Estimating the Burden of Influenza from Emergency Department Visits in Los Angeles County Pubblico
Massey, Jason (Spring 2020)
Published
Abstract
Currently, little is known about the holistic effect of flu on the emergency department. This study aims to explore the use of daily emergency department visits in Los Angeles County facilities from 2005-2014 to compare different models and their components to see which more accurately estimate the associated attributable burden of flu. Models for the outcomes: respiratory diseases, cardiovascular diseases, and pneumonia were fit using quasi-Poisson regression. Functions use to fit the models were 3rd degree polynomial and 40, 60, 80, and 120 knot splines. Comparisons were measured between the inclusion of both primary and secondary diagnoses codes versus primary. Each model reported a time series comparing each outcome including influenza as a predictor to excluding influenza as a predictor. The burden of influenza was found from taking the difference between the two. Both the burden estimates and standard errors were reported for each year for each model version. This methodology was also done for stratified groups of ages 1-4, 5-49, 50-64, and >=65. The 4 ages group distributions were then compared over the 9-year study period. The models selected for each outcome was then based on both graphical interpretations of model fit and level of noise and numerical interpretations of standard error. Based on the selection criteria, outcomes were fit with both diagnoses’ codes. The non-stratified results respiratory disease, cardiovascular disease, and pneumonia were fit with a 60-knot spline model, 40-knot spline model, and 3rd degree polynomial model respectively. For the stratified results, all three outcomes models selected were fit with a 40-knot spline model across all 4 age groups. The 5-49 age group showed the largest distribution of influenza burden especially from 2009-2010. This study was an exploratory descriptive analysis comparing the models used to more accurately measure influenza estimates. While the selected models seemed to decently fit the outcome data, much more investigation into modeling emergency department data across different regions and time periods needs to be done. The lack of knowledge on burden also emphasizes the need for both public health related and political actions towards increasing research and testing.
Table of Contents
TABLE OF CONTENTS
INTRODUCTION...............................................................................................................2
METHODS ........................................................................................................................4
RESULTS .........................................................................................................................12
DISCUSSION....................................................................................................................21
REFERENCES ..................................................................................................................25
APPENDIX A ...................................................................................................................27
APPENDIX B ...................................................................................................................40
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