Chicago Health Atlas Context, current status, and future work

April 30, 2013

Roderick (Eric) Jones, MPH

Chicago Department of Public Health

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

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

Chicago Health Atlas is a . . .

website

Chicago Health Atlas is a . . .

database

• De-identified electronic health record

data for ~1 million Chicagoans

• In-patient and out-patient visits spanning

2006-2011

• Individual patient records matched

across institutions

Chicago Context:

Person, Place, Time

Chicago: Person, Place, Time

Group Percent change,

2000-2010

Percent of total

in 2010

Chicago 7 [2.7 million]

Non-Hispanic black 17 32

Non-Hispanic white 6 32

Hispanic 3 29

Non-Hispanic Asian 14 5

Chicago: Person, Place, Time 229 Square miles

77 neighborhood “Community areas”

with population median of 31,000

(range, 3,000 – 99,000)

Stem Leaf # Boxplot

9 9 1 0

9 4 1 |

8 |

8 02 2 |

7 99 2 |

7 23 2 |

6 |

6 44 2 |

5 556667 6 |

5 223 3 |

4 559 3 +-----+

4 0124 4 | |

3 5666799 7 | + |

3 01112233 8 *-----*

2 55669 5 | |

2 01123334 8 | |

1 568888899 9 +-----+

1 01233334 8 |

0 6679 4 |

0 33 2 |

----+----+----+----+

Multiply Stem.Leaf by 10**+4

All but two community areas have

larger populations than the least-

populated Illinois county

O’Hare

Midway

Lake Michigan

Suburban Cook County

Loop

Chicago Context:

• Public policy and legislation (n=56)

• Health education and awareness (n=45)

• Interventions and programs (n=92)

Healthy Chicago sets goals for. . .

HEALTHY CHICAGO Chicago Department of Public Health

Infrastructure

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

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

Highlights Infrastructure

• Increase the

availability of

public health data

through the City

of Chicago

website

Chicago Context:

Health Information Exchange

Illinois Regional

Health Information Exchanges

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?

Process – getting started

• Coordinated IRB approval across multiple

institutions

• Limited to structured data, no free text

• Constrained to adults aged 18-89

• Focus on 606xx zip codes, with known

overlapping care institutions and high

population density

Data Dictionary

• Standardized specifications for data

extractions from participating sites

–Demographics

–Vital signs

–Encounter type

–Diagnoses

–Medications

–Laboratory tests

Methods

Patient de-identification and

matching across sites

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 987654329 20802322ED366A1EFD562A6219C4D7AF993BADAD

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.

X:\Data\Institution A patients

Institution C / Honest Broker

Institution B

Institution A

Pre -

Process

Hash

Fxn

StudyID

250 . xx

401.xx

Pre -

Process

Hash

Fxn

Replace

Matched

HashIDs with

Unique

StudyID 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

Diabetes

(250.xx)

HTN

(401.xx)

John

O’Dwyer

6/12/1970

987-65-4329

M

John

O dwyer

6/12/70

male

john

odwyer

06121970

m

john

odwyer

06121970

987654329

m

Two ways to de-identify information

(1) the removal of individual, familial,

household, employer identifiers

(2) a formal determination by a qualified

statistician . . .

Ensuring privacy and

minimizing risk of re-identification

23

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.”

. . .Formal Determination

(abridged)

24

Assessment of records that

are unique . . .

. . . with respect to age, sex,

race-ethnicity, and ZIP Code

of residence

Findings

A promising source of prevalence estimates

Data contribution summary,

April 2013

1 2 3 4 5 6

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

Institution Data Type

C: complete; N: not yet incorporated;

PP: partial time period; PC: partial cohort

Sample size/cohort comparison,

by residential ZIP code, BRFSS* vs. Chicago Health Atlas

Source Min Median Mean Max

IL BRFSS, Chicago

2011 respondents 4 15 16 33

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.

Challenge

Calculating disease prevalence estimates

Percent=

# of patients with > 1 diabetes mellitus diagnosis code

# of patients with visit in 2006-2010

Diabetes prevalence estimate by residential ZIP

Finding type 2 diabetes

in the health record • Diagnosis codes

• Labs

• Medications

• Number of visits Yes, patient has type 2 diabetes

No, patient does not

have type 2 diabetes

Minimum number of visits recorded

Percent

Percent of Atlas patients with

diabetes diagnosis in 2006-2010

Illinois BRFSS estimates the prevalence of diabetes in Chicago at 9-11%.

Challenge

Applying estimates to Chicago

– rather than patient – populations

Age groups

Perc

en

t

Age distribution comparison, 2010

Race-ethnicity comparison

Group Percent

Atlas

of total

2010 Census

Non-Hispanic black 31 32

Non-Hispanic white 20 32

Hispanic 14 29

Non-Hispanic Asian 4 5

Not given/Unknown 31 0

Percent=

# of patients with visit in 2010

2010 Census population

Geographic coverage by residential ZIP

Additional text

Making data available for use

To participating institutions

–Piloting query system

To public

–Chicago Health Atlas website

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

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

Chicago Health Atlas Funders

• Otho S.A. Sprague Institute

• Northwestern Memorial Hospital

Community Engagement

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)

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