Tuesday, March 18, 2008

Identifying Predictors of Pertussis Disease in Infants Less than 12 Months of Age in Texas Using Surveillance Records and Birth Certificate Data from 1999-2003

Lucille Palenapa and Rita R. Espinoza. Infectious Disease Control Unit, Texas Department of State Health Services, 1100 W 49th St, Austin, TX, USA

Learning Objectives for this Presentation:
By the end of the presentation participants will be able to describe the epidemiology of pertussis in Texas; list risk factors associated with pertussis among Texas infants; and describe the method for matching surveillance and birth certificate data.

Research on risk factors for pertussis disease in infants is limited. Infants who are unvaccinated or not fully vaccinated and are less than 12 months of age have the highest risk for severe and life threatening complications and death. Current data on risk factors in infants suggests that severe pertussis complications primarily occur most often in Hispanic infants less than 12 months of age. Previous studies have provided us with few important predictors to look for in infant cases; however, there is a demonstrated need for more detailed information on risk factor identification for this serious disease.

Identify potential risk factors for pertussis disease in infants less than 12 months of age in Texas.

This proposed study is a retrospective case-control study examining predictors for the development of pertussis in infants less than twelve months who are born in Texas. Demographic information for cases and controls will be compared for analysis and the differences between proportions will be tested by odds ratios using 95% confidence intervals. Potential predictors will be analyzed by comparing cases with controls. For each potential risk factor, a matched odds ratio and 95% confidence intervals will be calculated. Categories for all variables will be developed a priori. However, categories from some variables may be modified based on the preliminary findings. Multivariate logisitic regression models will be developed to assess the independent effects of risk factors that are found to be important in univariate analyses. Data will
be analyzed using SAS and SPSS.

Results will be presented at the meeting.

Conclusions will be presented at the meeting.