503- Mid-term Study Guide[ 1068]

1. Epidemiology is the science of public health.
2. Population health focuses on risk, data, demographics, and outcomes.
3. Outcomes is the end result that follows an intervention.
4. Aggregate is a defined population.
5. Community is composed of multiple aggregates.
6. Data is compiled information.
7. Prevalence measures the existence of a disease. Measures the number of all cases of a disease or attribute in a population at a given time.
8. Incidence measures the appearance of a disease. Measures the occurrence of new events in a population over a period of time.
9. Surveillance is the collection, analysis, and dissemination of data.
10. High-risk is an increased chance of poor health outcome.
11. Morbidity is the presence of illness in a population.
12. Mortality is related to the tracking deaths in an aggregate.
13. Vital statistics-statistics on live births, deaths, fetal deaths, marriages, and divorces.
14. Cases- set of criteria used in making a decision as to whether an individual has a disease or health event of interest.
15. Social Justice- the view that everyone deserves equal rights and opportunities —this includes the right to good health.
16. Inter-professional collaboration- The idea of sharing and implies collective action oriented toward a common goal, in this case, improving the quality and safety of patient care. It involves responsibility, accountability, coordination, communication, cooperation, assertiveness, mutual respect, and autonomy.
17. HP2020- aims to reach four overarching goals: 1. Attain high-quality, longer lives free of preventable disease, disability, injury, and premature death, 2. Achieve health equity, eliminate disparities, and improve the health of all groups 3. Create social and physical environments that promote good health for all. 4. Promote quality of life, healthy development, and healthy behaviors across all life stages.
18. Determinants of Care- The range of personal, social, economic, and environmental factors that influence health status are known as determinants of health.
19. Risk analysis- the characterization of the potential adverse health effects of human exposures to environmental hazards.
20. Health disparities- the difference in health statuses between various groups (populations).
21. Sensitivity- measures the proportion of actual positives that are correctly identified as such (e.g., the percentage of sick people who are correctly identified as having the condition).
22. Specificity- (also called the true negative rate) measures the proportion of actual negatives that are correctly identified as such (e.g., the percentage of healthy people who are correctly identified as not having the condition).
23. Positive Predictive Value- Positive predictive value is the probability that subjects with a positive screening test truly have the disease.
24. Epidemiological Triangle- A traditional model of infectious disease causation, known as the Epidemiologic Triad is depicted in Figure 2. The triad consists of an external agent, a host, and an environment in which host and agent are brought together, causing the disease to occur in the host. Confounding (Variables)- A confounding variable is an “extra” variable that you didn’t account for. They can ruin an experiment and give you useless results. They can suggest there is correlation when in fact there isn’t. They can even introduce bias. That’s why it’s important to know what one is, and how to avoid getting them into your experiment in the first place.
25. Study Methods Descriptive- describes person, place, and time. Provides data for program planning, resource planning, and generates a hypothesis. Types include correlational studies, case reports and studies, and cross-sectional studies. Analytic- consists of observational and experimental. Observational includes case control and cohort. Experimental includes random control trial (typically for new drug testing), field trial (conducted on those who have a high risk of obtaining a disease), and community trial (research is conducted on an entire community or neighborhood). Tests a hypothesis.
26. Rapid Cycle Improvement Models- Rapid-cycle improvement is a “quality improvement method that identifies, implements, and measures changes made to improve a process or a system.” Rapid-cycle improvement implies that changes are made and tested over periods of three or months or less, rather than the standard eight to twelve months. It consists of four stages: Plan: Identify an opportunity to improve and plan a change or test of how something works. Do: Carry out the plan on a small number of patients. The test period may be as short as one day for small PDSA cycles. Study: Examine the results. Did you achieve your goals? Act: Use your results to make a decision, incorporate changes into your workflow, and establish future quality improvement plans.
27. Is screening a tertiary intervention? If yes, why, if not, what is it? No, it is secondary.
28. How does a provider determine the usefulness, appropriateness, of a screening test? Where would an NP look to find a screening test? What determines if a screening test should be used? Determining whether a screening test is appropriate requires the APRN to address several aspects of the disease of interest. The target population needs to be identifiable. There should be enough people to make the study cost-effective. The preclinical period should be proficient to allow treatment before symptoms appear so that early diagnosis and treatment make a difference in terms of outcomes. The NP could look at the U.S. Preventive Services Task Force, Agency for Healthcare Research and Quality, and SAMHSA-HRSA to find a screening test. Sensitivity and specificity measure the validity of a test. Sensitivity is the number identified/ the number affected. Specificity is the number identified in the screening of not having the disease/ the actual number who do not have the disease.
29. Can you explain what “descriptive epidemiology” means? What is the purpose? How is it used? It covers time, place, and person. First, by looking at the data carefully, the epidemiologist becomes very familiar with the data. He or she can see what the data can or cannot reveal based on the variables available, its limitations (for example, the number of records with missing information for each important variable), and its eccentricities (for example, all cases range in age from 2 months to 6 years, plus one 17-year-old). Second, the epidemiologist learns the extent and pattern of the public health problem being investigated — which months, which neighborhoods, and which groups of people have the most and least cases. Third, the epidemiologist creates a detailed description of the health of a population that can be easily communicated with tables, graphs, and maps. Fourth, the epidemiologist can identify areas or groups within the population that have high rates of disease. This information in turn provides important clues to the causes of the disease, and these clues can be turned into testable hypotheses.
30. How are causation and descriptive epidemiology related, how do they work together to aid evidence-based care? Causation helps look at the cause of the issue or disease process. Descriptive epidemiology focuses on the person, place, and time. An example of how they are intertwined might be a person who was sick from E. Coli. The physician might look at what the individual ate to determine what made them sick. For instance, they may have decided to eat from the salad bar at a local restaurant.
31. What does “causation” mean? Can you relate causation to primary, secondary, and tertiary interventions? Causation is an increase in a casual factor or exposure causes an increase in the outcome of interest (disease). Causation related to primary intervention could be the use of flu vaccines yearly to prevent the flu from causing an illness. A secondary intervention would be to test for the influenza virus in a patient. A tertiary intervention would be giving Tamiflu to a flu positive patient. Since we know that the influenza virus causes the flu when can help to perform actions against it.
32. Are you able to discuss “surveillance” and its relationship to “causation”? Surveillance is the ongoing systematic collection, analysis, and interpretation of health data essential to the planning, implementation, and evaluation of public health practice closely integrated with the timely dissemination of these data to those who need to know. Passive surveillance involves using data to look at reportable diseases while active involves using individuals such as project staff interviewing physicians about cases. Using surveillance can help identify the causation of diseases particularly in a specific population.
33. What is the case-control study and how does it differ (or how is it the same) as the cohort study design? The cohort study design identifies a group of people exposed to a particular factor and a comparison group that was not exposed to that factor and measures and compares the incidence of disease in the two groups. A higher incidence of disease in the exposed group suggests an association between that factor and the disease outcome. This study design is generally a good choice when dealing with an outbreak in a relatively small, well-defined source population, particularly if the disease being studied was fairly frequent. The case-control design uses a different sampling strategy in which the investigators identify a group of individuals who had developed the disease (the cases) and a comparison of individuals who did not have the disease of interest. The cases and controls are then compared with respect to the frequency of one or more past exposures. If the cases have a substantially higher odds of exposure to a particular factor compared to the control subjects, it suggests an association. This strategy is a better choice when the source population is large and ill-defined, and it is particularly useful when the disease outcome was uncommon. Examples of two real outbreaks will be used to illustrate these differences in sampling strategy.
34. Can you talk about the ways bias shows up in a study design (such as, selection bias) etc? Selection bias occurs when subjects in a sample are not representative of the population of interest. Selection bias can occur when groups are not randomly assigned to study groups. For example, a sample may be composed of volunteers, leading to selection bias. It can also occur when groups are compared based on criteria other than their exposure. This type of bias is often associated with case-control studies or other designs where subjects are selected based on their disease status or exposure status, leading to inaccurate results.