Chapter 13: Relative Risk vs. Absolute Risk

Chapter 13: Relative Risk vs. Absolute Risk New Vocabulary: Absolute Risk: the chance of an outcome occurring, with no comparison group. Absolute risk is the number of people who have the outcome out of all those who could have had that result Relative Risk (RR): the incidence rate in the exposed sample divided by the incidence rate of those not exposed Absolute Risk Difference: the absolute risk in one group minus the absolute risk in the other Risk Factor: an exposure associated with increased rates of disease Attack Rate: the incidence rate in the exposed group or the number of cases divided by all those exposed to a particular agent Incidence: the number of new cases that occur among a sample during the duration of the study Prevalence: the number of cases that already exist in a population Odds Ratio (OR): an approximation of the relative risk using prevalence data (the odds that a case was exposed divided by the odds the control was exposed) Number needed to treat (NNT): the number of people who must be given an intervention to promote a single beneficial outcome or prevent a single adverse outcome Number needed to harm (NNH): the number of people who must be exposed to produce a single adverse outcome Study Designs and Epidemiology -​ Epidemiology is the study of the distribution and determinants of disease -​ There are 3 basic types of epidemiological studies: 1.​ Cohort study a.​ A cohort study is a prospective design that follows a group of individuals over time to see who develops the outcome of interest b.​ You eliminate prevalence cases, meaning those who already have the disease you are studying, because then you can’t find the exposure c.​ You can monitor incidence cases (which are the new cases that occur among your sample) which then allows you to calculate the relative risk (RR) aka the risk ratio (the incidence rate in the exposed group divided by the incidence of those not exposed) 2.​ Case-control study a.​ This type of study looks at the outcome of interest (disease) and looks back to determine what the exposure could be. This includes doing interviews, looking at medical records and asking questions about their pasts. b.​ Because you can’t remove prevalence cases, you cannot calculate the incidence cases and thus cannot calculate the relative risk i.​ However, you can calculate the odds ratio (OR), an estimate of the risk of being sick or not becoming sick 3.​ Cross-sectional study a.​ Cross-sectional studies collect data about the exposure and outcome simultaneously. All the data is collected at the same time. However, these studies can only show associations because you cannot determine the direction of the relationship. b.​ Cross-sectional studies offer preliminary results and can help form hypotheses, but they are usually only the beginning of examining any significant research issue Example of a Cohort Study -​ Suppose 1000 people live in a town that was recently devastated by a hurricane. Half the town (500 people) became sick within 2 weeks of the hurricane. It turns out that of the half that got sick, 468 were living in an area where the local water supply was compromised and high bacteria levels were detected. 63 of those who did not get sick lived in the same area with contaminated water. -​ With this information we can then begin to fill in the chi-square table! -​ -​ -​ -​ Half the town became sick, and half the town did not, so that means that 500 are disease present, and 500 are disease absent -​ Of the half that got sick, 468 lived in the exposure area, so we put that value in “A”. We then take 500-468=32 to get the number of people not exposed but with the disease and put that in “C” -​ 63 of those who did not get sick lived in the exposure area, so we put that value in “B”. We then take 500-63=437 to get the not exposed people without the disease and put that in “D” The next thing to do is to calculate the relative risk (RR) = (a/(a+b))/(c/(c+d)) -​ The formula will be given on our final exam but for the purpose of this question: RR = (468/531) / (32/469) = 0.88/0.07 = 12.57 -​ This means that the group living in the exposed area with the contaminated water were 12.57x more likely to be sick as compared to those who lived elsewhere in the town. -​ HOWEVER: we still don’t know if this is a statistically significant risk factor until you check the p-value or the confidence interval Interpreting Relative Risk Values -​ RR < 1 means that the group exposed had fewer cases develop (lower incidence) that the group that was not exposed -​ RR > 1 means that the exposed group has a higher incidence rate than the unexposed group -​ RR = 1 means that there is no association between exposure and the illness. -​ Don’t forget to check the p-value of the associated chi-square test to see if the results are statistically significant tho! Relative Risk vs. Absolute Risk -​ The relative risk looks at the risk comparison in two groups, the exposed group and the non-exposed group -​ Absolute risk is simply the chance of an outcome occurring, and has no comparison group. It is the number of people who have the outcome out of all those who could have had the outcome -​ The absolute risk difference is the absolute risk in the exposed group minus the absolute risk in the unexposed group. -​ It can tell you the amount of disease or outcome in an exposed group that is due to that particular exposure Numbers Needed to Treat (NNT) and Numbers Needed to Harm (NNH) -​ NNT is the number of patients who must be given the intervention to promote a single beneficial outcome or prevent a single adverse outcome -​ NNT = 100 / absolute risk difference as a percent value (when talking about beneficial outcomes) -​ Here's an example: -​ -​ We must first calculate the absolute risk difference by dividing people who have the disease by all of those who could have had the outcome. This means 60/100 (the incidence rate for the exposed group) and 10/100 (which is the incidence rate for the unexposed group) and subtract them. -​ (60/100)-(10/100)=50/100 which means that if tanning beds were eliminated you can prevent up to 50 cases of cataracts for up to 100 individuals -​ -​ -​ The absolute risk difference is 50% and thus the NNT is 100/50=2. In plain language this means that 2 patients must prevent tanning bed usage in order to prevent cataracts in 1 person NNH is the number of people who must be exposed to create a single adverse event -​ NNH = 100 / absolute risk difference as a percent value (when talking about adverse outcomes) -​ Basically go through the same process as above, but in the example you will be using you will look at adverse outcomes ALSOOOOO: make sure to check if the absolute risk difference is statistically significant before calculating the NNH or NNT because then it will be useless Attack Rates -​ Attack rates are used to determine the origin of an outbreak, particularly with foodborne illness. You calculate the attack rate by taking all those exposed to the agent of interest and putting that number in the denominator. You then consider out of this group, who got sick. That number goes in the numerator. Then you multiply it by 100 to get a percentage -​ Thus Attack Rate = (cases in exposed group) / (all those exposed) X 100 Case-Control Study Example -​ In this example, 10 patients with an unknown disease caused respiratory failure. 10 sick patients and 10 healthy controls were included in the study. The exposure is related to birds. The information provided is in the table below: -​ -​ -​ Because this is a case-control study, you calculate the OR. The OR divides the odds that the cases were exposed by the odds that controls were exposed. -​ OR = AD/BC → in this example OR = (8x6)/(4x2) = 6 -​ This means that the study estimates that individuals exposed to the birds are 6x as likely to become sick or that exposure to the birds is associated with a 6-fold increase in the risk of developing the illness Remember that OR is an estimate of the RR, and that they are not the same thing. Also don’t forget to check the p-value!