Sharing Surveillance Data across Jurisdictions: The DC/MD/VA Model - - PowerPoint PPT Presentation

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Sharing Surveillance Data across Jurisdictions: The DC/MD/VA Model

Anne Rhodes, Virginia Colin Flynn, Maryland Rupali Doshi, District of Columbia Marcia Pearl, Maryland

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Outline

• Background
• Black Box technology
• Black Box iterations/ results
• S

TD data sharing

• Future Directions

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National HIV Care Continuum

DATA and PROGRAM

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Data Quality: What, Why, How?

• S

urveillance, Ryan White, and other HIV data are not j ust utilized for funding oddslot formulas and static reports

• Real-time tracking of diagnosis, linkage, care

engagement, medication adherence and viral suppression are needed

• Current data systems – set up artificially with

barriers based on funding streams, j urisdictions, disease status, etc.

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Accuracy

How do people get included in/ excluded from Continuum

• f Care analyses?
• Death
• Proof of out of j urisdiction address
• No care in xx period of time?
• Modeling methods?
• Only care in xx period of time?

24%

• f current living cases in VA HIV

S urveillance system – no lab in last 5 years (n=6,005)

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Completeness

• Markers for care cannot all be tracked in current HIV

S urveillance system

• S

ystems outside of health department purview often have data on care status for PLWH

• Electronic medical records/ health information

exchanges/ all payer claims databases often available in j urisdictions

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Timeliness

• NHAS

– 4th Goal calls to “ strengthen the timely availability and use of data”

• National viral suppression rates for 2013 for

persons diagnosed with HIV as of 12/ 31/ 2012 (and alive as of 12/ 31/ 2013) released in July 2016

• AIDS

.GOV site has care continuum with 2011 data

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Black Box: Real Time HIV S urveillance

• Pilot proj ect from Georgetown, funded by NIH.

Involved DC, MD, and VA Departments of Health

• RIDR de-duplication proj ect, funded by CDC.

Used data from 8 j urisdictions: DC, MD, VA, NYS , NYC, WV, DE, NC, FL

• Utilizes privacy technology for sharing

surveillance data among j urisdictions where an algorithm for matching was set up in the “ black box” and returned matches of varying strengths (Exact to Very Low) to each j urisdiction

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ATra™: A new methodology for co-analyzing non-shareable data

Organization 1 Organization 2 Organization 3 Organization n Policy Body Patterns Pattern Matches

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Regional HIV data sharing

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S equence of Events in Cross- Jurisdictional HIV Data S haring

2013

• Dat a sharing

agreement s – discussions begin

2014

• Dat a sharing

agreement s signed

2015

• Black Box pilot

for DC/ MD/ VA complet ed

2016

• RIDR proj ect

begins wit h 8-10 j urisdict ions

• Weekly calls

wit h j urisdict ions

2017

• eHARS

dat a exchanges begin (large file back t o 2015), followed by prospect ive files for DC/ MD/ VA

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Matching - Pilot

HIV S urveillance Records: 1981-2015

• Tot al (N=161,343)
• Dist rict of Columbia (N=49,326)
• Maryland (N=66,200)
• Virginia (N=45,817)

Mat ching Variables:

• Last name of HIV case;
• First name of HIV case;
• Dat e of birt h of HIV case;
• S
• cial S

ecurit y number of HIV case;

• Hierarchical race/ et hnicit y assignment for HIV case; and
• Last name soundex of HIV case

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Matching - Pilot

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Black Box Results - Pilot

Output of person-matching across DC, MD, and VA eHARS databases:

Over half of matches were not known to jurisdictions

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Data S haring

• Exchanged data files with identification variables after

Black Box match and each state validated the accuracy

• f the matches
• Over 90%

acceptance for high, very high, and exact matches

• Exchanged data files on accepted matches with data on

diagnoses, demographics, risk, lab tests, residence, and vital status

• Used to update records, improve data quality, and

generate new HIV care continuum

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Black Box Results for VA (RIDR): August 2017 Match, Exact and High Categories

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1902 1513 856 628 629 491 294 104 6417 3056 2626 1752 1553 1278 679 302 159 11405

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

DC MD NC FL NYC NY st ate WV DE Tot al

Not Known Known

36%

• f matches in exact and high categories not previously known to Surveillance program

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S TD Data S haring

Cross-j urisdictional case investigations – index cases and named partners Monthly conference call S ecure FTP site

High volume clinical site (LGBT focus) DC Department

• f Health

Maryland Department of Health Virginia Department of Health

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Cross-Jurisdictional Case Investigations Letter to Providers, January 13, 2017

S igned by HIV/ S TD Leadership of DC, MD and VA Departments of Health

“ … Currently, the three health departments actively cooperate and share information on persons who seek medical care outside of their area of

• residence. We must operate in this way to prevent new infections and

assure individuals are linked to and retained in care and treatment. Please j oin us in in this cross-j urisdictional effort to increase the timeliness and effectiveness of our public health efforts to intervene in the spread of HIV and S

• TIs. As a front-line health care provider, you and

your office staff have access to critically important information that can aid the health departments in responding to new HIV and S TI cases. Therefore, on behalf of each of our health departments, we authorize and encourage you to respond to requests for information on HIV and S TI disease investigations of cross-j urisdiction cases from our partner health departments in the National Capital Region… ”

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Improved Accuracy of Case Numbers

• After address and

vital status updates, number of PLWH living in Virginia as of 12/ 31/ 2015 was reduced by 760 persons Increased Number of Care Markers for Continuum

• Care Markers
• utside of eHARS

, added 8% to retention rates in 2014 and 9% to viral suppression rates in 2015

Results: S

• Far

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Results: Continued

• Ongoing data sharing in DC/ MD/ VA – monthly meetings

to discuss issues

• Proj ects across j urisdictions, including Data to Care,

cluster investigations and coordination of prevention and care efforts

• Improved communication among j urisdictions

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Future Directions

Continued Quarterly Data S haring Building Relationships National Data S haring S haring across diseases

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Challenges

Leadership understanding and buy-in Bandwidth Comfort level with sharing identified disease data Information technology to support the proj ect Technical expertise Proj ect management

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Final Thoughts

• Data Improvement strategies should be part of plan for

addressing

• S

haring data across j urisdictions is important for tracking disease and care for S TDs and HIV

• Utilizing data for public health impact requires merging
• f multiple sources of information across systems,

agencies, and funding streams

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Acknowledgements

CDC: Benj amin Laffoon, Dr. Irene Hall DC Department of Health: Michael Kharfen, Garret Lum, Auntre Hamp, Adam Allston, Brittani S aafir-Callaway, Toni Flemming, Deontrinese Henderson, Alberta Roye, Francoise Uwimana Georgetown University: Jeff Collman, Joanne Michelle Ocampo, J S mart, Raghu Pemmaraj u HRS A: Jessica Xavier, John Hannay Maryland Department of Health: Colin Flynn, Reshma Bhattacharj ee Virginia Department of Health: Lauren Yerkes, Kate Gilmore, S ahithi Boggavarapu, S

• nam Patel, Amanda S

aia