Adolescent Pregnancy Trends and the Deficiencies of Dichotomous Measures of Rurality

OBJECTIVES: Adolescent pregnancy trends remain understudied in rural America. Recent publications cite rate decreases but may not be representative of teenagers living outside urban areas. This study examines the relationship between classifications of rurality and adolescent pregnancy trends. It hypothesizes that traditional classifications of rurality will misrepresent outcomes and mask rural inequities. METHODS: Analysis was conducted to estimate association between rurality and adolescent pregnancy rates in North Carolina counties (n=100). Counties were stratified by Urban Influence Codes and annual pregnancy rates were analyzed over a thirteen-year period. Pregnancy rates were compared using dichotomous geography (‘urban' and ‘rural') and categorical geography (‘urban', ‘micropolitan rural', ‘small adjacent rural', ‘remote rural'). Social measures were incorporated including poverty, insurance coverage and high school graduation. Bivariate correlation and linear regression models were constructed and analyzed. RESULTS: Adolescent pregnancy rates have declined throughout North Carolina as studies suggested. However, declines have been concentrated in urban counties. Within rural counties, more urbanized (micropolitan) have experienced comparable declines while remote counties have seen minimal pregnancy rate decreases and actual increases recently. Strong racial inequities remain consistent across all geographic strata. CONCLUSIONS: Dichotomous measures of rurality do not accurately represent adolescent pregnancy trends across North Carolina. Rural averages disproportionately capture declines of micropolitan counties while masking minimal improvements among small adjacent and remote rural counties. Dichotomous geographic classification is commonly utilized and may yield similar misconceptions across fields. Researchers studying geographic influences should reconsider traditional classification schemes for more comprehensive categorical methods to better capture differences and health inequities.