Saturday, 15 January 2005 - 10:00 AM

This presentation is part of: Sexual Health and Addiction Services in Adolescents

A Macro Level Study of Adolescent Sexual Health

Purpose: The purpose of this study was to examine adolescent sexual health on the macro level. The associations between county level characteristics and county level rates of teen births and teen sexually transmitted infections were assessed.

Methods: Secondary data analysis was conducted with county level data gathered in the state of Florida. This study involved two phases of research: one of which was cross-sectional and another that was longitudinal. In the first phase, a full set of analyses was conducted with each dimension of adolescent sexual health: teen birth rate for those aged 10 to 14, teen birth rate for those aged 15 to 19, sexually transmitted infection rate for those aged 10 to 14, and sexually transmitted infection rate for those aged 15 to 19. Bivariate correlations between each of the dimensions and the macro level county characteristics were used to identify key correlates of adolescent sexual health. Following this identification, multiple regression was conducted for each dimension. In the second phase of the study, the most successful multiple regression model (that which predicted the county birth rate for those aged 15 to 19) was reexamined using a growth curve analytic technique. Growth curve analysis was employed to test for the presence of significant variation in the birth rate at one time point and the rate of change in the birth rate over time.

Results: In the first phase of the study, the only model that had a substantial adjusted r-square (.404) was that predicting the teen birth rate for those aged 15 to 19, with significant independent variables representing the percent of the county's youths who were eligible for a free/reduced school lunch (standardized coefficient = .255) and the county's status of being urban or rural (standardized coefficient = -.302). The models predicting the county birth rate for younger teens (aged 10 to 14) and the county sexually transmitted infection rate for both younger (aged 10 to 14) and older teens (aged 15 to 19) were not successful in explaining a substantial amount of the variance. In the second phase of the study, the growth curve analysis revealed the presence of significant variation of the teen birth rate at time 1 between the counties. There was no significant variation between the counties in the trend of the birth rate spanning four years. As with the multiple regression model, the percent of the county's youths who were eligible for a free/reduced school lunch and the county's status of being urban or rural were both significant predictors of the birth rate at time 1 in the growth curve model.

Implication for Practice: This research points to the relevance of county characteristics in examining the teen birth rate for older adolescents and developing formulas for teen pregnancy prevention funding. However, it also points to the lack of a macro level model for the other aspects of sexual health (teen birth rates for younger teens and sexually transmitted infection rates for both younger and older teens).

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