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Multiple Service Systems Use Among Illinois Families

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Multiple Service Systems Use Among Illinois Families Robert M. Goerge Innovations in Design and Utilization of Measurement Systems to Promote Children s Cognitive, Affective, and Behavioral Health November
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Multiple Service Systems Use Among Illinois Families Robert M. Goerge Innovations in Design and Utilization of Measurement Systems to Promote Children s Cognitive, Affective, and Behavioral Health November 5-6, 2014 National Academy of Sciences Building 2101 Constitution Ave, N.W., Washington, DC Outline Setting for the research The population of at-risk children and their families The Integrated Database on Child and Family Progams in Illinois Identifying multi-system families Patterns of system involvement Barriers and potentials integrating data 2 Chapin Hall at the University of Chicago Organization that was brought to the University in order to have an impact on the well-being of children through better research and analysis Over the past 30 years, we have built relationships with policymakers in order to achieve that goal through conducting research that meets the needs of the public sector Known for our work with government social program administrative data, we employ a full range of methods and address many issues of children, families and their neighborhoods 3 Identifying children at-risk of bad things happening to them at scale We know which subgroups of families are at highest risk of their children experiencing adverse childhood experiences. They are characterized by a combination: Chronically unemployed parents Very low socioeconomic status Long-term welfare program participants Single-parent families Mothers who had their first child as an adolescent Families without grandparents living with them Alcohol, drug use, mental illness Inadequate parenting skills, family breakdown, parental stress and mental illness, domestic violence, and parental history of maltreatment during childhood. Community-level low socioeconomic status, living in an impoverished community, family size, and sibling spacing 4 Chicago Pre-k Chicago Head Start UI Wage Records UI Benefits Records WIA SSI/AABD TANF SNAP (Food Stamps) Chicago Public Schools Chapin Hall Child Care Subsidies IDB Child Care Licensing CPD arrest data Cook County Juvenile Court Juvenile Incarceration Adult Incarceration Foster Care Child Maltreatment Investigations Medicaid Providers Medicaid Claims Medicaid Eligible Population TANF Work Programs Integrated Database on Child and Family Programs in Illinois Data spans the period from , but mostly from 1990 forward 5 Survey vs. Administrative data Adapted from Wallgren and Wallgren (2007) Advantages Disadvantages Surveys based on data collection: sample surveys and censuses Register-based Surveys (administrative data and other non-traditional datasets) Can choose which questions to ask across multiple domains Can be up-to-date (depending on how big of an effort it is) No further burden on the respondent for the statistics Low costs Almost complete coverage of population Complete coverage of time Respondents answer carefully to important administrative questions Good possibilities for reporting for small areas, regional statistics and longitudinal studies Link records across datasets to take advantage of the relatively small amount of high quality data in each data source Some respondents..... do not understand the question... have forgotten how it was... do not respond (nonresponse)... respond carelessly Burden on respondents can be high Expensive Low quality for estimates for small study domains (for sample surveys) Cannot ask questions Dependent on the administrative system s population, object and variable definitions The reporting of administrative data can be slow; the time between the reference period and when data are available for statistical purposes can be long Changes in the administrative systems make comparisons difficult Variables that are less important for administrative work can be of lower quality Rationale for MSF analysis A small number of families in Illinois use a large portion of the State s service resources meaning the expensive and intensive health, human service, and corrections programs. If the State could develop a deeper understanding of the circumstances in which these families live, their histories, their geographic location, and their trajectories, along with what services they have used, the State could provide more adequate and efficient services. 7 High-cost services Mental health service, paid through Medicaid Substance abuse treatment, paid through Medicaid Adult incarceration Juvenile incarceration Foster care 8 Data Sources Human Services Children and Family Services Healthcare and Family Services Corrections Food Stamp and TANF recipients Child abuse and neglect reports and Foster care records Medicaid paid claims from Adult and juvenile admissions and exits from Chapin Hall Multi-service dataset 9 Preparing Administrative Data The process of making administrative data suitable for research use includes three important steps: De-duplication Identify records within a data set that belong to the same individual Record-linkage Identify and link individuals across data sets Employ probabilistic record-linkage Identify relationships among individuals 10 Definitions Definition of a family Individuals who were linked through the membership in Food Stamps or TANF cases, or were involved in a DCFS case 90% of these families had 10 or fewer members Definition of a problem An instance of mental health service, substance abuse treatment, adult incarceration, juvenile incarceration, or foster care placement. Definition of a Multi-system Family A family whose members had at least two different types of problems (e.g. adult incarceration and substance abuse) Costs Total per unit foster care, Medicaid, adult and juvenile incarceration dollars 11 The 80/20 rule (the Pareto Principle) 100% 90% 23% Families in multiple systems 80% Families in one system 70% 60% 50% 34% 63% 86% Families not in any systems 40% 30% 20% 10% 0% 43% Families 37% System Involvement 14% Expenditure MSFs accounted for: 23% of families 63% of system involvement 86% of costs Identifying Multi-system Families Family DHS: Cases with women age who received food stamps in DHS Supercases: Cases with members in common merged DHS-DCFS Families DCFS Supercases: Cases with members in common merged DCFS: All cases Julie Kyle Julie Kyle Julie Kyle Gillian Henry Gillian Henry Gillian Henry Alice Clarice Bill Clarice Frank Bill Alice Clarice Frank Alice Clarice Opal Bill Nancy Frank Alice Opal Bill Nancy Frank Alice Alice Opal Bill Nancy Frank Case Creation Steps Select cases by criteria Link individuals within agencies Link cases across agencies Link service records to families 4 Family with no problems Family with one problem Multi-system family (family with 4 unique problems) Julie Kyle Gillian Henry Substance abuse treatment Bill Foster care Alice Nancy Foster care Frank Incarceration Opal Clarice Juvenile detention Substance abuse treatment 7 Family composition 82% of families had 2-10 members* 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% 9% *Percentages do not total 100% due to rounding error 16% 45% 20% 9% Family Size 14 15 16 17 Mental health service was the most common problem (Total number of problems=465,036)* Findings Mental Health 220,878 Foster Care Substance Abuse Adult Incarceration 72,161 56, ,784 Juvenile Incarceration 8, , , , , ,000 Number of Families with Each Problem *The total number of problems (465,036) exceeds the total number of families with problems (285,722) because some families have multiple problems. 18 Mental health services Families with one or more problems Families with multiple problems Did not receive mental health service Did not receive Received 23% mental health mental health 77% service service 6% Received mental health 94% service 100% = 285,722 families 100% = 114,355 families 54% of families receiving any mental health service received both inpatient and outpatient services. 42% of families receiving any substance abuse treatment received both inpatient and outpatient services. 19 Incarceration and substance abuse treatment All Families with System involvement System Adult incarceration Juvenile incarceration Families in One System Families in Multiple Systems Total Number of Families Percent of Families in this System only 8,406 48,243 56,649 15% 366 8,198 8,564 4% Mental health 113, , ,878 51% Substance abuse Foster care 45,599 3,675 68,468 72,161 5% 61, ,784 43% 20 Child abuse and neglect 73% of MSFs had an abuse or neglect finding (83,944) 41% of Multi-system Families had both abuse or neglect findings and an instance of violent injury (46,444) 49% of MSFs had an instance of violent injury (55,471) Violent injury incidents correspond to Medicaid paid claims for CCS codes for injury due to violence and those ICD-9 codes that were found to be highly indicative of abuse, neglect or violence. 21 Multi-system Families: Future directions Potential research area Geography Recent problems Individual vs. family problems Magnitude of problems Additional family characteristics Trajectories Benefit Identifying unique characteristics of MSFs in specific places Discovering the problems that may have the greatest impact on the current state of the family Revealing whether single individuals account for multiple problems within a family or whether several family members encounter problems Delineating between families that have had few service spells versus families with multiple service spells. Estimating the cost of providing state services to multi-problem families. Problems: Asthma and chronic conditions Assets: Employment and education Conduct longitudinal analysis to determine when these families become MSF 22 Good news first States and cities are developing their administrative data sources faster than ever They are even using the data for many different purposes And they are making the data public, so that data entrepreneurs are creating apps that inform the public and policymakers There are a number of federal initiatives that are promoting the development of administrative data Examples Given the national effort to improve our competitiveness, a focus of the federal government has been in education and workforce development. In June 2012, the U.S. Department of Education (ED) awarded new Statewide Longitudinal Data Systems (SLDS) grants (started in 2005) and the U.S. Department of Labor (DOL) awarded new Workforce Data Quality Initiatives (WDQI) grants (started in 2011). Eight states received their first SLDS grants (Delaware, Oklahoma, New Jersey, South Dakota, Vermont, West Virginia, Puerto Rico, and the U.S. Virgin Islands). Three states (Hawaii, New Jersey, and Rhode Island) have new SLDS grants focused on workforce linkages and WDQI grants. Of course, the Longitudinal Employer-Household Dynamics (LEHD) program is the premier example of linking data to provide greater intelligence around employment. %20WDQI%20grants.pdf However It s happening to different degrees in different cities, counties, and states. There is a wide variation in who has access to the data that is being created and the quality of the data that is being built. It s also taking many years to develop these efforts in states and cities Best practices have not been disseminated to a sufficient extent States often rely on large corporate vendors, who will only go so far, and government agencies don t have the skilled staff necessary to take full advantage of the efforts Silos Special interests want us to believe that problems can be addressed one-by-one But everyone knows that: Early nutrition and good parenting is associated with learning Learning is associated with getting a job A parent having a job is associated with child well-being Lack of school success is associated with criminal behavior This is why integration or breaking down the silos is necessary in order to make policy and develop programs to improve the well-being of individuals and families. It all starts with the local public sector 30 years ago, when there was less data, most public sector agencies had handfuls of analysts Now, we re lucky if there is one per agency Increasing focus on compliance, but that s not all that new At the same time, the federal government is requiring evidence-based practice and evaluation in many areas of social programs, which is a major challenge, given the lack of research expertise in these agencies Obstacles for local government getting help Data sharing agreements More complicated as identity theft became more prevalent More complicated as FERPA, HIPPA, CFR 42 More complicated as leaders and their lawyers viewed information as power and that data sharing could lead to negative media Contracts Certainly the easiest way to work with government, even though Universities are generally concerned that they limit academic freedom Evaluations It s hard to justify using state tax dollars, so the few evaluations that are done are federally funded. Good luck getting the data sharing agreement through our lawyers. Connecting academics and government It s often the case that politics matter the most policy and facts come second There is not enough human capital in government to link to the researchers who can help Can they provide enough data? Can they deal with the legal problems in order to share the data? Skepticism about the data Most social scientists would rightly recommend the city make decisions based on evidence developed from high quality research. To them, that usually means data that they themselves collected or at least had a big hand in collecting data OR the data is blessed by the discipline AND a research design that fits the research question at hand. The end There are real barriers that lead to data not flowing to those that need it The nature of these barriers vary from sector to sector and place to place, but there are common themes These barriers can be addressed and the federal government has to learn how to learn from those places that have had success Incentives have to be put into place for all jurisdictions to use their data to get smarter about what they are doing Reviewing all federal research projects so that they are effectively using administrative data
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