Epidemiology Of Disease X And Other Health Factors

Prevalence and incidence of Disease X

Incidence portrays information about the risk of developing a disease while prevalence shows how the disease spreads (Spiegelman & Hertzmark, 2005). For the case of disease x, it will have high prevalence and incidence. This is because the disease is incurable so the number of people who are affected remains very high. When the incidence of disease x is constant, for the time of the disease, prevalence is the product of the disease incidence plus average disease duration. Therefore, when the incidence increase, the prevalence also increases  

1. Cumulative frequency of the disease is given by 15/500 = 0.03 or 3%
2. Incidence rate of the disease is given by  = (No. of new cases /person-time at risk) 15/1000 = 0.015 or 1.5%
3. Cumulative frequency of the disease is twice its incidence rate.

1. Prevalence of HBP at the age of 55years is 300/3000 = 0.1  or 10%
2. Prevalence of HBP at the age of  65 is 700/2700 = 0.26 or 26%
3. Cumulative frequency of HBP among the women  is 700/3000 = 0.23 or 23%
4. Number of person-times at risk of contracting HBP within 10 years is 3, 000+2300 at risk at the end of 10 years /2) *10years (5300/2)*10= 26500 person-year of risk

Incidence rate of HBP among women is 700/26500= 2.645%

1. Period prevalence of hypertension is 1500/30000= 5%
2. Cumulative incidence of hypertension  is 600/30000= 0.02
3. Incidence rate of hypertension is 600/36000= 1.7%
4. Cumulative incidence of hypertension is much higher compared to the incidence rate due to an increase in population and the number of hypertension incidences.

The  study report with relative risk of 1.8  at 95 percent confidence interval of  1.6–2.0 for the link between  alcohol consumption  and cancer is ideal since it shows precise plus statistically significant estimate because  the CI is narrow and doesn’t include 1.0 (Jekel et al., 2007). Also the 1.8 RR indicates an 80% increases relative risk of cancer due to alcohol consumption.

1. None group is 27/73= 0.35
2. Occasional group is 24/76= 0.015
3. Light group is 26/74= 0.35
4. Moderate group is 25/75= 0.333
5. Heavy group is 7/93= 0.075
6. From the table above there is no correlation between the number of deaths and alcohol consumption. There are higher death rates for the non-alcohol consumers than the heavy alcohol consumption group. For the occasional, light, and moderate alcohol consumers, the number of deaths is almost the same regardless of the amount of alcohol consumption; hence death rates are not merely due to alcohol consumption but other causes.
7. Conclusions based on the above table can be misleading for several reasons. First, studies exploring the relation between alcohol consumption and mortality have showed that low intake of alcohol can have a degree of protection. Compared with never drinker’s data from other cohort studies show a reduction in the risks of death at lower level of alcohol consumption relative to the heavy or frequent drinkers. The deaths are attributed to a number of factors such as suicides, alcohol related deaths and car accidents.
8. Limitations that would be done to prevent limitations of the previous study would include moving towards statistical techniques that can analyze complex heterogeneous trajectories like growth mixture models (Hayat et al., 2007). Also, analyses should adjust for survey design variable that can account for the clustered as well as hierarchical aspect of the sampling procedure.

Findings from this study shows that increase in number of cigarettes smoked per group does not necessarily lead to increased cases of lung cancer. This is evident in group “0” where out of 552 people who are none- smokers, the risk of death due to lung cancer is 62%. This means that there are other factors that contribute to lung cancer in group 0 and 25 such as alcohol consumption, exposure to second hand smoke or radon gas which are not mentioned in the study (Krieger et al., 2003). For group 1-14 and 15-24, there are high chances that increased number of cigarettes smoked in these groups leads to higher risks of death due to cancer related illness.

1. Age is a confounding variable that was not used in this case study because old age can lead to more deaths. Health status of the men working in civil service was not considered which could have contributed to more deaths after 7.5 years
2. Weakened immune system is a confounder that has not been used in the case study since it is evident that weak immune system is independent variable to preventing cold symptom (Thom et al., 2006). Another confounder in this case is smoking since its more likely for one to have severe colds especially if he/she is a smoker.
3. Age is a confounder that has not been considered in this study because oral contraceptives are linked to higher risks of breast cancer especially between 20 to 49 years. Hormones are another confounder that has not been included in this study. According to Di Maggio (213) this is because contraceptives that use hormones may increase the risks of breast cancer  reoccurring

Risk of LC among smokers is 504/4580=11%

Risk of LC among non smokers is 110/2670= 4%

Risk ratio is 11/4= 2.75; thus smokers are 2.75 times more likely to contract Lung cancer than non smokers

People aged 40 years and below

Risk of Lung cancer among smokers is 85/1374= 6.2%

Risk of Lung cancer among nonsmokers is 90/1622= 5.5%

Risk ratio is 6.2/5.5= 1.127

People aged 40 years and above

Risk of lung cancer among smokers is 419/3206= 13%

Risk of lung cancer among nonsmokers is 20/1048= 1.9%

Risk ratio is 13/1.9= 6.8

From the two tables above, it is evident that increase in age increases the risk ratio meaning smokers are more likely to develop LC than non smokers. Therefore age is independent variable and the risk of developing LC is dependent variable , so age is a confounder variable in this case

Studies on lung cancer, high blood pressure, and alcohol and cancer

Apart from independent variables such as low ventricular rejection fraction and age (over 60 years) other confounders that can be considered in this study include the body mass index of the patient. This is because BMI of ≥ 30kg/m² is highly associated with coronary after bypass grafting. Post operative complications is another confounder that need to have been considered in the cohort study because mortality rate as well as the incidences of post operative complications were found to increase with age (Pfaff et al., 2013). Therefore, there is no difference in CABG mortality between the two hospitals.

Magnetic fields of low power use were at higher risks of contracting cancer despite the fact that the association magnitude was found to be much less compared to what had been reported by the odds ratio 2.0 and 3.0. According to Kriegar et al. (2003), no connection was found between electric and magnetic fields under high power use. The findings were not because of confounding by prenatal, but differential mobility as well as non response of controls leads to significant limitations in this study. The results in the above table show close connection of wire codes and cancer with more restricted proof of the connection based on 24- hour measurements and spot measurements of the magnetic fields . Irrespective of the presumably   higher accuracy as an indicator of a long period historical exposure to cancer, measurements obtained over the 24-hour did not show a stronger connection with cancer compared to spot measurements.

1. Experimental mortality is a potential internal threat in the study. Although three participants were at high risks of suicide, one died in the allocated to exercise program and the other to allocate to usual medical care. Instrumentation is another internal threat that has demonstrated throughout the study because there were changes in the variables. For example out of the ten participants who had poor health one patient had poor health in allocated to usual medical care and allocated to exercise program.
2. Body mass index is a confounder that was used in the study. A self reported height as well as weight was used to calculate the mass index where participants with a BMI of ≥ 25kg/m²were classified as obese. History of anxiety and depression diagnosis is another confounder that has been used in the study alongside current antidepressant use.
3. The 50% reduction on depressive severity during a period of 26weeks has an exercise intervention of 48% and P value of 0.68 at 95% confidence interval ranging from 0.36 to 1.28.
4. The table shows depressive severity and diagnostic status between per-protocol study groups at 12, 26 and 52 weeks. From the table there is no  difference between groups regarding re mission of depressive severity illness and fifty percent or higher decline in depressive symptomatology throughout the study

References

DiMaggio, C. (2013). ANOVA. In SAS for Epidemiologists (pp. 159-186). Springer New York.

DiMaggio, C. (2013). Introduction. In SAS for Epidemiologists (pp. 1-5). Springer New York.

Hayat, M. J., Howlader, N., Reichman, M. E., & Edwards, B. K. (2007). Cancer statistics, trends, and multiple primary cancer analyses from the Surveillance, Epidemiology, and End Results (SEER) Program. The oncologist, 12(1), 20-37.

Jekel, J. F., Katz, D. L., Elmore, J. G., & Wild, D. (2007). Epidemiology, biostatistics and preventive medicine. Elsevier Health Sciences.

Krieger, N., Chen, J. T., Waterman, P. D., Rehkopf, D. H., & Subramanian, S. V. (2003). Race/ethnicity, gender, and monitoring socioeconomic gradients in health: a comparison of area-based socioeconomic measures—the public health disparities geocoding project. American journal of public health, 93(10), 1655-1671.

Pfaff, J. J., Alfonso, H., Newton, R. U., Sim, M., Flicker, L., & Almeida, O. P. (2013). ACTIVEDEP: a randomised, controlled trial of a home-based exercise intervention to alleviate depression in middle-aged and older adults. Br J Sports Med, bjsports-2013.

Spiegelman, D., & Hertzmark, E. (2005). Easy SAS calculations for risk or prevalence ratios and differences. American journal of epidemiology, 162(3), 199-200.

Thom, T., Haase, N., Rosamond, W., Howard, V. J., Rumsfeld, J., Manolio, T., … & Lloyd-Jones, D. (2006). Heart disease and stroke statistics–2006 update: a report from the American Heart Association Statistics Committee and Stroke Statistics Subcommittee. Circulation, 113(6), e85.

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