View list of archived news items.

Fifth Annual Syndromic Surveillance Conference
19 - 20 October, 2006
Baltimore, Maryland

The fifth annual Syndromic Surveillance Conference - "Putting Theory into Practice"
was organized by the International Society for Disease Surveillance (ISDS), whose mission is to improve population health by advancing the field of disease surveillance. In support of this mission, the Society provides an educational and scientific forum where epidemiologists, informaticists, public health practitioners, health care providers, statisticians, and others can work together to explore and address population health monitoring across institutional and professional boundaries.

Meena Doshi presented a poster titled ‘Enteric Disease Surveillance: Seasonal changes in Population Profile' in the conference. The abstract is as follows and it will be published in the ISDS journal this December. To view another previous article contributed by InForMID, click here [PDF].

Enteric Disease Surveillance: Seasonal Changes in Population Profiles
Meena Doshi, M.S.(1), Alfred DeMaria, M.D. (2), Elena N. Naumova, Ph.D.(1)
1Tufts University School of Medicine, Boston MA
2Massachusetts Department of Public Health, Boston MA

Objectives
The objective of this communication is two-fold: 1) to introduce an analytical approach for assessing changes in the surveillance reporting with respect to population profile; and 2) to demonstrate the utility of this method using four notifiable enteric infections (cryptosporidiosis, giardiasis, shigellosis, and salmonellosis) recorded by the Massachusetts Department of Public Health over the last 12 years.  The proposed approach is based on gender-specific single-year age distribution and allows comparing population profiles over extended and short time periods.

Background
In the last decade, the time series analysis became one of the most important tools of surveillance systems.  Understanding the nature of temporal fluctuations is essential for successful development of outbreak detection algorithms, aberration assessment, and control for seasonal variations.  Typically, in applying the time series methods to health outcomes collected over extended period of time it is assumed that population profiles remain constant.  In practice, such assumptions have been rarely tested. At best, the temporal analysis is performed using stratification by age or other discriminating factors if heterogeneity is suspected.  

Any community can experience population changes in various forms.  Long-term trends of inflow/outflow migration and rapid transient fluctuations associated with specific events are typical examples of changes in population profile.  Seasonality, as an intrinsic property of infectious diseases manifestation in a community, is typically attributed to periodic changes in transmissibility of pathogens.  To some extend, seasonal fluctuations in the incidence of infectious diseases could also be associated with the changes in population profiles.  An ability to detect and describe such changes would provide valuable clues into seasonally changing factors associated with an infection.  Here we present an analysis of changes in patient profiles for reported cases of four enterically-transmitted infections, focusing on decadal and seasonal changes.

Methods
We examined the decadal change of Massachusetts populationby comparing single-year age distributions based on Census 1990 and 2000.  Similarly, gender-specific single-year age distributions were calculated for all reported cases in four infections. We calculated single-year sex-specific daily rates using population estimates at the midpoint of the study period.  Next, sets of four time series of weekly rates were compiled for each infection.  Each set included time series for all population and for three age groups (<18y, 18-64y, 65y+). The periods of seasonal increase were determined using d-method [1].  Finally, single-year sex-specific daily rates were calculated for the periods of seasonal increase (in-season rates) and for the rest of the year (off-season rates).  The rates of reported infections were expressed as functions of age; differences in rates were assessed using Pearson correlation.

Results
As expected, the highest rate averaged across all ages was observed in reported salmonellosis (1.256±0.837 cases per day per 1,000,000 person), followed by giardiasis (0.764±0.497), shigellosis (0.133±0.162), and cryptosporidiosis (0.026±0.032).  The single-year age-specific rates were well correlated among all four infections: Pearson correlation coefficients were ranging from 0.94 (shigellosis vs giardiasis) to 0.63 (cryptosporidiosis vs salmonellosis), mostly due to the fact that for each infection, children (<5y) and adults (25-30y) exhibited high incidence rates.  However, in all infections these correlations became much weaker (as small as 0.43 in salmonellosis) when in-season and off-season rates were compared primarily due to increased rates of infections in older individuals (>50y) during off-season periods.

Conclusions
The proposed technique allows assessment of changes in surveyed populations: while population profiles as measured by single-year age-specific rate over extended time periods were similar, substantial seasonal changes were observed in four reported enterically-transmitted infections.  Such assessment provides valuable information on changes in patient profile and can be applied in various scenarios and readily available to supplement existing surveillance systems.

References
[1] Naumova EN, Jagai J, Matyas B, DeMaria A, MacNeill IB, Griffiths JK. Seasonality in six enterically transmitted diseases seasonality and ambient temperature. Epidemiol. Infect.  July 2006. (in press)