STAT6200 Statistics for Public Health Assignment Help
Individual/Group - Individual
Length - 1,200 words (+/- 10%)
This assessment addresses the following learning outcomes:
a) Critically apply the theories on key concepts in descriptive and inferential statistics
b) Analyze survey design and sampling methods to collect valid and reliable data and appraise methodologies
c) Assess the data and determine the appropriate parametric and non-parametric statistical tests, and how to control for confounding variables
d) Evaluate types of inferential statistics and interpret the results of these analyses using theoretical examples or as presented in published literature
e) Apply key concepts of statistics, including: sampling, hypothesis testing, distribution of data, validity and reliability, statistical significance and effect size
Submission Due Sunday following the end of Module 8 at 11:55pm
Weighting - 40%
Total Marks - 100 marks
This assessment requires you to read excerpts from four articles and answer a series of questions in no more than 1,200 words (+/- 10%).
Most public health and wider health science journals report some form of statistics. The ability to understand and extract meaning from journal articles, and the ability to critically evaluate the statistics reported in research papers are fundamental skills in public health. This type of assessment demonstrates how students can apply the skills that they learn in this course to real-world scenarios wherein they might need to interpret/review articles for public health use.
After reading published research articles, you will be asked to interpret, describe and report the following types of statistics for assignment help:
o State the null and alternative hypothesis
o Detail the demographic characteristics of the people in a sample
o Report summary descriptive and inferential statistics reported in the paper
o Describe what inferential statistics were used for the analysis of data in a study and why
o Interpret the odds ratios or hazard ratios for reported outcomes
o Evaluate the impact design limitations described by the researchers have on study or the extent to which results can be generalized to the population
Paper Excerpts for Interpretation
1. What was the purpose of the research?
2. What kind of data was used, and what statistical analysis was performed on the data?
3. Refer to Table 2. Describe the correlation between overweight, obesity, BMI and HDI for both men and women.
4. What inferential statistics were used for analysis of the data summarized in Table 2, and why?
5. What was the conclusion of the study?
1. Describe the research design of the study.
2. What demographic characteristics were considered for the people in the sample? Explain by referring to the descriptive statistics reported in the paper.
3. Which reported statistic was common to Figures 2, 4 and 5 in this paper? Please describe the outcome variable and statistic.
4. Refer to Figure 2. What did the researchers find for the number of disease-free years from age 40 when they categorized the participants by BMI?
5. What type of descriptive statistic is illustrated in Figure 3? List the variables included.
1. Describe the aim of the study. Based on the study design, can the aim be restated in terms of null and alternative hypotheses?
2. What type of statistical analysis was used to independently examine the effect of diabetes status and type on in-hospital death with COVID-19? Why?
3. Interpret and report the adjusted odds ratios for in-hospital COVID-19-related death associated with diabetes status.
4. How generalizable are the findings described by the researchers to the population, and why?
5. What was the main finding of the study?
1. Describe the aims of the study. Based on the study design, can the aim be restated in terms of null and alternative hypotheses? If so, state the null and alternative hypothesis.
2. Which four primary outcomes were examined by the study? Refer to Figure 2 - interpret and report the statistical results of these four primary outcomes.
3. Examine Figure 3, what was the outcome for Diabetes? Please report the statistics.
4. What was the most likely explanation for the effect of the intervention described in the discussion?
5. What were the limitations of the study reported in the discussion?
Previous researches have demonstrated that obesity is one of the known health threats that lead to serious non-communicable diseases like diabetes, cardiovascular disease, blood pressure and even cancer. Ataey et al. (2020), arguesin their study that Human Development Index (HDI) has significant impact on the prevalence of obesity. The study also assesses the degree to which HDI influence the prevalence of overweight and obesity.
In the analysis, Ataey et al. (2020) used secondary quantitative data and using the SPSS, descriptive and inferential statistical analysis has been used. To determine the association between prevalence of overweight and obesity with HDI based on gender, data for Eastern Mediterranean Region has been collected. UN resource for HDI data and WHO resource were used for gathering information regarding overweight, obesity and other non-communicable diseases.
The study has presented its finding in table 2 where correlation between HDI with overweight, obesity and BMI is presented. As per the correlation, for male HDI has significant impact on overweight, obesity and BMI as their p value is lower than 0.05. Correlation also demonstrates the HDI influence the overweight, obesity and BMI highly as correlation of .721, .714 and .549 shows, HDI can lead to change in overweight by 72.1%, 71.4% and 54.9% respectively (Ataey et al. 2020). When it comes to female, then the correlation is valid for overweight and obesity as their p value is lower than 0.05. Correlation of .615 and .617 demonstrates, HDI can change overweight by 61.5% and obesity by 61.7% respectively (Ataey et al. 2020). Hence, for as the inferential analysis, here correlation analysis has been used to summarise the outcome in table 2.
To conclude, the study stated that there is good level of significant association between HDI and obesity and overweight (Ataey et al. 2020). The study at last asks policymakers to consider HDI factors while making general health policy for controlling non-communicable diseases.
Previous studies have demonstrated that obesity is one of the major factors that lead to risks of several chronic diseases. However, it is not yet discovered, to which extent obesity is connected with the loss of diseases free years in different socioeconomic groups. Thus, the study authored by Nyberg et al. (2018) has performed a comparative analysis to determine the number of free years from any non-communicable disease for people overweight and obese with people who are normal weight. To analyse the research aim, present study has used quantitative approach of data analysis. Using the deductive approach of study, here analysis has been done of the Body Mass Index (BMI) with risk free years based on gender. To perform the study, researcher here considered variables like BMI, gender and age.
As per the statistical analysis, it can be seen that mean age of the male is 44.6 years and it is 43.4 years for female. Out of total respondents, 60.8% were female respondents and 39.2% were male respondents (Nyberg et al. 2018). Average BMI is 25.7 kg/m2 for males, and BMI is 21.468 for normal weight and 14.93 kg/m2 for overweight males. On the other hand, for females, mean BMI is 24.5 kg/m2 with 44.760 for normal weight and 5.670 for overweight (Nyberg et al. 2018).
Coming to the finding of the analysis, it has been observed that figure 2, 4 and 5 demonstrates mean age of disease-free years. Also, all three figures demonstrate gender wise significance of the disease-free years. As per the figure 2, it can be seen that when participants were arranged based by BMI, then average men age free year is 69.3 years for normal weight and 65.3 years for obese level I (Nyberg et al. 2018). For women, risk free years is 69.4 years and it is 66.7 years for obese level I (Nyberg et al. 2018). Using the descriptive statistics, prevalence level of obesity, has been presented based on socioeconomic status and gender. Hence, here graphical presentation has been used as descriptive statistics. Variable that was included in the study were obesity level, smoking, physical inactivity, gender and socio-economic status.
Various study has demonstrated that there is long standing debate that whether diabetes has association with morality related to covid19. To understand the phenomenon, analysis has been made to check how the relative risks for type 1 and type 2 diabetes impact the covid19 related mortality during march 1 2020 to May 11 2020 by (Barron et al. 2020). Underpinning the study design, the study aim can be restated as well like: how the mortality rate of covid19 is associated with the diabetes type 1 and type 2. To analyse the restated research aim, hypothesis can be developed as follows:
Null hypothesis (H0): Mortality rate of covid19 does not have association with type 1 and type 2 diabetes
Alternative hypothesis (H1): Mortality rate of covid19 have positive association with type 1 and type 2 diabetes
Using the actual research design, for analysing the effect of diabetes and the type of in-hospital death during covid19, odd ratio has been used and descriptive statistics were used to determine generic characteristics of the data. In figure 2, the study has presented adjusted odd ratios for demonstrating in-hospital covid19 related death for diabetes. As per the same, it can be seen that odd ratio is high for female and people who are ages more than 80 years and who have diabetes have odd ratio of 9.2 compared to control group. It has been further found that people who have diabetes type 1 are more prone to covid19 mortality and for type 2 diabetes people mortality rate is low (Barron et al. 2020). The finding of the study is valid for the England population who are having type 1 and type 2 diabetes. Thus, the study finding cannot be generalised for other part of the world where the factors like ethnicity, diabetes level, age benchmark changes. To conclude, the study found that there is independent association between the covid19 mortality and in hospital death for type 1 and type 2 diabetes.
Previous studies have demonstrated that lifestyle intervention can slow down the type 2 diabetes glucose tolerance level; however, it is uncertain that whether it result in fewer complication or enhanced longevity under uncertainty. Thus, Gong et al. (2019) authored their study to determine how long-term effect of lifestyle intervention with impaired glucose tolerance level have implication on diabetes and mortality. Based on the study design, it can be restated as null and alternative hypothesis.
Null hypothesis: there is no significant influence of lifestyle intervention in people with impaired glucose tolerance level on diabetes and mortality
Alternative hypothesis: there is positive significant influence of lifestyle intervention in people with impaired glucose tolerance level on diabetes and mortality
To analyse the association, four major variables were considered, as presented in figure 2, which diabetes, cardiovascular disease (CVD) event, CVD deaths and Compositive microvascular disease. Figure 2 demonstrated the difference in diabetes and mortality effect on control and intervention group. The finding demonstrated that intervention group has media delays in diabetes by 3.96 years (Gong et al. 2019). CVD event and CVD deaths represented a 1.44-year growth in life expectancy. Figure 3 finding demonstrates that intervention group has better hazard ratio during follow up time and significance level was 0.001. For the CVD event, CVD death, Composite microvascular disease also, intervention group demonstrated better outcome. Though the finding was good, it was limited in terms of size of sample. Apart from this, there was irregularities in participation examination and the finding was limited for diabetes type 2 patients only.
Ataey, A., Jafarvand, E., Adham, D., & Moradi-Asl, E. (2020). The relationship between obesity, overweight, and the human development index in world health organization eastern Mediterranean region countries. Journal of Preventive Medicine and Public Health, 53(2), 98. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7142010/
Barron, E., Bakhai, C., Kar, P., Weaver, A., Bradley, D., Ismail, H., ... & Valabhji, J. (2020). Associations of type 1 and type 2 diabetes with COVID-19-related mortality in England: a whole-population study. The lancet Diabetes & endocrinology, 8(10), 813-822. https://www.ncbi.nlm.nih.gov/pmc/articles/pmc7426088/
Gong, Q., Zhang, P., Wang, J., Ma, J., An, Y., Chen, Y., ... & Roglic, G. (2019). Morbidity and mortality after lifestyle intervention for people with impaired glucose tolerance: 30-year results of the Da Qing Diabetes Prevention Outcome Study. The lancet Diabetes & endocrinology, 7(6), 452-461. https://www.ncbi.nlm.nih.gov/pmc/articles/pmc8172050/
Nyberg, S. T., Batty, G. D., Pentti, J., Virtanen, M., Alfredsson, L., Fransson, E. I., ... & Kivimäki, M. (2018). Obesity and loss of disease-free years owing to major non-communicable diseases: a multicohort study. The lancet Public health, 3(10), e490-e497. https://www.sciencedirect.com/science/article/pii/S2468266718301397