Identification of US Counties at Elevated Risk for Congenital Syphilis Using Predictive Modeling and a Risk Scoring System.
Journal
Sexually transmitted diseases
ISSN: 1537-4521
Titre abrégé: Sex Transm Dis
Pays: United States
ID NLM: 7705941
Informations de publication
Date de publication:
05 2020
05 2020
Historique:
pubmed:
12
2
2020
medline:
27
4
2021
entrez:
12
2
2020
Statut:
ppublish
Résumé
Although preventable through timely screening and treatment, congenital syphilis (CS) rates are increasing in the United States, occurring in 5% of counties in 2015. Although individual-level factors are important predictors of CS, given the geographic focus of CS, it is also imperative to understand what county-level factors are associated with CS. This is a secondary analysis of reported county CS cases to the National Notifiable Diseases Surveillance System during the periods 2014-2015 and 2016-2017. We developed a predictive model to identify county-level factors associated with CS and use these to predict counties at elevated risk for future CS. Our final model identified 973 (31.0% of all US counties) counties at elevated risk for CS (sensitivity, 88.1%; specificity, 74.0%). County factors that were predictive of CS included metropolitan area, income inequality, primary and secondary syphilis rates among women and men who have sex with men, and population proportions of those who are non-Hispanic black, Hispanic, living in urban areas, and uninsured. The predictive model using 2014-2015 CS outcome data was predictive of 2016-2017 CS cases (area under the curve value, 89.2%) CONCLUSIONS: Given the dire consequences of CS, increasing prevention efforts remains important. The ability to predict counties at most elevated risk for CS based on county factors may help target CS resources where they are needed most.
Sections du résumé
BACKGROUND
Although preventable through timely screening and treatment, congenital syphilis (CS) rates are increasing in the United States, occurring in 5% of counties in 2015. Although individual-level factors are important predictors of CS, given the geographic focus of CS, it is also imperative to understand what county-level factors are associated with CS.
METHODS
This is a secondary analysis of reported county CS cases to the National Notifiable Diseases Surveillance System during the periods 2014-2015 and 2016-2017. We developed a predictive model to identify county-level factors associated with CS and use these to predict counties at elevated risk for future CS.
RESULTS
Our final model identified 973 (31.0% of all US counties) counties at elevated risk for CS (sensitivity, 88.1%; specificity, 74.0%). County factors that were predictive of CS included metropolitan area, income inequality, primary and secondary syphilis rates among women and men who have sex with men, and population proportions of those who are non-Hispanic black, Hispanic, living in urban areas, and uninsured. The predictive model using 2014-2015 CS outcome data was predictive of 2016-2017 CS cases (area under the curve value, 89.2%) CONCLUSIONS: Given the dire consequences of CS, increasing prevention efforts remains important. The ability to predict counties at most elevated risk for CS based on county factors may help target CS resources where they are needed most.
Identifiants
pubmed: 32044864
doi: 10.1097/OLQ.0000000000001142
pii: 00007435-202005000-00002
doi:
Types de publication
Journal Article
Research Support, U.S. Gov't, P.H.S.
Langues
eng
Sous-ensembles de citation
IM
Pagination
290-295Commentaires et corrections
Type : ErratumIn
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