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ACMT Board Review Course Population Health and Assessments

ACMT Board Review Course Population Health and Assessments. Jeffrey Brent, M.D., Ph.D. Toxicology Associates University of Colorado School of Medicine and Colorado School of Public Health. Topics for this Lecture. Exposure monitoring and sampling PPE

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ACMT Board Review Course Population Health and Assessments

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  1. ACMT Board Review Course Population Health and Assessments Jeffrey Brent, M.D., Ph.D. Toxicology Associates University of Colorado School of Medicine and Colorado School of Public Health

  2. Topics for this Lecture • Exposure monitoring and sampling • PPE • Study designs and measures of association • Statistical concepts • Bias and confounding • The Hill “criteria” • Sensitivity, specificity, predictive values

  3. Exposure monitoring and sampling Exposure monitoring • Environmental sampling: • Wipe sampling • Water sampling • Air sampling • Breathing zone measurements are best for inhalational exposures • Biological monitoring e.g.: • Blood Pb • Urine mercury

  4. Personal protective equipment • Respiratory • Chemically protective clothing

  5. Respiratory protection • Classification by size • Quarter face • Half face • Full face • Classification by function • Air-purifying • Uses chemical specific cartridges • Supplied air • SCBA

  6. Protection Factor • The factor by which exposure is reduced by use of a respirator • Ambient/protection factor = exposure • For example: • Ambient of 100 PPM • Protection factor of 10 • Exposure = 100/10 = 10 PPM • Protection factors range form 5 – 10,000 • The goal is to get exposure to below safe limits

  7. Chemically protective clothing • Simple protection • Ex. Aprons, boots, gloves • Nonencpasulating suits • 1 or 2 pieces, for example: • 1 piece hooded coveralls • Hooded jacket + chem protective pants • Encapsulating suit • Highest level of protection

  8. Chemically protective clothing is usually designated by the EPA rating system • Level A = Max protection • Encapsulating suit • SCBA • Level B • Supplied air respirator (or SCBA) • Non-encapsulating garment • Level C • Air purifying respirator • Non-encapsulating garment • Level D = standard work clothes

  9. NEXT Study design and measures of associationStatistical concepts

  10. Types of human data • Anecdotal • Case-reports and series • Controlled observational • Controlled epidemiological studies • Controlled interventional • Trials

  11. Controlled observational studies • Cohort • Cross-sectional • Mortality • Case-control • Ecologic For all epi studies: • Groups should be matched for relevant variables (e.g.) • Age • Sex • Anything else that can affect results

  12. Cohort studies • Compares exposed group to an unexposed group • Can be retrospective or prospective • Can assess incidence rates • Incidence = Rate of new cases (e.g. Cases/100,00/yr) • Prevalence = Number of cases in the population (e.g. cases/100,000) • Results expressed as Relative Risk (aka risk ratio or rate ratio)

  13. Cross-sectional studies • Compares exposed group to an unexposed group at one snapshot in time • Provides prevalence data • Example: Prevalence of drug abuse in medial toxicologists taking the board exam v those that are not • Results expressed as Relative Risk (aka risk ratio or rate ratio)

  14. Mortality Studies • Typically a variation of a cohort study • Assesses diagnoses at time of death • Results expressed as mortality rates corrected for relevant factors (“Standardized mortality rates”) • Usually expressed as a percentage (Mortality rate of exposed/rate in unexposed) X 100 = SMR) • Thus an SMR of 100 = no difference btw exposed and unexposed

  15. Case-control studies • Compares individuals with a specific condition with individuals that do not have that condition and compares exposures (or other risk factors) • For example: Comparing medical toxicologists with alcohol abuse (the cases) with those w/o this dx (controls) to see if there is a higher likelihood of alcoholic abuse if preparing to take the boards. • Thus assesses risk factors (e.g. exposures) related to specific conditions • Recall bias major problem • Results expressed as Odds Ratios

  16. Ecologic Studies • Assesses population numbers, not individuals • Example: Rate of admission for asthma exacerbations in a city with high airborne PM10 compared to a city with low PM10 • Results expressed as Relative Risk (aka risk ratio or rate ratio) • Ex: Snow’s study of cholera rates in London districts

  17. Assessment of results of epi studies • By convention a result is statistically significant if the likelihood that it is chance result is < 5% or approx 2SDs from the mean

  18. Interpretation of EPI Data • You can never assess the degree of association based only on the magnitude of the RR, OR or SMR • These values have an inherent uncertainty that is determined by the nature of the data • In modern epi this uncertainty is expressed as Confidence Intervals

  19. The 95% Convention • In science the uncertainty in a result is expressed as that range of data in which there is a 95% likelihood that the real value exists • CIs express this range • Ex: RR 1.6 (CI 0.7 – 2.4)

  20. The importance of confidence intervals • If RR 1.6 (0.7 – 2.4) Than there is a 95% likelihood that the real value lies between 0.7 – 2.4 • If the real RR is: • >1= association • 1= Non-association • <1 = negative association (protective effect) • The 95% rule defines “statistical significance” • Thus, in order to be a statistically significant result the CI must not include 1

  21. What about “p values”? • p Values are an older way of describing statistical significance • P < 0.05 means a result is statistically significant • OR 1.6 (0.7 – 2.4) = OR 1.6 p > 0.05 • OR 1.6 (1.1 – 2.1) = OR 1.6 p < 0.05

  22. Now the Bad NewsA statistical relationship never a priori means a causal relationship

  23. It is not the falling of the leaves that causes winter to come There are many more statistical associations in toxicology than there are causal relationships

  24. How to get from association to causation • Requires specific rigorous methodology • Stems from Doll and Hills’ observation of an association between smoking and lung cancer

  25. Hill’s Viewpoints • To be applied if a statistical association is shown to exist • Does not account for quality of studies showing such an association

  26. Hill’s “Viewpoints” • Strength of association • Consistency • Specificity • Biological gradient • Temporal precedence • Coherence • Plausibility • Experimental support • Analogy Also must consider the quality of the study

  27. Bias and confounding • Bias = systematic error • Ex: You are doing a study on childhood bl Pb concentrations and behavior. However, your lab technique inflates blood lead values by 20% = a bias. • Confounding = uncontrolled for factor affecting results. • Ex: You are doing a retrospective cohort study on chronic exposures to phosgene in laboratory workers and the incidence of lung cancer but you do not control for smoking. Smoking is a confounder

  28. A little tip -Know how to calculate sensitivity, specificity, and predictive values

  29. Sensitivity • The likelihood of a test being positive if the condition is present • Ex: Being under 16 yrs old has 100% sensitivity for the detection of childhood Pb poisoning. • Good for screening (few false negatives (FN)) Sensitivity = True positives (TP)/(TP + FN) Sensitivity is often expressed as a % In example above if screen 100 individuals and 10 had Pb poisoning: Sens = 10/(10+0) = 10/10 = 1 (or 100%)

  30. Another example To determine the sensitivity of a terminal R in lead AVR for the detecting of Na+ channel antagonist toxicity in all OD patients. • Screen 1,000 EKGs of OD patients, 100 had OD’d on Na+ channel blockers and 80 had a terminal R wave (TPs). 50 had a terminal R wave but did not OD on these agents. • TP = 80 • FN = 20 • Sens = TP/(TP + FN) = 80/(80 + 20) = 80/100 = 0.8 (80%)

  31. Specificity • The likelihood of the unaffected individuals correctly having a negative test • Test: using criteria of being under 16 for dx of childhood Pb poisoning. • N = 100 • 10 with Pb poisoning - the other 90 are false positives (FP) Specificity = True neg (TN)/(TN + FP)= 0/0+90 = 0

  32. The second experiment • Screen 1,000 EKGs of OD patients, 100 had OD’d on Na+ channel blockers and 80 had a terminal R wave. 50 others had a terminal R wave but did not OD on these agents (FPs). • TN= 850 • FP = 50 Sp = TN/(TN+FP) = 850/(850+50) = 850/900 =0.94 (94%)

  33. Comparison btw Sensitivity and specificity • Both= True/(True + False) • Sens = TP/(TP+FN) • Specificity is the mirror image Spec = TN/(TN+FP) • For both the “trues” in the numerator and denominator terms are the same. • The other denominator term is the complete opposite

  34. Positive predicative value • PPV = likelihood that the test will correctly Dx the condition • Test: using criteria of being under 16 for dx of childhood Pb poisoning. • N = 100 • 10 with Pb poisoning (TP) - the other 90 are false positives (FP) PPV = TP/(TP+FP) = 10/(10 + 90) = 0.1 So 10% PPV

  35. PPV – the second experiment • Screen 1,000 EKGs of OD patients, 100 had OD’d on Na+ channel blockers and 80 had a terminal R wave (TP). 50 others had a terminal R wave but did not OD on these agents (FPs). • TP = 80 • FP = 50 PPV = TP/(TP + FP) = 80/(80+50)= 80/130 = 0.6

  36. Negative predicative value • The likelihood that the disease is not present if the test is negative • Test: using criteria of being under 16 for dx of childhood Pb poisoning. • N = 100 • 0 are TN • 0 are FN NPV = TN/(TN+FN) = 0/(0+0) = 1 (100%)

  37. NPV – a more rational study • Screen 1,000 EKGs of OD patients, 100 had OD’d on Na+ channel blockers and 80 had a terminal R wave. 50 others had a terminal R wave but did not OD on these agents (FPs). NPV = TN/(TN + FN) TN = 850 FN = 20 NPV = 850/(850+20) = 850/870 = 0.97

  38. Predicative values - summary • PPV uses only positive terms • PPV = TP/(TP+FP) • NPV uses only negative terms and is exactly opposite of the PPV • NPV = TN/(TN+FN)

  39. If, when you are studying, this you have any questions call me (24/7) @ 303-765-3800 or e-mail me at Jeffrey.Brent@ucdenver.edu

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