enhancing std surveillance by matching to other data sources: a hot topic
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Enhancing STD Surveillance by Matching to Other Data Sources: A Hot Topic. Michael C. Samuel, DrPH California Department of Health Services Lori Newman, MD Centers for Disease Control and Prevention. Defining Matching. Case-based - PowerPoint PPT PresentationTRANSCRIPT
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,
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