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Table 1.  Exposure Variables
Exposure Variables
Table 2.  Characteristics of 1245 Study Participants
Characteristics of 1245 Study Participants
Table 3.  Characteristics of Study Cohort by Family Income Category and Family at Risk of Food Insecuritya
Characteristics of Study Cohort by Family Income Category and Family at Risk of Food Insecuritya
Table 4.  Multivariable Logistic Regression Model for the Association of Family Income and Family at Risk for Food Insecurity With Iron Deficiencya
Multivariable Logistic Regression Model for the Association of Family Income and Family at Risk for Food Insecurity With Iron Deficiencya
Table 5.  Multivariable Logistic Regression Model for the Association of Family Income and Family at Risk of Food Insecurity With Iron Deficiency Anemiaa
Multivariable Logistic Regression Model for the Association of Family Income and Family at Risk of Food Insecurity With Iron Deficiency Anemiaa
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    Original Investigation
    Pediatrics
    July 30, 2020

    Association of Family Income and Risk of Food Insecurity With Iron Status in Young Children

    Author Affiliations
    • 1Department of Family Medicine, Queen’s University, Kingston, Ontario, Canada
    • 2Centre for Studies in Primary Care, Queen’s University, Kingston, Ontario, Canada
    • 3Division of Paediatric Medicine and the Paediatric Outcomes Research Team (PORT), The Hospital for Sick Children, Toronto, Ontario, Canada
    • 4Institute of Health Policy, Management, and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
    • 5Department of Pediatrics, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
    • 6Child Health Evaluative Sciences, Sick Kids Research Institute, Toronto, Ontario, Canada
    • 7The Applied Health Research Centre, Li Ka Shing Knowledge Institute, Toronto, Ontario, Canada
    • 8St Michael’s Hospital, Toronto, Ontario, Canada
    JAMA Netw Open. 2020;3(7):e208603. doi:10.1001/jamanetworkopen.2020.8603
    Key Points español 中文 (chinese)

    Question  Are family income and risk of food insecurity associated with iron status in healthy young children?

    Findings  In this cross-sectional study of 1245 children aged 12 to 29 months, those with a family income of less than CAD $40 000 had 3 times higher odds of having iron deficiency and iron deficiency anemia than children in the highest family income group. Risk of food insecurity was not associated with iron deficiency or iron deficiency anemia.

    Meaning  The findings of this study suggest that low family income may be an important risk factor for iron deficiency and iron deficiency anemia, but family risk of food insecurity is not.

    Abstract

    Importance  Iron deficiency (ID) has the greatest prevalence in early childhood and has been associated with poor developmental outcomes. Previous research examining associations of income and food insecurity (FI) with ID is inconsistent.

    Objective  To examine the association of family income and family risk of FI with iron status in healthy young children attending primary care.

    Design, Setting, and Participants  This cross-sectional study included 1245 children aged 12 to 29 months who attended scheduled primary care supervision visits from 2008 to 2018 in Toronto, Canada, and the surrounding area.

    Exposures  Family income and risk of FI were collected from parent-reported questionnaires. Children whose parents provided an affirmative response to the 1-item FI screen on the Nutrition Screening Tool for Every Toddler or at least 1 item on the 2-item Hunger Vital Sign FI screening tool were categorized as having family risk of FI.

    Main Outcomes and Measures  Iron deficiency (serum ferritin level <12 ng/mL) and ID anemia (IDA; serum ferritin level <12 ng/mL and hemoglobin level <11.0 g/dL). All models were adjusted for age, sex, birth weight, body mass index z score, C-reactive protein level, maternal education, breastfeeding duration, bottle use, cow’s milk intake, and formula feeding in the first year.

    Results  Of 1245 children (595 [47.8%] girls; median [interquartile range] age, 18.1 [13.3-24.0] months), 131 (10.5%) were from households with a family income of less than CAD $40 000 ($29 534), 77 (6.2%) were from families at risk of FI, 185 (14.9%) had ID, and 58 (5.3%) had IDA. The odds of children with a family income of less than CAD $40 000 having ID and IDA were 3 times higher than those of children in the highest family income group (ID: odds ratio [OR], 3.08; 95% CI, 1.66-5.72; P < .001; IDA: OR, 3.28; 95% CI, 1.22-8.87; P = .02). Being in a family at risk of FI, compared with all other children, was not associated with ID or IDA (ID: OR, 0.43; 95% CI, 0.18-1.02; P = .06; IDA: OR, 0.16; 95% CI, 0.02-1.23; P = .08).

    Conclusions and Relevance  In this study, low family income was associated with increased risk of ID and IDA in young children. Risk of FI was not a risk factor for ID or IDA. These findings suggest that targeting income security may be more effective than targeting access to food to reduce health inequities in the prevention of iron deficiency.

    Introduction

    Iron is an essential nutrient, and iron deficiency (ID) in early life has been associated with potentially irreversible poor developmental outcomes.1-6 The prevalence of ID in childhood peaks between age 12 and 24 months7 because of rapid growth, depletion of prenatally acquired iron stores, and the transition to complementary foods. In high-income countries, an estimated 9% to 12% of young children have nonanemic ID and 1% to 3% have ID anemia (IDA).8-10 Optimizing iron status in early childhood may represent an opportunity to address a modifiable factor to improve young children’s health.

    Low family income is an important social determinant of children’s health and has been associated with profound negative consequences on child health, particularly when the exposure to poverty is prolonged or occurs in early childhood.11-13 Child poverty remains prevalent, affecting 16.2% of US children.14 Family income affects children’s growth and development,12 including nutritional, mental, and developmental health.15,16

    Food security is defined as the state “when all people, at all times, have physical, social, and economic access to sufficient, safe, and nutritious food which meets their dietary needs and food preferences for an active and healthy life.”17 An estimated 12% of US households reported experiencing some level of food insecurity (FI) in the preceding 12 months, including 16% of households with children under the age of 18 years.18 Household FI has been associated with poor child health outcomes,19 adversely affecting developmental health,20,21 children’s mental health,22,23 and parental mental health.24

    The American Academy of Pediatrics recommends screening for ID using risk assessment (including income)10 and screening for FI.25 It is unclear whether family income or FI are independently associated with ID or whether primary care screening should focus on 1 or both risk factors. Previous studies have examined the association between FI and iron status26-30 or income and ID9,31,32 in children residing in high-income countries with inconsistent results. The few studies accounting for both household income and FI are now dated, were conducted in high-risk populations, assessed varying levels of FI severity, or did not adjust for potential confounders, such as feeding practices.9,28

    Understanding the association of family income and family risk of FI with iron status in healthy young children attending primary care may help to inform guidelines for screening for ID as well as policy approaches aimed at reducing health inequities in the prevention of ID. The objective of this study was to examine the association of family income and family risk of FI with iron status in healthy young children attending primary care while adjusting for child characteristics and feeding practices.

    Methods
    Participants

    This was a cross-sectional study of healthy urban children who attended scheduled health supervision visits at The Applied Research Group for Kids (TARGet Kids!) participating pediatric or family medicine primary care practices between December 2008 and January 2018. TARGet Kids! is a primary care practice–based research network based in Toronto, Canada. TARGet Kids! is an ongoing, open, longitudinal cohort enrolling healthy children from birth to age 5 years. The profile of this cohort has been previously described.33 Study participants were recruited by trained research personnel embedded in participating practices. Parents completed a standardized questionnaire based on the Canadian Community Health Survey addressing child and family characteristics, nutrition, and feeding practices. Parents also completed the Nutrition Screening Tool for Every Toddler (NutriSTEP) questionnaire, a validated parent-completed measure of nutritional risk.34 These questionnaires included questions regarding family income and risk of FI. Blood samples (for serum ferritin, hemoglobin, and C-reactive protein [CRP] levels) and anthropometric measures were also collected at these visits.

    TARGet Kids! exclusion criteria at enrollment are as follows: genetic or chronic health conditions (except asthma); severe developmental delay; gestational age less than 32 weeks; acute illness; and parents unable to communicate in English.33 For the purpose of this study, children were included if they were aged 12 to 29 months (when children have the highest prevalence of iron deficiency7 and attend health supervision visits scheduled at ages 12, 15, 18, and 24 months) and had complete data on exposure and outcome variables, including parent-reported family income and family risk of FI, serum ferritin level, and CRP level. If a child’s record contained data from multiple visits, the first visit with serum ferritin testing was used for analysis. A child with an affirmative response for family at risk of FI at any visit during this short interval was considered to be at risk of FI. Children with serum ferritin levels greater than 200 ng/dL (to convert to micrograms per liter, multiply by 1.0) were excluded, given that this was beyond the upper limit of the reference interval.35 Children with CRP levels greater than 0.5 mg/dL (to convert to milligrams per liter, multiply by 10), suggesting acute systemic inflammation, were excluded because serum ferritin can be falsely elevated in this context.10,36 Children taking iron supplementation or a multivitamin containing iron were excluded.

    Consent was obtained from parents of participating children, and ethics approval was granted by the Research Ethics Boards at the Hospital for Sick Children and St Michael’s Hospital. This report followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline.

    Exposure Variables

    We used 2 exposure variables for analyses, ie, parent-reported family income and parent-reported family risk of FI. The questions, response options, and definition of an affirmative FI response and family income categories are shown in Table 1.

    Family income was collected in 4 categories, with the lowest income category and the lower middle income category approximating the low income cutoff (CAD $44 266 [$32 684] for a 4-person household) and the median family income (CAD $82 859 [$61 180] for a 4-person household), respectively, for Toronto, Canada.37

    Family risk of FI was collected using 2 approaches, as follows: a 1-item question used to screen for risk of FI from the NutriSTEP questionnaire34 (available from 2008-2018) and the Hunger Vital Sign, a 2-item FI screening tool38 (available from 2013-2018). The Hunger Vital Sign is based on the 18-item US Household Food Security Survey Module (HFSSM),39 and an affirmative response to either of the 2 items has a sensitivity of 97% and specificity of 83% for identifying families at risk of FI, using the 18-item HFSSM as the criterion measure.40 Our group previously validated the 1-item NutriSTEP question using the 2-item Hunger Vital Sign as the criterion measure.41 We found the NutriSTEP question had a sensitivity of 85% and a specificity of 91%. Family at risk of FI was defined as an affirmative response to the 1-item NutriSTEP question (“most of the time” or “sometimes”) or to 1 or both of the Hunger Vital Sign questions (“often true” or “sometimes true”) (Table 1).

    Outcome Variables

    The primary outcome was ID defined as serum ferritin of less than 12 ng/dL, as recommended by the World Health Organization and the American Academy of Pediatrics for this age group.10,42 Serum ferritin has been described as the best indicator of iron stores in the absence of inflammation.43 To account for acute systemic inflammation, children with CRP levels greater than 0.5 mg/dL were excluded, and CRP was included as a covariate.36,44 Blood samples were refrigerated and transported to the laboratory the same day. The secondary outcome was IDA, defined as serum ferritin levels of less than 12 ng/dL and hemoglobin levels of less than 11.0 g/dL (to convert to grams per liter, multiply by 10).35

    Other Variables

    Covariates that might influence serum ferritin level, family income, or risk of FI (based on peer-reviewed literature), were identified a priori, including child age; child sex; birth weight; sex- and age-adjusted body mass index (BMI) z score; CRP level; maternal education; infant feeding in the first year of life; breastfeeding duration; bottle use after age 15 months; and daily cow’s milk intake.45-48 Maternal education was determined based on parent-reported highest level of educational attainment. Infant feeding in the first year of life was determined from response to the question, “Which scenario best describes your child in the first year of life? (1) My child received infant formula 80% to 100% of the time; (2) My child received breast milk 80% to 100% of the time; (3) My child received both breast milk and formula equally.” Breastfeeding duration was determined based on parental recall, considered valid and reliable in the child’s first 3 years of life.44 Children who were never breastfed were assigned a breastfeeding duration of 0 months, and those currently breastfeeding were defined as having a breastfeeding duration equal to the child’s current age in months.

    Statistical Analysis

    Descriptive statistics of the exposures, outcomes, and covariates were calculated for the entire sample, as well as for each of the 4 family income categories (<CAD $40 000 [$29 534], CAD $40 000-$79 999 [$29 534-$59 068], CAD $80 000-$149 999 [$[59 068-$110 752], and ≥CAD $150 000 [$110 753]) and 2 family at risk of FI categories (yes and all other). To assess the association of family income and family risk of FI with ID, we examined univariate associations between each of the exposure variables and ID and IDA. Then, 2 multivariable logistic regression models were constructed, the first to evaluate the association between family income and ID (model 1a) and the second to evaluate the association between family risk of FI and ID (model 1b). Models 1a and 1b were compared with the fully adjusted model, which included family income and family risk for FI (model 2).

    We used similar models to evaluate the association between our exposure variables and our secondary outcome, IDA. All models were adjusted for all covariates previously described regardless of statistical significance.49 We used restricted cubic spline transformation to test the assumption of a linear association between age and serum ferritin; after nonlinearity was confirmed by Loess curves, splines were used to model the association of age. We selected 3 knot points to correspond to the age at scheduled visits (ie, 15, 18, and 24 months); these knot locations were selected at scheduled visit times, when data points were clustered.7

    A likelihood ratio test between the main effects model and the model with the interaction term (family income × family risk of FI) yielded a P = .59, suggesting that we could rule out the possibility of an interaction between the 2 exposure variables. Therefore, no interaction term was included in any model. Multicollinearity was assessed using the variance inflation factor.50 All variance inflation factors were less than 2, suggesting that multicollinearity was unlikely. The maximum rate of missing data for any covariate was 8%. Multiple imputation using the fully conditional specification method was used to impute missing covariates.51 To reduce the potential for bias, models were run on 20 imputed data sets using all identified covariates.52 Results of the 20 imputed data sets were combined, and the parameter estimates (95% CIs) for the adjusted pooled models were reported. Sensitivity analyses were performed, including children with a response of “rarely” to the 1-item NutriSTEP question in our definition of a family at risk of FI. Statistical significance was defined as P < .05, and all statistical tests were 2-sided. Statistical analysis was conducted using SAS version 9.4 (SAS Institute).

    Results

    A total of 4571 children were recruited to participate; there were 1446 children with available family income, family at risk of FI, and serum ferritin data. Of these, 201 were excluded, as shown in eBox 1 in the Supplement. Previous TARGet Kids! research found that children with and without blood testing had similar demographic characteristics,47 which we also observed (eBox 2 in the Supplement).

    The final study sample of 1245 children included 595 (47.8%) girls, and the median (interquartile range) age was 18.1 (13.3-24.0) months. Descriptive statistics are shown in Table 2, which includes a total column showing the distribution of participant characteristics for the final analytic sample (with imputation), demonstrating minimal selection bias owing to missing data.53

    Table 3 shows participant characteristics for the final analytic sample by exposure variables (ie, family income category and family at risk of FI). Notably, 34 of 131 children (26.0%) from households with a family income of less than CAD $40 000 ($29 534) were iron deficient; however, 7 of 77 children (9.1%) in families at risk of FI were iron deficient compared with 178 of 1168 children (15.2%) from all other FI categories. There was an income gradient for the variable family at risk for FI, ranging from 34 children (26.0%) living in the lowest-income families to 5 of 517 children (1.0%) living in the highest-income families. There was some variation in the distribution of covariates. Compared with children living in the highest-income families, those in the lowest-income families had higher body mass index z scores (≥2, 21 [4.1%] vs 9 [6.9%]), fewer were breastfed after age 12 months (294 [56.9%] vs 54 [41.2%]), more were mostly formula fed in the first year of life (59 [11.4%] vs 49 [37.4%]), more had cow’s milk intake greater than 2 cups per day (131 [25.3%] vs 60 [45.8%]), more used bottles after age 15 months (160 [31.0%] vs 75 [57.3%]), and more had mothers with a high school diploma or less (15 [2.9%] vs 48 [36.6%]). More children in families at risk of FI compared with all other children were mostly formula fed (28 [36.4%] vs 214 [18.3%]), and fewer were breastfed after age 12 months (30 [39.0%] vs 661 [56.6%]); however, there were no differences between children in families at risk of FI compared with all other children in cow’s milk intake greater than 2 cups per day, bottle use after age 15 months, and maternal educational attainment.

    In univariate analyses, we found increased odds of ID among children from households with a family income of less than CAD $40 000 ($29 534) compared with children in the highest income category (odds ratio [OR], 2.24; 95% CI, 1.41-3.56; P < .001) but not among children from the lower middle income and higher middle income groups. Compared with children in the highest income group, those in the lowest income group also had a higher odds of IDA (OR, 2.53; 95% CI, 1.19-5.37; P = .02). Among children in households at risk of FI, we did not find higher odds of ID (OR, 0.56; 95% CI, 0.25-1.23; P = .15) or IDA (OR, 0.24; 95% CI, 0.03-1.96; P = .16) on univariate analyses.

    Table 4 and Table 5 show that after adjusting for age, sex, birth weight, BMI z score, CRP level, maternal education, breastfeeding duration, bottle use, daily cow’s milk intake, infant feeding in the first year of life, and family risk of FI, family income of less than CAD $40 000 ($29 534) remained a risk factor for both ID and IDA. The multivariable logistic regression model (model 2) demonstrated that the odds of children from households with a family income of less than CAD $40 000 ($29 534) having iron deficiency was 3.08 times (95% CI, 1.66-5.72; P < .001) (Table 4) higher than that for children in the highest family income group, and having IDA was 3.28 times (95% CI, 1.22-8.87; P = .02) (Table 5) higher than that for children in the highest family income group.

    In the fully adjusted logistic regression model (model 2), there was no association between ID or IDA and risk of FI (ID: OR, 0.43; 95% CI, 0.18-1.02; P = .06; IDA: OR, 0.16; 95% CI, 0.02-1.23; P = .08) (Table 4 and Table 5). Results from sensitivity analyses were similar to our main findings (eAppendix in the Supplement).

    Discussion

    In this large cohort of healthy urban young Canadian children enrolled in primary care settings, we found that a family income of less than CAD $40 000 ($29 534) was associated with 3-fold higher odds of ID and IDA compared with children in the highest family income group. Our analyses included adjusting for important covariates including child factors (ie, age, sex, birth weight, BMI z score, CRP level, and maternal education), feeding practices (ie, breastfeeding duration, infant feeding in the first year, cow’s milk intake, and bottle use), and FI status. We found no association of odds of ID or IDA with risk of FI.

    Several observational studies from other high-income countries have examined the association between family income and iron status in early childhood.9,30-32 In the United States, there have been several publications using data from the National Health and Nutrition Examination Survey demonstrating a narrowing of the gap between young children living above and below the federal poverty line in successive survey cycles from 1976 to 2002.9,31,32 In Italy, Ferrara et al54 assessed iron status in 1250 children aged 8 to 36 months from 1980 to 2010 and found that children from low-income families had a 3-fold to 4-fold increase in ID compared with children from medium- to high-income families.54 In New Zealand, Soh et al55 assessed iron status in 323 children aged 6 to 24 months and did not identify an association with low family income with adjustment for dietary factors; however, their sample size was smaller.55

    Results of studies examining the association between FI and iron status in early childhood differ in high-risk vs average-risk populations. Children whose families reported more severe FI were more likely to have IDA,26,28,29 but research using US population-level data from the National Health and Nutrition Examination Survey found no association with FI.30,56 Our study assessed families at risk for food insecurity, a less severe form representing average risk.

    In our study, we found an association between family income and ID but not between risk of FI and ID. Income and FI are closely related but different constructs. Factors that may buffer low-income families from FI include savings and assets, particularly home ownership and income stability57; literature on the association between food buying and preparation skills and FI is mixed.58,59

    We found that having a low family income was associated with an increased risk of ID, even after controlling for important covariates. Children in the lowest income group were more likely to be mostly formula fed in the first year, to drink more than 2 cups of cow’s milk daily, and to have shorter breastfeeding durations. However, even after adjusting for these and other important covariates, including family risk of FI, low income was independently associated with a greater risk of ID. Identifying the causal pathway by which low income leads to ID is beyond the scope of this project, but our results may be related to unmeasured confounders, such as maternal iron status in pregnancy and delayed cord clamping at birth,60 both of which are associated with increased iron stores in the first year of life. Our study provides support for the inclusion of family income as an important risk factor in screening for ID in early childhood. Our group has derived and is currently collecting data to externally validate a clinical decision rule to inform targeted screening of young children for ID.61

    We did not find an association between family at risk of FI and ID, which may be explained by the relative distribution of risk and protective factors. For example, similar to children in the lowest income group, more children from families at risk of FI were mostly formula fed in the first year and had a shorter breastfeeding duration. However, in contrast with the lowest income group, they did not have high cow’s milk consumption, which may have been relatively protective. Our results are consistent with those reported by Metallinos-Katsaras et al,26 who also did not find an association between risk of FI and ID, suggesting that this result may be related to iron-rich foods being provided to Women, Infants, and Children–supported families.

    Extensive evidence has demonstrated the importance of socioeconomic factors in determining children’s health. The strong association between social determinants in childhood and health throughout the life course in turn is driving increased interest in screening for social needs in clinical care. Multiple health professional bodies have established campaigns encouraging health professionals to screen their patients for unmet social needs.62,63 However, many questions remain regarding optimal approaches for identifying and intervening on unmet social needs in health care settings. Dworkin and Garg64 advocate for a strength-based, family- and patient-centered approach that emphasizes shared decision-making and respect for family autonomy and priorities and has the capacity to link families to community-based programs and services. However, physicians identify multiple barriers to screening for social needs, including constraints in financial resources and time, misaligned incentives, and physician uncertainty regarding how best to address social needs.65,66 Furthermore, there remains a gap in robust studies of appropriate screening instruments and the association of interventions with health outcomes, rather than intermediate process outcomes.67

    It is unclear whether social needs screening should aim to identify financial strain generally or more specific needs, such as FI. Our study lends support to the approach supported by the College of Family Physicians of Canada, ie, using a general question to identify financial need. (“Do you ever have difficulty making ends meet at the end of the month?”)68 In contrast, the American Academy of Pediatrics recommends screening for FI.69 Some experts suggest that such an approach may be constrained by parents’ concern regarding the stigma of FI, fear of being judged, and in particular, fear that identifying difficulty with providing adequate food for their children may trigger reports to child welfare authorities and fear of losing their children.70,71

    Previous work suggests that both policy interventions targeting income security and large-scale food assistance programs can narrow health inequities (including FI).72 Canadian studies highlight the association between relatively small changes in income supports for low-income Canadians and improvement in food security status.73,74 Unlike the United States, Canada lacks large-scale food assistance programs, such as Women, Infants, and Children or the Supplemental Nutrition Assistance Program, which are associated with reduced but still substantial FI. Currently there is insufficient evidence showing that food programs more effectively reduce FI or improve child health compared with income support programs, suggesting that it is unclear whether public resources should preferentially target income security or food access programs.

    Strengths and Limitations

    Strengths of our study include the large sample size of 1245 healthy urban Canadian children recruited in primary care. We used parent-reported family income rather than ecological measures and prospectively collected data on multiple confounders, including child characteristics and feeding practices. The primary outcome, ID, was measured by serum ferritin level, which is considered the best indicator of iron stores.43

    Our study also had limitations. Our observational study demonstrated important associations with ID and IDA but cannot determine causation. Furthermore, the cross-sectional design precludes conclusions about changes in exposures and outcomes over time. Study participants were recruited from primary care practices in Toronto, Canada, and may not be generalizable to the general population of young children in Canada and other high-income countries. Measures of food insecurity were restricted to brief measures; use of the full HFSSM would have provided a more robust measure. Family income and risk of FI were parent-reported and may be subject to social desirability bias, perhaps particularly for FI reporting. This may have biased the results toward the null. Also, self-reported breastfeeding duration may be subject to recall bias, although this is considered a valid and reliable measure in the first 3 years of life.44

    Conclusions

    In this study, low family income was associated with an increased risk of ID and IDA in young Canadian children. Risk of FI was not associated with ID or IDA. These findings suggest that targeting income security may be more effective than targeting access to food to reduce health inequities in the prevention of iron deficiency.

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    Article Information

    Accepted for Publication: April 1, 2020.

    Published: July 30, 2020. doi:10.1001/jamanetworkopen.2020.8603

    Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2020 Bayoumi I et al. JAMA Network Open.

    Corresponding Author: Imaan Bayoumi, MD, MSc, Department of Family Medicine, Queen’s University, 220 Bagot St, PO Bag 8888, Kingston, ON K7L 5E9, Canada (bayoumi@queensu.ca).

    Author Contributions: Dr Bayoumi had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Drs Parkin and Borkhoff contributed equally as co–senior authors.

    Concept and design: All authors.

    Acquisition, analysis, or interpretation of data: All authors.

    Drafting of the manuscript: Bayoumi, Borkhoff.

    Critical revision of the manuscript for important intellectual content: All authors.

    Statistical analysis: Bayoumi, Borkhoff.

    Obtained funding: Parkin.

    Administrative, technical, or material support: Parkin, Birken, Maguire.

    Supervision: Parkin, Maguire, Borkhoff.

    Conflict of Interest Disclosures: Dr Bayoumi reported receiving a grant from the Academic Health Science Centre Alternative Funding Plan Innovation Fund outside the submitted work. Dr Parkin reported receiving a grant from the Canadian Institutes of Health Research, Danone Institute of Canada, and Dairy Farmers of Ontario and receiving nonfinancial support in the form of liquid iron supplements for an ongoing trial of iron deficiency in young children from Mead Johnson Nutrition outside the submitted work. Dr Maguire reported receiving a grant from the Dairy Farmers of Canada. Dr Birken reported receiving a grant from Walmart Canada Regional Grant outside the submitted work. Dr Borkhoff reported receiving a grant from the Sickkids Centre for Healthy Active Kids outside the submitted work.

    Funding/Support: Funding to support The Applied Research Group for Kids (TARGet Kids!) was provided by multiple sources, including the Canadian Institutes for Health Research, namely the Institute of Human Development, Child and Youth Health (grant No. FRN 114945 to Dr Maguire; grant No. FRN 115059 to Dr Parkin) and the Institute of Nutrition, Metabolism and Diabetes (grant No. FRN 119375 to Dr Birken) as well as the St Michael's Hospital Foundation. The Paediatric Outcomes Research Team is supported by a grant from The Hospital for Sick Children Foundation.

    Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

    Members of the TARGet Kids! Collaboration:

    Coleads: Catherine S. Birken, MD, and Jonathon L. Maguire, MD.

    Advisory Committee: Ronald Cohn, MD; Eddy Lau, MD; Andreas Laupacis, MD; Patricia C. Parkin, MD; Michael Salter, MD; and Shannon Weir-Seeley, MSc.

    Science Review and Management Committees: Laura N. Anderson, PhD; Cornelia M. Borkhoff, PhD; Charles Keown-Stoneman, PhD; Christine Kowal, MSc; and Dalah Mason, MPH.

    Site Investigators: Murtala Abdurrahman, MD; Kelly Anderson, MD; Gordon Arbess, MD; Jillian Baker, MD; Tony Barozzino, MD; Sylvie Bergeron, MD; Gary Bloch, MD; Joey Bonifacio, MD; Ashna Bowry, MD; Caroline Calpin, MD; Douglas Campbell, MD; Sohail Cheema, MD; Brian Chisamore, MD; Evelyn Constantin, MD; Karoon Danayan, MD; Paul Das, MD; Mary Beth Derocher, MD; Anh Do, MD; Anne Egger, BScN; Allison Farber, MD; Amy Freedman, MD; Sloane Freeman, MD; Sharon Gazeley, MD; Charlie Guiang, MD; Curtis Handford, MD; Laura Hanson, MD; Leah Harrington, MD; Sheila Jacobson, MD; Lukasz Jagiello, MD; Gwen Jansz, MD; Paul Kadar, MD; Tara Kiran, MD; Holly Knowles, MD; Bruce Kwok, MD; Sheila Lakhoo, MD; Margarita Lam-Antoniades, MD; Eddy Lau, MD; Denis Leduc, MD; Fok-Han Leung, MD; Alan Li, MD; Patricia Li, MD; Jessica Malach, MD; Roy Male, MD; Aleks Meret, MD; Elise Mok, MD; Rosemary Moodie, MD; Katherine Nash, MD; Sharon Naymark, MD; James Owen, MD; Michael Peer, MD; Marty Perlmutar, MD; Navindra Persaud, MD; Andrew Pinto, MD; Michelle Porepa, MD; Vikky Qi, MD; Noor Ramji, MD; Danyaal Raza, MD; Alana Rosenthal, MD; Katherine Rouleau, MD; Caroline Ruderman, MD; Janet Saunderson, MD; Vanna Schiralli, MD; Michael Sgro, MD; Hafiz Shuja, MD; Susan Shepherd, MD; Barbara Smiltnieks, MD; Cinntha Srikanthan, MD; Carolyn Taylor, MD; Stephen Treherne, MD; Suzanne Turner, MD; Fatima Uddin, MD; Meta van den Heuvel, MD; Thea Weisdorf, MD; Peter Wong, MD; John Yaremko, MD; Ethel Ying, MD; Elizabeth Young, MD; and Michael Zajdman, MD.

    Research Team: Vincent Bouchard, PhD; Marivic Bustos, RPN; Charmaine Camacho, BSc; Dharma Dalwadi, BSc; Christine Koroshegyi, MA; Tarandeep Malhi, MLT; Sharon Thadani, MLT; Julia Thompson, SSRP; and Laurie Thompson, MLT.

    Project Team: Mary Aglipay, MSc; Imaan Bayoumi, MD; Sarah Carsley, PhD; Katherine Cost, PhD; Karen Eny, PhD; Laura Kinlin, MD; Jessica Omand, PhD; Shelley Vanderhout, BASc; and Leigh Vanderloo, PhD.

    Applied Health Research Centre: Christopher Allen, BSc; Bryan Boodhoo, MSc; David W. H. Dai, MSc; Peter Juni, MD; and Gerald Lebovic, PhD.

    Mount Sinai Services Laboratory: Rita Kandel, MD, and Michelle Rodrigues, BSc.

    Additional Contributions: We thank all participating children and families for their time and involvement in the TARGet Kids! primary care practice–based research network and all practice site physicians, research staff, collaborating investigators, trainees, methodologists, biostatisticians, data management personnel, laboratory management personnel, and advisory committee members.

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