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Using Administrative/EMR Data to Understand Health Risk Behaviors among Teens in Foster Care Sarah Beal, PhD.

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Presentation on theme: "Using Administrative/EMR Data to Understand Health Risk Behaviors among Teens in Foster Care Sarah Beal, PhD."— Presentation transcript:

1 Using Administrative/EMR Data to Understand Health Risk Behaviors among Teens in Foster Care Sarah Beal, PhD

2 Goals Some background on the sample/population and data source Brief discussion of current plans for data analysis Discussion of future opportunities/things to think about

3 Background

4 80% of children in foster care experience neglect 18% experience physical abuse 9% experience sexual abuse 9% experience psychological maltreatment 2% experience medical neglect

5 Background There are roughly 402k children ages 0-17 in foster care on a given day – Legal custodian is child protective services Job and Family Services (JFS) in Ohio – Children may be living with: Group homes or residential treatment programs Licensed foster caregivers who are kin/unrelated Kin who JFS has identified/approved Parents/caregivers from family of origin (extended visit)

6 Background In Ohio: – Approximately 12,000 children on a given day – Hamilton County: 1,500 children on a given day 46% of children in foster care are 10 or older – Median age of entry is 6 years – Median age of children in care is 8 years 29% of children will spend more than 2 years in care – Federal law says kids need permanency after 2 years – In reality, families have 2 years to be reunified

7 Background Children enter foster care with more health problems than their peers (Simms et al., 2000) – 50% have at least 1 mental health problem 5 x more likely to have behavioral problems 16x more likely to receive psychiatric diagnoses 8x more likely to take psychotropic medications – 40% have a chronic medical condition 2x greater than the general population

8 Background States require a medical examination when children enter foster care – Screening for infectious diseases, acute health issues, chronic conditions, and abuse – Vaccine catch-up In Hamilton County, visits are required to occur at the CHECK Center at CCHMC – Seen within 5 and 30 days of entry/change of placement Foster children cost the medical system an age-adjusted $233/month compared to $166/month for peers on Medicaid (Halfon et al., 1992) Health Screening and Assessment for Children and Youth Entering Foster Care: State Requirements and Opportunities. Center for Health Care Strategies, Inc. Issue Brief. November 2010.

9 Background Young people leave foster care with more health problems than their peers

10 Background In the 18 months following exit from care, teens (Courtney et al., 2014) – Use more emergency healthcare services – Early and more frequent pregnancy – STIs and HIV infection – Substance use – Intentional and unintentional Injury – Obesity

11 Background Opportunities for Intervention

12 Background

13 Gaps in Knowledge To prevent health-risk behaviors, we need to know – When to target intervention/prevention efforts Missing population-based data Earlier age of onset for foster youth? Timing informed by child and case factors – Who to target Large number of predictors, variability in outcomes Few resources to address issues, need to stratify Less research that is specific to foster youth

14 Opportunity HCJFS has 700 youth ages 10+ in custody on a given day – 1200 youth ages 10+ in custody over 12 months State Automated Child Welfare Information System (SACWIS) used reliably for case management in Hamilton County – Federal mandates tied to funding dollars – Monthly audits – Semi-annual case file reviews

15 Opportunity

16 EMR (Epic) – All foster youth mandated to be seen at least twice at CCHMC, through CHECK Center Projected 2500 patient visits in 2015 – Foster youth may also be seen for primary, emergency, or specialty care (either while in or before entry into care) – Visits at CHECK, Teen Health Center, and ED include screening for substance use and sexual risk behaviors

17 Opportunity

18 Data Linking Eric Hall, PhD – SACWIS and EHR datasets will be linked using shared identifiers (e.g., name, date of birth, Medicaid billing number). Linked study data will be de-identified and managed using REDCap – A key to re-identify data will be kept separately in a secure file to allow for follow-up with this cohort in a future NIH grant Basis for a K01 application to NIDA – Opportunities for future grant submissions

19 K01 Aims Aim 1: Establish rates of substance use, STI, pregnancy, and HIV risk prior to age 21 for youth in HCJFS custody compared to a national sample of typical youth. Hypothesis 1: Rates for substance use, STI, pregnancy, and HIV risk will be higher for youth in foster care compared to maltreated girls not in foster care (FADS study) a matched comparison sample from the National Longitudinal Study of Adolescent Health (Add Health) Hypothesis 2: Peak age of onset for substance use, STI, and pregnancy will occur at earlier ages for youth in foster care compared to FADS and Add Health Aim 2: Examine differences in indicators of substance use, STI, pregnancy, and HIV risk based on youths’ experiences in foster care. Hypothesis 1: Youth who experience placement instability, are in foster care for longer periods of time, and are placed in group homes or residential treatment will have higher rates of substance use, STI, and pregnancy, and greater non- preventative healthcare utilization compared to those with stable placements, shorter time in care, and placements in family-based settings.

20 Planned Analyses Rates for youth ages 10- 21 Discrete-Time Survival Analysis to estimate proportional hazard functions – Hazard functions will be compared for foster youth and matched comparison samples using local and national data Predictive models

21 Future Directions Extensive amounts of data – Most SACWIS data is text Initial analyses using check boxes, structured data entry Queries with Epic or SACWIS could pull narratives, notes, etc. Some existing coding techniques for better describing characteristics from narratives (e.g., trauma history) JFS interested in ongoing data links between Epic and SACWIS – Access to Epic for updated information an ongoing challenge for JFS Potential solution with MyChart – SACWIS integrated with other private agencies

22 Thank you Questions/Discussion


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