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Background: Human capital (the knowledge, skills, and health that accumulate over life) can be optimized by investments in early childhood to promote cognitive and language development. Parents and caregivers play a crucial role in the promotion and support of cognitive development in their children. Thus, understanding caregiver perceptions of a child’s capabilities and attributes, including intelligence, may enhance investments early in life. To explore this question, we asked caregivers to rank their child’s intelligence in comparison with other children in the community, and compared this ranking with children’s scores on an assessment of developmental abilities across multiple domains. Methods: Our study examined cross-sectional data of 3361 children aged 16-42 months in rural Madagascar. Child intelligence, as perceived by their caregiver, was captured using a ladder ranking scale based on the MacArthur Scale for Subjective Social Status. Children’s developmental abilities were assessed using scores from the Ages and Stages Questionnaire: Inventory (ASQ-I), which measures cognitive, language, and socio-emotional development. Ranked percentiles of the ASQ-I were generated within communities and across the whole sample. We created categories of under-estimation, matched, and over-estimation by taking the differences in rankings between caregiver-perceived child intelligence and ASQ-I. Child nutritional status, caregiver belief of their influence on child intelligence, and sociodemographic factors were examined as potential correlates of discordance between the measures using multinomial logistic regressions. Results: We found caregiver perceptions of intelligence in Madagascar did not align consistently with the ASQ-I, with approximately 8% of caregivers under-estimating and almost 50% over-estimating their children’s developmental abilities. Child nutritional status, caregiver belief of their influence on child intelligence, caregiver education, and wealth were associated with under- or over-estimation of children’s developmental abilities. Conclusions: Our findings suggest parents may not always have an accurate perception of their child’s intelligence or abilities compared with other children. The results are consistent with the limited literature on parental perceptions of child nutrition, which documents a discordance between caregiver perceptions and objective measures. Further research is needed to understand the common cues caregivers that use to identify child development milestones and how these may differ from researcher-observed measures in low-income settings. Trial registration: Current Controlled Trials ISRCTN14393738. Registered June 23, 2015.
Given that specific developmental domains of cognition, language, vision, and hearing largely occur in the early years of life [26], we focus on children under 4 years of age. Cross-sectional data of rural women and children aged 16–42 months were collected as part of the endline evaluation of a cluster-randomized controlled trial in Madagascar in 2016. The sample represents five regions of the country, which were selected because they have some of the highest rates of stunting and food insecurity in Madagascar: Amoron’ i Mania, Androy, Atsimo Atsinanana, Haute Matsiatra, and Vatovavy-Fitovinany. A more detailed description of the study design and sampling is available elsewhere [27]. Briefly, the five-arm trial tested the effects of intensive nutrition counseling, lipid-based supplementation, or a home visit parenting program on child growth and development. All pregnant women and women with age-eligible children for the intervention were eligible to participate in the trial. A total of 3560 caregivers and children were interviewed and assessed. The measure of ECD, the ASQ-I, was complete for 3533 children. Missing values for caregiver age (N = 82), caregiver education (N = 14), and child birth order (N = 1) were imputed using baseline and midline values. After imputation, complete data on child-, caregiver-, and household-level characteristics were available for 3361 children. We modeled our caregiver-perceived intelligence scale on the existing MacArthur Scale for Subjective Social Status used in adults and adolescents and measures an individual’s perceived economic status [28]. The MacArthur Scale has been shown to be an independent predictor of adult health [29], adolescent self-rated health [30], and adolescent risky behaviors [31]. In the present study, caregivers were asked to look at a picture of a ladder with rungs labeled from 1 to 7, and asked: “At the top of the ladder are the children with the best intelligence status and at the bottom are children with the worst intelligence status. In your opinion, where is your child on this scale?” Caregivers were asked to focus specifically on the target child of the study and rank the child in relation to other children in the community using their own definition of intelligence. Prior to the survey administration, this scale was extensively pre-tested and the terms were discussed by investigators. The terms “Faharanitantsaina” (intelligence, or having a ‘sharp mind’) and “Maranitsaina” (to be intelligent) were selected and are commonly used terms in official Malagasy. In focus groups during the pre-testing, the term ‘intelligence’ was clearly perceived and understood by main caregivers as a broad concept of intelligence and child development that encompasses motor skills, problem solving, socio-emotional skills, language and cognitive capacity. In the instructions, interviewers were careful in asking caregivers to use their own definition of intelligence. Therefore, the subjective ranking was a function of individual and community characteristics. Child developmental abilities were assessed using the ASQ-I, an instrument focused on developmental milestones for children aged 1–54 months across five domains: communication, gross motor, fine motor, problem solving, and personal-social. At the time of this study, the authors were given access to the unpublished ASQ-I instrument courtesy of the Ages and Stages Questionnaire (ASQ) group at the University of Oregon. The validity and reliability of the ASQ-I has since been published [32], and has also been adapted and validated in China [33]. To adapt the ASQ-I to Madagascar, the authors worked in an iterative process for any changes to item ordering, text adaptations, administration protocols for Madagascar, and coding of items. One of the study authors [LR], a local Malagasy psychologist translated and adapted it to the local context, and incorporated interviewer-observed items. The ASQ-I was then back-translated and reviewed by the ASQ group in Oregon. Three iterations of piloting, testing, and updating occurred before the instruments were finalized. For each ASQ-I domain, a summary score was created, and a total ASQ-I score was calculated by summing across the domain-specific scores. The total, continuous score was age-standardized and controlled for interviewer. In order to compare this scale to the caregiver-perceived intelligence measure, we created seven percentile-based, rank-ordered (from 1 to 7) categories of total age-adjusted ASQ-I score. Given that caregivers used their own definition of intelligence on the ladder scale, their ranking is a function of individual and community characteristics. Therefore, we constructed two sets of ASQ-I ranked percentiles: within communities and across the whole analytic sample. A difference score was calculated between the ranks of caregiver-perceived child intelligence and the ASQ-I. This score ranged from − 5 to 6 for both the within-community and whole samples. A positive score indicates that a caregiver ranked the child higher on the perceived intelligence scale than the child ranked on the ASQ-I. Given that small differences between the ranks may not be meaningful, we categorized these scores into under-estimation (difference less than − 2), matched (difference between − 1 and 1), and over-estimation (difference greater than 2). We examined mean-centered child age (in months), gender, and birth order with caregiver under- and over-estimation. We also included height-for-age z-score (HAZ) and weight-for-age z-score (WAZ), measured according to the World Health Organization (WHO) Child Growth reference standards [34]. The WHO defines stunting and underweight as having a HAZ and WAZ, respectively, two standard deviations below the median of same age and sex of a global reference population [34]. All caregiver variables are for the primary caregiver of the child. In the majority of cases, the primary caregiver was the mother, but in some, an older relative was the primary caregiver. We included mean-centered caregiver age (in years), education, depression, and belief of own influence on child intelligence in our analyses. We classified caregiver education into (1) Did not attend school; (2) Primary or less; (3) Secondary or Higher. Caregiver depression was measured using an adapted version of the Center for Epidemiologic Studies Depression Scale (CESD), a screening tool used to assess depressive symptoms [35]. The CESD was adapted, translated, and tested before use. The ten-item scale examines depressed affect, somatic symptoms, and positive affect. Items were summed and standardized to create a depression score. A higher score indicates that a caregiver has more depressive symptoms. Caregiver perceived influence on child intelligence was measured by asking “How much does your child’s intelligence depend on you?” and categorized into (1) None; (2) Some; (3) A lot. We examined the associations of household size, wealth, and stimulation and early learning opportunities with caregiver under- and over-estimation of child development. Household size was reported by the caregiver as the total number of individuals living in the household. To assess household wealth, an asset index was created using principal components analysis [36, 37]. Items in the principal components analysis included housing materials, electricity, toilet, drinking water source, personal property, and livestock. The first component was retained and quintiles of the wealth index were created. We assessed the stimulation and learning opportunities of a household using the Family Care Indicator (FCI) Scale, which has been previously used in a low-resource setting [38]. Using factor analysis, we created an FCI score based on stimulation activities, play materials, and books. Additionally, we included treatment arm and region indicators as control variables. To describe the agreement between caregiver-perceived child intelligence and assessed abilities, we used the Kendall τb correlation test, a non-parametric measure of the association between two ordinal variables that accounts for tied data [39]. We conducted multivariable multinomial logistic regressions to examine the child, caregiver, and household characteristics that were associated with caregiver under- and over-estimation of child abilities, setting matched as the reference outcome category. Model (1) included HAZ while Model (2) included WAZ. We used two different samples to analyze discordance: a within-community sample in which ASQ-I percentiles were ranked within communities and a whole sample in which percentiles were ranked across the whole analytic sample. A multinomial model was favored because the test for the proportional odds assumption was violated [40, 41]. Given the high correlation between HAZ and WAZ (Pearson’s correlation coefficient, r- = 0.71), we included them in separate models. We examined collinearity between independent variables, not including HAZ and WAZ, using Pearson’s correlation coefficients and variance inflation factors. The highest correlation, using Pearson’s correlation coefficient, was between birth order and maternal age (r = 0.65). Multicollinearity was assessed using variance inflation factors (VIF), with a VIF greater than 10 indicative of severe collinearity between independent variables [42]. No independent variable had a VIF greater than three. Analyses were repeated using only the interviewer-observed items of ASQ-I in order to examine the potential of reporting bias by participants. Ranked percentiles of these items were created within communities and multinomial logistic regressions were performed using this sample. Participants with missing data for any variable after imputation were excluded from analyses. Odds ratios (OR) and 95% confidence intervals (CI) were reported.
