantly among all sociodemographic subgroups except for American Indian/Alaska Native children. The largest decreases were among 4-year-olds, Asian/Pacific Islander and Hispanic children, and children from higher-income households. However, from 2016 to 2020, the prevalence increased significantly overall and among sociodemographic subgroups, except for American Indian/Alaska Native and non-Hispanic white children. The largest increases occurred in 4-year-olds and Hispanic children. Among 56 Special Supplemental Nutrition Program for Women, Infants, and Children agencies, the prevalence significantly declined in 17 agencies, and 1 agency (Mississippi) showed a significant increase from 2010 to 2016. In contrast, 21 agencies had significant increases, and only Alaska had a significant decrease from 2016 to 2020.

CONCLUSIONS:

Although severe obesity prevalence in toddlers declined from 2010 to 2016, recent trends are upward. Early identification and access to evidence-based family healthy weight programs for at-risk children can support families and child health.

The prevalence of childhood obesity remains high in the United States; ~1 in 5 US children and adolescents have obesity.1,2 The trends of childhood obesity have been well-documented by using the National Health and Nutrition Examination Survey (NHANES). Among youth aged 2 to 19 years, obesity prevalence has plateaued or slightly increased in the recent decades,2–6 whereas the prevalence of severe obesity rose from 3.6% in 1999 and 2000 to 6.1% in 2017 and 2018.5 In addition to these trends, recent cohort studies revealed substantial weight gain, particularly among children with excessive weight, during the early phase of the coronavirus disease 2019 (COVID-19) pandemic.7,8 However, it is noteworthy that the accelerated weight gain during the early pandemic was largely attenuated later in the pandemic (January to November 2021).9 These findings indicate a potential fluctuation in weight changes among children and the need for ongoing monitoring and interventions.

Children with severe obesity, compared with their peers with moderate obesity (BMI ≥95th percentile to <120% of the 95th percentile), are at a greater risk of various health complications, including cardiovascular disease, metabolic syndrome, type 2 diabetes, fatty liver disease, and premature death.10–12 Despite these significant health implications, there has been limited attention given to understanding the prevalence of severe obesity among children aged 2 to 5 years, a critical age group for public health interventions.13–15 Nevertheless, the authors of a few NHANES studies examined the prevalence and trends of severe obesity among children aged 2 to 5 years. One study revealed that the prevalence of severe obesity among this age group was 3.0% in 2009 and 2010, 1.7% in 2015 and 2016, and 2.9% in 2017 and 2018.4 Hales and colleagues reported similar changes between 2009 to 2010 (2.7%) and 2015 to 2016 (1.8%).3 Another study revealed that the prevalence of severe obesity was 2.2% from 2015 to 2018.6 However, the 95% confidence intervals (CIs) for these estimates were wide, and no significant trends were observed over the study period. As a result of the small sample size and low cases of severe obesity, NHANES data provide limited ability to monitor trends among this age group, especially by sociodemographic characteristics.

Data from the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) Participant and Program Characteristics study have been used to monitor weight status among low-income children <5 years of age.16–20 WIC is a federal assistance program that provides healthy foods, nutrition education, health care referrals, and other services to millions of low-income pregnant and postpartum women, as well as infants and children up to age 5, who are at nutritional risk.21 In 2019, 20.4% of children in the United States received WIC benefits.22 A previous study that examined trends in severe obesity among young children enrolled in WIC reported an increase from 1.80% in 2000 to 2.11% in 2004, followed by a modest decline from 2.11% to 1.96% from 2004 to 2014.23 However, it is unknown whether this declining trend has been sustained, and little is known about the prevalence and trends in severe obesity at the state level. Understanding the ongoing state-specific trends can help inform targeted intervention strategies for children at risk. In this study, with the latest data from WIC Participant and Program Characteristics (WIC-PC), we aimed to provide an updated prevalence of severe obesity among low-income children aged 2 to 4 years enrolled in WIC, and we examined trends in severe obesity by sex, age, race and ethnicity, household income, and by WIC State agency from 2010 to 2020.

METHODS

Data Sources and Study Population

The WIC-PC is a biennial census of participants certified to receive WIC benefits as of April of the reporting years (even years). WIC benefits include nutritious supplemental foods, nutrition education and counseling, and referrals to health care and social services, etc.21 It is one of the largest federal nutrition programs, serving millions of people. The program helps maintain and improve the health of low-income pregnant, postpartum, and breastfeeding women, as well as infants and children up to age 5, who are at nutritional risk.21 Enrollees must meet residential, nutritional risk, and income requirements to qualify for the benefits. Income eligibility is based on a household income of no more than 185% of the federal poverty income guidelines or participation in other federal programs, such as the Supplemental Nutrition Assistance Program, Temporary Assistance for Needy Families, or Medicaid.24 The US Department of Agriculture’s (USDA’s) Food and Nutrition Service manages WIC at the federal level, and WIC is administered at the local level by state health departments or Indian tribal organizations in each state or territory. Children’s weight and height (or length) are measured and collected by trained program staff during certification or recertification visits, and the data are used to calculate BMI. Weights were measured to the nearest one-quarter pound, and heights to the nearest one-eighth inch according to the Centers for Disease Control and Prevention (CDC) nutrition surveillance program standards.25,26 The validity of these measurements was found to be sufficiently accurate.27 This study did not need review by the Institutional Review Board of the CDC because we used deidentified secondary data.

The data for this study are from 6 WIC-PC censuses (2010 to 2020). The initial population included ~17.2 million children aged 2 to 4 years enrolled in WIC from 50 states, the District of Columbia, and 5 territories. We excluded 17 children whose weight and height were measured >1 year before the reporting year and 206 912 children whose sex, weight, height, or BMI were missing or biologically implausible based on the suggested CDC cutpoints.28 We further excluded 308 091 children who were certified in March and April 2020 given data quality concerns due to the COVID-19 pandemic, which is ~15.8% of 2020 WIC participating children.29 This yielded an analytical sample of 16 557 172 children ranging from 3 307 442 in 2010 to 1 646 747 in 2020.

Defining Severe Obesity

Severe obesity was defined as a sex-specific BMI for age ≥120% of the 95th percentile based on the CDC growth charts or a BMI ≥35 kg/m2. Of the 4717 children in this analysis who had a BMI ≥35 kg/m2, none were <120% of the 95th percentile.

Sociodemographic Characteristics

Sociodemographic variables included sex, age group (2, 3, or 4 years), race and ethnicity, and household income. Race and ethnicity are social constructs and were based on self-reports. USDA’s WIC study team classified these self-reports into 5 mutually exclusive categories (American Indian/Alaska Native [AI/AN], Asian/Pacific Islander, Black, non-Hispanic [Black], Hispanic, or white, non-Hispanic [white]). Children with multiple race and ethnicity designations were not forced into a single category. Instead, a mapping approach developed by the USDA was employed to assign individuals to the appropriate category within the 5-category classification.25 Children whose race or ethnicity was unknown were not included in analyses that specifically focused on (or adjusted for) race and ethnicity. Household income included income from all sources that were assessed at each WIC certification or recertification appointment. It was expressed as a percentage of the federal poverty level (% FPL) and categorized into <50% FPL, 50% to <100% FPL, 100% to <150% FPL, and ≥150% FPL.

Statistical Analyses

Descriptive analyses were conducted in SAS 9.4 (SAS Institute, Cary, NC) for the overall population, by sociodemographic characteristics and WIC state or territorial agency. The unadjusted prevalence of severe obesity and 95% CI were computed overall and for each subgroup. For trend analyses, we first used Joinpoint trend analysis software (National Cancer Institute, version 4.9.0, https://surveillance.cancer.gov/joinpoint) to identify possible knots in the overall trend in severe obesity prevalence from 2010 to 2020, and the software identified 2016 as the inflection year. Therefore, we focused on 2 periods, 2010 to 2016 and 2016 to 2020, to study the prevalence differences between years. We used logistic regression controlling for age in months, sex, and race and ethnicity to obtain the adjusted prevalence differences (APDs) during each period. We included race and ethnicity as a confounding factor in the analysis, in addition to age and sex, because previous studies have revealed racial and ethnic disparities in severe obesity among young children.15,23 The APDs were computed as 100 times the average marginal effect of year (2010 vs 2016 and 2016 vs 2020). The calculations were conducted in R with the “margins” package.30,31 The APD was considered statistically significant if the 2-sided P value was <.05. A negative number of APD means that the adjusted prevalence in the latter year decreased compared with the earlier year.

RESULTS

The sociodemographic characteristics of children aged 2 to 4 years enrolled in WIC are shown in Table 1. The number of children included in this analysis was ~3.3 million in 2010, ~2.8 million in 2016, and ~1.6 million in 2020. The study population in 2010 had a slightly higher proportion of 4-year-olds than those in more recent years. The proportion of Black children increased from 18.7% in 2010% to 22.7% in 2020, whereas the proportion of Hispanic children decreased from 46.5% in 2010% to 43.3% in 2020. In addition, the proportion of children from families with household income <50% FPL increased from 2010 to 2020 (Table 1).

TABLE 1.

Sociodemographic Characteristics of Children Aged 2 to 4 Years Enrolled in WIC Program in Selected Years, 2010 to 2020a

Characteristics 2010 2016 2020b
n (%) n (%) n (%)
Total, n 3 307 442 2 818 594 1 646 747
Sex
 Male 1 676 395 (50.7) 1 431 197 (50.8) 837 069 (50.8)
 Female 1 631 047 (49.3) 1 387 397 (49.2) 809 678 (49.2)
Age, y
 2 1 333 334 (40.3) 1 152 176 (40.9) 678 668 (41.2)
 3 1 166 350 (35.3) 1 027 505 (36.5) 606 069 (36.8)
 4 807 758 (24.4) 638 913 (22.7) 362010 (22.0)
Race/ethnicity
 American Indian/Alaska Native 38 661 (1.2) 35 682 (1.3) 20 977 (1.3)
 Asian/Pacific Islander 121 667 (3.7) 136141 (4.8) 83 365 (5.1)
 Black, non-Hispanic 618 580 (18.7) 594060 (21.1) 373 567 (22.7)
 Hispanic 1 536 644 (46.5) 1 274 650 (45.2) 712 904 (43.3)
 White, non-Hispanic 966 673 (29.2) 776 843 (27.6) 455 005 (27.6)
 Unknown 25 217 (0.8) 1218 (0.04) 929 (0.1)
Household income, % FPL
 <50 980 903 (29.7) 904 683 (32.1) 522 630 (31.7)
 50 to <100 1 137 558 (34.4) 983 100 (34.9) 533 270 (32.4)
 100 to <150 630 706 (19.1) 497 656 (17.7) 317 305 (19.3)
 ≥150 331 316 (10.0) 225 424 (8.0) 169 206 (10.3)
 Unknown 226 959 (6.9) 207 731 (7.4) 104 336 (6.3)
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a

Included children who were enrolled in WIC from 50 states, the District of Columbia, and 5 US territories.

b

Children with anthropometric data examined in March and April 2020 were excluded because of the COVID-19 pandemic.

In all years, girls had slightly higher severe obesity prevalence than boys, the prevalence of severe obesity increased with age, and the highest prevalence was observed among 4-year-olds. Within racial and ethnic subgroups, AI/AN and Hispanic children had the highest prevalence. The prevalence of severe obesity was higher among children from families below the poverty level (<100% FPL) than children from families with relatively higher (100% to <150% FPL) or highest (≥150% FPL) household income (Table 2, Fig 1).

TABLE 2.

Trends in the Prevalence of Severe Obesitya Among Children Aged 2 to 4 Years Enrolled in WIC Program in Selected Years, 2010 to 2020

Characteristics Crude Prevalence, % (95% CI) APD,b % (95% CI)
2010 2016 2020c 2016 vs 2010 2020 vs 2016
Overall 2.12 (2.10 to 2.13) 1.84 (1.83 to 1.86) 2.03 (2.01 to 2.05) −0.19 (−0.21 to −0.16)d 0.23 (0.21 to 0.26)d
Sex
 Male 2.06 (2.03 to 2.08) 1.75 (1.73 to 1.77) 1.95 (1.92 to 1.98) −0.21 (−0.24 to −0.18)d 0.25 (0.21 to 0.28)d
 Female 2.18 (2.16 to 2.20) 1.94 (1.92 to 1.96) 2.11 (2.08 to 2.15) −0.16 (−0.19 to −0.13)d 0.22 (0.18 to 0.26)d
Age, y
 2 1.30 (1.28 to 1.32) 1.16 (1.14 to 1.18) 1.25 (1.23 to 1.28) −0.10 (−0.13 to −0.07)d 0.11 (0.08 to 0.15)d
 3 2.23 (2.20 to 2.26) 1.97 (1.95 to 2.00) 2.24 (2.20 to 2.27) −0.22 (−0.26 to −0.18)d 0.28 (0.24 to 0.33)d
 4 3.31 (3.27 to 3.35) 2.87 (2.83 to 2.91) 3.14 (3.09 to 3.20) −0.32 (−0.38 to −0.27)d 0.35 (0.28 to 0.42)d
Race/ethnicitye
 American Indian/Alaska Native 2.66 (2.50 to 2.82) 2.41 (2.26 to 2.57) 2.36 (2.17 to 2.58) −0.18 (−0.40 to 0.05) −0.01 (−0.27 to 0.25)
 Asian/Pacific Islander 1.51 (1.44 to 1.58) 1.14 (1.08 to 1.20) 1.43 (1.35 to 1.51) −0.32 (−0.41 to −0.23)d 0.28 (0.19 to 0.38)d
 Black, non-Hispanic 1.51 (1.48 to 1.54) 1.38 (1.35 to 1.41) 1.48 (1.44 to 1.52) −0.09 (−0.13 to −0.05)d 0.13 (0.08 to 0.18)d
 Hispanic 2.81 (2.78 to 2.83) 2.41 (2.38 to 2.44) 2.79 (2.76 to 2.83) −0.31 (−0.34 to −0.27)d 0.41 (0.36 to 0.46)d
 White, non-Hispanic 1.45 (1.42 to 1.47) 1.36 (1.34 to 1.39) 1.38 (1.35 to 1.42) −0.05 (−0.08 to −0.01)d 0.03 (−0.01 to 0.07)
Household income, % FPL
 <50 2.18 (2.16 to 2.21) 1.90 (1.88 to 1.93) 2.11 (2.07 to 2.15) −0.16 (−0.20 to −0.12)d 0.24 (0.19 to 0.29)d
 50 to <100 2.27 (2.25 to 2.30) 1.97 (1.94 to 1.99) 2.19 (2.15 to 2.23) −0.20 (−0.23 to −0.16)d 0.29 (0.24 to 0.34)d
 100 to <150 2.02 (1.98 to 2.05) 1.66 (1.63 to 1.70) 1.88 (1.83 to 1.93) −0.27 (−0.32 to −0.22)d 0.23 (0.18 to 0.29)d
 ≥150 1.66 (1.61 to 1.70) 1.45 (1.40 to 1.50) 1.72 (1.66 to 1.79) −0.16 (−0.23 to −0.09)d 0.24 (0.16 to 0.32)d
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a

Defined as a BMI of 120% or more of the 95th percentile for age and sex on the CDC growth charts or BMI ≥35 kg/m.

b

Represents 100 times the average marginal effect of year (2016 vs 2010, 2020 vs 2016) controlling for sex, age, and race/ethnicity. Children with missing information on race/ethnicity were excluded. A negative value indicates that the prevalence decreased.

c

Children with anthropometric data examined in March and April 2020 were excluded because of the COVID-19 pandemic.

d

Statistically significant difference at the 0.05 level based on logistic regression adjusting for age, sex, and race/ethnicity.

e

Children with multiple race/ethnicity designations were assigned to the appropriate race/ethnicity category presented in the table.

FIGURE 1.

FIGURE 1

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Trends in the prevalence of severe obesity among children aged 2 to 4 years enrolled in WIC from 2010 to 2020 by (A) sex, (B) age, (C) race and ethnicity, and (D) household income.

From 2010 to 2016, the overall prevalence of severe obesity decreased significantly by 0.19% points (95% CI [−0.21 to −0.16]; P <.001) after adjusting for age, sex, and race and ethnicity (Table 2). There were significant decreasing trends among all sociodemographic subgroups except for AI/AN children, for whom the adjusted prevalence decrease was not statistically significant (Table 2, Fig 1). Within the various subgroups, the largest adjusted prevalence decreases were observed among 4-year-olds (−0.32%, 95% CI [−0.38 to −0.27]), Asian/Pacific Islander (−0.32% [−0.41 to −0.23]), Hispanic children (−0.31% [−0.34 to −0.27]), and children from families with relative higher household income (100% to <150% FPL) (−0.27% [−0.32 to −0.22]; Table 2).

From 2016 to 2020, the prevalence of severe obesity increased significantly from 1.8% to 2.0% overall (APD, 0.23% [0.21 to 0.26], P <.001) and among most sociodemographic subgroups except for AI/AN and white children, for which the prevalence remained stable between 2016 and 2020 (Table 2, Fig 1). The adjusted prevalence increases were similar among boys and girls and among household income subgroups. The largest adjusted prevalence increases occurred among 4-year-olds (0.35% [0.28 to 0.42]) and Hispanic children (0.41% [0.36 to 0.46]; Table 2).

Table 3 reveals the crude prevalence and adjusted prevalence changes of severe obesity during the study period for the 56 WIC agencies, including 50 US states, the District of Columbia, and 5 territories. From 2010 to 2016, after adjusting for age, sex, and race and ethnicity, 14 states and 3 territories revealed significant decreases in the prevalence of severe obesity, and 1 state (Mississippi) showed a significant increase. The largest significant decrease among states was observed in Arizona (APD, −0.74% [−0.88 to −0.60]), and the largest decrease among territories was in the Northern Mariana Islands (APD, −1.99% [−2.78 to −1.20]). In contrast, from 2016 to 2020, the prevalence significantly increased in 20 states and Puerto Rico (APD, 0.30% [0.12 to 0.48]). Seven states had a significant increase of >0.3%, and the largest significant increase was in California (APD, 0.54% [0.45 to 0.62]). Alaska was the only WIC agency showing a significant decrease (APD, −0.59% [−1.18 to 0.0]) between 2016 and 2020.

TABLE 3.

Prevalence of Severe Obesitya Among Children Aged 2 to 4 Years Enrolled in WIC Program, by US State or Territory, 2010 to 2020

WIC Agency 2010 2016 2020 2016 vs 2010 2020 vs 2016
n %b (95% CI) n %b (95% CI) n %b (95% CI) APD,c % (95% CI) APD,c % (95% CI)
State
 Alabama 45 743 2.12 (2.00 to 2.26) 42 671 2.25 (2.12 to 2.40) 29 284 2.10 (1.94 to 2.27) 0.10 (−0.09 to 0.29) 0.09 (−0.13, 0.31)
 Alaska 10108 2.6 (2.31 to 2.93) 5983 2.36 (2.00 to 2.77) 3390 1.74 (1.35 to 2.24) −0.29 (−0.78 to 0.20) −0.59 (−1.18 to 0.0)d
 Arizona 72 933 2.07 (1.97 to 2.17) 58 054 1.29 (1.20 to 1.39) 40182 1.41 (1.30 to 1.53) −0.74 (−0.88 to −0.60)d 0.15 (0.0 to 0.29)
 Arkansas 31 245 1.9 (1.76 to 2.06) 23 647 1.81 (1.64 to 1.98) 11 735 2.06 (1.82 to 2.34) −0.07 (−0.30 to 0.16) 0.30 (−0.01 to 0.61)
 California 583 008 2.73 (2.69 to 2.77) 495 095 2.26 (2.22 to 2.30) 202 526 2.77 (2.70 to 2.84) −0.28 (−0.34 to −0.22)d 0.54 (0.45 to 0.62)d
 Colorado 39 612 1.12 (1.02 to 1.22) 31 307 1.02 (0.92 to 1.14) 21 702 1.23 (1.10 to 1.39) −0.10 (−0.25 to 0.05) 0.29 (0.10 to 0.48)d
 Connecticut 22 988 2.32 (2.13 to 2.52) 18 748 1.84 (1.66 to 2.04) 13 271 2.19 (1.96 to 2.46) −0.31 (−0.58 to −0.03)d 0.19 (−0.12 to 0.51)
 Delaware 7650 2.81 (2.46 to 3.21) 6906 2.11 (1.8 to 2.48) 4610 2.32 (1.92 to 2.8) −0.27 (−0.78 to 0.24) 0.19 (−0.36 to 0.73)
 District of Columbia 5182 2.06 (1.71 to 2.49) 5181 1.41 (1.12 to 1.77) 3480 1.52 (1.17 to 1.99) −0.45 (−0.95 to 0.05) 0.11 (−0.40 to 0.63)
 Florida 194 924 1.83 (1.77 to 1.89) 193 749 1.61 (1.55 to 1.66) 125 469 1.74 (1.67 to 1.82) −0.23 (−0.31 to −0.15)d 0.18 (0.08 to 0.27)d
 Georgia 104959 1.76 (1.68 to 1.84) 78 023 1.55 (1.46 to 1.63) 42 661 1.85 (1.73 to 1.98) −0.23 (−0.35 to −0.11)d 0.40 (0.24 to 0.56)d
 Hawaii 14 504 1.29 (1.12 to 1.49) 11 589 1.33 (1.14 to 1.55) 8441 1.55 (1.31 to 1.84) 0.03 (−0.25 to 0.31) 0.37 (0.02 to 0.72)d
 Idaho 18 704 1.29 (1.14 to 1.47) 14521 1.47 (1.29 to 1.68) 8859 1.47 (1.24 to 1.74) 0.16 (−0.09 to 0.41) 0.07 (−0.25 to 0.39)
 Illinois 108 762 2.02 (1.94 to 2.10) 79 949 2.05 (1.96 to 2.15) 41 503 2.20 (2.07 to 2.35) 0.12 (−0.02 to 0.25) 0.22 (0.05 to 0.39)d
 Indiana 63 220 2.07 (1.97 to 2.19) 55 955 1.54 (1.45 to 1.65) 35126 1.65 (1.52 to 1.79) −0.24 (−0.39 to −0.09)d −0.06 (−0.23 to 0.10)
 Iowa 29 481 2.01 (1.86 to 2.18) 24 427 1.99 (1.82 to 2.17) 14447 1.88 (1.67 to 2.11) 0.02 (−0.22 to 0.26) −0.12 (−0.40 to 0.16)
 Kansas 30 458 1.69 (1.56 to 1.85) 24 306 1.52 (1.37 to 1.68) 15 555 1.64 (1.45 to 1.85) −0.18 (−0.39 to 0.03) 0.22 (−0.04 to 0.47)
 Kentucky 45 761 2.73 (2.58 to 2.88) 38 361 2.42 (2.28 to 2.58) 17 697 2.34 (2.13 to 2.57) −0.30 (−0.52 to −0.09)d −0.06 (−0.34 to 0.21)
 Louisiana 48145 1.85 (1.74 to 1.98) 37 527 1.76 (1.63 to 1.90) 21 090 1.95 (1.77 to 2.14) −0.15 (−0.33 to 0.03) 0.26 (0.02 to 0.49)d
 Maine 10410 1.69 (1.46 to 1.96) 8233 1.63 (1.38 to 1.92) 4665 1.82 (1.48 to 2.25) −0.07 (−0.44 to 0.30) 0.22 (−0.25 to 0.69)
 Maryland 51 280 2.12 (2.00 to 2.25) 50 469 2.00 (1.88 to 2.12) 35 210 2.25 (2.10 to 2.41) −0.03 (−0.21 to 0.14) 0.26 (0.06 to 0.46)d
 Massachusetts 49178 2.16 (2.03 to 2.29) 41 740 2.07 (1.94 to 2.22) 28 562 2.36 (2.19 to 2.54) −0.13 (−0.32 to 0.06) 0.23 (0.01 to 0.45)d
 Michigan 85 293 1.67 (1.59 to 1.76) 84 387 1.40 (1.32 to 1.48) 61 119 1.61 (1.51 to 1.71) −0.12 (−0.23 to 0.0) 0.21 (0.08 to 0.34)d
 Minnesota 57 529 1.30 (1.21 to 1.40) 47 219 1.45 (1.35 to 1.56) 27 074 1.30 (1.18 to 1.45) 0.11 (−0.03 to 0.26) −0.08 (−0.25 to 0.09)
 Mississippi 36 519 2.07 (1.93 to 2.22) 28 493 2.39 (2.22 to 2.57) 19 685 2.15 (1.96 to 2.36) 0.26 (0.03 to 0.49)d −0.18 (−0.45 to 0.09)
 Missouri 50 575 1.58 (1.48 to 1.69) 43 404 1.42 (1.32 to 1.54) 22 856 1.50 (1.35 to 1.67) −0.15 (−0.31 to 0.0) 0.12 (−0.07 to 0.32)
 Montana 7194 1.60 (1.33 to 1.92) 6647 1.43 (1.17 to 1.74) 3621 1.16 (0.86 to 1.56) −0.22 (−0.62 to 0.19) −0.22 (−0.68 to 0.23)
 Nebraska 15 622 1.86 (1.66 to 2.09) 13 807 2.14 (1.91 to 2.39) 7376 1.84 (1.56 to 2.18) 0.24 (−0.08 to 0.56) −0.26 (−0.65 to 0.13)
 Nevada 25 855 2.02 (1.85 to 2.19) 24 493 1.70 (1.55 to 1.87) 15 790 1.64 (1.45 to 1.85) −0.23 (−0.47 to 0.0) 0.07 (−0.19 to 0.33)
 New Hampshire 7263 1.65 (1.38 to 1.97) 6042 1.77 (1.47 to 2.14) 4402 2.02 (1.65 to 2.48) 0.08 (−0.36 to 0.52) 0.31 (−0.23 to 0.84)
 New Jersey 59 000 2.45 (2.33 to 2.58) 53 917 2.09 (1.97 to 2.21) 42 528 2.28 (2.15 to 2.43) −0.40 (−0.58 to −0.23)d 0.39 (0.20 to 0.58)d
 New Mexico 21 968 1.86 (1.69 to 2.05) 18619 1.57 (1.40 to 1.76) 11 781 1.98 (1.74 to 2.25) −0.27 (−0.52 to −0.01)d 0.33 (0.02 to 0.63)d
 New York 186 760 2.10 (2.03 to 2.16) 182 401 1.66 (1.60 to 1.72) 103 959 1.95 (1.87 to 2.04) −0.27 (−0.36 to −0.19)d 0.19 (0.09 to 0.29)d
 North Carolina 89 798 1.85 (1.77 to 1.94) 97 286 1.83 (1.74 to 1.91) 57101 1.91 (1.8 to 2.03) 0.0 (−0.12 to 0.13) 0.17 (0.03 to 0.31)d
 North Dakota 5484 1.53 (1.24 to 1.89) 4723 1.69 (1.36 to 2.10) 3072 1.89 (1.46 to 2.43) 0.14 (−0.35 to 0.64) 0.26 (−0.35 to 0.87)
 Ohio 102 803 1.62 (1.55 to 1.70) 74 753 1.45 (1.37 to 1.54) 35 864 1.61 (1.49 to 1.75) −0.17 (−0.28 to −0.05)d 0.18 (0.02 to 0.33)d
 Oklahoma 37 849 1.75 (1.62 to 1.88) 34 486 1.68 (1.55 to 1.83) 19 665 1.64 (1.47 to 1.83) −0.07 (−0.26 to 0.12) −0.04 (−0.27 to 0.18)
 Oregon 43 209 1.73 (1.61 to 1.85) 34 485 1.77 (1.63 to 1.91) 21 315 1.97 (1.79 to 2.17) 0.06 (−0.13 to 0.24) 0.27 (0.04 to 0.51)d
 Pennsylvania 96 762 1.65 (1.57 to 1.73) 80 202 1.56 (1.48 to 1.65) 55 283 1.71 (1.61 to 1.82) −0.02 (−0.14 to 0.1) 0.05 (−0.09 to 0.19)
 Rhode Island 10 783 2.28 (2.02 to 2.58) 6984 1.93 (1.64 to 2.28) 4938 2.37 (1.98 to 2.83) −0.36 (−0.79 to 0.07) 0.41 (−0.12 to 0.95)
 South Carolina 39 785 1.65 (1.53 to 1.78) 32 399 1.56 (1.43 to 1.70) 16 461 1.75 (1.56 to 1.96) 0.04 (−0.14 to 0.23) 0.20 (−0.04 to 0.44)
 South Dakota 7884 1.84 (1.57 to 2.16) 6771 1.76 (1.47 to 2.10) 4194 1.74 (1.39 to 2.18) −0.14 (−0.58 to 0.29) −0.11 (−0.61 to 0.39)
 Tennessee 57153 2.12 (2.01 to 2.24) 51 157 2.01 (1.89 to 2.13) 30 061 2.09 (1.93 to 2.25) −0.14 (−0.31 to 0.03) 0.20 (0.0 to 0.41)
 Texas 361 823 2.33 (2.29 to 2.38) 268 787 2.02 (1.97 to 2.07) 180 615 2.40 (2.33 to 2.47) −0.14 (−0.21 to −0.06)d 0.45 (0.36 to 0.54)d
 Utah 26 045 1.22 (1.09 to 1.36) 21 599 0.97 (0.85 to 1.11) 11 707 1.17 (0.99 to 1.38) −0.24 (−0.43 to −0.06)d 0.25 (0.01 to 0.49)d
 Vermont 6964 1.71 (1.43 to 2.04) 5254 1.79 (1.46 to 2.18) 3904 1.74 (1.38 to 2.2) 0.06 (−0.41 to 0.54) −0.04 (−0.59 to 0.50)
 Virginia 48 920 2.72 (2.58 to 2.87) 47 376 1.99 (1.86 to 2.12) 28 038 2.16 (2.00 to 2.34) −0.55 (−0.74 to −0.36)d 0.16 (−0.05 to 0.37)
 Washington 78 336 1.81 (1.72 to 1.90) 69 870 1.74 (1.65 to 1.84) 43618 1.90 (1.78 to 2.03) −0.09 (−0.22 to 0.05) 0.38 (0.22 to 0.55)d
 West Virginia 17 669 2.33 (2.12 to 2.56) 14 222 2.52 (2.27 to 2.79) 7598 2.34 (2.03 to 2.71) 0.15 (−0.19 to 0.49) −0.12 (−0.55 to 0.31)
 Wisconsin 48 511 1.79 (1.68 to 1.92) 37116 1.79 (1.66 to 1.93) 26177 1.90 (1.74 to 2.08) 0.04 (−0.14 to 0.22) 0.26 (0.04 to 0.47)d
 Wyoming 4413 0.95 (0.70 to 1.28) 3458 1.33 (1.00 to 1.77) 2007 1.74 (1.26 to 2.42) 0.34 (−0.13 to 0.82) 0.45 (−0.24 to 1.14)
Territory
 American Samoa 3221 1.55 (1.18 to 2.04) 2824 1.10 (0.77 to 1.55) 1421 1.76 (1.19 to 2.58) −0.45 (−1.02 to 0.13) 0.62 (−0.16 to 1.39)
 Guam 3248 1.45 (1.09 to 1.92) 2710 0.70 (0.45 to 1.09) 2234 0.81 (0.51 to 1.27) −0.76 (−1.28 to, −0.24)d 0.11 (−0.38 to 0.59)
 Northern Mariana Islands 2157 2.60 (2.00 to 3.36) 1418 0.63 (0.33 to 1.20) 1095 0.82 (0.43 to 1.55) − 1.99 (−2.78 to −1.20)d 0.16 (−0.52 to 0.84)
 Puerto Rico 70 699 3.09 (2.97 to 3.22) 63 251 1.90 (1.80 to 2.01) 40 056 2.14 (2.00 to 2.29) −1.17 (−1.33 to −1.00)d 0.30 (0.12 to 0.48)d
 Virgin Islands 2093 1.72 (1.24 to 2.37) 1593 1.76 (1.22 to 2.53) 667 1.65 (0.92 to 2.93) 0.03 (−0.82, 0.88) −0.09 (−1.25 to 1.08)
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a

Defined as a BMI of 120% or more of the 95th percentile for age and sex on the CDC growth charts or BMI ≥35 kg/m.

b

Crude prevalence of severe obesity.

c

Represents 100 times the average marginal effect of year (2016 vs 2010, 2020 vs 2016) controlling for sex, age, and race or ethnicity. Children with missing information on race/ethnicity were excluded. A negative value indicates that the adjusted prevalence decreased.

d

Statistically significant difference at the 0.05 level based on logistic regression adjusting for age, sex, and race/ethnicity.

DISCUSSION

We found a modest declining trends in severe obesity prevalence from 2010 to 2016, followed by a modestly increasing trend from 2016 to 2020 among low-income children aged 2 to 4 years enrolled in WIC. Although the magnitudes of the decline and the increase were small, the finding of a reversal in trends from decreasing to increasing is concerning, particularly if the upward trend continues. Our data reveal that the prevalence of severe obesity increased significantly from 2016 to 2020 overall and among all age, sex, household income groups, and race and ethnicity groups, except for American Indian/Alaska Native and non-Hispanic white children. Additionally, we found that 21 of 56 WIC agencies had a significant increase, and only Alaska had a significant decrease in severe obesity prevalence between 2016 and 2020. Our study revealed that 2.0% of low-income young children had severe obesity in 2020. A previous WIC-PC study revealed that the severe obesity prevalence increased from 2000 to 2004 and decreased from 2004 to 2014 among low-income children aged 2 to 4 years.23 The decrease is similar to what we observed in our study.

A few earlier studies have revealed decreasing trends in obesity among WIC infants and young children from 2000 to 2016 or 2018, but none of these studies examined trends in severe obesity.18–20 Multiple factors might have contributed to the declines, as noted in these studies. For example, the decreasing trends could be influenced by the revised 2009 WIC food packages that provided cash allowances for various healthy food options in addition to federal, state, and local obesity prevention initiatives and programs. The revised WIC food package includes extra cash allowances for fruits, vegetables, and whole grains, reductions in milk, cheese, and juice allowances, restrictions on milk fat content, and incentives to encourage breastfeeding.32 Several studies have revealed that these revisions improved the dietary intake habits of WIC participants, potentially contributing to the reduction in obesity prevalence among WIC children.33–35 However, it is important to note that the effects of the revised food packages on the prevalence of obesity may vary by sex.36,37 For instance, the food package changes appear to have limited benefits for girls, suggesting that other factors beyond the food packages may influence obesity trends. Additionally, the recent upward trends in severe obesity since 2016 are concerning, indicating that current population-level preventive efforts may not be fully effective. This highlights the need for the ongoing evaluation and refinement of obesity prevention strategies to address the persistent challenge of severe obesity among WIC children and identify additional interventions that can complement the impact of the revised food package.

In addition, we found significant increases in severe obesity among 21 of 56 WIC agencies from 2016 to 2020. The reasons for these increases remain unclear and are likely influenced by a complex interplay of various factors. These factors may include levels of state social resources to families (eg, earned income tax credit, wage supports, Medicaid, housing supports) and variations in funding to support local WIC agencies and clinics, as well as the implementation of the WIC benefits (such as breastfeeding support, provision of supplemental foods, and nutrition education and counseling). Additionally, state policy and environmental changes to improve the availability of healthier food and opportunities for physical activity in communities, alongside efforts to incorporate breastfeeding support, nutrition, and physical activity requirements, into early care and education programs,38,39 may have played a role. However, further research is needed to understand the specific factors driving the increases in severe obesity across different states.

Although our study did not capture data during the COVID-19 pandemic, it is important to acknowledge the substantial impact of the pandemic on the daily routines of children and adolescents. The pandemic has introduced various challenges, including reduced opportunities for physical activity, increased sedentary behaviors, limited access to healthy food, and heightened stress levels within households.40 These factors can have significant implications for weight gain, particularly among children with excessive weight, and may potentially influence future trend in severe obesity.41

An important avenue for addressing the problem of severe obesity for all children, including those with low household incomes, is ensuring quality treatment. For young children aged 2 to 5 years who are obese, the American Academy of Pediatrics’ new clinical practice guideline recommends intensive health behavior and lifestyle treatment (IHBLT), also called Family Healthy Weight Programs.42,43 The most effective IHBLT interventions include 26 or more hours of intense, in-person, family-based multicomponent treatment from health care providers over a 3- to 12-month period. IHBLT includes coaching on nutrition, physical activity, and behavioral change support, such as parent modeling of healthy behaviors. Such intensive treatment is not universally available and needs training and support to deliver.42 Collaborative and continued efforts involving multiple sectors, including state, local, communities, and clinicians, can help ensure that these family-centered intervention strategies are accessible for low-income young children with severe obesity.

The findings in this study are subject to several limitations that should be considered when interpreting the results. Firstly, the findings apply to low-income children enrolled in WIC, limiting the generalizability of the results to children from families of all income levels. Secondly, we did not include children participating in the tribal WIC programs. Therefore, the prevalence and trends of severe obesity among AI/AN children may not be representative of all AI/AN children enrolled in WIC. Thirdly, although height and weight were measured by trained program staff following standardized protocols, potential variations in data collection practices across different WIC agencies could exist. Moreover, it is important to note that the number of children enrolled in the WIC program has steadily decreased since 2010,44 and nearly 16% of records in 2020 were excluded from the analysis because of data quality issues related to the COVID-19 pandemic. Consequently, children’s demographic characteristics in the sample may have changed throughout the study. We accounted for some of these changes in the trend analyses. However, residual confounding resulting from other factors, such as socioeconomic status, parental education level, and household composition, may remain.

CONCLUSIONS

Despite the declining trend of severe obesity among young children enrolled in the WIC program from 2010 to 2016, the small upward trend in severe obesity since 2016 is concerning. Given that children with higher BMI are at greater risk of future health consequences, continued understanding of the ongoing state trends of obesity, especially severe obesity among young children, is warranted. Ensuring that children and families from low-income households have access to early clinical detection, and referrals to effective and sustainable family-based interventions, could help promote healthy child growth.

WHAT’S KNOWN ON THIS SUBJECT:

The prevalence of severe obesity among low-income children modestly declined from 2004 to 2014. However, little is known about (1) whether this declining trend has continued and (2) the state-level prevalence and trends in severe obesity.

WHAT THIS STUDY ADDS:

The declining trends in severe obesity among 16.6 million low-income children between 2010 and 2016 have been reversed. The upward trends are concerning. Early identification and referral to family healthy weight programs for at-risk children can support healthy child growth.

ACKNOWLEDGMENTS

We thank Amanda Reat (USDA) for the critical review of the article. We also acknowledge the staff from USDA Food and Nutrition Service for providing WIC-PC data.

ABBREVIATIONS

AI/AN

American Indian/Alaska Native

APD

adjusted prevalence difference

CDC

Centers for Disease Control and Prevention

CI

confidence interval

COVID-19

coronavirus disease 2019

FPL

federal poverty level

IHBLT

intensive behavior and lifestyle treatment

NHANES

National Health and Nutrition Examination Survey

USDA

US Department of Agriculture

WIC

Special Supplemental Nutrition Program for Women, Infants, and Children

WIC-PC

WIC Participant and Program Characteristics

Footnotes

The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention.

CONFLICT OF INTEREST DISCLOSURES: The authors have indicated they have no potential conflicts of interest relevant to this article to disclose.

COMPANION PAPER: A companion to this article can be found online at www.pediatrics.org/cgi/doi/10.1542/peds.2023-063799.

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