1. Youth Psychiatric Inpatient Data
Emergency Detention and Crisis Stabilization Workgroup
February 18, 2015
Kate McCoy

2. Data Sources WMHI (2013-2014) Medicaid claims and encounters (2013)
Aggregate # of kids by county
Medicaid claims and encounters (2013)
All records related to enrollees’ mental health services
WI Hospital Association (WHA) admission and discharge information ( )
Basic info for each admission (age, gender, reason for stay and length of stay)

3. Guide to data sources on slides
WMHI: blue shaded slide
Medicaid data: gray shaded slide
All hospitalizations: un-shaded

4. Caveats! Short time span or point in time only (2011-2013)
WHA data is # of admissions, not kids
No good way to distinguish ED’s vs. voluntary
Tribes are not accounted for; only counties
A service that’s working well in one county might not show up in this analysis if the same service isn’t working in another county.
There is more that could be done with this data!
Results=best available at this time
Some results may change as new variables added

5. Recap: Trends at State Institutes
From DMHSAS during a previous meeting of this group

6. Trends in All Youth Psychiatric Admissions
No dip in overall admissions during the years when State institutes saw a dip.

7. WI’s Rates Are High and Growing
US data: Centers for Disease Control and Prevention, National Center for Health Statistics. National Hospital Dis­charge Survey, Annual File Unpublished data.
WI Data: WHA (All admissions)

8. Just looking at WMHI doesn’t tell the whole story…
Top 5 Counties by Per Capita Admission Rate, 2013
WMHI
MEDICAID
All HOSPITALIZATIONS
1) ADAMS
1)MILWAUKEE
1) MILWAUKEE
2)FOND DU LAC
2)MENOMINEE
2) RACINE
3) VILAS
3) OUTAGAMIE
4) GREEN
4)SHAWANO
4) WOOD
5) DODGE
5) BROWN
5) VILAS

9. Characteristics of hospitalized kids

10. Foster Children
Are 3% of Medicaid enrollees, but 15% of those hospitalized.
Have more hospitalizations per child
Tend to stay in inpatient settings longer
BUT: Very service-intensive group
Not more likely to be hospitalized when compared to other service-intensive kids
Foster kids are 12% of those using outpatient MH services.
Foster care ceases to be significant when you control for # of outpatient mental health services.
Service intensity: they use 2.5x as many mental health services as their counterparts who are not in foster care.

11. Rates of All Hospitalizations Differ Greatly by Race: (2013)
Source: WHA data. Racial differences are highly significant in regression.
You see similar differences in the Medicaid data as well, even accounting for the racial composition of the MA population.

12. Breakdown of Admissions by Gender
All Admissions: More girls
Medicaid only: Almost 50/50

13. Age by Sex
BOYS
GIRLS
All admissions 2013
Boys are more likely than girls to be hospitalized at a younger age;
girls’ hospitalizations shoot up at age 13.

14. Age Distribution for Black vs. White Boys
Black: 52% of admissions under age 13
White: 35% of admissions under age 13
All admissions 2013

15. Age Distribution for Black Boys on Medicaid
Teens are only 1/3 here
35% under age 10
2x as many 0-5 year olds (6% vs. 3% overall)
Big spike at 7 years old, also 9.
Looks very similar when we look at “Other/Unknown” race: 1/3 under age 10, 43% teens, 24%
Pattern is less pronounced for American Indian boys. More of a spike in the later years, with some spike around 7-9.

16. Hospitalization patterns

17. When? A: During the school year April, May Oct as most common months.
Summer
WHA 2013 Data

18. How much and how often?
Most Medicaid children were hospitalized only once in 2013
When kids were readmitted, half the time it happened within 30 days
Almost always <90 days
Median time to readmission was just under 1 month
75% of kids who were readmitted, did so in under 70 days
Note: can’t tell readmissions from current WHA data source; only from Medicaid.

19. How Long?
Average length of stay (LOS) for all admissions just under 7 days
LOS differs by age, race and gender
Boys, African-Americans and elementary age kids stay longest
Differences are slight, but statistically significant:
Average LOS across groups: 6-9 days
From WHA Data:
Overall LOS for 2013, all youth under 18
no real difference in LOS by payer type (MA vs. Private)

20. Additional Services

21. Of Medicaid Kids Hospitalized in 2013…
92% had some outpatient services during the year
36% had crisis intervention
20% had mental health day treatment
5% had in-home services
3% were in CCS
Contrast with other kids using MH services, but who weren’t hospitalized:
100% had outpatient services
10% used crisis intervention
1% used in-home services
1% were in CCS

22. What county-level factors impact hospitalizations?

23. What Has the Biggest Impact?
Geography
Rural vs. Urban
DHS Region
Proximity to hospital
Racial Composition
Especially American Indian
Based on 3-year average admission rates using all admissions (WHA ).
In most models, rural loses significance when controlling for DHS region and proximity to hospital. Proximity to hospital has a huge effect.

24. Rural vs. Urban Matters…
…but similarly rural counties differ a lot.
Based on WHA admissions. Rural indicator is from County Health Rankings. 0=most urban; 100=most rural.
Rates here are based on 3 year averages.

25. Locations of Hospitals Admitting Youth
Primary Hospitals
(90% of admissions)
(MN)
Secondary Hospitals (10% of admissions)

26. Proximity to Hospital vs. Inpatient Rates
Outliers: Vilas and Menominee. They have higher hospitalization rates than expected based on proximity to hospital.
This effect is very strong and significant: Counties housing a hospital are more likely to have high rates of hospitalization.

27. Inpatient Rates by DHS Region
WHA Data

28. What about services?
Looking across the state, we don’t see any blanket effect for programs (CCS, CST, C-LTS)
There is a small effect for rate of outpatient MA services
More services, lower hospitalizations
There is a modest effect for crisis services
More crisis services, fewer hospitalizations
BUT, not quite statistically significant
Better ways to capture crisis than MA data?

29. Possible Explanations for Lack of Program Effects
Service effectiveness differs widely at the local level, so programs cancel each other out when lumped together
Data is not up to date and includes counties with lapsed programs, thereby diluting the effect
Counties are using hospitalizations as a reason to enroll kids in the program (vs. prevention)
Services are effective but the reach is too small
Services ineffective
Counties that opted for services (before statewide expansion) differ in some important way from other counties
OTHERS?

30. Summary Wisconsin has a high rate of youth hospitalization
Foster children and youth of color have the highest rates of hospitalization and are hospitalized at a younger age
On average, counties that house or adjoin a hospital with youth psychiatric beds have higher admission rates.
DHS region is another factor