data and trends...2016-2020 strategic plan 27 strategic trends device mesh • expanding set of...

2016-2020 Strategic Plan

1

Data and Trends


2

Strategic Planning • Remembering our history. • Looking forward for the next five

(5) years.

• Positioning the Department for the future.


3

Requirements Under Chapter 46.23 (3)(am)1.of the Wisconsin State Statutes: • Inventory of existing resources; • Identifies needed new resources and services; • Has a plan for meeting the health, mental health, and

social needs of families and individuals; • Be based on a need survey of prevalence and

incidence of various disabilities; • Long-range goals and intermediate-range plans; • Details priorities and estimated costs; • Provides for coordination of local services and

coordination of care; • Requires a public hearing.


4

Presentation will examine: DCDHS

• Workforce • Information Technology • Community Demographics • Income & Poverty • Housing • Employment • Disability Areas


5

In the next five years: There will be a new President finishing up his/her current term in office. Mid term elections for the House and Senate will have passed. There will be another three (3) years of the current Governor. There will either be a second term of the current Wisconsin Governor starting or a new leader.


6

Workforce


7

5.7 4.4 5.0

84.9

4.0 8.5 8.1

78.8

0

10

20

30

40

50

60

70

80

90

Asian Black/A.A. Hispanic White

Perc

ent o

f Per

sons

Age

20-

74

Race/Ethnic Category

Comparison of Dane County Workforce Age 20-74 to DCDHS Active Employees by Race/Ethnic Status

Dane County

DCDHS

Presenter

Presentation Notes

Sources: U.S. Census Bureau, American Fact Finder, 2014 American Community Survey 1-Year Estimates. Teri Taylor, Account Clerk II, DCDHS. Report received 10.16.2015.


8

6.8

28.5

8.9 9.3 5.7

10.2

7.6 1.1

21.9

DCDHS Active Employees - Length of Time Based on Hire Date

<1 Year

1-3 years

4-6 years

7-9 years

10-12 years

13-15 years

16-18 years

19 years

20 years +

Presenter

Presentation Notes

Source: Data compiled from October 16, 2015 report prepared by Teri Taylor, Account Clerk II, DCDHS from multiple data sources including Access database, E-Personality, and Payroll.


9

56.7

35.8 33.2

24.8

0

10

20

30

40

50

60

Admin ACS CYF EAWS

Perc

ent o

f Em

ploy

ees

Percent of DCDHS Employees Age 50 and Older by Selected Divisions

Presenter

Presentation Notes

Source: Data compiled from October 16, 2015 report prepared by Teri Taylor, Account Clerk II, DCDHS from multiple data sources including Access database, E-Personality, and Payroll. 157 employees (excluding Badger Prairie) are 50 years of age or older. That is 33.3% of the workforce.


10

11.0 7.7

71.4

11.1 12.8 7.3

7.5

7.7 44.4

4.3 3.7

77.4 84.6

28.6

44.4

78.7 84.1

92.9

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

CYF Admin ProgramLeader

SSS SW SR SW SW Sup

Perc

ent o

f Em

ploy

ees

Division or Class Specification

Race/Ethnic Status of Active CYF Employees by Select Class Specifications as of 10.16.2015

White

Hispanic

Black/A.A.

Asian

Nat.Amer

Presenter

Presentation Notes



11

33.2

57.7

14.3

50.0

2.1

38.6

46.2

0

10

20

30

40

50

60

70

CYF Admin Program Leader SSS SW SR SW SW Sup

Perc

ent o

f Em

ploy

ees

Percent of Active CYF Employees Age 50 and Older

Presenter

Presentation Notes



12

2.5 7.4 6.7 3.3 2.9

3.7 1.7

7.4

92.5 97.1

81.5 93.3

100.0

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

ACS Admin Info & Asst. Spec. SW SR SW

Perc

ent o

f Em

ploy

ees


Race/Ethnic Status of Active ACS Employees by Select Class Specifications as of 10.16.2015

White

Hispanic

Black/A.A.

Asian

Presenter

Presentation Notes



13

35.8

61.8

33.3

0.0

43.1

0

10

20

30

40

50

60

70

ACS Admin Info & Asst. Spec. SW SR SW

Perc

ent o

f Em

ploy

ees

Percent of Active ACS Employees Age 50 and Older

Presenter

Presentation Notes



14

0.8 8.3 8.3 6.7 9.2

8.3

11.1

9.9 6.7

13.2

17.4

13.3

19.7 33.3

63.6 73.3

57.9

83.3

55.6

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

EAWS Admin ESS Lead ESS ESS Sup

Perc

ent o

f Em

ploy

ees


Race/Ethnic Status of EAWS Active Employees by Select Class Specifications as of 10.16.2015

White

Hispanic

Black/A.A.

Asian

Native Am.

Presenter

Presentation Notes



15

24.8 26.7

15.8

33.3

44.4

0

5

10

15

20

25

30

35

40

45

50

EAWS Admin ESS Lead ESS ESS Sup

Perc

ent o

f Em

ploy

ees

Percent of Active EAWS Employees Age 50 and Older

Presenter

Presentation Notes



16

0

10

20

30

40

50

60

Admin ACS CYF EAWS

Perc

ent

Generation of Active DCDHS Employees by Division

Silent

Baby Boomer

Generation X

Millennials

Presenter

Presentation Notes

Source: Data compiled from October 16, 2015 report prepared by Teri Taylor, Account Clerk II, DCDHS from multiple data sources including Access database, E-Personality, and Payroll. While there is some variation to the dates for each generation, Pew Research Centers defines the generations as follows: The Millennial Generation Born: 1981-1997 Age of Adults in 2015: 18 to 34 Generation X Born: 1965 to 1980 Age in 2015: 35 to 50 The Baby Boom Generation Born: 1946 to 1964 Age in 2015: 51 to 69 The Silent Generation Born: 1928 to 1945 Age in 2015: 70 to 87


17

2.1 13.0

31.3

50.6

66.7

36.4

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

CYF Social Worker Econ Support Spec.

Perc

ent

Generation for Active DCDHS Employees in Select Job Classifications

Millennials

Generation X

Baby Boomer

Presenter

Presentation Notes



18

Characteristics of Millennials • They are used to working in teams and want to make friends with

people at work. • They work well with diverse coworkers. • They have a “can-do” attitude about work and look for feedback

about how they are doing frequently – even daily. • They are positive and confident, ready to take on the world. • They seek a challenge and do not want to experience boredom. • They are used to balancing many activities such as teams,

friends, philanthropic activities and want flexibility in scheduling and a life away from work.

• Millennials need to see where their career is going and what they need to do to get there.

• Millennials are the most connected generation in history.

Presenter

Presentation Notes

Source: National Child Welfare Workforce Institute, Workforce Development Framework, June 2015, web. 10/20/2015.


19


20

285,700

146,200

114,200

61,200

328,800

185,500

140,200

67,000

0 50,000 100,000 150,000 200,000 250,000 300,000 350,000

CYF and School Social Workers

Healthcare Social Workers

MH and AODA Social Workers

All Other Social Workers

Number of Jobs

2012 National Employment of Social Workers by Occupational Title and Projected Employment 2022

2022 Projected Employment 2012 Employment

Presenter

Presentation Notes

Source: U.S. Bureau of Labor Statistics, Employment Projections. Accessed, 10.9.2015. 19% employment increase overall. 23% increase for mental health and substance abuse social workers. 27% increase for healthcare social workers. 15% increase for CYF and school social workers.


21

4,074

3,050

1,628

873

4,237

3,409

1,745

867

0 500 1,000 1,500 2,000 2,500 3,000 3,500 4,000 4,500

CYF and School Social Workers

Healthcare Social Workers

MH and AODA Social Workers

All Other Social Workers

Number of Jobs

2012 Employment of Social Workers by Occupational Title and Projected Employment in 2022 for Wisconsin

2022 Projected Employment 2012 Employment

Presenter

Presentation Notes

Source: Wisconsin Long-Term Occupational Employment Projections, 2012-2022, WorkNet. Accessed: 10.9.2015. 11.8% increase for healthcare social workers. 7% increase for mental health and substance abuse social workers. 4% increase for CYF and School Social Workers.


22

Recruitment Strategies for Child Welfare • Increase presence at job and career fairs

at universities, community colleges, middle and high schools and maintain connections with interested students over time;

• Provide fiscal incentives and benefits, i.e., housing, salary bonuses, flexible scheduling;

• Develop a scholarship program for BSW and MSW degrees;

• Conduct targeted recruitment (historically Black and Hispanic universities) and from non-traditional audiences (teachers, law enforcement);

• Develop career ladders in-house; • Develop realistic job preview videos.

Presenter

Presentation Notes

Source: National Child Welfare Workforce Institute, Workforce Development Framework, June 2015, web. 10/20/2015.


23

Recruiting Millennials Social Networks – both on-line and off-line such as, LinkedIn, Twitter, Google+, and even blogs. Employee Referrals.

Presenter

Presentation Notes

Source: Eric Holtzclaw, Company Strategist, Hiring Millennials? Here’s What You Need to Know. , Inc. September 24, 2012.


24

Key Points • 1/3 of DCDHS Staff (excluding BPHCC) are

currently age 50 and older – meaning they will be 55 or older in five years.

• Millennials are not just coming, they have

arrived. They bring different expectations to the work place.

• There is a pending nation-wide shortage of

social workers, CNAs, and nurses.


25

Feedback from presentations • EAWS - Developing social media strategy for

hiring.

• EAWS – How do we better retain and train Millenials?

• CYF – Online application is a barrier to applicants.

• ACS & CYF – Need to home-grow staff and career ladders.

• ACS – Need to have a more ethnically diverse workforce within DCDHS and POS community.

• ACS – Retirement in DCDHS management.


26

Information Technology


27

Strategic Trends Device Mesh • Expanding set of endpoints people use

to access applications and information or to interact with people, social communities, governments and businesses. This includes mobile devices, wearable, consumer and home electronics, sensors, etc.

3-D Printing Adaptive Security Architecture

Presenter

Presentation Notes

Source: ITBusinessEdge, quoting Gartner in article Top 10 Strategic Technology Trends for 2016, web. 23 October 2015.


28

Advanced Machine Learning and Smart Machine Implementations • Machine learning gives rise to a

spectrum of smart machine implementations – i.e., robots, autonomous vehicles, virtual personal assistants (VPAs). Instead of interacting with menus, forms and buttons on a smartphone, the user speaks to an app, which is an intelligent agent. Siri and Cortana are precursors to autonomous agents.

Information of Everything • To link data from all different data

sources.

Presenter

Presentation Notes

Source: ITBusinessEdge, quoting Gartner in article Top 10 Strategic Technology Trends for 2016, web. 23 October 2015.


29

Internet of Things to Internet of Services • Movement to being able to conduct

business on the Internet – Texas Department of Family and Protective Services allows on-line reporting of non-emergency suspected cases of abuse, neglect, etc. for adults and children; Los Angeles County – Suspected Child Abuse Reporting System (SCAR) for mandated reporters.

Videoconferencing, Webinars, etc. • DCDHS Staff are participating more

frequently in webinars and video conferences plus creating on-line training for external staff. It is expected this trend will continue.

Presenter

Presentation Notes

For SCAR system, also need to make phone call and obtain a referral number.


30

DCDHS Devices in the field, i.e.,

I-Pads. Need to replace every 4 years or so.

Device Management – pending action by DIM. Will be upgrading solution in 2016 or 2017.


31

DCDHS Enhance DCDHS Web

App to incorporate additional workflow and integration of budget and accounting systems.

Redesign external web site to make it more user friendly.


32

DCDHS Reporting – improve

quality and information available.

Dashboards and data analytics.

Increased electronic document management.

User training.


33

Component 1997 2015

DIM IT Application Development Staff • Application Development Manager

(HS Team Lead) • IT Specialists

1.0 FTE 2.0 FTE

1.0 * 2.0 FTE**

Number of End Users 85 421+

Applications Supported with External Users

No Yes

*Application Development Manager - in 2015 has other assignments besides Human Services. ** Currently 1 vacancy.

Division of Information Management – HS Application Development Support


34

Component 1997 2015

Systems Coordinator/Data Base Support Supervisor

1.0 FTE

1.0 FTE

HS IT Specialists/Data Base Coordinators 3.0 FTE 5.0* FTE 1 LTE

Number of End Users 85 421+

* Currently 1 vacancy.

DCDHS IT Support


35

Key Points • Staffing levels in the Division of Information

Management are at 1997 levels and do not reflect the ever-increasing number of supported applications and end-users.

• There is an extensive backlog of current projects. • There is a trend to providing more service-

related functions on-line.


36

Feedback from presentations • EAWS – We need to meet the technological

needs of our both high-tech and low-tech customers and staff.

• EAWS – Using new technology to conduct our business such as LiveChat or EBT on mobile devices.

• EAWS – New Call Center Software in late 2016.

• All - Lack of IT resources is an impediment to efficiency and effectiveness of the Department.

• Need for new and better data.

• IT not staffed to meet current or projected needs.


37

Facilities


38

Northport Office HVAC System – bring up to standards along with lighting and electrical. (Currently working with H&H on identifying building needs before moving into design phase. Project will most likely last into 2018). Parking Lots Job Center and BPHCC – budgeted in 2016. Stoughton and upper NPO lot. Re-lamping Lighting to LED Lights Job Center begins in Nov. 2015. Based on results, may consider other DCDHS locations.

Presenter

Presentation Notes

We have budgeted for the HVAC upgrade in our 2016 Capital Budget, as well as, carrying over from previous years. Source: Greg Brockmeyer, Director of Facilities & Services per e-mail on 10.23.2015.


39

Job Center There is discussion regarding moving the library services out of the Job Center which may come to realization in the next five (5) years.


40

Demographics


41

0

100,000

200,000

300,000

400,000

500,000

600,000

700,000

1980 1985 1990 1995 2000 2005 2010 2015 2020 2025 2030 2035 2040

Popu

latio

n

Dane County Population - 1980-2040

Actual

Projection

Presenter

Presentation Notes

Actuals: U.S. Census Bureau – July 1 Estimates Projections: Demographic Services Center, Division of Intergovernmental Relations, WI Department of Administration: http://www.doa.state.wi.us/Divisions/Intergovernmental-Relations/Demographic-Services-Center/Wisconsin-Population-Projections/ County Age-Sex Population Projections, 2010-2040 – http://www.doa.state.wi.us/Documents/DIR/Demographic%20Services%20Center/Projections/Proj_cofinal_2010_2040Web.xlsx 1980-1989 - Intercensal Estimates of the Resident Population of States and Counties 1980-1989 Issued March 1992 Source: U.S. Bureau of the Census Population Estimates and Population Distribution Branches – Dane County FIPS code is 55025 http://www.census.gov/popest/data/counties/totals/1980s/tables/e8089co.txt 1990-2000 - Table CO-EST2001-12-55 - Time Series of Wisconsin Intercensal Population Estimates by County: April 1, 1990 to April 1, 2000 Source: Population Division, U.S. Census Bureau Release Date: April 17, 2002 -http://www.census.gov/popest/data/intercensal/st-co/files/CO-EST2001-12-55.pdf 2000-2010 - http://www.census.gov/popest/data/intercensal/county/CO-EST00INT-01.html 2010-2014 - https://www.census.gov/popest/data/counties/totals/2014/files/CO-EST2014-alldata.csv


42

Presenter

Presentation Notes

Source: Capital Area Regional Planning Commission – Page 17 of 96 https://danedocs.countyofdane.com/PDF/capd/2012_postings/Publications/2010_Regional_Trends_web.pdf


43

2010 2020 % Change

Cities (ex. Madison) 104,780 117,275 11.9

Villages 76,931 81,130 5.5

Towns 68,924 76,075 10.4

Total (ex. Madison) 250,635 274,480 9.5

City of Madison 233,209 251,550 7.9

Projected Growth in Dane County Population By Municipality Type

Presenter

Presentation Notes

Source: Wisconsin Department of Administration, Demographic Services Center.


44

Growth by Annexation

Municipality Annexed To Begins Complete

T. of Madison City of Madison City of Fitchburg

2002 10.30.2022

T. of Blooming Grove City of Madison 2015 10.31.2027

T. of Burke City of Madison City of Sun Prairie V. of DeForest

10.27.2036

Presenter

Presentation Notes

Sources: City of Madison, City of Fitchburg and Town of Madison Cooperative Plan dated 2.12.2007. Town of Blooming Grove and City of Madison Cooperative Plan Under Section 66.0307, Wisconsin Statutes dated 4.20.2006. Final Town of Burke, Village of DeForest, City of Sun Prairie and City of Madison Cooperative Plan dated 1.5.2007.


45

2010 2020 % Change

City of Verona 10,619 12,800 20.5

City of Sun Prairie 29,364 34,770 18.4

Town of Bristol 3,765 4,455 18.3

V. of Cottage Grove 6,192 7,190 16.1

V. of Waunakee 12,097 13,850 14.5

V. of Dane 995 1,135 14.1

Town of Middleton 5,877 6,695 13.9

Projected Growth in Dane County Population For Select Municipalities

Presenter

Presentation Notes



46

2010 2020 % Change

City of Verona 10,619 12,800 20.5

City of Sun Prairie 29,364 34,770 18.4

Town of Bristol 3,765 4,455 18.3

V. of Cottage Grove 6,192 7,190 16.1

V. of Waunakee 12,097 13,850 14.5

V. of Dane 995 1,135 14.1

Town of Middleton 5,877 6,695 13.9

Projected Growth in Dane County Population For Select Municipalities

14730 14537

14138 16346

23487 20655

17917 15960 16245

17854 12205

16240 12684

8342 5728

4878 4366

5350

15510 15337 14735

16523 23765

21786 18495

16236 16343

17073 16677

15354 12097

7558 4931

3707 2860 2424

30000 20000 10000 0 10000 20000 30000

Under 55-9

10-1415-1920-2425-2930-3435-3940-4445-4950-5455-5960-6465-6970-7475-7980-84

85 and older

Age

Gro

up

Dane County Population Distribution 2010: All Races Male Female

Presenter

Presentation Notes



47 30,000 20,000 10,000 0 10,000 20,000 30,000

0-45-9

10-1415-1920-2425-2930-3435-3940-4445-4950-5455-5960-6465-6970-7475-7980-8485-89

90 & over

Age

Gro

oups

Dane County Age-Sex Pyramid, 2015 and 2040 Population Projections

2040 Males 2015 Males 2040 Females 2015 Females

Presenter

Presentation Notes

Projections: Demographic Services Center, Division of Intergovernmental Relations, WI Department of Administration: http://www.doa.state.wi.us/Divisions/Intergovernmental-Relations/Demographic-Services-Center/Wisconsin-Population-Projections/ County Age-Sex Population Projections, 2010-2040 – http://www.doa.state.wi.us/Documents/DIR/Demographic%20Services%20Center/Projections/Proj_cofinal_2010_2040Web.xlsx


48

16,785 17,210 16,080

11,500 8,040 8,530

552

8,000 8,400

10,700

8,100 4,650

0

5,000

10,000

15,000

20,000

25,000

30,000

2015 2020 2025 2030 2035 2040

Projected Population Growth in Dane County in Five Year Bins, 2015 to 2040, Age 0-59 vs. 60+

Age 0-59

Age 60+

Presenter

Presentation Notes

Projections: Demographic Services Center, Division of Intergovernmental Relations, WI Department of Administration: http://www.doa.state.wi.us/Divisions/Intergovernmental-Relations/Demographic-Services-Center/Wisconsin-Population-Projections/ County Age-Sex Population Projections, 2010-2040 – http://www.doa.state.wi.us/Documents/DIR/Demographic%20Services%20Center/Projections/Proj_cofinal_2010_2040Web.xlsx


49

60,000

70,000

80,000

90,000

100,000

110,000

120,000

Popu

latio

n

Dane County Population Age 0-17

108,748

84,455

Presenter

Presentation Notes

Source: Office of Juvenile Justice and Delinquency Prevention – Easy Access to Juvenile Populations http://www.ojjdp.gov/ojstatbb/ezapop/asp/comparison_selection.asp?selState=1 National Center for Health Statistics (2015). Vintage 2014 postcensal estimates of the resident population of the United States (April 1, 2010, July 1, 2010-July 1, 2014), by year, county, single-year of age (0, 1, 2, .., 85 years and over), bridged race, Hispanic origin, and sex. Prepared under a collaborative arrangement with the U.S. Census Bureau. Available online from http://www.cdc.gov/nchs/nvss/bridged_race.htm as of June 26, 2015, following release by the U.S. Census Bureau of the unbridged Vintage 2014 postcensal estimates by 5-year age group on June 26, 2014. [Retrieved 7/8/2015]


50

1,260,000

1,280,000

1,300,000

1,320,000

1,340,000

1,360,000

1,380,00019

90

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

Popu

latio

n

WI Population Age 0-17

1,302,869

1,300,189

Presenter

Presentation Notes



51

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

80,000

90,000

Popu

latio

n

Dane County - White vs. Persons of Color, Age 0-17

78,017 75,522

8,933

30,731

Presenter

Presentation Notes



52

0

250,000

500,000

750,000

1,000,000

1,250,00019

90

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

Popu

latio

n

WI - White Non-Hispanics vs. Persons of Color, Age 0-17

957,116

1,127,211

343,073

175,658

Presenter

Presentation Notes



53

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

Popu

latio

n

Dane County Minority Populations Age 0-17

Hispanic

Black

Asian

Am. Ind.

Presenter

Presentation Notes



54

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

Dane County - White vs. Persons of Color, Age 0-17

PoC

White

Presenter

Presentation Notes



55

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013Number 306 343 321 325 364 315 330 258 225 243 164

0

50

100

150

200

250

300

350

400

Num

ber o

f Birt

hs

Year

Births to Teens in Dane County 2003-2013

Presenter

Presentation Notes

Source: Wisconsin Department of Health Services, Division of Public Health, Office of Health Informatics.


56

Presenter

Presentation Notes

Source: 2010 U.S. Census Block Group Data. Prepared by Aaron Krebs, Capitol Area Regional Planning Commission.


57

0%

5%

10%

15%

20%

25%

30%

35%

2008-2010 2009-2011 2010-2012 2011-2013

% w

ith a

Dis

abili

ty

Dane County - Percent of Population with a Disability by Age Group

Age 65+

Age 18-64

Age 5-17

Under 5

Presenter

Presentation Notes

Source: U.S. Census Bureau – American Community Survey (ACS) – 3-Year Averages – Table S1810 – Disability Characteristics http://factfinder.census.gov/faces/nav/jsf/pages/searchresults.xhtml


58

0%

2%

4%

6%

8%

10%

12%

14%

16%

18%

20%

2008-2010 2009-2011 2010-2012 2011-2013

% w

ith a

Dis

abili

ty

Dane County - Percent of Population with a Disability by Race

BlackWhiteAsian

Presenter

Presentation Notes



59

0%

1%

2%

3%

4%

5%

6%

7%

8%

9%

10%

2008-2010 2009-2011 2010-2012 2011-2013

% w

ith a

Dis

abili

ty

Dane County - Percent of Population with a Disability by Ethnicity

White Non-Hispanic

Hispanic or Latino

Presenter

Presentation Notes



60

0.0%

0.2%

0.4%

0.6%

0.8%

1.0%

2008-2010 2009-2011 2010-2012 2011-2013

% w

ith a

Dis

abili

ty

Dane County - Type of Disability for those Under 5

Hearing Difficulty

Vision Difficulty

Presenter

Presentation Notes



61

0%

1%

2%

3%

4%

5%

2008-2010 2009-2011 2010-2012 2011-2013

% w

ith a

Dis

abili

ty

Dane County - Type of Disability for those Age 5-17

Cognitive Difficulty

Self-Care Difficulty

Ambulatory Difficulty

Hearing Difficulty

Vision Difficulty

Presenter

Presentation Notes



62

0%

1%

2%

3%

4%

5%

2008-2010 2009-2011 2010-2012 2011-2013

% w

ith a

Dis

abili

ty

Dane County - Type of Disability for those Age 18-64

Cognitive DifficultyAmbulatory DifficultyIndependent Living DifficultyHearing DifficultySelf-Care DifficultyVision Difficulty

Presenter

Presentation Notes



63

0%

5%

10%

15%

20%

25%

30%

2008-2010 2009-2011 2010-2012 2011-2013

% w

ith a

Dis

abili

ty

Dane County - Type of Disability for those Age 65+

Ambulatory DifficultyHearing DifficultyIndependent Living DifficultyCognitive DifficultySelf-Care DifficultyVision Difficulty

Presenter

Presentation Notes



64

Income and Poverty


65

2007 2008 2009 2010 2011 2012 2013Income $53,737 $60,794 $61,562 $58,002 $60,489 $60,119 $60,326

$48,000

$50,000

$52,000

$54,000

$56,000

$58,000

$60,000

$62,000

$64,000

Median Household Income in Dane County 2007-2013

Presenter

Presentation Notes

Source: Wisconsin Department of Health Services, Office of Health Information, Health Analytics Section, Public Health Profiles, Wisconsin web. 22 October 2015.


66

2005 2006 2007 2008 2009 2010 2011 2012 2013 2014Dane $53,537 $57,693 $60,791 $61,441 $57,687 $58,661 $60,345 $59,792 $60,081 $61,582Wisconsin $47,105 $48,772 $50,578 $52,094 $49,993 $49,001 $50,395 $51,059 $51,467 $52,622

$-

$10,000

$20,000

$30,000

$40,000

$50,000

$60,000

$70,000

Dolla

rs

Median Household Income in Past 12 Months Dane County and Wisconsin

Presenter

Presentation Notes

Source: U.S. Census Bureau, S1901, Income in the Past 12 Months, American Community Survey 1-Year Estimates. Median Household Incomes peaked in Dane County and Wisconsin in 2008. Median household income in Dane County by 2014 was estimated to have increased by $141 or 0.2% since 2008. Median household income in Wisconsin by 2014 was estimated to have increased by $528 or 1.0% since 2008.


67

2005 2006 2007 2008 2009 2010 2011 2012 2013 2014White $55,594 $59,949 $62,664 $64,610 $60,940 $61,593 $62,445 $63,303 $62,925 $64,946Black $31,426 $33,228 $34,564 $26,593 $30,472 $34,293 $20,664 $26,423 $26,351 $30,744Hispanic $33,147 $42,573 $60,459 $39,836 $48,340 $37,438 $36,292 $30,293 $31,712 $44,490

$-

$10,000

$20,000

$30,000

$40,000

$50,000

$60,000

$70,000

Median Household Income in Past 12 Months in Dane County for Selected Races

Presenter

Presentation Notes

Source: U.S. Census Bureau, S1903, Median Income in the Past 12 Months, American Community Survey 1-Year Estimates.


68

2005 2006 2007 2008 2009 2010 2011 2012 2013 201415-24 Years $23,958 $21,352 $23,981 $22,138 $18,675 $23,304 $23,066 $22,555 $22,212 $24,35825-44 Years $56,350 $59,016 $63,286 $64,355 $62,844 $60,977 $61,262 $61,180 $61,105 $66,02345-64 Years $68,217 $73,730 $74,138 $76,171 $76,205 $71,439 $76,857 $75,579 $74,624 $78,96865+ Years $39,449 $41,304 $41,537 $48,829 $44,886 $49,803 $47,943 $48,764 $46,783 $47,232

$-

$10,000

$20,000

$30,000

$40,000

$50,000

$60,000

$70,000

$80,000

$90,000

Median Household Income in the Past 12 Months in Dane County by Age of Householder

Presenter

Presentation Notes

Source: U.S. Census Bureau, S1903, Median Income in the Past 12 Months, American Community Survey 1-Year Estimates.


69

Presenter

Presentation Notes

Source: 2008-2014 Madison Neighborhood Indicators Project, City of Madison and the University of Wisconsin Applied Population Lab, web., 7 October 2015.


70

$9.

07

$9.

31

$9.

62

$9.

93

$10

.20

$10

.61

$10

.61

$10

.75

$11

.09

$11

.33

$11

.47

$11

.66

$-

$2.00

$4.00

$6.00

$8.00

$10.00

$12.00

$14.00

2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016

Dolla

rs

Dane County Hourly Living Wage Amounts by Year

Presenter

Presentation Notes

Source: Dane County Purchasing, Living Wage Information web. 24 October 2015. The Dane County Living Wage (the hourly wage equal to 100% of the federal poverty level for a family of four divided by 2,080 hours) increased by 14% between 2008 and 2014.


71

Presenter

Presentation Notes

Source: 2008-2014 Madison Neighborhood Indicators Project, City of Madison and the University of Wisconsin Applied Population Lab, web., 7 October 2015.


72

12.3% 11.6%

12.0% 12.3% 12.9%

0%

1%

2%

3%

4%

5%

6%

7%

8%

9%

10%

11%

12%

13%

14%

15%

2005-2009 2006-2010 2007-2011 2008-2012 2009-2013

S1701 - Poverty Status in the past 12 Months

Presenter

Presentation Notes

Source: U.S. Census Bureau, S1701, Poverty Status in the Past 12 Months, American Community Survey 5-Year Estimates.


73

2005-2009 2006-2010 2007-2011 2008-2012 2009-2013200 % of P. L. 23.9% 24.0% 24.8% 25.6% 26.4%185 % of P. L. 22.1% 22.1% 22.8% 23.7% 24.4%150 % of P. L. 17.9% 17.5% 18.1% 18.8% 19.3%125 % of P. L. 15.0% 14.6% 15.1% 15.6% 16.1%100% of P.L 12.3% 11.6% 12.0% 12.3% 12.9% 50 % of P. L. 6.6% 6.1% 6.3% 6.5% 6.8%

0%

5%

10%

15%

20%

25%

30%

% o

f Pop

ulat

ion

for w

hom

po

vert

y st

atus

is d

eter

min

ed

Percent of Population below Given Poverty Level's

Presenter

Presentation Notes



74

2005-2009 2006-2010 2007-2011 2008-2012 2009-2013Male 11.1% 10.6% 10.7% 11.2% 11.7%Female 13.4% 12.6% 13.2% 13.4% 14.0%

0%

1%

2%

3%

4%

5%

6%

7%

8%

9%

10%

11%

12%

13%

14%

15%

S1701 - Poverty Status in the past 12 Months by Sex

Presenter

Presentation Notes



75

2005-2009 2006-2010 2007-2011 2008-2012 2009-2013 Under 18 years 10.7% 11.1% 12.0% 12.7% 13.4% 18 to 64 years 13.6% 12.7% 13.1% 13.4% 14.0% 65 years and over 5.7% 5.1% 4.5% 4.1% 4.6%

0%

1%

2%

3%

4%

5%

6%

7%

8%

9%

10%

11%

12%

13%

14%

15%

S1701 - Poverty Status in the past 12 Months by Age Group

Presenter

Presentation Notes



76

2005-2009 2006-2010 2007-2011 2008-2012 2009-2013 Black or African American 37.0% 34.2% 36.9% 37.5% 39.6% Asian 22.2% 21.0% 22.8% 21.6% 22.4% White 10.3% 9.5% 9.6% 9.9% 10.2%

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

Pove

rty

Stat

us

S1701 - Poverty Status in the past 12 Months by Race

Presenter

Presentation Notes



77

2005-2009 2006-2010 2007-2011 2008-2012 2009-2013Hispanic or Latino origin (of any

race) 17.6% 14.8% 16.4% 19.6% 24.1%

White alone, not Hispanic or Latino 10.0% 9.3% 9.3% 9.4% 9.5%

0%

5%

10%

15%

20%

25%

30%

Pove

rty

Stat

us

S1701 - Poverty Status in the past 12 Months by Ethnicity

Presenter

Presentation Notes



78

Presenter

Presentation Notes

Source: FoodShare Data from Antonio Esterrich – Received in March 2014 - Collect Events>Kernel Density Cell Size 150x150 with bands of 1320ft. = ¼ Mile


79

Presenter

Presentation Notes

Source: FoodShare Data from Antonio Esterrich – Received in March 2015 – Collect Events>Kernel Density Cell Size 150x150 with bands of 1320ft. = ¼ Mile


80

2005-2009 2006-2010 2007-2011 2008-2012 2009-2013 Percent high school graduate or higher 93.90% 94.30% 94.40% 94.70% 94.70% Percent bachelor's degree or higher 44.40% 45.40% 45.40% 45.80% 46.60%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

S1501 - Percent of Population with High School Graduate or Higher vs. Bachelor's Degree or Higher, Age 25+

Presenter

Presentation Notes



81

0%

10%

20%

30%

40%

50%

60%

70%

80%

2005-2009 2006-2010 2007-2011 2008-2012 2009-2013

Dane County - Educational Attainment Bachelor's Degree or Higher, Age 25+

Asian Alone

White Alone, Not Hispanic or Latino

Hispanic or Latino

Black Alone

Presenter

Presentation Notes

Source: U.S. Census Bureau, C15002A-C15002I, Sex By Educational Attainment for the Population 25 Years and Over by Race and Ethnicity, American Community Survey 5-Year Estimates.


82

2005-2009 2006-2010 2007-2011 2008-2012 2009-2013 Less than high school graduate 23.4% 20.5% 20.2% 21.0% 23.9% High school graduate (includes

equivalency) 8.3% 9.1% 9.2% 9.9% 10.4%

Some college, associate's degree 5.4% 6.2% 6.8% 7.4% 7.9% Bachelor's degree or higher 3.2% 3.3% 3.5% 3.4% 3.5%

0%

5%

10%

15%

20%

25%

30%

S1701 - Poverty Status in the past 12 Months by Educational Attainment

Presenter

Presentation Notes



83

23.9 26.2

28.5 30.7 30.2 31.3 31.6 31.1 32.1

33.6

37.1 39.2

40.7 41.5 42.2 41.5

0

5

10

15

20

25

30

35

40

45

2008 2009 2010 2011 2012 2013 2014 2015

Perc

ent o

f You

th Q

ualif

ying

Percent of Youth Enrolled in Public Schools Qualifying for Free & Reduced Lunches: Dane County Compared to Statewide 2008 -

2015 Dane County State

Presenter

Presentation Notes

Source: Wisconsin Department of Public Instruction, Wisconsin School Free/Reduced Eligibility Data, Web. 5 October 2015.


84

Presenter

Presentation Notes

Source: 2008-2014 Madison Neighborhood Indicators Project, City of Madison and University of Wisconsin Applied Population Lab, web. 7 October 2015.


85

19.6% 18.5%

6.2% 5.6%

0%

5%

10%

15%

20%

25%

2008-2012 2009-2013

Receipt of SNAP in past 12 Months by Disability Status for Households

House with 1 or more persons with adisability

Households with no persons with adisability

Presenter

Presentation Notes

Source: U.S. Census Bureau, B22010 , RECEIPT OF FOOD STAMPS/SNAP IN THE PAST 12 MONTHS BY DISABILITY STATUS FOR HOUSEHOLDS, American Community Survey 5-Year Estimates.


86

0

2000

4000

6000

8000

10000

12000

14000

16000

18000

20000

22000

J11

M M J S N J12

M M J S N J13

M M J S N J14

M M J S N J15

M M J S

Pers

ons

WI Medicaid Certified Members by Month in Dane County for Elderly & Disabled Coverage and Other Coverage

Elderly & Disabled Other

5,179 3,661

12,024

13,546

Presenter

Presentation Notes

Other Coverage = TB related, Medicare Beneficiaries, Well Women MA, Subsidized Adoption, etc. Source: Wisconsin Medicaid


87

0

10000

20000

30000

40000

50000

60000

J11

M M J S N J12

M M J S N J13

M M J S N J14

M M J S N J15

M M J S

Num

ber o

f Mem

bers

BadgerCare Plus Members Certified by Dane County by Month

42,883

50,695

Presenter

Presentation Notes

Source: Wisconsin Medicaid.


88

Housing


89

42.4 40.4

42.9

52.3 50.7 51.9

0

10

20

30

40

50

60

2000 2010 2014

Perc

ent o

f Uni

ts

Percent of Occupied Housing Units That Are Renter Occupied - Dane County and City of Madison

Dane Madison

Presenter

Presentation Notes

Source: U.S. Census Bureau, American Fact Finder, Profile of General Population & Housing Characteristics.


90

2009 2010 2011 2012 2013 2014 2015QT1 3.23 3.73 2.85 2.00 1.88 2.25 2.33QT2 4.40 4.72 3.54 3.21 2.73 3.46 3.43QT3 4.55 3.70 2.57 2.44 2.70 2.83 3.05QT4 3.74 2.94 2.16 2.07 2.05 2.39

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

Perc

ent

MGE Madison Area Rental Vacancy Rates by Year and Quarter

Presenter

Presentation Notes

Source: Madison Gas and Electric, Madison Area Rental Vacancy Rates, Web. 21 October 2015. The vacancy rate represents the percentage of apartments in MGE's service area that appear to be vacant because the electric service is inactive or the service has been moved to the owner's name. By zip code, the third quarter 2015 vacancy rate is lowest in Waunakee at 0.66%, followed by Oregon at 1.38, Cross Plains at 1.41, and Middleton at 1.56. The highest vacancy rate is in the Madison 53715 zip code at 5.39.


91

2006 2007 2008 2009 2010 2011 2012 2013 2014 2015Efficiency 510 525 547 574 610 595 588 614 620 6401 Bedroom 636 656 683 716 761 742 733 734 742 7662 Bedrooms 752 775 807 846 899 877 867 889 898 9283 Bedrooms 1009 1040 1083 1135 1206 1177 1164 1226 1239 12804 Bedrooms 1250 1288 1341 1406 1494 1458 1441 1366 1380 1426

0

200

400

600

800

1000

1200

1400

1600

Dolla

r Am

ount

Fair Market Rents by Federal Fiscal Year for Dane County Efficiency 1 Bedroom 2 Bedrooms 3 Bedrooms 4 Bedrooms

Presenter

Presentation Notes

Source: HUD User, FY 2015 FMR and IL Summary System, FMR History for Dane County, WI, web. 10/14/2015. The fair market rent is the 40th percentile of gross rents for typical, non-substandard, rental units occupied by recent movers in a local housing market. Fair Market Rents (FMR) set limits on what units can be rented; in the HUD voucher program, FMRs set limits on the subsidy provided to the household. Certificate program households cannot rent units with gross rents exceeding the FMR; the recipients receive a subsidy equal to the difference between the gross rent and 30 percent of their incomes. Voucher program households receive a subsidy equal to the difference between the FMR and 30 percent of their monthly incomes.


92

Presenter

Presentation Notes

Source: HUD, Community Planning and Development Maps, http://egis.hud.gov/cpdmaps/, accessed October 14, 2015. LIHTC – stands for Low-Income Housing Tax Credits.


93

7,440

7,727

7,904

8,628

6,800

7,000

7,200

7,400

7,600

7,800

8,000

8,200

8,400

8,600

8,800

2006 2010 2011 2012

Num

ber o

f Uni

ts

Federally Subsidized Rental Units in Dane County by Year

Presenter

Presentation Notes

Source: Homeless Services Consortium, Community Plan to Prevent & End Homelessness in Dane County: 2013 Results, City of Madison, http://www.cityofmadison.com/cdbg/docs/community_plan_to_end_homelessness_final.pdf, p. 11. Web. 14 October 2015.


94

Dane County Section 8 Housing Choice Voucher Program: • Opened waiting list for 4.5 hours on

August 4, 2015. • Said they would accept 1,500 pre-

applications, the number they expected to serve in 1-2 years.

• Received 2,212 pre-applications. • Waiting list was last open in 2007.

Demographics of Pre-Applicants: • 2,212 applications covering 4,865 total

family members. • 1,736 (78.5%) of applications have

female Heads-of-Households. • 314 (14%) of applications have Head-

of-Household age 55+.

• 727 (32.8%) report being homeless. • 1,178 (53.2%) report paying more than

40% of their income in rent and utilities.

• 332 (15%) report living in substandard housing.

Presenter

Presentation Notes

Source: Section 8 Wait List – Who Applied, Housing Dane – The Official Blog of the Dane County Housing Authority., web. 21 October 2015.


95

3046 3062

2836

2386

2170

0

500

1000

1500

2000

2500

3000

3500

2010 2011 2012 2013 2014

Evictions Filed - Dane County by Year

Presenter

Presentation Notes

Sources: Homeless Services Consortium, Community Plan to Prevent & End Homelessness in Dane County: 2013 Results, City of Madison, http://www.cityofmadison.com/cdbg/docs/community_plan_to_end_homelessness_final.pdf, p. 8. Web. 14 October 2015. Kerry Widish, Dane County Clerk of Courts, “RE: Dane County Eviction Filings 2014.” Message to Lori Bastean. 21 October 2015. E-mail.


96

495 432 444 472

423

636 666

522

394 445 475

1131

865 831 875 848

1310 1294

1055

803 881

946

0

200

400

600

800

1000

1200

1400

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

Num

ber

Year

Families and Children in Shelter in Dane County by Year Families Children

Presenter

Presentation Notes

Source: City of Madison, Annual Report on Homeless Persons Served in Dane County, 2013, p. 41. http://www.cityofmadison.com/cdbg/documents/2013DaneCountyHomelessnessReport.pdf . Web. October 14, 2015.


97

1520

1343

1129

1481 1559

1300 1310 1235 1257

1362 1304

433 464 482 523 516 504 497 509 508 549 497

0

200

400

600

800

1000

1200

1400

1600

1800

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

Num

ber

Year

Single Men and Women in Shelter in Dane County by Year Single Men Single Women

Presenter

Presentation Notes

Source: City of Madison, Annual Report on Homeless Persons Served in Dane County, 2013, p. 41. http://www.cityofmadison.com/cdbg/documents/2013DaneCountyHomelessnessReport.pdf . Web. October 14, 2015


98

69 67

61

43 40

27 31

29 32 31

36

0

10

20

30

40

50

60

70

80

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

Num

ber

Year

Unaccompanied Minors In Shelter in Dane County by Year

Presenter

Presentation Notes

Source: City of Madison, Annual Report on Homeless Persons Served in Dane County, 2013, http://www.cityofmadison.com/cdbg/documents/2013DaneCountyHomelessnessReport.pdf . Web. October 14, 2015


99

8092

2484 2476

3207 2978

3636

2622

1605 2003

1654 1654

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

Individuals Turned Away Without Shelter - Dane County by Year

Presenter

Presentation Notes

Source: City of Madison, Annual Report on Homeless Persons Served in Dane County, 2013, p. 41, http://www.cityofmadison.com/cdbg/documents/2013DaneCountyHomelessnessReport.pdf . Web. October 14, 2015.


100

Families Single Men Single WomenUnknown 7 1 51 Year or More 69 70 67< 1 Year 23 28 27< 1 month 1 1 1

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Perc

ent

2013 Homeless Population by Type by Length of Residency in Dane County (Percent)

Presenter

Presentation Notes



101

10

29

18

38

25

41

0

5

10

15

20

25

30

35

40

45

Families Single Men Single Women

Perc

ent

2013 Homeless Population in Dane County by Living Condition Prior to Seeking Shelter (Percent)

Sleeping on Street or in Vehicle Doubling Up

Presenter

Presentation Notes



102

390

347 357 354 346 315

329

370

223 249

210

156 166 193

234

170

0

50

100

150

200

250

300

350

400

450

2006 2007 2008 2009 2010 2011 2012 2013

Num

ber

Year

Number of Single Men and Women in Dane County Shelter System with Mental Health Issues

Single Men Single Women

Presenter

Presentation Notes



103

596

531

469 433

380

280 318 322

132 114

165

73 89 86 76 70

0

100

200

300

400

500

600

700

2006 2007 2008 2009 2010 2011 2012 2013

Num

ber

Year

Number of Single Men and Women in Dane County Shelter System with AODA Issues

Single Men Single Women

Presenter

Presentation Notes



104

Employment


105

3.1% 3.3% 3.5% 3.4%

5.8% 5.9%

5.3% 4.9% 4.7%

3.8% 3.4%

0%

1%

2%

3%

4%

5%

6%

7%

Unemployment Rate

Presenter

Presentation Notes

Source: Bureau of Labor Statistics - Local Area Unemployment Statistics - http://data.bls.gov/cgi-bin/dsrv?la –LAUCN550250000000003


106

0%

5%

10%

15%

20%

25%

2005-2009 2006-2010 2007-2011 2008-2012 2009-2013

Unemployment Rate by Race

Black

Asian

White

Presenter

Presentation Notes

Source: U.S. Census Bureau, S2301- Employment Status, American Community Survey 5-Year Estimates.


107

0%

5%

10%

15%

20%

25%

2005-2009 2006-2010 2007-2011 2008-2012 2009-2013

Unemployment Rate by Ethnicity

Hispanic or Latino

White Non-Hispanic

Presenter

Presentation Notes

Source: U.S. Census Bureau, S2301- Employment Status, American Community Survey 5-Year Estimates.


108

2,320

2,130

1,920

1,850

1,850

1,590

1,350

1,140

1,070

1,020

- 500 1,000 1,500 2,000 2,500

Customer Service Representatives

Laborers and Freight, Stock, and Material Handlers

Registered Nurses

Office Clerks, General

Farmworkers and Laborers

Personal Care Aides

Janitors and Cleaners

Heavy and Tractor-Trailer Truck Drivers

Sales Reps

Nursing Assistants

Number of Average Annual Job Openings

Average Annual Number of Job Openings by Occupations with Most Projected Openings in Wisconsin 2012-2022

Presenter

Presentation Notes

Source: Occupations with Most Openings, Wisconsin Worknet, web. 10.9.2015. Excludes retail sales, food preparation and service workers, cashiers, waiters/waitresses, and bartenders.


109

Feedback from presentations • ACS – There is a lack of workers to fill low

wage caregiver positions.

• ACS – How do we better match people who have less education with the job shortages that are projected?


110

Arrest Data


111

6.3

5.5 5.2

4.5 3.9

7.8

8.5 8.1

7.1

5.8

0

1

2

3

4

5

6

7

8

9

2009-2010 2010-2011 2011-2012 2012-2013 2013-2014

Susp

ensi

on R

ate

School Year

School Suspension Rates: MMSD Compared to Statewide by School Year Statewide MMSD

Presenter

Presentation Notes

Source: Wisconsin School District Performance Report, Wisconsin Department of Public Instruction, WISE Dash Public Portal, Web. 6 October 2015.


112

7.8 8.5 8.1 7.1

5.8

21.9

24.8 23.7

20.8

17.1

0

5

10

15

20

25

30

2009-2010 2010-2011 2011-2012 2012-2013 2013-2014

Susp

ensi

on R

ate

School Year

MMSD School Suspension Rates: All Students Compared to African-American Students by School Year

MMSD African-American

Presenter

Presentation Notes



113

40

18 17

37 33

18

128

173 186

158

172

137

0

20

40

60

80

100

120

140

160

180

200

2008-2009 2009-2010 2010-2011 2011-2012 2012-2013 2013-2014

Num

ber o

f Inc

iden

ts

School Year

MMSD AODA and Drug Incidents Resulting in Suspension Alcohol Drug

Presenter

Presentation Notes



114

0

5,000

10,000

15,000

20,000

25,000

30,000

1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

Adult Arrests by Race Other Black White

Presenter

Presentation Notes

Source: Wisconsin Department of Justice – Bureau of Justice Information and Analysis – Extract of Uniform Crime Reporting (UCR) Data – 1997-2014


115

0

2,000

4,000

6,000

8,000

10,000

12,000

1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

Juvenile Arrests by Race Other Black White

Presenter

Presentation Notes



116

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

9,000

1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

Juvenile Arrests by Race

White Black

Presenter

Presentation Notes



117

0

100

200

300

400

500

600

700

800

900

1,000

1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

Juve

nile

Arr

ests

per

1,0

00 Y

outh

Dane County Juvenile Arrest Rate per 1,000 Youth (age 10-17)

White Black

54.1% decrease 79.4% decrease

Calculation based on Non-Hispanic Population

Presenter

Presentation Notes

Source: Arrest Data - Wisconsin Department of Justice – Bureau of Justice Information and Analysis – Extract of Uniform Crime Reporting (UCR) Data – 1997-2014 Population Data - Office of Juvenile Justice and Delinquency Prevention – Easy Access to Juvenile Populations http://www.ojjdp.gov/ojstatbb/ezapop/asp/comparison_selection.asp?selState=1 National Center for Health Statistics (2015). Vintage 2014 postcensal estimates of the resident population of the United States (April 1, 2010, July 1, 2010-July 1, 2014), by year, county, single-year of age (0, 1, 2, .., 85 years and over), bridged race, Hispanic origin, and sex. Prepared under a collaborative arrangement with the U.S. Census Bureau. Available online from http://www.cdc.gov/nchs/nvss/bridged_race.htm as of June 26, 2015, following release by the U.S. Census Bureau of the unbridged Vintage 2014 postcensal estimates by 5-year age group on June 26, 2014. [Retrieved 7/8/2015]


118

0

1

2

3

4

5

6

7

8

1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

RRI a

t Arr

est

Dane County Juvenile Arrest Relative Rate Index (RRI)

White Black

122.2% increase In 2014, the Juvenile Black Arrest rate was 327 while the Juvenile White Arrest rate was 43 – 327/43 = 7.6

Presenter

Presentation Notes

Source: The Relative Rate Index (RRI) in this slide is the Arrest Rate per 1,000 Black youth divided by the Arrest Rate per 1,000 White youth. It is a measure of disparity which generally uses the majority group, in this case Whites, as the comparison group, which always has a value of 1.0.


119

70.5% 69.9% 71.1% 70.9% 70.4%

29.5% 30.1% 28.9% 29.1% 29.6%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

2010 2011 2012 2013 2014

Total Arrests by Sex

Female

Male

Presenter

Presentation Notes



120

72.3% 71.5% 73.1% 72.6% 71.3%

27.7% 28.5% 26.9% 27.4% 28.7%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

2010 2011 2012 2013 2014

Adult Arrests by Sex

Female

Male

Presenter

Presentation Notes



121

63.6% 63.9% 62.8% 63.7% 65.6%

36.4% 36.1% 37.2% 36.3% 34.4%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

2010 2011 2012 2013 2014

Juvenile Arrests by Sex

Female

Male

Presenter

Presentation Notes



122

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

Arrests by Age Group

2010

2011

2012

2013

2014

Presenter

Presentation Notes



123

91%

62% 67% 71% 71% 71% 70% 72% 74% 78% 77% 82%

9%

38% 33% 29% 29% 29% 30% 28% 26% 22% 23% 18%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Arrests by Sex by Age Group, Aggregated 2010-2014

Female

Male

Presenter

Presentation Notes



124

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Grand Total n= 113832

36 - All Other (Except Traffic) n= 29154

34 - Disorderly Conduct n= 16713

07 - Theft n= 14783

33 - Liquor Laws n= 12765

32 - Driving While Intoxicated n= 11334

09 - Other Assaults n= 6773

27 - Marijuana Possession n= 5934

15 - Vandalism n= 3438

05 - Aggravated Assault n= 1904

26 - Opium/Cocaine Possession n= 1159

12 - Fraud n= 1133

06 - Burglary n= 984

16 - Weapons n= 973

37 - Curfew and Loitering n= 916

29 - Other Drug Possession n= 854

18 - Sex Offenses n= 613

31 - Family Offenses n= 609

Arrest Description by Sex - Aggregated - 2010-2014, Pt 1

Male %

Female %

Presenter

Presentation Notes



125

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

04 - Robbery n= 571

08 - Motor Vehicle Theft n= 472

22 - Marijuana Sales n= 399

21 - Opium/Cocaine Sales n= 361

14 - Stolen Property n= 353

11 - Forgery n= 343

38 - Runaways n= 269

28 - Synthetic Narcotic Possession n= 244

03 - Forcible Rape n= 204

17 - Prostitution n= 141

13 - Embezzlement n= 114

24 - Other Drug Sales n= 107

35 - Vagrancy n= 72

23 - Synthetic Narcotic Sales n= 56

01 - Murder n= 36

10 - Arson n= 33

02 - Negligent Manslaughter n= 10

30 - Gambling Violations n= 8

Arrest Description by Sex - Aggregated - 2010-2014, Pt 2

Male %

Female %

Presenter

Presentation Notes



126

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Grand Total n= 113832

36 - All Other (Except Traffic) n= 29154

34 - Disorderly Conduct n= 16713

07 - Theft n= 14783

33 - Liquor Laws n= 12765

32 - Driving While Intoxicated n= 11334

09 - Other Assaults n= 6773

27 - Marijuana Possession n= 5934

15 - Vandalism n= 3438

05 - Aggravated Assault n= 1904

26 - Opium/Cocaine Possession n= 1159

12 - Fraud n= 1133

06 - Burglary n= 984

16 - Weapons n= 973

37 - Curfew and Loitering n= 916

29 - Other Drug Possession n= 854

18 - Sex Offenses n= 613

31 - Family Offenses n= 609

Arrest Description by Race - Aggregated - 2010-2014, Pt 1

% White

% Other

% Black

Presenter

Presentation Notes



127

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

04 - Robbery n= 571

08 - Motor Vehicle Theft n= 472

22 - Marijuana Sales n= 399

21 - Opium/Cocaine Sales n= 361

14 - Stolen Property n= 353

11 - Forgery n= 343

38 - Runaways n= 269

28 - Synthetic Narcotic Possession n= 244

03 - Forcible Rape n= 204

17 - Prostitution n= 141

13 - Embezzlement n= 114

24 - Other Drug Sales n= 107

35 - Vagrancy n= 72

23 - Synthetic Narcotic Sales n= 56

01 - Murder n= 36

10 - Arson n= 33

02 - Negligent Manslaughter n= 10

30 - Gambling Violations n= 8

Arrest Description by Race - Aggregated - 2010-2014, Pt 2

% White

% Other

% Black

Presenter

Presentation Notes



128

0

500

1,000

1,500

2,000

2,500

2010 2011 2012 2013 2014

Arre

sts

Drug Arrests - Possession vs. Sales

Possession

Sales

Presenter

Presentation Notes



129

0

100

200

300

400

500

600

700

800

900

2007 2008 2009 2010 2011 2012 2013 2014

Law

Enf

orce

men

t Ref

erra

ls

Dane County - Law Enforcement Referrals

White

Black

57.7% decrease

33.1% decrease

Presenter

Presentation Notes

Source: Dane County Department of Human Services Department (DCDHS) – Delinquency Database


130

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

2007 2008 2009 2010 2011 2012 2013 2014

% o

f Arr

ests

Ref

erre

d to

DCD

HS

Percent of Juvenile Arrests Referred to DCDHS

Black

Total

White

Presenter

Presentation Notes

Juvenile Referrals divided by Juvenile Arrests Source: Arrest Data – Wisconsin Department of Justice – Bureau of Justice Information and Analysis – Extract of Uniform Crime Reporting (UCR) Data – 1997-2014 Juvenile Referrals - Dane County Department of Human Services Department (DCDHS) – Delinquency Database


131

0 100 200 300 400 500 600

Weapon @ School

Disorderly Conduct

OMVWOC

Burglary

Damage to Property

Retail Theft

Resisting/Obstructing

Theft

Battery

Disorderly Conduct

Most Frequent Charges by Year

201220132014

(Non-Firearm)

The top 10 Charges from 2014 along with their respective counts in 2012 and 2013 account for ~ 71.5% of all Charges for 2012-2014.

Presenter

Presentation Notes



132

38.5%

26.1% 23.6%

0%

10%

20%

30%

40%

50%

60%

Black Hispanic White

6 Month Recidivism by Race

52.7%

42.1%

29.9%

0%

10%

20%

30%

40%

50%

60%

Black Hispanic White

1 Year Recidivism by Race

2013 -Law Enforcement Recidivism - 6 Month vs. 12 Month by Race/Ethnicity

Presenter

Presentation Notes



133

18.2%

27.1%

38.6% 37.0% 38.6%

22.7%

37.5%

45.6%

50.0% 48.7%

0%

10%

20%

30%

40%

50%

60%

11 12 13 14 15

Recidivism by Age at First 2013 Law Enforcement Referral – 6 month vs. 12 month

6 Month Recidivism 12 Month Recidivism

Presenter

Presentation Notes



134

CPS and Out-of-Home Placement

Data


135

CPS Referral Flow Chart

Population, Age 0 - 17

Access ReportReferred to CPS (Allegation of

Maltreatment)

ReferralScreened In

Substantiation ofMaltreatment

Allegations are not Substantiated

Referral Screened Out

InitialAssessment

(Investigation)


136

2007 2008 2009 2010 2011 2012 2013 2014Total 3,971 4,335 5,078 5,821 6,087 7,024 7,502 7,825

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

9,000

Num

ber o

f Uni

que

Yout

h pe

r Acc

ess R

epor

t Ref

erre

d to

CPS

Number of Unique Youth per Access Report Referred to CPS

Presenter

Presentation Notes

Source: Dane County Department of Human Services (DCDHS) Wisconsin Statewide Automated Child Welfare Information System (eWiSACWIS) Replication Data


137

0

500

1,000

1,500

2,000

2,500

3,000

3,500

2007 2008 2009 2010 2011 2012 2013 2014

Yout

h Re

ferr

ed to

CPS

Number of Unique Youth per Access Report Referred to CPS by Race

White

Black

Hispanic

Asian

Presenter

Presentation Notes



138

0

50

100

150

200

250

300

2007 2008 2009 2010 2011 2012 2013 2014

Yout

h Re

ferr

ed to

CPS

per

1,0

00 Y

outh

Youth Referred to CPS per 1,000 by Race

Black

Hispanic

White

Asian

Presenter

Presentation Notes

Source: Child Protective Services (CPS) Data - Dane County Department of Human Services (DCDHS) Wisconsin Statewide Automated Child Welfare Information System (eWiSACWIS) Replication Data Population Data - Office of Juvenile Justice and Delinquency Prevention – Easy Access to Juvenile Populations http://www.ojjdp.gov/ojstatbb/ezapop/asp/comparison_selection.asp?selState=1 National Center for Health Statistics (2015). Vintage 2014 postcensal estimates of the resident population of the United States (April 1, 2010, July 1, 2010-July 1, 2014), by year, county, single-year of age (0, 1, 2, .., 85 years and over), bridged race, Hispanic origin, and sex. Prepared under a collaborative arrangement with the U.S. Census Bureau. Available online from http://www.cdc.gov/nchs/nvss/bridged_race.htm as of June 26, 2015, following release by the U.S. Census Bureau of the unbridged Vintage 2014 postcensal estimates by 5-year age group on June 26, 2014. [Retrieved 7/8/2015]


139

0

1

2

3

4

5

6

7

8

9

2007 2008 2009 2010 2011 2012 2013 2014

RRI V

alue

- Yo

uth

Refe

rred

to C

PS

Youth Referred to CPS Relative Rate Index (RRI)

Black

Hispanic

White

Asian

Presenter

Presentation Notes

Source: The Relative Rate Index (RRI) in this slide is the CPS referral rate per 1,000 Black youth divided by the CPS referral rate per 1,000 White youth. It is a measure of disparity which generally uses the majority group, in this case Whites, as the comparison group, which always has a value of 1.0.


140

2007 2008 2009 2010 2011 2012 2013 2014Total 1,746 1,768 1,953 2,162 1,992 2,506 2,522 2,580

0

500

1,000

1,500

2,000

2,500

3,000

Num

ber o

f Uni

que

Yout

h pe

r Acc

ess R

epor

t Scr

eene

d In

Number of Unique Youth per Access Report Screened In

Presenter

Presentation Notes



141

0

200

400

600

800

1,000

1,200

1,400

2007 2008 2009 2010 2011 2012 2013 2014

Yout

h Re

ferr

ed to

CPS

Number of Unique Youth per Access Report Screened In by Race

White

Black

Asian

Hispanic

Presenter

Presentation Notes



142

0

10

20

30

40

50

60

70

2007 2008 2009 2010 2011 2012 2013 2014

Yout

h Sc

reen

ed In

to C

PS p

er 1

00 Y

outh

Ref

erre

d

Youth Screened In per 100 Youth Referred by Race

Black

Hispanic

Asian

White

Presenter

Presentation Notes



143

0.00

0.25

0.50

0.75

1.00

1.25

1.50

1.75

2.00

2007 2008 2009 2010 2011 2012 2013 2014

RRI V

alue

- Yo

uth

Refe

rred

to C

PS

Youth Screened In Relative Rate Index (RRI)

White

Black

Asian

Hispanic

Presenter

Presentation Notes

Source: The Relative Rate Index (RRI) in this slide is the CPS Screen-In rate per 1,000 Black youth divided by the CPS Screen-In rate per 1,000 White youth. It is a measure of disparity which generally uses the majority group, in this case Whites, as the comparison group, which always has a value of 1.0.


144

2007 2008 2009 2010 2011 2012 2013 2014Total 333 281 261 286 278 302 344 333

0

50

100

150

200

250

300

350

400

Num

ber o

f Uni

que

Yout

h pe

r Acc

ess R

epor

t Scr

eene

d In

Number of Unique Youth per Access Report Substantiated

Presenter

Presentation Notes



145

0

20

40

60

80

100

120

140

160

180

2007 2008 2009 2010 2011 2012 2013 2014

Yout

h Su

bsta

ntia

ted

Number of Unique Youth per Access Report Substantiated by Race

Black

Asian

White

Hispanic

Presenter

Presentation Notes



146

0

5

10

15

20

25

2007 2008 2009 2010 2011 2012 2013 2014

Yout

h Su

bsta

ntia

ted

per 1

00 Y

outh

Scr

eene

d In

Youth Substantiated per 100 Youth Screened In by Race

White

Black

Hispanic

Presenter

Presentation Notes



147

0.00

0.25

0.50

0.75

1.00

1.25

1.50

1.75

2.00

2007 2008 2009 2010 2011 2012 2013 2014

RRI V

alue

- Yo

uth

Subs

tant

iate

d

Youth Substantiated Relative Rate Index (RRI)

White

Black

Hispanic

Presenter

Presentation Notes

Source: The Relative Rate Index (RRI) in this slide is the CPS Substantiation rate per 1,000 Black youth divided by the CPS Substantiation rate per 1,000 White youth. It is a measure of disparity which generally uses the majority group, in this case Whites, as the comparison group, which always has a value of 1.0.


148

2009 2010 2011 2012 2013 2014 2015 ProjIA's Completed 1144 1239 1470 1735 1706 1737 1849

0

200

400

600

800

1,000

1,200

1,400

1,600

1,800

2,000

Initial Assessments Completed

Presenter

Presentation Notes

Source: Wisconsin Statewide Automated Child Welfare Information System (eWiSACWIS) SM06A111 Recurrence of Maltreatment


149

2009 2010 2011 2012 2013 2014 2015 ProjIA's Substantiated 179 189 228 222 238 239 209

0

50

100

150

200

250

300

Initial Assessments - Substantiated

Presenter

Presentation Notes



150

2009 2010 2011 2012 2013 2014 2015 ProjSubstantiation Rate 15.65% 15.25% 15.51% 12.80% 13.95% 13.76% 11.28%

0%

4%

8%

12%

16%

20%

Initial Assessments - Substantiation Rate

Presenter

Presentation Notes



151

2010 2011 2012 2013 2014 2015.01-09Previous Screen-in within 12 Months 36.48% 29.57% 25.82% 32.42% 29.72% 34.31%Previous Screen-in within 6 Months 26.23% 22.59% 19.61% 23.85% 21.98% 29.41%

0%

5%

10%

15%

20%

25%

30%

35%

40%Re-Screen-In Rate

Previous Screen-in within12 MonthsPrevious Screen-in within 6Months

Presenter

Presentation Notes

Source: Wisconsin Statewide Automated Child Welfare Information System (eWiSACWIS) SM06A111 Recurrence of Maltreatment. The denominator of the Re-Screen-in Rate is the number of unique youth substantiated of maltreatment in given calendar year.


152

2010 2011 2012 2013 2014 2015.01-09Recurrence within 12 Months 9.84% 5.65% 5.56% 7.95% 5.88% 6.86%Recurrence within 6 Months 6.56% 3.65% 4.58% 7.34% 5.57% 6.37%

0%

2%

4%

6%

8%

10%

12%Substantiation Recurrence

Recurrence within 12MonthsRecurrence within 6Months

Presenter

Presentation Notes

Source: Wisconsin Statewide Automated Child Welfare Information System (eWiSACWIS) SM06A111 Recurrence of Maltreatment. The denominator of the Substantiation Recurrence is the number of unique youth substantiated of maltreatment in given calendar year.


153

0

100

200

300

400

500

600

700

800

900

Dane County Initial Assessment: Current, Completed and Backlog

Completed - Current (under 60 days) Completed - Backlog (over 60 days)

Current IAs (under 60 days, not approved) New Backlog (61 - 90 days, not approved)

Old Backlog (over 90 days, not approved)

Presenter

Presentation Notes

Source: Wisconsin Statewide Automated Child Welfare Information System (eWiSACWIS) eReports - SM06A109_IA_REPORT


154

0

50

100

150

200

250

300

350

Aver

age

Daily

Pop

ulat

ion

Kinship Care Center and Foster Care ADP

Kinship Foster Care

Presenter

Presentation Notes

Source: Dane County Department of Human Services (DCDHS) Wisconsin Statewide Automated Child Welfare Information System (eWiSACWIS) Alternate Care Payroll Data


155

0

10

20

30

40

50

60

70

Aver

age

Daily

Pop

ulat

ion

Congregate Care ADP by Type

Residential Care Center

Group Home

Corrections

Presenter

Presentation Notes

Source: Dane County Department of Human Services (DCDHS) Wisconsin Statewide Automated Child Welfare Information System (eWiSACWIS) Alternate Care Payroll Data


156

2007 2008 2009 2010 2011 2012 2013 2014White 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00Black 13.47 14.86 14.58 15.13 14.81 14.09 14.93 14.72Hispanic 1.87 1.99 2.09 2.30 1.80 1.97 2.37 1.79

0

5

10

15

20

25

30

35

RRI V

alue

s

Total Alternate Care RRI Values

Presenter

Presentation Notes

Source: Population Data - Population Data - Office of Juvenile Justice and Delinquency Prevention – Easy Access to Juvenile Populations http://www.ojjdp.gov/ojstatbb/ezapop/asp/comparison_selection.asp?selState=1 National Center for Health Statistics (2015). Vintage 2014 postcensal estimates of the resident population of the United States (April 1, 2010, July 1, 2010-July 1, 2014), by year, county, single-year of age (0, 1, 2, .., 85 years and over), bridged race, Hispanic origin, and sex. Prepared under a collaborative arrangement with the U.S. Census Bureau. Available online from http://www.cdc.gov/nchs/nvss/bridged_race.htm as of June 26, 2015, following release by the U.S. Census Bureau of the unbridged Vintage 2014 postcensal estimates by 5-year age group on June 26, 2014. [Retrieved 7/8/2015] Alternate Care Data Dane County Department of Human Services (DCDHS) Wisconsin Statewide Automated Child Welfare Information System (eWiSACWIS) Alternate Care Payroll Data


157


0

5

10

15

20

25

30

35

RRI V

alue

s

Foster Care RRI Values

Presenter

Presentation Notes



158


0

5

10

15

20

25

30

35

RRI V

alue

s

Kinship Care RRI Values

Presenter

Presentation Notes



159


0

5

10

15

20

25

30

35

RRI V

alue

s

Group Home Care RRI Values

Presenter

Presentation Notes



160


0

5

10

15

20

25

30

35

RRI V

alue

s

RCC RRI Values

Presenter

Presentation Notes



161

0%

10%

20%

30%

40%

50%

60%

70%

80%

2009 2010 2011 2012 2013 2014 2015.01-09

Out of Home Discharges by Length

% 0-12 Months

% 13-24 Months

% 25+ Months

Presenter

Presentation Notes



162

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

2009 2010 2011 2012 2013 2014 2015.01-09

Rate

of P

lace

men

t Sta

bilit

y

Placement Stability - % with <=2 Placements by Length of Episode

0-12 Month Benchmark

0-12 Months

13-24 Month Benchmark

13-24 Months

25+ Month Benchmark

25+ Months

Presenter

Presentation Notes

Source: Wisconsin Statewide Automated Child Welfare Information System (eWiSACWIS) eReports -SM10A116_PLACEMENT_STABILITY


163

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

2009 2010 2011 2012 2013 2014 2015.01-09

Rate

of P

lace

men

t Sta

bilit

y

Placement Stability - % with <=2 Placements within 12 months

Standard

WI

Dane

Presenter

Presentation Notes



164

0%

10%

20%

30%

40%

50%

60%

70%

2009 2010 2011 2012 2013 2014 2015.01-09

Rate

of P

lace

men

t Sta

bilit

y

Placement Stability - % with <=2 Placements within 13-24 months

Standard

WI

Dane

Presenter

Presentation Notes



165

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

50%

2009 2010 2011 2012 2013 2014 2015.01-09

Rate

of P

lace

men

t Sta

bilit

y

Placement Stability - % with <=2 Placements within 25+ months

Standard

WI

Dane

Presenter

Presentation Notes



166

2009 2010 2011 2012 2013 2014 2015.01-09Dane 31.9% 32.5% 31.3% 24.6% 31.2% 28.4% 21.9%WI 21.1% 20.3% 22.7% 20.2% 20.8% 19.7% 21.2%Benchmark 9.9% 9.9% 9.9% 9.9% 9.9% 9.9% 9.9%

0%

5%

10%

15%

20%

25%

30%

35%

Re-E

ntry

Rat

e

Re-entry w/i 12 Months - WI vs. Dane - 2009-2015.01-09

Presenter

Presentation Notes

Source: Wisconsin Statewide Automated Child Welfare Information System (eWiSACWIS) eReports -SM10A115-Re-Entry


167

Feedback from presentations • CYF – Reduced arrests & referrals leading to

decreased funding.

• CYF – Restorative Justice impact on the JJ system.

• CYF – Increasing CPS Access & IA caseloads are unsustainable with current staffing.

• CYF – Insufficient # of teen Foster Homes. • CYF – Foster Homes and Kinship Care Homes

are not replenishing at sufficient rates leading to increased use of Group Homes for teens.

• CYF – Need a greater emphasis on coordination

for the prevention of child abuse.


168

Feedback from presentations • CYF – Independent Living funds will shift to a

regional model in 2017.

• CYF – Impact of CCS.

• CYF – Impact of Family Care.

• CYF – RCC Administrative Rules are up for modification.


169

AODA


170

3148

3239 3216

3526

3706 3688

2800

2900

3000

3100

3200

3300

3400

3500

3600

3700

3800

2004 2005 2006 2008 2010 2012

Num

ber o

f Hos

pita

lizat

ions

Year

Alcohol-Related Hospitalizations in Dane County

Presenter

Presentation Notes

Source: Wisconsin Department of Health Services, Wisconsin Epidemiological Profile on Alcohol and Other Drug Use, 2008-2014, https://www.dhs.wisconsin.gov/stats/aoda.htm, web. 12 October 2015.


171

1014 1054 1032

1167

1251

1075

0

200

400

600

800

1000

1200

1400

2004 2005 2006 2008 2010 2012

Num

ber o

f Hos

pita

lizat

ions

Year

Drug-Related Hospitalizations in Dane County

Presenter

Presentation Notes



172

0.9 1.0

1.1 1.2

1.2 1.3

1.5

1.7

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2008-2009 2009-2010 2010-2011 2011-2012

Hosp

italiz

atio

n Ra

te

Moving Two-Year

Opiod-Related Hospitalizations Per 1,000 Population, Ages 12-25 in Dane County and Wisconsin

Dane Wisconsin

Presenter

Presentation Notes



173

Heroin Cases 2009

Presenter

Presentation Notes

Wisconsin Department of Justice, Cases by County, Heroin Cases by County, 2009 Heroin Map, web. https://www.doj.state.wi.us/dci/2009-heroin-map.


174

Presenter

Presentation Notes

Wisconsin Department of Justice, Cases by County, Heroin Cases by County, 2015 Heroin Map, web. https://www.doj.state.wi.us/dci/heroin-awareness/cases-county.


175

175 173 183

218

0

50

100

150

200

250

2011 2012 2013 2014

Num

ber o

f Cas

es

Hepatitis C Positive Cases in Dane County by Year

Presenter

Presentation Notes

Source: Lauren Stockman, MPH, Hepatitis C Epidemiologist, AIDS/HIV Program, Wisconsin Division of Public Health, “RE: Hepatitis C - Dane County,” message to Lori Bastean, 22 October 2015. E-mail. These numbers represent past, present, and acute HCV cases that meet the confirmed or probable CSTE definition. Numbers represent the HCV reports in final status in the Wisconsin Electronic Disease Surveillance System (WEDSS) as of 4/17/2015, the same data was used for the 2014 Surveillance Report. If a Dane County resident is incarcerated and HCV is detected and reported initially by the Department of Corrections (DOC), they are considered a DOC case and not included in Dane County numbers.


176

35.5 34.8 36.4

43.3

44.9 45.8 46.0

56.1

0

10

20

30

40

50

60

2011 2012 2013 2014

Rate

Per

100

,000

Res

iden

ts

Rate of Newly Reported Hepatitis C Virus Cases Per 100,000 Residents for Dane County and the State of Wisconsin

Dane WI

Presenter

Presentation Notes

Source: Lauren Stockman, MPH, Hepatitis C Epidemiologist, AIDS/HIV Program, Wisconsin Division of Public Health, “RE: Hepatitis C - Dane County,” message to Lori Bastean, 22 October 2015. E-mail. These numbers represent past, present, and acute HCV cases that meet the confirmed or probable CSTE definition. Numbers represent the HCV reports in final status in the Wisconsin Electronic Disease Surveillance System (WEDSS) as of 4/17/2015, the same data was used for the 2014 Surveillance Report. If a Dane County resident is incarcerated and HCV is detected and reported initially by the Department of Corrections (DOC), they are considered a DOC case and not included in Dane County numbers.


177

33.1 31.3 29.4

66.9 68.7 70.6

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

2013 2014 2015 to 9.30.2015

Perc

ent

Gender of Persons with New AODA Episode by Year (Percent)

Male

Female

Presenter

Presentation Notes

Source: DCDHS Information System.


178

13.7 12.7 11.4

9.0 7.9 8.9

72.9 75.4 75.4

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

2013 2014 2015 to 9.30.2015

Race/Ethnic Status of Persons with New AODA Episode by Year (Percents)

White

Unknown

Pacific Is.

Native Amer.

Hispanic

Bi-Racial

Black

Asian

Presenter

Presentation Notes

Source: DCDHS Information System.


179

Substance Problem 2013 2014 2015 (to 9.30.2015)

Alcohol 78.9 80.2 82.6

Marijuana 5.6 7.3 5.8

Cocaine/Crack 2.8 2.3 1.8

Heroin 8.0 6.6 6.5

Other Opiates 2.1 2.0 1.5

Primary Substance Abuse Problem for Persons with New Admissions by Year (Percent)

Presenter

Presentation Notes

Source: DCDHS Information System. This data reflects only the service authorizations that have been attached to an episode.


180

Dane County Youth Assessment – 2015 High School Youth – Drug Use in Past 12 Months

Drug Count Percent

Alcohol Not provided

34.8

Over the Counter (Non-Prescription Drugs) to get high

928 5.4

Synthetic Marijuana 843 4.9

Prescription Drugs not prescribed for you

827 4.8

Ecstasy 619 3.5

Cocaine or Crack 461 2.7

Inhalants 359 2.7

Presenter

Presentation Notes

While the count of youth who reported alcohol use in the past 12 months was not included in the overview report, it is approximately 5,980 youth. Source: Dane County Youth Commission, Dane County Youth Assessment: 2015 Dane County Youth Assessment Overview Report, (2015, September), pgs. 12-13. Retrieved from: https://danecountyhumanservices.org/yth/dox/asmt_survey/2015/2015_exec_sum.pdf.


181

Feedback from presentations • AODA – Who will be responsible for childless

adults after their 4 years of eligibility are up? • CYF – There are increased AODA/MH needs in

the JJ population. • ACS – We need an integrated service system

for AODA/MH.

• ACS – There is a desire to have more services for people leaving Jail/Prison.

• ACS – There are pressures to use DCDHS

funded inpatient hospitalization as locked preventative detention for individuals deemed potentially dangerous, regardless of treatment need.


182

Mental Health


183

394 385 372 381 400

477 472

0

100

200

300

400

500

600

2008 2009 2010 2011 2012 2013 2014

Num

ber o

f Cas

es F

iled

Number of Mental Health Cases Filed in Dane County Probate Court by Year

Presenter

Presentation Notes

Source: Dane County Clerk of Courts, Annual Case Filings, https://courts.countyofdane.com/court/cases, web. 21 October 2015.


184

38

47

59

44

50 49

57 54

50

57

49

69 66 65

0

10

20

30

40

50

60

70

80

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

Num

ber

Year

Number of Deaths Due to Intentional Self-Harm (Suicide) in Dane County by Year

Presenter

Presentation Notes

Source: Wisconsin Department of Health Services, Division of Public Health, Office of Health Informatics, Health Analytics Section, Public Health Profiles – Profile for Dane County, https://www.dhs.wisconsin.gov/stats/phprofiles/dane.htm, 22 October 2015.


185

2007 2008 2009 2010 2011 2012 201365+ 198 192 159 180 164 231 19945-64 519 575 598 582 685 695 67018-44 1013 995 961 1,093 1,197 1,303 1,324<18 353 346 348 405 443 518 573

0

500

1000

1500

2000

2500

3000

Num

ber

Psychiatric Hospitalizations by Age Group and Year - Dane County

65+

45-64

18-44

<18

Presenter

Presentation Notes



186

2007 2008 2009 2010 2011 2012 2013<18 9.2 9.4 8.9 7.7 7.9 7.1 7.218-44 7.3 8.7 6.8 6.6 6.0 6.1 6.645-64 8.2 8.7 7.3 7.3 7.0 6.7 7.165+ 11.4 11.4 13.8 11.8 12.6 11.3 11.8

0

2

4

6

8

10

12

14

16

Num

ber o

f Day

s

Psychiatric Hospitalizations by Length of Stay by Age Group and Year - Dane County

Presenter

Presentation Notes



187

10.2

8.0

1.7

16.7

20.3

4.4

0

5

10

15

20

25

Depression Anxiety Eating Disorder

Perc

ent

Percent of High School Youth in Dane County Reporting Long-Term Mental Health Problems Dane County Youth Assessment

2012 2015

Presenter

Presentation Notes

Sources: Dane County Youth Commission, 2015 Dane County Youth Assessment, (Madison, 2015), https://danecountyhumanservices.org/yth/yth_asmt_2015.aspx Dane County Youth Commission, 2012 Dane County Youth Assessment, https://danecountyhumanservices.org/yth/yth_asmt_2012.aspx .


188

12.3

4.6

18.7

5.8

0

2

4

6

8

10

12

14

16

18

20

Suicidal Thoughts Attempted Suicide

Perc

ent

Percent of Youth in Dane County Who Reported Having Suicidal Thoughts in Past 30 Days and Youth Who Have Attempted Suicide in Past

12 Months

2012 2015

Presenter

Presentation Notes

Sources: Dane County Youth Commission, 2015 Dane County Youth Assessment, (Madison, 2015), https://danecountyhumanservices.org/yth/yth_asmt_2015.aspx Dane County Youth Commission, 2012 Dane County Youth Assessment, https://danecountyhumanservices.org/yth/yth_asmt_2012.aspx . In 2015, 1,508 youth reported that during the past 12 months, they have attempted to kill themselves.


189

Access Appropriate& Quality Participation Outcomes Functioning Social

Connect. Satisfaction

Dane 72.7 65.9 63.6 69.5 66.1 67.1 82.6WI 75.1 78.0 63.6 58.2 60.2 63.2 75.0U.S. 85.1 88.5 80.1 69.7 70.9 72.8 89.0

0

10

20

30

40

50

60

70

80

90

100

Perc

ent

Perception of Care by Domain: Dane County Compared with 2014 State and U.S. CMHS Data

Presenter

Presentation Notes

Sources: Dane County Department of Human Services, Client Perception of Care: Dane County Mental Health System, January 2015, https://danecountyhumanservices.org/reports.aspx . U.S. Department of Health and Human Services, Substance Abuse and Mental Health Services Administration, 2014 CMHS Uniform Reporting System Output Tables, http://www.samhsa.gov/data/reports-by-geography?tid=673&map=1 . Reflects percent of respondents to MHSIP survey reporting positively about access, quality and appropriateness, outcomes, participation in treatment planning, and general satisfaction; and those reporting improved social connectedness and improved functioning as a result of services.


190

Feedback from presentations • CYF – Increase in MH needs for youth. • ACS – AODA/MH Silos – need for integrated

service delivery. • ACS – Increased cost of institutionalization.

• ACS – Conflict between those who need

services vs. those that are will to accept services.

• ACS – Need for more Long Term care options for the severely mentally ill.


191

EAWS


192

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

45,000

50,000

2008 2009 2010 2011 2012 2013 2014 2015

Tota

l Und

uplic

ated

Cas

es

EAWS - Total Unduplicated Cases

Presenter

Presentation Notes

Source: WI Department of Health Services - CARES System – ACD Active Case Summary Report


193

0

1

2

3

4

5

6

7

Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

Aver

age

Wai

t Tim

e (M

inut

es)

EAWS - Average Wait Time (Phone)

Average Wait Time (Minutes) Average Wait Time Target

2013 2014 2015

Presenter

Presentation Notes

Source: WI Department of Health Services - CCA System – C03.Capital Agent Performance Monthly Report


194

0%

20%

40%

60%

80%

100%

0

5

10

15

20

25

30

35

Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

Answ

er R

ate

Calls

Rec

eive

d (in

thou

sand

s)

EAWS - Calls Received and Answer Rate

Calls Received Answer Rate

2013 2014 2015

Presenter

Presentation Notes

Source: WI Department of Health Services - CCA System – C03.Capital Agent Performance Monthly Report


195

Feedback from presentations • EAWS – Income Maintenance Operational

Analysis…requires a Performance Improvement Plan, implemented in 2016.

• EAWS – Sheboygan County will be added to the Capital Consortium – 25 staff and 10,500 cases and impacts ongoing workflow.

• EAWS - Open Enrollment trends at the Marketplace which impact our intakes and cases.

• EAWS - FoodShare on Demand in May 2016.


196

Seniors


197

2010 2015 2020 2025 2030 2035 2040Age 60+ 74,925 91,700 108,920 125,000 136,500 144,540 153,070Age 65+ 50,144 62,160 78,020 93,500 107,250 115,490 121,470Age 85+ 7,774 8,600 9,030 9,870 12,540 17,800 23,700

-

20,000

40,000

60,000

80,000

100,000

120,000

140,000

160,000

180,000

Pers

ons

Dane County Population Projections for Persons Age 60 and Older by Age Group and Year

Presenter

Presentation Notes

Source: Wisconsin Department of Administration Updated Population Projections for Counties by Age: 2010-2014; Vintage 2013. Prepared by Eric Grosso, Bureau of Aging and Disability Resources 8/2015. Between 2015 and 2020, the number of persons in Dane County age 60 and older is projected to increase by 18.8% or 17,220 persons.


198

320 318 307

267 285

300 320

135 147

120 116 108 115

144

0

50

100

150

200

250

300

350

2007 2008 2009 2010 2011 2012 2013

Num

ber

Select Hospitalizations for Persons age 65 and Older in Dane County

Hip Fracture Diabetes

Presenter

Presentation Notes



199

6.9 6.7 6.3

5.3 5.4 5.6 5.5

2.9 3.1

2.5 2.3 2.0 2.2

2.5

0

1

2

3

4

5

6

7

8

2007 2008 2009 2010 2011 2012 2013

Rate

Per

1,0

00 P

opul

atio

n

Hospitalization Rate for Persons Age 65 and Older in Dane County for Select Causes

Hip Fracture Diabetes

Presenter

Presentation Notes



200

2010 2015 2020 2025 2030 2035 2040Age 65+ 7,020 8,702 10,923 13,090 15,015 16,169 17,006Age 85+ 3,732 4,128 4,334 4,738 6,019 8,544 11,376

-

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

18,000

Pers

ons

Dane County Population Projections for Persons with Alzheimer's Disease or Related Dementia by Year

Presenter

Presentation Notes

Sources: Wisconsin Department of Administration Updated Population Projections for Counties by Age: 2010-2014; Vintage 2013. Prepared by Eric Grosso, Bureau of Aging and Disability Resources 8/2015. Alzheimer's Disease and Related Dementia projections are based on dementia prevalence rates recommended by Denis A. Evans, M.D., et al. "Prevalence of Alzheimer's Disease in a Community Population of Older Persons, " Journal of the American Medical Association, 262(19), 1989, as quoted in Redesigning Wisconsin's Dementia Care System: A Stakeholder's Summit October 1-2, 2013 sponsored by Wisconsin Department of Health Services and The Johnson Foundation at Wingspread. Note these rates may include individuals without a confirmed dementia diagnosis. Rate is estimated at one out of every seven (14%) individuals age 65 and older has Alzheimer’s Disease or a related dementia. After age 85, the proportion of the population with dementia increases to just under half (48%). (Wisconsin Department of Health Services, p.4)


201

1,857 1,851 1,834 1,755 1,731 1,721 1,710

1,600 1,566 1,578 1,502

1,440 1,420 1,419

-

200

400

600

800

1,000

1,200

1,400

1,600

1,800

2,000

2007 2008 2009 2010 2011 2012 2013

Dane County Nursing Homes - Licensed Beds and Residents on December 31 2007-2013 Licensed Beds Residents on Dec. 31

Presenter

Presentation Notes

Source: Wisconsin Department of Health Services, Division of Public Health, Office of Health Informatics, Health Analytics Section, Public Health Profiles, Wisconsin web. 22 October 2015. Between 2007 and 2013, the number of licensed nursing home beds in Dane County has decreased by 7.9% while the number of residents in a licensed nursing home as of December 31 has decreased by 11.3%.


202

1,6

85

1,6

14 1

,918

2,1

20

1,8

73

2,0

21

2,0

27

1,8

22 2

,142

2,5

17

2,1

59

2,0

93

2,5

97

2,5

65

2,5

65 2,8

45

2,7

59

2,8

64

3,0

12

2,8

92

2,9

59

-

500

1,000

1,500

2,000

2,500

3,000

3,500

J2014

F M A M J J A S O N D J2015

F M A M J J A S

Num

ber

ADRC - Number of Contacts Per Month Jan. 2014 - Sept. 2015

Presenter

Presentation Notes

Sources: Dane County Department of Human Services, Aging & Disability Resource Center, ADRC Data Nov 28 2012 to September 30 2015, p. 2.


203

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Perc

ent

ADRC: Consumer Age Groups by Percent 1.1.2014 - 9.30.2015

Unknown

60+

18-59

Presenter

Presentation Notes



204

7.6

44.2

12.4

29.7

1.1

5.0

ADRC: Disability Group 2014

DD

Elderly

MH

PD

SA

U

N= 28,984

Presenter

Presentation Notes



205

624

275

183

109

101

91

85

84

71

0 100 200 300 400 500 600 700

Subsidized Housing

Long-Term Care Waiver

Transportation

Assisted Living

Home Care - Non Medical

MH CM

Employment

Utility Asst.

Home Care - Medical

Count

ADRC: Top Unmet Needs 10.2014 - 9.2015

Presenter

Presentation Notes

Source: Dane County Department of Human Services, Aging & Disability Resource Center, SAMS Statistics September 2015, pgs. 8-9.


206

2008 2009 2010 2011 2012 2013 2014Reports 485 473 462 455 460 484 421Substantiated 352 334 323 326 347 351 282

0

100

200

300

400

500

600

Coun

t

Number of Elder Adults-at-Risk (Age 60+) Abuse Reports for Dane County by Year

Presenter

Presentation Notes

Source: WITS Statistical Summary Report for Elder Adults-at-Rsk Age 60+ Dane County. Reports pulled by Shari Gray-Dorn, DCDHS Adult Protective Services Supervisor. Information is based on raw, county-level data which may not have been reconciled or edited by WI DHS.


207

2008 2009 2010 2011 2012 2013 201460-69 27.7 31.0 30.7 27.0 30.0 30.8 37.870-79 33.6 34.3 37.7 34.6 30.0 33.1 27.180-89 29.1 28.0 24.2 31.9 31.8 28.9 27.190+ 9.6 6.7 7.4 6.5 8.2 7.2 8.1

0

5

10

15

20

25

30

35

40

Perc

ent

Percent of Elder Abuse Reports by Adults-At-Risk Age Group - Dane County

Presenter

Presentation Notes



208

2008 2009 2010 2011 2012 2013 2014Mobility Impaired 12.1 14.0 17.7 18.9 17.0 18.2 19.7Mental Illness 18.9 18.5 17.3 14.8 10.2 11.4 14.3Alzheimer's/Dementia 24.5 27.4 24.2 24.2 26.3 24.0 23.0Frail Elderly 58.9 50.6 50.0 51.3 45.5 38.0 36.8

0

10

20

30

40

50

60

70

Perc

ent

Percent of Elder Abuse Reports by Adults-at-Risk Select Characteristics - Dane County

Presenter

Presentation Notes



209

Fin. Dep. On Elder Alcohol Drug Mentally Ill Unemployed2008 14.3 19.6 17.5 9.8 72009 15.9 17 17.4 9.8 7.22010 19.8 17.5 16 10.9 9.32011 14.7 14.3 17.4 8.9 3.62012 8.6 14.5 19.3 11.9 15.22013 3.8 10.2 12.6 9.9 12.62014 6.1 10 15.7 10.9 14.8

0

5

10

15

20

25

Percent of Elder Abuse Reports by Select Characteristics of Alleged Abusers - Dane County

Presenter

Presentation Notes



210

Feedback from presentations • ACS – Impact on all parts of DCDHS due to no

longer managing/controlling long term care services.

• ACS – Transportation needs are greater than

current capacity and growing. • ACS – Impact of Family Care.


211

For further questions, please contact the Manager of the

Program and Evaluation Unit, Jean Kuehn at 608-242-6418 or

[email protected]

mailto:[email protected]

mailto:[email protected]