Gestational diabetes screening changes and impacts on diagnosis
Bibliographic record
Abstract
Background: Gestational diabetes mellitus (GDM) affects between 2-40% of pregnancies worldwide, depending on diagnostic and screening methods. Changes in screening practices are not well understood because administrative sources lack data on whether or how individuals were screened. The objectives of this thesis were to: 1) validate a method to identify prenatal screening for GDM and other conditions in administrative health data; 2) describe changes in GDM screening; 3) evaluate the relative contributions of screening and population characteristics to changes in GDM risk; 4) characterize the impact of the COVID-19 pandemic on pregnancy weight gain and infant birthweight. Methods: Laboratory billing records from BC’s universal health insurance system for prenatal screening tests were compared with medical records by calculating validation properties. All pregnancies (birth >20wks or >500g) in British Columbia, Canada, 2005-2019, with linked perinatal health and administrative data, were used to examine time trends in GDM screening methods, trends within subgroups, and the effect of screening changes on prevalence. A second cohort from Washington State, 2016-2020, was analyzed using an interrupted time series design, to assess COVID-19 impacts on pregnancy weight gain and infant birthweight using z-scores. Results: GDM screening in laboratory billing records had a high sensitivity (97% [95% CI: 90, 99]) and specificity (>99% [95% CI: 86, <99]) compared with medical records. GDM diagnoses in BC more than doubled from 7.2% in 2005 to 14.7% in 2019 (n=550,783 pregnancies). Most of this increase was explained by changes in screening; adjustment for population factors had minimal impact. In Washington state, using an interrupted time series, pregnancy weight gain z-score increased by 0.08 (95% CI 0.03, 0.13) after the COVID-19 pandemic onset and infant birthweight z-scores were unchanged (-0.004, 95% CI (-0.04, 0.03)). Conclusion: Prenatal screening tests can be accurately ascertained using BC insurance billing data. Changes in GDM screening completion and in screening methods accounted for most of the increase in GDM diagnosis in BC since 2005. Covid-19 pandemic countermeasures were associated with an increase in pregnancy weight gain but not infant birthweight. Public health and future researchers should understand how screening changes can directly affect disease prevalence.
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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 | 0.005 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".