health disparities webinar 2/28/2013 michael l. dennis, chestnut health systems. normal, il...

Health Disparities Webinar 2/28/2013

Michael L. Dennis, Chestnut Health Systems. Normal, IL

Available from www.gaincc.org/presentations

Created for: Substance Abuse and Mental Health Services Administration’s (SAMHSA) Center for

Substance Abuse Treatment (CSAT) under contract number 270-2012-0001 / Project no. 270-12-0397.

2

Goals for the Presentation

1. Summarize the quality chasm in behavioral health and how it relates to health disparities

2. Provide data from the National Survey on Drug Use and Health (NSDUH) on the rates of health disparities in the community

3. Provide data from Treatment Episode Data Set – Discharge (TEDS-D) on health disparities in the rate of treatment completion and retention

4. Provide data from the Global Appraisal of Individual Needs (GAIN) on health disparities in rate of co-occurring problems and services targeted at them

5. Review strategies for reducing health disparities

3

IOM’s Crossing the Quality Chasm Series

The most relevant and recent of attempts to summarize an emerging consensus of research, practice, and policy makers of the state of the practice in our field

It also includes detailed recommendations for how to address these problems and health care reform

Free from http://www.iom.edu/Reports/2005/Improving-the-Quality-of-Health-Care-for-Mental-and-Substance-Use-Conditions-Quality-Chasm-Series.aspx

4

Regardless of Diagnosis or Where Patients Enter, High Quality Care Should be:

1. Safe – do no harm

2. Effective – based on scientific knowledge and average practice (based on actual data)

3. Patient-centered – respectful and responsible to individual preferences, needs, values and participation in clinical decision making (vs. staff centered)

4. Timely - reducing waits and delays; when care is most effective

5. Efficient - avoiding waste of time, energy and money

6. Equitable – providing effective care based on clinical criteria that does not vary by gender, race, age, geography or social economic status

4

5

The Quality Chasm in Behavioral HealthPast Year

CharacteristicsMental Health

DisordersSubstance Use

Disorders

CommunityAny Disorder 58 Million (25%) 21 Million (9%)

Severe 14 M (6% / 24%*) 7 M (3% / 33%*)

Co-occurring 15 M. (6% / 26%*) 15 M (6% / 71%*)

TreatmentAny Treatment* 25 Million (45%) 2 Million (9%)

% Compliance Under 30% Under 50%

% Co-occurring** 23-50% 60-80%

% Identified*** 17% 19%

% Getting CC Tx 12-15% 15-20%

* Percent of those with disorder** Based on formal screening and assessment** * Based on staff rating/ treatment records

>>Low Treatment Penetration>>Low Treatment Compliance>>Low Identification of Co-Occ.

6

Thus Health Disparities have to examine differences by subgroup in the rates of

1. Having problems

2. Accessing treatment for those in need

3. Getting those who start treatment to engage in adequate dosages of treatment

4. Getting those with co-occurring problems the services targeted at those needs

7

Subgroups might include things like

1. Race/Ethnicity, Age, Gender, Sexual Orientation

2. Poverty, Justice Involvement, Insurance Coverage, Geography, Provider

3. Or some combination of the above

8

General Approach to Evaluating Health Disparities

1. Have a reliable and valid measure defining a clinical problem

2. Identify a class or specific service associated with better outcomes for people with that clinical problem

3. Examine differences by subgroup in the rate of problem and failure to access or engage in effective service (unmet need)

9

Three SUD Measures from the SAMHSA 2009National Survey on Drug Use and Health

Any Past Year Substance Use Disorder(% of total)

Any Past Year SUD Treatment(% of total)

Unmet Need for SUD Treatment(% of SUD)

Note, all differences are statistically significant at the p<.001 level

X

10

Substance Use Disorder & Treatment by Race

Source: SAMHSA 2009 National Survey on Drug Use and Health

Higher rates of need for Hispanic & Mixed

Higher rates of unmet need for Hispanic, Mixed, & Other

10

11

Substance Use Disorder & Treatment by Age


Higher rates of need for young adults

Higher rates of unmet need for adolescents and young adults

11

12

Substance Use Disorder & Treatment by Gender


Higher rates of need for Males overall & for adolescent girls

Higher rates of unmet need for adolescent girls than boys

12

13

Substance Use Disorder & Treatment by Poverty


Higher rates of need for families with lower incomes

Higher rates of unmet need for families with higher incomes

13

14

Substance Use Disorder & Treatment by Justice


Higher rates of need for people involved with justice system

Higher rates of unmet need for people not in the justice systems

14

15

Three MDD Measures from the SAMHSA 2009National Survey on Drug Use and Health

Any Past Year Major Depressive Disorder(% of total)

Any Past Year MDD Treatment(% of total)

Unmet Need for MDD Treatment(% of SUD)


X

16

Major Depressive Episode & Treatment by Race


16

17

Major Depressive Episode & Treatment by Age


Higher rates of unmet need for adolescents and young adults

17

18

Major Depressive Episode & Treatment by Gender


Higher rates of need for adolescent girls

Higher rates of unmet need for adolescent girls and boys

18

19

Major Depressive Episode & Treatment by Poverty


19

20

Major Depressive Episode & Treatment by Justice


Higher rates of need for people involved with justice system

20

21

Native Americans (NA) are disproportionately effected by Substance Use Disorders (SUD)

Source: SAMHSA, 2011 National Survey on Drug Use and Health (NSDUH; (p8 of SAMHSA 1/13 newsletter)

Native Americans (NA) have higher* than average rates of Substance Use Disorders (SUD)

* p<.001

22

Rates Unmet Need for SUD by Age within NA

Source: SAMHSA 2011 National Survey on Drug Use and Health subset to Native Americans (n=1151, population estimate=2,281,422). * p<.001

Higher rates* of need for young adults

Higher rates* of unmet need for adolescents and young adults

22

23

Source: SAMHSA 2011 National Survey on Drug Use and Health subset to Native Americans (n=1151, population estimate=2,281,422). * p<.001

Higher rates* of need for males overall and for adolescent girls

Higher rates* of unmet need for adolescent girls than boys

Rates Unmet Need for SUD by Gender & Age within NA

23

24

Existing Data Sources on Substance Use Among Native Americans (NA)* by Age

Source (NA % of total) Pros & Cons

Native Americans (n)Under

18 18-25 26+ Total

2011 NSDUH Pop Est. (0.9%) raw n raw n with SUD

202,039363(52)

369,683412

(109)

1,709,700376(53)

2,281,4421,151(214)

2010 TEDS-Admission (3%) 5,142 9,772 39,732 54,646

2009 TEDS Discharge (3%) 4,760 8,886 36,247 49,893

2007-2010 SAMHSA (7%)GAIN datasets

2,908 432 589 3,929

2008-2012 All Systems of Care GAIN data (4.5%)

3,978 1,200 2,387 7,849

* Including Native Alaskans, Hawaiians and Pacific Islanders

25

Three Measures from the SAMHSA 2009Treatment Episode Data Set – Discharge (TEDS-D)

Completed Treatment or Transferred(% of admissions)

Engagement for 45 or more days(% of admissions)

Engagement for 90 or more days(% of admissions)


26

Substance Use Disorder & Treatment by Race

Source: SAMHSA 2009 Treatment Episode Data Set – Discharges (TEDS-D)

Lengths of stay are shorter for Black and Whites

Treatment completion/ transfer lower for Blacks, Hispanic & Mixed

26

27

Substance Use Disorder & Treatment by Age


Lengths of stay are shorter for young adults and adults

Completion rates are lower for adolescents and young adults

27

28

Substance Use Disorder & Treatment by Gender


Lengths of stay shorter for males

Treatment completion/ transfer for females

28

29

Four Measures from the SAMHSA 2011Global Appraisal of Individual Needs (GAIN) Data Set

Need for Service at Intake(% of Need / All admissions)

Unmet Need 3 months after Intake(% No targeted service / Clients with mod/high need)

Any Services Targeted at Need(% targeted service / All admissions)

Untargeted Services (% targeted services / Clients with low need)

* P <05 as marked

30

Any Substance Use Disorder at Intake vs. Any SUD Treatment by 3 Months

*Any past year AOD problems, use, abuse, or dependence** ‘Services’ is self-report of any days of SA treatment at 3 months

SAMHSA 2011 GAIN SA Data Set subset to has 3m Follow up (n=18,899)

Services for drug use are well targeted with those in need receiving services and services not being

spread to those without need

31

Unmet Need for Any SUD Treatment by 3 Months

* p<.05

GenderRace*


Higher drop out for Non-Whites

32

Physical Health Problem at Intake vs. Any Medical Treatment by 3 Months

*Current Need on ASAM dimension B2 criteria (past 90 days)** ‘Services’ is self-report of any days of physical health treatment at 3 months SAMHSA 2011 GAIN SA Data Set subset to has 3m Follow up (n=21,379)

Need, unmet need, and untargeted services are all of approximately equal concern

33

Unmet Need for Medical Treatment by 3 Months

* p<.05

Gender*Race*

Significantly higher for Males


Higher for Hispanics

34

Unmet Need for Medical Treatment by 3 Months

* p<.05

Age*


Higher for Young Adults

35

Mental Health Problem at Intake vs. Mental Health Treatment by 3 Months

High rate of co-occurring mental health problems; large unmet need

*Current Need on ASAM dimension B3 criteria (past 90 days)** ‘Services’ is self-report of any days of mental health treatment at 3 months SAMHSA 2011 GAIN SA Data Set subset to has 3m Follow up (n=20,433)

36

Unmet Need for Mental Health Treatment by 3 Months

* p<.05

Gender*Race*

Significantly higher for males


Higher for African Americans &

Hispanics

37

Unmet Need for Mental Health Treatment by 3 Months

* p<.05

Age*


Higher for adolescents and young adults

38

Risk Recovery Environment at Intake vs. Any Self-Help by 3 Months

*Current Need on ASAM dimension B6 criteria (past 90 days)** ‘Services’ is self-report of any days of self-help attendance at 3 monthsSAMHSA 2011 GAIN SA Data Set subset to has 3m Follow up (n=21,451)

Extremely high rate of recovery environment problems; large unmet need

39

* p<.05

Race* Gender*Higher for African

Americans & HispanicsSignificantly

higher for males


Unmet Need for Any Self-Help at 3 Months

40

* p<.05

Age*

Higher adolescents and young adults


Unmet Need for Any Self-Help at 3 Months

41

Reducing Health Disparities

1. Standardized screening and assessment to identify need

2. Clinical decision support systems to recommend targeted services

3. Data-based performance monitoring overall and by subgroups/problems for which there are health disparities

4. Increase patient centeredness of care

5. Use of motivational interviewing and problem solving

6. Taking services to the person

42

Some Lessons From IOM

1. Health disparities may involve differences in need requiring the targeting of a subgroup

2. They may also involve the lack of efficacious services among the subset in need

3. Health disparities are often difficult to see and may vary from what people expect

4. Health disparities can be reduced and eliminated

5. Matching patient and providers gender, race, etc. does not necessarily eliminate health disparities

43

Detailed Acknowledgements• Any opinions about this data are those of the authors and do not reflect official positions of the

government or individual grantees. • Please include the following acknowledgement and disclaimer if you use these data: • This presentation was supported by analytic runs using data provided by Substance Abuse and

Mental Health Services Administration's (SAMHSA's) Center for Substance Abuse Treatment (CSAT) under Contracts 207-98-7047, 277-00-6500, 270-2003-00006, 270-07-0191, 270-12-0397 using data provided by the following 230 grantees: TI11317 TI11321 TI11323 TI11324 TI11422 TI11423 TI11424 TI11432 TI11433 TI11871 TI11874 TI11888 TI11892 TI11894 TI13190 TI13305 TI13308 TI13313 TI13322 TI13323 TI13344 TI13345 TI13354 TI13356 TI13601 TI14090 TI14188 TI14189 TI14196 TI14252 TI14261 TI14271 TI14272 TI14283 TI14311 TI14315 TI14376 TI15413 TI15415 TI15421 TI15433 TI15438 TI15446 TI15447 TI15458 TI15461 TI15466 TI15467 TI15469 TI15475 TI15478 TI15479 TI15481 TI15483 TI15485 TI15486 TI15489 TI15511 TI15514 TI14261 TI14267 TI14271 TI14272 TI14283 TI14311 TI14315 TI14376 TI15413 TI15415 TI15421 TI15433 TI15438 TI15446 TI15447 TI15458 TI15461 TI15466 TI15467 TI15469 TI15475 TI15478 TI15479 TI15481 TI15483 TI15485 TI15486 TI15489 TI15511 TI15514 TI15524 TI15527 TI15545 TI15562 TI15577 TI15584 TI15586 TI15670 TI15671 TI15672 TI15674 TI15677 TI15678 TI15682 TI15686 TI16386 TI16400 TI16414 TI16904 TI16915 TI16928 TI16939 TI16961 TI16984 TI16992 TI17046 TI17070 TI17071 TI17334 TI17433 TI17434 TI17446 TI17475 TI17476 TI17484 TI17486 TI17490 TI17517 TI17523 TI17534 TI17535 TI17547 TI17589 TI17604 TI17605 TI17638 TI17646 TI17648 TI17673 TI17702 TI17719 TI17724 TI17728 TI17742 TI17744 TI17751 TI17755 TI17761 TI17763 TI17765 TI17769 TI17775 TI17779 TI17786 TI17788 TI17812 TI17817 TI17821 TI17825 TI17830 TI17831 TI17847 TI17864 TI18406 TI18587 TI18671 TI18723 TI18735 TI18849 TI19313 TI19323 TI19911 TI19942 20084 20085 20086 TI20017 TI20759 TI20781 TI20798 TI20806 TI20827 TI20828 TI20847 TI20848 TI20849 TI20852 TI20865 TI20870 TI20910 TI20921 TI20924 TI20938 TI20941 TI20946 TI21551 TI21580 TI21585 TI21597 TI21624 TI21632 TI21639 TI21682 TI21688 TI21705 TI21714 TI21748 TI21774 TI21788 TI21815 TI21874 TI21883 TI21890 TI21892 TI21948 TI22424 TI22425 TI22443 TI22513 TI22544 TI22695 TI22874 TI22907 TI23037 TI23056 TI23064 TI23096 TI23101 TI23174 TI23186 TI23188 TI23195 TI23196 TI23197 TI23200 TI23202 TI23204 TI23224 TI23228 TI23244 TI23247 TI23265 TI23270 TI23278 TI23279 TI23296 TI23298 TI23304 TI23310 TI23312 TI23316 TI23322 TI23323 TI23325 TI23336 TI23345 TI23346 TI23348 655373 655374

• The authors thank these grantees and their study clients for agreeing to share their data

health disparities webinar 2/28/2013 michael l. dennis, chestnut health systems. normal, il...

Documents