DATA LINKAGE FOR EVALUATING MATERNAL INFLUENCES ON INFANT MORTALITY AND MALTREATMENT IN CANADA
Bibliographic record
Abstract
Abstract BACKGROUND A number of social risk factors are reported to increase infant mortality rates and child maltreatment. Public health programs attempt to mitigate risk factors and improve outcomes for infants. This study aimed to explore the association of exposure factors in mothers with infant mortality and maltreatment in Ontario. OBJECTIVES Objectives for this study included: 1. Describe prevalence of infant mortality and maltreatment in Ontario. 2. Explore how maternal risk factors influence infant mortality and maltreatment. DESIGN/METHODS This was a population-based study of 845, 567 infants born between April 1, 2005 and March 31, 2015 using administrative and healthcare databases available at the Institute of Clinical Evaluative Sciences (ICES). Maternal risk factors were selected based on public health home visiting referral criteria. These exposures included, maternal adversity (substance abuse, intimate partner violence, homelessness), newcomer status (new to Canada in past 3 years) and young maternal age (less than 22 years of age). The primary outcome measure was all-cause mortality of infants less than 12 months age. The secondary outcome measures were combined fatal and non-fatal child maltreatment outcomes and were defined using International Classification of Diseases for maltreatment diagnoses. Baseline characteristics and outcomes were described. The association between maternal risk factors and infant mortality and maltreatment was analysed using multivariable logistic modelling, including analysis by type of maternal risk factors and number of risk factors. RESULTS All-cause deaths were present in 0.14% and combined fatal and non-fatal maltreatment outcomes were present in 0.05% of the study population. Young maternal age increased the risk of all-cause mortality 2.4 times (n 171, OR 2.4, 95% CI 2.0–3.0) and maltreatment 6.3 times (n 292, OR 6.3, 95% CI 5.0–7.8). Mental health diagnosis increased the odds of maltreatment by 90% (n 209, OR 1.9, 95% CI 1.5–2.4). Adversity increased the odds of maltreatment by 63% (n 40, OR 1.63, 95% CI 1.0–2.6). The risk of maltreatment also increased as the number of risk factors increased with an OR of 3.5 (95% CI 2.9–4.4) with one risk factor, an OR of 8.2 (95% CI 5.9–11.4) with two risk factors, and an OR of 10.9 (95% CI 5.7 20.7) with three or more risk factors. Newcomer status was not associated with increased risk of maltreatment and mortality. Gestational age showed increasing ORs as prematurity increased. Material deprivation was included as a covariate and was associated with increased risk of maltreatment with increased level of deprivation. CONCLUSION Young maternal age carried the greatest risk of death and maltreatment in infants. There was also an increasing risk of infant mortality and maltreatment with increasing number of risk factors. These findings are important for ensuring public health interventions are targeting the most vulnerable populations with the aim of preventing maltreatment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.072 | 0.226 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.016 | 0.039 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".