Epidemiology in Medicine

1. Epidemiology in Medicine Sandra Rodriguez Internal Medicine TTUHSC

2. Epidemiology and Research • Study of the frequency and cause of disease in human populations. • Research allows understanding of risk factors, progression of diseases, treatment effectiveness, outcomes and highlights research needs. • After critical evaluation of data, physicians can make clinical decisions and improve populations health (EBM).

3. Types of Statistical Studies • Descriptive • Take data, arrange them, and present them to demonstrate associations or to generate a research hypothesis for further studies • Analytic • To compare exposures to risk factors with disease states, allow hypothesis testing and statistical analysis • Could be without or with intervention

4. Types of Statistical Studies • Descriptive • Case Reports and Series • Correlation studies • Large sample size to identify associations between disease and variables • Best to generate research ideas • Cross-Sectional Studies • Evaluates a group at one point in time • Causal links can be speculated but no conclusions can be drawn.

5. Types of Statistical Studies • Analytic • Case control • Study group analyzed to identify associations • Begins with a population and it’s disease and can be evaluated retrospective to determine exposure • Prospective Cohort Study • Disease-free subjects are followed overtime to identify onset of disease, or incidence • Used to establish relative risk

6. Types of Statistical Studies • Analytic • Meta-Analysis • Quantitative analysis of two or more independent studies into a large one for analysis of variables and results. Gives a statistic summary and is used to increase knowledge beyond one study, guide diagnosis and treatments and point toward research. • Interventional study • Clinical trial • Ideally randomized, blind, designed to minimize impact of bias and confounding factors. • Crossover study design.

7. Reviewing literature • Study design • Hypothesis: Research and Null • Theory/natural law • Sample • Inclusion/Exclusion criteria • Randomization • Matching controls • Test: Threshold for normal, sensitivity, specificity, use depending prevalence of disease in your population. • Measures for assessment results • Accuracy • Precision • Statistical analysis. • Intention to treat. • Validity • Reliability • Bias: Selection, Observational • Confounding factors • Efficacy • Effectiveness

8. Reviewing literature • Statistical power • Accepting or rejecting the null hypothesis is the basic thrust of any study • Power: Likelihood that a statistically significant difference would be found between two groups given that a difference truly exists. • Sample size: Rule of 3, if a condition occurs 1 in to 10, then the population needed is three times this number (30) for a statistically significant study. • Experiment design

9. Reviewing literature • Type I error • Rejecting a true null hypothesis/accepting a false positive research hypothesis; hence a false positive result • Alpha is the frequency of occurrence of a type I error • The probability of committing a a type I error is the P-value • P-value is the probability that the null hypothesis is true, and the lower the more significant, <0.05 means that less than 5% possibility that result is by chance, <0.01 means that less than 1% possibility that result is by chance.

10. Reviewing literature • Type II error • Accepting a false null hypothesis/rejecting a true positive research hypothesis; hence false-negative result • Beta signifies the frequency of a type II error occurring • Protect with statistical power

11. Statistical Analysis • Noncontinuous • P-values • The chi-square which concerns the frequency of event occurrence • The Fisher’s exact test which estimate the P-value when small samples are used

12. Statistical Analysis • Continuous • One sample T-Test • Compares the sample mean value to a known mean of a standard variable • Two sample T-Test or paired T-Test • Compares the mean values with two independent groups • ANOVA • Compares values in more than two independent groups, the variation is between and within group

13. Statistical Analysis • The normal distribution • Mean: Sum of all values divided by number of them • Median: The middle most observation • Mode: The most frequent observation • Standard deviation: A measure of spread • 1 SD: About two thirds of data • 2 SD: About 95% of date will fall within • 3 SD: Virtually comprises 100% of the data

14. Statistical Analysis • Sensitivity refers to ability of the test to correctly identify patients who have disease. s= All positive results in disease (TP) x 100 All specimens with disease (TP+FN) • Specificity refers to the ability of a test to correctly identify patients who do not have a disease. sp= All negative results without disease (TN) x 100 All specimens without disease (TN+FP)

15. Statistical Analysis • Prevalence • Proportion of persons with the disease among a group to whom test is applied P= Disease present (TP+FN) All group (TP+FP+TN+FN) • Pretest probability • % of patients who have the target disorder as determined before test is performed

16. Statistical Analysis • Likelihood Ratio summarizes in a single number the clinical utility of a test, and it is added to the pretest probability to increase certainty: • LR of a positive test: s/(1-sp) • 2 increases probability of disease by 15% • 5 increases probability of disease by 30% • 10 increases probability of disease by 45%. • LR of a negative test: (1-s)/sp • 0.5 decreases probability of disease by 15% • 0.2 decreases probability of disease by 30% • 0.1 decreases probability of disease by 45%

17. Statistical Analysis • Odds Ratio • Compare a portion of the affected population with the unaffected population and is expressed as a ratio • The OR gives the odds of having a risk factor if the condition is present as compared to having a risk factor if the condition is not present • The higher, the stronger association. Odds ratio= risk factor with disease presentA/C risk factor without disease B/D

18. Predictive values • Positive predictive value • Describes the probability that a patient who has an abnormal test actually has the disease • Directly proportional to the prevalence of the disease PPV= Number of true positive results (TP) x 100 All positive test results (TP+FP)

19. Predictive Values • Negative predictive value • Describes the probability that a patient who has a normal test is actually free of disease • Inversely proportional to the prevalence of the disease NPV= Number of true negative results (TN) x100 All negative test results (TN+FN)

20. Number necessary to treat • NNT is how many patients must receive a treatment to produce one additional improved outcome compared to control. The lower the NNT, the more effective the treatment. • NNT: 1/ARR • ARR: Event rate w/o tx-event rate w tx. • RRR: Event rate w/o tx/event rate w tx.

21. Confidence Intervals • Provides an interval that is likely to capture the population mean with a level of confidence. • 95% CI: Indicates that if a test were repeated 100 times, a result within the specified range of values would be expected 95% of the time. In studies is presented as mean minus and plus two standard deviations: “4.5 ( 95% CI, 3.8 to 5.4). • Larger studies typically have narrower CI’s.

22. Board-type Questions • PSA has a sensitivity of 75% and specificity of 80%. Prevalence of prostatic carcinoma in your referral male population is 10%. If your patient has positive result on the blood test, what is the chance that he has prostate carcinoma? • What they are asking for? • Calculate TP, FP, FN, TN then PPV, NPV.

23. Board-type Questions • A 40 years old woman wants to have a mammogram to make sure that she does not have breast carcinoma because the prevalence of breast cancer in her population is known to be 20 in 1000. Sensitivity of mammogram test is 70% and specificity is 90%. If her mammogram is positive, what is the likelihood that she has breast cancer?

24. Board-type Questions • An HIV pregnant patient has 2% risk of transmitting infection to the baby if delivered with CS compared to 7% risk if delivered vaginally. How many c-sections you would have to do to prevent one HIV infection?

25. Board-type Question • Rates of re-admission for HF diminished from 46% to 41% after close multidisciplinary monitoring program initiated upon discharge. • What is the number necessary to monitor to prevent one re-admission? • What is the sample size required to give power to a study? (Rule of 3). • What could be the number to start with? Rule of 3.