Do relationships between ambient temperature and serious adverse health outcomes vary among users of different antidiabetes drugs? A retrospective cohort study of US Medicaid beneficiaries with type 2 diabetes
Bibliographic record
Abstract
OBJECTIVE: Prior studies demonstrate that some untoward clinical outcomes vary by outdoor temperature. This is true of some endpoints common among persons with diabetes, a population vulnerable to climate change-associated health risks. Yet, prior work has been agnostic to the antidiabetes drugs taken by such persons. We examined whether relationships between ambient temperature and adverse health outcomes among persons with type 2 diabetes (T2D) varied by exposure to different antidiabetes drugs. DESIGN: Retrospective cohort. SETTING: Healthcare and meteorological data from five US states, 1999-2010. PARTICIPANTS: US Medicaid beneficiaries with T2D categorised by use of antidiabetes drugs. EXPOSURE: Maximum daily ambient temperature (t-max). OUTCOMES: Hospital presentation for serious hypoglycaemia, diabetic ketoacidosis (DKA) or sudden cardiac arrest (examined separately). METHODS: We linked US Medicaid to US Department of Commerce data that permitted us to follow individuals longitudinally and examine health plan enrolment, healthcare claims, and meteorological exposures-all at the person-day level. We mapped daily temperature from weather stations to Zone Improvement Plan (ZIP) codes, then assigned a t-max to each person-day based on the residential ZIP code. Among prespecified subcohorts of users of different pharmacologic classes of antidiabetes drugs, we calculated age and sex-adjusted occurrence rates for each outcome by t-max stratum. We used modified Poisson regression to assess relationships between linear and quadratic t-max terms and each outcome. We examined effect modification between t-max and a covariable for current exposure to a specific antidiabetes drug and assessed significance via Wald tests. RESULTS: We identified ∼3 million persons with T2D among whom 713 464 used sulfonylureas (SUs), dipeptidyl peptidase-4 inhibitors (DPP-4is), meglitinides, or glucagon-like peptide 1 receptor agonists (GLP1RAs). We identified a positive linear association between t-max and serious hypoglycaemia among non-insulin users of glimepiride and of glyburide but not glipizide (Wald p value for interaction among SUs=0.048). We identified an inverse linear association between t-max and DKA among users of the DPP-4i sitagliptin (p=0.016) but not the GLP1RA exenatide (p=0.080). We did not identify associations between t-max and sudden cardiac arrest among users of SUs, meglitinides, exenatide, or DPP-4is. CONCLUSIONS: We identified some antidiabetes drug class-specific and agent-specific differences in the relationship between ambient temperature and untoward glycaemic but not arrhythmogenic, safety outcomes.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".