Andreatos Nikolaos, Grigoras Christos, Shehadeh Fadi, Pliakos Elina Eleftheria, Stoukides Georgianna, Port Jenna, Flokas Myrto Eleni, Mylonakis Eleftherios
Infectious Diseases Division, Warren Alpert Medical School of Brown University, Rhode Island Hospital, Providence, Rhode Island, United States of America.
PLoS One. 2017 Sep 1;12(9):e0183938. doi: 10.1371/journal.pone.0183938. eCollection 2017.
Gonorrhea is the second most commonly reported identifiable disease in the United States (U.S.). Importantly, more than 25% of gonorrheal infections demonstrate antibiotic resistance, leading the Centers for Disease Control and Prevention (CDC) to classify gonorrhea as an "urgent threat".
We examined the association of gonorrhea infection rates with the incidence of HIV and socioeconomic factors. A county-level multivariable model was then constructed.
Multivariable analysis demonstrated that HIV incidence [Coefficient (Coeff): 1.26, 95% Confidence Interval (CI): 0.86, 1.66, P<0.001] exhibited the most powerful independent association with the incidence of gonorrhea and predicted 40% of the observed variation in gonorrhea infection rates. Sociodemographic factors like county urban ranking (Coeff: 0.12, 95% CI: 0.03, 0.20, P = 0.005), percentage of women (Coeff: 0.41, 95% CI: 0.28, 0.53, P<0.001) and percentage of individuals under the poverty line (Coeff: 0.45, 95% CI: 0.32, 0.57, P<0.001) exerted a secondary impact. A regression model that incorporated these variables predicted 56% of the observed variation in gonorrhea incidence (Pmodel<0.001, R2 model = 0.56).
Gonorrhea and HIV infection exhibited a powerful correlation thus emphasizing the benefits of comprehensive screening for sexually transmitted infections (STIs) and the value of pre-exposure prophylaxis for HIV among patients visiting an STI clinic. Furthermore, sociodemographic factors also impacted gonorrhea incidence, thus suggesting another possible focus for public health initiatives.
淋病是美国第二大最常报告的可识别疾病。重要的是,超过25%的淋病感染表现出抗生素耐药性,导致疾病控制与预防中心(CDC)将淋病列为“紧急威胁”。
我们研究了淋病感染率与HIV发病率及社会经济因素之间的关联。随后构建了一个县级多变量模型。
多变量分析表明,HIV发病率[系数(Coeff):1.26,95%置信区间(CI):0.86,1.66,P<0.001]与淋病发病率呈现出最强的独立关联,并预测了淋病感染率中40%的观察到的变异。社会人口统计学因素,如县城市排名(Coeff:0.12,95%CI:0.03,0.20,P = 0.005)、女性比例(Coeff:0.41,95%CI:0.28,0.53,P<0.001)和贫困线以下个体比例(Coeff:0.45,95%CI:0.32,0.57,P<0.001)产生了次要影响。纳入这些变量的回归模型预测了淋病发病率中56%的观察到的变异(P模型<0.001,R2模型 = 0.56)。
淋病和HIV感染呈现出强相关性,因此强调了对性传播感染(STIs)进行全面筛查的益处以及对性病门诊患者进行HIV暴露前预防的价值。此外,社会人口统计学因素也影响淋病发病率,从而为公共卫生举措提出了另一个可能的重点。