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ISDS 2012 Conference Abstracts

Use of Syndromic Surveillance Information for
Expanded Assessment of Wildfire Disaster
Jeffrey Johnson*1, Michele Ginsberg1, Nancy French2, Brian Thelen2 and Benjamin Koziol2

1San Diego County Public Health Services, San Diego, CA, USA; 2Michigan Tech Research Institute, Ann Arbor, MI, USA

Objective
This presentation describes how syndromic surveillance informa-

tion was combined with fire emission information and spatio-tempo-
ral fire occurrence data to evaluate, model and forecast climate
change impacts on future fire scenarios.

Introduction
Syndromic surveillance information can be a useful for the early

recognition of outbreaks, acute public health events and in response
to natural disasters. Inhalation of particulate matter from wildland
fire smoke has been linked to various acute respiratory and cardio-
vascular health effects. Historically, wildfire disasters occur across
Southern California on a recurring basis. During 2003 and 2007,
wildfires ravaged San Diego County and resulted in historic levels
of population evacuation, significant impact on air quality and loss of
lives and infrastructure. In 2011, the National Institutes of Health-
National Institute of Environmental Health Sciences awarded Michi-
gan Tech Research Institute a grant to address the impact of fire
emissions on human health, within the context of a changing climate.
San Diego County Public Health Services assisted on this project
through assessment of population health impacts and provisioning of
syndromic surveillance data for advanced modeling.

Methods
Various historical data sets were used during this study. These in-

cluded: emergency department syndromic surveillance from 17 hos-
pitals, as well as air quality and particulate matter, meteorological,
wildland burn fuel, and atmospheric dispersion data. The study area
was San Diego County. These data were linked temporally and spa-
tially to create statistical models based on selected modeling ap-
proaches including generalized additive modeling. Future fire
frequency was modeled for the entire region to determine the impacts
of climate change on future fire and health outcomes. Modeling based
upon previous fire occurrence was used to develop models for future
fire indexes, risk of ignition, potential burning, and fire weather.

Results
Several models were developed to produce expected respiratory

health impacts under future climate conditions for the San Diego
County region. Model results showed that at peak fire par-ticulate
concentrations, the odds of a person seeking emergency care in-

creased by approxi-mately 50% compared to non-fire conditions.
Also developed was a model to forecast future fire occurrence based
on regional climate model predictions. This forecast covering the next
the next three decades reveals that San Diego County will experience
approximately two extreme fire seasons each decade by 2040.

Conclusions
Syndromic surveillance data are useful during disasters for situa-

tional awareness. These data may also provide value for post-disas-
ter analytic work and predictive modeling for future disas-ters. This
study demonstrates utility of syndromic data for collaborative work
resulting in better understanding of environmental interactions on
human health. We do know that wildfire oc-curs with some degree of
historic regularity and these results may be useful for preparedness
planning. We also know the conditions which must be present for
medium to high wildland fire impact upon the population. This study
further supports the notion that agencies should be able to strategi-
cally deploy resources and messaging immediately preceding the fire
period with the goal of reducing human health risk factors and en-
couraging changes in community behaviors before, during and after
a fire. Future fire model shows San Diego County will experience
approximately two extreme fire seasons each decade by 2040. This
project also promoted collaboration between public health and envi-
ronmental health entities to better understand de-terminants of health
during a disaster. In addition to developing a better understanding of
the consequences of climate change on fire-induced respiratory ill-
ness, the project funding has provided support for San Diego County
to improve their syndromic surveillance capacity and infrastructure.

Keywords
Syndromic; Disaster; Forecasting; Wildfire; San Diego

Acknowledgments

This work has been supported by the NIH-NIEHS Climate Change Grant
#1RC1ES018612-01. We would also like to acknowledge the research
teams at Michigan Tech Research Institute, Michigan Technology Uni-
versity, and the University of Maryland Department of Geography.

*Jeffrey Johnson
E-mail: jeffrey.johnson@sdcounty.ca.gov

Online Journal of Public Health Informatics * ISSN 1947-2579 * http://ojphi.org * 5(1):e95, 2013