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EPI235: Epi Methods in HSR

Sebastian Schneeweiss, MD, ScD Associate Prof. of Medicine (HMS) and Epi (HSPH) Division of Pharmacoepidemiology and

Pharmacoecon

Malcolm Maclure, ScD Adj. Prof. of Epidemiology (HSPH), Prof. of Health

Information Sciences, U Victoria, BC

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What is this course about ?

This course is designed to introduce Epidemiology students to the application of standard epidemiologic methods to Health Services Research. The course helps students to recognize the principles of Epidemiology in Health Services Research, and understand the terminology and methods specific to the field

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We will cover …

Threats to validity including selection bias, confounding, information bias, and methods for their control will be discussed in a variety of settings emphasizing practical considerations.

EPI 202 and BIO 200 or BIO 201 required or signature of instructor

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Where does Health Services Epidemiology fit in?

Epidemiology: “The study of the distribution and determinants of diseases”

Health Services Epidemiology: “The study of the distribution and

determinants of health services and their consequences”

Health Services Research Clinical Epidemiology Pharmacoepidemiology Outcomes Research Health Economics

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All fundamental principles of Epidemiology apply to

Health Services Research

Typical exposures are: Conditions that may lead to inequalities in access to care, e.g.

low income status, rural area of residence Health states that defined certain needs for care, e.g. mental

illness Two types of medical interventions, e.g. stent implants vs.

bypass surgery to prevent heart attacks Different health care delivery systems, e.g. HMO vs. capitated

PPO vs. FFS Programs that aim to improve the quality of care, e.g. disease

management programs Programs to contain the costs of care, e.g. drug formularies

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Typical study outcomes are: Access to care, e.g. preventive services (vaccination),

therapeutics Health status, e.g. incidence of pre-specified tracer diagnoses Life years gained, i.e. reduction of mortality Patient reported outcomes, e.g. health related quality of life Quality of care scores, e.g. HEDIS measures Appropriateness of care measures, e.g. appropriateness

evaluation protocol, AEP Cost of care, e.g. increases (additional costs or losses) or

decreases (savings)

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Bias: Confounding (risk adjustment) Selection bias Misclassification

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Extension to chronic disease epidemiology:

While disease outcomes are mostly either present or absent (binary), quality of life measures, quality of care score, or costs are multi-categorical or continuous measures that often have non-symmetric distributions.

Another complicating factor is that often observations in health care delivery systems are not independent.

  Patients are clustered

• In physicians• In group practices• In health care organizations

This clustering of observations on multiple levels has lead to the adoption of multi-level regression models to the standard tools of HSR.

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Data sources frequently used in HSR

Primary data collection

Survey research (longitudinal or panel data structure)

Administrative databases and medical records:

Advantages / limitations?

Many measures, few parameter: data reduction techniques

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Study designs frequently used in HSR

All epidemiologic designs also apply to HSR. Data structures usually dictate analytic designs. To make full use of the longitudinal nature of claims data with

long strings of repeated measures of health services for individual patients: Analytic techniques that can handle time-varying exposures,

repeated outcomes and with-in person correlations must be applied.

  The lack of detailed disease severity information in claims

data is a reason to use case-based study designs, e.g. case crossover studies to allow cases to be their own controls.

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Controlling confounding in non-randomized research

Confounding

Unmeasured Confounders

Measured Confounders

Design

•Restriction

•Matching

Analysis

•Standardization

•Stratification

•Regression

Unmeasured, but measurable in

substudy

•2-stage sampl.

•Ext. adjustment

•Imputation

Unmeasurable

Design Analysis

•Cross-over

•Active comparator (restriction)

•Instrumental variable

•Sensitivity analysis

Propensity scores

•Marginal Structural Models

Schneeweiss, PDS 2006

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Typical example (1): Statins

Statin are wonder drugs in non-randomized studies 50% reduction in hip fractures Improvement in cognitive status Reduction of psychiatric conditions

Can restriction of the study population lead to less biased estimates? Statin and 1-year mortality

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0) Incident and prevalent drug users vs. non-users (matched by exact date)

1a) Incident drug users vs. non-users (matched by exact date)

1b) Incident drug users vs. non-users (matched by date and system use)

Restrict to incident drug users

Match non-users on system use

2) Incident drug users vs. incident comparison drug users

Restrict to incident comparison drug users

Restrict to pats w/o contra-indications

Restrict to adherent patients

Restrict to RCT inclusion criteria

4) Adherent incident drug users v. adherent incident comparison drug users without contraindications

3) Incident drug users vs. incident comparison drug users without contraindications

RCT population

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Results* in comparison to RCTs

0

0.2

0.4

0.6

0.8

1

1.2

0 1a 1b 1c 2 3 4 5

Cohort restriction

Mor

talit

y R

ate

Rat

io .

4S

(65+, secondary prevention)

PROSPER

(70-82, prim +sec prevention)Pravastatin

(pooling 65+, LIPID, CARE)

* Unadjusted mortality rate ratios

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Causal inference and decision making

Importance of causal inference in HSR and preventive medicine

Formulate clear hypotheses as well as causal statements using counterfactuals.

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Cultural changes when migrating from chronic disease

epidemiology to applied Health Services Research (Maclure)

The challenge is to keep your rigorous thinking while participating in imprecise discussion.

Inevitably you must relax some of your standards. But which standards? The principle that should almost never be relaxed is that of

having a control group. Without a meaningful control group research can rarely contribute to a planning and decision process.

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Relaxing standards …

You will find that hypotheses are stated vaguely. Decision makers need help and they are always in a hurry. You must learn to use interviews, focus groups, literature review and expert consultation to help articulate and screen hypotheses rapidly

You will find crude associations are over-interpreted and the interpretations uncritically accepted. You will see trends of aggregated data and small area variations treated with respect, with little consideration of ecologic bias, i.e. uncontrolled confounding. Should you refuse to do such studies?

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Relaxing standards …

You will be asked to use administrative data sets. Besides misclassified and missing information you will realize that administrative data were not designed for measures of occurrence. So what kind of epidemiologic measures can be obtained? Epidemiologic concepts of prevalence, cumulative incidence and incidence rates should still be in your mind, but you will often need to construct a proxy for an unmeasurable epidemiologic measure.

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Relaxing standards …

You will start using the word “appropriate.” People acknowledge that utilization of a service by some patients is necessary and beneficial, whereas the same service used by other patients is ineffective or even harmful. To keep discussion of utilization from an impasse, you will use the term “appropriate care” as if it is a definable thing. But drawing a line between those who should and should not receive the services requires agreeable concepts and measures of need. Good luck!

You will design a study from an undesigned dataset, i.e. designed for administrative purposes. You will discover it is like sculpting a symmetrical figure from a asymmetric hunk of marble. You are limited by the shape and structure of the marble, but sometimes you can choose among several pieces of marble once you have the intended figure in mind.

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