Date of Award

January 2013

Document Type

Open Access Thesis

Degree Name

Master of Public Health (MPH)

Department

School of Public Health

First Advisor

Kimberly Yousey-Hindes

Abstract

Objective: To examine the relationship between area-based socioeconomic (SES) measures and incidence of all laboratory-confirmed influenza, laboratory-confirmed non-hospitalized influenza, influenza-associated hospitalizations, and influenza-associated deaths, in Connecticut.

Methods: Laboratory-confirmed influenza cases in Connecticut from October 1, 2006 to April 30, 2012 were geocoded, and in accordance with the methods of Harvard's Public Health Disparities Geocoding Project, linked to census tract measures of SES. Total and seasonal incidence rates were determined for each of the four influenza-associated health outcomes by SES measure. For each outcome, a relative rate ratio was calculated between the highest and the lowest percent quantile of each SES measure. For the poverty and crowding variables, this relative rate was then calculated by season for each of the four influenza outcomes, and compared to overall seasonal incidence.


Results: When laboratory-confirmed influenza incidence is examined by measures of SES, there is a positive linear relationship between the four percent quantiles of each SES measure and incidence of each outcome. For all laboratory-confirmed influenza, within each season the quantiles of each SES measure are significantly linearly related to total incidence. However, it is not clear whether or not the change in poverty or crowding high versus low incidence rate ratios correlates with the seasonal fluctuations in overall incidence rates.


Conclusions: Laboratory-confirmed influenza incidence varies by area-based SES. Continued evaluation of the relationship between influenza-associated health outcomes and census tract SES allows for public health interventions to more effectively target vulnerable populations. In addition, routine use of these methods may help elucidate previously unrecognized disparities in public health surveillance data.

Comments

This is an Open Access Thesis.

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