Week 7 USMLE Step 1 Review: Biostatistics, Behavioral Science, and Nutrition

1. Steven Katz MSIV Week 7 USMLE Step 1 Review: Biostatistics, Behavioral Science, and Nutrition

2. PART 1: BIOSTATISTICS • Terms: • Independent variable: values that are controlled or selected by the person experimenting to determine its relationship to an observed phenomenon (the dependent variable). • Dependant variable: the observed phenomenon, usually cannot be changed. • In summary: • Independent variables answer the question "What do I change?" • Dependent variables answer the question "What do I observe?"

3. Types of Studies (p.60) • Case Control: Compares a group of people with a disease to a group without. • Asks “what happened?” • Two types: • Observational and Retrospective • Famous example is lung cancer link to smoking • Issues: • Confounding: a variable that correlates to both dependant and independent variables. • Cannot prove cause and effect of risk factor to variable

4. Types of Studies (p.60) • Cohort: Compares a group with a given risk factor to a group without • Assesses whether the risk factor increases the likelihood of disease • Asks “what will happen” • Two types: • Observational and Prospective • Used to prove cause and effect of smoking to lung cancer.

5. Types of Studies (p.60) • Cross-Sectional: 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?” • Example: political polls

6. Types of Studies (p.60) • Twin Concordance: Compares the frequency with which both monozygotic twins or both dizygotic twins develop a disease • Measures heritability • Example: look at incidence of diabetes in twins

7. Types of Studies (p.60) • Adoption: Compares siblings raised by biologic v. adoptive parents • Measures heritability and influence of environmental factors • Famous examples are Swedish adoption studies

8. Clinical trials (p.60) • Experimental study involving humans. Compares therapeutic benefits of 2 or more treatments, or of treatment and placebo. • Highest quality study is double-blind randomized control trial.

9. Clinical trials (p.60)

10. Meta-analysis (p.60) • Pools data from several studies to come to an overall conclusion. • Achieves greater statistical power and integrates results of similar studies • Highest echelon of clinical evidence • May be limited by quality of individual studies or bias in study selection

11. Evaluation of diagnostic tests (p.61) • 2 x 2 table (TN = True neg, TP = True pos, FP = false pos, FN = false neg)

12. Evaluation of diagnostic tests (p.61) • Sensitivity = TP/(TP+FN) = 1-FN rate • Proportion of all people with disease who test positive • Value approaching 1 is desirable for RULING OUT disease and indicates low false negative rate. • Used for SCREENING in diseases with low prevalence • SNOUT = SeNsitivity rules OUT • If sensitivity = 100% then all negative tests are TN (TP/(TP+FN) = 1) because FN = 0

13. Evaluation of diagnostic tests (p.61) • Specificity = TN/(TN+FP) = 1-FP rate • Proportion of all people without disease who test negative • Value approaching 1 is desirable for RULINGIN disease and indicates a low FP rate • Used as a CONFIRMATORY test after a positive screening test • SPIN = SPecificity rules IN • If specificity = 100% then all positive tests are TP (TN/(TN+FP) = 1) because FP = 0

14. Evaluation of diagnostic tests (p.61) • Positive Predictive Value (PPV) = TP/(TP+FP) • Proportion of positive tests that are true positives • Probability that a person actually has the disease given a positive test result • Note: If the prevalence of a disease is low then even tests with high specificity or sensitivity will have LOW PPV

15. Evaluation of diagnostic tests (p.61) • Negative Predictive Value (NPV) = TN /(TN+FN) • Proportion of negative tests that are true negatives • Probability that a person actually is disease free given a negative test result • http://gim.unmc.edu/dxtests/bayes.htm

16. Evaluation of diagnostic tests (p.61) • A = 100% sensitivity • B= most accurate • C = 100% specificity

17. Prevalence v. Incidence (p.62) • Prevalence = TOTAL cases in a population at a given time total population at risk at a given time • Incidence = NEW cases in a population over a time period total population at risk during that time • Prevalence = incidence X disease duration • Prevalence > Incidence for chronic dz’s • Prevalence = incidence for acute dz’s

18. Odds ratio (p.62) • For case control studies • (a/b)/(c/d) = ad/bc • Odds of having disease in exposed group divided by odds of having disease in unexposed group • Approximates the relative risk if prevalence of disease is not too high

19. Relative risk (p.62) • For cohort studies • Relative probability of getting a disease in the exposed group compared to the unexposed group • [a/(a+b)]/[c(c+d)] • Calculated as a percent of exposed pts with dz to unexposed pts with dz

20. Attributable risk (p.62) • The difference in risk between exposed and unexposed groups OR • The proportion of disease occurrences that are attributable to the to the exposure • (e.g. smoking causes 1/3 of cases of pna) • [a/(a+b)] – [c/(c+d)]

21. Odds ratio, relative risk, attributable risk (p.62) Attributable risk = [a/(a+b)] – [c/(c+d)] Odds ratio: (a/b)/(c/d) = ad/bc Relative Risk: [a/(a+b)]/[c(c+d)]

22. Precision v. accuracy (p.62) • Precision: • The consistency and reproducibility of a test • RELIABILITY • The absence of random variation in a test • Random Error: reduced precision in a test • Accuracy: • The trueness of test measures • VALIDITY • Systematic error: reduced accuracy in a test

23. Precision v. accuracy (p.62) Neither Precise Nor Accurate Precise, Not Accurate Precise and Accurate Accurate, Not Precise

24. Bias (p.63) • Occurs when 1 outcome is systematically favored over another • Systematic errors: • Selection bias: nonrandom assignment to study group • Recall bias: knowledge of presence of disorder alters recall by subjects • Sampling bias: subjects are not representative relative to general pop; therefore, results are not generalizable • Late-look bias: information gathered at an inappropriate time • Procedure bias: subjects in different groups are not treated the same • E.g. more attention is paid to treatment group, stimulating greater compliance • Lead time bias: early detection confused with increased survival

25. Bias (p.63) • Confounding bias: occurs with 2 closely associated factors • The effect of the 1 factor distorts or confuses the effect of the other • Pygmalion effect: occurs when a researcher’s belief in the efficacy of the treatment changes the outcome of that treatment • Hawthorne effect: occurs when the group being studied changes its behavior to meet the expectations of the researcher • Ways to reduce bias: • Blind studies • Placebo responses • Crossover studies (each subject is its own control) • Randomization

26. Statistical distribution (p.63) • Normal, Gaussian, bell-shaped curved • Mean = mode = median • Bimodal = 2 humps • Positive skew—mean >median>mode • Asymmetry with tail on right • Negative skew—mean<median<mode • Asymmetry with tail on left • Mode is least affected by outliers

27. Statistical hypotheses (p.63) • Null (H0): Hypothesis of NO DIFFERENCE • e.g. there is no difference between the dz and the risk factor in the population • Alternative (H1): Hypothesis that the is some difference • e.g. there is some association between the dz and the risk factor in the population

28. Error types (p.64) • Type I error (a): Stating that there IS an effect or difference when none exists (to mistakenly accept the experimental hypothesis and reject the null hypothesis) • p = probability of making a type I error • p is judged against a, a preset level of significance (usually <0.05) • “False positive error”

29. Error types (p.64) • Type II error (b): Stating that there is NOT an effect or difference when one exists (to fail to reject the null hypothesis when in fact H0 is false) • b is the probability of making a type II error • “False negative error”

30. Error types (p.64) • If p < 0.05 then there is a less than 5% chance that the data will show something that is really not there. • a = you “saw” a difference that did not exist • b = you did NOT “see” a difference that does exist

31. Power (1-b) (p.64) • Definition: • Probability of rejecting a null hypothesis when it is in fact false • The likelihood of finding a difference if one in fact exists • Depends on: • Total number of endpoints experienced by the population • Difference in compliance between treatment groups (diff in the mean values of the groups) • Size of expected effect • If you increase sample size you increase power

32. Standard deviation v. standard error (p.64) • n = sample size • s = standard deviation • SEM = standard error of the mean • SEM = s/square root (n) • Therefore, SEM < s and SEM decreases as n increases

33. t-test v. ANOVA v. c2 (p.65) • t-test checks difference between the MEANS of 2 groups • Mr. T is MEAN • ANOVA checks difference between the means of 3 or more groups • ANOVA = ANalysis Of VAriance of 3 or more variables • c2 checks difference between 2 or more percentages or proportions of categorical outcomes (NOT mean values) • c2 = compare percentages or proportions

34. Correlation coefficient (r) (p.65) • r is always between -1 and +1. • The closer the absolute value of r is to 1, the stronger the correlation between the 2 variables • Coefficient of determination = r2 (value that is usually reported)] • Provides a measure of how well future outcomes are likely to be predicted by the model.

35. Disease prevention (p.65) • 1o – prevent disease occurrence (e.g. vaccination) • 2o – early detection of disease (e.g. Pap smear) • 3o – reduce disability from disease (e.g. exogenous insulin for diabetics) • PDR: • Prevent • Detect • Reduce disability

36. Important prevention measures (p.65)

37. Reportable diseases (p.65) • Only some infectious diseases are reportable in ALL states • AIDS Chickenpox • Gonorrhea Hepatitis A and B • Measles Mumps • Rubella Salmonella • Shigella Syphilis • TB • Other diseases (including HIV) vary by state

38. Reportable diseases (p.65) • Hep Hep Hep, Hooray, the SSSMMARTChick is Gone! • HepA • Hep B • Hep C • HIV • Salmonella • Shigella • Syphilis • Measles • Mumps • AIDS • Rubella • TB • Chickenpox • Gonorrhea

39. Leading causes of death in US by age (p.66)

40. Part 2: NUTRITION

41. Basal Metabolic Rate • Metabolism of the body at rest • Heat production of the body when in a state of complete mental and physical rest and in the post-absorptive state. • BMR can be estimated at 20-25 Cal/kg/day • Varies between people and changes throughout life. • High when you are young, slows as you age.

42. Resting Energy Expenditure • Energy expended in the post-absorptive state and is approx 10% higher than BMR • Males: REE = 900 + 10W • Females: REE = 700 + 7W • W is weight in kilograms • REE is then adjusted for physical activity by multiplying 1.2 for sedentary, 1.4 for moderately active, or 1.8 for very active individuals.

43. Caloric Requirement • Age and Caloric requirements: • 3 mo: 28 Cal/kg • 9-12 mo: 6 Cal/kg • 2-5 y/o: 2 Cal/kg • 9-17 y/o: 1 Cal/kg • 10% reduction in energy allowance for adults > 50 y/o.

44. Caloric Requirement • Unstressed hospitalized pts require 1.2 times their REE • Stressed, febrile, catabolic pts require 1.5-2 times their REE

45. Question 7 of 40

46. C   Respect her decision if she can demonstrate and communicate ability to reason • Competence is a legal term, capacity is a medical term. Physicians are often called on to make a determination of a patient's capacity to make medical decisions. The patient's primary care provider is an ideal person to make the assessment as they have background knowledge of the patient's educational level, values, and medical history. • A psychiatrist may be needed if overlying psychiatric problems make it difficult to determine capacity for judgment or ability to reason. Courts make the ultimate determination of competence, although there is usually concordance with the medical determination of capacity. Only lack of competence has legal ramifications, however. • A bedside mental status examination may help to determine capacity, but in and of itself does not determine competence. If the patient is deemed to have the capacity to make her own decisions, it may be detrimental to encourage family member involvement in the decision making process. • Adult protective services are usually called to investigate cases of abuse or neglect, not issues of capacity or competence. If still unclear, a psychiatrist or ethics board consultation could be utilized to help determine the patient's capacity to make her own decisions. • Four main criteria should be used to determine a patient's capacity to make medical decisions. • They can demonstrate understanding of the treatment options. • They can demonstrate understanding of how the different options affect their own individual situation. • 3) They can demonstrate ability to reason with the above information, using either evidence based in fact, or personal beliefs rooted in their value system. • 4) They are able to demonstrate 1-3 and can communicate a choice.

47. Question 37 of 40

48. D   It is in the differential diagnosis for diarrhea The definition of stress is an individual's negative emotional response to a perceived inability to meet demands place on him or her. It may express itself as anger, hostility, or feelings of helplessness, loss of control, or victimization. It is believed to be a factor in 60-80% of all health problems, and is the leading cause of disability claims in California. Major symptoms include fatigue, exhaustion, tight back and shoulders, insomnia, anxiety, anger, headaches, depression, sadness, hopelessness, colds, indigestion, diarrhea, and ulcer symptoms. Effective prevention and avoidance techniques include assertiveness training and the development of communication skills. Treatment methods include relaxation techniques, meditation, exercise, and participation in enjoyable activities.

49. Question 1 of 40

50. E   Opioids The patient is experiencing the classic symptoms of withdrawal from opioids which are anxiety, insomnia, anorexia, sweating, piloerection, fever, rhinorrhea, nausea, stomach cramps, diarrhea, yawning. Symptoms usually appear within 8 to 10 hours after abstinence. The onset is longer if methadone has been withdrawn. These symptoms peak within 48 to 72 hours and then disappear in 7 to 10 days. Methadone lessens the effects of withdrawal. It should be given no more than 20-50mg/day. Alcohol withdrawal appears within a few hours of stopping or decreasing alcohol consumption. It lasts for three to four days and sometimes as long as a week. The patient experiences tachycardia, tremulousness, diaphoresis, nausea, orthostatic hypotension, malaise, anxiety, and irritability. Benzodiazepine should be administered in a tapering dose over three days. Cocaine withdrawal is classified by psychological symptoms such as increased sleep, REM rebound causing nightmares, lassitude, increased appetite, depression, and suicide attempts. Treatment would consist of an antidepressant such as bupropion. Amphetamine withdrawal would include a post use crash, including anxiety, lethargy, headache, stomach cramps, hunger, severe depression, dysphoria mood, fatigue, and insomnia or hypersomnia. Barbiturate withdrawal is characterized by anxiety, seizures, delirium, and life threatening cardiovascular collapse.