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Physical punishment as a predictor of early cognitive development: Evidence from econometric approaches.

ABSTRACT: This study estimates the effect of physical punishment on the cognitive development of 1,167 low-income Colombian children (Mage = 17.8 months old) using 3 analytic strategies: lagged-dependent variables, a difference-in-differences-like approach (DD), and a novel strategy combining matching with a DD-like approach. Across approaches, physical punishment at ages 9-26 months predicted reductions in children's cognitive development of 0.08-0.21 SD at ages 27-46 months. These results, plus null results of falsification tests, strengthen the argument that physical punishment leads to slower cognitive growth and illustrate the utility of alternative statistical methods to reduce problems of selection bias in developmental research. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

SUBMITTER: Cuartas J

PROVIDER: S-EPMC7983059 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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Physical punishment as a predictor of early cognitive development: Evidence from econometric approaches.

Cuartas Jorge J McCoy Dana Charles DC Grogan-Kaylor Andrew A Gershoff Elizabeth E

Developmental psychology 20200907 11

This study estimates the effect of physical punishment on the cognitive development of 1,167 low-income Colombian children (Mage = 17.8 months old) using 3 analytic strategies: lagged-dependent variables, a difference-in-differences-like approach (DD), and a novel strategy combining matching with a DD-like approach. Across approaches, physical punishment at ages 9-26 months predicted reductions in children's cognitive development of 0.08-0.21 SD at ages 27-46 months. These results, plus null res ...[more]

PMID: 32897084

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Project description:BackgroundDespite growing evidence that early life experiences and exposures can impact child development, there is limited research on how prenatal and early life nutrition and early life parenting practices predict specific domains of child development in resource-limited settings. This study examines the association between prenatal factors, birth outcomes, and early life characteristics with motor, cognitive/language, and socioemotional development in Tanzania.MethodsWe assessed motor, cognitive/language, and socioemotional development among a cohort of 198 children aged 20-39 months in Dar es Salaam, Tanzania, whose mothers were previously enrolled in a randomized, placebo-controlled trial of prenatal vitamin A and zinc supplementation. Linear regression models were used to assess standardized mean differences in child development scores for randomized prenatal regimen and pregnancy, delivery, and early childhood factors.ResultsChildren born to mothers randomized to prenatal vitamin A had significantly lower reported motor scores in minimally adjusted and multivariate analyses, -0.29 SD, 95% CI [-0.54, -0.04], p = 0.03, as compared with children whose mothers did not receive vitamin A. There was no significant effect of randomized prenatal zinc on any development domain. Greater caregiver-child stimulation was associated with 0.38 SD, 95% CI [0.14, 0.63], p < 0.01, better cognitive/language scores, whereas children who experienced both verbal and physical punishment had 0.29 SD, 95% CI [-0.52, -0.05], p = 0.02, lower scores in socioemotional development. Maternal completion of primary school was associated with higher reported motor and cognitive/language development. Further, children of mothers who were <155 cm tall had lower cognitive and language scores.ConclusionPrenatal vitamin A supplements in a setting with low levels of vitamin A deficiency may not provide child development benefits. However, integrated environmental, educational, parenting, and stimulation interventions may have large positive effects across child development domains in resource-limited settings.

Project description:BackgroundRecently, tau PET tracers have shown strong associations with clinical outcomes in individuals with cognitive impairment and cognitively unremarkable elderly individuals. flortaucipir PET scans to measure tau deposition in multiple brain areas as the disease progresses. This information needs to be summarized to evaluate disease severity and predict disease progression. We, therefore, sought to develop a machine learning-derived index, SPARE-Tau, which successfully detects pathology in the earliest disease stages and accurately predicts progression compared to a priori-based region of interest approaches (ROI).Methods587 participants of the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort had flortaucipir scans, structural MRI scans, and an Aβ biomarker test (CSF or florbetapir PET) performed on the same visit. We derived the SPARE-Tau index in a subset of 367 participants. We evaluated associations with clinical measures for CSF p-tau, SPARE-MRI, and flortaucipir PET indices (SPARE-Tau, meta-temporal, and average Braak ROIs). Bootstrapped multivariate adaptive regression splines linear regression analyzed the association between the biomarkers and baseline ADAS-Cog13 scores. Bootstrapped multivariate linear regression models evaluated associations with clinical diagnosis. Cox-hazards and mixed-effects models investigated clinical progression and longitudinal ADAS-Cog13 changes. The Aβ positive cognitively unremarkable participants, not included in the SPARE-Tau training, served as an independent validation group.ResultsCompared to CSF p-tau, meta-temporal, and averaged Braak tau PET ROIs, SPARE-Tau showed the strongest association with baseline ADAS-cog13 scores and diagnosis. SPARE-Tau also presented the strongest association with clinical progression in cognitively unremarkable participants and longitudinal ADAS-Cog13 changes. Results were confirmed in the Aβ+ cognitively unremarkable hold-out sample participants. CSF p-tau showed the weakest cross-sectional associations and longitudinal prediction.DiscussionFlortaucipir indices showed the strongest clinical association among the studied biomarkers (flortaucipir, florbetapir, structural MRI, and CSF p-tau) and were predictive in the preclinical disease stages. Among the flortaucipir indices, the machine-learning derived SPARE-Tau index was the most sensitive clinical progression biomarker. The combination of different biomarker modalities better predicted cognitive performance.

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Physical punishment as a predictor of early cognitive development: Evidence from econometric approaches.

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Physical punishment as a predictor of early cognitive development: Evidence from econometric approaches.

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