Report

Introduction to Risk Factors & Measures of Effect Meg McCarron, CDC Introduction to Risk Analysis 2 What is a risk analysis? • The analysis of an association between a variable (e.g. underlying condition) and an outcome (e.g. death) • Why do risk analysis? • The probability of an outcome is often dependent on the interplay between a variety of factors • Follow up on suggested associations observed in descriptive analysis (e.g. the elderly appear to die more frequently than healthy young adults; a risk analysis might tell you whether or not that is a true observation) • Determine the severity of risk • Identify significant risk factors • Using this type of analysis we can measure risk ratio (RR), odds ratio (OR) 3 What is a risk factor? A risk factor is a factor that is associated with increased chance of getting a disease. In epidemiological terms: A risk factor is a variable (determinant) associated with an increased risk of disease or infection (outcome). Example: Obesity (determinant/exposure) is associated with increased risk of heart attack (outcome) When we measure risk factors we assess Strength Direction Shape 4 Risk factors in SARI surveillance • Information about a number of potential risk factors and outcomes is often recorded • e.g. Outcomes: death, influenza status • Risk factors: age, co-morbid conditions • Surveillance data can be analyzed to increase the understanding of the association of risk factors with severe outcomes • Surveillance data describing exposures allows analysis of associations without expensive in-depth studies 5 Is a risk factor the cause of a disease? Risk factors are correlational and not necessarily causal Correlation does not imply causation The statistical methods used do not consider the direction of effects For an effect to be causal the exposure must have occurred before the outcome e.g. young age does not cause measles (Morbillivirus causes measles), but young people are at greater risk because they are less likely to have developed immunity due to previous exposure or vaccination 6 The Correlation-Causation Problem Somalia has many pirates, but low carbon emissions How are risk factors/disease determinants identified? Individual-level data Two key variables Outcome: e.g. influenza Exposure: e.g. vaccination Should consider multiple risk factors Epidemiological study designs used to identify risk factors Case-control Cohort Surveillance data may approximate a cohort study Biological plausibility e.g. age and influenza infection Exposure (risk factor) must occur prior to outcome (disease) Types of variables Continuous E.g. Age Categorical variables Binary E.g. Gender, vaccination status Ordinal E.g. Age group, socioeconomic status (SES) Nominal/Categorical E.g. Geographic region Count E.g. number of ILI symptoms How are risk factors/disease determinants identified? Clinical and epidemiological comparison of hospitalized SARI patients with and without laboratory-confirmed influenza week 40/20xx to (current week)/20xx, Country X (NOTE: Numbers in table are not real and for example only) Characteristics Percent of influenza-negative SARI hospitalizations with selected demographic and epidemiological characteristics Percent of SARI hospitalizations confirmed as influenza with selected demographic and epidemiological characteristics Sex Information available for N = 100 Information available for N = 50 54/100 (54%) 46/100 (46%) 0 27/50 (54%) 23/50 (46%) 0 Information available for N = 98 30/98 (31%) Information available for N = 48 28/48 (58%) 15/98 (15%) 15/98 (15%) 11/98 (11%) 5/98 (5%) 3/98 (3%) 4/98 (4%) 7/98 (7%) 0/98 (0%) 68/98 (69%) 20/48 (42%) 10/48 (21%) 11/48 (23%) 5/48 (10%) 3/48 (6%) 4/48 (8%) 6/48 (13%) 1/48 (2%) 20/48 (42%) N=2 Information available for N = 50 women 11/50 (22%) 39/50 (78%) N=0 Information available for N = 90 N=2 Information available for N = 23 women 8/23 (35%) 15/23 (65%) N=0 Information available for N = 35 25/90 (28%) 65/90 (72%) 10 Information available for N = 100 40/100(40%) 25/100(25%) 10/100 (10%) 5/100 (5%) 5/100 (5%) 15/100 (15%) N=0 Information available for N = 98 40/98 (41%) 15/35 (42%) 20/35 (58%) 15 Information available for N = 48 10/48 (21%) 8/48 (17%) 10/48 (21%) 11/48 (23%) 8/48 (16%) 1/48 (2%) N=2 Information available for N = 40 2/40 (5%) 58/98 (59%) 38/40 (95%) N=2 Information available for N = 100 10/100 (10%) N=10 Information available for N = 44 8/44 (18%) 90/100 (90%) 36/44 (82%) N=0 4.0 days N=6 4.5 days Male Female Sex unknown Chronic Medical Illnesses Number of cases with at least one of the * chronic medical illness listed below Chronic respiratory disease Asthma Diabetes Chronic cardiac disease Chronic renal disease Chronic liver disease Chronic neurological impairment Immune-compromised Number of cases without any of the above chronic medical illnesses Unknown if risk factors present Pregnancy status Pregnancy in any trimester Not-pregnant Pregnancy status unknown Obesity (or other conditions as determined by national priorities) Obese (BMI>30 or judged obese clinically) Not obese (BMI<30 or not clinically judged obese) Obesity status unknown Age-groups (years) 0-1 2-4 5-14 15-29 30-64 65+ Age unknown Vaccination Status Received monovalent or trivalent vaccine during the current influenza season Did not receive monovalent or trivalent vaccine during the current influenza season Vaccination status unknown Oseltamivir/zanamivir (Tamiflu/Relenza) Use Received oseltamivir/zanamivir within 48 hours of symptom onset Did not receive oseltamivir/zanamivir within 48 hours of symptom onset Oseltamvir use unknown Median days from symptom onset to hospital admission 10 How are risk factors/disease determinants identified? (… continue …) Clinical and epidemiological description of hospitalized SARI patients with laboratory-confirmed influenza, by outcome status, year x to year y, Country X (NOTE: Numbers in table are not real and for example only) Characteristics Hospitalised SARI cases with laboratory-confirmed influenza Sex Male Female Sex unknown Chronic Medical Illnesses Number of cases with at least one of the chronic medical * illness listed below Chronic respiratory disease Asthma Diabetes Chronic cardiac disease Chronic renal disease Chronic liver disease Chronic neurological impairment Immune-compromised Number of cases without any of the above chronic medical illnesses Unknown if risk factors present Pregnancy status Pregnancy in any trimester Not-pregnant Pregnancy status unknown Obesity (or other conditions as determined by national priorities) Obese (BMI>30 or judged obese clinically) Not obese (BMI<30 or not clinically judged obese) Obesity status unknown Age-groups (years) 0-1 2-4 5-14 15-29 30-64 65+ Age unknown Vaccination Status Received monovalent or trivalent vaccine during the current influenza season Did not receive monovalent or trivalent vaccine during the current influenza season Vaccination status unknown Oseltamivir/zanamivir (Tamiflu/Relenza) Use Received oseltamivir/zanamivir within 48 hours of symptom onset Did not receive oseltamivir/zanamivir within 48 hours of symptom onset Oseltamvir use unknown Median days from symptom onset to hospital admission Percent of hospitalized (non-ICU/nonsevere) cases with selected demographic and epidemiological characteristics Percent of severe (severe outcome/or died) cases with selected demographic and epidemiological characteristics Information available for N = 100 Information available for N = 30 54/100 (54%) 46/100 (46%) 0 15/30 (50%) 15/30 (50%) 0 Information available for N = 98 30/98 (31%) Information available for N = 28 19/28 (58%) 25/98 (25%) 15/98 (15%) 11/98 (11%) 5/98 (5%) 3/98 (3%) 0/98 (0%) 3/98 (3%) 0/98 (0%) 68/98 (69%) 20/28 (71%) 4/28 (14%) 54/28 (23%) 5/28 (18%) 3/28 (11%) 4/28 (14%) 7/28 (25%) 1/28 (4%) 9/28 (42%) N=2 Information available for N = 50 women 11/50 (22%) 39/50 (78%) N=0 Information available for N = 90 N=2 Information available for N = 15 women 10/15(67%) 5/15 (33%) N=0 Information available for N = 28 23/90 (26%) 66/90 (73%) 10 Information available for N = 100 35/100(35%) 30/100(30%) 10/100 (10%) 4/100 (4%) 6/100 (6%) 15/100 (15%) N=0 Information available for N = 98 20/98 (20%) 19/28 (68%) 9/28 (32%) 2 Information available for N = 30 5/30 (17%) 2/30 (6%) 5/30 (17%) 3/30 (10%) 10/30 (33%) 5/30 (17%) N=0 Information available for N = 30 2/30 (7%) 78/98 (80%) 28/30 (93%) N=2 Information available for N = 100 15/100 (15%) N=0 Information available for N = 27 2/27 (7%) 85/100 (85%) 25/27 (93%) N=0 3.5 days N=3 7.5 days 11 Cohort study 1 D 2 3 Participant Follow people over time Collect data on their exposures (risks) Monitor their outcomes Compare risk of disease among exposed versus unexposed D 4 5 6 0 1 2 time 3 4 Example: cohort study e.g. Risk of death among SARI admissions Outcome: death Risk factors: age, underlying conditions, influenzapositive Source population: all patients admitted with SARI, followed until death or discharge 13 Case control study Cases: people with disease Deliberately over-selected E Controls: people without disease Find out their exposure status Compare risk of exposure among diseased and nondiseased E 1 D 2 D 3 4 Participant Represent exposure distribution of the source population D 5 E 6 time 14 Example: case-control study Risk of influenza among vaccinated patients Cases: people with influenza Controls: people without influenza Outcome: influenza status Risk factors: vaccination status, age, underlying comorbidity 15 Statistical significance: is the association due to chance alone? A statistical test is used to assess if an association may be due to chance alone (random error) In statistics, a result is called statistically significant if it is unlikely to have occurred by chance alone, according to a pre-determined threshold probability, the significance level (e.g. α: 0.05). 16 Common statistical tests Categorical data: Chi-square (2) test, Fisher’s test McNemar’s test Continuous data: T-test Wilcoxon rank-sum test ANOVA These tests can tell if there’s a difference between groups but do not convey the size or direction of effects Common measures of association / effect Measure the size of an association (effect) Compare some measure of disease in exposed versus unexposed Absolute difference Y1-Y2 Risk difference Relative difference (ratio) Y1/Y2 Odds ratio Risk ratio Incidence rate ratio Hazard ratio (survival data) Attributable risk 18 Odds ratios Most common measure of association used in epidemiology Binary outcome Odds Ratios (OR): compares the odds of exposure among cases (people with disease) with controls (people without disease) Odds: ratio of the probability (p) of an event occurring versus it not occurring Calculation of the RR & OR Cases Controls Exposed a b Unexposed c d OR = (a/c) / (b/d) OR = 1 = no association OR < 1 = negative association (reduces risk) OR > 1 = positive association (increases risk) Odds = p/(1-p) 19 Example of OR Calculations Outcome (Influenza patients that died) Calculation of the RR & OR Outcome (Influenza patients that died) Calculation of the RR & OR Died Alive Flu+ 200 (a) 150 (b) Female Flu- 50 (c) 100 (d) Male Died Alive 200 (a) 180 (b) 98 (c) 100 (d) OR = (a/c) / (b/d) = (a*d) / (b*c) OR=(200/50)/(150/100)=2.7 OR=(200*100)/(180*98)=1.1 20 Confidence intervals OR is a point estimate Confidence interval (CI) is a measure of uncertainty around your point estimate CI is based on the standard error (SE) SE=narrower confidence interval If CI includes 1, then not statistically significant wide CI also a problem Usually use 95%CI Cases Controls Exposed a b Unexposed c d SE = √1/a + 1/b + 1/c + 1/d 95%CI = e(OR 1.96 * SE) • OR=1.1 • 95%CI=1.01,1.4 22 Confidence intervals e.g. 2007 Victorian surveillance data, adults, influenza B Flu+ Flu- Vaccinated 44 (a) 95 (b) Unvaccinated 205 (c) 260 (d) OR = (44/205) / (95/260) = 0.59 ln(OR) = ln(0.25) = -0.53 SE = √1/44 + 1/95+ 1/205 + 1/260 = 0.20 95%CI = e(-0.53 + 1.96*0.20) = e(0.09) = e(-0.53 - 1.96*0.20) = e(-2.87) = 0.39 (UL) = 0.88 (LL) Interpreting Results Size of the CI is an indicator of uncertainty Wide CI = uncertainty Narrow CI = uncertainty If CI includes 1, then not statistically significant The observed effect could just be due to chance P-values are often used to convey statistical significance The p-value for a OR is calculated from a chi-squared test The p-value reference for a 95%CI is 5% or 0.05 P-values The p-values help us to determine whether the difference between the two groups might be due to random variation CI and p-values 95%CI=1.0, 2.3 indicates that the two-sided p-value for no association is about 0.05. 95%CI=0.9, 2.4 suggests p>0.05 95%CI=0.9, 2.4 indicate that the data are compatible with a two-fold higher risk (i.e. upper limit includes 2) The p-value is a measure of the compatibility of the data and the null hypothesis Implementation of a statistical test We start with a research hypothesis State the relevant null (H0) No effect (effect is due to chance) Alternative hypotheses (HA) An effect exists Decide which test is appropriate (see earlier list) Compute the test statistic and the associated p-(probability) value Compare the computed p-value to a reference p value (usually 0.05) to accept or reject the null hypothesis If the p-value of the test is lower than the reference value the H0 is rejected The effect is not likely to be due to chance Example: Implementation of a statistical test Influenza prevalence in hospitalized patients: Non pregnant women: 100/1000 = 10% Pregnant women: 30/200 = 15% Question: Is the influenza prevalence in hospitalized pregnant women different to non-pregnant women? Hypothesis H0: p1 = p2 ; p1 - p2 = 0 HA: p1 = p2 ; p1 - p2 = 0 Reject H0 if p (test) is < α: 0.05 Test results: Z (test statistic): 0.119 p value: 0.037 0.037<0.05 → Reject H0 Example: factors associated with influenza-positive diagnosis among ILI patients OR Vaccinated 0.54 95% CI p-value Lower limit Upper limit 0.02 0.32 0.89 Underlying condition 1.20 0.47OR=0.540.72 2.00 Adjusted (95%CI=0.32,0.89) Epi week 1.04 0.01 1.01 1.08 Crude OR=0.59 (95%CI=0.39,0.88) Age group <20 20-64 65+ ref 0.76 1.09 0.17 0.85 0.51 0.45 1.13 2.62 Summary A risk factor is a variable which increases (or decreases) the risk of an outcome We can assess the influence of risk factors using individual-level data from case-control and cohort studies The size of the effect can be measured by effect measures Most common effect measure is the odds ratio The uncertainty of the effect can be measured by the confidence interval Understanding whether an effect is due to random error is indicated by the p-value and tested using a statistical test Multivariable methods can tell us how much influence one risk factor has compared with others