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ISDS 2012 Conference Abstracts

Long-Term Asthma Trend Monitoring in New York City: A
Mixed Model Approach
Stephen E. Schachterle*, Robert W. Mathes, Marc Paladini and Don Weiss

New York City Department of Health and Mental Hygiene, Queens, NY, USA

Objective
Show the benefits of using a generalized linear mixed model

(GLMM) to examine long-term trends in asthma syndrome data.

Introduction
Over the last decade, the application of syndromic surveillance

systems has expanded beyond early event detection to include long-
term disease trend monitoring. However, statistical methods em-
ployed for analyzing syndromic data tend to focus on early event
detection. Generalized linear mixed models (GLMMs) may be a use-
ful statistical framework for examining long-term disease trends be-
cause, unlike other models, GLMMs account for clustering common
in syndromic data, and GLMMs can assess disease rates at multiple
spatial and temporal levels (1). We show the benefits of the GLMM
by using a GLMM to estimate asthma syndrome rates in New York
City from 2007 to 2012, and to compare high and low asthma rates
in Harlem and the Upper East Side (UES) of Manhattan.

Methods
Asthma related emergency department (ED) visits, and patient age

and ZIP code were obtained from data reported daily to the NYC De-
partment of Health and Mental Hygiene. Demographic data were ob-
tained from 2010 US Census. ZIP codes that represented high and
low asthma rates in Harlem and the UES of Manhattan were chosen
for closer inspection. The ratio of weekly asthma syndrome visits to
total ED visits was modeled with a Poisson GLMM with week and
ZIP code random intercepts (2). Age and ethnicity were adjusted for
because of their association with asthma rates (3).

Results
The GLMM showed citywide asthma rates remained stable from

2007 to 2012, but seasonal differences and significant inter-ZIP code
variation were present. The Harlem ZIP code asthma rate that was es-
timated with the GLMM was significantly higher (5.83%, 95% CI:
3.65%, 9.49%) than the asthma rate in UES ZIP code (0.78%, 95% CI:
0.50%, 1.21%). A linear time component to the GLMM showed no ap-
preciable change over time despite the seasonal fluctuations in asthma
rate. GLMM based asthma rates are shown over time (Figure 1).

Conclusions
GLMMs have several strengths as statistical frameworks for mon-

itoring trends including:
1. Disease rates can be estimated at multiple spatial and temporal

levels,
2. Standard error adjustment for clustering in syndromic data al-

lows for accurate, statistical assessment of changes over time and dif-
ferences between subgroups,

3. “Strength borrowed” (4) from the aggregated data informs small
subgroups and smooths trends,

4. Integration of covariate data reduces bias in estimated rates.
GLMMs have previously been suggested for early event detection

with syndromic surveillance data (5), but the versatility of GLMM
makes them useful for monitoring long-term disease trends as well.
In comparison to GLMMs, standard errors from single level GLMs

do not account for clustering and can lead to inaccurate statistical hy-
pothesis testing. Bayesian hierarchical models (6), share many of the
strengths of GLMMS, but are more complicated to fit. In the future,
GLMMs could provide a framework for grouping similar ZIP codes
based on their model estimates (e.g. seasonal trends and influence on
overall trend), and analyzing long-term disease trends with syndromic
data.

Figure 1: Harlem ZIP code (red), the Upper East Side ZIP code (blue), and
citywide (black) estimates are shown as dotted lines surrounded by 30%
credibility bands in solid lines.

Keywords
Asthma; Long term trends; Generalized Mixed Models

Acknowledgments

This work was carried out in conjunction with a grant from the Alfred P.
Sloan Foundation (#2010-12-14). We thank the members of the New York
City Department of Health and Mental Hygiene Syndromic Surveillance
Unit.

References

1. Diggle, P.J., Liang K.Y., & Zeger, S. L. (1994). Analysis of Longitudi-
nal Data. Oxford: Oxford University Press. 

2. Gelman, A., Hill, J., (2007). Data Analysis Using Regression and Mul-
tilevel/Hierarchical Models. New York: Cambridge University Press. 

3. Claudio, L. et al. (1999) Socioeconomic Factors and Asthma Hospi-
talization Rates in New York City. Journal of Asthma. 36(4): pp. 343-
350.

4. Jones, K., Johnston R. J., and Pattie C. J. (1992). People, Places and Re-
gions: Exploring the Use of Multi-Level Modelling in the Analysis
of Electoral Data. British Journal of Political Science, 22, pp 343-380.

5. Kleinman, K., Lazarus, R., Platt, R., (2004). A Generalized Linear
Mixed Models Approach for Detecting Incident Clusters of Disease in
Small Areas, with an Application to Biological Terrorism. AJE, 159,
pp 217-224.

6. Chan, T.C., et al. Probabilistic Daily ILI Syndromic Surveillance with
a Spatio-Temporal Bayesian Hierarchical Model. PLoS ONE. 5(7):
e11626.

*Stephen E. Schachterle
E-mail: steve.schachterle@gmail.com

Online Journal of Public Health Informatics * ISSN 1947-2579 * http://ojphi.org * 5(1):e123, 2013