CENSUS BUREAU

ALICE ZAWACKI

PRESENTER

JOEY MARSHALL

NATIONAL CENTER FOR 

HEALTH STATISTICS 

DONALD CHERRY

XIANGHUA YIN

BRIAN W. WARD

DISCLAIMER. Any opinions and conclusions expressed herein are those of the authors and do 

not represent the views of the National Center for Health Statistics, the Centers for Disease 

Control and Prevention, or the U.S. Census Bureau . The Census Bureau has ensured appropriate 

access and use of confidential data and has reviewed these results for disclosure avoidance 

protection (Project 7529814: CBDRB-FY23-CES019-007).

Federal Committee on 

Statistical Methodology 

October 25, 2023

Methodology on Creating the 

Linked Retail Health Clinic 

(LiRHC) Database
What are 

RHCs and 

why do we 

care about 

them?

“An in-store clinic with 

healthcare professional(s) who 

provides medical care”

(2017 Census of Retail Trade, Census Bureau)

Consumer appeal

• Convenient locations 

and hours

• Walk-in accessibility

• Short wait times

• Transparent pricing

Services

• Minor illnesses/injuries

• Health screenings

• Medication 

management

• Immunizations
What are 

RHCs and 

why do we 

care about 

them?

Role of RHCs?

Population health

Healthcare access

Healthcare provision

36% 214%

2008  2015

Change in visit rate for 

low-acuity conditions

RHCs Hospital 

Emergency 

Departments

Poon, Schuur, and Mehrotra (2018)
Interagency

Collaboration

CENSUS BUREAU & NCHS COLLABORATION

Agencies jointly saw benefits in creating a 

database of RHCs and their locations 

throughout the United States

Benefits

• Create new dataset 

• Nationally representative sample of RHCs

• Support analysis of this relatively “new” health care setting 

• Produce new estimates describing areas & populations near RHCs

• Potential use 

• Solicit EHR data from RHCs

• Collect National Ambulatory Medical Care Survey data
2018-2020

LiRHC

Database

2,000 Number of RHCs operating

97% RHC share in MSAs

47% Share in the South region 
Methods

Data

1. Convenient Care Association (CCA)

• Members name and address

• 2018-2020, multiple pulls each year

• RHCs and other walk-in health centers

2. Census Bureau’s Business Register (BR)

• Universe of U.S. businesses

• Name, location, payroll, employment size, industry, etc.

3. National Plan and Provider Enumeration System (NPPES)

• Centers for Medicare and Medicaid Services

• Includes individual health care providers, organizations

• Evaluate match quality, distinguish clinic type
Methods

Matching

Probabilistic matching: Similarity of addresses 

STEPS

1. Preprocess original records

2. Link CCA + BR

3. Match NPPES

4. Postprocess matched records
Methods

Matching

STEPS

1. Preprocess original records: CCA, BR, NPPES

2. Link CCA + BR

3. Match NPPES

4. Postprocess matched records
Methods

Matching

1. Preprocess original records: CCA, BR, NPPES

 Standardize basic address text

 Remove address-related “stop words”

 Concatenate address and city names

 Deduplicate CCA  1 Observation/Year

2. Link CCA + BR

3. Match NPPES

4. Postprocess matched records
Methods

Matching

1. Preprocess original records

2. Link CCA + BR

3. Match NPPES

4. Postprocess matched records
Methods

Matching

1. Preprocess original records

2. Link CCA + BR

 Use Fellegi-Sunter probabilistic linkage model 

 Enamorado, Fifield, and Imai, 2019

 fastLink R library

 Match records within each state-year block 

 Conduct 8 sequential matching passes

 Address “next-door neighbor” problem

3. Match NPPES

4. Postprocess matched records
Methods

Matching

• Preprocess original records

1. Link CCA + BR

 Use Fellegi-Sunter probabilistic linkage model 

 Match records within each state-year block 

 Conduct 8 sequential matching passes

 Address “next-door neighbor” problem

2. Match NPPES

3. Postprocess matched records
Methods

Matching

• Preprocess original records

1. Link CCA + BR

 Use Fellegi-Sunter probabilistic linkage model 

 Match records within each state-year block 

 Conduct 8 sequential matching passes

 Address “next-door neighbor” problem

2. Match NPPES

3. Postprocess matched records
Methods

Matching

1. Preprocess original records

2. Link CCA + BR

 Use Fellegi-Sunter probabilistic linkage model 

 Match records within each state-year block 

 Conduct 8 sequential matching passes

 Same address including exact number match

 Mixed-use retail settings

 Accept matches with quality score > 14 

 Remove year and state blocking

 Address “next-door neighbor” problem

3. Match NPPES

4. Postprocess matched records
Methods

Matching

• Preprocess original records

1. Link CCA + BR

 Use Fellegi-Sunter probabilistic linkage model 

 Match records within each state-year block 

 Conduct 8 sequential matching passes

 Address “next-door neighbor” problem

2. Match NPPES

3. Postprocess matched records
Methods

Scoring 

Matches

Match Quality Score >14  Accept match

Score Criteria

23 Maximum possible score
Methods

Matching

Match Quality Score >14  Accept match

Score Criteria

+5 BR name references a known RHC or contains “clinic” or “health”

+5 All numbers in address match (“123 main, Suite 500” = 123500)

23 Maximum possible score
Methods

Matching

Match Quality Score >14  Accept match

Score Criteria

+5 CBPBR name references a known RHC or contains “clinic” or “health”

+5 All numbers in address match (“123 main, Suite 500” = 123500)

+3 Address number match

+3 BR name = Name of known grocer or major retailer

+3 Industry code = Healthcare provider, retail trade

23 Maximum possible score
Methods

Matching

Match Quality Score >14  Accept match

Score Criteria

+5 CBPBR name references a known RHC or contains “clinic” or “health”

+5 All numbers in address match (“123 main, Suite 500” = 123500)

+3 Address number match (“123 Main, Suite 500” = 123)

+3 CBPBR name = Name of known grocer or major retailer

+3 Industry code = Healthcare provider, retail trade

+1 Positive payroll (BR)

+1 Positive employment (BR)

+1 Active business (BR)

+1 Included in Census Bureau tabulations

23 Maximum possible score
Methods

Matching

• Preprocess original records

• Link CCA + BR

• Match 2018 NPPES to CCA-BR matches

Probabilistic street address match with(out) state block

• Postprocess matched records
Methods

Matching

1. Preprocess original records

2. Match CCA + BR

3. Match 2018 NPPES to CCA-BR matches

4. Postprocess records

1. Deduplicated RHCs operating in multiple years

2. Remove unmatched CCA records
Methods

Quality 

Assessments

1. Benchmarking

2. Match statistics

3. Manual review
Methods

Quality 

Assessments

2017 2018 2019 2020

2018-2020 LiRHC

2,000 RHCs operating

2,008 RHCs 

(Merchant 

Medicine)

1,949 RHCs 

(Statista)
Methods

Quality 

Assessments

1. Benchmark

2. Review match statistics

95% CCA records match BR

Firm name assigned to CCA record vs. firm name in BR

3. Manual review

Match Quality Score (0-100) Percent

High (75-100) 81

Moderate to low (0-74) 19

Pass Number Percent

1 or 2 92

3-8 8

Source. Based on authors’ merging of the 2018-2020 

Convenient Care Association data (CCA) with the 

Census Bureau’s 2017-2010 Business Register (BR).
Methods

Quality 

Assessments

1. Benchmarking

2. Match statistics

3. Manual review 

500 randomly selected matches

 1 CCA record  > 1 BR record

 NPPES taxonomy vs. BR industry 

 NPPES taxonomy  Urgent care centers?

 Geographic distribution by match status
LiRHC

97% 

Metropolitan Statistical Areas 
LiRHC

47% South region

27% South Atlantic division
REGION SHARE COUNT

South 47% 950

Midwest 30% 600

Northeast 12% 240

West 11% 230

TOTAL 100% 2,020
Next Steps

Using LiRHC

SECOND OBJECTIVE

Use LiRHC to improve understanding on 

how RHCs might affect population health and 

access and provision of healthcare services
Next Steps

Using LiRHC

SECOND OBJECTIVE

Use LiRHC to improve understanding on 

how RHCs might affect population health and 

access and provision of healthcare services

Characteristics of nearby populations? 

Demographic ~ Socioeconomic

Insurance coverage ~ Presence of children

Characteristics of other healthcare providers? 

Types ~ Distribution ~ Visits

Revenues ~ Payroll ~ Employment size
Is There a Retail Health 

Clinic Near You? 

(census.gov)

Methodology on Creating the U.S. 

Linked Retail Health Clinic (LiRHC) 

Database 

(census.gov)
Thank you

Alice.M.Zawacki@Census.gov