IS BEING A PREMATURE OR HAVING ABNORMAL BIRTH WEIGHT ASSOCIATED WITH DEPRESSION AMONG 6 - 17 YEARS OLD U.S. CHILDREN?
Bibliographic record
Abstract
Abstract BACKGROUND Reports in 2015 showed that premature birth rate in the United States increased when compared to 2014 data, and this was the first increment since 2007. Major complications of prematurity and birth weight abnormalities are well known, but other complications including mental health abnormalities require more investigation to understand their association well. OBJECTIVES We aimed in this study to determine if prematurity and birth weight abnormalities including very low birth weight (VLBW) and low birth weight (LBW) are associated with depression among United States children aged between six and seventeen years old. DESIGN/METHODS This is a cross sectional study using data from the National Survey of Children’s Health (NSCH) 2011–2012. When we applied our selection criteria, 84,182 children out of the total 95,677 NSCH population were selected. Our exclusion criteria were: age less than six years, child’s history of cerebral palsy, and mental retardation. Multivariable logistic regression was done to control for confounding effects when studying the association of prematurity, birth weight abnormalities and depression. RESULTS Our results reveal that 3.6% of our population had history of depression, 11% were born prematurely, 7.4% had low birth weight, and 1.5% had very low birth weight. Depression was more frequent in children who were born prematurely (prevalence 4.3%) when compared to children born at term. Different models were built to analyze the association between prematurity, birth weight abnormalities and depression. There was no detectable statistically significant association when controlling for demographic data (age, gender, race, family structure) and mental health risk factors (parental poor mental health, chronic health conditions) as well as other factors. Results reveal that children who had chronic health conditions or had adverse family experiences have greater odds of having depression. On the other hand, African-American, male, and younger (6–11 years old) children have lower odds of depression. CONCLUSION Further longitudinal studies are required to establish a causal relationship of behavioral and psychological complications, and to determine the biological mechanisms of brain development that could be associated with depression among premature infants or those who have birth weight abnormalities.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".