1. Solutions Consulting Group, LLC Prevalence Rates: A Working Model and 2 State experiences Presenters: Karleen Goldhammer-Consultant Mary Ann Discenza (VA) - Part C Coordinator Lizette Stiehr (AK) - Agency Director February 7, 2005

2. Why is Prevalence important to know? Benchmarks and planning System design Financing Identifying resource and support needs Quality assurance Equity Well being of children Long and short term service gap identification

3. Virginia’s Plan - Context Re-design the finance system to reflect the: –Demographic, –Political and –Economic context of the Commonwealth Implement an integrated data system to assist –Local EI systems in decision-making and management Renew state interagency agreements and service contracts –Clearly establish the parameters of the service delivery approach Increase knowledge of, and access to: –Potential formal funding at the state, local, and community level –Informal community resources and supports

4. Virginia - Why a Cost Study? The purpose and design of the cost study were to understand the total cost of Virginia’s Part C System; and To answer the question how many children should be served given Virginia’s definition of eligibility.

5. Information gained through the Cost Study? Statewide total cost of early intervention in Virginia Average hourly cost per direct service person Ratio of direct services to administrative and support costs Cost difference relating to different personnel types/disciplines. Number of children we should be serving The cost for serving all potentially eligible children

6. Alaska-Why did we do it? Leaders within Alaska’s early intervention system have identified this question as a critical priority area to be addressed within the scope of a strategic work plan and have dedicated resources to address the issue.

7. Alaska- Project Status Spent time with a Prevalence Committee pulling a working model together through 2004 Presented information to a larger stakeholder group for input and reaction (11/04) Collected stakeholder thought and input Established targets and other recommendations Identified recommended use for the information Draft report currently in review

8. IDEA Part C Eligibility Categories Children who have a diagnosed mental or physical condition that has a high probability of resulting in developmental delay. (Required) Children experiencing developmental delays. (Required) Children at-risk of having substantial delays (9/ 56=16%). (Optional)

9. National Early Intervention Longitudinal Study (NEILS) Data Report

10. WASHINGTON OREGON MONTANA IDAHO NEVADA CALIFORNIA UTAH WYOMING COLORADO ARIZONA NEW MEXICO NORTH DAKOTA SOUTH DAKOTA NEBRASKA KANSAS OKLAHOMA TEXAS ALASKA HAWAII PUERTO RICO LA ARKANSAS MS ALABAMA GEORGIA FL SO. CAROLINA NO. CAROLINA TENNESSEE KENTUCKY MISSOURIVIRGINIA WV ILLINOISIN OHIO IOWA MINNESOTA WISCONSIN MICHIGAN PENNSYLVANIA NEW YORK MAINE CT VT MA DC NJ NH MD DE RI Percent Served Less than 1% 1% to <1.5% 1.5% to <2% 2% to <3% Map 1 Percentage (Based on 2000 Census Population) of Infants and Toddlers Ages Birth Through 36 Months Served Under IDEA, Part C in 2002 (excludes At-Risk) Note:Data as of August 30, 2003. Because the criteria for Part C eligibility varies widely across states, differences in identification rates on this map should be interpreted with caution. Please see Data Notes for an explanation of individual state differences on how data are reported. Source:U.S. Department of Education, Office of Special Education Programs, Data Analysis System (DANS). 3% or higher

11. IDEA Part C Percentage of children under the age of 3 receiving services as of 12/1/2003 (excludes at-risk) = 2% level Broad Eligibility Narrow Eligibility Moderate Eligibility Data Source: Westat Hawaii7.70 Massachusetts5.92 Indiana3.62 Wyoming3.57 Vermont 3.42 Pennsylvania2.94 Delaware 2.90 New Mexico 2.89 Maine (ED)2.77 West Virginia2.73 South Dakota (ED) 2.66 Wisconsin2.66 New Hampshire 2.61 Maryland (ED)2.60 Arkansas2.46 Kansas2.40 Florida2.28 Michigan (ED)2.13 Iowa (ED)1.95 Ohio1.81 Minnesota (ED)1.78 Louisiana1.75 North Carolina1.66 Colorado (ED)1.56 Washington1.56 Mississippi1.53 Virginia1.40 Alabama1.20 New York4.42 Rhode Island3.48 Connecticut2.96 Idaho2.44 Illinois2.42 Kentucky2.37 New Jersey 2.36 Tennessee (ED) 1.81 Texas1.81 California1.76 Nebraska (ED)1.70 Utah1.69 Oregon (ED)1.38 Georgia1.19 South Carolina 1.04 Oklahoma (ED) 2.24 Alaska 2.17 North Dakota 2.13 Montana 1.95 Missouri (ED) 1.51 Arizona 1.39 DC 1.13 Nevada.94

12. Virginia’s Part C - Eligibility “Early intervention services" means services provided through Part C of the Individuals with Disabilities Education Act (20 U.S.C. § 1431 et seq.), as amended, designed to meet the developmental needs of each child and the needs of the family related to enhancing the child's development and provided to children from birth to age three who have (i) a twenty-five percent developmental delay in one or more areas of development, (ii) atypical development, or (iii) a handicapping condition.

13. Virginia - Children in Service YearChild Count% of Population 19972,393.9% 19982,6511.0% 19993,0101.1% 20003,1101.1% 20013,4971.2% 20024,1631.4% 20034,1731.4% Note: May not include 2 year olds served by the schools

14. How Many Children Are Currently in Service? Virginia has a mandate for 2 year olds to optionally be served within the public school system. Using DOE combined data for the 2002 and 2003 Child Count, Virginia is currently serving 5,197 children or 1.9% of children 0-3 in the Commonwealth.

15. Alaska’s Part C Eligibility Children who experience developmental delays of 50% or greater, or who experience a diagnosed condition (such as Down syndrome, Autism, Fetal Alcohol Syndrome (FAS)), likely to result in a 50% developmental delay, are entitled to services.

16. Alaska - Children in Service YearChild Count% of Population 19974661.6% 19984991.7% 19995852.0% 20006512.3% 20016342.2% 20026462.1% 20036382.2%

17. How do you count the number of children currently served in the system? –Child Count 12/1 of each year –Aggregate Count –Referrals not moving to eligibility –Eligible children not completing an Individualized Family Service Plan

19. How many children should we serve? Establish the population base Review the eligibility definition Tally the number of children currently served Select a projection model Collect data to build a statewide composite number Decide how it will play a role in your system Continue to review, refine and enhance

20. Model Options A single statistic, such as low birth weight Epidemiological Model Variables Model

21. Prevalence Concept The premise of the estimated prevalence model used for Alaska and Virginia is rooted in the notion that all communities within a single Part C system should serve the same percentage of children EXCEPT for accounting (indexing) for community differences in population characteristics that are likely predictors of participation in early intervention.

22. Variables Model Concept Proposed as an alternative to an epidemiological model –Limited data exists regarding the prevalence rate of children with developmental delay –Complexity of identifying which diagnosis are eligible Establishes the highest credible percentage of children currently in service as the benchmark –Review for issues of over or under identification –Look for possible weaknesses in the eligibility determination process –Conduct forums or interviews in targeted communities Compare community differences to benchmark community –Review county level characteristics that influence early intervention participation Project the target prevalence rate given today’s service levels –Establishes the minimum threshold rather than a maximum Review and adjust

23. Model Mechanics 1.0-3 or 0-4 population numbers by county (FIPS code) 2.Identify desired variables such as Pre Term Births, Children in Poverty, No Prenatal Care, Maternal Education etc. Sum the variable Optionally you may weight the variables then sum 3.Child Count/Aggregate by geography Establish the county with the highest % of children served Validity and credibility are crucial 4.Compute the index Highest percentage of children in service divided by summary percentage of variables 5.Establish the percentage of children to be served based Apply the index to all of the other sums of variables 6.Compare the number of children in service to the projected estimate of eligible children Growth will not occur immediately

24. Geographic Issues to Consider Cities, towns & villages Census tracts Counties, Boroughs Metropolitan area Census areas Federal Information Processing Standards (FIPS) Regions

25. Demographic & Health Risk Factors Being a member of a minority Being in foster care Being in a low-income household ($25,000 or less annually) Having a primary female caregiver with less than a high school education Having a female caregiver who was < 17 years old Living in a household with only one parent Living in a household with one or more other children with special needs Living in a household with four or more children Adequacy of housing rated as fair or poor Adequacy of transportation rated as fair or poor. Birth Weight Gestational Age Medical Complications

26. Suggested Characteristics for Variables Population based rather than participatory counts. The information should be readily available with a long history of collection. Alignment with state demographics. The quantity of information for both the numerator and denominator need to be sufficient enough in size to be statistically reliable.

27. Alaska-Initial Variables Discussed –Number of children diagnosed with Fetal Alcohol Syndrome (FAS) –Number of children diagnosed with Fetal Alcohol Effect (FAE) –Unduplicated count of substantiated reports of harm –Number of children whose mother smoked during pregnancy –Number of children born at or below 32 weeks gestation –Number of children born to mothers age 17 yrs or less at time of birth –Number of children in foster care

28. AK – Variables Modeled Substantiated Reports of Harm 1999-2003 –A data report from PROBER© through the DHSS/OCS was used that tallies the number of substantiated reports of harm for children under age 3 for five (5) year period from fiscal year 1999 through fiscal year 2003. Poverty Index 1999 –(http://www.ers.usda.gov/Data/PovertyRates) and includes Related Children under 18 years of agehttp://www.ers.usda.gov/Data/PovertyRates Pre Term Births 1998-2000 –Data from the Alaska Department of Health website (http://www.hss.state.ak.us/dph/bvs/birth_statistics/Profiles_Census /default.htm) was used and represents the number of infants born at less than 37 weeks gestation over the total number of live births for the period.http://www.hss.state.ak.us/dph/bvs/birth_statistics/Profiles_Census /default.htm Late or No PNC 1998-2000 –This variable includes the combined percentage of No Prenatal Care and the percentage where prenatal care began in the third trimester. < 12 Yrs education 1998-2000 –The percentage of the population with less than 12 years of education was obtained from the DOH vital statistics website

29. Virginia- Variable Categories Discussed  General Demographics  Race/ethnicity Information  Populations Of Special Consideration  Pregnancy And Birth Information  Health Challenges And Child Welfare Issues  Family Households  Child Count Data  Part B/3-5 Preschool Eligibility  Medicaid/SCHIP Eligible And/or Enrolled  Income Information  Eligibility Information  Local Economic Resources

30. Virginia - Selected Variables HS Dropouts-2002 Very Low Birth Weight 1998-2001 Poverty Rates for Related Children under 18 years 1999(Weighted)

31. AK & VA Model Results Statewide Projection Range (Hi, Low)

32. Fitting it All Together Decide on your approach Build your model at the appropriate geographic level Collect your data/identify missing data Routinely use the information Review and adjust the estimate Don’t be afraid to start!

33. Practical Uses for the Data Discussion at Local Coordination Meetings with other community members –Head Start –Local public school –Other health care providers –Other community resources Grant application planning –Child Find and intake activity –Provide for additional review at the community level –Establish a targeted growth plan using the estimated prevalence number Evaluation of system resources (people, time & money) –Provider availability –Funding –Allocation systems Data analysis and verification –Validity of Child Count numbers –Relationship between child count and aggregate count (turnover ratio) Quality assurance

34. Population Base by Alaska Census Area (2000) FIPSGeography (N=27)T. Pop 0-3% of Total 0-3T. Pop 0-4% of Total 0-4 02013Aleutians East710.3% 910.2% 02016Aleutians West1230.4% 1800.5% 02020Anchorage1200042.9% 15,78842.2% 02050Bethel9333.3% 1,2603.4% 02060Bristol Bay500.2% 730.2% 02068Denali640.2% 780.2% 02070Dillingham2771.0% 3811.0% 02090Fairbanks North Star397014.2% 5,32114.2% 02100Haines690.2% 1020.3% 02110Juneau City and Borough11604.1% 1,5234.1% 02122Kenai Peninsula19426.9% 2,6006.9% 02130Ketchikan Gateway5271.9% 7632.0% 02150Kodiak Island7932.8% 1,0682.9% 02164Lake and Peninsula790.3% 970.3% 02170Matanuska-Susitna22728.1% 3,1828.5% 02180Nome4511.6% 6441.7% 02185North Slope4191.5% 5551.5% 02188Northwest Arctic4411.6% 5951.6% 02201Prince of Wales-Outer Ketchikan2821.0% 3591.0% 02220Sitka City and Borough3201.1% 3841.0% 02232Skagway-Hoonah-Angoon1200.4% 1550.4% 02240Southeast Fairbanks2340.8% 3380.9% 02261Valdez-Cordova3741.3% 5141.4% 02270Wade Hampton4041.4% 5941.6% 02280Wrangell-Petersburg2821.0% 3611.0% 02282Yakutat City and Borough250.1% 420.1% 02290Yukon-Koyukuk2791.0% 3871.0% State of Alaska27961 37,435

35. Alaska’s Population Base 0-3: 2000

36. Percentage of Children in Service – Alaska 2.28% Statewide Within 1 standard deviation of the meanBelow 1 standard deviation of the mean 1.36%Northwest Arctic Borough0.00%Aleutians West 1.41%Aleutians East Borough0.00%Denali 1.58%Matanuska-Susitna Borough0.00%Yakutat City and Borough 1.60%Valdez/Cordova0.31%City & Borough of Sitka 1.67%North Slope Borough1.01%Kodiak Island Borough 1.71%Southeast Fairbanks 1.79%Yukon-KoyukukAbove 1 standard deviation of the mean 1.99%Fairbanks North Star Borough3.77%Nome 2.09%Ketchikan Gateway Borough3.80%Lake & Peninsula Borough 2.27%Kenai Peninsula Borough4.00%Bristol Bay Borough 2.48%Prince of Wales4.35%Haines Borough 2.57%Bethel 2.58%Municipality of Anchorage 2.67%City & Borough of Juneau 2.84%Wrangell-Petersburg 2.89%Dillingham 3.22%Wade Hampton 3.33%Skagway-Angoon

37. Virginia’s Early Intervention System Number of children served4,173 Eligibility definition (broad) (25% delay in one or more developmental areas, atypical development or a handicapping condition) Local autonomy

39. Alaska- Community Variables Modeled 1.Substantiated Reports of Harm 1999-2003 2.Pre-Term Births 1998-2000 3.Late or No PNC 1998-2000 4.< 12 Years Education 1998-2000 5.Poverty Index 1999

40. Alaska-Community Variables Census Area Pov. Index 99 PreTerm Bir. 98- 00Late or No PNC 98-00< 12 Yrs Edu.98-00**Substan. Rpts of Harm 99-03 Aleutians East6.8%4.8%1.6%6.9%2.80% Aleutians West6.0%4.3%6.4%12.0%22.80% Anchorage8.8%10.7%2.5%12.7%18.10% Bethel24.5%12.3%10.6%20.0%40.30% Bristol Bay10.7%7.5%3.8%8.0%0.80% Denali10.2%10.5%3.7%10.7%3.10% Dillingham26.6%11.6%18.6%16.1%26.00% Fairbanks North Star8.4%9.6%5.9%9.9%17.30% Haines14.6%7.6%5.1%6.9%2.90% Juneau City and6.7%9.3%2.1%10.3%23.40% Kenai Peninsula12.0%10.1%4.9%13.5%17.20% Ketchikan Gateway7.5%9.7%6.0%14.6%16.70% Kodiak Island6.7%9.4%5.1%12.5%7.10% Lake and Peninsula21.0%12.5%3.8%24.4%13.90% Matanuska-Susitna13.2%9.1%5.9%12.8%12.90% Nome20.0%13.7%12.0%22.8%38.10% North Slope9.0%13.1%7.8%30.2%39.40% Northwest Arctic19.7%14.9%5.3%29.2%23.40% Prince of Wales-Outer Ketchikan13.7%6.9%3.1%14.0%12.10% Sitka City and9.2%8.4%3.8%11.5%14.10% Skagway-Hoonah-Angoon15.0%5.7%2.6%18.8%9.20% Southeast Fairbanks20.8%5.6%10.6%13.1%23.10% Valdez-Cordova9.7%9.0%4.4%9.8%8.80% Wade Hampton29.4%12.7%11.4%26.3%53.50% Wrangell-Petersburg9.3%5.1%2.4%12.9%16.70% Yakutat City and22.5%5.6% 6.3%4.00% Yukon-Koyukuk26.7%11.1%8.2%18.9%43.70% State of Alaska11.2%10.3%4.7%13.7%20.80%

41. Alaska - Models Model 1 Substantiated Reports of Harm 1999-2003 Pre-Term Births 1998-2000 Late or No PNC 1998-2000 < 12 Yrs education 1998- 2000 Model 2 Pre-Term Births 1998- 2000 Late or No PNC 1998- 2000 < 12 Yrs education 1998-2000 Model 3 Pre-Term Births 1998-2000 Late or No PNC 1998-2000 < 12 Yrs education 1998- 2000 Poverty Index 1999

42. Alaska - Local Community Stakeholder Meetings Collaboration w/ other community stakeholders who were helpful to validate the numbers. Concern about the system impact of doubling current service participation numbers.

43. Virginia - Variables Stakeholders reviewed models with more than a dozen variables Unanimously agreed to use the following variables: High school drop-out rate Very low birth weight Poverty indicator –No one variable should have any more or less of an influence in the final calculations. First two variables were weighted to an equivalency of 11.9 An index was created and universally applied to each geographic designation to estimate the minimum number of children that should be served.

44. Virginia - Data Sources Census data plus information traditionally collected by the State agency responsible for the Maternal Child Health Grant was used in this study

45. Virginia - Demographic Analysis The Regional Profile is: –Designed to assist local and state planners in short- and long-term planning; –Identifying the potential prevalence locally of children eligible; and –Targeting specific regional or local challenges that serve as barriers to accessing or providing services for the eligible population under Part C.

46. How Many Children Should Be Served in Virginia’s Part C System? Virginia’s integrated work plan developed by stakeholders identified this question as a critical priority area to be addressed. An outcome of the cost study was to identify the cost of serving all eligible children. For the 2002 data, Virginia ranked 24 th of 29 of states with a broad eligibility definition. Based on the 12/1/2002 Child Count, Virginia is serving at 1.4% of the 0-2 population.

47. Virginia-The Process Virginia attempted to use an epidemiological model to compute the number of children to be served within the early intervention system. Challenges with using this model: –Not all children having a particular medical condition will be in need of service. –There is a lack of data regarding very young children with developmental delay. –The system relies on passive reporting requirements –Children are misdiagnosed –Children could be missed

48. 48 Virginia Cares: Birth Defect Surveillance Data 1989-1998 The report: –Summarizes epidemiological and statistical information about children born to Virginia residents. –Spans 20 years. Of the 95,000 children born annually in Virginia, approximately 4,600 children are known to have birth defects. Nationally, between 3 and 5 percent of children born annually have birth defects. Virginia is within the range of 4.9%.

49. Virginia - Common Characteristics - Communities with Highest Levels of % of Children Served Small community Well-known by the physician community Communicate back with referral sources Stable program Longevity of the primary contact person

50. Alaska - Common Characteristics of Communities with Highest Levels of % of Children Served Small community The person and/or the organization are well known by the physician community Communication back with referral source about the outcome of the referral exists Existence of longstanding inclusive playgroups Stable program Longevity of the primary contact person

51. In Conclusion The prevalence data from Virginia’s Cost Study provides a baseline of information that can be used in future system evaluation processes. Virginia’s Challenges: –Lack of actual delivered service information collected on a routine basis –Revenue information is substantially different across the region and this has implications for fund stability Virginia has a solid foundation to build an improved system for Virginia’s infants and toddlers and their families

52. What do you think? How clear is the model? Does any of this make sense? What are the strengths? What are the weaknesses? How does this relate for you and your community? Other thoughts?

53. Discussion Items: Referral 1.Is there a broad array of referral sources? 2.What are the referral patterns? 3.How aggressive is the child find effort? 4.What kind of training occurs for potential referral sources? 5.How do you examine/review that over identification of children is not playing a part in the number of children being served? 6.What is the rate of children referred to the number of children actually eligible?

54. Discussion Items: Eligibility Determination 1.Describe the process of determining child eligibility. 2.Who is primarily responsible for determining eligibility? 3.Do they use a standardized process for eligibility determination? 4.What kind of training occurs for persons determining eligibility? 5.How often is informed clinical opinion used?

55. Discussion Items: Finance 1.Are there pockets of special considerations? 2.What are the service patterns? 3.Financial considerations? 4.Other community partnerships?

56. Request for Additional Information - VA Mary Ann Discenza Part C Coordinator Infant & Toddler Connection of Virginia (804) 371-6592 maryann.discenza@co.dmhmrsas.virginia.gov

57. Request for Additional Information- AK Lizette Stiehr-Director Family Outreach Center Under-standing Special Needs, Inc. (FOCUS) PO Box 671750 Chugiak, AK 99567 Tel: 907-688-0282 E-mail: lizette-stiehr@gci.net

58. Thank you for your participation! Karleen R. Goldhammer Solutions Consulting Group, LLC 725 Riverside Drive Augusta, ME 04330 Tel: 207-623-8994 E-mail: Kgoldhamm@aol.com