NR 503 DeVry Week 3 Relative Risk Calculation Worksheet? NR 503 DeVry Week 3 Relative Risk Calculation Worksheet ? NR 503 Relative Risk Calculation Worksheet? Purpose The purpose of this assignment is to help you to begin to understand and apply the important counts, ratios, and statistics presented in healthcare and epidemiological research. Remember to use the list of formulas presented prior to the problems and to carefully consider the purpose of each calculation and how it is interpreted. Course Outcomes Through this assignment, the student will demonstrate the ability to: (CO #3) Identify appropriate outcome measures and study designs applicable to epidemiological subfields such as infectious disease, chronic disease, environmental exposures, reproductive health, and genetics. (CO #4) Apply commonly used measures of health risk. (CO #6) Identify important sources of epidemiological data. Due Date: Sunday 11:59 p.m. (MT) at the end of Week 3 Total Points Possible: 50 CLICK HERE TO ORDER YOUR NR 503 DeVry Week 3 Relative Risk Calculation Worksheet? Requirements: 1. Complete the Risk Calculation Worksheet located in Course Resources. 2. For each question identify the correct answer. 3. Submit the worksheet to the DropBox by 11:59 p.m. MT Sunday of Week 3 Epidemiological Formulas and Statistics Incidence (exposed) Definition Incidence of new cases of disease in persons who were exposed Formula number (exposed with disease)/Total number of exposed Incidence (unexposed) Definition Incidence of new cases of disease in persons who were not exposed Formula number (unexposed with disease)/Total number of unexposed Incidence of Disease Definition Measure of risk. Total number in a population with a disease divided by the total number of the population. Formula Number with the disease/ Total population number Relative Risk Definition Risk of disease in one group versus another. Risk of developing a disease after exposure. If this number is one, it means there is no risk. R (exposed)/Risk (unexposed) Formula (# exposed with disease (divided by)/total of all exposed) (# of non-exposed with disease/(divided by)total of all non-exposed) Odds Ratio Definition A measure of exposure and disease outcome commonly used in case control studies. Formula R (exposed)/ R (unexposed) 1- R (exposed) 1-R (unexposed) Prevalence Definition The number of cases of a disease in a given time regardless of when it began. (new and old cases) Formula (Persons with the disease/ Total population) X 1000 Attributable Risk Definition The difference in disease in those exposed and unexposed and is calculated from prospective data. Directly attributed to exposure (if exposure gone, disease would be gone) Formula R(exposed) ? R(unexposed) Crude Birth Rate Definition The number of live births per 1,000 people in the population Formula (# of births/estimated mid-year population) X 1000 Crude Death Rate Definition The number of deaths per 1,000 people in the population Formula (# of deaths/estimated mid-year population) X 1000 Fetal Death Rate Definition The number of fetal deaths (20 weeks or more gestation) per 1,000 live births. Formula (# of fetal deaths/ # of live births + fetal deaths) X 1000 Annual Mortality Rate Definition Usually an expression of a specific disease or can be all causes per 1,000 people for a year. Formula (# of deaths of all causes (or a specific disease)/Mid-year population) X 1000 Case Fatality Rate Definition The parentage of individuals who have a specific disease and die within a specific time after diagnosis. Formula (# of persons dying from a disease after diagnosis or set period/ # of persons with the disease) X 100 Relative Risk Calculation Worksheet Prior to completing this worksheet, review the lessons, reading and course text up to this point. Also review the tables of calculations. Each question is worth five (5) points. There is only one right answer for each of the ten problems. 1. The population in the city of Springfield, Missouri in March, 2014 was 200,000. The number of new cases of HIV was 28 between January 1 and June 30th 2014. The number of current HIV cases was 130 between January 1 and June 30th 2014. The incidence rate of HIV cases for this 6 month period was: A. 7 per 100,000 population B. 14 per 100,000 population C. 28 per 100,000 population D. 85 per 100,000 population 2. The prevalence rate of HIV cases in Springfield, Missouri as of June 30, 2014 was: A. 14 per 100,000 population B. 28 per 100,000 population C. 79 per 100,000 population D. 130 per 100,000 population 3. In a North African country with a population of 5 million people, 50,000 deaths occurred during 2014. These deaths included 5,000 people from malaria out of 10,000 persons who had Malaria. What was the total Annual Mortality Rate for 2014 for this country? (Please show your work) 4. What was the cause-specific mortality rate from malaria? (Please show your work) 5. What was the case-fatality percent from malaria? 6. Fill in and total the 4 X 4 table for the following disease parameters: Total number of people with lung cancer in a given population = 120 Total number of people with lung cancer who smoked = 90 Total number of people with lung cancer who did not smoke = 30 Total number of people who smoked = 150 Total number of people in the population = 350 Fill in the missing parameters based on the above. YES LUNG CANCER NO LUNG CANCER TOTALS YES SMOKING NO SMOKING TOTALS 7. From Question 6, what is the total number of people with no lung cancer? 8. From question 6, what is the total number of people who smoked, but did not have lung cancer? 9. Set up the problem for relative risk based on the table in #6. 10. Calculate the relative risk.

ADDITIONAL INFORMATION;

How to apply the important counts, ratios, and statistics presented in healthcare and epidemiological research.

Introduction

The purpose of this article is to introduce you to the key counts, ratios, and statistics used in healthcare and epidemiological research. These measures are all common in research related to health and medicine.

Prevalence and incidence

Prevalence is the number of cases of a disease in a population at a given point in time. In other words, it’s how many people have the disease. Incidence is the number of new cases of a disease in a population at a given point in time.

Prevalence and incidence are often used interchangeably because they both refer to different aspects of morbidity (the number or rate) but they do have some important differences:

• Prevalence refers to all members within an entire population; incidence refers only to new cases occurring during one particular time period.

• Prevalence can be measured over time, whereas incidence cannot be measured over time

Relative risk and odds ratio

Relative risk and odds ratio

Relative risk is the ratio of the probability of an event in one group to the probability of the event in another group. For example, if you were to ask 100 people if they had been bitten by a spider within the past year and 40 said yes, then your relative risk would be 40/100 = 0.4 (i.e., four out of 100). This means that for every 100 people who have been bitten by spiders during their lifetime, 4 will come back with symptoms such as nausea or dizziness after being bitten by a spider. In contrast, if you asked 100 people whether they had ever been bitten by a spider within their lifetime and only 10 responded affirmatively (i.e., 1 out of 10), then this would represent an odds ratio greater than 1 because it shows that those who responded affirmatively have more chance of being infected with venom than those who didn’t respond affirmatively at all!

Sensitivity and specificity

Sensitivity and specificity are two important measures of a test’s performance. A positive test result indicates that the disease is present, while a negative result means that it isn’t. Sensitivity tells you how often a given test will be positive if there really is a disease present; specificity tells you how often such a test will be negative when no actual disease exists.

Sensitivity and specificity are independent of prevalence (i.e., how many people have the disease), but they’re not entirely independent either: sensitivity depends on prevalence for some diseases but not others

Positive predictive value and negative predictive value

The positive predictive value and negative predictive value are two important concepts in diagnostics. They’re also called sensitivity, or the likelihood that a test will be positive if the condition is present. The negative predictive value is the likelihood that a test will be negative if the condition is not present—it’s what we call “the absence of disease.”

In general, you can think of these terms as follows:

• Positive predictive value (PPV) = True positives / Total positives – False positives

• Negative predictive value (NPV) = True negatives / Total negatives – False negatives

Likelihood ratio

The likelihood ratio is a measure of the strength of evidence for or against the null hypothesis. It is calculated using the odds ratio and prevalence of disease in controls, as shown below:

• Odds Ratio = (Odds * Prevalence)/(1 + Odds * Prevalence)

• Likelihood Ratio = [P(Disease|H0)/P(Disease|H1)]/(1-P(Disease|H0))

These measures are all common in research related to health and medicine.

• Prevalence and incidence

• Relative risk and odds ratio

• Sensitivity and specificity

• Positive predictive value and negative predictive value (PPV) (or likelihood ratio)

Conclusion

The information presented in this section is a great tool for anyone who wants to understand the concepts behind healthcare research and epidemiological studies. The figures, ratios and statistics should be used as a supplement to your understanding of these topics instead of being used alone as an end goal or method.