A PHP Error was encountered

Severity: Warning

Message: file_get_contents(https://...@pubfacts.com&api_key=b8daa3ad693db53b1410957c26c9a51b4908&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests

Filename: helpers/my_audit_helper.php

Line Number: 176

Backtrace:

File: /var/www/html/application/helpers/my_audit_helper.php
Line: 176
Function: file_get_contents

File: /var/www/html/application/helpers/my_audit_helper.php
Line: 250
Function: simplexml_load_file_from_url

File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3122
Function: getPubMedXML

File: /var/www/html/application/controllers/Detail.php
Line: 575
Function: pubMedSearch_Global

File: /var/www/html/application/controllers/Detail.php
Line: 489
Function: pubMedGetRelatedKeyword

File: /var/www/html/index.php
Line: 316
Function: require_once

Local weather effects on emergency department visits: a time series and regression analysis. | LitMetric

Local weather effects on emergency department visits: a time series and regression analysis.

Pediatr Emerg Care

Department of Emergency Medicine, Mount Sinai School of Medicine, New York, NY 10029-6574, USA.

Published: February 2006

Objective: The ability to forecast atypical emergency department (ED) volumes may aid staff/resource allocation. We determine whether deviations from short-term predictions of weather can be used to forecast deviations from short-term predictions of ED volumes.

Methods: In this retrospective study, we attempted to predict the volume of patient visits to an academic pediatric ED based on short-interval local weather patterns (2000). Local temperature and precipitation data in 1- and 3-hour increments were obtained. Precipitation was coded to be present if it exceeded 0.04 in and subclassified as cold rain/snow if the ambient temperature was lower than 40 degrees F. ED visits were categorized as injuries, emergent, or nonemergent visits. For each category of visit, Box-Jenkins Auto-Regressive Integrated Moving Average time-series models were created of natural trends and cycles in temperature and patient volumes. From these models, differences (residuals) between predicted and observed values of these variables were estimated. The correlation between residuals for temperature and ED volumes was derived for various kinds of ED visit, after controlling for type/volume of precipitation.

Results: Residuals for ambient temperature controlled for precipitation correlated poorly with residuals for patient volumes, accounting for 1% to 6% of the variability in the volume of injuries, emergent, and nonemergent visits (R2 = 1%, 1%, and 6%, respectively).

Conclusions: Deviations from short-term predictions of temperature correlate poorly with deviations from predictions of patient volume after adjusting for natural trends and cycles in these variables and controlling for precipitation. These weather variables are of little practical benefit for predicting fluctuations in the rates of ED utilization.

Download full-text PDF

Source
http://dx.doi.org/10.1097/01.pec.0000199561.34475.29DOI Listing

Publication Analysis

Top Keywords

deviations short-term
12
short-term predictions
12
local weather
8
emergency department
8
ambient temperature
8
injuries emergent
8
emergent nonemergent
8
nonemergent visits
8
natural trends
8
trends cycles
8

Similar Publications

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!