Enhancing STD Surveillance by Matching to Other Data Sources: A Hot Topic

Michael C. Samuel, DrPHCalifornia Department of Health Services

Lori Newman, MDCenters for Disease Control and Prevention

Defining Matching

• Case-based• Matching individually line-listed data to another

individually line-listed source of data

• Ecologic• Correlate stratum-specific (e.g. county level) rates

of one disease or condition with rates of another

Why Match?

• Assess co-morbidity or the co-occurrence of diseases/conditions –> identify “hot spots”

• Answer specific research questions

• Complete missing data or correct data

• Case finding

• Analyze patterns of re-infection

Why Match?

• Encourage collaboration and communication between programs

• “Mining” existing data

• Prioritize program activities / target limited resources

Data Sources

• Diseases• Syphilis• Gonorrhea• Chlamydia • NGU• Herpes• AIDS/HIV

• Cancer

• TB

• Enterics

• Vital Statistics• Births

• Deaths

• Other related data• Substance use Tx

• Incarceration Records

• Behavioral Data • e.g., BRFS

• SES, etc. Data• e.g., Census

Technical Issues

• Confidentiality/Security

• Data formats

• Software • SAS, Access, etc.

• Dataflux (and other matching software)

• STD*MIS and HARS

• NEDSS

Matching Criteria

• Unique identifiers

• Algorithms• Incorrect matches (false positive)

• Missed matches (false negative)

• Database size

Matching Examples:Assessing Co-Morbidity

ChlamydiaGonorrhea

Syphilis

HIV

STDs and HIV/AIDSCo-morbidity and STDs as markers of HIV risk

California Matching Algorithms

• Match 1 (Automated Exact Match)• Exact matches on: Last Name, First Name, DOB

• Match 2 (“Best” Match)• Exact matches + manually reviewed matches with

point values ≥ 35

• Match 3 (Loosest Match)• “Best” match + HARS records with no names that

match STD records on SOUNDEX, DOB, SEX

Point System

15Month and day are transposedTRANSPOSITION

10Month, day, year of birth date all matchMDY

5Year matches identicallyIDENTICALYEAR

15Year of birth date within 5 yearsYEAR

5Day of birth dateDAY

10Month of birth dateMONTH

10All letters in first and last names matchALLNAME

15First 3 letters of first nameFIRST

PointsDescriptionVariable Name

*All matches with a total point value ≥ 35 were manually reviewed by two individuals to determine match validity

Co-morbidity from Three Matches

150Exact Match

Loosest Match

"Best" Match

Syphilis-AIDS Cases

1990-2001

Matching Algorithm

184

244

0

200

400

600

800

1000

1200

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001

Nu

mb

er o

f P

&S

Syp

hil

is C

ases

0

2

4

6

8

10

12

14

16Syphilis CasesPercent with AIDS

Percent of Male Syphilis Cases with AIDS Diagnosis

Per

cen

t w

ith

AID

S D

iag

no

sis

California Department of Health Services, Office of AIDS. Epidemiological Studies Section

Washington State - HIV Prevalence AmongInfectious* Syphilis Cases, 1994 - 2002

*Primary, secondary and early latent syphilis

1994 1995 1996 1997 1998 1999 2000 2001 2002

Year

0

20

40

60

80

100Number of Cases

0

10

20

30

40

50

60Percent HIV+

All Infectious Syphilis Cases

Percent HIV+

Washington State - HIV Prevalence Among Reported Chlamydia Cases, 1994 - 2002

1994 1995 1996 1997 1998 1999 2000 2001 2002

Year

0

5000

10000

15000

Number of CT Cases

0

1

2

3

4

5Percent HIV+

All Chlamydia Cases Percent HIV+

Trend in Rate of Change, Reported STDs*, PLWHA and STDs Reported Among PLWHA 1998 - 2002

*Chlamydia, gonorrhea, P, S & EL syphilis only

98-99 99-00 00-01 01-02

Interval

0

5

10

15

20

25

30

35Percentage Increase

All STD Cases

PLWHA

STDs Among HIV+

Detroit HIV/STD Match

• 1997-2004

• 2.8% to 4.9% (per year) of syphilis cases co-infected with HIV

• 67% of these were infected with syphilis after HIV diagnosis

Matching Example:Answering a Research Question

California Chlamydia/Birth Match

• Assess adverse birth outcomes associated with chlamydia (CT) during pregnancy

• 1997-1999; 675,000 births, 101,000 female CT cases

• 14,000 matched cases with CT during pregnancy

CA Chlamydia/Birth MatchResults

Low birth weight (LBW):

• 6.6% LBW among women with CT

• 4.7% LBW among women without CT

• Adjusted (for age, race, education, prenatal care) Odds Ratio = 1.2 (95% CI 1.1-1.3)

Matching Example: Completing Data

California “Family PACT” Administrative / Unilab Chlamydia Test Data, 2000

Data Elements Unilab Data Administrative Data

Merged Data

Test Results Complete Missing 100% Complete

Race/Ethnicity Missing 100% Complete Complete

Gender Missing 7% Complete Complete

Unilab and FPACT Claims Data :Female CT Positivity

By Age and Race/ Ethnicity Dec00-Jul01

14.6

6.9 6.44.9

7.4

4.8

2.2

3.8

2.0 2.4

0

2

4

6

8

10

12

14

16

Black Latina A/PI White Other

Race/Ethnicity

CT

Pos

itivi

ty

15-25

26-55

Family PACT Match Results/Conclusions

• Precise estimates of age/race specific chlamydia prevalence rates

• Demonstrates racial disparities in CT rates from large state “safety net” provider, not otherwise available

• Required no additional data collection

Matching Example:Case Finding

Virginia HIV/AIDS Case Finding

• TB match with HIV/AIDS found few new cases, but helped complete risk factor data (IDU)

• ADAP (AIDS Drug Assistance Program) match with HIV/AIDS identified many new cases and improved timeliness of reporting

Matching Example:Re-infection

California – Repeat Gonorrhea Infection Assessment

• Exact match on name and date of birth

• 1/1/2001-12/31/2002

• >26,000 unique cases

• >1,650 (6%) re-infections or duplicates

Patients with Two or More Gonorrhea Infections*California Project Area, 2001–2002

0

100

200

300

<1 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

# Months Between Infections(based on Report Date)

# P

ati

en

ts

* Repeat infections identifier based on patient last name and date of birth.

Duplicate?

Treatment Failure?

True Re-infections?

OASIS Matching Findings

• Substantial and increasing STD cases after HIV/AIDS; highlights potential for HIV transmission (CA, SF, WA, MA…)

• Lack of chlamydia / HIV co-morbidity screening of CT cases for HIV not resource efficient (WA)

• Little TB / STD co-morbidity (multiple sites)

• Successful for building data mart across diseases (NY)

Strengths of Matching

• Inexpensive, efficient way to augment knowledge

• Can be made easy/simple• Automated matches• Data warehouses• NEDSS-like systems

• Can help build bridges• Can provide actionable results

• Interpret carefully• Even negative match can provide info

Weakness/Limitations of Matching

• Technically may be difficult or impossible• No unique identifiers

• Database/registry may cover small and/or biased population

• Can be time consuming and difficult

• May be better ways to get data• e.g., ask cases with one disease if they have

another

• Confidentiality concerns

• May not provide information for action

General Recommendations

• Know data sources

• Assure data protection

• Assess technical capacity and technical issues before beginning

• Assess likely “juice for squeeze”

• Collaborate with OASIS team

• Think ……………………….…..outside the box

Thanks to the California Matching Team

STD Control Branch

• Joan Chow

• Denise Gilson

• Mi-Suk Kang

Office of AIDS

• Maya Tholandi

• Allison Ellman

• Juan Ruiz

• Kathryn Macomber, Michigan Department of Health• Mark Stenger, Washington State Department of Health• Jeff Stover, Virginia Department of Health

And,

For more information contact:

Michael C. Samuelmsamuel@dhs.ca.gov

510-540-2311or

Lori Newmanlen4@cdc.ogv404-639-6183

Timing of Syphilis-AIDS Diagnoses (1999-2001, “Best” Match)

Timing of Infections“Best” Match

(%)

Syphilis >1 after AIDS diagnosis 29 (76)

Syphilis within 1 year of AIDS diagnosis 9 (24)

Syphilis >1

before AIDS diagnosis0 (0)

Total 38

California Department of Health Services, Office of AIDS. Epidemiological Studies Section

Scatter plot of Gonorrhea and Chlamydia Rates by Gender and State, United States 2002

0 100 200 300 400 500

0

200

400

600

800

GC Rate

CT

Ra

te

AL

AK

AZAR

CACO

CT

DE DC

FL

GAHI

ID

IL

INIA

KSKY

LA

ME

MD

MA

MI

MN MSMO

MT NENV

NHNJ

NM

NY NCND

OHOKOR

PARI

SC

SD

TNTXUT

VT

VA

WA

WV

WI

WY

AL

AK

AZ

AR

CACO

CT

DE

DC

FL

GA

HI

ID

IL

IN

IA

KS

KY

LA

ME

MD

MA

MI

MN

MS

MO

MT NE

NV

NH

NJ

NM

NY

NC

ND

OHOK

OR

PARI

SC

SDTN

TX

UTVT

VA

WA

WV

WI

WY

Female

Male