Maternal depression and anxiety disorders (MDAD) and child development: A Manitoba population-based study

<div><p>Objective</p><p>To examine the association between maternal depression and anxiety disorders (MDAD) and child development assessed during the kindergarten year.</p><p>Methods</p><p>Administrative data from several health and social databases in Manitoba, Canada, were used to study 18,331 mother-child pairs. MDAD over the period from one year prior to the child's birth to the kindergarten year was defined using physician diagnoses and filled prescriptions. Child development was assessed during the kindergarten year using the Early Development Instrument (EDI) which measures vulnerability across five domains of development. Structural equation modeling was used to examine associations between timing, recurrence and severity of MDAD and child outcomes. Health at Birth (preterm, low birth weight, neonatal intensive care stay and long birth hospitalization), Family Context (teen mother, lone parent, socio-economic status (SES)), child age and child sex were covariates.</p><p>Results</p><p>MDAD had a modest negative association with child EDI scores across all models tested, particularly for social, emotional and physical development. Prenatal MDAD had a stronger negative association with outcomes than other time periods; however, recurrent MDAD had a stronger negative association with outcomes than any specific time period or MDAD severity. The influence of MDAD was mediated by Family Context, which had a strong, negative association with outcomes, particularly language and cognitive development.</p><p>Conclusion</p><p>The number of time periods a child was exposed to MDAD in early childhood was more negatively associated with five areas of child development than timing or severity. Prenatal exposure may be more sensitive to MDAD than other time periods. The familial context (teen mother, lone parenthood and low SES) had a stronger influence on child outcomes than MDAD. Findings can be used to inform interventions which address maternal mental health from the prenatal period onward, and to support disadvantaged families to encourage healthy birth outcomes, early childhood development and school readiness.</p></div

Structural equation model for recurrent MDAD and social competence at kindergarten.

<p>Structural equation model for recurrent MDAD and social competence at kindergarten.</p

Data sources used in the study.

<p>Data sources used in the study.</p

Conceptual model.

<p>(A) Model for the prenatal period with path from MDAD to Health at Birth. (B) Model for time periods following birth with path from Health at Birth to MDAD.</p

Latent constructs used in the statistical modeling.

<p>Latent constructs used in the statistical modeling.</p

Time periods used in the study.

<p>Time periods used in the study.</p

Descriptive statistics for model variables measured at one time.

<p>Descriptive statistics for model variables measured at one time.</p

ICD-9-CM and ICD-10-CA codes.

IntroductionTraumatic physical injuries are the number one cause of hospitalization and death among children in Canada. The majority of these injuries are preventable. The burden from injury can be reduced through prevention programs tailored to at-risk groups, however, existing research does not provide a strong explanation of how social factors influence a child’s risk of injury. We propose a theoretical framework to better understand social factors and injury in children and will examine the association between these social factors and physical traumatic injury in children using large population-wide data.Methods and analysisWe will examine data from 11,000 children hospitalized for traumatic physical injury and 55,000 matched uninjured children by linking longitudinal administrative and clinical data contained at the Manitoba Centre for Health Policy. We will examine 14 social determinants of child health measures from our theoretical framework, including receipt of income assistance, rural/urban status, socioeconomic status, children in care, child mental disorder, and parental factors (involvement with criminal justice system, education, social housing, immigration status, high residential mobility, mother’s age at first birth, maternal Axis I mental disorder, maternal Axis II mental disorder and maternal physical disorder) to identify groups and periods of time when children are at greatest risk for traumatic physical injury. A conditional multivariable logistic regression model will be calculated (including all social determinant measures) to determine odds ratios and adjusted odds ratios (95% confidence interval) for cases (injured) and controls (non-injured).Ethics and disseminationHealth Information Privacy Committee (HIPC No. 2017/2018-75) and local ethics approval (H2018-123) were obtained. Once social measures have been identified through statistical modelling, we will determine how they fit into a Haddon matrix to identify appropriate areas for intervention. Knowing these risk factors will guide decision-makers and health policy.</div

Minimum detectable effect size (odds ratios).

IntroductionTraumatic physical injuries are the number one cause of hospitalization and death among children in Canada. The majority of these injuries are preventable. The burden from injury can be reduced through prevention programs tailored to at-risk groups, however, existing research does not provide a strong explanation of how social factors influence a child’s risk of injury. We propose a theoretical framework to better understand social factors and injury in children and will examine the association between these social factors and physical traumatic injury in children using large population-wide data.Methods and analysisWe will examine data from 11,000 children hospitalized for traumatic physical injury and 55,000 matched uninjured children by linking longitudinal administrative and clinical data contained at the Manitoba Centre for Health Policy. We will examine 14 social determinants of child health measures from our theoretical framework, including receipt of income assistance, rural/urban status, socioeconomic status, children in care, child mental disorder, and parental factors (involvement with criminal justice system, education, social housing, immigration status, high residential mobility, mother’s age at first birth, maternal Axis I mental disorder, maternal Axis II mental disorder and maternal physical disorder) to identify groups and periods of time when children are at greatest risk for traumatic physical injury. A conditional multivariable logistic regression model will be calculated (including all social determinant measures) to determine odds ratios and adjusted odds ratios (95% confidence interval) for cases (injured) and controls (non-injured).Ethics and disseminationHealth Information Privacy Committee (HIPC No. 2017/2018-75) and local ethics approval (H2018-123) were obtained. Once social measures have been identified through statistical modelling, we will determine how they fit into a Haddon matrix to identify appropriate areas for intervention. Knowing these risk factors will guide decision-makers and health policy.</div

Administrative datasets to be used in the study.

IntroductionTraumatic physical injuries are the number one cause of hospitalization and death among children in Canada. The majority of these injuries are preventable. The burden from injury can be reduced through prevention programs tailored to at-risk groups, however, existing research does not provide a strong explanation of how social factors influence a child’s risk of injury. We propose a theoretical framework to better understand social factors and injury in children and will examine the association between these social factors and physical traumatic injury in children using large population-wide data.Methods and analysisWe will examine data from 11,000 children hospitalized for traumatic physical injury and 55,000 matched uninjured children by linking longitudinal administrative and clinical data contained at the Manitoba Centre for Health Policy. We will examine 14 social determinants of child health measures from our theoretical framework, including receipt of income assistance, rural/urban status, socioeconomic status, children in care, child mental disorder, and parental factors (involvement with criminal justice system, education, social housing, immigration status, high residential mobility, mother’s age at first birth, maternal Axis I mental disorder, maternal Axis II mental disorder and maternal physical disorder) to identify groups and periods of time when children are at greatest risk for traumatic physical injury. A conditional multivariable logistic regression model will be calculated (including all social determinant measures) to determine odds ratios and adjusted odds ratios (95% confidence interval) for cases (injured) and controls (non-injured).Ethics and disseminationHealth Information Privacy Committee (HIPC No. 2017/2018-75) and local ethics approval (H2018-123) were obtained. Once social measures have been identified through statistical modelling, we will determine how they fit into a Haddon matrix to identify appropriate areas for intervention. Knowing these risk factors will guide decision-makers and health policy.</div