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David E. Pollio, PhD George Warren Brown School of Social Work David E. Pollio, PhD George Warren Brown School of Social Work H OMELESSNESS, D RUG ABUSE,

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Presentation on theme: "David E. Pollio, PhD George Warren Brown School of Social Work David E. Pollio, PhD George Warren Brown School of Social Work H OMELESSNESS, D RUG ABUSE,"— Presentation transcript:

1 David E. Pollio, PhD George Warren Brown School of Social Work David E. Pollio, PhD George Warren Brown School of Social Work H OMELESSNESS, D RUG ABUSE, AND S ERVICE USE CHALLENGES FOR PRACTITIONERS, PROGRAM PLANNERS, AND POLICY MAKERS

2  Early 20th century:  Hoboes, bums, winos  Middle aged alcoholic men  1980s:  Deinstitutionalized mentally ill  Famous descriptions:  75% schizophrenic (Torrey 1986)  50% schizophrenic, 100% mentally ill (Lipton et al 1983)  One-third “mentally ill” C HANGING P OPULATION

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8 O VERVIEW OF P RESENTATION A. ST. PATRICK STUDY B. SUNCODA METHODS SUBTUDIES CHANGE OVER DECADES MODELING SERVICE ACCESS LONGITUDINAL SERVICE USE C. TOWARDS THE FUTURE…

9 S T. P ATRICK S TUDY SERVICE USE OVER TIME AND ACHIEVING STABLE HOUSING IN A MENTALLY ILL HOMELESS POPULATION

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11  Individuals initially obtain services to meet “survival needs” S ERVICE R EADINESS T HEORY  Favorable outcomes occur after: 1) readiness to change 1) readiness to change 2) services are available 2) services are available  Service use increases before change and decreases afterward

12  Change occurs at “strategic moment”  If change fails to occur, service use returns to its previous level and purpose (survival)  Service use, like homelessness, tends to be cyclical S ERVICE R EADINESS T HEORY

13 T HEORETICAL M ODEL FOR S ERVICE U SE AND O UTCOMES T HEORETICAL M ODEL FOR S ERVICE U SE AND O UTCOMES Pre-outcome achievement Post-outcome achievement Pre-engagement / Consolidation Stages Engagement Stage Strategic Moment: outcome achieved Consolidation Stage: outcome maintained Disengagement Outcome not maintained No Strategic Moment: outcome not achieved

14 T ESTING THE M ODEL - versus - - versus -  Unhoused group (n=55) = Next unhoused service user selected from the agency = Next unhoused service user selected from the agency  Housed group (n=58) = Individuals maintaining housing for 24 consecutive months = Individuals maintaining housing for 24 consecutive months Data collected at St. Patrick Center comparing service use by two groups:

15 S ERVICE U SE  Monthly service use data collected for both groups starting the month the housed group achieved housing, proceeding through next 24 months  Utilization of drop-in center, counseling, health, and general services compared between the two groups  Comparison on total post-housing period; separate analysis of pre-housing and immediate post-housing periods -- different patterns hypothesized

16 C OMPARISON OF T OTAL S ERVICES U SED BY G ROUPS A TTAINING AND N OT A TTAINING H OUSING 051015202530 0 10 20 30 40 50 60 70 80 Month # services per person = Attained housing = Did not attain housing

17 2 - P HASE M ODEL: H OUSED V S. U NHOUSED Intercept 2 Slope 2 Intersection: X (months) Y (services) HOUSED GROUP UNHOUSED GROUP Intercept 1 Slope 1 108.50 (19.99) 108.50 (19.99) -16.78 (6.07) 45.89 (12.14) -5.14 (3.76) 34.08 (7.16) 34.08 (7.16) -0.80 (0.26) -0.80 (0.26) 24.68 (9.78) -0.83 (0.40) 4.65 (1.12) 30.38 (6.40) 4.92 (3.10) 4.92 (3.10) 20.62 (8.66) Tests for equality of the two lines: Chi-square (df=2) P-value20.94<.0014.20.122

18 2 - P HASE M ODEL FOR D ROP- I N C ENTER U SE, C OUNSELING, AND H EALTH S ERVICES: H OUSED O NLY DROP-IN HEALTH Intercept 1 Slope 1 48.97 (9.14) -7.84 (2.65) 29.05 (4.56) -4.32 (1.34) 7.33 (3.60) -1.20 (1.20) Intercept 2 Slope 2 13.59 (3.17) -0.39 (0.13) Intersection: X (months) Y (services) 4.75 (1.13) 11.76 (2.71) Tests for equality of the two lines: Chi-square (df=2) P-value 19.50 <.001 26.93 <.001 5.58.062 8.95 (1.87) -0.13 (0.08) 2.08 (0.81) -0.03 (0.03) 4.48 (2.74) 1.96 (0.72) 4.81 (0.79) 8.32 (1.72)COUNSELING

19 R ESULTS  Data support model for general service use, drop-in center, and counseling use  Findings suggest benefit may be gained by facilitating broad service use among homeless populations …but not for health services

20 S ERVICE I MPLICATIONS Findings suggest need for:  Multi-phase intervention with changing intensity:  Lower in initial stages when relationships established  High at “strategic moment”  Lower during consolidation of gains  Relation-building services (e.g., counseling or drop-in center) in services package  Repeated opportunities for service engagement

21 SUNCODA S TUDY 1998-2003 FUNDED BY NIDA GRANT #10713 Service Use, Needs, Costs, and Outcomes for the Drug Abusing Homeless Population

22 R ATIONALE  Research has not examined complex simultaneous interactions among:  demographics  homelessness  mental illness/drug use disorders  services  Understanding multiple interrelationships in a more comprehensive model will ultimately improve our ability to deliver effective services to the homeless population

23 PredictorsUtilizationOutcome Predisposing characteristics Sociodemographics Risk and protective factors Social support Enabling characteristics Availability Accessibility Acceptability Affordability Need factors Severity of drug use disorder Comorbid conditions Assessed/perceived need for services Treatment mandates Structural characteristics Organization Management Financing Service use Type of service Integration of individual service use (drug use, homelessness, substitute) Extent Direct cost of services Individual outcomes Substance use Housing status Psychiatric status HIV risk behaviors Indirect costs SUNCODA HOMELESS CONCEPTUAL MODEL

24  Following a homeless sample (N=397) for 24 months S ervice U se, N eeds, C osts, and O utcomes for D rug A buse in homelessness S ervice U se, N eeds, C osts, and O utcomes for D rug A buse in homelessness  Random recruitment from shelters and street locations  Interviewed annually for substance use, mental illness, service utilization, and other key outcomes  Interviewed every 3 months for intermediate outcomes

25  Collecting service use data from 35 participant agencies using MIS and other standardized collection methods Service Use, Needs, Costs, and Outcomes for Drug Abuse in homelessness Service Use, Needs, Costs, and Outcomes for Drug Abuse in homelessness  Collecting agency organizational data  Costing services for each participant agency

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27  Baseline interviews completed (N=397) Service Use, Needs, Costs, and Outcomes for Drug Abuse in homelessness Service Use, Needs, Costs, and Outcomes for Drug Abuse in homelessness W HERE W E A RE N OW  1-year follow-up interviews ongoing (N=275)  First year service data collection completed (N=385)  Cost methods finalized  Agency cost data collection ongoing

28 Demographics Male76% Male76% Nonwhite 82% Nonwhite 82% Age (median years) 42 Age (median years) 42 Education (median years) 11.7 Education (median years) 11.7 Ever married 46% Ever married 46% S AMPLE C HARACTERISTICS Homelessness # shelters in last year 1.9 # shelters in last year 1.9 # shelter nights in last year 119 # shelter nights in last year 119 Median age first shelter 34 Median age first shelter 34 Median lifetime years homeless 2.5 Median lifetime years homeless 2.5.

29 P SYCHIATRIC D ISORDERS 0 10 20 30 40 50 60 Major depression PTSD Bipolar disorder Schizophrenia Percentage

30 0 10 20 30 40 50 60 70 80 Cocaine Opioids Any drug Alcohol and/or drug Percentage S UBSTANCE U SE D ISORDERS

31 M ETHODS S UBSTUDIES M ETHODS S UBSTUDIES

32 T EST- R ETEST A NALYSES IN D RUG U SERS AND N ONUSERS H OMELESS S UPPLEMENT TO THE D IAGNOSTIC I NTERVIEW S CHEDULE:

33  M ETHODS Instruments: Diagnostic Interview Schedule/Homeless Supplement CIDI Short Form (DSM-III-R) Test-retest 1-7 days apart (mean, 1.7; SD,1.3) Independent interviewers Data analysis:  Categorical data: kappa (compared with  2 tests)  Continuous data: intra-class correlations (ICCs) Convenience sample (N=51) from 2 homeless day shelters  P URPOSE To determine reliability, esp. for substance dependent

34 SECTIONS OF THE HOMELESS SUPPLEMENT SECTIONS OF THE HOMELESS SUPPLEMENT  R ESIDENTIAL H ISTORY  C OURSE OF H OMELESSNESS  S HELTER U SE  T RANSIENCE

35 0.1.2.3.4.5.6.7.8.9 1.0 kappa  R ESULTS: R ESIDENTIAL H ISTORY “Where have you stayed overnight in the past 12 months?” family's home partner's home friend's home motel or hotel streets hospital jail own home Substance dependent Not substance dependent

36 .1.2.3.4.5.6.7.8.9 0 1.0 kappa or ICC Homeless residential variables  R ESULTS: C OURSE OF H OMELESSNESS 0.1.2.3.4.5.6.7.8.9 1.0 “What are the main reasons you don’t have a regular place of your own to live right now?” benefits, medical, psychiatric jail, drugs, alcohol family, housing partner intervening housing intervening doubled-up housing * # lifetime years homeless * p<.05 Substance dependent Not substance dependent # years since first homeless job, money

37  R ESULTS: S HELTER U SE.1.2.3.4.5.6.7.8.9 0 1.0 ICC Years, nights, and numbers of shelters used # shelter nights last year * # shelters last year * Substance dependent Not substance dependent # years since first shelter use * * p<.05

38  R ESULTS: T RANSIENCE.1.2.3.4.5.6.7.8.9 0 1.0 Moves and residence elsewhere lived in St. Louis >1 year prior residence in another state * p<.05 # other cities resided past 5 yrs * # moves past year Substance dependent Not substance dependent

39 Poor to excellent reliability Substance dependent subjects less reliable on only 4 variables (more reliable on 1 variable)  S UMMARY /D ISCUSSION Modifications: Corrected the poor questions Designed methods to anchor homelessness history - e.g., calendars, memory aids

40 C OMPARING M ETHODS OF D RUG U SE D ATA C OLLECTION: C OMPARING M ETHODS OF D RUG U SE D ATA C OLLECTION: S CREENER V S. U RINE T EST VS. CIDI-SAM

41 P URPOSE To compare substance use self report with urine testing in a homeless population sample

42 M ETHODS Comparison data: Screener: “Are you presently using/abusing alcohol or drugs?” Screener: “Are you presently using/abusing alcohol or drugs?” Urine testing (cocaine, heroin, amphetamines, cannabis, EtOH) Urine testing (cocaine, heroin, amphetamines, cannabis, EtOH) CIDI/SAM DSM-III-R diagnoses (current/last 2 wks) CIDI/SAM DSM-III-R diagnoses (current/last 2 wks) Data analysis: T-tests,  2, kappa, Yules Y T-tests,  2, kappa, Yules Y

43 10 20 30 40 50 60 70 80 90 0 100 % of sample EtOH Cannabis Ampheta- mines Heroin Cocaine Any drug S CREENER VS. CIDI/SAM * CIDI/SAM & screener (–) CIDI/SAM (–), screener (+) CIDI/SAM (+), screener (–) κ =.57 κ =.41 κ =.57 Y=.73 Y=.13 CIDI/SAM & screener (+)

44 10 20 30 40 50 60 70 80 90 0 100 % of sample (–) urine (+) report (–) report κ =.40 Y=.55 Y=.42 Y=.52 κ =.35 κ =.34 R ESULTS: S ELF R EPORT (S CREENER ) VS. U RINE T ESTING (+) urine EtOH Cannabis Ampheta- mines Heroin Cocaine Any drug (+) report (–) report With negative urine test, self report added little to detection of drug use (10% of positive cases); with negative self report, urine test detected 53% of users identified Drug use was detected better by urine test (90% of positive cases) than by self report (47% of positive cases) Alcohol use was detected better by self report (94% of positive cases) than by urine test (31% of positive cases) With negative self report, urine test added little to detection of EtOH use (6% of positive cases); with negative urine test, self report detected 67% of users identified

45 Variable time frames (“current” vs. last 2 weeks)  thus CIDI/SAM data not compared to urine test data L IMITATIONS Urine drug testing limited to cocaine, heroin, amphetamines, cannabis, & EtOH Most drugs tested detectable in urine only for 2-3 days; EtOH poorly detected Severity of substance abuse not considered

46 CIDI/SAM vs. screener - agreement:  good for drugs (except poor for cannabis)  fair for EtOH S UMMARY /D ISCUSSION Unknown additional substance use may be undetected by self report and urine test in this study

47 Detection of drug use:  test the urine (self report adds little) C ONCLUSIONS Detection of alcohol use:  not necessary to test the urine

48 TRACKING (How do you track HOMELESS people?)

49 SUNCODA approach to tracking - exhaustive & creative Future contacts forms - eg, girlfriend’s phone, favorite bar Future contacts forms - eg, girlfriend’s phone, favorite bar Photo IDs Photo IDs Reminder letters - to subjects and their contacts Reminder letters - to subjects and their contacts Calendars Calendars Networking and extended agency contact Networking and extended agency contact Barbecues Barbecues SUNCODA drop-in center SUNCODA drop-in center Open house events Open house events Lotteries Lotteries Monthly check-in (paid) Monthly check-in (paid) Visits to shelters and day centers Visits to shelters and day centers Searches of databases (e.g., death certificates) Searches of databases (e.g., death certificates) Posters at bus stops, agencies Posters at bus stops, agencies Hanging out - street routes, public areas, “hangouts” that were reported at baseline interviews (plasma/blood donor sites, recycling centers, areas where clothes/food are given out, library, museums, zoo, etc.); city festivals Hanging out - street routes, public areas, “hangouts” that were reported at baseline interviews (plasma/blood donor sites, recycling centers, areas where clothes/food are given out, library, museums, zoo, etc.); city festivals

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51 SUNCODA TRACKING STATISTICS Follow-up interviews completed:  Wave 1 (12 month) - 71% (275 of 386)  Wave 2 (24 month) - 75% (60 of 80 due so far) 85% of baseline sample tracked to date Others 6 deceased 2 in prison long term 3 too ill 4 dropped out of study

52 0 5 10 15 20 25 30 letter to subject subject called project subject dropped in located at shelter/day center located on street unknown phone call to contact saw poster phone call to subject letter to subject subject called project subject dropped in located at shelter/day center located on street unknown phone call to contact saw poster phone call to subject Wave 1 Wave 2 Percent of subjects found S UBJECTS F OUND BY T RACKING M ETHOD

53 C HANGE OVER D ECADES

54  P URPOSE To examine rates of psychiatric disorders in the homeless population in ~1980, 1990, and 2000 Assumption: the homeless population is static over time  B ACKGROUND: C HANGE O VER T IME Limited evidence for this assumption: No longitudinal data available Comparison of studies across time limited by inconsistent definitions, sampling, focus of study, and instruments of measure

55 YEAR ~1980~1990~2000 STUDY… ECA Homeless Health Survey SUNCODA DATES April 1981  July 1982April 1989  April 1990Oct 1999  Feb 2001 N 1395 ♀ & 828 ♂ General population 300 ♀ & 600 ♂ Homeless 98 ♀ & 298 ♂ Homeless Sample Residential (69 ♀ & 81 ♂ homeless) Shelters & streets Homelessness definition - Lifetime - 1. >1 mo. unplanned travel 2. >1 mo. no regular abode (from DSM criteria for ASP) - Current - no stable residence, living in public shelter or on streets without regular mailing address x 30 days - Current - no stable residence, living in public shelter or on streets without regular mailing address x 30 days 3 CROSS-SECTIONAL STUDIES USING SYSTEMATIC, RANDOMLY SAMPLED HOMELESS ADULTS IN METRO ST. LOUIS AREA 3 CROSS-SECTIONAL STUDIES USING SYSTEMATIC, RANDOMLY SAMPLED HOMELESS ADULTS IN METRO ST. LOUIS AREA

56  D ATA C OLLECTION ~1980~1990~2000 ECA Study Homeless Health Survey SUNCODA ● Diagnostic Interview Schedule for DSM-III (lifetime diagnoses) ● Diagnostic Interview Schedule for DSM-III-R (lifetime diagnoses) ● Diagnostic Interview Schedule for DSM-IV ● Composite International Diagnostic Interview/ Substance Abuse Module for DSM-III-R (lifetime diagnoses)

57 R ESULTS Sample ~1980~1990~2000 StudyECA Homeless Health Survey SUNCODA N150900397 Male54%67% (predetermined gender ratio) 76% African-American Caucasian Other race 46% (oversampled) 53% 1% 75% 22% 3% 74% 18% 8% Mean age (SD)35 (15)34 (11)42 (11)  D EMOGRAPHICS

58 % with bipolar disorder 0 10 20 30 40 50 60 70 80 90 100 Bipolar disorder 1990 † 2000 % with major depression 0 10 20 30 40 50 60 70 80 90 100 Major depression *** * 1990 †† *** ††† *** 2000 Men Women 1980 Non-substance Axis I Dx % with non-substance Axis I disorder 0 10 20 30 40 50 60 70 80 90 100 1980 Men Women % with schizophrenia 0 10 20 30 40 50 60 70 80 90 100Schizophrenia ††† *** ††† *** 2000 * 1990 MenWomen 1980 *** 1990 *** *** 2000 †† ††† Men Women 1980 R ESULTS  D IAGNOSES * p<.05 ** p<.01 *** p<.001

59 % with any substance use disorder 0 10 20 30 40 50 60 70 80 90 100 Any substance use disorder % with any substance use disorder 0 10 20 30 40 50 60 70 80 90 100 Any diagnosis % with drug use disorder 0 10 20 30 40 50 60 70 80 90 100 Drug use disorder % with alcohol use disorder 0 10 20 30 40 50 60 70 80 90 100 Alcohol use disorder 1990 † ** 2000 *** * 1990 ††† *** ††† *** 2000 * 1990 ††† *** ††† *** 2000 1990 ††† *** ††† *** 2000 Men Women 1980 Men Women 1980 Men Women 1980 Men Women 1980 * p<.05 ** p<.01 *** p<.001

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61 S UMMARY  Alcohol abuse/dependence - ♀ increasing; still more prevalent in ♂ (~60%)  Drug abuse/dependence - steep increase, ♂ & ♀  Antisocial personality disorder - flatline  Major depression - increasing, ♂ & ♀  Bipolar affective disorder - brisk increase in last decade  Schizophrenia - increasing slowly in ♀, still not common 3 St. Louis epidemiologic studies, ~1980, ~1990, ~2000 with data on psychiatric disorders in homeless people

62  Implications for services to the homeless Services based on outdated prevalence rates will be wrong for the population’s needs Increasing psychiatric and especially drug abuse disorders would indicate increasing need for mental health and drug abuse services (particularly for women)  Speculation: Are increases in drug use disorders an unintended byproduct of public policies such as zero tolerance? - flooding the existing homeless population with new drug addicted individuals

63 MODELING SERVICE ACCESS

64 0 10 20 30 40 50 60 70 80 Percentage Amelioration (aimed at attaining stable housing - eg voc rehab) Maintenance (aimed at maintaining during homelessness - eg soup kitchens) Mental health (inpatient, outpatient) Chemical dependency (inpatient, outpatient residential) Ever Last 30 days S ERVICES U SED BY C ATEGORY

65 P URPOSE To model self-reported service access in a homeless population across multiple service sectors, testing three conceptual models of service use

66 D ATA A NALYSIS For each service sector, separate logistic regression analyses performed with variables entered in blocks based on conceptual model Tests calculated for significant improvements in models A final logistic regression model run for each services sector including all variables, then tested for improvement over best conceptual model

67 Homeless Amelioration Services Homeless Maintenance Services Mental Health Treatment Substance Abuse Treatment Conceptual Model 1: Services predicted by needs Conceptual Model 2: Services predicted by needs and prevalence Conceptual Model 3: Services predicted by needs, prevalence, and other services Homelessness Non-substance Psychiatric Disorders Substance Abuse/ Dependence Diagnoses Demographics sexrace # lifetime episodes total # years major depression, bipolar disorder, schizophrenia, panic disorder, generalized anxiety, ASP alcohol, cocaine, cannabis, other drug aimed at attaining stable housing subsistence while homeless (e.g., soup kitchens, shelters) inpatientoutpatient inpatientoutpatientresidential

68 Homeless Amelioration Services Homeless Maintenance Services Mental Health Treatment Substance Abuse Treatment Homelessness Non-substance Psychiatric Disorders Substance Abuse/ Dependence Diagnoses Demographics Significant pathways between any variable and service sector ( lifetime ) Note: Pathways are for analyses of Model 3

69 Homelessness Non-substance Psychiatric Disorders Substance Abuse/ Dependence Diagnoses Demographics Significant pathways between any variable and service sector ( 30 days ) Note: Pathways are for analyses of Model 3 Homeless Amelioration Services Homeless Maintenance Services Mental Health Treatment Substance Abuse Treatment

70 SIGNIFICANT VARIABLES BY SERVICE SECTOR (LIFETIME) Alcohol (3.1) Cocaine (6.1) MH Services (2.4) Depression (3.4) Schizophrenia (6.9) White (.2) HA services (2.1) SA services (2.4) HA services (2.2)HM services (2.2) Model 3 Alcohol (3.0) Cocaine (5.3) Depression (2.2) Depression (3.6) Schizophrenia (6.5) White (.2) White (1.5)None Model 2 Alcohol (3.2) Cocaine (5.6) Depression (3.9) Anxiety (2.6) Schizophrenia (5.7) None Model 1 Substance Abuse Services Mental Health Services Homeless Maintenance Homeless Amelioration Numbers in parentheses are odds ratios

71 Alcohol (2.7) Cocaine (6.1) GAD (.2) Male(.4) Depression (6.2) Schizophrenia (9.1) White (.5) Male (2.5) MH services (2.1) None Model 3 Alcohol (2.7) Cocaine (5.8) GAD (.2) Depression (7.1) Schizophrenia (10.3) White (.5) White (.4) Male (2.2) None Model 2 Alcohol (2.2) Cocaine (6.2) Depression (7.4) Schizophrenia (9.2) None Model 1 Substance Abuse Services Mental Health Services Homeless Maintenance Homeless Amelioration SIGNIFICANT VARIABLES BY SERVICE SECTOR (30 DAYS) Numbers in parentheses are odds ratios

72 SO WHICH CONCEPTUAL MODEL IS BEST (lifetime)? Model 3 +  2 =14.0, df=3 p<.01 Model 3 +  2 =15.5, df=3 p<.01 Model 3 +  2 =13.0, df=3 p<.01 Model 3 +  2 =15.8, df=3 p<.01 Model 3 compared to Model 2 NSModel 2 +  2 =18.5,df=6 p<.01 Model 2 +  2 =9.5, df=2 p<.01 NS Model 2 compared to Model 1 Substance Abuse Services Mental Health Services Homeless Maintenance Homeless Amelioration In all analyses of Model 3 compared to model with all available variables, improvement was not significant for any service sector

73 SO WHICH CONCEPTUAL MODEL IS BEST (30 day)? NSModel 3 +  2 =7.8, df=3 p<.05 Model 3 +  2 =8.0, df=3 p<.05 NS Model 3 compared to Model 2 Model 2 +  2 =19.3, df=6 p<.01 NSModel 2 +  2 =15.6, df=2 p<.001 NS Model 2 compared to Model 1 Substance Abuse Services Mental Health Services Homeless Maintenance Homeless Amelioration In all analyses of Model 3 compared to model with all available variables, improvement was not significant for any service sector

74 S UMMARY  Types of service access best predicted by type of need, prevalence factors (race, gender, and comorbidity), and other service use (Model 3)  Psychiatric diagnosis (MH & CD) predicted use of relevant services  Use of homelessness services not predicted

75   Services should facilitate appropriate cross- sector use at the consumer level   Service provision in the homelessness sector should be more needs-driven   Service providers should attend to potential barriers (e.g., shelters requiring workers to be in at certain hours) S ERVICE I MPLICATIONS

76  Homeless individuals may use housing and subsistence services not according to need but rather when they are convenient and available A LTERNATIVE H YPOTHESIS

77 LONGITUDINAL SERVICE USE AND DIAGNOSIS

78 P URPOSE   To examine the impact of diagnoses on agency- generated longitudinal service use across multiple service sectors

79 SERVICE USE   Total # service units in 12-month period aggregated in monthly increments   Service Units defined naturalistically   Five types of services: 1) Shelter services (# of nights) 2) Inpatient substance abuse (# of nights) 3) Outpatient substance abuse (# of direct contact hours) 4) Inpatient mental health (# of nights) 5) Outpatient mental health (# of direct contact hours)

80 DATA ANALYSIS   For each of 5 service types, 12-month totals compared between those with diagnoses and those not meeting criteria   Kruskall-Wallis (non-parametric sum-rank) tests

81 D IAGNOSES (12-MONTH)   Psychiatric diagnoses:  Major depression, mania, schizophrenia   Substance abuse/dependence (SA/D) diagnoses:  Cocaine, cannabis, opioid, alcohol   Aggregate variables for:  Mood/psychotic Dxs (depression, mania, schizophrenia)  Any diagnosis

82 SERVICE USE % used in past 12 months

83 MEAN SERVICE UNITS PER SECTOR units of use

84 SERVICE USE AND ANY DRUG ABUSE/DEPENDENCE DIAGNOSIS All relationships significant at p >.05 units of use

85 SERVICE USE AND MOOD/PSYCHOTIC DIAGNOSIS All relationships significant at p >.05 units of use

86 SERVICE USE AND SPECIFIC DRUG/ALCOHOL DIAGNOSES Cocaine diagnosis (n=146):  less shelter use  greater outpatient and inpatient SA Cannabis diagnosis (n=48):  no differences Opioid diagnosis (n=12):  less shelter use Alcohol Diagnoses (n=173):  no significant differences

87 SERVICE USE AND SPECIFIC MOOD/PSYCHOTIC DIAGNOSIS Schizophrenia diagnosis (n=31):  greater outpatient MH Mania (bipolar) diagnosis (n=54):  greater outpatient MH Major depression diagnosis (n=112):  less shelter use  greater outpatient MH

88 QUARTERLY SERVICE USE: MOOD/PSYCHOTIC DIAGNOSIS AND SHELTER SERVICE USE * * p<.05 for quarter units of use

89 QUARTERLY SERVICE USE: MOOD/PSYCHOTIC DIAGNOSIS AND OUTPATIENT SERVICE USE 0 10 20 30 40 50 60 Q1Q2Q3Q4 SMI Dx/OMHNo SMI Dx/OMH * * * * p<.05 for quarter units of use

90 DISCUSSION   Similar to the modeling data, amounts of service use is related to diagnosis, both for substance abuse and mental health services.

91 DISCUSSION   Shelter findings somewhat indeterminate - data unable to separate between individuals getting housed (decreasing need) and those expelled or departing (changing accessibility)

92 TOWARDS THE FUTURE….

93 TOWARDS THE FUTURE… Completion of SUNCODA data will allow us to examine the interrelationships among longitudinal service use, needs, and outcomes over time. Wave I follow-ups on SUNCODA will be completed in May 2002, Wave II in May 2003. CONTINUING RESEACH

94 TOWARDS THE FUTURE… Cost-per-service data are being generated for all service providers, allowing extensive examination of costs over time Data on subset of SUNCODA families has been collected (M. Polgar, PI) and are being analyzed CONTINUING RESEACH

95 St. Louis City Needs Assessment Short-Term Assertive Community Treatment (ST-ACT) Towards a Spatial Explanation of Homeless Service Use SUNCODA Continuation TOWARDS THE FUTURE… GRANT APPLICATIONS

96 ST. LOUIS CITY NEEDS ASSESSMENT Purpose: To conduct a service needs assessment for St. Louis DHHS Relation to previous research: SUNCODA Data, Decades Paper Results Data include: –SUNCODA prevalence data –Focus groups of stakeholders (providers, participants, community members) Current Status: Ongoing grant from St. Louis DHHS

97 SHORT-TERM ASSERTIVE COMMUNITY TREATMENT Purpose: To implement a stage-based model of ACT, maximizing treatment efficiency for a comorbid MI/DA homeless population Relation to previous research: Builds on St. Patrick study Community partners: Behavioral Health (BJC), St. Patrick Center Status: Under development

98 ST-ACT MODEL Drop-InServices Treatment Readiness group CommunityResources:AppropriateAftercare Pre-engagement/ Engagement Stage Strategic Moment Stage Outcomeachieved ConsolidationStage Brief-ACT

99 TOWARDS A SPATIAL EXPLANATION OF HOMELESS SERVICE USE Purpose: To test whether homeless service use over time can be predicted by convenience of service access Relation to previous research: Alternative explanation to modeling results Data include: SUNCODA monthly service data, GIS system mapping all St. Louis agencies Current Status: Under review NIDA (February 2002)

100 SUNCODA II Purpose:   To follow the SUNCODA sample over 5 more years   To recruit additional homeless individuals and a comparison sample of sociodemographically equivalent housed individuals   To study not only impact of drug abuse on homelessness but also impact of homelessness on drug abuse Current Status: Under review NIDA (February 2002)

101 Pollio DE, North CS, Foster DA, et al. A comparison of agency-based and self- report methods of measuring services across an urban environment by a drug abusing homeless population. North CS, Eyrich KM, Pollio DE, Cottler LB, Spitznagel E. The homeless supplement to the DIS: Reliability and validity results. North CS, Eyrich KM, Pollio DE. Are rates of psychiatric disorders changing over time in the homeless population? Pollio DE, North CS, Eyrich KM. Modeling service use in a homeless population. Cowell AJ, Pollio DE, North CS, et al. Deriving service costs for a psychosocial rehabilitation clubhouse: Case study and methodological considerations. TOWARDS THE FUTURE… MANUSCRIPTS UNDER REVIEW

102   C REATING SERVICES RESPONSIVE TO UNIQUE INDIVIDUAL NEEDS   D ESIGNING AND IDENTIFYING SYSTEMS OF CARE APPROPRIATE TO CHANGING POPULATION   C OORDINATING CARE ACROSS MULTIPLE TREATMENT SECTORS C HALLENGES T O P ROVIDERS

103   I DENTIFYING UNINTENDED BY-PRODUCTS OF POVERTY POLICY P UBLIC P OLICY C HALLENGES P UBLIC P OLICY C HALLENGES   C REATING COST-EFFICIENT SYSTEMS OF CARE   E XAMINING REALISTIC SOLUTIONS …will lifetime welfare receipt limits be next? …ending the game of musical chairs

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105 Enola Proctor, PhD Michael Polgar, PhD Karin Eyrich, MSW, MPE Enola Proctor, PhD Michael Polgar, PhD Karin Eyrich, MSW, MPE Investigators: Carol S. North, MD, MPE (PI) Ed Spitznagel, PhD Linda Cottler, PhD

106 ADAPT of Missouri Archway Community Treatment Center Barnes-Jewish Hospital BJC Behavioral Health St. Louis Veterans Administration Grace Hill Neighborhood Services Black Alcohol Drug Service Information Center Christian Service Center Haven of Grace Hope House Hopewell MH Center Metropolitan St. Louis Psychiatric Center Our Lady’s Inn Peter and Paul Community Services Places for People Queen of Peace Lutheran Medical Center St. Alexius Hospital YWCA Phyllis Wheatley Program ADAPT of Missouri Archway Community Treatment Center Barnes-Jewish Hospital BJC Behavioral Health St. Louis Veterans Administration Grace Hill Neighborhood Services Black Alcohol Drug Service Information Center Christian Service Center Haven of Grace Hope House Hopewell MH Center Metropolitan St. Louis Psychiatric Center Our Lady’s Inn Peter and Paul Community Services Places for People Queen of Peace Lutheran Medical Center St. Alexius Hospital YWCA Phyllis Wheatley Program Missouri Department of Mental Health St. Louis DHHS Community Alternatives Salvation Army Family Haven Salvation Army Harbor Light Center Salvation Army CSTAR St. Louis Psychiatric and Rehabilitation Center St. Patrick Center St. Phillipine Emergency Shelter Sunshine Ministries St. Louis University Hospital (Tenet) BREM Catholic Social Ministries Drug and Alcohol Rehabilitation Good Samaritan Center for the Homeless Harris House Independence Center New Beginnings CSTAR United Methodist Ministries Shalom COMMUNITY PARTNERS

107 Any Questions


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