Severe Hypoglycemia Rates Are Highest among those with Suboptimal Reporting Behaviour—Results from the InHypo-DM Study
Bibliographic record
Abstract
Many patients fail to report severe hypoglycemia (SH) events to their healthcare provider (HCP). This limits the ability of HCPs to adjust treatment regimens and optimize glycemia. This study aimed to explore the independent association between patients’ disclosure of SH events to their HCP and self-reported rates of SH. Data were obtained from the InHypo-DM population-based study, Canada’s largest hypoglycemia-specific survey. To indicate SH disclosure, patients at risk for hypoglycemia rated how often they reported their SH events (using ADA definitions) to their HCP on a scale from Never to Always. Univariable analyses identified significant clinical or demographic confounders (p≤0.2). Based on the distribution of SH events, zero-inflated negative binomial (ZINB) regression was used to test the relationship between hypoglycemia disclosure and the retrospective incidence of SH events. A total of 552 people with T1DM (17%) or T2DM (83%) completed the InHypo-DM survey. Forty percent of respondents reported at least one or more SH events. The overall incidence rate of SH was 2.45 events/person-year. Thirty-four percent of respondents indicated that they always report SH events. Results of the ZINB model showed that the odds of experiencing any event was not significantly associated with disclosure behaviour (p=0.86). However, the rate of repeated events was almost double (IRR=1.9, 95% CI: 1.2 to 3.0, p=0.004) among individuals who under-reported their SH as compared to those who did not. These results persisted after adjusting for age, medication type, income, most recent HbA1c, and presence of comorbidities. This population-based study revealed a nearly two-fold higher rate of SH among those who under-report to their HCP. Thus, HCPs should be aware that patient reporting may not be a true reflection of SH events and make efforts to elicit accurate information regarding SH events from their patients, especially for those at risk for repeated SH. Disclosure A. Ratzki-Leewing: None. J.E. Black: None. S. Mequanint: None. N.H. Au: None. B.L. Ryan: None. S.M. Reichert: Other Relationship; Self; Novo Nordisk Inc., Sanofi, Abbott, AstraZeneca. Advisory Panel; Self; Servier. Speaker's Bureau; Self; Eli Lilly and Company. Other Relationship; Self; Boehringer Ingelheim Pharmaceuticals, Inc.. Speaker's Bureau; Self; Merck & Co., Inc., Janssen Pharmaceuticals, Inc.. J.B. Brown: None. S. Harris: Advisory Panel; Self; Novo Nordisk A/S, Sanofi, Merck, AstraZeneca, Amgen Inc., Lilly/Boehringer Ingelheim, Abbott, Janssen. Consultant; Self; Novo Nordisk A/S, Sanofi, Merck, AstraZeneca, Lilly/Boehringer Ingelheim, Abbott, Janssen. Research Support; Self; Novo Nordisk A/S, Sanofi, Merck, Abbott, AstraZeneca, Janssen. Other Relationship; Self; CIHR, CDA, The Lawson Foundation.
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How this classification was reachedexpand
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".