Crosssectional study
 Study Type
 Design
 Measures/Example
 Study Type
 Observational
 Design
 Collects data from a group of people to assess frequency of disease (and related risk factors) at a particular point in time.
 Asks, “What is happening?””
 Measures/Example
 Disease prevalence.
 Can show risk factor association with disease, but does not establish causality.
 Observational
 Collects data from a group of people to assess frequency of disease (and related risk factors) at a particular point in time.
 Asks, “What is happening?””
 Disease prevalence.
 Can show risk factor association with disease, but does not establish causality.
Casecontrol study
 Study Type
 Design
 Measures/Example
 Study Type
 Observational and retrospective
 Design
 Compares a group of people with disease to a group without disease.
 Looks for prior exposure or risk factor.
 Asks, “What happened?”
 Measures/Examples
 Odds ratio (OR).
 “Patients with COPD had higher odds of a history of smoking than those without COPD had.”
 Observational and retrospective
 Compares a group of people with disease to a group without disease.
 Looks for prior exposure or risk factor.
 Asks, “What happened?”
 Odds ratio (OR).
 “Patients with COPD had higher odds of a history of smoking than those without COPD had.”
Cohort study
 Study Type
 Design
 Measures/Example
 Study Type
 Observational and prospective or retrospective
 Design
 Compares a group with a given exposure or risk factor to a group without such exposure.
 Looks to see if exposure increased the likelihood of disease.
 Can be prospective (asks, “Who will develop disease?”) or retrospective (asks, “Who developed the disease [exposed vs. nonexposed]?").
 Measures/Example
 Relative risk (RR).
 “Smokers had a higher risk of developing COPD than nonsmokers had.”
 Observational and prospective or retrospective
 Compares a group with a given exposure or risk factor to a group without such exposure.
 Looks to see if exposure increased the likelihood of disease.
 Can be prospective (asks, “Who will develop disease?”) or retrospective (asks, “Who developed the disease [exposed vs. nonexposed]?").
 Relative risk (RR).
 “Smokers had a higher risk of developing COPD than nonsmokers had.”
Twin concordance study
 Design
 Measures/Example
 Design
 Compares the frequency with which both monozygotic twins or both dizygotic twins develop same disease.
 Measures/Example
 Measures heritability and influence of environmental factors (“nature vs. nurture”).
 Compares the frequency with which both monozygotic twins or both dizygotic twins develop same disease.
 Measures heritability and influence of environmental factors (“nature vs. nurture”).
Adoption study
 Design
 Measures/Example
 Design
 Compares siblings raised by biological vs. adoptive parents.
 Measures/Example
 Measures heritability and influence of environmental factors.
 Compares siblings raised by biological vs. adoptive parents.
 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 doubleblinded (i.e., neither patient nor doctor knows whether the patient is in the treatment or control group).
 Tripleblind refers to the additional blinding of the researchers analyzing the data.
Drug Trials: Phase I
 Typical Study Sample
 Purpose
 Typical Study Sample
 Small number of healthy volunteers.
 Purpose
 “Is it safe?”
 Assesses safety, toxicity, and pharmacokinetics.
 Small number of healthy volunteers.
 “Is it safe?”
 Assesses safety, toxicity, and pharmacokinetics.
Drug Trials: Phase II
 Typical Study Sample
 Purpose
 Typical Study Sample
 Small number of patients with disease of interest.
 Purpose
 “Does it work?”
 Assesses treatment efficacy, optimal dosing, and adverse effects.
 Small number of patients with disease of interest.
 “Does it work?”
 Assesses treatment efficacy, optimal dosing, and adverse effects.
Drug Trials: Phase III
 Typical Study Sample
 Purpose
 Typical Study Sample
 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.
 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.
Drug Trials: Phase IV
 Typical Study Sample
 Purpose
 Typical Study Sample
 Postmarketing surveillance trial of patients after approval.
 Purpose
 “Can it stay?”
 Detects rare or longterm adverse effects.
 Can result in a drug being withdrawn from market.
 Postmarketing surveillance trial of patients after approval.
 “Can it stay?”
 Detects rare or longterm adverse effects.
 Can result in a drug being withdrawn from market.
Evaluation of diagnostic tests
 Uses 2 × 2 table comparing test results with the actual presence of disease.
 TP = true positive
 FP = false positive
 TN = true negative
 FN = false negative
 Sensitivity and specificity are fixed properties of a test (vs. PPV and NPV).
 TP = true positive
 FP = false positive
 TN = true negative
 FN = false negative
Sensitivity (truepositive rate)
 Definition
 Equations
 Definition
 Proportion of all people with disease who test positive, or the probability that a test detects disease when disease is present.
 Value approaching 100% is desirable for ruling out disease and indicates a low falsenegative rate.
 High sensitivity test used for screening in diseases with low prevalence.
 Equations
 = TP / (TP + FN)
 = 1 – falsenegative rate
 If sensitivity is 100%
 TP / (TP + FN) = 1
 FN = 0
 All negatives must be TNs
 SNNOUT = highly SeNsitive test, when Negative, rules OUT disease
 Proportion of all people with disease who test positive, or the probability that a test detects disease when disease is present.
 Value approaching 100% is desirable for ruling out disease and indicates a low falsenegative rate.
 High sensitivity test used for screening in diseases with low prevalence.
 = TP / (TP + FN)
 = 1 – falsenegative rate
 If sensitivity is 100%
 TP / (TP + FN) = 1
 FN = 0
 All negatives must be TNs
Specificity (truenegative rate)
 Definition
 Equations
 Definition
 Proportion of all people without disease who test negative, or the probability that a test indicates nondisease when disease is absent.
 Value approaching 100% is desirable for ruling in disease and indicates a low falsepositive rate.
 High specificity test used for confirmation after a positive screening test.
 Equations
 = TN / (TN + FP)
 = 1 – falsepositive rate
 If specificity is 100%
 TN / (TN + FP) = 1
 FP = 0
 All positives must be TPs
 SPPIN = highly SPecific test, when Positive, rules IN disease
 Proportion of all people without disease who test negative, or the probability that a test indicates nondisease when disease is absent.
 Value approaching 100% is desirable for ruling in disease and indicates a low falsepositive rate.
 High specificity test used for confirmation after a positive screening test.
 = TN / (TN + FP)
 = 1 – falsepositive rate
 If specificity is 100%
 TN / (TN + FP) = 1
 FP = 0
 All positives must be TPs
Positive predictive value (PPV)
 Definition
 Equation
 Definition
 Proportion of positive test results that are true positive.
 Probability that person actually has the disease given a positive test result.
 PPV varies directly with prevalence or pretest probability
 High pretest probability > high PPV
 Equation
 = TP / (TP + FP)
 Proportion of positive test results that are true positive.
 Probability that person actually has the disease given a positive test result.
 PPV varies directly with prevalence or pretest probability
 High pretest probability > high PPV
 = TP / (TP + FP)
Negative predictive value (NPV) (51)
 Definition
 Proportion of negative test results that are true negative.
 Probability that person actually is disease free given a negative test result.
 NPV varies inversely with prevalence or pretest probability
 High pretest probability > low NPV
 Equation
 = TN / (FN + TN)
 Proportion of negative test results that are true negative.
 Probability that person actually is disease free given a negative test result.
 NPV varies inversely with prevalence or pretest probability
 High pretest probability > low NPV
 = TN / (FN + TN)
Incidence vs. prevalence
 Equations
 Comparison
 Equations
 Incidence rate = # of new cases in a specified time period / Population at risk during same time period
 Incidence looks at new cases (incidents).
 Prevalence = # of existing cases / Population at risk
 Prevalence looks at all current cases.
 Comparison
 Prevalence ≈ incidence rate × average disease duration.
 Prevalence > incidence for chronic diseases (e.g., diabetes).
 Incidence and prevalence for common cold are very similar since disease duration is short.
 Incidence rate = # of new cases in a specified time period / Population at risk during same time period
 Incidence looks at new cases (incidents).
 Prevalence = # of existing cases / Population at risk
 Prevalence looks at all current cases.
 Prevalence ≈ incidence rate × average disease duration.
 Prevalence > incidence for chronic diseases (e.g., diabetes).
 Incidence and prevalence for common cold are very similar since disease duration is short.
Odds ratio (OR)
 Definition
 Equations
 Definition
 Typically used in casecontrol studies.
 Odds that the group with the disease (cases) was exposed to a risk factor (a/c) divided by the odds that the group without the disease (controls) was exposed (b/d).
 Equations
 OR = (a/c) / (b/d) = ad / bc
 Typically used in casecontrol studies.
 Odds that the group with the disease (cases) was exposed to a risk factor (a/c) divided by the odds that the group without the disease (controls) was exposed (b/d).
 OR = (a/c) / (b/d) = ad / bc
Relative risk (RR)
 Definition
 Equations
 Definition
 Typically used in cohort studies.
 Risk of developing disease in the exposed group divided by risk in the unexposed group
 e.g., if 21% of smokers develop lung cancer vs. 1% of nonsmokers, RR = 21/1 = 21
 If prevalence is low, RR ≈ OR.
 Equations
 RR = [ a / (a+b) ] / [ c / (c+d) ]
 Typically used in cohort studies.
 Risk of developing disease in the exposed group divided by risk in the unexposed group
 e.g., if 21% of smokers develop lung cancer vs. 1% of nonsmokers, RR = 21/1 = 21
 If prevalence is low, RR ≈ OR.
 RR = [ a / (a+b) ] / [ c / (c+d) ]
Relative risk reduction (RRR)
 Definition
 Equations
 Definition
 The proportion of risk reduction attributable to the intervention as compared to a control.
 e.g., if 2% of patients who receive a flu shot develop flu, while 8% of unvaccinated patients develop the flu, then RR = 2/8 = 0.25, and RRR = 1 – RR = 0.75
 Equations
 RRR = 1 – RR
 The proportion of risk reduction attributable to the intervention as compared to a control.
 e.g., if 2% of patients who receive a flu shot develop flu, while 8% of unvaccinated patients develop the flu, then RR = 2/8 = 0.25, and RRR = 1 – RR = 0.75
 RRR = 1 – RR
Attributable risk (AR)
 Definition
 Equations
 Definition
 The difference in risk between exposed and unexposed groups, or the proportion of disease occurrences that are attributable to the exposure
 e.g., if risk of lung cancer in smokers is 21% and risk in nonsmokers is 1%, then 20% (or .20) of the 21% risk of lung cancer in smokers is attributable to smoking.
 Equations
 AR = [ a / (a+b) ]  [ c / (c+d) ]
 The difference in risk between exposed and unexposed groups, or the proportion of disease occurrences that are attributable to the exposure
 e.g., if risk of lung cancer in smokers is 21% and risk in nonsmokers is 1%, then 20% (or .20) of the 21% risk of lung cancer in smokers is attributable to smoking.
 AR = [ a / (a+b) ]  [ c / (c+d) ]
Absolute risk reduction (ARR)
 Definition
 Equations
 Definition
 The difference in risk (not the proportion) attributable to the intervention as compared to a control
 e.g., if 8% of people who receive a placebo vaccine develop flu vs. 2% of people who receive a flu vaccine, then ARR = 8%  2% = 6% = .06.
 Equations

ARR = [ c / (c+d) ]  [ a / (a+b) ]
 The difference in risk (not the proportion) attributable to the intervention as compared to a control
 e.g., if 8% of people who receive a placebo vaccine develop flu vs. 2% of people who receive a flu vaccine, then ARR = 8%  2% = 6% = .06.

ARR = [ c / (c+d) ]  [ a / (a+b) ]
Number needed to treat
 Definition
 Equation
 Definition
 Number of patients who need to be treated for 1 patient to benefit.
 Equation
 NNT = 1/ARR.
 Number of patients who need to be treated for 1 patient to benefit.
 NNT = 1/ARR.
Number needed to harm
 Definition
 Equation
 Definition
 Number of patients who need to be exposed to a risk factor for 1 patient to be harmed.
 Equation
 NNH = 1/AR.
 Number of patients who need to be exposed to a risk factor for 1 patient to be harmed.
 NNH = 1/AR.
Precision
 The consistency and reproducibility of a test (reliability).
 The absence of random variation in a test.
 Random error—reduces precision in a test.
 Increased precision > decreased standard deviation.
Accuracy
 The trueness of test measurements (validity).
 The absence of systematic error or bias in a test.
 Systematic error—reduces accuracy in a test.
Selection bias
 Definition
 Examples
 Berkson bias
 Loss to followup
 Healthy worker and volunteer biases
 Strategies to reduce bias
 Definition
 Nonrandom assignment to participate in a study group.
 Most commonly a sampling bias.
 Examples
 Berkson bias
 A study looking only at inpatients
 Loss to followup
 Studying a disease with early mortality
 Healthy worker and volunteer biases
 Study populations are healthier than the general population
 Strategies to reduce bias
 Randomization
 Ensure the choice of the right comparison/reference group
 Nonrandom assignment to participate in a study group.
 Most commonly a sampling bias.
 Berkson bias
 A study looking only at inpatients
 Loss to followup
 Studying a disease with early mortality
 Healthy worker and volunteer biases
 Study populations are healthier than the general population
 Randomization
 Ensure the choice of the right comparison/reference group
Recall bias
 Definition
 Example
 Strategy to reduce bias
 Definition
 Awareness of disorder alters recall by subjects
 Common in retrospective studies.
 Example
 Patients with disease recall exposure after learning of similar cases
 Strategy to reduce bias
 Decrease time from exposure to followup
 Awareness of disorder alters recall by subjects
 Common in retrospective studies.
 Patients with disease recall exposure after learning of similar cases
 Decrease time from exposure to followup
Measurement bias
 Definition
 Example
 Strategy to reduce bias
 Definition
 Information is gathered in a way that distorts it.
 Example
 Hawthorne effect — groups who know they’re being studied behave differently than they would otherwise
 Strategy to reduce bias
 Use of placebo control groups with blinding to reduce influence of participants and researchers on experimental procedures and interpretation of outcomes
 Information is gathered in a way that distorts it.
 Hawthorne effect — groups who know they’re being studied behave differently than they would otherwise
 Use of placebo control groups with blinding to reduce influence of participants and researchers on experimental procedures and interpretation of outcomes
Procedure bias
 Definition
 Example

Strategy to reduce bias
 Definition
 Subjects in different groups are not treated the same.
 Example
 Patients in treatment group spend more time in highly specialized hospital units
 Strategy to reduce bias
 Use of placebo control groups with blinding to reduce influence of participants and researchers on experimental procedures and interpretation of outcomes
 Subjects in different groups are not treated the same.
 Patients in treatment group spend more time in highly specialized hospital units
 Use of placebo control groups with blinding to reduce influence of participants and researchers on experimental procedures and interpretation of outcomes
Observerexpectancy bias
 Definition
 Example

Strategy to reduce bias
 Definition
 Researcher’s belief in the efficacy of a treatment changes the outcome of that treatment
 aka Pygmalion effect; selffulfilling prophecy
 Example
 If observer expects treatment group to show signs of recovery, then he is more likely to document positive outcomes
 Strategy to reduce bias
 Use of placebo control groups with blinding to reduce influence of participants and researchers on experimental procedures and interpretation of outcomes
 Researcher’s belief in the efficacy of a treatment changes the outcome of that treatment
 aka Pygmalion effect; selffulfilling prophecy
 If observer expects treatment group to show signs of recovery, then he is more likely to document positive outcomes
 Use of placebo control groups with blinding to reduce influence of participants and researchers on experimental procedures and interpretation of outcomes
Confounding bias
 Definition
 Example
 Strategies to reduce bias
 Definition
 When a factor is related to both the exposure and outcome, but not on the causal pathway
 Factor distorts or confuses effect of exposure on outcome
 Example
 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 to reduce bias
 Multiple/repeated studies
 Crossover studies (subjects act as their own controls)
 Matching (patients with similar characteristics in both treatment and control groups)
 When a factor is related to both the exposure and outcome, but not on the causal pathway
 Factor distorts or confuses effect of exposure on outcome
 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
 Multiple/repeated studies
 Crossover studies (subjects act as their own controls)
 Matching (patients with similar characteristics in both treatment and control groups)
Leadtime bias
 Definition
 Example
 Strategy to reduce bias
 Definition
 Early detection is confused with increased survival
 Seen with improved screening techniques.
 Example
 Early detection makes it seem as though survival has increased, but the natural history of the disease has not changed
 Strategy to reduce bias
 Measure “backend” survival (adjust survival according to the severity of disease at the time of diagnosis)
 Early detection is confused with increased survival
 Seen with improved screening techniques.
 Early detection makes it seem as though survival has increased, but the natural history of the disease has not changed
 Measure “backend” survival (adjust survival according to the severity of disease at the time of diagnosis)
Measures of central tendency
 Mean
 Median
 Mode
 Mean = (sum of values)/(total number of 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.
 If there is an even number of values, the median will be the average of the middle two values.
Measures of dispersion
 Standard deviation
 Standard error of the mean
 Standard deviation = how much variability exists from the mean in a set of values.
 Standard error of the mean = an estimation of how much variability exists between the sample mean and the true population mean.
 σ = SD, n = sample size
 SEM = σ / sqrt(n)
 SEM decreases as n increases
 σ = SD, n = sample size
 SEM = σ / sqrt(n)
 SEM decreases as n increases
Normal distribution
 Gaussian, also called bellshaped.
 Mean = median = mode.
Bimodal distribution
 Suggests two different populations
 e.g., metabolic polymorphism such as fast vs. slow acetylators; suicide rate by age
Positive skew
 Typically, mean > median > mode.
 Asymmetry with longer tail on right.
Negative skew
 Typically, mean < median < mode.
 Asymmetry with longer tail on left.
Null Hypothesis (H_{0})
 Hypothesis of no difference
 e.g., there is no association between the disease and the risk factor in the population
Alternative Hypothesis (H_{1})
 Hypothesis of some difference
 e.g., there is some association between the disease and the risk factor in the population
Table: Power, Type 1 Error, Type 2 Error, and Correct
Correct result
 Stating that there is an effect or difference when one exists
 Null hypothesis rejected in favor of alternative hypothesis
 Stating that there is not an effect or difference when none exists
 Null hypothesis not rejected
 Null hypothesis rejected in favor of alternative hypothesis
 Null hypothesis not rejected
Type I error (α)
 Definition
 α & p
 Definition
 Also known as falsepositive error
 Stating that there is an effect or difference when none exists
 Null hypothesis incorrectly rejected in favor of alternative hypothesis
 α = you saw a difference that did not exist (e.g., convicting an innocent man).
 α & p
 α is the probability of making a type I error.
 p is judged against a preset a level of significance (usually < .05).
 If p < 0.05, then there is less than a 5% chance that the data will show something that is not really there.
 Also known as falsepositive error
 Stating that there is an effect or difference when none exists
 Null hypothesis incorrectly rejected in favor of alternative hypothesis
 α = you saw a difference that did not exist (e.g., convicting an innocent man).
 α is the probability of making a type I error.
 p is judged against a preset a level of significance (usually < .05).
 If p < 0.05, then there is less than a 5% chance that the data will show something that is not really there.
Type II error (β)
 Definition
 β & power
 Definition
 Also known as falsenegative error.
 Stating that there is not an effect or difference when one exists
 Null hypothesis is not rejected when it is in fact false
 β = you were blind to a difference that did exist (e.g., setting a guilty man free).
 β & power
 β is the probability of making a type II error.
 β is related to statistical power (1 – β), which is the probability of rejecting the null hypothesis when it is false.
 Increase power and decrease β by:
 Increasing sample size
 There is power in numbers.
 Increasing expected effect size
 Increasing precision of measurement
 Also known as falsenegative error.
 Stating that there is not an effect or difference when one exists
 Null hypothesis is not rejected when it is in fact false
 β = you were blind to a difference that did exist (e.g., setting a guilty man free).
 β is the probability of making a type II error.
 β is related to statistical power (1 – β), which is the probability of rejecting the null hypothesis when it is false.
 Increase power and decrease β by:
 Increasing sample size
 There is power in numbers.
 Increasing expected effect size
 Increasing precision of measurement
 Increasing sample size
Metaanalysis
 Pools data and integrates results from several similar studies to reach an overall conclusion.
 Increase statistical power.
 Limited by quality of individual studies or bias in study selection.
Confidence interval
 Definition
 Equation
 95% & 99% CI
 If the 95% CI for a mean difference between 2 variables includes 0
 If the 95% CI for odds ratio or relative risk includes 1
 If the CIs between 2 groups do not overlap
 If the CIs between 2 groups overlap
 Definition
 Range of values in which a specified probability of the means of repeated samples would be expected to fall.
 Equation
 CI = range from [mean – Z(SEM)] to [mean + Z(SEM)].
 95% & 99% CI
 For the 95% CI, Z = 1.96.
 The 95% CI (corresponding to p = .05) is often used.
 For the 99% CI, Z = 2.58.
 If the 95% CI for a mean difference between 2 variables includes 0
 Then there is no significant difference and H0 is not rejected.
 If the 95% CI for odds ratio or relative risk includes 1
 H0 is not rejected.
 If the CIs between 2 groups do not overlap
 Significant difference exists.
 If the CIs between 2 groups overlap
 Usually no significant difference exists.
 Range of values in which a specified probability of the means of repeated samples would be expected to fall.
 CI = range from [mean – Z(SEM)] to [mean + Z(SEM)].
 For the 95% CI, Z = 1.96.
 The 95% CI (corresponding to p = .05) is often used.
 For the 99% CI, Z = 2.58.
 Then there is no significant difference and H0 is not rejected.
 H0 is not rejected.
 Significant difference exists.
 Usually no significant difference exists.
ttest
 Checks differences between means of 2 groups.
 Tea is meant for 2
 Example: comparing the mean blood pressure between men and women.
 Tea is meant for 2
ANOVA
 Checks differences between means of 3 or more groups.
 3 words: ANalysis Of VAriance
 Example: comparing the mean blood pressure between members of 3 different ethnic groups.
 3 words: ANalysis Of VAriance
Chisquare (χ²)
 Checks difference between 2 or more percentages or proportions of categorical outcomes (not mean values).
 Pronounce Chitegorical
 Example: comparing the percentage of members of 3 different ethnic groups who have essential hypertension.
 Pronounce Chitegorical
Pearson correlation coefficient (r)
 Definition
 Positive vs. negative r value
 Coefficient of determination
 Definition
 r is always between 1 and +1.
 The closer the absolute value of r is to 1, the stronger the linear correlation between the 2 variables.
 Positive vs. negative r value
 Positive r value > positive correlation.
 Negative r value > negative correlation.
 Coefficient of determination = r^{2} (value that is usually reported).
 r is always between 1 and +1.
 The closer the absolute value of r is to 1, the stronger the linear correlation between the 2 variables.
 Positive r value > positive correlation.
 Negative r value > negative correlation.
Disease Prevention
 Primary
 Secondary
 Tertiary
 Quaternary

Primary

Prevent disease occurrence (e.g., HPV vaccination).

Secondary

Screening early for disease (e.g., Pap smear)

Tertiary

Treatment to reduce disability from disease (e.g., chemotherapy)

Quaternary
 Identifying patients at risk of unneccessary treatment, protecting from the harm of new interventions
 Prevent disease occurrence (e.g., HPV vaccination).
 Screening early for disease (e.g., Pap smear)
 Treatment to reduce disability from disease (e.g., chemotherapy)
 Identifying patients at risk of unneccessary treatment, protecting from the harm of new interventions
Medicare and Medicaid
 Both
 Medicare
 Medicaid
 Both
 Federal programs that originated from amendments to the Social Security Act.
 Medicare
 Available to patients ≥ 65 years old, < 65 with certain disabilities, and those with endstage renal disease.
 MedicarE is for Elderly
 Medicaid
 Joint federal and state health assistance for people with very low income.
 MedicaiD is for Destitute
 Federal programs that originated from amendments to the Social Security Act.
 Available to patients ≥ 65 years old, < 65 with certain disabilities, and those with endstage renal disease.
 MedicarE is for Elderly
 Joint federal and state health assistance for people with very low income.
 MedicaiD is for Destitute