National Center for Environmental Health

Assessing the Impacts of 

Differential Privacy on Public 

Health Surveillance at Varying 

Geographic Resolutions

Angie Werner, PhD, MPH; Nicholas Skaff, PhD; Adam Vaughan, PhD, MPH 

October 24, 2023

2023 FCSM Research & Policy Conference

Division of Environmental Health Science and Practice

The findings and conclusions in this presentation are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention (CDC).
Q: What is differential privacy? 

• Creates uncertainty about the identities of people behind the 

numbers and allows for measurement of privacy risk.

• Adds precisely calibrated “statistical noise” to data tables 

published by Census (inserts small differences into counts of 

people and households) to protect each respondent’s identity.

• Invariants (reported as enumerated)

• Total population (state and state-equivalents level)

• Total housing units (census block level) – not block-level resident 

population

• Number of group quarters by facilities type (census block level) – 

not population in group quarters
County level: Assessing the impact 

on age-adjusted incidence of COVID-19
Data and Methods

Data

 Numerator: Number of COVID-19 cases in 2020 by county, race, and age*^

 Denominators: Summary File 1 (SF1) 2010 population data and differential privacy 

(DP) demonstration data (v4-3-2023)

Methods

*included if case report date, age, race, and county of residence were submitted

^limitation for all use cases: 2010 published data used swapping instead of disclosure avoidance

age-standardized 

rates with SF1 

denominator

county -

level rates 

(metro and 

non-metro) age-standardized 

rates with DP 

denominator

COVID-19
Age-adjusted incidence rates for 

people from racial minority groups 

were disproportionately affected.

County-level age-adjusted incidence rates for people from 

racial minority groups were disproportionately affected.

COVID-19
Age-adjusted incidence rates for people from racial minority 

groups were disproportionately affected.

COVID-19
COVID-19

|% difference| Age-adjusted rates 

SF vs. DP for Black persons

NA values occur where no cases were reported among 

Black persons in that county during 2020.
County level: Assessing the impact on heart 

disease mortality overall and by sub-group
Data and Methods

Data

 Numerator: 2010 county-level heart disease death counts from 

National Vital Statistics System 

 Denominators: National Center for Health Statistics bridged-race 

estimates and DP demonstration data (v4-3-2023)

Bayesian 

spatiotemporal 

conditional 

autoregressive 

model

age-standardized 

rates by sex

age-specific rates 

(stratified by 10-year 

age groups) by sex

% change 

between rates 

with different 

denominators

countylevel rates

Methods

Heart Disease Mortality
Positive differences 

indicate the rate 

estimated using DP 

was a higher value. 

*Outliers truncated.

Percent difference in age-adjusted 

county-level heart disease mortality 

and in age-specific rates by sex

Heart Disease Mortality
County and census tract: Assessing the impact 

on age-adjusted rates of asthma emergency 

department (ED) visits and acute myocardial 

infarction (AMI) hospitalizations
Data and Methods

Data

 County-level asthma 

• ED visit counts from 30 states 

• AMI hospitalization counts from 31 

states

 Census tract-level asthma 

• ED visit counts from 6 states 

• AMI hospitalization counts from 7 

states

 Denominators: SF1 2010 population 

data and DP demonstration data (v4- 3-2023)

Methods

 Calculated age-adjusted rates with 

and without DP

 Value of percent difference 

calculated

 Evaluated changes in rates 

between 2000 and 2010 using SF1 

and DP denominators 

Hosp/ED

age-adjusted rates 

with SF1 

denominator county - and 

tract -level 

rates age-adjusted rates 

with DP 

denominator
Hosp/ED

Counties and 

tracts with ≥ 20% 

change from the 

SF1 rate are in red.
Hosp/ED

Counties and 

tracts with ≥ 20% 

change from the 

SF1 rate are in red.
Hosp/ED

Counties where the age-adjusted rates of asthma emergency department visits from 

2000 to 2010 changed signs due to differential privacy are shown in yellow.
Hosp/ED

Counties where the age-adjusted rates of acute myocardial infarction hospitalizations 

from 2000 to 2010 changed signs due to differential privacy are shown in yellow.
Block: Assessing the impact on the Community 

Assessment for Public Health Emergency 

Response (CASPER)
Data and Methods

Data

 2020 Census data at the block level

• Occupied households (H3)

 Uses additional data, where needed

• Total population (P1)

• Population in households by age (P16)

• Race of householder (H6)

• Hispanic or Latino origin of householder 

by race of householder (H7)

Methods

 Block-level data necessary for 

selecting sample and providing 

maps to send staff to the field

 Census data used to select 30 

“clusters” (blocks) with probability 

proportional to size

 Seven households interviewed 

within each cluster

 Survey data are weighted based on 

2020 Census data to gain accurate 

estimates of needs

CASPER
CASPER

Sample cluster (#16) showing the 

approximate number of households (42) in 

the block according to 2020 Census data

Satellite imagery of the cluster (#16) where 

7 interviews should have occurred through 

systematic selection of the 42 households
CASPER

Satellite imagery of the cluster (#16) where 

7 interviews should have occurred through 

systematic selection of the 42 households

 No households actually in the census 

block

• Assumption that one of the buildings 

was an apartment and could have 

accounted for the 42 households

• No households – only a food bank

 Interviewed a few persons 

experiencing homelessness
CASPER

Sample cluster (#20) showing the 

approximate number of households (185) in 

the block according to 2020 Census data

Satellite imagery of the cluster (#20) where 

14 interviews should have occurred through 

systematic selection of the 185 households
CASPER

Satellite imagery of the cluster (#20) where 

14 interviews should have occurred through 

systematic selection of the 185 households

 Teams arrived at cluster expecting 

185 households and no households 

were available to interview

 Invalidated the entire assessment 

• At least one household must be 

interviewed in each cluster

• A minimum of 168 households must 

be interviewed overall

 CASPER could not be completed after 

several months of planning and effort
Conclusions From CDC Use Cases

• Data showed some overall 

improvement in ageadjusted rates*

• Data remain problematic 

for age-specific rates

• Data remain problematic 

when calculating ageadjusted rates

• Changes in age groups 

impact overall rate 

calculations–even if total 

population counts don’t 

change significantly

• Data are problematic for 

characterizing risks 

accurately and identifying / 

targeting populations at 

higher risk

• Can not characterize 

communities accurately, 

particularly for situational 

awareness and emergency 

response activities

• Significant differences in 

rates, particularly in counties 

with smaller populations 

and when stratifying ageadjusted rates

County Census tract Block

*no stratification by sex or by race/ethnicity 

or urban/rural or any sub-population
Public Health Implications

Measuring baselines and progress of CDC health equity goals

Under/overestimation of disease, disparity, or healthcare use rates

Trend monitoring 

over time

• Detecting true increases or decreases

• Setting targets

• Implementing evidence-based interventions

• Monitoring progress

Finer spatial resolution 

& sub-populations data

• Disseminating more local-level data

• Targeting populations accurately

• Allocating CDC resources
Acknowledgments

 CDC Differential Privacy Interest Group

 CDC Tracking Program Differential Privacy Content Workgroup

 CDC Office of Science

 Mel Smith and Ryan Davis, Geospatial Research, Analysis, and 

Services Program

 Jane Henley, Division of Cancer Prevention and Control

 Amy Schnall, Health Studies Section

 National Center for Health Statistics (NCHS)

 IPUMS 
For more information, contact NCEH

1-800-CDC-INFO (232-4636)

TTY: 1-888-232-6348 www.cdc.gov

The findings and conclusions in this report are those of the authors and do not necessarily represent the 

official position of the Centers for Disease Control and Prevention.

Follow Tracking on social media:

facebook.com/CDCEPHTracking

@CDC_EPHTracking

Contact Me:

Angela Werner, PhD, MPH

awerner@cdc.gov