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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,

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Presentation on theme: "Enhancing STD Surveillance by Matching to Other Data Sources: A Hot Topic Michael C. Samuel, DrPH California Department of Health Services Lori Newman,"— Presentation transcript:

1 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

2 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

3 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

4 Why Match? Encourage collaboration and communication between programs “Mining” existing data Prioritize program activities / target limited resources

5 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

6 Technical Issues Confidentiality/Security Data formats Software SAS, Access, etc. Dataflux (and other matching software) STD*MIS and HARS NEDSS

7 Matching Criteria Unique identifiers Algorithms Incorrect matches (false positive) Missed matches (false negative) Database size

8 Matching Examples: Assessing Co-Morbidity

9 Chlamydia Gonorrhea Syphilis HIV STDs and HIV/AIDS Co-morbidity and STDs as markers of HIV risk

10 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

11 Point System 15 Month and day are transposed TRANSPOSITION 10 Month, day, year of birth date all match MDY 5 Year matches identically IDENTICALYEAR 15 Year of birth date within 5 years YEAR 5 Day of birth date DAY 10 Month of birth date MONTH 10 All letters in first and last names match ALLNAME 15 First 3 letters of first name FIRST PointsDescriptionVariable Name *All matches with a total point value ≥ 35 were manually reviewed by two individuals to determine match validity

12 Co-morbidity from Three Matches 150 Exact Match Loosest Match "Best" Match Syphilis-AIDS Cases Matching Algorithm

13 Percent of Male Syphilis Cases with AIDS Diagnosis Percent with AIDS Diagnosis California Department of Health Services, Office of AIDS. Epidemiological Studies Section

14 Washington State - HIV Prevalence Among Infectious* Syphilis Cases, *Primary, secondary and early latent syphilis          Year Number of Cases Percent HIV+ All Infectious Syphilis Cases Percent HIV+ 

15 Washington State - HIV Prevalence Among Reported Chlamydia Cases,

16 Trend in Rate of Change, Reported STDs*, PLWHA and STDs Reported Among PLWHA *Chlamydia, gonorrhea, P, S & EL syphilis only         Interval Percentage Increase All STD Cases PLWHA STDs Among HIV+  

17 Detroit HIV/STD Match % to 4.9% (per year) of syphilis cases co- infected with HIV 67% of these were infected with syphilis after HIV diagnosis

18 Matching Example: Answering a Research Question

19 California Chlamydia/Birth Match Assess adverse birth outcomes associated with chlamydia (CT) during pregnancy ; 675,000 births, 101,000 female CT cases 14,000 matched cases with CT during pregnancy

20 CA Chlamydia/Birth Match Results 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 )

21 Matching Example: Completing Data

22 California “Family PACT” Administrative / Unilab Chlamydia Test Data, 2000 Data ElementsUnilab DataAdministrative Data Merged Data Test ResultsCompleteMissing 100%Complete Race/EthnicityMissing 100%Complete GenderMissing 7%Complete

23 Unilab and FPACT Claims Data : Female CT Positivity By Age and Race/ Ethnicity Dec00-Jul01

24 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

25 Matching Example: Case Finding

26 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

27 Matching Example: Re-infection

28 California – Repeat Gonorrhea Infection Assessment Exact match on name and date of birth 1/1/ /31/2002 >26,000 unique cases >1,650 (6%) re-infections or duplicates

29 Patients with Two or More Gonorrhea Infections* California Project Area, 2001–2002 * Repeat infections identifier based on patient last name and date of birth. Duplicate? Treatment Failure? True Re-infections?

30 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)

31 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

32 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

33 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

34 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,

35 For more information contact: Michael C. Samuel or Lori Newman

36

37 Timing of Syphilis-AIDS Diagnoses ( , “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 diagnosis 0 (0) Total 38 California Department of Health Services, Office of AIDS. Epidemiological Studies Section

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


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