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  1. Low WY, Binns C
    Asia Pac J Public Health, 2014 Sep;26(5 Suppl):7S-8S.
    PMID: 25143527 DOI: 10.1177/1010539514545287
    Matched MeSH terms: Behavioral Risk Factor Surveillance System
  2. Reidpath DD, Davey TM, Kadirvelu A, Soyiri IN, Allotey P
    Prev Med, 2014 Feb;59:37-41.
    PMID: 24270054 DOI: 10.1016/j.ypmed.2013.11.011
    OBJECTIVES: Evidence that age of smoking initiation represents a risk factor for regular smoking in adolescence is complicated by inconsistencies in the operational definition of smoking initiation and simultaneous inclusion of age as an explanatory variable. The aim of this study was to examine the relationship between age, age of smoking initiation and subsequent regular smoking.
    METHODS: A secondary analysis was conducted of the U.S. Youth Risk Behavior Survey 2011. A sex stratified multivariable logistic regression analysis was used to model the likelihood of regular smoking with age and age of smoking initiation as explanatory variables and race/ethnicity as a covariate.
    RESULTS: After controlling for race/ethnicity, age and age of smoking initiation were independently associated with regular smoking in males and females. Independent of age, a one year's decrease in the age of smoking initiation was associated with a 1.27 times increase in odds of regular smoking in females (95% CI: 1.192-1.348); and similar associations for males (OR: 1.28; 95% CI: 1.216-1.341).
    CONCLUSION: While the majority of high school students do not become regular smokers after initiating smoking, earlier initiation of smoking is associated with subsequent regular smoking irrespective of sex or race/ethnicity. These findings have potentially important implications for intervention targeting.
    KEYWORDS: Adolescent; Epidemiology; Smoking
    Matched MeSH terms: Behavioral Risk Factor Surveillance System*
  3. Ross JL, Teeraananchai S, Lumbiganon P, Hansudewechakul R, Chokephaibulkit K, Khanh TH, et al.
    J Acquir Immune Defic Syndr, 2019 06 01;81(2):e28-e38.
    PMID: 30865173 DOI: 10.1097/QAI.0000000000002008
    BACKGROUND: Adolescents living with HIV (ALHIV) have poorer adherence and clinical outcomes than adults. We conducted a study to assess behavioral risks and antiretroviral therapy outcomes among ALHIV in Asia.

    METHODS: A prospective cohort study among ALHIV and matched HIV-uninfected controls aged 12-18 years was conducted at 9 sites in Malaysia, Thailand, and Vietnam from July 2013 to March 2017. Participants completed an audio computer-assisted self-interview at weeks 0, 48, 96, and 144. Virologic failure (VF) was defined as ≥1 viral load (VL) measurement >1000 copies/mL. Generalized estimating equations were used to identify predictors for VF.

    RESULTS: Of 250 ALHIV and 59 HIV-uninfected controls, 58% were Thai and 51% females. The median age was 14 years at enrollment; 93% of ALHIV were perinatally infected. At week 144, 66% of ALHIV were orphans vs. 28% of controls (P < 0.01); similar proportions of ALHIV and controls drank alcohol (58% vs. 65%), used inhalants (1% vs. 2%), had been sexually active (31% vs. 21%), and consistently used condoms (42% vs. 44%). Of the 73% of ALHIV with week 144 VL testing, median log VL was 1.60 (interquartile range 1.30-1.70) and 19% had VF. Over 70% of ALHIV had not disclosed their HIV status. Self-reported adherence ≥95% was 60% at week 144. Smoking cigarettes, >1 sexual partner, and living with nonparent relatives, a partner or alone, were associated with VF at any time.

    CONCLUSIONS: The subset of ALHIV with poorer adherence and VF require comprehensive interventions that address sexual risk, substance use, and HIV-status disclosure.

    Matched MeSH terms: Behavioral Risk Factor Surveillance System*
  4. Reidpath DD, Masood M, Allotey P
    Int J Public Health, 2014 Jun;59(3):503-7.
    PMID: 24045784 DOI: 10.1007/s00038-013-0510-1
    OBJECTIVES: Four metrics to characterise population overweight are described.

    METHODS: Behavioural Risk Factors Surveillance System data were used to estimate the weight the US population needed to lose to achieve a BMI 

    Matched MeSH terms: Behavioral Risk Factor Surveillance System
  5. Lee LK, Chen PC, Lee KK, Kaur J
    Singapore Med J, 2006 Jun;47(6):476-81.
    PMID: 16752015
    INTRODUCTION: Sexual intercourse among Malaysian adolescents is a major concern, especially with the worry of HIV/AIDS. This study was done to determine the prevalence of sexual intercourse among secondary school students aged 12 to 19 years in Negeri Sembilan, Malaysia.
    METHODS: This is a cross-sectional school survey conducted on 4,500 adolescent students based on a structured questionnaire. Data were collected using the self-administered questionnaire (translated version of the Youth Risk Behaviour Surveillance in Bahasa Malaysia).
    RESULTS: The study showed that 5.4 percent of the total sample were reported to have had sexual intercourse. The proportion among male students who had had sex was higher (8.3 percent) compared with female students (2.9 percent). The mean age at first sexual intercourse was 15 years. One percent of students reported that they had been pregnant or had made someone else pregnant. Adolescent sexual intercourse was significantly associated with (1) socio-demographical factors (age, gender); (2) environmental factors (staying with parents); and (3) substance use (alcohol use, cigarette smoking, drug use), even after adjustment for demographical factors. The survey showed that 20.8 percent of respondents had taken alcohol, 14.0 percent had smoked cigarettes, 2.5 percent had tried marijuana, 1.2 percent had tried ecstasy pills, 2.6 percent had tried glue sniffing, 0.7 percent had tried heroin, and 0.7 percent had intravenous drugs.
    CONCLUSION: Prevalence of sexual intercourse among Malaysian adolescents was relatively low compared to developed countries. However, certain groups of adolescents tend to be at higher risk of engaging in sexual intercourse. This problem should be addressed early by targeting these groups of high-risk adolescents.
    Matched MeSH terms: Behavioral Risk Factor Surveillance System
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