Introduction to Epidemiology

Discuss about the Epidemiology for Students and Health Professionals.

Epidemiology is the study of the distribution and the determinant of health events in a population and its applications in solving health problems. This paper examines various area of epidemiology. These areas include study designs, measures of association, research analysis, bias and errors. These areas have been covered by means of answering given test questions.

1. The study is a prospective cohort study. The advantages of a prospective cohort study is that it can be used to calculate incidence of the disease and the risk of developing the outcome from the exposure. It is also suitable to study rare exposures. The study can also be used to study an exposure with multiple outcome (Wang & Clayton, 2006).
2. Crude incidence rate is a of incidence that takes into account the time taken to observe the subjects. It incorporates time into the denominator. The denominator is usually in person-time units (Wang & Clayton, 2006)

Crude incidence rate =

                                     =

                                     = 0.0026

                                     = 2.6 cases per 1000 person-year.

1. Quintile specific incidence rate =

Quintile 1 =

                    = 2.7 per 1000 person-year

 Quintile 2    =

                      = 2.4 per 1000 person-year

 Quintile 3    = 

                        = 2.6 per 1000 person-year

  Quintile 4   =

                         = 2.5 per 1000 person-year

   Quintile    5

                          = 2.8 per1000 person-year

                 Quintile crude relative risk

                                                          2 = 0.86 x 0.43

                                                = 0.3698

                                                3 = 0.85 x 0.83

                                                = 0.7055

                                                4 =0.77 x 1.31

                                                = 1.00

                                                5 = 0.84 x 2.70

                                                = 2.268

No pattern of association can be can be observed from the unadjusted crude data.

1. The pattern of association in Table 2 Model 2 is a positive correlation. If a line of fit was to be drawn it will have a positive slope (Webb & Bain, 2011)
2. The authors adjusted their analysis for alcohol, cigarette smoking, family history of diabetes and physical activity because these factors are cofounders. If these cofounders are not adjusted then they will affect and create a biased association. Some of these factors vary greatly from quintile to quintile. An example is physical activity with a baseline adjustment of 36, 41, 44, 47 and 50 across the quintiles respectively. The trend also applies to cigarette smoking.

1. The main possible bias in this study is loss of follow up. Loss of follow up is a form of selection bias. It is common in prospective cohort study. In this study loss of follow up can be seen by the varying person-year values. This means some subjects left the study. This bias might affect the credibility of the results as the subjects who left the study might be different from the ones who remained.

1. Prevalence is the proportion in a defined population that has an outcome of interest at a defined period of time (Dicker & Koo, 2006)

Prevalence =

Prevalence of child abuse in the control = 9/158

Prevalence of child abuse in the cases =  9/158 x 3.9

                                                                                   = 0.22215

No.  of children abused in the cases = 0.22215 x 63

                                                                                       = 14

Odds ratio is the ratio of exposure in the diseased group divided by the ratio of the exposure in the non-diseased group.

Odd ratio =

                 =

                  =

                  = 4.73

The odd ratio of exposure to child abuse and the risk of mental illness is 4.73. this means that there is 4.73 more likelihood of someone developing mental illness if they were abused as a child as compared to those who were not abused as children.

1. Factors that can bias the estimate obtained include confounders. These factors may influence the both the cases and the controls and give a distortion in the estimates. The confounders can include substance abuse among others.

Recall bias might also influence the estimate. This is because the exposure number was retrieved from the past.

1. Attributable risk fraction is the proportion of the disease in the exposed group that is attributed to the exposure. It is an estimate of how much of the disease is due to the factor or exposure of interest (Olsen, Christensen &Murray, 2006).

Attributable fraction, AF=

                                           =

                                          = 0.7886 x 100

                                           = 78.86 %

The proportion of mental illness that is actually due to childhood abuse is 78.86%. 

1. The exposure has no association with the disease. This is concluded by calculating the relative risk.

Relative risk is the ratio that shows the probability of developing an health outcome in an exposed group (Webb & Bain, 2011).  It is calculated by taking the incidence risk of disease in the exposed group divided by the incidence risk of the disease in the non-exposed group.

Relative risk, RR =                             =

Exploring Study Designs in Epidemiology

                            = 1

Relative risk of 1 means that the exposure has no effect on the disease. A relative risk of less than 1 means that the exposure is protective against the disease while relative risk of above 1 means that the exposure increases the risk of having the disease.

1. Relative risk

 RR = a/(a+b)/c/(c+d)

 Relative risk in men

             = 

              = 0.75

Relative risk in female

              =   = 1.33

1. The relative risk in men is 0.75. this value is less than 1. This means that the exposure in men is protective against the disease.

The relative risk in female is 1.33. this value is more than 1. This means that the exposure increases the risk of having the disease in female by 1.33 folds.

Type of bias in a survey of prevalence of various electrocardiographic abnormalities after a heart attack.

Selection bias this is a misrepresentation of the association between the risk factor and the outcome that is brought about by how the subjects/items are selected for the study. Given that the study took place on patients who were admitted in the hospital, it failed to equally represent all classes that are supposed to be represented in the study.

Confounding bias. This bias occurs when another exposure exists in the study. This exposure is related to both the exposure and the disease under study. It is also unequally distributed in the study between the cases and the control. In the stated case the electrocardiographic abnormality might have been cause by other health issue in addition to the heart attack.

Restriction. The subject that were chosen for this study were limited to the hospital. This will make generalizability of the study to the general population hard as they were only limited to the hospital.

Diagnostic bias. Based on the researcher’s knowledge some subjects with no electrocardiographic abnormalities may have been counted so just because they had a heart attack.

Loss of participants represent a form of bias. It is known as loss of follow up bias or migration bias. It is a bias because those who withdraw may be different from those who remained in the study. If the loss occurs in large scale it might affect the validity of the conclusion. It might also reduce the sample size.

Conclusion

Measures of associations include risk ratio, odds ratio and attributable risk among others. Adjusted incidence rates are vitals in order to obtain correct associations without the influence of confounders. Some of the biases that affect results of research include selection bias, measurement bias and confounding factors.

References

Dicker, R., Coronado, F., & Koo, D. (2006). Principles of Epidemiology in Public Health Practice: An introduction to applied epidemiology and biostatistics. Waldorf: PHF

Olsen, J., Christensen, K., Murray, J., & Ekbom, A. (2010). An Introduction to Epidemiology for    Health Professionals. New York: Springer

Wang, D., Clayton, T., & Bakhai, A. (2006).  A practical guide to design, analysis and reporting. London: Remedica

Webb, P., & Bain, C. (2011). Essential epidemiology. An Introduction for Students and Health Professionals. Cambridge:University press