Chicago Health Atlas Context, current status, and future work

Transcription

1 Chicago Health Atlas Context, current status, and future work April 30, 2013 Roderick (Eric) Jones, MPH Chicago Department of Public Health

2 Session Preview What is the Chicago Health Atlas? Background: Contextual factors that play a role in the collaboration Current work: Getting started, developing matching algorithms, minimizing reidentification risk Challenges and lessons learned: Deriving meaning and delivering it to people who can use it

3 Chicago Health Atlas is a... collaboration Informatics researchers from multiple healthcare institutions Chicago Regional Extension Center (CHITREC) Chicago Community Trust Chicago Department of Public Health

4 Chicago Health Atlas is a... website

5 Chicago Health Atlas is a... database De-identified electronic health record data for ~1 million Chicagoans In-patient and out-patient visits spanning Individual patient records matched across institutions

6 Chicago Context: Person, Place, Time

7 Chicago: Person, Place, Time Group Percent change, Percent of total in 2010 Chicago 7 [2.7 million] Non-Hispanic black Non-Hispanic white 6 32 Hispanic 3 29 Non-Hispanic Asian 14 5

8 Chicago: Person, Place, Time 229 Square miles O Hare Lake Michigan 77 neighborhood Community areas with population median of 31,000 (range, 3,000 99,000) Suburban Cook County Midway Loop Stem Leaf # Boxplot *-----* Multiply Stem.Leaf by 10**+4 All but two community areas have larger populations than the leastpopulated Illinois county

9 Chicago Context:

10 Healthy Chicago sets goals for... Public policy and legislation (n=56) Health education and awareness (n=45) Interventions and programs (n=92)

11 HEALTHY CHICAGO Chicago Department of Public Health Infrastructure

12 Highlights Infrastructure Establish an Office of Epidemiology and Public Health Informatics Expand epidemiology capacity through an increase in staff and the development of strategic partnerships with other entities who use or collect public health data

13 NYC Macroscope Scientific Advisory Group New York City has embarked on a study to validate population health estimates from its Primary Care Information Project CDPH involvement has lead to collaboration on developing vision and methodology for more widespread use of EHR data for public health

14 Highlights Infrastructure Increase the availability of public health data through the City of Chicago website

15 Chicago Context: Health Information Exchange

16 Illinois Regional Health Information Exchanges

17 Even if we don t have a mature HIE or a Regenstrief Institute, is it possible to... Leverage existing EHR data, Weave together data from multiple institutions with publicly available data, Measure disease burden and care delivered?

18 Process getting started Coordinated IRB approval across multiple institutions Limited to structured data, no free text Constrained to adults aged Focus on 606xx zip codes, with known overlapping care institutions and high population density

19 Data Dictionary Standardized specifications for data extractions from participating sites Demographics Vital signs Encounter type Diagnoses Medications Laboratory tests

20 Methods Patient de-identification and matching across sites

21 How we Hashed our Data One-way hash algorithms take in identifiers and produce a fixed-size output called a hash value or message digest John O Dwyer 6/12/1970 M ED366A1EFD562A6219C4D7AF993BADAD X:\Data\Institution A patients Java application is run on institution side of firewall, creates 5 hash IDs depending on availability of last name, first name, date of birth, gender, Social security number.

22 Diabetes (250.xx) John O Dwyer 6/12/ M John O dwyer 6/12/70 male Pre - Process Pre - Process Institution A john odwyer m john odwyer m Hash Fxn Hash Fxn Hash ID-1 Hash ID-2 Hash ID-3 Hash ID-4 Hash ID-5 Hash ID-1 Hash ID-2 Hash ID-3 Hash ID-4 Hash ID-5 Institution C / Honest Broker Replace Matched HashIDs with Unique StudyID StudyID 250. xx 401.xx HTN (401.xx) Institution B

23 Ensuring privacy and minimizing risk of re-identification Two ways to de-identify information (1) the removal of individual, familial, household, employer identifiers (2) a formal determination by a qualified statistician... 23

24 ...Formal Determination (abridged) Determine through generally accepted statistical and scientific principles and methods, that the risk is very small that the information could be used, alone or in combination with other reasonably available information, by the anticipated recipient to identify the subject of the information. 24

25 Assessment of records that are unique with respect to age, sex, race-ethnicity, and ZIP Code of residence

26 Findings A promising source of prevalence estimates

27 Data contribution summary, April 2013 Data Type Institution Demographics C C C C C PC Diagnoses C C C C C PC Visit type C C C C C PC BMI, BP C PP N N N PC Glucose, HbA1c C C C N N PC Medications C C C N N PC C: complete; N: not yet incorporated; PP: partial time period; PC: partial cohort

28 Sample size/cohort comparison, by residential ZIP code, BRFSS* vs. Chicago Health Atlas Source Min Median Mean Max IL BRFSS, Chicago 2011 respondents Chicago Health Atlas, patient with 2010 visit 1,339 10,031 9,270 21,289 *CDC Behavioral Risk Factor Surveillance System survey, Chicago sub-sample from Illinois dataset.

29 Challenge Calculating disease prevalence estimates

30 Diabetes prevalence estimate by residential ZIP Percent= # of patients with > 1 diabetes mellitus diagnosis code # of patients with visit in

31 Finding type 2 diabetes No, patient does not have type 2 diabetes in the health record Diagnosis codes Labs Medications Number of visits Yes, patient has type 2 diabetes

32 Percent Percent of Atlas patients with diabetes diagnosis in Minimum number of visits recorded Illinois BRFSS estimates the prevalence of diabetes in Chicago at 9-11%.

33 Challenge Applying estimates to Chicago rather than patient populations

34 Percent Age distribution comparison, 2010 Age groups

35 Race-ethnicity comparison Group Atlas Percent of total 2010 Census Non-Hispanic black Non-Hispanic white Hispanic Non-Hispanic Asian 4 5 Not given/unknown 31 0

36 Geographic coverage by residential ZIP Percent= # of patients with visit in Census population Additional text

37 Making data available for use To participating institutions Piloting query system To public Chicago Health Atlas website

38 Website work has involved Identifying health-related data from potential partners Evaluating need for data-sharing agreements Securing and importing the data Developing procedures and best practices for ongoing integration of data

39 Developing procedures and best practices Public health indicators from City Data Portal can be viewed for temporal and neighborhood trends Incorporating CDC guidelines for classification of map categories How to make metadata easily accessible to users How to deal with aggregated geographies and time periods

40 Chicago Health Atlas Funders Otho S.A. Sprague Institute Northwestern Memorial Hospital Community Engagement

41 Health Atlas Research Team Northwestern University: Abel Kho, John Cashy, Anna Roberts, Sara Lake Univ. of Illinois-Chicago: Bill Galanter, John Lazaro Cook County Hospital System: Bala Hota, Amanda Grasso Univ. of Chicago Medical Center: Chris Lyttle, Ben Vekhter, David Meltzer Alliance of Chicago: Erin Kaleba, Fred Rachman, Jermaine Dellahousaye Rush University Medical Center: Shannon Sims, Aaron Tabor Vanderbilt University: Brad Malin UIC Intern team: Ariadna Garcia, Pravin Babu Karuppaiah, Shazia Sathar, Ulas Keles (Sid Battacharya, Faculty mentor)

42 CityofChicago.org/Health