MIDTERM Flashcards
Prevalence formula
Number of cases of a disease present in the population at a specified time / # of persons in the population at that specified time
Case fatality formula
Number of individuals dying from a specific disease during a specified period of time after disease onset or diagnosis / # of individuals with the specified disease
Proportionate mortality formula
Deaths caused by a specific disease / total number of deaths
Cumulative incidence (risk) formula
Number of new cases of a disease occurring in a population during a specified period of time / # of persons who are at risk of developing the disease during that period of time
Incidence density (rate) formula
Number of new cases of a disease occurring in a population during a specified period of time / Total person-time at risk
Mortality rate formula
Total # of deaths from all causes in one year / # of persons in the population at midyear
Cause-specific mortality rate formula
Number of deaths from a specific cause in one year / # of persons in the population at midyear
Incidence studies: risk difference
(A/A+B) - (C/C+D)
Incidence studies: Risk ratio
(A/A+B) / (C/C+D)
Incidence studies: Rate difference
(A/person-time exposed) - (C/person-time unexposed)
Incidence studies: Rate ratio
(A/person-time exposed) / (C/person-time unexposed)
Incidence studies: Risk odds ratio
(A/B) / (C/D) = AD / BC
Prevalence studies: Prevalence difference
(A/A+B) - (C/C+D)
Prevalence studies: Prevalence ratio
(A/A+B) / (C/C+D)
Prevalence studies: Odds ratio
(A/B) / (C/D) = AD / BC
Type of prevalence study
cross-sectional
Types of incidence studies
- RCTs
- prospective & retrospective cohorts
- Hypothesized the existence of the fecal-oral disease transmission route
- Identified that cholera causes were originating from the Broad Street pump in London
- Went from house to house counting all deaths from cholera in each house, and determined which company supplied water to each home. He determined that houses that drank water form on company had higher mortality rates that those who used the other
contributions of John Snow
Levels of prevention:
To prevent disease, before it develops so as to maintain health
Primary prevention
Levels of prevention:
Smoking prevention, condom use, wearing a seatbelt
Primary prevention
Levels of prevention:
To diagnosis and treat disease in its early stages so as to restore or improve health
- often a subclinical diagnosis
Secondary prevention
Levels of prevention:
Pap test or colon cancer screening, routine mammograms for breast cancer
Secondary prevention
Levels of prevention:
To reduce complications of disease and improve functioning and quality of life where possible
- often already have clinical symptoms
Tertiary prevention
Levels of prevention:
Hospice programs for AIDs patients, physical therapy that is designed to relieve complication from advanced arthritis
Tertiary prevention
- Observed elevated incidence of childbed fever between two maternity clinics
- Proposed a hand washing solution of chlorinated lime to be implemented in first clinic
Ignaz Semmelewis
The study of the distribution and determinants of health-related states or events in specified populations and the application of this study for the control of health problems
Epidemiology
As countries become industrialized they increasingly manifest the mortality patterns correctly seen in developed countries, with mortality from chronic diseases becoming the major challenge
Epidemiologic transitions
A smaller and more manageable representation of a larger group
Sample
The pool of individuals from which a statistical sample is drawn for a study
Population
The act of generalizing from a sample to a population with calculated degree of certainty
Statistical inference
we are curious about parameter in the _
population
we calculate statistics in the _
sample
An attribute of a population
- population parameters are unknowable (usually)
- we believe there is a true value for the parameter
Parameter
An attribute of a parameter
- a _ provides an estimate of a parameter
statistic
For an attribute “is what it is”
Distribution
- there are no _ involved in creating the distributions
- we are simply collecting (and then plotting) the data
- we can summarize a distribution in several ways:
- mean ( a measure of central tendency)
- median (an alternative of central tendency)
- standard deviation (a measure of the spread)
- range and interquartile range (other measures of spread)
statistics
A very important point is that sample sizes increases, the precision increases, and the _ _ decreases
- thus, the _ _ will get narrower as the sample sizes increases
- standard error
- confidence interval
The mean and the _ _ (ex: heights) do not change as a function of sample size
standard deviation
The probability, given that the null hypothesis is true, of obtaining a statistic as extreme or more extreme than the statistic actually observed
P-value
- Calculating a p-value requires you to specify an _ _
- Think p-value as a measure of your data’s _ with the null hypothesis
- A _ p-value indicates data is not very compatible with the null hypothesis
- expected value
- compatibility
- low
The role of statistics: why do we do statistical tests?
There will always be some _ from random processes
- Statistics are exceptionally good at characterizing random variability (“chance”)
variability
The role of statistics: why do we do statistical tests?
Using statistics we can characterize the _ in the data that would be present if the null hypothesis were true
- Based on this distribution we can calculate a p-value and use it to make a decision about the null hypothesis
variability
Confidence intervals (CI):
The narrower the CI, the more precise the interval
- How narrow the CI can be determined by subtracting the lower CI from the upper CI
Precision
Confidence intervals (CI):
Ratios (Ex: risk ratio, odds ratio) with CI that include _ are NOT statistically significant
1
Confidence intervals (CI):
- Absolute differences such as risk difference, prevalence difference, etc. with CI that include _ are NOT statistically significant
0
Confidence intervals (CI):
_ _ _ have the property that if you repeated the study many times (always with the same sample size), _ of the intervals would contain the true value of the parameter
- 95% confidence intervals
- 95%
A quantity calculated to indicate the extent of deviation for a group as a whole (of some attribute of a population)
Standard deviation
A measure of the statistical accuracy of an estimate, equal to standard deviation of the theoretical distribution of a large population of such estimates (an attribute of a statistic)
Standard error
A range of values so defined that there is a specified probability that the value of a parameter lies within it
Confidence intervals
The product of an interaction of the human host, an infectious or other type of agent, and the environment that promotes the exposure
Epidemiologic triad
Epidemiologic triad
Must be susceptible (for disease to occur), which is determined by a number of factors, including but not limited:
- Biological factors
- Socio-demographic characteristics
- Host behaviors
Host
Epidemiologic triad
Cause of disease
- Types:
- Biological (bacterium, virus, prions)
- chemical
- physical (traumatic injury, radiation)
- nutritional
Agent
Epidemiologic triad
Supports transmission of the agent from a source to that host
- Examples:
- temperature/altitude
- crowding
- neighborhood/population density
- water/milk/food
- radiation
- air pollution
- noise
- economic deprivation
Environment
An organism that transmits a pathogen/disease
- not required for all diseases, but essential for some diseases
- Examples:
- mosquito
- deer tick
Vector
The resistance of group of people to an attack by a disease to which a large proportion of the members of the group are immune
- if a large percentage of the population is immune, the entire population is likely to be protected, not just those who are immune
Herd immunity
The time interval from receipt of infection to the time of onset of clinical illness
- may be affected by initial dose received
- in some diseases, individuals may be able to transmit the disease prior to showing clinical signs and symptoms of the disease
incubation period
SIR model stands for:
Susceptible, Infected, Resistant
SIR model:
The number of new cases from one infected case is R0
Susceptible
SIR model:
dead
Infected
SIR model:
Can be recovered or vaccinated
Resistant
The distribution of the times of onset of a disease
Epidemic curve
The number of diseased persons in a population at a specific time divided by the number of persons in the population at that time
Prevalence
Prevalence:
- Does not account for the _ of a disease and therefore is not a measure of risk
- Useful to measure the _ _ _ in a community (population) for:
- policy designs, where to locate clinics and other resources
- can be useful for diseases that are difficult to determine the _ or _ such as asthma or obesity
- duration
- burden of disease
- onset or beginning
The rate of disease in a population
Morbidity
Deaths
Mortality
Reflect the transition from a non-diseased to a diseased state
Incidence
Incidence:
- _ disease cases
- Generally represented by two measures:
- New
1. Cumulative incidence (or risk)
2. Incidence rate (or incidence density)
The number of people who develop a disease divided by the total number of people at risk of developing that disease over a specified period of time
Cumulative incidence (or risk)
Rate of new cases of a disease in a population at risk for the disease (Ex: new cases that occurs per unit of time)
- person-time
Incidence rate ( or incidence density)
Individuals do not have to be followed up for the entire time period specified to calculate a rate, individuals may be followed for differing periods of time and thus contribute different amounts of
- (all times added together)
person-time
Surveillance in which available data on reportable diseases are used, or in which disease reporting is mandated or requested, with the responsibility for the reporting often falling on the health care provider or district health officer
Passive surveillance
Passive surveillance:
- No active search for cases
- Advantage: _
- Disadvantage: _ and _
- Examples; clinics, hospitals, schools
Advantage: less expensive
Disadvantage: cases and outbreaks might be missed
Surveillance in which project staff are recruited to carry out a surveillance program; they make periodic visits to healthcare facilities such as clinics and hospitals
Active surveillance
Active surveillance:
- Advantage: _
- Disadvantage: _
- Example: field epidemiologists recruited to collect data and interview health care workers
Advantage: more complete than passive surveillance
Disadvantage: requires more time and resources
Disease duration is a major determinant of population _
- short term/long term duration
prevalence
Increase/decrease/stays the same prevalence?
A new measure is developed that prevents new cases of disease from occurring
decreases prevalence
Increase/decrease/stays the same prevalence?
There is immigration of a large number of healthy people into the population
decreases prevalence
Increase/decrease/stays the same prevalence?
There is immigration of a large number of unhealthy people (most of whom are cases of the disease)
increases prevalence
Increase/decrease/stays the same prevalence?
A treatment is developed that prolongs the life of people suffering from the disease
increases prevalence
Increase/decrease/stays the same prevalence?
A treatment is developed that prolongs the life of people suffering from the disease.
increases prevalence
Increase/decrease/stays the same prevalence?
A new test is developed that increases the number of new cases of disease that are diagnosed
increases prevalence
- Notice the size of the total population, total number who died, and proportion of total who died
- check overall patterns (number and death rate for each population subgroups separately, notice marginal row & column totals and rates)
- Examine subgroups according to sex, age and SES
Reading tabulated data (2x2 table)
A measure of the severity of a disease
Case fatality rates
Mortality rate from a specified cause for a population
Cause-specific mortality
A measure of the frequency of occurrence of death in a defined population during a specified interval
Mortality rate
Describes the proportion of deaths in a specified population over a period of time attributed to a specific disease
Proportionate mortality
Descriptive epidemiology of a certain population (Ex: age,sex, SES)
Distribution
A sample of persons from a population is enrolled and their exposures and health outcomes are measured simultaneously
- Alternate names: survey, prevalence
Cross-sectional study
Type of observational study in which two groups differing in outcome are identified and compared on the basis of some supposed causal attribute
- Alternate names: retrospective
Case-control study
Epidemiologist records whether each study participant is exposed or not, and then tracks the participants to see if they develop the disease of interest; similar to an experimental study
- Alternate names: prospective, prospective cohort, longitudinal, follow-up, incidence
Cohort study
Use groups or populations as the unit of analysis (study groups can be countries, states, counties, census tracts)
- Alternate names: correlation
Ecological study
- measures incidence of disease (risk, rates)
- good for studying rare exposures
- clear temporal relationship between exposures and outcome
- it is possible to evaluate multiple exposures and multiple diseases in the same study
Cohort study advantages
- insufficient for studying rare diseases
- requires large sample sizes
- can be expensive to follow people over time
- some potential bias
- information bias
- bias from nonresponse and loss follow up
Cohort study disadvantages
Investigator moves through time with study
- investigator can decide what data to collect during study because follow-up time has not yet occurred
- can take a long time (have to wait for disease to develop)
- Expensive
Prospective cohort studies (concurrent)
Rely on historical records (not necessarily a problem)
- limited to what information was recorded - investigator cannot go back in time to add additional data collection
- efficient for diseases with long induction and latent period
- Takes fewer resources
Retrospective cohort studies (non-concurrent)
A cohort study where individuals are randomized to be exposed or unexposed
Randomized control study
- RCT uses the same measures and methods as a _
- Randomization is the _ because it is expected to balance the risk factors for disease in both the exposed group and the unexposed group
- Example: randomization increases the likelihood the groups will be comparable in regards to: gender, race, severity of disease, SES
- cohort study
- “gold standard”
- Reduces bias
- Provides most reliable evidence on the effectiveness of interventions because the processes used during conduct of an RCT minimize the risk of confounding factors influencing the results
Advantages of a randomized control study
- Expensive and time consuming
- problems with generalisability (participants that volunteer to participate might not be representative of the population being studied)
- loss of follow-up
Disadvantages of a randomized control study
Interpreting ratio measures:
If the relative risk = 1 the risk is equal and there’s _ _ _ _
no evidence of association
Interpreting ratio measures:
If the relative risk > 1 the risk in the exposed group is _, _ _ _ and exposure is possibly _ for the disease
- greater, association is positive
- causal
Interpreting ratio measures:
If the relative risk < 1 the exposed group is _, _ _ _, and exposure is possibly _
- less, association is negative
- protective
Interpreting ratio measures:
Risk ratio = 1.9
Harmful
Interpreting ratio measures:
Risk ratio = 0.5
preventive
Participants exposed to (exposure) have (blank times) the risk of developing (outcome) compared to the unexposed
Interpreting ratio measures
Another measure sometimes used in incidence or prevalence studies, and always used in case-control studies
- The number of times an event occurs divided by the number of times it does not occur
Odds ratio
If the overall prevalence of diseases (Ex: (A+C) / (A+B+C+D)) is less than 5% than the OR is a good approximation of the RR
odds ratio in a cohort study
- A ratio of two measures of disease frequency
- Gives information about the strength of the association
Relative measures
- The difference between two measures of disease frequency
- Give info about public health impact of exposure
- When baseline risk is really low, _ _ may be more telling
Absolute measure
The percentage of disease-free UNR freshman who contract tuberculosis for their first time before the end of their freshman year
Cumulative incidence
The number of new HIV cases diagnosed among injection drug users during 100 person years of follow-up
incidence rate
The percent of UNR seniors who currently have SARS-CoV-2 antibodies
Prevalence
The proportion of people who were diagnosed with lymphoma cancer who died within 1 year of their diagnosis
Case fatality rate
In a randomized control trial that you perform, you conclude that a significant difference exists between your experimental group and the control group. What action can you take in relation to the null hypothesis?
Reject the null hypothesis
You are doing a study to identify the mean cholesterol level of university students who eat at Panda Express every day. What will be strongly affected by your sample size:
Standard error of the student’s mean cholesterol level in your sample
A term for all of the new cases/person time at risk
incidence density
If the number of new cases is known in a population, what is needed in order to calculate the incidence density (incidence rate)?
Total person-time
At the beginning of the school year, the prevalence of students who have chlamydia at UNR is 30%. At UNLV, the prevalence of chlamydia is 50%. Can we definitively conclude that cumulative incidences of chlamydia is higher at UNLV than at UNR?
No, because we do not know how long students have had chlamydia
Women who have had hysterectomies (removal of uterus) should not be included in incidence studies for uterine cancer, because they are not at risk of having the cancer. What would be the effect on incidence rates of uterine cancer if women with hysterectomies were included in the the denominator of the calculations?
The incidence rate would tend to decrease
The exposure distribution among the cases (i.e., people with disease) is compared with the exposure distribution among the controls
case-control study
