Public Health Sciences - First Aid Flashcards
Observational Studies:
- frequency of disease and frequency of risk-related factors are assessed in the present
- “What is happening?”
- disease prevalence
- can show risk factor association with disease, but does not establish causality
Cross-Sectional Study
Observational Studies:
- compares a group of people with disease to a group without disease.
- looks to see if odds of prior exposure or risk factor differs by disease state
- “What happened?”
- Odds Ratio (OR)
Case-Control Study
Observational Studies:
- compares a group with a given exposure or risk factor to a group without such exposure
- looks to see if exposure or risk factor is associated with later development of disease
- Prospective—“Who will develop disease?”
- Retrospective—“Who developed the disease [exposed vs. nonexposed]?”
- Relative Risk (RR)
Cohort Study
Observational Studies:
- compares the frequency with which both monozygotic twins vs. both dizygotic twins develop the same disease
- measures heritability and influence of environmental factors (“nature vs. nurture”)
Twin Concordance Study
Observational Studies:
- compares siblings raised by biological vs. adoptive parents
- measures heritability and influence of environmental factors
Adoption Study
A _____ is an experimental study involving humans. Compares therapeutic benefits of 2 or more treatments, or of treatment and placebo.
Clinical Trial
Study quality improves when the study is randomized, controlled, and _____ (ie. neither patient nor doctor knows whether the patient is in the treatment or control group).
Double-Blinded
_____ refers to the additional blinding of the researchers analyzing the data.
Triple-Blind
Four Phases of Clinical Trials
“Does the drug SWIM?
- Is it Safe?
- Does it Work?
- Any Improvement?
- Can it stay in the Market?
Phases of Clinical Trials:
- small number of healthy volunteers or patients with disease of interest
- “Is it safe?”
- assesses safety, toxicity, pharmacokinetics, and pharmacodynamics
Phase I
Phases of Clinical Trials:
- moderate number of patients with disease of interest
- “Does it work?”
- assesses treatment efficacy, optimal dosing, and adverse effects
Phase II
Phases of Clinical Trials:
- large number of patients randomly assigned either to the treatment under investigation or to the best available treatment (or placebo)
- “Is it as good or better?”
- compares the new treatment to the current standard of care
Phase III
Phases of Clinical Trials:
- postmarketing surveillance of patients after treatment is approved
- “Can it stay?”
- detects rare or long-term adverse effects
- can result in treatment being withdrawn from market
Phase IV
Evaluation of Diagnostic Tests
- Uses 2 × 2 table comparing test results with the actual presence of disease.
- Sensitivity and specificity are fixed properties of a test. PPV and NPV vary depending on disease prevalence in population being tested.
Evaluation of Diagnostic Tests:
- proportion of all people with disease who test positive, or the probability that when the disease is present, the test is positive
- value approaching 100% is desirable for ruling out disease and indicates a low false-negative rate
- used for screening in diseases with low prevalence
Sensitivity (True-Positive Rate)
- Sn = TP / (TP + FN)
- Sn = 1 – FN rate
- SN-N-OUT = highly SeNsitive test, when Negative, rules OUT disease
- if sensitivity is 100%, then FN is zero
- all negatives must be TNs
Evaluation of Diagnostic Tests:
- proportion of all people without disease who test negative, or the probability that when the disease is absent, the test is negative
- value approaching 100% is desirable for ruling in disease and indicates a low false-positive rate
- used for confirmation after a positive screening test
Specificity (True-Negative Rate)
- Sp = TN / (TN + FP)
- Sp = 1 – FP rate
- SP-P-IN = highly SPecific test, when Positive, rules IN disease
- if specificity is 100%, then FP is zero
- all positives must be TPs
Evaluation of Diagnostic Tests:
probability that a person who has a positive test result actually has the disease
Positive Predictive Value
- PPV = TP / (TP + FP)
- PPV varies directly with pretest probability (baseline risk, such as prevalence of disease)
- high pretest probability → high PPV
Evaluation of Diagnostic Tests:
probability that a person with a negative test result actually does not have the disease
Negative Predictive Value
- NPV = TN / (TN + FN)
- NPV varies inversely with prevalence or pretest probability
Possible Cutoff Values
_____ is the likelihood that a given test result would be expected in a patient with the target disorder compared to the likelihood that the same result would be expected in a patient without the target disorder.
Likelihood Ratio
- LR+ > 10 and/or LR– < 0.1 indicate a very useful diagnostic test
- LRs can be multiplied with pretest odds of disease to estimate posttest odds
Quantifying Risk
Definitions and formulas are based on the classic 2 × 2 or contingency table.
Quantifying Risk:
- typically used in case-control studies
- depicts the odds of a certain exposure given an event (eg. disease; a/c) vs. the odds of exposure in the absence of that event (eg. no disease; b/d)
Odds Ratio
Quantifying Risk:
- typically used in cohort studies
- risk of developing disease in the exposed group divided by risk in the unexposed group (eg. if 5/10 people exposed to radiation get cancer, and 1/10 people not exposed to radiation get cancer, the _____ is 5, indicating a 5 times greater risk of cancer in the exposed than unexposed)
- for rare diseases (low prevalence), OR approximates _____.
Relative Risk
- RR = 1 → no association between exposure and disease
- RR > 1 → exposure associated with ↑ disease occurrence
- RR < 1 → exposure associated with ↓ disease occurrence
Quantifying Risk:
the difference in risk between exposed and unexposed groups (eg. if risk of lung cancer in smokers is 21% and risk in nonsmokers is 1%, then the attributable risk is 20%)
Attributable Risk
Quantifying Risk:
the proportion of risk reduction attributable to the intervention as compared to a control (eg. if 2% of patients who receive a flu shot develop the flu, while 8% of unvaccinated patients develop the flu, then RR = 2/8 = 0.25, and _____ = 0.75)
Relative Risk Reduction
- RRR = 1 − RR
Quantifying Risk:
the difference in risk (not the proportion) attributable to the intervention as compared to a control (eg. if 8% of people who receive a placebo vaccine develop the flu vs. 2% of people who receive a flu vaccine, then
_____ = 8% − 2% = 6% = .06).
Absolute Risk Reduction
Quantifying Risk:
- number of patients who need to be treated for 1 patient to benefit
- lower number = better treatment
Number Needed to Treat
- NNT = 1/ARR
Quantifying Risk:
- number of patients who need to be exposed to a risk factor for 1 patient to be harmed
- higher number = safer exposure
Number Needed to Harm
- NNH = 1/AR
Incidence vs. Prevalence
- Incidence looks at new cases (incidents)
- Prevalence looks at all current cases
- prevalence ∼ pretest probability
- ↑ prevalence → ↑ PPV and ↓ NPV
_____ is the consistency and reproducibility of a test. It is the absence of random variation in a test.
Precision (Reliability)
- random error ↓ precision in a test
- ↑ precision → ↓ standard deviation
- ↑ precision → ↑ statistical power (1 − β)
_____ is the trueness of test measurements. It is the absence of systematic error or bias in a test.
Accuracy (Validity)
- systematic error ↓ accuracy in a test
Bias and Study Errors:
Recruiting Participants
Selection Bias
Bias and Study Errors:
- nonrandom sampling or treatment allocation of subjects such that study population is not representative of target population
- most commonly a sampling bias
Selection Bias
Bias and Study Errors:
- selection bias
- study population selected from hospital is less healthy than general population
Berkson Bias
Bias and Study Errors:
- selection bias
- participating subjects differ from non-respondents in meaningful ways
Non-Response Bias
Bias and Study Errors:
Selection Bias can be reduced by _____.
- randomization
- choice of the right comparison/reference group
Bias and Study Errors:
Performing Study
- Recall Bias
- Measurement Bias
- Procedure Bias
- Observer-Expectancy Bias
Bias and Study Errors:
- awareness of disorder alters recall by subjects
- common in retrospective studies
- patients with disease recall exposure after learning of similar cases
Recall Bias
Bias and Study Errors:
Recall Bias can be reduced by _____.
decreasing the time from exposure to follow-up
Bias and Study Errors:
- information is gathered in a systemically distorted manner
- association between HTN and MI not observed when using faulty automatic sphygmomanometer
Measurement Bias
Bias and Study Errors:
- measurement bias
- participants change behavior upon awareness of being observed
Hawthorne Effect
Bias and Study Errors:
Measurement Bias can be reduced by _____.
- using objective, standardized, and previously tested methods of data collection that are planned ahead of time
- using a placebo group
Bias and Study Errors:
- subjects in different groups are not treated the same
- patients in treatment group spend more time in highly specialized hospital units
Procedure Bias
Bias and Study Errors:
Procedure Bias can be reduced by _____.
blinding and using of placebo to reduce influence of participants and researchers on procedures and interpretation of outcomes as neither are aware of group allocation
Bias and Study Errors:
- researcher’s belief in the efficacy of a treatment changes the outcome of that treatment (aka. Pygmalion effect)
- an observer expecting treatment group to show signs of recovery is more likely to document positive outcomes
Observer-Expectancy Bias
Bias and Study Errors:
Observer-Expectancy Bias can be reduced by _____.
blinding and using of placebo to reduce influence of participants and researchers on procedures and interpretation of outcomes as neither are aware of group allocation
Bias and Study Errors:
Interpreting Results
- Confounding Bias
- Lead-Time Bias
- Length-Time Bias
Bias and Study Errors:
- when a factor is related to both the exposure and outcome, but not on the causal pathway, it distorts or confuses effect of exposure on outcome
- contrast with effect modification
- pulmonary disease is more common in coal workers than the general population; however, people who work in coal mines also smoke more frequently than the general population
Confounding Bias
Bias and Study Errors:
Confounding Bias can be reduced by _____.
- repeating studies
- using crossover studies (subjects act as their own controls)
- matching (patients with similar characteristics in both treatment and control groups)
Bias and Study Errors:
- early detection is confused with ↑ survival
- early detection makes it seem like survival has increased, but the disease’s natural history has not changed
Lead-Time Bias
Bias and Study Errors:
Lead-Time Bias can be reduced by _____.
measuring “back-end” survival (adjust survival according to the severity of disease at the time of diagnosis)
Bias and Study Errors:
- screening test detects diseases with long latency period, while those with shorter latency period become symptomatic earlier
- a slowly progressive cancer is more likely detected by a screening test than a rapidly progressive cancer
Length-Time Bias
Bias and Study Errors:
Length-Time Bias can be reduced by _____.
a randomized controlled trial assigning subjects to the screening program or to no screening
Statistical Distribution:
Measures of Central Tendency
- Mean
- Median
- Mode
Measures of Central Tendency:
- (sum of values)/(total number of values)
- most affected by outliers (extreme values)
Mean
Measures of Central Tendency:
- middle value of a list of data sorted from least to greatest
- if there is an even number of values, it will be the average of the middle two values
Median
Measures of Central Tendency:
- most common value
- least affected by outliers
Mode
Statistical Distribution:
Measures of Dispersion
- Standard Deviation
- Standard Error
Measures of Dispersion:
how much variability exists in a set of values, around the mean of these values
Standard Deviation
- σ = SD
- Variance = (SD)2
Measures of Dispersion:
an estimate of how much variability exists in a (theoretical) set of sample means around the true population mean
Standard Error
- σ = SD
- n = sample size
- SE = σ/√n
- SE ↓ as n ↑
Normal Distribution
- Gaussian, also called bell-shaped.
- Mean = Median = Mode
Non-Normal Distributions:
- suggests two different populations
- metabolic polymorphism such as fast vs. slow acetylators
- age at onset of Hodgkin lymphoma
- suicide rate by age
Bimodal
Non-Normal Distributions:
- Mean > Median > Mode
- asymmetry with longer tail on right
Positive Skew
Non-Normal Distributions:
- Mean < Median < Mode
- asymmetry with longer tail on left
Negative Skew
Statistical Hypotheses:
- hypothesis of no difference or relationship
- there is no association between the disease and the risk factor in the population
Null (H0)
Statistical Hypotheses:
- hypothesis of some difference or relationship
- there is some association between the disease and the risk factor in the population
Alternative (H1)