Public health sciences Flashcards
Cross-sectional study
Frequency of disease and frequency of risk-related factors are assessed in the present. Asks, “What is happening?”
measures
Disease prevalence. Can show risk factor association with disease, but does not establish causality
Case-control study
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. Asks, “What happened?”
measures
Odds ratio (OR). Patients with COPD had higher odds of a smoking history than those without COPD.
Cohort study
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. Can be prospective (asks, “Who will develop disease?”) or retrospective (asks, “Who developed the disease [exposed vs nonexposed]?”).
measures
Relative risk (RR). Smokers had a higher risk of developing COPD than nonsmokers.
Twin concordance study
Compares the frequency with which both monozygotic twins vs both dizygotic twins develop the same disease.
measues
Measures heritability and influence of environmental factors (“nature vs nurture”).
Adoption study
Compares siblings raised by biological vs adoptive parents.
measures
Measures heritability and influence of environmental factors.
Clinical trial
Experimental study involving humans. Compares therapeutic benefits of 2 or more treatments, or of treatment and placebo. Study quality improves when study is randomized, controlled, and double-blinded (ie, neither patient nor doctor knows whether the patient is in the treatment or control group). Triple-blind refers to the additional blinding of the researchers analyzing the data. Four phases (“Does the drug SWIM?”).
Phase I
Small number of healthy volunteers or patients with disease of interest.
purpose
“Is it Safe?” Assesses safety, toxicity, pharmacokinetics, and pharmacodynamics
Phase II
Moderate number of patients with disease of interest.
purpose
“Does it Work?” Assesses treatment efficacy, optimal dosing, and adverse effects
Phase III
Large number of patients randomly assigned either to the treatment under investigation or to the best available treatment (or placebo)
purpose
“Is it as good or better?” Compares the new treatment to the current standard of care (any Improvement?)
Phase IV
Postmarketing surveillance of patients after treatment is approved.
purpose
Can it stay?” Detects rare or long-term adverse effects. Can result in treatment being withdrawn from Market.
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.
Sensitivity (true-positive rate)
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. High sensitivity test used for screening in diseases with low prevalence.
= TP / (TP + FN) = 1 – FN rate
SN-N-OUT = highly SeNsitive test, when Negative, rules OUT disease If sensitivity is 100%, then FN is zero. So, all negatives must be TNs.
Specificity (truenegative rate)
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. High specificity test used for confirmation after a positive screening test.
= TN / (TN + FP) = 1 – FP rate
SP-P-IN = highly SPecific test, when Positive, rules IN disease If specificity is 100%, then FP is zero. So, all positives must be TPs.
Positive predictive value
Probability that a person who has a positive test result actually has the disease.
PPV = TP / (TP + FP)
PPV varies directly with pretest probability (baseline risk, such as prevalence of disease): high pretest probability high PPV
Negative predictive value
Probability that a person with a negative test result actually does not have the disease.
NPV = TN / (TN + FN)
NPV varies inversely with prevalence or pretest probability
Likelihood ratio
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.
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.
LR+ = sensitivity / 1 – specificity = TP rate/FP rate
LR– = 1 – sensitivity/ specificity = FN rate/TN rate
Odds ratio
Typically used in case-control studies. OR 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).
Relative 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 relative risk is 5, indicating a 5 times greater risk of cancer in the exposed than unexposed). For rare diseases (low prevalence), OR approximates RR.
RR = 1 –> no association between exposure and disease.
RR > 1 –> exposure associated with increased disease occurrence.
RR < 1 –> exposure associated with decreased disease occurrence.
Absolute risk reduction
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 ARR = 8% − 2% = 6% = .06)
Attributable 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%).
Relative risk reduction
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 RRR = 0.75).
Number needed to treat
Number of patients who need to be treated for 1 patient to benefit. Lower number = better treatment.
Number needed to harm
Number of patients who need to be exposed to a risk factor for 1 patient to be harmed. Higher number = safer exposure
Incidence = # of new cases/ # of people at risk
(during a specified time period)
Prevalence = # of existing cases/Total # of people
(at a point in time)
Prevalence/ 1 – prevalence = Incidence rate × average duration of disease
Prevalence ≈ incidence for short duration disease (eg, common cold).
Prevalence > incidence for chronic diseases, due to large # of existing cases (eg, diabetes).
Prevalence ∼ pretest probability. increased prevalence –> increased prevalence–> increased PPV and decreased NPV
Precision (reliability)
The consistency and reproducibility of a test. The absence of random variation in a test.
Random error decreased precision in a test.
increased precision –> decresed standard deviation.
increased precision –> increased statistical power (1 − β).
Accuracy (validity)
The trueness of test measurements. The absence of systematic error or bias in a test
Systematic error decreased accuracy in a test.
Selection bias
Recruiting participants
definition
Nonrandom sampling or treatment allocation of subjects such that study population is not representative of target population. Most commonly a sampling bias.
examples
Berkson bias—study population selected from hospital is less healthy than general population
Non-response bias— participating subjects differ from nonrespondents in meaningful ways
strategies
Randomization
Ensure the choice of the right comparison/reference group
Recall bias
definiton: Awareness of disorder alters recall by subjects; common in retrospective studies
examples: Patients with disease recall exposure after learning of similar cases
strategies: Decrease time from exposure to follow-up
Measurement bias
definition: Information is gathered in a systemically distorted manner.
Example: Association between HTN and MI not observed when using faulty automatic sphygmomanometer Hawthorne effect—participants change behavior upon awareness of being observed
strategies: Use objective, standardized, and previously tested methods of data collection that are planned ahead of time Use placebo group
Procedure bias
defintion: Subjects in different groups are not treated the same.
Example: Patients in treatment group spend more time in highly specialized hospital units
Observer-expectancy bias
definition: Researcher’s belief in the efficacy of a treatment changes the outcome of that treatment (aka, Pygmalion effect).
examples: An observer expecting treatment group to show signs of recovery is more likely to document positive outcomes
both strategies for procedure and observer-expectancy: Blinding and use of placebo reduce influence of participants and researchers on procedures and interpretation of outcomes as neither are aware of group allocation
Confounding bias
definition: 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.
examples: 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
Strategies: Multiple/repeated studies Crossover studies (subjects act as their own controls) Matching (patients with similar characteristics in both treatment and control groups)
Lead-time bias
Definition: Early detection is confused with increased survival.
example: Early detection makes it seem like survival has increased, but the disease’s natural history has not changed
Strategies: Measure “back-end” survival (adjust survival according to the severity of disease at the time of diagnosis)
Length-time bias
Definition: Screening test detects diseases with long latency period, while those with shorter latency period become symptomatic earlier.
Example: A slowly progressive cancer is more likely detected by a screening test than a rapidly progressive cancer
Strategies: A randomized controlled trial assigning subjects to the screening program or to no screening
Statistical distribution
Measures of central tendency
Mean = (sum of values)/(total number of values). Most affected by outliers (extreme values).
Median = middle value of a list of data sorted from least to greatest. If there is an even number of values, the median will be the average of the middle two values.
Mode = most common value. Least affected by outliers.
Measures of dispersion
Standard deviation = how much variability exists in a set of values, around the mean of these values.
Standard error = an estimate of how much variability exists in a (theoretical) set of sample means around the true population mean.
Normal distribution
Gaussian, also called bell-shaped. Mean = median = mode.