Participant characteristics.

<p>All percentages were across columns.</p

Details of the leadership training programme.

<p>Details of the leadership training programme.</p

The assoication between preferred vaccine (Cervarix® vs. Gardasil® vs. no preference) and the reasons to choose vaccines (A to N).

<p>(A: Stronger protection; B: Safety; C: Lower price; D: More HPV types; E: Protect against genital warts; F: Better antibody response; G: Long-lasting immunity; H: Cross-protection for other cancer-associated HPV types; I: Better adjuvant; J: Patients think it’s better; K: Practice management; L: Selected by the Government; M: Credibility of manufacturer; N: Comprehensive service of manufacturer). All comparisons among vaccine preference groups across reasons to choose vaccine categories (A to N) were statistically significant (p<0.05).</p

Knowledge on human papillomavirus (HPV) infection (N = 444).

<p>Knowledge on human papillomavirus (HPV) infection (N = 444).</p

Attitude towards the importance of public health strategies to promote human papillomavirus vaccination.

<p>(A: Partial (∼30%) subsidy by the Government; B: Fully paid by the Government; C: Price offered at 50% discount; D: School immunization program with market price; E: Immunization program jointly organized by school and community doctors with market price).</p

Practice of human papillomavirus (HPV) vaccination according to recommendations, age groups and sex.

<p>Practice of human papillomavirus (HPV) vaccination according to recommendations, age groups and sex.</p

Preference and reasons to choose human papillomavirus (HPV) vaccines (bivalent vs. quadrivalent) in practice for adolescent girls aged 10–17 years.

a<p>Percentages in brackets were valid % which excluded missing variables.</p

Participant Characteristics (N = 444).

<p>HPV: Human Papillomavirus.</p

Bivariate association between independent variables.

<p>◊ Cramer's V computed for association between a nominal variable and another variable</p><p>* Gamma statistic computed for association between ordinal variables.</p><p>Bivariate association between independent variables.</p

Alcohol Tax Policy and Related Mortality. An Age-Period-Cohort Analysis of a Rapidly Developed Chinese Population, 1981–2010

<div><p>To delineate the temporal dynamics between alcohol tax policy changes and related health outcomes, this study examined the age, period and cohort effects on alcohol-related mortality in relation to changes in government alcohol policies. We used the age-period-cohort modeling to analyze retrospective mortality data over 30 years from 1981 to 2010 in a rapidly developed Chinese population, Hong Kong. Alcohol-related mortality from 1) chronic causes, 2) acute causes, 3) all (chronic+acute) causes and 4) causes 100% attributable to alcohol, as defined according to the Alcohol-Related Disease Impact (ARDI) criteria developed by the US Centers for Disease Control and Prevention, were examined. The findings illustrated the possible effects of alcohol policy changes on adult alcohol-related mortality. The age-standardized mortality trends were generally in decline, with fluctuations that coincided with the timing of the alcohol policy changes. The age-period-cohort analyses demonstrated possible temporal dynamics between alcohol policy changes and alcohol-related mortality through the period effects, and also generational impact of alcohol policy changes through the cohort effects. Based on the illustrated association between the dramatic increase of alcohol imports in the mid-1980s and the increased alcohol-related mortality risk of the generations coming of age of majority at that time, attention should be paid to generations coming of drinking age during the 2007–2008 duty reduction.</p></div