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Linkage to HIV Care on Release from Incarceration: Data from the LINCS Project 2010-2012 in RI and NC Brian Montague, DO MS MPH Assistant Professor of.

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Presentation on theme: "Linkage to HIV Care on Release from Incarceration: Data from the LINCS Project 2010-2012 in RI and NC Brian Montague, DO MS MPH Assistant Professor of."— Presentation transcript:

1 Linkage to HIV Care on Release from Incarceration: Data from the LINCS Project in RI and NC Brian Montague, DO MS MPH Assistant Professor of Medicine Alpert School of Medicine at Brown University Michael Costa, MPH Senior Associate Abt Associates Funding: NIH, NIDA 1R01DA

2 Presentation Outline Study Overview and Rationale Data Access – Systems-Level Challenges and Solutions Study Findings from Rhode Island and North Carolina Sites Conclusions and Implications 2

3 STUDY OVERVIEW AND RATIONALE 3

4 LINCS Study Overview Goal: to develop scalable metrics to assess quality of linkage to for persons with HIV on release from corrections using existing corrections and clinical data sources Framework: Uses eUCI from HRSA/HAB as means of confidentially linking records between corrections release data and Ryan White HIV/AIDS Program clinical data sources Key metrics assessed include time to linkage for persons linking to care and virologic status at time of first community assessment 4

5 Participating LINCS Study Sites 5 Rhode Island North Carolina Massachusetts Georgia Dallas, Texas

6 Why Is This Important? HIV treatment is critical to maintaining the health of HIV+ individuals Sicker individuals are harder and more expensive to treat Treated, individuals are less much less likely to pass HIV on to uninfected partners 6

7 HIV in Corrections Since the early years of the HIV epidemic, HIV has disproportionately impacted prisoners. In 2008, the prevalence of HIV was 1.6% among US state prisoners, representing 20,449 people. 1 Approximately 150,000 HIV-infected persons, 14% of all Americans with HIV, pass through corrections each year. 2, 3 The prevalence of HIV within correctional settings ranges from 2.5 to more than 3 times that of the general population, with prevalence in high prevalence communities such as Baltimore and Washington D.C. as high as 6.6%. 1, 3, 4 Minority disparities in HIV care are amplified in corrections. 1.Maruschak LM. December 2009, revised 1/28/10. Bureau of Justice Statistics Bulletin: HIV in Prisons Washington, DC: US Department of Justice. 2.Spaulding AC, Seals RM, Page MJ, Brzozowski AK, Rhodes W, et al. (2009) HIV/AIDS among Inmates of and Releasees from US Correctional Facilities, 2006: Declining Share of Epidemic but Persistent Public Health Opportunity. PLoS ONE 4(11): e Boutwell A, Rich JD. HIV infection behind bars. Clin Infect Dis Jun 15;38(12): Solomon L, Flynn C, Muck K, Vertefeuille J. Prevalence of HIV, syphilis, hepatitis B, and hepatitis C among entrants to Maryland correctional facilities. J Urban Health Mar;81(1):

8 Disproportionate Impact on Minorities African Americans are incarcerated at 6 times the rate of whites. 1 HIV disproportionately impacts African Americans. 7 times the rate of HIV infection Constitute 45% of new HIV infections nationwide 2 Nearly twice as likely to lack health insurance 3 Nearly 50% of Ryan White program clients are African American. 4 1.Sabol WJ, West HC, Cooper M. Prisoners in Bureau of Justice Statistics. December 2009, NCJ Revised 6/30/2010. Available at: 2.Hall HI, Song R, Rhodes P, Prejean J, An Q, Lee LM, Karon J, Brookmeyer R, Kaplan EH, McKenna MT, Janssen RS; HIV Incidence Surveillance Group. Estimation of HIV incidence in the United States. JAMA Aug 6;300(5): Schwartz K, Howard J, Tolbert J, Lawton E, Chen V. The Uninsured: A Primer. October Publication # Washington, DC: The Kaiser Commission on Medicaid and the Uninsured, Kaiser Family Foundation. Available at: 4.Heath Resources and Services Administration, HIV/AIDS Bureau. Going the distance: The Ryan White HIV/AIDS Program, 20 years of leadership, a legacy of care. August Rockville, MD: Health Resources and Services Administration. Available at: welcometomyworld.blogspot.com/2011 /06/infographic-not-guilty-program- seeks-to.html 8

9 HIV in Corrections and on Reentry Incarceration is often the only time these individuals access HIV testing, education, counseling, and treatment services. Limited data regarding the experience of HIV-infected persons on reentry. Often marginalized in their communities due to addiction, mental health disorders, unemployment, and racial disparities Decreased access to health care High rates of relapse to substance abuse and other transmission risk behaviors High mortality 9

10 Risks on Reentry ―CD4 declines, viral load increases at reincarceration and return to care 1,2 ―Texas experience: 5.4% of prison inmates receiving ART while incarcerated fill ARV prescription in time to avoid gap in treatment 3 1.Springer SA, Pesanti E, Hodges J, Macura T, Doros G, Altice FL. Effectiveness of antiretroviral therapy among HIV-infected prisoners: reincarceration and the lack of sustained benefit after release to the community. Clin Infect Dis. 2004;38 (12):1754– Stephenson BL, Wohl DA, Golin CE, Tien HC, Stewart P, Kaplan AH. Effect of release from prison and re-incarceration on the viral loads of HIV-infected individuals. Public Health Rep Jan-Feb;120(1): Baillargeon J, Giordano TP, Rich JD, Wu ZH, Wells K, Pollock BH, Paar DP. Accessing antiretroviral therapy following release from prison. JAMA Feb 25;301(8): sf/2010/01/mentoring_program_focused_on_c _1.html Health gains during the stay in corrections are often lost at the time of reentry 10

11 Key Assumptions Ryan White providers, as the safety net providers, will be the first service provider for persons on release from corrections Sentenced persons who are incarcerated will be offered antiretroviral therapy and in most cases achieve virologic suppression prior to release Given limited supplies of medicine on release and social instability in the post release period, early engagement in community HIV care is critical to treatment success 11

12 DATA ACCESS – SYSTEMS-LEVEL CHALLENGES AND SOLUTIONS 12

13 The Problem of Data Linkage Corrections Data Incarceration Release HIV Care Clinical Data Clinical Services Prescription HIV Status & Care Barriers Procedural Confidentiality/HIPAA Legal Motivational Linkage 13

14 Assessing Linkage to Care Lack of scalable metrics to assess linkage to care How were they at the time of release? How long did it take on reentry to return to care? What was their condition at the time of the first visit? Ryan White client level data reporting (RSR) offers a unique opportunity to assess linkage and retention National Corrections Reporting Program (NCRP) provides case-level data on released prisoners 14

15 National Corrections Reporting Program (NCRP) Bureau of Justice Statistics (BJS) funds and directs data collection from state depts of correction and the Federal Bureau of Prisons Centralized incarceration/release data Contains data on sentenced persons in 41 states 15

16 HRSA, HAB Ryan White HIV/AIDS Program Grant-based payor of last resort. Ryan White Program - only care option for most releasees. Ryan White Reporting System (RSR): Demographics Dates of service Key clinical variables (CD4, VL) Other services provided 16

17 Confidentiality in Ryan White CLD File Client-level data records identified by electronic Unique Client Identifier (eUCI) Encrypted identifier derived from 1st & 3rd letters of first and last name, DOB, and gender Hash algorithm SHA-1 prevents reverse engineering client identifiers 17

18 eUCI Source: Coombs E, O’Brien-Strain P. “UCI and You.” Webcast. SPHERE Institute. November 10,

19 Matching RSR & NCRP Data eUCI RSR Remove PII Create eUCI NCRP Mask Dates (PHI) HIPAA De-identified Matched Records Data File 19

20 Data Access Challenges The RSR data for this study is provided directly by participating RW grantees HAB does not provide any data Grantees own and determine the use of their data for such studies 20

21 Data Challenges - Permissions For just Rhode Island and North Carolina sites we have initiated: 22 Data Use Agreements 8 IRB Reviews 3 Letters of Authorization with OHRP 21

22 Data Challenges - Quality NCRP and RSR have undergone many years of QI/QC State HIV surveillance data is a possible source Uneven completeness and quality across states State laws vary regarding the ability share surveillance data with outside institutions State DPHs have severe capacity constraints to conduct such analyses in-house 22

23 Data Access Solutions We have taken the door to door approach out of necessity. Once a partnership is established, data transfer can become routinized There are still grantees that did not participate due to burden or caution – we respect both Clear implications for State and Federal Leadership to institutionalize ongoing assessment 23

24 STUDY FINDINGS FROM RHODE ISLAND AND NORTH CAROLINA SITES 24

25 Study Phases Validation Rhode Island Quantitative data Rhode Island North Carolina 25

26 Correctional & Clinical Data Sources Rhode Island Corrections: NCRP, single correctional institution Clinical: Ryan White Data from Miriam Hospital (primary provider serving patients following release) High level of ascertainment of HIV status in incarceration Small release cohort so fewer false positive matches North Carolina Corrections: NCRP, multiple correctional institutions Clinical: Ryan White data from 37 of 71* Ryan White providers Larger release cohort with more potential for false positives Not all Ryan White providers provided study data, so only partial coverage of clinical care sites serving patients post release * Source for total number of providers: 2011 HAB State Profile (number of providers can change year to year) 26

27 Correctional & Clinical Data Sources Rhode Island Corrections: NCRP, single correctional institution Clinical: Ryan White Data from Miriam Hospital (primary provider serving patients following release) High level of ascertainment of HIV status in incarceration Small release cohort so fewer false positive matches North Carolina Corrections: NCRP, multiple correctional institutions Clinical: Ryan White data from 37 of 71* Ryan White providers Larger release cohort with more potential for false positives Not all Ryan White providers provided study data, so only partial coverage of clinical care sites serving patients post release * Source for total number of providers: 2011 HAB State Profile (number of providers can change year to year) 27

28 Validation of the eUCI in RI Gold standard linkage assessment performed in RI eUCIs generated for names and for known aliases. Match for any eUCI was treated as a match for the individual Linkage as assessed by eUCI compared to gold standard 28

29 Key Observations: eUCI matching performed comparably to probabilistic matching Inclusion of aliases significant improves matching performance False positives and false negatives occurred but bias in estimates of time to linkage and rates of virologic suppression was small Density plots demonstrated clustering of false positives at earlier linkage times 29

30 Validation of eUCI False positives Linkage time distribution comparison False Positives days

31 Handling False Positive Matches Based on RI validation sample, false positives tended to be clustered at earlier linkage times. Similar clustering seen in North Carolina sample Statistically, models created in which estimates with earlier linkage times are weighted less than those with later linkage time for the purposes of analyses of linkage to care Clustering can may change over time as practice patterns change (spacing out routine care visits) and programs to impact linkage shorten time to linkage to care 31

32 Metrics Time to linkage: time from release date to first ambulatory care service in the community Clinical status at linkage: viral load at first assessment following release (on treatment/off treatment) Retention: remaining in care based on visit frequency or persistent virologic suppression following linkage 32

33 Benchmarks and Comparisons In+Care (2014): nationwide, cross-section HIV providers Virologic suppression (<200): 75% Texas (Baillargeon et al) TX reentry prisons 33 <10 days<30 days<60 days ART Rx (2009)5%18%30% Linkage to Care (2010)20%28% Althoff (2013): Multisite, jail reentry 0-90 days daysSustainedLost to FU Linkage/Retention19%8%38%34%

34 Demographics of Sample RINC Full Release Set n (%) Full Release Set n (%) Known HIV+ n (%) Follow-Up Services post release101 (86%)410 (74%)314 (79%) Viral load data post release96 (81%)397 (71%)294 (74%) Services 15 months follow-up71 (70%)159 (39%)158 (50%) Repeat VL > 10m post release49 (48%)143 (35%)125 (40%) Sex Male81 (84%)444 (80%)316 (79%) Female16 (16%)111 (20%)82 (21%) Ethnicity Hispanic20 (17%)12 (2%)9 (2%) Race White48 (42%)89 (16%)65 (16%) Black65 (57%)448 (80%)318 (80%) Other1 (1%)18 (4%)15 (4%) Education Less than High School74 (63%)247 (44%)142 (47%) High school or more43 (37%)308 (56%)162 (53%) HIV Risk Factor MSM47 (40%)135 (24%)92 (23%) IDU44 (37%)71 (13%)55 (14%) Heterosexual Contact26 (22%)330 (59%)236 (59%) Other1 (<1%)19 (4%)15 (4%) 34

35 Linkage RINC Time to first clinical visit post-release Full Release Set n (%) Full Release Set n (%) HIV Status Confirmed n (%) ≤ 30 days 41 (41%) 194 (47%) 128 (41%) days 14 (14%) 55 (13%) 40 (13%) days 5 (5%) 29 (7%) 25 (8%) > 90 days 41 (41%) 132 (32%) 121 (38%) 35

36 Linkage RINC Full Release Set Full Release Set HIV Status Confirmed Med time to 1 st service44 days34 days52 days VL > 200 at 1st assessment36%34%37% VL > 200 at months post release 24%26%29% 36

37 Time to Linkage To Care Rhode Island North Carolina: Known HIV+ 37 Linkage time assessed among those who ultimately link to care

38 Site Variability sites with > 5 release events Range meeting retention metrics 27% to 55% Considerations: Low volume sites <=10 (what happens if you deal with this rarely) range 29%-78% High volume sites (greater experience, potentially more complex patients) range 27% - 55% Gives ability to identify sites with demonstrated success and potentially provide mechansim for identifying and sharing best practices

39 Key Findings Despite significant differences between correctional and care systems between RI and NC, linkage experience was comparable Delays in engagement in care are frequent with median time to first service in range of days 37% have recurrent viremia at first visit indicating significant lapses in treatment Of those retained in care, about 30% have persistent viremia one year post release RI and NC experience better than prior reports but significant gaps remain 39

40 Additional Analytic Questions Given known barriers to linkage, to what extent can these be identified using additional data sets Mental health: service data in corrections Substance abuse: ? charge data, ? Ryan White service data Unstable housing: homeless service data from community Access to insurance: program policy, ? Ryan White data Access to case management or other social supports post release: care providers accessed (if designated CM providers), service data Distance to care providers: site of release, site of care 40

41 CONCLUSIONS AND IMPLICATIONS 41

42 Conclusions This method provides a valuable framework for assessing the quality of linkage to care Additional support is needed at all levels to promote engagement and retention following release Release planning in corrections Active community based case management peri-release Active programs at community care sites to engage these high risk patients ACA initiated Medicaid payment for care may present an opportunity to strengthen urgency for linkage Funding: NIDA 1R01DA

43 Acknowledgements Abt Associates Liza Solomon (PI) Michael Costa Lisa LeRoy David Izrael Sara Donohue Tom Rich Michael Shively Jennifer Davis Alyssa Kogan Brown University Josiah Rich (PI) Cara Sammartino Roee Gutman Fizza Gillani Nick Zaller RI Dept of Corrections Jeff Renzi Pauline Marcussen Erin Boyar Co-Investigators Jacques Baillargeon David Wohl David Rosen Carmen Albizu Garcia Funding: NIH, NIDA 1R01DA BJS/NCRP William Sabol E. Ann Carson Texas Ank Nijhawan Princess Iroh North Carolina DPH: Jacquelyn Clymore Brian Berte All 17 Participating RW grantees DPS: David Edwards Georgia Jane Kelly, DPH Ron Henry, DOC Christine Helms, DOC Massachusetts DPH: Kevin Cranston Noelle Cocoros Betsey John DOC: Rihanna Kohl

44 Viral Load Suppression: NC & RI North Carolina (HIV+ only) Rhode Island 44

45 Viral Load Suppression: Rhode Island Individuals with VL<= 200 at first test post-release npercent(%) Yes, Meets Metric No, Does Not Meet Metric Total96 Individuals with VL <= 200 at 1 year post engagement npercent(%) Yes, Meets Metric No, Does Not Meet Metric Total49 45

46 Virologic Suppression: North Carolina Individuals with VL <= 200 at 1 year post engagement npercent(%) Yes, Meets Metric No, Does Not Meet Metric Total125 Individuals with VL<= 200 at first test post-release npercent(%) Yes, Meets Metric No, Does Not Meet Metric Total299 46


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