1. Objective 1 Compares the levels of AOD use at Pre and Post-school celebration
H0: There is no significant difference in AOD use at Pre and Post-school celebration
H2: There is no significant difference in AOD use at at Pre and Post-school celebration
Objective 2 relates the experience of harms experienced at the celebrations to levels of use and engagement in harm-minimization strategies.
H0: Harms experienced at the celebration do not varies with levels of use and engagement in harm-minimization strategies.
H1: Harms experienced at the celebration varies with levels of use and engagement in harm-minimization strategies.
Hypothesis tests: To compare whether there is any significant difference in AOD consumption between the last event and an average day at the leavers' celebrations.
Confidence intervals: The sample estimate of the population parameter obtained will be uncertain due to sampling error, so the confidence intervals are calculated by using standard error. It creates a range of value for population parameters and population value will likely fall between this range.So
Logistic Regression: To assess the likelihood of negative experiences with given factors/independent variables included in the study.
5.The odds ratio of unprotected sex compared with those who engaged in safety strategies with the greatest frequency is 10.92.
Interpretation: Respondents who fall under the 66.84 PBSS group were 10.92 times as likely to report unprotected sex to compare with those who fall under the safest PBSS group.
1.The study aimed to find out the trends in the prevalence of diabetes in households of Hong Kong, China. Identifying high-risk categories for diabetes by using socioeconomic factors. Either aim can be restated as follow
Objective 1:To study the trends in the prevalence of diabetes over time
H0: There is no significant increase in the prevalence of diabetes among households from 2001 to 2008.
H1: There is significant growth in the prevalence of diabetes among household from 2001 to 2008
2. The sample size used for the study was N=121895.Out of the total sample, a large number of respondents fall under the age group 35-44 years. Out of the total sample, the proportion of male and female respondents are almost equal, female respondents were marginally high by 1767 number (1.45%). A large number of households(50,648) fall under the monthly income category 10,000 to 24,999 HK dollars.
3. To compare the diabetes prevalence among sample respondents over the year across different socio economic characteristics diabetes prevalence rate was used. Logistic regression was employed to know the factors responsible for the prevalence of self-reported diabetes. The factors which are considered to run the binary logistic regression model were age, sex, and monthly household income. The hypotheses test was employed to see the significant differences in the prevalence of self-reported diabetes across different socio-economic groups. To study the differences of the association between age and diabetes across different gender category a separate regression model was built. To test the significant differences between these groups (gender category) hypothesis testing was done. A P-value less than or equal to 0.05 was considered to test the hypothesis. The confidence interval was used to give ranges of variable estimates.
4.The researchers found out that after adjusting the prevalence rates of diabetes for age and sex there was a drastic rise in the prevalence rate in both the sex groups as age increases.
5.The odds ratio of the higher age group was 120.08 which indicates respondents who fall under more than 65 age groups were 120 times more likely to have diabetes compared with those of the benchmark category. Similarly, the odds ratio of the male category was found to be 1.10 stating the males were1.10 times more likely to have diabetes compared to females. The odds ratio of household income is highest in the lowest income group category(2.19) indicating that the lower-income group category was 2.19 times more likely to get diabetes compared to the highest income group.
6. the authors indicated this study was completely based on self-reported data and authors quoted a study by Gu et al. (2003) which showed, in China 75% of diabetes patients were undiagnosed. The authors also mentioned the role of other factors in the prevalence of diabetes. In the case of self-reported data, the respondent may tend to give a socially acceptable answer. This limitation will impact the acceptability of the report as the self reported data are biased and the conclusion should not be drawn by only considering this. This research would have been best when considering other factors such as individual life style,obesity, food habits &, family background, etc.which influences the occurrence of diabetes. So this study can be used to understand the generality of the topic. Future research studies can be framed taking the outcome of the study like a high-risk group and conducting research by combining self-reported as well as individual behavior data.
Gu,D., Reynolds ,K., Duan ,X., Xin, X., Chen,J., Wu,X., Mo J, Whelton PK,& He,J.(2003).Prevalence of diabetes and impaired fasting glucose in the Chinese adult population: international collaborative study of cardiovascular disease in Asia (InterASIA). Diabetologia, 46:1190–1198.
Lam, T., Liang, W., Chikritzhs, T., & Allsop, S. (2014). Alcohol and other drug use at school leavers' celebrations. Journal of Public Health, 36(3), 408-416. Retrieved from:http://jpubhealth.oxfordjournals.org/content/early/2013/08/26/pubmed.fdt 087.full.p df+html
Wong, M. C., S., Leung, M. C., M., Tsang, C. S., H., . . . Griffiths, S. M. (2013). The rising tide of diabetes mellitus in a Chinese population: A population-based household survey on 121,895 persons. International Journal of Public Health, 58(2), 269- 276. Retrieved from: http://dx.doi.org.ezproxy.laureate.net.au/10.1007/s00038- 012-0364-y
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