Abstract
Limited data exist regarding the relationship between socioeconomic risk factors and failure to thrive (FTT). Using data from the National Health and Nutrition Examination Survey (NHANES) from years 1999 to 2014, we sought to determine whether there was a higher prevalence of underweight (<5th percentile weight-for-age [WFA], weight-for-length [WFL], or body mass index-for-age [BFA]), and, therefore, likely a higher risk of FTT, in US children <3 years with low household income or food insecurity compared with children without these factors. Among 7356 evaluated children, there were no significant differences in the prevalence of underweight by adjusted household income quintile, food security, household Women, Infants, and Children (WIC) status, or federal poverty income ratio. These findings do not support a link between low income or food security and underweight in children and, therefore, do not provide support for an association between low income or food security and FTT.
Keywords: failure to thrive, socioeconomic factors, food security, malnutrition, underweight
Introduction
In some children with borderline low weight or rate of weight gain it can be difficult to determine whether they have some degree of undernourishment, also known as failure to thrive (FTT). For these children, a clinician must determine whether the child is healthy and small or has FTT. Socioeconomic status (SES) and food insecurity have often been considered to play a role in children having FTT, mediated by factors such as caregiver stress and a lower amount of food offered.1-4 There is no widely accepted definition of FTT in the United States, and no protocol for diagnosing FTT. Anecdotally, many pediatricians report considering SES when making a multifactorial assessment of children with slow weight gain, and some experts have recommended doing so. 5 Socioeconomic disadvantage is associated with a higher prevalence of multiple conditions. 6 However, there is limited evidence to support an association between low SES and an increased prevalence of FTT, or to support an association between low SES and an increased prevalence of underweight which could reflect a higher risk of FTT. A recent systematic review found conflicting evidence on the topic, with little data from the United States.2,3,7-10 Furthermore, a recent prospective study in the United States did not find an increase in underweight prevalence in children with public insurance experiencing food insecurity in tertiary centers in 5 US cities. 11
It is important to understand factors that do or do not provide information about the risk of FTT because underdiagnosis and overdiagnosis of FTT both have potential harms.3,12 Evaluations of children with FTT sometimes include costly testing and can involve detailed questioning about parenting practices or home visits. Management of FTT may involve strict feeding routines, purchase of specific foods, and frequent in-person follow-up, all of which may be more challenging for families with less money and control over their schedules or with fewer transportation options. 13 Underdiagnosis of FTT may also disproportionately affect children with lower SES because they are at higher risk of poor outcomes from some medical conditions.14,15 Socioeconomic status and food insecurity are also associated with race and ethnicity, leading to the potential for further health care disparities if children with these risk factors are overidentified, or underidentified, as having FTT.16-18
We used a nationally representative US sample to determine whether there was a higher prevalence of children with underweight in children with low income, a component of SES, or food insecurity, compared to those without. A higher prevalence of underweight in these children would likely reflect a higher risk of FTT.
Methods
Study Design
Our study included children under 3 years of age in the National Health and Nutrition Examination Survey (NHANES) during the years 1999 to 2014. 19 NHANES is a prospective survey that uses multistage probability sampling; when analyzed with appropriate sampling weights, it can provide nationally representative estimates. We restricted the sample to those under 3 years of age because, in our experience, children in this age group are most likely to present with new diagnoses of FTT, and most studies included in the systematic review for the recent guideline by the National Institute for Health and Care Excellence in the United Kingdom had an upper age limit of 3 years. 3
The Penn State College of Medicine Institutional Review Board considered this study of publicly available deidentified data to not be human subject research.
Definitions
Our primary definition of underweight was weight-for-age (WFA or weight-for-length (WFL <5th percentile on the World Health Organization (WHO) growth curve for children <2 years of age, or WFA or body mass index-for-age (BFA <5th percentile on the Centers for Disease Control and Prevention (CDC) growth curve for those 2 to 3 years of age. 20 We excluded children with a birth weight <2500 g in our primary analysis because a history of low birth weight is associated with both SES and with underweight that is less likely to be caused by undernourishment.21-23
Primary Analyses
Our 2 primary analyses were a comparison of underweight status in children with and without low income and with and without food insecurity. We adjusted household income for household size by dividing the income by the square root of the number of people living in the household. 24 These adjusted values were then categorized in quintiles. Our goal was to evaluate risk of underweight in children with low income; quintiles were used because of prior research suggesting a possible U-shaped relationship of underweight with income. 3 As alternate measures of low income, we compared the prevalence of underweight in children receiving and not receiving Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) benefits and in children with a family income below (vs above) 130% of the federal poverty level. This corresponds to a federal poverty income index below 1.3, a threshold used to determine eligibility for the Supplemental Nutrition Assistance Program. The index was calculated in NHANES by dividing family income by the Department of Health and Human Services guidelines specific for that participant’s year, location, and family size. 25
Household and child food insecurity were categorized as full, marginal, low, or very low based on responses to the 18 questions of the US Household Food Security Survey Module from the United States Department of Agriculture. 26 Analyses of household and child food security were performed separately. We compared children with full household or child food security with all others, and compared children with full or marginal food security with low or very low food security.
We also evaluated the prevalence of underweight in 3 categories of race/ethnicity: Non-Hispanic White, Non-Hispanic Black, and Other. We evaluated race and ethnicity because they are associated with family/household income and food insecurity, in other words, if children from families with lower income or with food insecurity are managed differently, that could translate to children being treated differently by race and/or ethnicity.
We first used logistic regression to model the presence of underweight by income measures, food security, and race/ethnicity. We then performed multivariable analyses including all of the above measures and survey year. All models were fit using SAS proc surveylogistic that accounted for sampling weights, clustering, and stratification of the complex sampling design as specified in the instructions for NHANES data to ensure nationally representative estimates. 27 Briefly, the sampling weights accounted for the probabilities of being selected in the study (for a given year), non-response to the survey, and differences between the sample population and the US total population (2000 Census). For years 1999 to 2002, 4-year mobile examination center (MEC) sampling weights were used. In all other years, 2-year MEC sampling weights were used. Sampling weights were combined across survey cycles by dividing the sample weights for each 2-year (or 4-year) cycle by the total number of cycles. The models also accounted for clustering of individuals (within counties, and for individuals within households), and variance estimates used a stratification factor to account for primary sampling units (via a pseudo-stratum variable to mask actual sampling units).
Missing Data
We performed unweighted analyses to determine whether the percentages of missing survey values were associated with adjusted household income, food security, race/ethnicity, household WIC status, and poverty income ratio individually.
Secondary Analyses
As the prevalence of obesity may be higher among children with low SES, 28 we compared children with underweight to all other children and, in a sensitivity analysis, to children without obesity (<95th percentile WFA and WFL for <2 years, <95th percentile BFA for ≥2 years).29-31 We also performed a sensitivity analysis of the multivariable model including children with low birth weight.
Results
The initial sample included 8160 participants <3 years of age with 7356 children available for the analysis after exclusions (Table 1, Figure 1).
Table 1.
Survey Participant Demographics for Children Age 0 to <3 Years.
| Survey participants by weight status | ||
|---|---|---|
| Variable | Unweighted | Sample weighted |
| Number of participants | 7356 | |
| Age in months | ||
| M (SD) | 16.0 (10.37) | 18.0 (11.15) |
| Median | 15.0 | 17.6 |
| Biological gender | ||
| Male | 3800 (51.7%) | 48.8% |
| Female | 3556 (48.3%) | 51.2% |
| Race/Ethnicity | ||
| Non-Hispanic White | 2327 (31.6%) | 56.2% |
| Non-Hispanic Black | 1540 (20.9%) | 12.8% |
| Mexican American | 2370 (32.2%) | 16.8% |
| Other Hispanic | 629 (8.6%) | 7.6% |
| Other | 490 (6.7%) | 6.6% |
Excludes children with low birth weight (<2500 g).
Abbreviation: SD, standard deviation.
Figure 1.
Study participant flowchart.
Abbreviation: NHANES, National Health and Nutrition Examination Survey.
Missing Data
Among the sample, 546 children (7.4%) did not have height and/or weight recorded (Figure 1). Among the 6810 children with height and weight recorded, 286 (4.2%) met the criteria for underweight.
The most common missing covariates were household income (n = 967, 13.1%) and family income (n = 522, 7.1%), needed to calculate the poverty income ratio. The other covariates were missing in <2% of participants. Adjusted household income, household food security, child food security, race/ethnicity, and poverty income ratio were not associated with missingness. The percentage of missing values was significantly lower for the group of participants who received household WIC benefits compared with those who did not.
Population Characteristics
Of those with household food security recorded, 2778 (38.5%) had marginal, low, or very low household food security, and of those with child food security recorded, 1408 (19.5%) had marginal, low, or very low child food security (Table 2). A majority with data available were receiving WIC benefits (59%) and had a family income below 130% of the federal poverty index (52%), represented here as a poverty income ratio below 1.3.
Table 2.
Percentages of Survey Participants in Adjusted Household Income Quintiles and Food Security Categories.
| Variable | Survey participants by weight status | ||||
|---|---|---|---|---|---|
| All participants (column %) | Underweight (row %) | Typical weight (row %) | Obesity (row %) | Growth measurements missing | |
| Adjusted household income | |||||
| Q1 (≤7000) | 1247 (19.5%) | 45 (3.9%) | 934 (80.7%) | 179 (15.5%) | 89 |
| Q2 (7001-11 000) | 1271 (19.9%) | 49 (4.2%) | 972 (82.4%) | 159 (13.5%) | 91 |
| Q3 (11 001-16 000) | 1290 (20.2%) | 57 (4.8%) | 967 (81.3%) | 166 (13.9%) | 100 |
| Q4 (16 001-28 000) | 1301 (20.4%) | 55 (4.5%) | 1000 (81.7%) | 169 (13.8%) | 77 |
| Q5 (>28 000) | 1280 (20.0%) | 40 (3.4%) | 1020 (86%) | 126 (10.6%) | 94 |
| Income data missing | 967 | 40 | 724 | 80 | 95 |
| Household food security category | |||||
| Fully secure | 4443 (61.5%) | 178 (4.3%) | 3413 (83.2%) | 510 (12.4%) | 342 |
| Marginally secure | 1150 (15.9%) | 48 (4.4%) | 890 (82.1%) | 146 (13.5%) | 66 |
| Low security | 1092 (15.1%) | 33 (3.3%) | 818 (80.8%) | 161 (15.9%) | 80 |
| Very low security | 536 (7.4%) | 21 (4.3%) | 405 (82.2%) | 67 (13.6%) | 43 |
| Food data missing | 135 | 6 | 91 | 23 | 15 |
| Child food security category | |||||
| Fully secure | 5804 (80.5%) | 232 (4.3%) | 4441 (82.4%) | 715 (13.3%) | 416 |
| Marginally secure | 572 (7.9%) | 20 (3.8%) | 441 (84.8%) | 59 (11.3%) | 52 |
| Low security | 755 (10.5%) | 26 (3.7%) | 576 (82.3%) | 98 (14%) | 55 |
| Very low security | 81 (1.1%) | 2 (2.7%) | 62 (83.8%) | 10 (13.5%) | 7 |
| Food data missing | 144 | 6 | 97 | 25 | 16 |
| Household WIC received | |||||
| Yes | 4307 (59.1%) | 170 (4.2%) | 3274 (81.3%) | 584 (14.5%) | 279 |
| No | 2984 (40.9%) | 113 (4.2%) | 2303 (84.6%) | 307 (11.3%) | 262 |
| WIC data missing | 65 | 3 | 41 | 16 | 5 |
| Poverty income ratio | |||||
| ≥1.3 | 3301 (48.3%) | 124 (4.1%) | 2558 (83.7%) | 374 (12.2%) | 245 |
| <1.3 | 3533 (51.7%) | 136 (4.2%) | 2670 (81.7%) | 464 (14.2%) | 263 |
| Ratio data missing | 522 | 26 | 389 | 69 | 38 |
| Race/Ethnicity | |||||
| Non-Hispanic White | 2327 (33.9%) | 100 (4.7%) | 1821 (84.7%) | 228 (10.6%) | 178 |
| Non-Hispanic Black | 1540 (22.4%) | 64 (4.5%) | 1151 (80.5%) | 214 (15%) | 111 |
| Other Race/Ethnicity (including multiple) | 2999 (43.7%) | 122 (3.8%) | 2645 (81.8%) | 465 (14.4%) | 257 |
Underweight: <5th percentile for WFA or WFL on the WHO growth chart for children <2 years and <5th percentile for WFA or BFA on the CDC growth chart for children ≥2 years. Obesity: >95th percentile for WFL on the WHO growth chart for children <2 years and >95th percentile for BMI on the CDC growth chart. Typical Weight: not classified as having underweight or obesity.
Abbreviations: WIC, special supplemental nutrition program for women, infants, and children; WFA, weight-for-age; WFL, weight-for-length; WHO, World Health Organization; CDC, Centers for Disease Control and Prevention; BMI, body mass index.
Primary Analyses
Our primary analyses showed no significant difference in the odds of underweight when comparing each adjusted income quintile to the lowest quintile or when comparing children with and without food security (Table 3). There were also no univariable associations between underweight and other measures of income (household WIC status or a poverty income ratio below 1.3) or race and ethnicity. We did not find associations between underweight and the evaluated risk factors in our multivariable analysis (Table 4). Including multiple measures of income did not result in collinearity in this analysis.
Table 3.
Odds Ratios of Underweight Status (vs Not Underweight) From Univariable Logistic Regression Models Including Sampling Weights.
| Parameter | Odds ratio (95% CI) |
|---|---|
| Income quintile | |
| Q1 (≤7000), reference | 1 |
| Q2 (7001-11 000) | 1.28 (0.78-2.10) |
| Q3 (11 001-16 000) | 1.45 (0.90-2.31) |
| Q4 (16 001-28 000) | 1.42 (0.85-2.38) |
| Q5 (>28 000) | 0.73 (0.41-1.28) |
| Household WIC status | |
| Yes, reference | 1 |
| No | 0.92 (0.66-1.28) |
| Poverty income ratio | |
| <1.3, reference | 1 |
| ≥1.3 | 0.93 (0.69-1.27) |
| Household food security | |
| Marginal or insecure, reference | 1 |
| Fully secure | 0.93 (0.65-1.32) |
| Insecure, reference | 1 |
| Fully or marginally secure | 1.16 (0.79-1.71) |
| Child food security | |
| Marginal or insecure, reference | 1 |
| Fully secure | 0.87 (0.57-1.33) |
| Insecure, reference | 1 |
| Fully or marginally secure | 0.92 (0.56-1.51) |
| Race/Ethnicity | |
| Non-Hispanic White, reference | 1 |
| Non-Hispanic Black | 1.11 (0.77-1.59) |
| Other | 0.98 (0.75-1.29) |
Abbreviations: CI, confidence interval; WIC, Special Supplemental Nutrition Program for Women, Infants, and Children.
Table 4.
Odds Ratios of Underweight Status (Vs Not Underweight) From a Multivariable Logistic Regression Model.
| Parameter | Odds ratio (95% CI) |
|---|---|
| Adjusted household income | |
| Q1 (≤7000), reference | 1 |
| Q2 (7001-11 000) | 1.37 (0.69-2.71) |
| Q3 (11 001-16 000) | 1.23 (0.70-2.17) |
| Q4 (16 001-28 000) | 1.25 (0.53-2.96) |
| Q5 (>28 000) | 0.56 (0.19-1.68) |
| Household WIC | |
| Yes, reference | 1 |
| No | 1.25 (0.81-2.04) |
| Poverty income ratio | |
| <1.3, reference | 1 |
| ≥1.3 | 1.22 (0.48-2.27) |
| Household food security category | |
| Insecure, reference | 1 |
| Fully or marginally secure | 1.28 (0.77-2.10) |
| Race/Ethnicity | |
| Non-Hispanic White, reference | 1 |
| Non-Hispanic Black | 0.88 (0.55-1.41) |
| Other | 0.82 (0.55-1.25) |
| NHANES survey year | |
| 99-00 | 0.21 (0.10-0.46) |
| 01-02 | 0.58 (0.31-1.10) |
| 03-04 | 0.72 (0.36-1.47) |
| 05-06 | 0.79 (0.35-1.81) |
| 07-08 | 0.71 (0.36-1.42) |
| 09-10 | 0.52 (0.29-0.94) |
| 11-12 | 0.70 (0.36-1.37) |
| 13-14, reference | 1 |
Abbreviations: CI, confidence interval; WIC, Special Supplemental Nutrition Program for Women, Infants, and Children; NHANES, National Health and Nutrition Examination Survey.
Secondary Analyses
Excluding children with obesity or including children with low birth weight in sensitivity analyses did not change these results for measures of income, food security, or race/ethnicity. The point estimate of the odds ratio for underweight status was always below 1 for the highest income quintile, but was not statistically significantly below 1 in any of the evaluated models. The statistically significant lower likelihood of underweight status in 2 sets of survey years seen in our primary multivariable model was not found consistently in sensitivity analyses.
Discussion
Young children from families with low income or food insecurity in the United States did not have an increased likelihood of underweight in this nationally representative sample. Our findings echo those of a recent prospective study with a smaller sample and do not support an increased risk of FTT in children from families with lower income and/or lower food security. 11 Socioeconomic status is associated with race, and we also did not find an association between race and underweight status.16-18 This lack of an association with the prevalence of underweight makes it less likely that income, food insecurity, or race/ethnicity are associated with an increased risk of FTT, which is the subset of underweight caused by undernourishment. Because over- and underdiagnosis of FTT have potential harms, and because income and food security are associated with race and ethnicity in the United States, diagnosing FTT in underweight children differently based on household income or food security could contribute to both socioeconomic and racial disparities in health care.
The lowest risk of FTT was in children in the highest income quintile, but the difference from the lowest quintiles was nonsignificant in our primary and other analyses. It is possible that this represents a true difference, and that children from families with the highest incomes are at decreased risk of underweight and, therefore, of FTT. However, our study objective was to evaluate risk in children with the lowest incomes, and we evaluated income quintiles rather than solely binary measure of income primarily because of prior research suggesting a U-shaped distribution of underweight risk, with the potential for an increased risk of underweight in the highest income children. 3 Therefore, there is reason to be cautious about concluding that children in the highest income quintiles have a lower incidence of underweight (and FTT) beyond the lack of statistical significance.
Our findings indicate that clinicians should use caution when incorporating household income and food security into their assessments of whether a child with a mild-to-moderate degree of underweight is small and healthy or has FTT, to avoid treating children with similar risk differently.3,11 There are potential benefits to asking about financial stressors or food insecurity in children with underweight, such as identifying concerns for which assistance may be available, regardless of whether this information contributes to a determination of whether a child has FTT. Even if our findings are replicated, low income may play a role in poor growth in some children. 5 For example, children from families with the most severe socioeconomic stressors, such as homelessness, may be more likely to have FTT and are likely not represented well in NHANES data or in many studies.32,33
We did not identify an association between receipt of WIC and underweight status. Importantly, WIC is a marker both of risk and of connection to resources. Ensuring adequate nutrition is a primary goal of the program, and enrollment in WIC may be protective against underweight status. Therefore, the lack of an association could reflect 2 competing effects.
A recent systematic review from the US Preventive Services Task Force identified few randomized controlled trials of screening and intervention for social risk factors that evaluated health outcomes. 34 The few existing trials in children show variable results. The most clear benefit appeared to be in the single trial of priority placement in housing and service provision for homeless families; this intervention resulted in improved parent-reported health. 35 There was 1 trial of screening and intervention for food insecurity. 36 The intervention was effective in increasing identification of food insecurity, but the prevalence of food insecurity remained stable in both the intervention and control groups at 6 months. There were 2 trials of similar multicomponent interventions in populations with substantial frequencies of food insecurity; the interventions resulted in improved health outcomes in the first but not the second trial.37,38
The COVID-19 pandemic has significantly impacted food security in the United States39,40 Food insecurity may change eating and feeding behavior for children in the household, an effect that may be heightened or altered by the pandemic.41,42 Therefore, it is important to acknowledge that the relationship between income and/or food insecurity with underweight may have been significantly altered by the pandemic.
The primary strengths of our study are the use of a nationally representative sample with prospective weight and linear growth measurements, the use of multiple measures of income and food insecurity, and the performance of sensitivity analyses. The most important limitation is the lack of data on clinician-diagnosed FTT, and, even more fundamentally, the lack of a gold standard or even a widely accepted definition for FTT diagnosis. Longitudinal growth data, which would help to differentiate children who are small but gaining weight typically and those who are small with decelerating weight gain, are not available in NHANES. Longitudinal data would also help to detect children who are above the 5th percentile with abnormally slow weight gain. In addition, we did not have a sufficient sample to analyze children with more extreme degrees of underweight. We did not analyze other types of undernourishment, such as micronutrient deficiencies. We did not study other aspects of SES or families with more extreme socioeconomic stressors, such as homelessness. In addition, publicly available NHANES data do not allow identification of geographic area, so we did not evaluate income adjusted for local cost-of-living or stratify the analysis by rural status.
More research is needed to further delineate the relationship between underweight, FTT, and SES, overall and in the US population. These analyses could focus on children with more severe poverty who may not be represented in NHANES, such as those who do not have stable housing. Examination of other factors related to SES that may have a more direct relationship with FTT such as parental stress, adequate housing, and availability of transportation would also be useful, as would evaluation of the combination of low SES with other risk factors, such as developmental delay. 8 Another important avenue for research is to evaluate the association of SES and race with clinician diagnoses of FTT, using secondary data analysis or a survey with clinical vignettes.
Conclusion
Our data do not support a link between low income or food insecurity and underweight in children using a nationally representative sample. More research examining the potential relationship between these factors and underweight status is needed in the US population. Providers should be aware of the current lack of evidence regarding a relationship between household income and FTT. Clinicians who believe that a child with low weight or low rate of weight gain is more likely to be undernourished if they have lower household income or food insecurity may inadvertently contribute to health care disparities for patients by SES and race.
Author Contributions
Bathai Edwards contributed to development of the research question, preparation of data for analysis, interpretation of results, and drafted the manuscript. Eric Schaefer performed the statistical analysis. Laura Murray-Kolb contributed to study design and interpretation of results. Carrie Daymont contributed to development of the research question, study design, interpretation of results, and drafting of the manuscript. All authors edited the manuscript for intellectual content.
Footnotes
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding: The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by Penn State College of Medicine and the Children’s Miracle Network (Grant No. 211009). The funding sources had no involvement in the design of the study, collection of the data, analysis and interpretation of the data, or the writing of the article.
ORCID iD: Carrie Daymont 
https://orcid.org/0000-0003-1555-1502
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