ojphi-07-e109.pdf


ISDS Annual Conference Proceedings 2014. This is an Open Access article distributed under the terms of the Creative Commons Attribution-
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ISDS 2014 Conference Abstracts

The Need for Address Based Data: Disaggregation of 
Syndromic Surveillance Systems

James N. Blackwell, Ashley Hickson, David Heinbaugh, Kimberlee McGee, William 
Stephens and Sharefa Aria*

Public Health Informatics Office, Tarrant County, Fort Worth, TX, USA

Objective
The justification for address based syndromic surveillance systems, 

and building syndrome weighting mechanisms.

Introduction
Epidemiological surveillance is used to monitor time trends in 

diseases and the distribution of the diseases in the population. To 
streamline the process of identifying outbreaks, and notification of 
disease, syndromic surveillance has emerged as a method to report 
and analyze health data. Rather than report data by disease status (ie 
disease/no disease), clinical symptoms are used to detect outbreaks 
as early as possible.1

Currently, only data collected via active surveillance (notifiable 
disease investigations) are usable for identifying communities that 
require attention. Therefore, any interventions performed using said 
data is reactive in nature. Syndromic surveillance systems must be 
disaggregated to enable proactive health promotion, and responses.

Furthermore, a common method must be established to assess the 
overall impact of syndromes. Diseases are not equal; some have a 
greater impact on health, and life. To address this issue, the World 
Health Organization (WHO) has created disability weights to be used 
in calculating disability adjusted life years (DALY).2 DALYs are 
effective in calculating the overall impact of disease in a community. 
DALYs estimate the burden of disease, not syndromes; therefore, it 
is reactive tool. To create a more effective syndromic surveillance 
system, syndromes must be associated with an overall impact weight.

Methods
ESSENCE and BioSense 2.0 are syndromic surveillance systems 

used by Tarrant County. The data from these systems are aggregated 
into zipcodes. To demonstrate the necessity of address based data, and 
syndrome weights, data collected via active surveillance techniques 
were used as a proxy.

The active surveillance data are infectious disease data collected 
by Tarrant County. To demonstrate the utility address data, a heat 
map with pertussis cases was generated (Figure 1). All maps were 
created in QGIS 2.4.

Results
The heat map shows that the highest pertussis densities are smaller 

than zipcodes. The heat map also give more information on outlying 
cases, as well.

Conclusions
To perform localized interventions, community based participatory 

research, and create better syndromic predictors it is necessary to 
disaggregate health data. Address level data will be key in creating 
more precise outbreak predictions. The tools to assess disease 
distribution are readily available; it is the structure of syndromic data 
that is limiting widespread adoption of existing software.

Keywords
GIS; Disaggregation; spatial weights; Tarrant; surveillance

References

1. Kramer A, Kretzschmar M, Krickeberg K. Modern Infectious Disease
Epidemiology. London: Springer; 2010.

2. World Health Organization. Metrics: Disability Adjusted Life Year.
http://www.who.int/healthinfo/global_burden_disease/metrics_daly/
en/. Updated 2014. Accessed August 08,2014.

*Sharefa Aria

E-mail: saaria@tarrantcounty.com    

Online Journal of Public Health Informatics * ISSN 1947-2579 * http://ojphi.org * (1):e , 201