Understanding income-related differences in distribution of child growth, behaviour and development using a cross-sectional sample of a clinical cohort study

Abstract

Objectives: Children from low-income households are at an increased risk of social, behavioural and physical health problems. Prior studies have generally relied on dichotomous outcome measures. However, inequities may exist along the range of outcome distribution. Our objective was to examine differences in distribution of three child health outcomes by income categories (high vs low): body mass index (BMI), behaviour difficulties and development.

Design and setting: This was a cross-sectional study using data from a primary care-based research network with sites in three Canadian cities, and 15 practices enrolling participants.

Participants, independent variable and outcomes: The independent variable was annual household income, dichotomised at the median income for Toronto (<$C80 000 or ≥$C80 000). Outcomes were: (1) growth (BMI z-score (zBMI) at 5 years, 1628 participants); (2) behaviour (Strengths and Difficulties Questionnaire (SDQ) at 3-5 years, 649 participants); (3) development (Infant Toddler Checklist (ITC) at 18 months, 1405 participants). We used distributional decomposition to compare distributions of these outcomes for each income group, and then to construct a counterfactual distribution that describes the hypothetical distribution of the low-income group with the predictor profile of the higher-income group.

Results: We included data from 1628 (zBMI), 649 (SDQ) and 1405 (ITC) children. Children with lower family income had a higher risk distribution for all outcomes. For all outcomes, thecounterfactual distribution, which represented the distribution of children with lower-income who were assigned the predictor profile of the higher-income group, was more favourable than their observed distributions.

Conclusion: Comparing the distributions of child health outcomes and understanding different risk profiles for children from higher-income and lower-income groups can offer a deeper understanding of inequities in child health outcomes. These methods may offer an approach that can be implemented in larger datasets to inform future interventions.

Keywords: community child health; epidemiology; paediatrics; social medicine.

Figure 1

These flow diagrams show cohort definitions for each outcome and reasons for participant exclusion due to missing data. BMI, body mass index; ITC, Infant Toddler Checklist; SDQ, Strengths and Difficulties Questionnaire.

Figure 2

Distributions and distributional decomposition of BMI z-score, including observed distributions of BMI z-score by income (A); differences between observed distributions (B); observed distribution plus the counterfactual distribution of the low-income group with predictor profile of high-income group (C); and the residual difference between the high-income and counterfactual distributions (D). BMI, body mass index.

Figure 3

Distributions and distributional decomposition of SDQ Total Difficulties Score, including observed distributions of Total Difficulties Score by income (A); differences between observed distributions (B); observed distribution plus the counterfactual distribution of the low-income group with predictor profile of high-income group (C); and the residual difference between the high-income and counterfactual distributions (D). SDQ, Strengths and Difficulties Questionnaire.

Figure 4

Distributions and distributional decomposition of total ITC Score, including observed distributions of ITC Score by income (A); differences between observed distributions (B); observed distribution plus the counterfactual distribution of the low-income group with predictor profile of high-income group (C); and the residual difference between the high-income and counterfactual distributions (D). ITC, Infant Toddler Checklist; SDQ, Strengths and Difficulties Questionnaire.