european best information through regional outcomes in diabetes risk adjusted diabetes indicators...

EUropean Best Information through Regional Outcomes in Diabetes

Risk Adjusted Diabetes

Indicators Fabrizio Carinci

Technical Coordinator

The BIRO Academy2nd Residential Course

Brussels, 23rd-25th January 2011

The BIRO Academy 2nd Residential Course Brussels, 23rd-25th January 2011

Quality of Care and Outcomes Monitoring

• Meaningful assessment of quality of care and outcomes requires:– Measure of quality of care or outcome

(indicator)– A way to risk adjust for the different risk of

patients for various outcomes– Risk adjustment methods allow us to

compare quality of care and outcomes for groups of patients with different risk factors


Unexplained variability in patient outcomes

“...your results are quite different from the mean”

“...my patients are different!”


These are everywhere today!

Case-mixProvider Profiling


Standardization

(the epidemiologist way....)

Two main forms of standardisation:

1.Direct Applies the strata-specific rates of outcomes

of the population of interest to the standard population (calculates the rate of events IF the populations of interest had the same structure as a reference population)


Standardization (2)

2.Indirect

Adjusts for differences in case-mix by calculating the number of events expected for the outcome of interest in a specific population, based on its structure, if it had the same distribution of outcomes as a reference population.


Standardization (3)

• Standardized Ratio:– O/E%

• Risk Adjusted Average– Standardized Ratio*Population Average


Provider Comparisons

8/800 (1%)20/200 (10%)

A

50% (1%)50% (5%)

2/200 (1%)80/800 (10%)

B

10/800 (1.25%)25/200

(12.50%)C

Low Risk

High Risk

Standard Population2.8%

8.2%

3.5%

3%


Provider Comparisons

8/800 (1%)20/200 (10%)

A

50% (1%)50% (5%)

2/200 (1%)80/800 (10%)

B

10/800 (1.25%)25/200

(12.50%)C

Low Risk

High Risk

Standard Population2.8%

8.2%

3.5%

3%

4.7%

5.8%

5.8%


Statistical Models

Allow performing standardization in more complex situations, where risk factors may be several and the sources of variability at different levels

Risk adjustment methods require the development of statistical models that explain the outcome variables of interest based on patient characteristics we wish to control.


Key questions

• Risk of what?

• Over what time frame?

• What population?

• What is the purpose of the model?

• What are the risk factors?

• What are the data sources?

• What tools are available to build the models?


The unreliability of individual physician "report cards" for assessing the costs and quality of care of a chronic disease, Hofer TP et al., JAMA. 1999 Jun 9;281(22):2098-105.

1991 1992

-0.4

-0.2

0.0

0.2

0.4

-0.4

-0.2

0.0

0.2

0.4

Outlier Physicians (1991)

Non-outlier Physicians (1991)


Functional forms to model risk


Diabetes Indicators

• Many diabetes indicators are expressed in terms of percentages. They can be read as a deviation from an optimal target (e.g. 100% patients with at least one GP visit per year)

• Measurements at the level of each patient take the numeric form of 0=No / 1=Yes (binary outcomes). They are easy to interpret and can be easily managed by policy makers in terms of performance indicators.


Odds Ratio

The level of association between a set of potential risk factors

and a binary outcome is usually expressed in terms of odds

ratios. For each trial i a set of explanatory variables is linked

to the outcome through the following relationship:

ln[pi/(1-p

i)] = +

1 X

1,i +

2X

2,i +...

k X

k,i

Slope: coefficient

Multiplier of "log odds" for each unit increase in X

exp() is the effect of the independent variable on the "odds ratio“

OR<1 factor “associated” with a DECREASE in risk

OR=1 no association

OR>1 factor “associated” with an INCREASE in risk


Interpreting the results


US AHRQuality Indicators


Risk models

• A risk model is usually required separately for each outcome of interest (one for each diabetes indicator)

• Age and gender are routine candidates to be included in risk adjustment models


AHRQ Standardization

Risk adjustment model (overall or within a region)

Y(%) = 0+1(females)+2(age_class1)+...k(age_class4)

Source unit

Yi expected= 0+1(females)+2(age_class1)+...k(age_class4)

Predi x 100 = Expected Rate

Standardized Rate= (observed rate/expected rate)*population rate


Logistic regression without individual data

Complete Sample

Combinations ofLevels of Covariates

Same results !


BIRO System Standardization


Adjusted Outcome Indicators

• Predicted Value for each combination=

e(+1 X1,i +2X2,i +...k Xk,i )/(1+e(+1 X1,i +2X2,i +...k Xk,i ))• Expected Rate=Sum of the Total Predicted

Values over a unit (multiply each predicted value by the total number of observations per stratum, then sum)

• Compute Adjusted Rate


Application

• BIRO Data• R program

d <-read.csv("riskdata_528.csv", header=TRUE)

d<-d[d$age2_c!="[0 - 18)",]

m<-glm(hypert_med~sex*age2_c, d, family=binomial, weights=n)

summary(m)

s<-m$fitted*d$n

exp<-aggregate(s,by=list(dbname=d$dbname),FUN=sum)

write.csv(m$coefficients,file="coeff_riskdata_528.csv")

european best information through regional outcomes in diabetes risk adjusted diabetes indicators...

Documents

biro academy

nd residential course

diabetes risk

population average slide

different risk of patients

different risk factors

specific population

reference population