Cuffe Kendra M, Torrone Elizabeth A, Hong Jaeyoung, Leichliter Jami S, Gift Thomas L, Thorpe Phoebe G, Bernstein Kyle T
From the Division of STD Prevention, Centers for Disease Control and Prevention, Atlanta, GA.
Sex Transm Dis. 2022 Mar 1;49(3):184-189. doi: 10.1097/OLQ.0000000000001561.
The persistence of congenital syphilis (CS) remains an important concern in the United States. We use the 2018 data to refine a previous predictive model that identifies US counties at elevated risk for CS in 2018.
Using county-level socioeconomic and health-related data from various sources, we developed a logistic regression predictive model to identify county-level factors associated with a county having had 1 or more CS case reported to the National Notifiable Diseases Surveillance System in 2018. We developed a risk scoring algorithm, identified the optimal risk score cutpoint to identify counties at elevated risk, and calculated the live birth to CS case ratio for counties by predicted risk level to compare counties at elevated risk with counties not at elevated risk.
We identified several county-level factors associated with a county having 1 or more CS case in 2018 (area under the curve, 88.6%; Bayesian information criterion, 1551.1). Using a risk score cutoff of 8 or higher (sensitivity, 83.2%; specificity, 79.4%), this model captured 94.7% (n = 1,253) of CS cases born in 2018 and identified 850 (27%) counties as being at elevated risk for CS. The live birth to CS case ratio was lower in counties identified as at elevated risk (2,482) compared with counties categorized as not at elevated risk (10,621).
Identifying which counties are at highest risk for CS can help target prevention efforts and interventions. The relatively low live birth to CS case ratio in elevated risk counties suggests that implementing routine 28-week screening among pregnant women in these counties may be an efficient way to target CS prevention efforts.
先天性梅毒（CS）的持续存在仍是美国的一个重要问题。我们使用2018年的数据来完善之前的预测模型，该模型可识别出2018年美国先天性梅毒风险较高的县。
利用来自各种来源的县级社会经济和健康相关数据，我们开发了一个逻辑回归预测模型，以确定与2018年向国家法定疾病监测系统报告有1例或更多先天性梅毒病例的县相关的县级因素。我们开发了一种风险评分算法，确定了识别高风险县的最佳风险评分切点，并按预测风险水平计算各县的活产数与先天性梅毒病例数之比，以比较高风险县和非高风险县。
我们确定了几个与2018年有1例或更多先天性梅毒病例的县相关的县级因素（曲线下面积，88.6%；贝叶斯信息准则，1551.1）。使用8或更高的风险评分临界值（敏感性，83.2%；特异性，79.4%），该模型捕获了2018年出生的94.7%（n = 1253）的先天性梅毒病例，并确定了850个（27%）县为先天性梅毒高风险县。与分类为非高风险的县（10621）相比，被确定为高风险的县（2482）的活产数与先天性梅毒病例数之比更低。
确定哪些县先天性梅毒风险最高有助于确定预防工作和干预措施的目标。高风险县的活产数与先天性梅毒病例数之比相对较低，这表明在这些县的孕妇中实施常规的28周筛查可能是针对先天性梅毒预防工作的一种有效方法。
