Familial aggregation of childhood health and the socioeconomic gradient of disease: a longitudinal population-based sibling analysis
Bibliographic record
Abstract
This study explores the relationships that emerge between socioeconomic status (SES) and the prevalence of several health outcomes in children of different ages utilizing administrative data housed at The Manitoba Centre for Health Policy (MCHP). This research also determines the effect that family has on a child developing (or not developing) a specific health outcome. Finally, the relationship between prevalence and familial aggregation are examined. The Johns Hopkins ACG(r) Case-Mix System grouped various physician and hospital diagnosis codes into 32 Aggregated Diagnostic Groups (ADGs). Eight of these ADGs were assessed at four age groups (0-3, 4-8, 9-13 & 14-18) for each member of the final study population. Each member was assigned to one of six SES groups, five income quintile groups and one social assistance group. Familial aggregation was determined for eight selected ADGs using an intraclass correlation coefficient (ICC). Statistical contrasts were made for SA vs. Q1-Q5 and an overall linear trend (SA – lowest; Q5 – highest) to establish the SES differences for the prevalence and familial aggregation of a particular condition. Many of the conditions across SES had statistically significant (p<0.05) linear and SA vs. Q1-Q5 contrasts for 3 both ICCs and prevalence at all age groups. Of the eight ADGs that familial aggregation was calculated, chronic conditions related to the eye had the highest ICCs at all age groups. Injury ADGs had consistently lower ICCs for all age groups. Factors that affected the results of ICC estimation for binary outcomes include the number of bootstrap selections, the width of the age group and the event rate for the outcome of interest. Suggested future research includes a validity review of ICC estimates for binary outcomes, exploring the variables that may reduce or eliminate the SES gradient for ICCs and exploring the aggregation for different study samples within Manitoba.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".