cdc.gov/coronavirus

Representativeness of Non-probability Serology Samples

from Multiple Commercial Laboratories in the United States

Yun Kim, ICF

Ronaldo Iachan, ICF

Lee Harding, ICF

Adam Lee, ICF

Davia Moyse, ICF

2022 FCSM Research & Policy Conference

October 26, 2022

Kristie Clarke, CDC

Kevin Barney, CDC

Ruchi Pancholy, CDC
 What is the Nationwide Commercial 

Laboratory Seroprevalence (NCLS)?

 CDC’s NCLS study is designed to produce 

weighted estimates for the percentage of 

people with antibodies to SARS-CoV-2, the 

virus that causes COVID-19,

Background

Source: https://covid.cdc.gov/covid-data-tracker/#national-lab
 Objectives of Nationwide Commercial Laboratory Seroprevalence (NCLS) Study

 To produce jurisdiction-level estimates weighted to the population totals of each jurisdiction 

based on available demographics (e.g., age, sex, metro/non-metro status) using residual sera 

from commercial laboratories across the United States. 

 To produce national estimates by demographic groups and to assess changes over time

Background
 Design, Setting, and Participants

 Included all 50 states, the District of Columbia, and Puerto Rico

 Repeated monthly cross-sectional study (Rounds)

 Used non-probability samples of people who visited commercial 

laboratories for non-COVID-19 related reasons and had blood 

drawn

 Collected specimens provided by people of all ages originally for 

routine screening or clinical management from the commercial 

laboratories

 Excluded people with COVID-19 related conditions

Background

Sample 

Representativeness?
Research Questions

 Do the serology samples provide good 

geographic representation at the jurisdiction 

level?

 How do the counties in the serology sample 

differ from the counties not in the serology 

sample in terms of demographic and 

socioeconomic (SES) characteristics?
Harris 

County

Maricopa 

County

Los Angeles 

County

U.S. Population by County in 2020

Source: American Community Survey (ACS) 2020 Public Use Microdata Sample (PUMS) 
Total Number of Specimens (September 2021)

Source: CDC Nationwide Commercial Laboratory Seroprevalence Round 25 (September 2021)

Harris 

County

Maricopa 

County

Los Angeles 

County
Total Number of Specimens (November 2021)

Source: CDC Nationwide Commercial Laboratory Seroprevalence Round 27 (November 2021)

Harris 

County

Maricopa 

County

Los Angeles 

County
Total Number of Specimens (January 2022)

Source: CDC Nationwide Commercial Laboratory Seroprevalence Round 29 (January 2022)

Harris 

County

Maricopa 

County

Los Angeles 

County
Specimens per 100,000 Population in Rounds 25-30

(September 2021 – February 2022) 

Source: American Community Survey (ACS) 2020 Public Use Microdata Sample (PUMS) 

CDC Nationwide Commercial Laboratory Seroprevalence Round 25-30 (September 2021 – February 2022)

Harris 

County

Maricopa 

County

Los Angeles 

County
Methods: Overview

Research Questions Methods Summary

1 Does the serology sample provide good 

geographic representation at the state 

level?

Dissimilarity Index 

2 How do the counties in the serology 

sample differ from the counties not in 

the serology sample in terms of 

demographic and SES characteristics?

County-level multivariable logistic 

regression model
Method 1

 Dissimilarity Index

Measure of the evenness with which two groups are distributed across component 

geographic areas that make up a larger area. 

= 

1

2෍

Specimen counts in county i in the state sample

Total sample in the state −

County i population in the state

Total population in the state

ே

௜ୀଵ

where N = the number of counties in the state.

Reference: Agresti, A. Categorical Data Analysis, Second Edition. 2002. 
Method 2

 Statistical Analysis

 Chi-square Test

Compared demographic and SES characteristics between counties in the 

sample and counties not in the sample

 County-level Multivariate Logistic Regression Model 

Examined the association of county average response in the serology 

sample with county-level demographic and SES characteristics
Method 2

 Dependent Variable

 1: counties having at least one sample in the serology data (Sep. 2021- Feb. 2022)

 0: counties having no sample in the serology data (Sep. 2021- Feb. 2022)

 Independent Variables – from American Community Survey (ACS)

 Categorical variables based on the terciles within the state of the following countylevel characteristics :

% Black, Hispanic, or Asian/Pacific Islander people

% Age 65 and older

% Under poverty line

% With college education

% Employed

Population size

 Census Regions (Midwest, Northeast, South, West)
Geographic 

Representativeness 

Jurisdiction Number of County Sample Size Dissimilarity Index

South Dakota 66 751 74.26

North Dakota 53 75 63.89

Texas 254 3942 63.01

Missouri 115 8837 51.96

Montana 56 4851 40.92

Pennsylvania 67 9036 38.59

Indiana 92 4446 38.35

Virginia 133 7450 37.47

Iowa 99 8899 37.46

Vermont 14 7289 37.20

West Virginia 55 9107 35.09

Massachusetts 14 8847 34.34

New Jersey 21 7645 33.85

Louisiana 64 9332 33.27

Hawaii 5 9054 30.74

Puerto Rico 78 9222 30.72

Wyoming 23 878 30.49

Maryland 24 7777 29.96

Washington 39 8754 29.18

Oklahoma 77 9172 29.12

New Mexico 33 9100 29.01

Wisconsin 72 7887 28.52

Oregon 36 8681 27.21

Kansas 105 9122 27.13

Minnesota 87 8987 26.23

New York 62 9126 25.95

Table 1. Dissimilarity index for geographic 

representativeness by states 

(Sep. 2021 – Feb. 2022)
Jurisdiction Number of County Sample Size Dissimilarity Index

Maine 16 8464 25.16

Tennessee 95 9796 24.95

Idaho 44 8646 23.44

Alaska 29 8656 22.16

Colorado 64 9073 21.88

Ohio 88 9477 21.82

Georgia 159 9164 20.81

Michigan 83 7791 20.18

Nebraska 93 8823 20.03

New Hampshire 10 8896 18.76

Kentucky 120 9914 18.18

Mississippi 82 8931 15.11

California 58 9852 14.91

Arkansas 75 6016 13.65

Utah 29 5510 13.21

North Carolina 100 9867 11.93

Illinois 102 10519 10.86

Florida 67 9998 10.54

Connecticut 8 9041 9.92

South Carolina 46 9971 9.76

Alabama 67 9917 9.55

Rhode Island 5 8333 7.28

Arizona 15 7305 6.84

Nevada 17 6756 4.28

Delaware 3 7739 0.83

District of Columbia 1 8969 0.00

Table 1. Dissimilarity index for geographic 

representativeness by states 

(Sep. 2021 – Feb. 2022) (continued)

Geographic 

Representativeness 
 CA

Geographic Representativeness 
 FL

Geographic Representativeness 
Table 2. Demographic and 

socioeconomic characteristics 

of counties nationally 

(Sep. 2021 – Feb. 2022)

* Significance of the difference 

between two county groups are tested 

using Chi-square tests.

* County characteristics were defined 

based on the tercile within the state.

County Characteristics

Counties with 

specimens in sample 

(n=2522)

Counties without 

specimens in sample 

(n=698)

n(%) n(%) P-value

% 65 and older

Lowest tercile 973 (34.7) 91 (21.8)

Middle tercile 909 (32.4) 135 (32.4) <0.001

Highest tercile 921 (32.9) 191 (45.8)

% Black people

Lowest tercile 826 (29.5) 175 (42.0)

Middle tercile 938 (33.5) 152 (36.5) <0.001

Highest tercile 1039 (37.1) 90 (21.6)

% Hispanic people

Lowest tercile 787 (28.1) 152 (36.5)

Middle tercile 920 (32.8) 153 (36.7) 0.002

Highest tercile 1096 (39.1) 112 (26.9)

% Asian/Pacific Islander people 

Lowest tercile 874 (31.2) 166 (39.8)

Middle tercile 948 (33.8) 123 (29.5) <0.001

Highest tercile 981 (35.0) 128 (30.7)

Comparisons by County Characteristics
Comparisons by County Characteristics

Table 2. Demographic and 

socioeconomic characteristics 

of counties nationally 

(Sep. 2021 – Feb. 2022)

(continued)

County Characteristics

Counties with 

specimens in sample 

(n=2522)

Counties without 

specimens in sample 

(n=698)

n(%) n(%) P-value

% Higher education 

Lowest tercile 898 (32.0) 159 (38.1)

Middle tercile 911 (32.5) 153 (36.7) <0.001

Highest tercile 994 (35.5) 105 (25.2)

% Poverty 

Lowest tercile 944 (33.7) 121 (29.0)

Middle tercile 897 (32.0) 151 (36.2) 0.112

Highest tercile 962 (33.3) 145 (34.8)

% Employed 

Lowest tercile 900 (32.1) 160 (38.4)

Middle tercile 909 (32.4) 151 (36.2) <0.001

Highest tercile 994 (35.5) 106 (25.4)

Population size

Lowest tercile 847 (30.2) 227 (54.4)

Middle tercile 924 (33.0) 132 (31.7) <0.001

Highest tercile 1032 (36.8) 58 (13.9)

* Significance of the difference 

between two county groups are tested 

using Chi-square tests.

* County characteristics were defined 

based on the tercile within the state.
County characteristics *

Having at least one specimen in the sample

OR 95% CI

% 65 and older (ref: Lowest tercile)

Middle tercile 0.963 (0.700, 1.320)

Highest tercile 0.921 (0.652, 1.295)

% Black people (ref: Lowest tercile)

Middle tercile 1.058 (0.825, 1.358)

Highest tercile 1.711*** (1.276, 2.304)

% Hispanic people (ref: Lowest tercile)

Middle tercile 1.086 (0.833, 1.419)

Highest tercile 0.872 (0.656, 1.160)

% Asian/Pacific Islander people (ref: Lowest tercile)

Middle tercile 0.799 (0.614, 1.040)

Highest tercile 0.748 (0.547, 1.026)

% College degree or higher (ref: Lowest tercile)

Middle tercile 0.905 (0.695, 1.178)

Highest tercile 0.898 (0.646, 1.251)

% Poverty (ref: Lowest tercile)

Middle tercile 0.831 (0.624, 1.106)

Highest tercile 0.893 (0.648, 1.230)

% Employed (ref: Lowest tercile)

Middle tercile 0.950 (0.723, 1.247)

Highest tercile 1.056 (0.740, 1.511)

Population size (ref: Lowest tercile)

Middle tercile 2.002*** (1.549, 2.595)

Highest tercile 4.969*** (3.410, 7.331)

Results

Table 3. Multivariate logistic 

regression on sample 

representation 

(Sep. 2021 – Feb. 2022, 

national data)

OR: Odds Ratio; CI: Confidential Interval

County characteristics were defined based on the 

tercile within the state.

Significance level: *p<0.05 **p<0.01 ***p<0.001 
Conclusions

 The people in the serology sample are more likely to come from the counties with 

highest tercile of percentage of people who are Black, and highest tercile of size. – The odds of being represented in the sample is 1.7 times higher for the counties with the 

highest tercile of percentage of people who are Black, compared to the lowest tercile 

counties. – The odds of being represented in the sample is 4.9 times higher for the counties with the 

highest tercile of population size, compared to the lowest tercile counties.

 The sample population overrepresents people living in counties with a higher 

percentage of people who are Black and higher population counties. 
Limitations

 Analyses were conducted using the county characteristics due to the absence of 

individual demographic and SES information of people. Therefore, while counties 

with a higher percentage of people who are Black are more represented in the 

survey, the true racial and ethnic representativeness of survey data cannot be 

calculated.

 Analyses used only the serology sample collected Sep. 2021 – Feb. 2022 (Rounds 

25-30). The representativeness can fluctuate over rounds and the sample 

population characteristics may differ from round to round. 
Implications

 The geographic analysis and dissimilarity index help commercial labs improve data 

collection and geographic representativeness. – For example, with very high dissimilarity indices, it is recommended to revisit data 

collection practice in Texas and Missouri to secure more geographic representation and 

validity of seroprevalence estimates. 

 The potential bias of the sample can provide information about sample 

characteristics that can better inform data interpretation for any research using this 

serology sample. – Due to the sample characteristics, the seroprevalence estimates from the serology 

sample is likely to be related to the behavioral or healthcare-related characteristics of 

people living in large counties or counties with high concentrations of Black population. 
For more information, contact CDC

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.