Which factors make a difference when identifying pockets of under-immunization? Gayle Moxness Hennepin County Community Health Department Minneapolis, MN October 2002

Agenda l Identify Sources and Tools l Plan the Analysis l Analyze the Data - Look for Patterns/Relationships l See the Results - Hennepin County Analysis

Identify Sources/Tools l ImmuLink Immunization Registry l 2000 Census Data l CASA (Clinic Assessment Software Application) l GIS (Geographic Information System)

What is ImmuLink? Immunization Registry serving the Minneapolis/St. Paul Metro Region (population 2.6 million)

History Behind ImmuLink l Retrospective studies Conducted by the Minnesota Department of Health Identified pockets within Hennepin County that had very low immunization rates l ImmuLink launched in 1995 Hennepin County launched ImmuLink in response to these research results

ImmuLink Today l Nearly 600,000 records (people of all ages with shots) l Over 4 million shots 111 Participating Sites (public and private clinics, including large clinic systems, and schools)

Plan the Analysis l Focus on Hennepin County l Develop hypotheses l Determine geographic areas Analyze the data

Focus on Hennepin County, MN l Minneapolis and Suburbs l Population: 1.1 million l Increasingly diverse

Develop Hypotheses: Do any of these factors relate to immunization UTD rates? l Poverty level l Inability to speak English well l Gender l Race/ethnicity l Immigrant density l Population density l Single parent household

Possible Geographic Areas l Urban/Suburban l Cities (47 in Hennepin County) l Communities/Planning Districts (11 in Minneapolis) l Neighborhoods (34 in Minneapolis) l Census Tract (298 in Minneapolis) Zip Code

Analyze the Data Extract Data from ImmuLink l From ImmuLink, extract immunization data for children who: l Live in Hennepin County l Are months old l For each child: l Vaccinations given/dates l Street address l Gender (if available) l Race (if available) Result: 7023 records

Analyze the Data Match Addresses l Match children’s addresses from ImmuLink with defined Census geographic areas l Test for sufficient sample size l Result: “Communities” for Minneapolis “Cities” for suburban areas -

Analyze the Data CASA Use CASA to analyze ImmuLink data to determine UTD rates l Total Hennepin County -Gender -Race l Each geographic area

Analyze the Data Census l Analyze Census 2000 data by geographic area: Population density, poverty, etc. Above/below County average l Integrate with CASA analysis Look for patterns: UTD rates and Census

Hennepin County Analysis Census and UTD Rates l Calculate indices to easily compare above/below the County average for different types of data Example: If Hennepin County has average HH income of $30,000 per year City XYZ has an average HH income of $60,000 We see that the city has above average income City XYZ =60,000 = 200% (index County 30,000 of 200)

Hennepin County Analysis Total Hennepin County Does Gender make a difference? l6,926 kids had a “gender” code in ImmuLink (only 97 records without gender code) Compare UTD rates for M & F Is the rate for one gender above average, the other below average?

Hennepin County Analysis Hennepin County ImmuLink Data Index to County Avg. %UTD* (49.8%=100) Female Male *includes MOGEs Conclusion: Gender doesn’t make a difference

Hennepin County Analysis Hennepin County ImmuLink Data Does race make a difference? 2486 kids with “race” code in ImmuLink ( Nearly 2/3 of all records lack the optional “race”code) Compare UTD rates for different racial groups

Hennepin County Analysis Hennepin County ImmuLink Data Index to County Avg. %UTD (49.8%=100) White Black Asian Am. Indian No race code Results: Inconclusive. Questionable whether samples represent each racial group since all groups are below the County average

Hennepin County Analysis Does Suburban versus Urban make a difference? l Suburban Cities combined Minneapolis Communities combined

Hennepin County Analysis County Average =100

Hennepin County Analysis County Average=100

Hennepin County Analysis l Minneapolis Communities Combined Greater population density Greater poverty, etc. And, higher UTD rate l Look at each geographic area Minneapolis Communities Suburban Cities

Hennepin County Analysis County Average

Hennepin County Analysis County Average

Hennepin County Analysis l Minneapolis Communities: Inverse relationship with UTD rates Poverty (As poverty increases, UTD rate decreases) Poor English-speaking skills Continue analysis for other factors Look at Suburban Cities

Hennepin County Analysis County Average

Hennepin County Analysis County Average

Hennepin County Analysis l Suburban Cities UTD rates and other factors don’t show expected relationships Do further analysis with GIS

Hennepin County Analysis GIS Use GIS lUTD Rates by geographic area l24-35 month olds in ImmuLink as a percent of population lDifferent assumptions

Hennepin County Analysis l Minneapolis Communities Highest UTD Rates Highest percent of population represented in ImmuLink l Look at “High end” and “Low end” Assumptions

Hennepin County Analysis l Look at “High end” and “Low end” Assumptions High end: Assumes that all children not in ImmuLink ARE Up-To-Date Low end: Assumes that all children not in ImmuLink are NOT Up-To-Date

Hennepin County Analysis l Dramatic differences in the picture depending on assumptions l Retrospective Survey Results show “High End” closer to reality

Hennepin County Analysis l When registry penetration is high, as in Minneapolis, analysis of patterns is more reliable l When registry penetration is low, analysis is less reliable

Hennepin County Analysis l Strong ImmuLink expansion in metro area continues l Additional Factors at Work Successful Outreach Program- Baby Tracks

Hennepin County Analysis Baby Tracks - Outreach Program Focuses on high risk 0-2 year olds in 23 zip codes in Minneapolis and inner ring suburbs Personal reminders and incentives to parents to immunize their children

Conclusions How to identify pockets of under-immunization l Integrate your sources/tools ImmuLink Registry 2000 Census Data CASA GIS l Analyze data for patterns l Consider level of registry penetration l Look further, e.g. targeted programs