KlimaNot – Effects of climate change on emergency and acute care: protocol for a multicenter, registry-based observational cohort study (Preprint)
Bibliographic record
Abstract
BACKGROUND: Due to climate change, the population and health care systems face an increasing burden of weather-related health risks. Emergency departments (EDs) are one of the first points of contact for acute and emergency care and insights into population health. Previous research has demonstrated that climate change-based weather phenomena have an impact on ED usage and morbidity. However, research shows inconsistent results for some weather phenomena and disease groups, and no corresponding evidence is yet available for Germany. OBJECTIVE: This study aims to investigate the association between climate-related weather conditions and ED usage and morbidity in Germany. It focuses on identifying particularly vulnerable patient groups, developing indicators for syndromic surveillance, and testing prediction models to support clinical and public health decision-making. METHODS: KlimaNot is a multicenter, registry-based observational cohort study with retrospective and prospective components. The primary analysis is a prespecified retrospective evaluation using routinely collected encounter data from the German National Emergency Department Data Registry (AKTIN), comprising approximately 6.35 million ED visits from up to 56 EDs (2019-2024). Hospital-level environmental exposures (eg, temperature and selected air pollutants) will be linked to each participating ED based on location and summarized to daily metrics. The primary endpoint is daily all-cause ED visit volume at the hospital-day level. Secondary endpoints include hospital admission probability, syndromic and diagnostic case-mix, referral source and mode of transport, and routinely recorded proxies of clinical severity (eg, triage acuity). Heat effects will be quantified using models allowing for nonlinear exposure-response relationships and delayed (lagged) effects, with adjustment for site, seasonality and time trends, weekday, and public holidays; effect modification by age, sex, multimorbidity proxies, and area-level socioeconomic deprivation will be assessed. Additional analyses include a predefined case study for the region Stuttgart, leveraging extended longitudinal and more detailed pathway data, development and validation of heat-sensitive syndromic surveillance indicators, evaluation of short-term forecasting models of ED usage, and an ancillary prospective geriatric substudy collecting patient-reported and functional outcomes to better characterize vulnerability in the oldest-old. RESULTS: Retrospective data analyses are ongoing and scheduled for completion by April 1, 2026. The prospective study will expect results by September 1, 2026. CONCLUSIONS: This study will provide the first robust evidence on the impact of climate change-related weather conditions on ED usage and morbidity in Germany. The findings aim to support early detection, preparedness, and targeted protection strategies for vulnerable populations and inform clinical and public health decision-making. TRIAL REGISTRATION: German Clinical Trials Registry DRKS00033214; https://drks.de/search/en/trial/DRKS00033214 and German Clinical Trials Registry DRKS00037822; https://drks.de/search/en/trial/DRKS00037822. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR1-10.2196/82267.
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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 | 0.040 | 0.065 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.052 | 0.014 |
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".