Concept Equation Units Use Pros/Cons Ratio Event or People (A)/ Event of People (B) None Descriptive Proportion

= + %

Rate # Person-yrs Frequency of event

Risk # # % Prevalence # #

Point = specific time pt; period = time interval OR Incidence x Disease Duration

% Burden disease in popn

Affected by survival; no measure risk; mix chronic/acute cases

Cumulative/Crude Incidence (CD)

# () Person-yrs Assume entire popn followed through; always smaller than ID

Incidence Density (ID) # Person-years Disease causation Not include time not followed up

Morbidity rate #

Mortality rate #

Case-fatality # # Attack rate #

Years of Potential Life Loss (YPLL)

Age at death predetermined age at death (predetermined standard = 65)

years Premature mortality index

Research/resource priorities, surveillance trends/interventions

Epidemiological Study Designs Descriptive Describe health events with attention to person, place, time. Generate hypothesis and resource allocation. Analytic Examine associations with methodological rigor. Observational = cohort & case control. Experimental = RCT

Concept Equation Use Interpretation Relative Risk (RR)

= + + Likelihood devng Outcome (O

+) in Exposed (E) group relative to Unexposed (UE) group; ratio incidence in E vs. incidence in UE

Those with [E] are [RR%] [more/less] likely to develop [O] than those with [UE]

Attributable Risk/Risk Difference (AR)

RateE RateUE =

+ + Rate of O that can be attributed to the E in the E group [AR#] cases of [O] per [x ppy] among the [E] group can be attributed to [E] Attributable Fraction Exposed (AR%) 100 = 100 1

Proportion of the disease amongst the E that is attributable to the E

[AR%] of [O] amongst the [E] group can be attributed to [E]

Population Attributable Risk (PAR)

Incidencetotal IncidenceUE

OR AR x PrevelanceE

Excess rate of O in the total population that is attributable to the E

[PAR]excess cases of [O] per [x ppy] in the population can be attributed to [E]

Attributable Fraction population (PAR%) 100 = + ( 1)

+ ( 1) + 1

Proportion of O in total popn which is attributable to E; assume causation

[PAR%] of [O] in the population can be attributed to [E]

Odds Ratio (OR) // = Ratio of odds of E in O+ to odds of E in O-; for case-control studies. No change with O- #; no

change if examine % E in O+/O-. Equals RR if O rare (prevalence disease; no intermediates Indirect: factor-> A -> disease; factor causes disease through step(s) Necessary: no factor = no disease; w/o factor disease never develops Sufficient: factor -> disease. w/ factor the disease always develops

O+ O-

E a b UE c d

Hillis Causal Criteria 1. Strength large effect 2. Consistency repeated observations in different settings 3. Specificity cause leads to single effect 4. Temporality cause precedes effect 5. Biologic gradient dose response relationship 6. Plausibility biologic 7. Coherence no conflict natural history/biology 8. Analogy

Direct Standardization (Type 1) - Requires: (a) age specific rates from study population

(b) age distribution from standard population - Represents what crude rate would be if study population

had same age distribution as standard Cons: adjusted rate not meaningful for population, not suitable for resource allocation, choice of standard affects comparison

1. Calculate age-specific rates for each study population (a) 2. Choose a standard population

a. Reference Population b. Average of Study Populations

3. Calculate % per age category (%) as decimal 4. Calculate age-standardized rates for each population

[(a1)(%1)+(a2)(%2)+(a3)(%3)] Indirect Standardization (Type 2)

- Requires: (a) age specific rates from standard population (b) age distribution of study population

- Outcome= expected events; represent # occurring if study population had same rates as standard population & SMR

Standardized Mortality Ratio (SMR) = observed deaths/expected deaths (= 1.71)

- If [panama] had the same [age]-specific rates as [Sweden] then wed expect [1.71] times the mortality

- No need category specific rates in study population - Useful when dealing with small number events and

unstable rates in study population; when no internal comparison group

Pro: summary measure, statistical stability, minimize confounder efx Con: not useful for multiple comparisons due to differing popn structures, rate no longer meaningful by itself Proportionate Mortality = proportion death due to factor/ total deaths in popn

- Not useful because competing values (more deaths due to factors means less death due to other factors)

- When population structure unknown, info from death certificates

Incidence Density Ratio = ID(Exposed)/ ID(Unexposed)

- For cohort studies with varying follow up with ppy in den. Case-Controls Studies Use When

- Disease with very long latency periods - Rare diseases - Wide ranges of exposures in single study

Case Selection Incidence, dont use prevalence b/c:

- Difficult determine if factor related to disease occurrence/duration

- No temporality - Prevent longer time for recall bias

Control Selection - Select to represent popn which wouldve been included as

case had they developed disease - Represent frequency of exposure in underlying source

popn (may differ from general popn) - Characteristics and sources of cases (comparable)

Individual Matching - Each ctrl matched to case, matched analysis - For each case select one or more ctrls with same

characteristics on potential confounders Pro: ctrls factors difficult to measure; easier obtain comparable ctrl group, gain precision of OR estimate (tighter CI) Cons: complexity in ctrl accrual; info from ctrls on matching variable need obtain before study inclusion (need screen more), matching on many variables difficult, cant study matched variable, decrease OR precision if not true confounder; only small gain if factor not strong for disease Frequency Matching

- Select controls with similar distribution of confounder - Control in analysis

Analysis of Individually Matched Data - Each pair (case-ctrl) contributes to one observation/count - OR = #(CasesE & ControlsUE)/ # (CasesUE & ControlsE)

Stratification - Stratify by confounder, examine OR within lvls of

confounder - Want summary estimate, estimate of risk adjusted for

effects of confounder - Use ORMH when strata OR similar - Factor is true confounder if adjusted OR and unadjusted

OR differ by greater than 10% Cohort Studies Uses

- Rare exposures - Multiple effects of single exposure - Identify temporal sequence - Expense follow-up not an issue

Fixed Cohorts: identify popn at time and no include more eligible Dynamic Cohorts: open popn and includes ppl who enter later Changes in exposure over time: re-classify during study or allow exposure status to vary in analysis Internal Comparisons

- Study gradient (D-R) of disease - Variation with amount of exposure (often no unexposed)

External Comparisons - Estimate disease incidence in exposed group in absence of

exposure - As similar as possible to exposed group

Follow-Up 1. Length needs to be considered in design - Base apriori knowledge of time needed for disease show - Induction time (to induce) & latency time (express/detect) - Usually no know induction/latency; try estimate 2. Attempt high levels of follow up (prevent loss) - Be persistent

Bias 1. Selection (systematic differences b/w E & UE groups) 2. Information (Misclassification, measurement error) 3. Non-participation (systematic reason for non-Ps?) 4. Attrition (differential loss to follow-up) 5. Healthy Worker Effect (decrease O+ in worker) - Use internal comp, external comp of workers, artificially

adjust risk (inflate) Nested Case-Control Study

- Conduct cohort but no full evaluation of exposure - After follow-up, conduct case-control within cohort - Cases = identified cases of disease in cohort - Ctrls= sample of cohort free of disease at time of cases - Analysis as case-control

Randomized Control Trials Therapeutic: conducted with diseased ppl (diminish Sy, prevent recurrence, decrease mortality risk) Preventative: disease-free ppl (decrease risk, inds or communities) Efficacy Trials (Explanatory)

- Does Tx work under ideal circumstances? - Tx more harm than good? - Only patients who cooperate

Effectiveness Trials (Pragmatic) - Does Tx work in ordinary settings? - Offer Tx to subjects and let them reject or accept - Intention to treat analysis - Failure Tx effect may be due lack efficacy or subject accept

Blinding (Masking) - Observers/subjects kept ignorant of group subject

assigned to - Avoid bias - Single blind = subject - Double blind = subject and interviewer/evaluator - Triple blind = subject, evaluator and analyst

Noncompliance - Potentially due to randomization, drop out, stop following

prescribed Tx - Build in checks to ensure compliance

Phases of RCTs 1. Tx any effect (pharm/tox)? All get Tx (single arm) 2. What dose achieves effect (efficacy)? What toxic effects

observed with Tx (safety)? Single arm 3. Large scale RCT for effectiveness and safety (work in ideal?

Ordinary?) Usually 2 or more arms 4. Post-marketing surveillance, cost, benefit, etc

Randomized Community Trials Why?

- Public health: interventions at this level - Feasibility: individual trials expensive - Benefits: intervention under control of experimenter so

can adjust for differences in communities Community Selection/Recruitment Size (expense) vs. statistic, similarity of communities, favourable community relations, accessibility, consent, communication plan Baseline Surveillance Selection of outcomes, key population characteristics, approaches to data collection, comparability of baseline and follow-up measures Development of Interventions Protocol (intervention and ctrl arm), Options (education, municipal policy), random assignment Data Collection and Analysis

1. Periodic Surveillance (outcomes, intermediate outcomes, potential side effects)

2. Evaluation (intervention effective? Adjust community differences, assumption of independence)

Natural History of Diseases Normal: No disease, before onset (1 prevention, remove cause) Preclinical: B/w bio onset and Sy appearance (2prevention, screen) Clinical: Sy to disease outcome (3 prevention, Tx) Lead time: Detected by screening/Dx to usual time for Dx Screening

- Early detection disease - Not diagnosis; pos+ screen = diagnostic tests after - Often for disease with long latency periods - Improve outcome of disease amongst those affected

Conditions for Screening 1. Long detectable preclinical phase 2. High prevalence amongst screened popn (cost/benefit)

3. Seriousness (cost effectiveness for reduce mortality; consequences fail detect vs. risk/discomfort of screen)

Measurement Validity - Degree to which method used correctly categorizes

False Pos+: # screen is pos+ but diagnosis is neg- False Neg-: # screen is neg- but diagnosis is pos+ True Disease Present No Disease Test Positive a b Test Negative c d Sensitivity: probability testing pos+ if disease truly present = a/(a+c) Specificity: probability testing neg- if disease truly absent = d/(b+d)

- Depending on cut off, increase sensitivity or specificity - Everything pos+ = 100% sensitivity, 0% specificity and v-v

High Consequences for both false pos+ and neg- Missing a case (false neg-): increase sensitivity (decrease false neg-) Identifying non-case (false +): increase specificity (decrease false +) Feasibility

- Acceptable to popn being screened (uncomfortable) - Cost effectiveness (screen+ diagnostic tests, cost per case) - Yield of cases (predictive value of screen)

Predictive Value Pos+ PV: probability person truly has disease given pos+ test = a/ (a+b) Neg- PV: probability person truly no disease given neg- test = d/(c+d) Measurement Reliability Observed agreement (O) = (a+d)/N; no ctrl chance Expected agreement (E)= [c(a+c) + (b+d)(c+d)]/N2

Kappa = (O-E)/(1-E); perfect agreement K=1, only chance K=0 Fair agreement = 0.21-0.40 Evaluating Screening Programs Effectiveness: screening effective to reduce morbidity/mortality Short-term outcome: severity of disease at diagnosis Mortality: compare screen and unscreened popns Volunteer Bias: Systematic differences in comparison groups Lead Time Bias: Increased time b/w diagnosis and death (survival) purely due to earlier diagnosis; compare age-specific mortality Length Bias: Amongst those w/ disease who are screened, may be over-rep of those with long pre-clin phases (and maybe more favourable prognosis/benign disease); may never have shown Sy Prevalence and Screening Tests Increase Prevalence: No change in sensitivity and specificity If rare disease: decrease PPV, and v-v Measurement Error, Sensitivity & Specificity

Non-differential error with respect to case-ctrl status with a sensitivity of [80%] and a specificity of {90%}.

1. Calculate new cases and controls: A=a[80%], B=b[80%],C= c{90%},D= d{90%}

2. Calculate differences between old and new numbers and move these numbers to create the final measure: Af=A+(c-C), Bf= B+ (d-D).

Hypothesis Testing Null Hypothesis (H0): p0=p1 or OR=1.0 Alternative Hypothesis (HA): p0p1 or OR1.0

- Assume null is true to begin with Chi-Square (X2) = ( )2/E

O+ (Cases) O- (Controls) Exposed a b UnExposed c d

P-values - Square root of X2 to compare to standard distrubtn - Standard normal, p=0.06 (less than 0.05, reject null) - Represents probability of observing result at least as

extreme as that observed by chance alone Interpretation of Significance Tests Type 1 Error: Reject H0 when it is true Area under 0.05 (alpha = 0.05 in 2-tailed test) Type 2 Error: Fail to reject H0 when it is false Significance Values

- P-value function of sample size and effect magnitude Confidence Intervals (CIs)

- Range within which true effect magnitude lies - If CI includes 1.0, then p>0.05 (accept null) - If CI excludes 1.0, then p