Effects of weather and moon phases on emergency medical use after fall injury: A population-based nationwide study.
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ABSTRACT: Background
Previous studies reported that changes in weather and phases of moon are associated with medical emergencies and injuries. However, such studies were limited to hospital or community level without explaining the combined effects of weather and moon phases. We investigated whether changes in weather and moon phases affected emergency department (ED) visits due to fall injuries (FIs) based on nationwide emergency patient registry data.Methods
Nationwide daily data of ED visits after FI were collected from 11 provinces (7 metropolitan cities and 4 rural provinces) in Korea between January 2014 and December 2018. The daily number of FIs was standardized into FI per million population (FPP) in each province. A multivariate regression analysis was conducted to elucidate the relationship between weather factors and moon phases with respect to daily FPP in each province. The correlation between weather factors and FI severity was also analyzed.Results
The study analyzed 666,912 patients (418,135 in metropolitan and 248,777 in rural areas) who visited EDs on weekdays. No regional difference was found in age or gender distribution between the two areas. Precipitation, minimum temperature and wind speed showed a significant association with FI in metropolitan areas. In addition, sunshine duration was also substantial risk factors for FI in rural areas. The incidence of FIs was increased on full moon days than on other days in rural areas. Injury severity was associated with weather factors such as minimum temperature, wind speed, and cloud cover.Conclusion
Weather changes such as precipitation, minimum temperature, and wind speed are associated with FI in metropolitan and rural areas. In addition, sunshine duration and full moon are significantly associated with FI incidence only in rural areas. Weather factors are associated with FI severity.
SUBMITTER: Yuh MA
PROVIDER: S-EPMC8719656 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BACKGROUND:A national Helicopter Emergency Medical Service (HEMS) was introduced in Denmark in 2014 to ensure the availability of physician-led critical care for all patients regardless of location. Appropriate dispatch of HEMS is known to be complex, and resource utilisation is a highly relevant topic. Population-based studies on patient characteristics are fundamental when evaluating and optimising a system. The aim of this study was to describe the patient population treated by the Danish HEMS in terms of demographics, pre-hospital diagnostics, severity of illness or injury, and the critical care interventions performed. METHOD:The study is a retrospective nationwide population-based study based on data gathered from the Danish HEMS database. We included primary missions resulting in a patient encounter registered between October 1st 2014 and April 30th 2018. RESULTS:Of 13.391 dispatches registered in the study period we included 7133 (53%) primary missions with patient encounter: 4639 patients were air lifted to hospital, 174 patients were escorted to hospital by the HEMS physician in an ambulance, and in 2320 cases HEMS assisted the ground crew on scene but did not escort the patient to hospital. Patient age ranged from 0-99 years and 64% of the population were men. The median age was 60 years. The main diagnostic groups were cardio-vascular emergencies (41%), trauma (23%) and neurological emergencies (16%). In 61% of the cases, the patient was critically ill/injured corresponding to a NACA (National Advisory Committee for Aeronautics) score between 4 and 7 (both included). In more than one third of the missions a critical care intervention was performed. Ultrasound examination and endo-tracheal intubation were the critical care interventions most frequently performed (21% and 20%, respectively). CONCLUSION:The national Danish HEMS primarily attends severely ill or injured patients and often perform critical care interventions. In addition, the Danish HEMS provides rapid transport to highly specialised treatment for patients in the more rural parts of the country. Patients with cardio-vascular emergencies, trauma and neurological emergencies are among those patient groups most commonly seen. We conclude that the overall dispatch profile appears appropriate but emphasise that continuous development and refinement is essential.
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Project description:OBJECTIVES:To examine: (1) prevalence of fall risk-increasing drug (FRID) use among older adults with a fall-related injury, (2) which FRIDs were most frequently prescribed, (3) whether FRID use was reduced following the fall-related healthcare episode, and (4) which interventions have reduced falls or FRID use in older adults with a history of falls. DESIGN:Systematic review. PARTICIPANTS:Observational and intervention studies that assessed (or intervened on) FRID use in participants aged 60?years or older who had experienced a fall. MEASUREMENTS:PubMed and EMBASE were searched through June 30, 2019. Two reviewers independently extracted data and evaluated studies for bias. Discrepancies were resolved by consensus. RESULTS:Fourteen of 638 articles met selection criteria: 10 observational studies and 4 intervention studies. FRID use prevalence at time of fall-related injury ranged from 65% to 93%. Antidepressants and sedatives-hypnotics were the most commonly prescribed FRIDs. Of the 10 observational studies, only 2 used a design adequate to capture changes in FRID use after a fall-related injury, neither finding a reduction in FRID use. Three randomized controlled studies conducted in various settings (hospital, emergency department, and community pharmacy) with 12-month follow-up did not find a reduction in falls with interventions to reduce FRID use, although the study conducted in the community pharmacy setting was effective in reducing FRID use. In a nonrandomized (pre-post) intervention study conducted in an outpatient geriatrics clinic, falls were reduced in the intervention group. CONCLUSIONS:Limited evidence indicates high prevalence of FRID use among older adults who have experienced a fall-related injury and no reduction in overall FRID use following the fall-related healthcare encounter. There is a need for well-designed interventions to reduce FRID use and falls in older adults with a history of falls. Reducing FRID use as a stand-alone intervention may not be effective in reducing recurrent falls. J Am Geriatr Soc 68:1334-1343, 2020.
| S-EPMC7299782 | biostudies-literature
Project description:BackgroundFalls impact over 25% of older adults annually, making fall prevention a critical public health focus. We aimed to develop and validate a machine learning-based prediction model for serious fall-related injuries (FRIs) among community-dwelling older adults, incorporating various medication factors.MethodsUtilizing annual national patient sample data, we segmented outpatient older adults without FRIs in the preceding three months into development and validation cohorts based on data from 2018 and 2019, respectively. The outcome of interest was serious FRIs, which we defined operationally as incidents necessitating an emergency department visit or hospital admission, identified by the diagnostic codes of injuries that are likely associated with falls. We developed four machine-learning models (light gradient boosting machine, Catboost, eXtreme Gradient Boosting, and Random forest), along with a logistic regression model as a reference.ResultsIn both cohorts, FRIs leading to hospitalization/emergency department visits occurred in approximately 2% of patients. After selecting features from initial set of 187, we retained 26, with 15 of them being medication-related. Catboost emerged as the top model, with area under the receiver operating characteristic of 0.700, along with sensitivity and specificity rates around 65%. The high-risk group showed more than threefold greater risk of FRIs than the low-risk group, and model interpretations aligned with clinical intuition.ConclusionWe developed and validated an explainable machine-learning model for predicting serious FRIs in community-dwelling older adults. With prospective validation, this model could facilitate targeted fall prevention strategies in primary care or community-pharmacy settings.
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