Identifying Peer Areas for Community Health Collaboration and Data Smoothing

1. Utah Department of Health 1 Identifying Peer Areas for Community Health Collaboration and Data Smoothing Brian Paoli Utah Department of Health 6/6/2007

2. Utah Department of Health 2 Acknowledgments Dr. Lois Haggard, UDOH Dr. David Mason, Univ. of Utah Mohammed Chaara, Univ. of Utah Michael Friedrichs, UDOH Kathryn Marti, UDOH Thanks to Dr. Lois Haggard, Dr. David Mason and his assistant Mohammed Chaara for statistical consultation, and my colleagues Michael Friedrichs and Kathryn Marti for their insights and suggestions.Thanks to Dr. Lois Haggard, Dr. David Mason and his assistant Mohammed Chaara for statistical consultation, and my colleagues Michael Friedrichs and Kathryn Marti for their insights and suggestions.

3. Utah Department of Health 3 Outline Background: Why Peer Areas? Data Smoothing Previous Peer Area Attempts Methodology and Procedures Utah�s 61 Small Areas Demographic Similarity Producing Smoothed Estimates Here is a brief outline of what I�ll cover today Some background why we are interested in Peer Areas Our approach to data smoothing and creating stable rates when we have to deal with a small population Discuss some previous attempts at smoothing and how we came to our current method A little background on what we call �Small Areas� in Utah and what we will call demographic similarity And I�ll end with how we produce smoothed estimates. Here is a brief outline of what I�ll cover today Some background why we are interested in Peer Areas Our approach to data smoothing and creating stable rates when we have to deal with a small population Discuss some previous attempts at smoothing and how we came to our current method A little background on what we call �Small Areas� in Utah and what we will call demographic similarity And I�ll end with how we produce smoothed estimates.

4. Utah Department of Health 4 Why Peer Areas? Community Collaboration Identify areas that are similar for purposes of comparison Collaborate on strategies, interventions Data Smoothing �Borrow strength� from geographic areas that are similar. Especially useful when multi-year trend data are not available Why Peer Areas? �We have two goals in mind, Community collaboration and Data Smoothing: Help communities view their health status relative to neighboring communities that are similar with regard to certain demographic variables such as income, poverty, age, race/ethnicity. It is hoped that group membership will help promote collaboration on public health strategies and interventions at the local level. Creating peer groups will also help when we smooth the rates, the additional data from peer group members will help to stabilize and improve the estimate of the true underlying rate. This is important in the case where estimates involve rare events, small populations, or when data from multiple years are not available.Why Peer Areas? �We have two goals in mind, Community collaboration and Data Smoothing: Help communities view their health status relative to neighboring communities that are similar with regard to certain demographic variables such as income, poverty, age, race/ethnicity. It is hoped that group membership will help promote collaboration on public health strategies and interventions at the local level. Creating peer groups will also help when we smooth the rates, the additional data from peer group members will help to stabilize and improve the estimate of the true underlying rate. This is important in the case where estimates involve rare events, small populations, or when data from multiple years are not available.

5. Utah Department of Health 5 Data Smoothing Why smooth? We calculate measures, such as rates of death and disease, to assess the underlying disease risk in a population. Measures from small populations are inherently erratic � subject to sampling variation. Rare events such as infant mortality can vary widely from year to year. We calculate rates in order to assess the disease risk in a population. Rates from small populations are subject to greater sampling variation. We smooth in order to get stable estimates� essentially reducing the sample variation within an area. At times this is done when we are dealing with rare events or when looking at small populations where the number of cases can vary widely from year to year. Rates can vary dramatically when we begin to examine population subgroups where we categorize by race, age, or gender.We calculate rates in order to assess the disease risk in a population. Rates from small populations are subject to greater sampling variation. We smooth in order to get stable estimates� essentially reducing the sample variation within an area. At times this is done when we are dealing with rare events or when looking at small populations where the number of cases can vary widely from year to year. Rates can vary dramatically when we begin to examine population subgroups where we categorize by race, age, or gender.

6. Utah Department of Health 6