Date

2-23-2012

UMMS Affiliation

Graduate School of Biomedical Sciences, Clinical and Population Health Research Program

Document Type

Dissertation, Doctoral

Subjects

Dissertations, UMMS; Influenza A Virus, H1N1 Subtype; Influenza, Human; Hospitalization; Population Surveillance; Population Characteristics; Pandemics; Massachusetts

Disciplines

Clinical Epidemiology | Epidemiology | Influenza Humans | Life Sciences | Medicine and Health Sciences | Virus Diseases

Abstract

The spread of pandemic influenza A (2009 H1N1 influenza) virus resulted in a global influenza pandemic in 2009. During the early stages of the pandemic, population surveillance was crucial. However, officials around the world realized that many of our surveillance and reporting systems were not prepared to respond in a coordinated, integrated way, which made informed public health decision-making very difficult. More accurate estimates of the total number of hospitalized 2009 H1N1 influenza cases were required to calculate population-based 2009 H1N1 influenza-associated mortality, morbidity and hospitalization rates. For instance, how many people were hospitalized with 2009 H1N1 influenza in Massachusetts? Of these, how many were admitted to the ICU and how many died? Compared to seasonal influenza, were some race/ethnic and age groups affected more than others, and what types of characteristics led to more severe manifestations of 2009 H1N1 influenza among these groups in Massachusetts?

To address the above questions, I proposed a retrospective cohort study using data from the Hospital Discharge Database (HDD), which contains data for all inpatients discharged from 76 acute care hospitals in Massachusetts, as well as Census information to provide a measure of socioeconomic status (SES). My specific aims are as follows: 1. Develop methods to identify influenza cases precisely and describe characteristics of those hospitalized with ILI in MA between April 26-Sept 30, 2009; 2. Conduct analyses to identify race/ethnicity-related trends in reference to 2009 H1N1 influenza-related hospitalizations; 3. Conduct analyses to identify age-related trends in reference to 2009 H1N1 influenza-related hospitalizations.

First, I established influenza case selection criteria using hospital discharge data. I addressed limitations in the published methods on defining cases of influenza using administrative databases, and evaluated ICD-9 codes that correspond with common and relatively serious respiratory infections and influenza using a ‘maximum’ and ‘minimum’ approach. Results confirmed that 2009 H1N1 influenza affected a younger population, and disproportionately affected racial minorities in Massachusetts. There were also higher rates of ICU admission compared to seasonal influenza.

I then presented epidemiological data indicating race/ethnic disparity among 2009 H1N1 influenza cases in Massachusetts. I found that Hispanics had significantly lower odds of 2009 H1N1 influenza-related ICU stay. SES gradients calculated using five-digit zip code information did not account for these differences. Within race/ethnic strata, Hispanics

Finally, I presented epidemiological data indicating differences among 2009 H1N1 influenza cases by age group in Massachusetts. I calculated measures of Diagnostic Cost Group (DxCG) comorbidity for the study population to provide a comorbidity measure at baseline. Main results indicate that although comorbidity scores were similar between the 2009 H1N1 influenza and seasonal influenza groups, 2009 H1N1 influenza caused more severe disease in younger age groups.

This is the first study to report population-based statewide outcomes in all acute care centers in MA. In this dissertation I address challenges surrounding influenza surveillance to create case selection criteria within an administrative database. Using my case selection criteria, I then provide data related to fatality and severity of 2009 H1N1 influenza in Massachusetts in reference to sociodemographic variables such as racial/ethnicity and age groups, and provide evidence for patient-level interventions to those hardest hit by influenza. These findings provide valuable information about using large administrative databases to describe pandemic influenza cases and guide resource allocation to reduce disparities in relation to pandemic influenza preparedness.

 
 

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