Secondary Database Analysis II Case-Mix Adjustment

What is the Purpose of Health Services Research? Income Ethnicity EMR CPOE Utilization A1c Immunization Medical Errors

Last Time – Uses Secondary Data: Can provide new information on health care delivery (quality; geographic variation; post-marketing adverse events; cost), on the natural history of disease, and on regulatory matters Can save data-collection resources: time, money, personnel, participant burden Can save researcher resources: your time, your money May permit you to build your CV without anyone’s help or funds – just your time (but co-authors are a good thing) Can provide preliminary data for a grant proposal May enable research on rare events or difficult populations

Last Time – Precautions Take note of the study design; is it suitable? Consider: What are inclusion & exclusion criteria? Why? How does that affect the sample and generalizability?

This Time – Precautions What bias may be inherent in the database – perhaps from the population or from the method of measurement? Selection bias: who got included Migration bias: who was lost/gained and why Other sources of systematic error (bias)?

This Time – Precautions & Solutions Control for bias – Restrict Match Stratify Adjust with covariates

Validity Truth in the Universe Truth in the Study Findings in the Study infer ERRORS RESEARCH QUESTION STUDY PLAN ACTUAL STUDY Target Population Phenomenon of Interest Intended Sample Intended Variables Actual Subjects Actual Measurements EXTERNAL VALIDITY INTERNAL VALIDITY Risk Adjustment Nesting/Clustering of data

Bias Systematic error in measurement or a systematic difference (other than the one of interest) between groups  Selection For cohorts, assembly, migration, contamination, and referral bias  Measurement  Confounding

Restriction (may lose generalizability) Matching (limited # factors) Stratification Standardization Multivariate Adjustment Assuming the worst (sensitivity analyses)  If needed, conduct sensitivity analyses on multivariable models >> Always discuss potential impact of uncorrected bias on your results Bias: Anticipate and Control

Bias: Systematic Error Selection Bias  Case-Mix/Disease Severity  Nesting/clustering Measurement Bias Recall Bias Investigator Bias

11/05/03 Example: Hospital Mortality Report Cards Originally unadjusted Hospitals without trauma centers, doing primarily elective surgery, etc., looked really good Made hospitals who took care of the sickest of the sick look bad

Quality Assessment Data Quality: Garbage in, garbage out Risk Adjustment: To remove the confounding effect of different institutions providing care to patients with dissimilar severity of illness and case complexity

Risk Adjustment  Controls for patient characteristics that are related to the outcomes of interest  Removes the confounding effect, e.g., of different institutions providing care to patients with dissimilar severity of illness and case complexity  Addresses regional variations  Inadequate case-mix adjustment can lead to misclassification of outlier status

Essential Elements of Risk Adjustment Outcome-specific Contains specification of the principal diagnosis Contains demographics as proxies for preexisting physiological reserve Measures # of comorbidities and allows all the most important comorbidities to assume their own empirically derived coefficients

Additional considerations – outcome-specific: is one available or do you need to construct & validate one yourself? – an index incorporates many predictors – do you need to study some separately? – established comorbidity indices for case-mix adjustment include Charlson, Elixhauser, Selim, developed on various patient pops – suitable for yours? – other predictors based on your reading of the literature – do you need omitted constructs? – propensity scores (for treatment choice; good for small datasets, for non-randomized studies of treatment effect)

Risk Adjustment & Outcomes Primary data collection vs. administrative data Disease-specific vs. generic Commercial vs. developed for your study Predictors vary by outcomes being predicted

Classification of Disease States ICD-9: too many specific codes (n~10,000) Clinical Classifications for Health Policy Research (CCHPR): good for chronic illness and longitudinal care [ Primary diagnosis: good for studies that focus on a single episode of care

Famous Methods of Risk Adjustment DRGs: diagnosis related groups  Used by Medicare to set hospital reimbursement APACHE III  Adult ICU PRISM  Pediatric ICU Charlson Score  Adult 1 year survival after hospitalization

Risk Adjustment: Charlson Advantages:  Commonly used case-mix classification system in the health care industry  System with which most clinicians and reviewers are familiar

Risk Adjustment: Charlson Disadvantages  Principal diagnosis not differentiated  Original work did not specify ICD-9 codes that went into the disease categories  Developed on inpatients predicting mortality; may not be well suited to outpatients at low risk of death  Not good for longitudinal care / chronic illness

Demographic Factors in Risk Adjusters Age (e.g., age-adjusted Charlson) Proxies of Social Support  Marital status Race Gender SES (occupation, employment status, education) Proxies of Socioeconomic Status  Health insurance status  Home address zip code average income

Race and Gender Don’t adjust for automatically Ideally adjust for variation in the patients’ physiological reserve and disease burden but not for variation in care rendered to patients

Propensity Scores Useful when sample size is small, to conserve power An alternative to including a lot of covariates Ask: propensity for what? Include as many predictors as possible to get predicted probability of group membership (Rosenbaum & Rubin) Published schema may include predictors you want to study separately Best for non-randomized studies of treatment effect where you want to adjust for the factors that may have influenced the treatment choices

Study Design: Minimize Bias Decision #1: Alter events under study? Decision #2: Make measurement on more than one occasion? Experimental Study Apply intervention, observe effect on outcome yes Observational Study no Cross-Sectional Study Each subject examined on only one occasion no yes Longitudinal Study Each subject followed over a period of time Case-controlCohort

Risk adjustment is needed… When subjects are not randomly assigned People do not randomly distribute by  Setting  Provider Risk Adjust  Outcomes = f (intrinsic pt factors; treatment applied; quality of treatment; random chance)

On What Factors Should We Risk Adjust? Risk of what outcome?  Ejection Fraction  Readmission  Activities of Daily Living (feeding, dressing, etc.) For what population?  Inpatient  Outpatient  Nursing Home For what purpose?  Clinical  Quality  Payment

Classes of potential risk factors Demographics: age, sex, etc. Physiologic status Number and type of medical diagnoses Cognitive and mental status Sensory function Social, economic, environmental factors Functional/overall health status

An Important Distinction Disease Severity Case-mix or co-morbidity What is the difference?

Disease Severity v. Case Mix

Nesting or Clustering of Observations A threat to validity

Nesting/Clustering of Observations Traditional methods of analysis assume observations are independent: people who see same MD are not! By setting  Multicenter studies  By clinic  By clinician Key issue is understanding SOURCE of variance in the observed data  Within group (clinic)  Between groups (clinics)

Survival by OR Team

Common Terminology Intra-class Correlation Coefficient (ICC)  The extent to which individuals within the same group are more similar to each other than they are to individuals in different groups  The proportion of the true variation in the outcome that can be attributed to differences between the clusters

ICC Values 20 primary care clinics patients with type 2 DM per clinic For process of care measures (foot exam, labs ordered etc.) ICC = 0.32 For A1c values, ICC = 0.12 Question: Which is more dependent upon site of care: processes of care or outcomes of care (A1c control)?

Sample Size Implication Design Effect = 1 + (n-1)ICC (n= number of subjects per cluster) So if estimated sample size of 300 per group for an intervention study, what sample size would you need for 30 subjects per cluster (10 clinics) and ICC of 0.12?

Assignment Design a study:  Experimental  Cross-sectional  Prospective cohort  Case-Control What factors you would measure to “risk adjust” and how measure them? How would you would adjust for nesting/clustering of data?

Questions? Common Research Design Issues in Health Services Research