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133rd Annual Meeting & Exposition December 10-14, 2005 Philadelphia, PA |
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Joy P. Nanda, DSc, MS, MHS, Population and Family Health Sciences, Johns Hopkins Medical Institutions, 624 North Broadway, Baltimore, MD 21205, 410-614-3485, jnanda@jhsph.edu, Mian B. Hossain, MSC, MHS, PhD, MPH/DrPH Public Health Program, Morgan State University, 343 Jenkins Building, 1700 E. Cold Spring Lane, Baltimore, MD 21251-0001, Yolanda A. Cavalier, MPH, HIV/AIDs, HRSA, 6303 Monika Place #1309, Baltimore, MD 21207, Laurencia Hutton, MPH, CHES, Public Health Program, Morgan State University, 1700 E Cold Spring Lane, Jenkins Building 343, Baltimore, MD 21251, and Y. Bronner, ScD, MPH/DrPH Program, Morgan State University, 1700 E. Cold Spring Lane, Baltimore, MD 21251.
While condom use is presumed to protect against STI, it may not be universal. This was evident from our analysis of 4,882 youth ages 18-24 years who were interviewed in the National Longitudinal Survey of Adolescent Health (ADDHEALTH) during 2001-2002. We hypothesized that consistent condom was inversely related, whereas ATOD use was directly related, to STI among youth. To test this hypothesis, we analyzed the 3rd wave public use ADDHEALTH dataset (PUF3) to examine main and interaction effects of ATOD and consistent condom use (CCU) on STI.
The PUF3 had 4,882 youth from the original 1994 survey. STI measures were: confirmed diagnosis of chlamydia, gonorrhea, syphilis, trichomonas, herpes, HIV during past year. ATOD measures were cigarette, chewing tobacco, marijuana, cocaine use, and IDU. Youth who used condoms most/all the time were termed consistent condom (CC) users.
Among the 18-24 year old youth, 247 (5.2%) had a diagnosed STI, 44% used ATOD, and 31% were CC users. ATOD use predicted STI risk (p=0.01) but CC users did not differ from non-CC users on STI risk (p=0.59), even after ATOD use (O.R. 0.84; 95%CI: 0.6-1.2) was controlled.
Subsequently we created four subgroups of ATOD/CC users to predict STI risk by subgroup. ATOD and non-CC users had the greatest STI risk (O.R. 2.3;95%CI:1.6-3.3;p<0.001) compared to non-ATOD users and non-CC users. This interaction effect remained significant even after socio-demographic, other behavioral, environmental and health care utilization factors were controlled (O.R. 2.1; 95%CI:1.3-3.5;p<0.01).
Subgroup analysis facilitates identifying vulnerable targets for effective prevention program efforts.
Learning Objectives: At the end of the presentation, participants will be able to
Keywords: Adolescent Health, STD
Presenting author's disclosure statement:
I wish to disclose that I have NO financial interests or other relationship with the manufactures of commercial products, suppliers of commercial services or commercial supporters.
The 133rd Annual Meeting & Exposition (December 10-14, 2005) of APHA