Development of a Risk Algorithm to Better Target STI Testing and Treatment Among Australian Aboriginal and Torres Strait Islander People

Development of a Risk Algorithm to Better Target STI Testing and Treatment Among Australian... Identifying and targeting those at greatest risk will likely play a significant role in developing the most efficient and cost-effective sexually transmissible infections (STI) prevention programs. We aimed to develop a risk prediction algorithm to identify those who are at increased risk of STI. A cohort (N = 2320) of young sexually active Aboriginal and Torres Strait Islander people (hereafter referred to as Aboriginal people) were included in this study. The primary outcomes were self-reported high-risk sexual behaviors and past STI diagnosis. In developing a risk algorithm, our study population was randomly assigned to either a development (67%) or an internal validation data set (33%). Logistic regression models were used to create a risk prediction algorithm from the development data set for males and females separately. In the risk prediction models, older age, methamphetamine, ecstasy, and cannabis use, and frequent alcohol intake were all consistently associated with high-risk sexual behaviors as well as with a past STI diagnosis; identifying as gay/bisexual was one of the strongest factors among males. Those who had never tested for STIs, 52% (males) and 66% (females), had a risk score >15, and prevalence of undiagnosed STI was estimated between 30 and 40%. Since universal STI screening is not cost-effective, nor practical in many settings, targeted screening strategies remain a crucial and effective approach to managing STIs among young Aboriginal people. Risk prediction tools such as the one developed in this study may help in prioritizing screening for STIs among those most at risk. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Archives of Sexual Behavior Springer Journals

Development of a Risk Algorithm to Better Target STI Testing and Treatment Among Australian Aboriginal and Torres Strait Islander People

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Publisher
Springer US
Copyright
Copyright © 2017 by Springer Science+Business Media New York
Subject
Psychology; Sexual Behavior; Public Health; Social Sciences, general
ISSN
0004-0002
eISSN
1573-2800
D.O.I.
10.1007/s10508-017-0958-9
Publisher site
See Article on Publisher Site

Abstract

Identifying and targeting those at greatest risk will likely play a significant role in developing the most efficient and cost-effective sexually transmissible infections (STI) prevention programs. We aimed to develop a risk prediction algorithm to identify those who are at increased risk of STI. A cohort (N = 2320) of young sexually active Aboriginal and Torres Strait Islander people (hereafter referred to as Aboriginal people) were included in this study. The primary outcomes were self-reported high-risk sexual behaviors and past STI diagnosis. In developing a risk algorithm, our study population was randomly assigned to either a development (67%) or an internal validation data set (33%). Logistic regression models were used to create a risk prediction algorithm from the development data set for males and females separately. In the risk prediction models, older age, methamphetamine, ecstasy, and cannabis use, and frequent alcohol intake were all consistently associated with high-risk sexual behaviors as well as with a past STI diagnosis; identifying as gay/bisexual was one of the strongest factors among males. Those who had never tested for STIs, 52% (males) and 66% (females), had a risk score >15, and prevalence of undiagnosed STI was estimated between 30 and 40%. Since universal STI screening is not cost-effective, nor practical in many settings, targeted screening strategies remain a crucial and effective approach to managing STIs among young Aboriginal people. Risk prediction tools such as the one developed in this study may help in prioritizing screening for STIs among those most at risk.

Journal

Archives of Sexual BehaviorSpringer Journals

Published: May 26, 2017

References

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