Epidemiology and Prospective Observational

Question: Discuss about the Epidemiology and Prospective Observational. Answer: Introduction: The study design used in this study is prospective observational study. In this design, the author observed the nurses feeding patterns with relation to the risk of getting the type 2 diabetes mellitus (McKeown et al., 2002). The researcher then collected the information for a period of ten years using a detailed questionnaire with no influence to them. The use of this design enables the observation of some rare exposures to health since a researcher is able to identify a subject who developed a disease at some point. It is also possible to calculate the incidence of a disease which has been exposed to the subjects. The crude incidence rate is the number of new cases of diabetes mellitus that occurred in one year per one hundred thousand nurses under investigation. Therefore the incidence is arrived at by counting the number of new cases, divides by the total population under study and multiplied by one hundred thousand. Incidence rates for total grain Percentiles Counts Population Crude rate 1 392 144,698 270.9 2 356 144,403 246.5 3 368 144,438 254.8 4 358 144,471 247.8 5 405 144,409 280.5 Incidence rates for whole grain Percentiles Counts Population Crude rate 1 426 144,914 2934.0 2 391 147,351 265.4 3 407 143,856 282.9 4 320 145,133 220.5 5 335 144,164 232.4 Incidence rates for refined grain Percentiles Counts Population Crude rate 1 349 144,742 241.1 2 369 144,817 254.8 3 337 144,095 233.9 4 378 144,252 262.0 5 446 144,512 308.6 These results indicate that the crude rates were not consistent with the quintiles of meal consumption. Unadjusted incidence rate Percentiles Counts Population Crude rate 1 349 144,742 0.0024 2 369 144,817 0.0025 3 337 144,095 0.0023 4 378 144,252 0.0026 5 446 144,512 0.0031 The relative ratio of diabetes mellitus reduces with increasing quintile of whole grain. This means that the higher the amount of consumed whole grain, the lower the likelihood of diabetes mellitus type 2 occurring (de Munter et al., 2007). The physical form of the whole grain as well as its high content of the fibers makes them to be digested slowly. Moreover, the low rate of whole grains absorption makes it to have low glycemic levels. The consumption of foods which have low levels of glycemia is linked to low glycosylates hemoglobin excretion. The finely ground fibers cannot produce glucose response to the postprandial blood. The adjustment of other factors were meant to determine whether there were other factors apart from the composition of meals that were linked to type 2 diabetes. Physical activity for instance is linked to a reduction of the blood glucose levels. This is because during exercises, the muscles use glucose in blood to derive energy. In case an individual is resistant to insulin, physical activities lower the resistance making cells to take up glucose effectively (He et al., 2010). Family history is also linked to diabetes as a result of inheritance of genes for diabetes type 2. Smoking causes damage to the blood vessels complicating the diabetes. The biasness in this diabetes type 2 studies among the nurses is the assumption that nurses are closer to the medical care and hence access to treatments. In some instances, the nurses offer services to the patients such that they do not remember that they too need medical services (Egger and Smith, 1998). Moreover, the nurses may have the fear of being stigmatized by their colleagues and escape treatment or diagnosis. The association of diabetes type 2 with other factors apart from the whole grain meal does not have a clear cut line. The study did not include males to determine the effect of gender differences to type 2 diabetes (Fung et al., 2002). Fat intake Control Study cases High 100 80 Moderate 270 270 Low 130 150 For the control and study groups, there was low number of skin cancer cases i.e. 100 and 80 respectively. On the other hand, for the control and study groups the low fat intake there were high number of skin cancer cases i.e. 130 and 150 respectively. However, moderate intake of fats resulted had similar number of skin cancers of 270. The low number of skin cancer in high fats intake is because the fats raise the prostaglandin E2 levels which function as T cell function immunoregulators and in turn lower the ultraviolet related skin cancers. Relative risk= the number of subject with a positive or bad outcome divided by the sum of the number of subject with bad and good outcomes in the case study. This figure obtained is then divided by the number of subjects with a positive or bad outcome divided by the sum of the number of subject with bad and good outcomes in the control group. 150/230 divided by 130/230 =0.65/0.57 Relative risk=1.14 This means that the people who take fats in low levels would be approximately 1.14 more times likely to develop skin cancer as compared to those who take high fat levels. Relative risk=1.3 This means that the people who take fats in moderate levels would be approximately 1.30 more times likely to develop skin cancer as compared to those who take high fat levels (Prochaska et al., 2005). The association between the parenchymal cells and low fats intake is that low fats intake increases the risk of skin cancer (Black et al., 1995). On the other hand, high fats intake leads to oxidative stress and increase in the number of cytokines responsible for inflammation while at the same time reducing the death of skin cells via apoptosis. The exposure to low fats leads to melanoma because the skin cells are not able to counter the effects of oxidative damage. In the long run the skin cells begin dividing uncontrollably leading to the cancer of the skin. There is no association between rare exposure and development of a disease. This is because there when there is a rare exposure, sixty people develop the disease while one hundred and eighty of them do not. On the other hand when there is no exposure, the same effect is observed as when there is a rare exposure. Relative risk in younger adults 30/30+90 divided by 40/40+80 = 0.76 Relative risk in older adults 40/120 divided by 30/120 =1.32 The relative risk of 0.76 observed in the younger adults means that the people who are exposed to a disease are about 0.76 times less likely to develop a disease as compared to those who have been exposed. The relative ratio of 1.32 in the older adults indicates that the older exposed adults are about 1.32 times more likely to develop a disease when they are exposed than those who are not exposed. Bias in cohort studies can occur especially in the process of making of a selection of the study subjects. This means that the selection method is based on the exposure as well as the outcomes of the exposure (Greenland, 1977). In some cases, it can be easy to view the relationship between exposure and selection of subjects. However, it is difficult for the researcher to determine how the awareness of an outcome can influence the outcomes of a study. For instance in a study to determine the effects of the emission of some chemicals like sulfur to the people for a period of seven years, there were beliefs that employees who worked in that factory were the most affected. However, there was no data to support this assumption. At the time of enrollment, the health records which existed by then were used while many of the old records had been misplaced or lost. Therefore there was a likelihood of either underestimation or overestimation of the association between exposure to sulfur and disease development. Occurs when the subjects selected for the control are not a true representative of the population hence cannot estimate the distribution of exposure. case Controls Exposed 5 8 unexposed 4 54 On the other hand, taking two hypothetical situations, the researchers chose similar controls which had a high probability of having the exposure as shown below. Case Controls Exposed 5 14 unexposed 4 48 The loss of some participants during a follow up represents data biasness in a study. This is because there is an introduction of a deviation in the observed values during follow up as compared to the observation if all the subjects were present. In some cases, the loss of participants by about five percent is acceptable but more losses are likely to cause alarm because they have a different prognosis as compared to those who make it to the follow up. In the end, the validity and accuracy of the study is not acceptable in this study. As a result of this, the researchers do their best to lower the number of loss of participants during follow ups. Such measures include maintaining regular contacts by making calls or sending emails, maintaining baseline information which enables them to track the subjects easily and using the participants who are easy to track. 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