Public Aggregate Reporting – DHCS Business Reports Overview

Linette Scott, MD, MPH

Chief Medical Information Officer, DHCS

July 1, 2015

Public Aggregate Reporting for DHCS Business Reports (PAR-DBR)

HIPAA Standard for De-identification

Overview of the PAR-DBR

Steps of the PAR-DBR

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HIPAA STANDARD FOR DE-IDENTIFICATION

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HIPAA standard for de-identification of protected health information:

“Health information that does not identify an individual and with respect to which there is no reasonable basis to believe that the information can be used to identify an individual is not individually identifiable health information”

DHCS is a HIPAA Covered Entity

Health Insurance Portability and Accountability Act (HIPAA)

http://www.hhs.gov/ocr/privacy/hipaa/understanding/coveredentities/De-identification/guidance.html

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HIPAA De-identification Standard

Two methods described in the standard:

Safe Harbor 18 identifiers of the individual or of relatives,

employers, or household members of the individual must be removed

In the context of other publicly available information

Expert Determination

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HIPAA Safe Harbora) Names

b) All geographic subdivisions smaller than a state, including street address, city, county, precinct, ZIP code, and their equivalent geocodes, except for the initial three digits of the ZIP code if, according to the current publicly available data from the Bureau of the Census:- The geographic unit formed by combining all ZIP codes with the

same three initial digits contains more than 20,000 people; and- The initial three digits of a ZIP code for all such geographic units

containing 20,000 or fewer people is changed to 000

c) All elements of dates (except year) for dates that are directly related to an individual, including birth date, admission date, discharge date, death date, and all ages over 89 and all elements of dates (including year) indicative of such age, except that such ages and elements may be aggregated into a single category of age 90 or older

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HIPAA Safe Harbor Cont.d) Telephone numbers

e) Fax numbers

f) Email addresses

g) Social security numbers

h) Medical record numbers

i) Health plan beneficiary numbers

j) Account numbers

k) Certificate/license numbers

l) Vehicle identifiers and serial numbers, including license plate numbers

m) Device identifiers and serial numbers

n) Web Universal Resource Locators (URLs)

o) Internet Protocol (IP) addresses

p) Biometric identifiers, including finger and voice prints

q) Full-face photographs and any comparable images

r) Any other unique identifying number, characteristic, or code

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What Usually Leads to Expert Determination? Time

The time period is less than a year Geography

Less than statewide Other

Rare diagnosisSpecific combinations of variables

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Expert Determination

Apply statistical or scientific principles

Very small risk that the anticipated recipient could identify an individual

Documents the methods and results of the analysis that justify such determination

http://www.hhs.gov/ocr/privacy/hipaa/understanding/coveredentities/De-identification/guidance.html#standard

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OVERVIEW OF THE PAR-DBR

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Purpose of the PAR-DBR

Establish guidelines to be used for reports and documents generated by DHCS programs for public release that include data (tables, charts, graphics)

Create consistency in the analysis and presentation of data in reports and documents

Protect confidentiality of personal data held by DHCS

Compliance with laws that govern data release

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Public Aggregate ReportingDHCS Business Reports These Guidelines provide a method and process

for Expert Determination

Key Steps:

Evaluate the Numerator

Evaluate the Denominator

Use the Publication Scoring Criteria

Suppress data that has higher risk

Departmental document review processes12

Public Aggregate Reporting

http://www.dhcs.ca.gov/dataandstats/statistics/Documents/3_1_Population_Distribution_Age_Gender.pdf 13

A Table with Suppressed Data

http://www.dhcs.ca.gov/dataandstats/statistics/Documents/RASD_Issue_Brief_MC_Births.pdf 14

STEPS FOR THE PAR-DBR

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The Cells in the table are the boxes that have values in them, as opposed to the row and column headers

Table Cell

Defining Table Cell

Year# of Medi-Cal Members

in Fee For Service (in thousands)

# of Medi-Cal Membersin Managed Care

(in thousands)2012 2,775 4,853

2011 3,067 4,527

http://www.dhcs.ca.gov/dataandstats/statistics/Documents/1_6_Annual_Historic_Trend.pdf - Data in the Table16

Numerator – number of events with the characteristics of the given row and column

Denominator – the population from which the events arise

Defining Numerator & Denominator

Year# of Medi-Cal Members

in Fee For Service (in thousands)

# of Medi-Cal Membersin Managed Care

(in thousands)2012 2,775 4,853

2011 3,067 4,527

Numerator# of Medi-Cal Members in Fee For

Service (in thousands) 2,775

Denominator # Medi-Cal Members in 2012 (in thousands)

7,628

http://www.dhcs.ca.gov/dataandstats/statistics/Documents/1_6_Annual_Historic_Trend.pdf - Data in the Table 17

Step 1 – Numerator Condition

Have the Numerators (the table cells) been derived from greater than 10 members (beneficiaries)?

If Yes, Go to Step 2 If No, Go to Step 3

Step 2 – Denominator Condition

Is the population denominator for the numerators in the table cells greater than 20,000 individuals?

If Yes, Go to Step 5 If No, Go to Step 3

Step 3 – Apply Publication Scoring Criteria to assess risk:

If the score is ≤ 12, Go to Step 5 If the score is > 12, Go to Step 4

Step 5 – Submit Aggregate Data Analysis for Document Review

Program Management Review Expert Determination Review* OLS Review for legal risk OPA Review OPA Review

Step 4 – Suppress Small Cells and Complimentary Cells

Small Cells are those with numerators fewer than 11 and Complimentary Cells are those that could be used to recalculate the Suppressed Small Cells

Figure 3: Reporting Assessment Decision Tree

Assesses risk for data release of aggregate data through a stepwise process. Aggregate data may be derived from record level data with identifiers, record level data without identifiers or previously aggregated data.

NO

NO

> 12

YES

YES

≤ 12

* l Review for Expert Determination will be performed by individuals who have been qualified as experts by OLS and who meet the HIPAA Privacy Rule implementation specifications: “A person with appropriate knowledge of and experience with generally accepted statistical and scientific principles and methods for rendering information not individually identifiable.” [45 CFR Section 164.514(b)(1)]

A stepwise decision tree to assess aggregate data for de-identification

Serves as a tool and guideline for the Expert Determination

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A minimum cell size is set for the Numerator

A minimum value is set for the Denominator

Both the minimum cell size for the numerator and denominator must be met

DHCS has identified a minimum value of 11 for the numerator rule and a minimum value for the denominator 20,000.

Both conditions must be met to release the data in the table cell, otherwise proceed to Step 3

Reporting Assessment Decision Tree Steps 1 & 2

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Step 3 – Apply Publication Scoring Criteriato assess risk

Step 4 – Suppress Small Cells and Complimentary Cells if score is greater than 12

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A symbol standing in for an unknown numerical value in an equation

Common variables in health data aggregation: Age Sex Race Ethnicity Time

Common Public Reporting Variables

Geography (State, County, Medical Service Study Area, ZIP Code)

Diagnosis/ConditionProvider (Type, Specialty,

Location)

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A given variable my have different ranges assigned to it

Ranges assigned to the variable may be defined many ways

Example – Age Groupings 0-10, 11-20, 21-30, etc. (years old) …

provides equal groupings, commonly used, may not apply to a particular program

0-4, 5-11, 12-18, 19-21 (years old) … correlates to school environments: pre-school, elementary, junior high/high school, post-school

Variables - Ranges

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Variable Characteristics Score

Sex Male or Female +1

Age Range >10-year age range +2

  6-10 year age range +3

  3-5 year age range +5

  1-2 year age range +7

Race Group White, Asian, Black +3

  Detailed Race +5

Hispanic Ethnicity

yes or no +2

  Detailed ethnicity +3

Language Spoken

English, Spanish, Other Language +2

Publication Scoring Criteria – Step 3

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Variable Characteristics Score

Events 1000+ events in a specified population +2

 (Numerator) 100-999 events +3

  11-99 events +5

  <11 events +8

Geography State or geography with population >2,000,000

-5

  Population 560,001 - 2,000,000 -3

  Population 20,001 - 560,000 0

  Population ≤ 20,000 +5

Data Year 5 years aggregated -5

  2-4 years aggregated 0

  1 year (e.g., 2001) +3

  Bi-Annual +4

  Quarterly +5

  Monthly +7

Publication Scoring Criteria – Cont.

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Release Scoring Criteria approximately quantifies two re-identification risks:

size of potential population

variable specificity

Add the score assigned to each variable characteristic:

If the score is more than 12, cell sizes must be 11 or more before releasing data

Compiling the Score

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Suppression – Step 4 Complimentary Cells

Any time a single cell could be calculated based on row or column totals when it is suppressed, then additional cells in that row and/or column will also need to be suppressed

The total value of the cells suppressed should be 11 or more

Additional Aggregation Examples Extend the time period included

Group regional geography

Goal – large enough groups that suppression is no longer necessary

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Aggregate Variables - Examples

Higher Aggregation Lower Aggregation

Age Groups: 0-21, 22-64, 65 and older

Ethnicity: Hispanic or Not Hispanic

Geography: Medical Service Study Areas (542 in CA)

Time: three years

Diagnosis: Diagnosis Related Groups (DRGs), Episode Grouper

Age Groups: one year increments (1, 2, 3, etc.)

Race: White, Black, American Indian, Asian Indian, Chinese, Filipino, Japanese, Korean, Vietnamese, Native Hawaiian, Guamanian, Samoan, Other

Geography: ZIP Code (2,591 in CA)

Time: monthly

Diagnosis: Specific ICD-9 Codes

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To achieve a minimum number in the given cell, results are combined over the associated variable:

Geographic areas,

Multiple years, or

Subgroups (e.g., age groups)

The number of variables in a table will affect the amount of aggregation necessary

For example, if the results for a 5 year age group (ages 1-5 years of age) do not yield an adequate number of cases, then the age group is extended to cover more ages (1-10 years of age)

Aggregation

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Program Management

Expert Determination

Office of Legal Services Privacy Team

Office of Public Affairs

Step 5 – Approval Processes

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Public Aggregate Reporting for DHCS Business Reports

A multi-step process supports public reporting

Balances public reporting with protecting confidentiality

Will continue to review and revise as the data landscape changes and matures

DHCS is committed to supporting data publishing while being consistent with the HIPAA De-identification Standard

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Thank you!