1.(a) The introduction of new drug that cures a previously incurable but non-fatal non-communicable disease.
Effect on incidence rate: The incidence rate is likely to remain constant or increased because the drug does not prevent the risk of being infected with the disease thus does not prevent the rise of new cases. The incidence rate is number of new cases of a disease in a population of people who are at risk of being infected. Therefore, if the risks for the disease are not eliminated or reduced, then the chances of new cases still remain the same.
Effect on prevalence: the prevalence will be reduced as the disease will be cured, therefore, reducing the total number of cases of the disease. Prevalence is the total number of cases out of a population at risk of the disease.
Effect on the duration of the disease: the duration will be reduced since the disease will be cured. When the disease is incurable, the duration is prolonged. The introduction of a cure manages the symptoms of the disease and cure it therefore, a shorter duration of the disease in the body.
(b) The introduction of a new drug that prolongs survival but does not cure a fatal non-communicable disease
Effect on incidence rate: The incidence rate will either be constant or increase. This is because the drug does not reduce the risk of being infected therefore, new will still be there.
Effect on prevalence: The prevalence will remain constant or increase since the disease will not be cured. It could also increase because of the number of survivors with the disease will be increased since the mortality rate will have reduced.
Effect on the duration of the disease: The duration will be increased since the drug will ensure survival but not cure. One will likely have the disease for a longer time since the drug will help prolong life therefore, prolonging the duration of the disease too.
(c) The introduction of new vaccine that prevents most people from developing a disease. Vaccinated people who still develop the disease recover more quickly than unvaccinated people.
Effect on incidence rate: The incidence rate will be lowered due to prevention of new cases of the disease. Therefore, the occurrence of the new cases will be reduced by the vaccine.
Effect on prevalence: The prevalence will be reduced because the number of new cases will have reduced therefore; the total number of cases will definitely be reduced.
Effect on the duration: The duration will remain constant for those not vaccinated and those vaccinated but developed the disease, the duration will reduce because they will recover more quickly as compared to the unvaccinated group.
January 2006:
Therefore, the prevalence in 2006 was 52.3%
January 2016: 3400 – 200= 3200
3200 + 1000 = 4200
The prevalence is 64.6%
(b) Incidence proportion:
(c) Incidence of becoming healthy weight:
If in 100,000 there are 10
Then in 300,000 there will be
(a) From the results the percentage of residents with Bachelor degree/higher qualification in Australian council areas in 2013 ranged between 6-12%. Majority of them earned about $ 40000 as personal income with the highest earning $ 93, 000 while the lowest earned $ 0. Even so, only two were not earning and this means that majority of individuals with Bachelor degree/higher qualification earned a personal income in council areas in 2013 in Australia. More than 45% of residents with Bachelor’s degree and/or higher qualifications earn above $ 50,000 as personal income.
(c) From the scatter plot, it is clear that if a best-line-of-fit is drawn it will give a clear picture that the figures indicate that attaining a Bachelor degree or higher educational qualification can lead to a higher income. Even so, the data only focuses on the population of highly educated people and not the whole population. This makes comparison difficult in regard to personal income for highly educated individuals and that of those below the parameter.
(d) Cohort Study. This is because cohort studies are the cheapest observational studies. A cohort study will also enable one to obtain the incidence other than the prevalence as is the case for cross-sectional study used above. Further, cohort study will help establish a temporal sequence that exists between having bee in highly educated and earning high income i.e. between exposure and outcome,
(b) Case control study. This will involve having the case group of smokers and a control group of non-smokers then collect information on their experience with common cold in terms of duration. This design helps to determine an association between an exposure and an outcome. The exposure in this case is smoking while the outcome is common cold. This study is faster and cheaper. This design is important in studying rare conditions. The association between smoking and common cold is a rare condition, therefore, this design proves more appropriate. It also allows one to study multiple risk factors since there could be other factors that are contributing to the development of common cold other than smoking.
(c) Cross-sectional study. This is because random samples are collected systematically and observed at the fruit serving to determine the association. A cross-sectional study design determines a relationship between a condition and given variables in a defined population at a given time, usually over a short period of time. This study is also preferred because the data is collected only once, provides descriptive analyses and can also be used to generate hypotheses. It also allows the study of multiple outcomes and exposure. In this study, the factors associated with fruit serving in the university and the group that goes for more fruit serving can be studied
(d) Randomized control trials. It is used in testing new treatments. It is study in participants are allocated at random to either a group receive the new intervention or the one receiving the standard intervention as a control. The groups will help compare the effects of the interventions and therefore, it will be used to determine which method is more effective during vaccination. This study is considered the best in clinical trials.
(b) The target population would be the children, the parents and the health care providers. The children will be studied in terms of their health information because they are the affected group while the parents will be able to give background information of the children since they are the caregivers of the children at home and the health providers, who are the ones who diagnose and have the medical information, will be able to give the symptoms and prognosis of the disease.
(c) The study should have a representative sample in order to capture the whole population. This would include every child that is infected from every residence and economical class; helps in determining the factors that contribute to the occurrence of the disease. The sample size should be large enough to be able to precisely determine the prevalence of the condition. The larger the sample size the more precise the results. The data should include interviews, questionnaires and medical examinations to be able to collect data on exposures and outcomes. It is also important to determine the inclusion and exclusion criteria at the design stage.
(b) Chances of having freckles and melanoma. This is because out of 183 cases, there were 61 who had freckles. This means that the 61 also have melanoma.
Example:
In a particular prospective cohort study that was aimed at establishing hormone replacement therapy (HRT) for the coronary artery disease among women that had past the menopause stage. The researchers calculated the coronary artery disease incidence rate among these post-menopausal women that had been put on HRT and then compared this to the then incidence rate among the post-menopausal women that had not been put on HRT. From the study, the following findings were established.
|
Post-menopausal Hormone Replacement Therapy Use |
Found with Coronary Artery Disease |
Disease-free Follow-up Person-years |
|
Exposed(Yes) |
30 |
54,308.7 |
|
Not Exposed(No) |
60 |
51,477.5 |
The incidence rate among the exposed(women that used HRT) is calculated as below:
The incidence rate among the unexposed (women that did not use HRT) is calculated as below:
The incidence rate ratio is calculated = (Incidence rate in exposed)/(Incidence rate in unexposed).
=(55.2)/(116.6)
=0.47
The incidence rate ratio is thus=0.47.
This shows that a post-menopausal woman is 0.47 times more likely to acquire coronary artery disease if exposed to HRT. From the question, an incidence rate ratio of 10 in exposed persons implies that one is 10 times more likely to acquire a particular condition when exposed to the agent under study.
Low level of screen-time incidence ratio:
Intermediate level of screen-time incidence ratio:
Highest level of screen-time incidence ratio:
(b) There is an association between the screen time and the incidence but it is not strong since the average incidence rate ratio is.75%. The association grows stronger with increase in screen-time as calculated with high level screen-time has a higher incidence rate ratio. Therefore, increase in screen-time level is directly proportional to incidence rate ratio of sight problem.
(c) 100% – 0.47%= 99.53% of sight problem would be theoretically prevented if all children had low levels of average daily screen time.
(b) The relative risk shows that the community is more disadvantaged by walking on the gold coast. This is because the relative risk is more than 1. Therefore, they are associated. The relative risk is the ratio of the probability of the occurrence of an event in an exposed group to the probability of the occurrence of the event in a group that has not been exposed which will be the comparison group.
(c) There is an association between walking on an average of at least one hour per day. This is because the odds ratio shows that walking on sunshine coast is associated with the community disadvantage while according to the risk ratio, the community is at risk of being disadvantaged when walking for an average of at least one hour.
References
Gallagher, L. (2012). Session Title: Ethics and Epidemiologic Decision-Making for Population Benefits. Annals of Epidemiology, 22(9), 680.
Kim, J. (2013). Epidemiology and Causation. Kosin Medical Journal, 28(2), 87.
Li, H. (2013). Systems biology approaches to epidemiological studies of complex diseases. Wiley Interdisciplinary Reviews: Systems Biology And Medicine, 5(6), 677-686.
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