Epidemiology World Health Organization

Question: Discuss about theEpidemiology for World Health Organization. Answer: Introduction The design used in the research is food frequency questionnaire which is commonly referred to as FFQ design. The main advantage of this design is that the method is suitable in the measurement of long term dietary intake as it is based on average intakes of food over a period of time. Another advantage of this design is that the design is pre-corded and therefore its data preparation does not require a nutritional expert. The model is also very efficient and its cost of administration is very low .the design can also be administered in one visit (Friis, 2010). During the follow up period of five years, 1879 cases of the type two diabetes mellitus were detected from the cohort study. When both the quintiles were compiled and compared, the energy and age attuned relative risks were as follows; whole grain=0.62 (95% [CI] 0.53, 0.71, P inclination .0001). Refined grain=1.31 (95% CI=1.12, 1.53, P trend = .0003. Ratios of polished to whole grain intake =1.57 (95% CI=1.36, 1.82, P trend .0001. RR=1.26, 95% Confidence interval= 1.08, 1.46, P=.01 for tendency when the extreme quintiles were compared. From the table, we can easily observe that the total grain consumption is directly related to a reduction in the level of risk of contracting diabetes mellitus 2.there is an inverse relationship between the consumption of whole grains and the risk of suffering from diabetes type 2.The whole grain is associated to the intake of cereal fiber. Cereal fiber intake reduces the risk of suffering from diabetes mellitus 2. From the study, the research indicates that total grain consumption is inversely related with the possibility of suffering from diabetes mellitus II. Lower intake of whole grain was highly associated to an increase in the likelihood of suffering from diabetes mellitus II. The whole grains are rich in cereal fiber which are highly associated with prevention against diabetes mellitus 2.Dietary fiber intake is one nutrient that can greatly help in protection against the diabetes disease. The main reason for adjustments of analysis was because cigarette smoking, alcohol consumption and family history appeared to be a major factor affecting the body mass index (BMI).The above also showed a strong quadratic relationship causing underweight risk especially in women. Alcohol consumption, smoking, age and the geographical area are considered to be potential confounding factors that required adjustments so as to reduce bias in the study since diabetes mellitus 2 risks is positively associated to them. The use of food frequency questionnaire in the study tends to overestimate the energy and total grain consumption but it does not account for protein and fat intake (Coulston, Boushey, Ferruzzi, 2013). Validation and calibration studies are important in food frequency questionnaire in nutrition epidemiology for the purpose of interpreting the findings and other factors that affect validation. These factors may include memory and the nutrients data. Table of association between dietary fat intake and skin cancer. Cancer patient= CP Melanoma (high)= H Melanoma (low)=L Total CP CPH=150 CPL=80 CP=230 Control=C CH=130 CL=100 C=230 Non control=NC NCH=280 CN=40 CN=40 Total CH=300 CL=200 500 RR= [{am / (am+bn)} / {cm/ (cm+dn)}] = [{a (d+bq) / b(c+aq)} / {b(c+aq)}] = [ad {1+ (b/d)} / bc {1+ (a/c)}] High vs. low fat intake [50{1+ (80/500)} / 25{1+ (250/500)] =68/37.5 OR 1.813. Medium vs. high fat intake. = [25{1+ (40/500)} / 50{1+ (80/500)}] =27/68 or 0.397 This is because the risk of suffering from melanoma reduces with the reduction of low dietary fat. Population attributed to risk of melanoma due to low fat intake; =25% of 500= 125 people. Twenty five percent of the study cohort who consumed low dietary fat still faced the risk of suffering from melanoma. This can be attributed to other factors such as alcohol consumption and smoking. By using the same instrument in a dietary based study, it is visible that there is no concrete evidence that supports the relationship between high fat intake and increased risk of suffering from melanoma. Rather, the research indicates the likelihood of the above happening. Up to date, there is no study that has investigated the association between dietary fat intake and the risk of basal cell carcinoma, the risk of CMM and squamous cell carcinoma in a same cohort and while using the same design of fat intake. There is no major association between exposure and the cohort suffering from the disease. When the cohort is exposed and when not exposed, the likelihood of suffering from the disease is constant. The spread of the disease can be attributed to other factor apart from exposure. Exposure to the UV radiation is the single major factor that is highly attributed to skin cancer (Rom, Markowitz, 2007). Also, the variation of incidences such as the coetaneous malignant melanoma in a cohort living in a similar altitude has suggested that fat dietary intake may have a role in a cohort suffering from melanoma. Relative risk of exposure causing disease in young adults. RR= [{am / (am+bn)} / {cm/ (cm+dn)}] = [{a (d+bq) / b(c+aq)} / {b(c+aq)}] = [ad {1+ (b/d)} / bc {1+ (a/c)}] = [30{1+970/120)} / 80{1+ (50/120)}] =47.50 / 113333 =0.419 Relative risk of exposure causing disease older adults. RR= [ad {1+ (b/d)} / bc {1+ (a/c)}] [40{1+ (70/120)} / 90{1+ (50/120)}] =63.333/127.5 =0.4967 The relative exposure to risk in older adults is higher compared to that of younger adults. Exposure increases the probability of an older adult suffering from the disease but it does not affect the younger adults. This is inverse when there is no exposure. More young adults are at a risk of getting sick when there is no exposure than the -older adults. An example of a typical bias in a cohort study is the subject selection bias. It is least expected that the factors that may affect enrollment of subjects into a probable cohort study can introduce bias. To create this bias, the selection of the subject has to be related to both the outcome and exposure (Lachin, 2014). Area under discussion is usually enrolled into a group of study before they have the result and the experience of concern. This indicates how easily an enrolment may be related to exposure. This type of cohort study bias is likely to be more common in a cohort study that is retrospective especially if the cohort being studied is required to provide informed consent for participation (Gordis, 2014). This occurs because a retrospective cohort study usually starts after all the cases of a disease have occurred and the subjects may be aware of both the exposure they are in and the outcomes. Example; Consider an investigation that is hypothetical of an occupational exposure that was conducted 15-20 years ago in a factory. Over the years, there emerged some suspicion that working in the factory was greatly associated with some adverse health effects (Savitz Wellenius, 2016). Finally, a retrospective cohort study was carried out as indicated in the contingency table below; Unbiased data sickly Non-diseased overall exposure 1000 9000 10000 Not exposed 500 9500 10000 The above data is unbiased and its RR would be as follow; RR unbiased= [(1000/10000) / (500/10000)} =2.0 However, in cases where many of the old data records were lost but was given the suspicion of estimation and approximation, the accounts of the employees who were solvent but later had health problems are likely to be 99% retained(Wang, 2011). This would result to the data shown in the table below; Biased result sickly Non-diseased overall exposure 990 7200 8190 Not exposed 400 7600 8000 The biased RR would be ;={( 990/8190) / (400/6000)} =0.1209/0.05 or 2.42 From the above, we can confidently say that loss record results in over estimation and selection bias in a case study. Yes. Loss of data and records results in overestimation and selection bias depending on the scenario of the cohort study. This type of bias mainly arises from retrospective cohort study. It might occur as a result of retention or loss of the study subject as these are related to both the outcome status and the exposure selection bias is likely to occur if the choice or selection of the exposed or the unexposed subject in a retrospective cohort study is in a way related to the outcome of the interest. We can relate this to question 4.The table given shows the relative risk before and after loss of data. From the above, the loss of record resulted in an over estimation and selection bias. Reference Bonita, R., Beaglehole, R., KjellstroÃÅ'ˆm, T., World Health Organization. (2006). Basic epidemiology. Geneva: World Health Organization. Chan, S. G. (2008). Development of food frequency questionnaire and database for assessing soy isoflavone intake in the Chinese population. Coulston, A. M., Boushey, C., Ferruzzi, M. (2013). Nutrition in the prevention and treatment of disease. Dietary reference intakes for energy, carbohydrate, fiber, fat, fatty acids, cholesterol, protein, and amino acids. (2006). Washington, D.C: National Academy Press. Friis, R. H. (2010). Epidemiology 101. Sudbury, Mass: Jones and Bartlett Publishers. Gordis, L. (2014). Epidemiology. Lachin, J. M. (2014). Biostatistical Methods: The Assessment of Relative Risks. New York, NY: John Wiley Sons. Rom, W. N., Markowitz, S. (2007). Environmental and occupational medicine. Philadelphia: Wolters Kluwer/Lippincott Williams Wilkins. Savitz, D. A., Wellenius, G. A. (2016). Interpreting epidemiologic evidence: Connecting research to applications. Wang, S. Q. (2011). Beating melanoma: A five-step survival guide. Baltimore: Johns Hopkins University Press.