Trends and Factors Associated With Mortality Rates of Leading Causes of Infant Death: A CDC Wide-Ranging Online Data for Epidemiologic Research (CDC WONDER) Database Analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Infant mortality is a critical indicator of a nation's healthcare system and social well-being. This study explores trends and factors associated with mortality rates for three leading causes of infant death: congenital malformations, deformations, and chromosomal abnormalities; disorders related to short gestation and low birth weight, not elsewhere classified; and sudden infant death syndrome (SIDS). METHODS: Utilizing the CDC WONDER (CDC Wide-Ranging Online Data for Epidemiologic Research) database, we conducted a retrospective observational analysis of infant mortality rates and associated factors. Data encompassed multiple years, allowing for trend analysis and exploration of influencing variables. Study variables included demographic, maternal, prenatal, and leading cause as factors. RESULT: Trends in infant mortality rates varied across causes. The overall mortality rate was 2.69 per 1,000 (p=0.000) people during 2007-2020. The highest rates were observed in 2007 (3.05), 2008 (3.01), and 2009 (2.93) per 1,000 infants. For congenital malformations, deformations, and chromosomal abnormalities, the rate ranged from 1.35 to 1.12 (2007-2020). Gender-based mortality differences were subtle (male rate 2.88 per 1,000 infants, p=0.000; female infants 2.50 per 1,000 infants, p=0.000). The examination of infant mortality trends also explored maternal variables, including maternal age, education, and delivery method. The analysis revealed disparities across variables. Teenage maternal age correlated with higher mortality rates, while maternal education was associated with lower rates. Vaginal delivery (2.61 per 1,000 infants, p=0.199) showed slightly lower rates compared to cesarean section (2.86 per 1,000 infants, p=0.076). CONCLUSION: This study utilizes the CDC WONDER database and offers evidence of changing trends in infant mortality rates for the selected causes. Factors such as maternal age (30-34 years and 35-39 years), race/ethnicity (Black or African-American and White), birthplace (in hospital), and mother's education (master's degree) were identified as influencing mortality rates. These findings contribute to informed policymaking and interventions aimed at mitigating infant mortality and improving the well-being of infants and their families. Further research is needed to fully understand the underlying dynamics of these trends and factors.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it