midterm Flashcards
what is epidemiology
SCREENING, utilizing interventions, and preventing bad health outcomes in the community
- Major success-fluoride in drinking water, iodine in salt, hand washing to prevent infections, vaccinations-flu vaccine, disposing of biological wastes, etc..
Good health outcomes-decrease in MORBIDITY and MORTALITY in the population:
- increased life expectancy,
-increased quality of life,
-economic efficiency,
-positive social changes
Field which emphasizes increase scrutiny of disease states and events in an:
- objective
- scientific
- controlled manner
Two fundamental
assumptions of
Epidemiology
1) Human disease does NOT occur at RANDOM
- there are factors which can increase or decrease the likelihood of disease.
The FACTORS CAN BE IDENTIFIED (some are causal, and some are preventive) can be identified by SYSTEMIC investigation of populations or subgroups within populations.
Greek origin of epidemiology
Greek Origin: Epi (on or upon) and Demos (the common people)
Therefore- “ a study that falls upon the common people”
The study of the distribution (prevalence) and determinants of disease frequency (incidence) in human populations and the application of this study to control health problems
Prevalence vs Incidence
Prevalence:
- proportion of existing cases within a population (both old and new)
- can increase or decrease
- How many people in a given scenario have this condition
-ex: prevalence = how many people wear eye glasses in a sample, but then some got laser surgery and no longer need glasses
Incidence:
- measure the rapidity with which newly diagnosed cases of a disease develop
- Refers to the proportion of new cases per unit of time (usually one year)
●New cases
● How many people started wearing eye glasses this year?
types of prevalence
Point Prevalence:
- A measure of the number of cases at a specific point in time
Period Prevalence
-Number of cases over a defined period of time (usually 6 mo or 1 yr)
Lifetime Prevalence:
- proportion of individuals who have been affected by a disorder at any time during their lives
● Ie: how many people in this class, during their lifetime, have suffered
from a concussion
Sample, mean, mode, median
Sample:
- Subgroup of the whole affected population
● Ex: all patients with flu in Old Westbury as opposed to all of New York State
Average:
- obtained by adding up all the numbers and dividing by the total (N) of numbers
median:
- Center number in the ordered sequence of data points
Mode:
- The number which occurs most often in a sequence
Sampling error
Natural variability, not caused on purpose
-ex: If conducting a study on the capacity of school buses for middle school-not all 4-6 graders weigh the same or have a similar height.
- Can be mitigated by INCREASING sample size
- unbiased error
Selection bias
The sample was NOT CHOSEN RANDOMLY for the study
- the researcher inadvertently had a bias -> researchers chose who was included
- data does not accurately represent the population in the context of interest on PURPOSE DUE TO BIAS
- results can’t be applied to the general population
Ex: researcher has more female patients in the group receiving a promising medication because they reminded him of his mother who was ill
Validity vs. Reliability
Validity
- the accuracy of a test
- The test measures accurately the information sought by the researcher
Reliability
- repeated administration of the test leads to the same result
- test is consistent and repeatable
→ A valid test is generally reliable. A reliable test does not necessarily mean it’s valid.
Independent
Vs.
Dependent variable
Independent variable:
- the one that influences the change (the one being manipulated)
- Usually graphed on the horizontal axis
- The independent variable causes a change in the dependent variable
Dependent variable
- is the result of applying the independent variable (the one being studied)
- Usually graphed on the vertical axis
Ex- A new medication for headache
- dose of the drug = independent variable
- resulting relief reported by the patient = dependent variable
Range and standard deviation
Range:
- Difference between the highest and lowest value
Standard deviation:
- The spread of the data being observed relative to the MEAN
- The range in a normal bell curve
- 1 standard deviation 68%, 2 SD 95%, 3 SD 99%
Nominal vs Ordinal data
Nominal Data
- Definition: Categories without natural order
- Characteristics: Mutually exclusive, no ranking
- Examples: Gender, Race, Color, city
- Analysis: Mode, Chi-square tests
Ordinal Data:
- Definition: Categories with a natural order
- Characteristics: Ranked categories, intervals not equal
- Examples: Satisfaction ratings, Stages of disease
- Analysis: Median, Mode, Non-parametric tests
Interval Data:
- Definition: NUMERIC scale with equal intervals, arbitrary zero
- Characteristics: Differences are meaningful, no true zero
- Examples: Temperature (Celsius), Calendar years
- Analysis: Mean, Median, Mode, Parametric tests
sub-disciplines of epidemiology
Disease
exposure
population
sequence of epidemiologic investigation
1) suspect exposure influences disease occurence
2) form specific hypothesis about exposure-disease association
3) conduct epidemiologic studies to measure relationship between exposure and disease
4) judge whether the association is valid and causal:
- accumulated evidence
-chance, bias, confounding variable (affects both independent and dependent variable)
- positives and negatives of the study design
5) evaluate preventions and tx
Pt 1+ 2: DESCRIPTIVE
Pt 3-5: analytic/scientific
descriptive vs analytic/scientific epidemiology
Descriptive:
- Addresses the “what, who, where, and when” questions about diseases
- descriptive studies precede analytic studies in the sequence of investigation
- distribution of health-related states or events by characteristics of persons, places, and time
Analytic:
- Addresses “how and why” diseases occur, looking at causes and effects
- Determines if there is an association between exposure factors and health outcomes.
- Uses methods like case-control and cohort studies.
examples of descriptive studies
identifying and counting the cases of disease in populations. simple studies
- case reports
- case series
- cross-selectional study
It’s like being a detective at the start of an investigation. You gather basic facts:
- What? What is the disease or health problem?
- Who? Who is getting the disease? Are there more cases in kids, adults, or the elderly?
- Where? Where are the cases popping up? Is it in one neighborhood or all over the place?
- When? When did the disease start occurring? Is it getting more common or less?
You use this info to see patterns and trends, like if a flu is hitting a particular city hard or if injuries are more common after a new skate park opens.
examples of analytical and scientific studies
comparing groups and systematically determining if there is an association
- clinical trials
- experimental study
- case- control study
- cohort study
Analytic Epidemiology:
Now, you take the investigation deeper to figure out the story behind the facts:
- How? How might people be getting sick? Is it something they ate, the air they breathe, or maybe a bug bite?
- Why? Why are some people getting sick and not others? Does it have to do with genes, behaviors, or perhaps where they work or live?
To find answers, you compare groups of people.
- For example, you might look at smokers versus non-smokers to see who gets lung problems more often.
It’s all about finding clues to cause-and-effect relationships.
confounding variable
- additional variable that could be skewing the results
- leads to paradoxical results
- confounding variable is something that wasn’t considered in the study design but could influence the results, leading to a mistaken conclusion about the relationship between the things you’re really interested in studying
- like an unexpected guest who crashes a party and mixes things up, making it hard to figure out who originally brought what to the table
Fundamental vs operational hypothesis
Fundamental:
- broad, theoretical, and conceptual
- not testable
operational:
- specific, measurable, testable
-detailing how those relationships will be examined in a particular study through the operationalization of variables.
Alternative and null hypothesis
Alternative:
- hypothesis that the researcher tries to prove
null:
- there is no relationship between the two variables
- A hypothesis that the researcher tries to disprove, or nullify
outcome of john snow and cholera
-proposed hypothesis for how cholera was transmitted
- tested hypothesis from comparing groups of ppl
- he establish a strong association between contaminated drinking water and the incidence of cholera
- argued for an INTERVENTION that prevented more cases
selected epidemiology milestones
main causes of death in framingham
1) cancer
2) heart disease
higher mortality in high school or less education, older age
some variability with race:
sources of epidemiologic data: where when you get data from
WHO (Who Provides the Data):
- Governments
- NGOs (Non-Governmental Organizations)
- HMOs (Health Maintenance Organizations)
- Hospitals
- Independent researchers
WHAT (Types of Data Collected):
- US Census data
- Health surveys
- Death certificates
- Birth certificates
- Cancer registries
- Hospital discharge registries
- Infectious disease reporting
National Survey of Family Growth, National Health Interview Survey, National Health and Nutrition Examination Survey
National Survey of Family Growth:
- national data on marriage, divorce, family planning, and fertility
National Health Interview Survey:
- Collects data on health problems, impairments, and utilization of health services.
National Health and Nutrition Examination Survey:
- data on the HEALTH and DIET of U.S. citizens
- includes interviews and health tests
Behavioral Risk Factor Surveillance System; National health care surveys, notifiable disease surveillance system
Behavioral Risk Factor Surveillance System :
- telephone survey on health risk factors related to chronic ds, injuries, death
- screening and preventative services
National health care surveys:
- surveys about the use and quality of health care and the impact of medical technology in a variety of settings
notifiable disease surveillance system:
- A system that tracks the incidence of over 60 notifiable diseases
- conditions by law, must be reported to health authorities,
- infectious diseases: AIDS, HIV infection, botulism, gonorrhea, hepatitis, syphilis, plague, and malaria
