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A sexually transmitted infection screening algorithm based on semiparametric regression models.

Zhuokai Li1, Hai Liu2, Wanzhu Tu2

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Statistics in Medicine
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Summary
This summary is machine-generated.

A new model-based screening algorithm accurately identifies individuals at high risk for common sexually transmitted infections (STIs) like Chlamydia trachomatis, reducing costs associated with universal screening.

Keywords:
bivariate surfacesmultiple binary outcomespenalized likelihoodresamplingsplines

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Area of Science:

  • Epidemiology and Public Health
  • Biostatistics and Mathematical Modeling

Background:

  • Sexually transmitted infections (STIs) caused by Chlamydia trachomatis, Neisseria gonorrhoeae, and Trichomonas vaginalis are prevalent, particularly among young women in the US.
  • Asymptomatic infections necessitate screening for control, but universal screening is costly.
  • Existing screening methods may not optimally identify high-risk individuals efficiently.

Purpose of the Study:

  • To propose and evaluate a novel semiparametric model-based screening algorithm for common STIs.
  • To quantify individual infection risks, accounting for organism interdependence and repeated measures.
  • To assess the algorithm's accuracy in identifying at-risk individuals for targeted STI screening.

Main Methods:

  • Development of a semiparametric model incorporating bivariate thin-plate regression splines for age and sexual partner influences.
  • Estimation of model parameters using a penalized likelihood method.
  • Utilized a likelihood-based resampling procedure for inference and receiver operating characteristic (ROC) analysis for predictive performance assessment.

Main Results:

  • The model demonstrated distinct age and sexual partner effect patterns for C. trachomatis, N. gonorrhoeae, and T. vaginalis.
  • The partner effect for C. trachomatis was notably stronger in younger adolescents.
  • The model-based screening algorithm exhibited excellent accuracy in identifying individuals at increased risk for STIs.

Conclusions:

  • The proposed model-based screening algorithm offers an accurate and potentially cost-effective approach to STI screening.
  • It can assist clinicians in identifying individuals who would benefit most from targeted STI testing.
  • This approach supports efficient resource allocation in public health initiatives for STI prevention.