Identifying Communities at Risk for Sudden and Unexpected Infant Deaths Using ArcGIS
Bibliographic record
Abstract
In 2004, approximately 4,500 cases of sudden, unexpected infant death occurred in the United States and between 1994 and 2004 there were 1,007 SUID deaths in Kentucky. Linking morbidity and mortality rates to geographic areas is a fundamental epidemiological tool, which can be applied to preventing infant death. In 2006 the Centers for Disease Control and Prevention funded seven states to record and collect statewide data to clarify certification practices, and identify if state performances fall short of national expectations. Statewide SUID data were retrospectively collected in Kentucky for the years 1999-2005. There were 575 evaluable SUID cases during the study period. For visual analysis of the data, cases were geocoded then spatially joined to the county GIS data layer in a combined data set in order to create maps. Standardized mortality ratio and probability maps were generated and areas with unexpectedly high or low SUIDs were identified. Of Kentucky's 120 counties, 42 were found to have SUIDs higher than expected (including 20 counties, 48% considered Appalachian and 86% non-metropolitan). The remaining 78 counties were considered average with an expected number of SUID cases or a lower number of SUIDs than would be expected. Identifying regions with higher than expected SUID rates allows specific communities and regions to be targeted. Understanding geographically based risk factors allows for more effective and focused prevention strategies. Similar analyses in other states could target needy areas with limited resources to optimize risk reduction and promote more effective pregnancy planning. Keywords: Sudden infant death syndrome, sudden unexplained infant death, mapping analysis, Appalachian, rural
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".