KlimaNot – Effects of climate change on emergency and acute care: protocol for a multicenter, registry-based observational cohort study (Preprint)
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,040 | 0,065 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,003 |
| Méta-épidémiologie (sens large) | 0,003 | 0,004 |
| Bibliométrie | 0,003 | 0,005 |
| Études des sciences et des technologies | 0,005 | 0,002 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,003 | 0,003 |
| Intégrité de la recherche | 0,002 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,052 | 0,014 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».