Pinto Casey N, Dorn Lorah D, Chinchilli Vernon M, Du Ping, Chi Guangqing
Department of Public Health Sciences, The Pennsylvania State University College of Medicine, Hershey.
College of Nursing & Department of Pediatrics, The Pennsylvania State University, University Park.
Ann Epidemiol. 2017 Sep;27(9):606-610.e2. doi: 10.1016/j.annepidem.2017.08.018. Epub 2017 Aug 24.
American adolescents and young adults between the ages of 15 and 24 account for 50% of all sexually transmitted diseases (STDs) annually. Rural populations in this age group are often understudied, despite having factors that place them at higher risk for STDs.
The purpose of this study was to evaluate the utility of time series analysis in the assessment of rural Pennsylvania county-level chlamydia and gonorrhea rates overtime (2004-2014) for 15- to 19- and 20- to 24-year-old age groups by gender.
An exploratory analysis was completed using Pennsylvania STD surveillance case report and census data, to develop a linear mixed-effects model of the STD rate for each Pennsylvania county for the years 2004 through 2014 using 3-month increments. A cubic polynomial spline regression model was assumed over the 44 time points for each county to account for possible oscillations in the STD rate during the 11-year period.
Eight out of 12 rural counties had a significant increase in chlamydia or gonorrhea rates, and five rural counties had significant decreases in chlamydia or gonorrhea rates from 2004 to 2014.
Results from this study provide the first analysis of change in rates of STDs in rural settings and demonstrate the utility of time series analysis for populations with small sample sizes.
年龄在15至24岁之间的美国青少年每年占所有性传播疾病（STD）病例的50%。尽管该年龄组的农村人口存在使其感染性传播疾病风险更高的因素，但对他们的研究往往不足。
本研究的目的是评估时间序列分析在评估宾夕法尼亚州农村地区2004年至2014年期间15至19岁和20至24岁年龄组按性别划分的衣原体和淋病发病率随时间变化情况时的效用。
利用宾夕法尼亚州性传播疾病监测病例报告和人口普查数据完成探索性分析，以建立一个线性混合效应模型，该模型以3个月为增量，用于分析2004年至2014年宾夕法尼亚州每个县的性传播疾病发病率。假设每个县在44个时间点上采用三次多项式样条回归模型，以考虑11年期间性传播疾病发病率可能出现的波动。
从2004年到2014年，12个农村县中有8个县的衣原体或淋病发病率显著上升，5个农村县的衣原体或淋病发病率显著下降。
本研究结果首次分析了农村地区性传播疾病发病率的变化，并证明了时间序列分析在小样本量人群中的效用。
