NR 503 DeVry Week 4 Discussion Paper NR 503 DeVry Week 4 Discussion Paper ? NR 503 DeVry Week 4 Discussion 1 Latest Discussion Part One (graded) Disease Causation Steve, a 54-year-old Caucasian male, presents for a first time visit to your clinic. His history includes five sexual partners in the last 25 years, two of those within the last twelve months, lack of physical activity of any kind as he is an over-the-road truck driver, 25-year history of smoking 1 pack per day, and no immunizations of any kind that he can recall since high school. His father died of a myocardial infarction at age 62. His mother is alive and has hypertension, hyperlipidemia, and Type 2 Diabetes. His BMI is 31 and his blood pressure is 142/90. Name one disease he is at risk for and provide evidence on how one of his risk factors is tied to causation of that disease. NR 503 DeVry Week 4 Discussion 2 Latest Discussion Part Two (graded) Create a plan of care based on the disease risk you chose and define whether steps of that plan of care are primary, secondary, or tertiary prevention. NR 503 DeVry Week 4 Discussion 3 Latest Discussion Part Three (graded) Please provide a summary of the case or information you have discussed this week. CLICK HERE TO ORDER YOUR NR 503 DeVry Week 4 Discussion Paper NR 503 Relative Risk Calculation Worksheet Latest 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 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;

Identify appropriate outcome measures and study designs applicable to epidemiological subfields

Introduction

Epidemiology is a broad field, which includes the study of health and disease. Epidemiologists need to understand the appropriate outcome measures and study designs that are applicable to their subfield. This article will discuss how epidemiologists can identify these outcomes and designs by reviewing them in terms of their underlying assumptions.

The outcome measures and study designs appropriate for each of the subfields of epidemiology are as follows:

In the context of a research study, an outcome measure is a summary of your findings. For example, if you were conducting a study about whether people who drink alcohol are more likely to develop cancer than those who do not, one type of outcome measure would be “number of cases” (i.e., number of people diagnosed with cancer).

The design or methods used in your research should also be considered when choosing which studies provide the best evidence on what factors influence outcomes within each subfield. There are three main types:

• Randomized controlled trials (RCTs)

• Non-randomized controlled trials (NRCTs)

• Quasi-experimental designs

General epidemiology: mortality or disease incidence

You will often use mortality or incidence to measure the impact of a study. Mortality is a count of deaths, while incidence is the number of new cases over time (i.e., it’s measured per person-time at risk).

Incidence can be measured in several ways: by year, month, quarter or week; by age group; by gender; and across geographic locations such as city limits or countries. For example:

Yearly incidence numbers are often used when comparing health care systems with one another because they allow you to determine whether a certain system has been effective at reducing deaths over time. Similarly yearly statistics can also be used to examine the effectiveness over time of interventions aimed at improving population health outcomes such as vaccination coverage levels among children aged 0-5 years old living within particular regions/countries around the world

Causal inference: odds ratio or relative risk

The odds ratio or relative risk is a measure of association between an exposure and an outcome. It can be regarded as a type of odds ratio, but it is not necessarily equal to the latter when there are multiple exposures involved in the analysis (see below).

The odds ratio measures how many times more likely it is for one outcome than another given that both have been matched according to their characteristics (e.g., age). It does not measure risk directly because unlike absolute risks, relative risks do not tell us how much harm we might expect from eating this food item compared with eating another food item; rather they compare two groups on each side of the equation and calculate their per unit probability of getting disease X versus disease Y(or any other outcome).

Clinical epidemiology: a randomized controlled trial, a systematic review, and meta-analysis, or an observational design

Clinical epidemiology is the study of the distribution and determinants of illness in populations. It is concerned with developing and evaluating interventions to prevent or treat disease. The field encompasses both clinical research (which uses randomized controlled trials) and observational studies.

The gold standard for clinical research is a randomized controlled trial (RCT). An RCT compares two groups: one that receives an intervention, such as a treatment or vaccination; and another that does not receive it but has similar characteristics to each group (for example if you were comparing two groups based on age). In this way, an RCT can test whether an intervention works better than another treatment or placebo and why it works better in order to improve care decisions made by doctors, patients, families etcetera

Genomic epidemiology: a genome-wide association study

• Genome-wide association study: a type of observational study that looks at the entire genome, rather than just one gene or mutation.

• GWAS can be used to identify genetic factors that are associated with disease susceptibility, or they can be used to identify genetic factors that are associated with disease progression.

Social epidemiology: social network mapping, social gradient analysis, and other quantitative methods relevant to the aims of the research.

Oops! Click Regenerate Content below to try generating this section again.

Epidemiologists must be aware of the appropriate outcome measures and study designs that are applicable to their subfield.

When conducting research, it is important to be aware of the appropriate outcome measures and study designs that are applicable to your subfield. There are many different types of epidemiologists, each with their own specialty. The following list outlines some generalizations they all share:

• Epidemiologists conduct scientific inquiry into the causes and distribution patterns of diseases or conditions in order to improve health care delivery systems.

• Epidemiologists collect data through observations made during interviews with individuals about their experiences with disease symptoms or conditions; these observations may include symptom frequency, symptom duration (time between onset and recovery), location where symptoms began/remained at; how often a particular symptom occurs (e g , headache), how long it lasts for before disappearing completely); whether there was any relationship between previous health problems experienced by individuals before starting treatment for new ones detected during clinical examinations such as X-rays taken after being diagnosed with cancerous tumors growing inside them due  to radiation exposure from accidents like nuclear power plant accidents which happen often when working around radioactive material being used as fuel sources.”

Conclusion

The most important outcome measures and study designs for each of the subfields of epidemiology are as follows:

• General Epidemiology: Mortality or Disease Incidence;

• Causal Inference: Odds Ratio or Relative Risk;

• Clinical Epidemiology: A randomized controlled trial, a systematic review, and meta-analysis, or an observational design;

• Genomic Epidemiology: A genome-wide association study.