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Linking STD and HIV Morbidity and Risk Behaviors in Indiana

Linking STD and HIV Morbidity and Risk Behaviors in Indiana. James D. Beall, MA Sr. Public Health Advisor Indiana State Department of Health. OASIS. O utcomes A ssessment Through S ystems of I ntegrated S urveillance grant awarded October 2000. Reason for Applying.

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Linking STD and HIV Morbidity and Risk Behaviors in Indiana

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  1. Linking STD and HIV Morbidity and Risk Behaviors in Indiana James D. Beall, MA Sr. Public Health Advisor Indiana State Department of Health

  2. OASIS • Outcomes • Assessment Through • Systems of • Integrated • Surveillance • grant awarded October 2000

  3. Reason for Applying • Syphilis epidemic in Marion County (Indianapolis) Indiana in 1999-2000 • Wanted to identify common behavioral risk factors shared by STDs and HIV • Focused on a limited geographic area

  4. Pre-Application Approvals • Program, Division, State Health Department and Local Health Department approval • to match STD and HIV databases • even though the databases were combined in a blinded fashion • allow access to databases by contracted SAS programmer

  5. Pre-Application Approvals • Community Approval • Stamp Out Syphilis Coalition (SOS) • 40 City, State, County Community Group members and the affected neighborhood association • HIV Prevention Community Planning (CPG) • 35 individuals representing geographic, HIV service organizations, and risk behaviors reflecting Indiana HIV epidemic

  6. Pre-Application Approvals • Community Approval • for the mutual beneficial outcome, not a search for recalcitrant behavior • building confidence in our maintaining the confidentiality of the individual STD and HIV databases

  7. Purpose of Project • Promote integrated interpretation and use of STD and HIV surveillance data • Identify STD and HIV behavioral risk profiles • Share risk profiles with public health and community-based prevention and intervention programs • Improve planning and evaluation of public health programs directed toward STD and HIV prevention • Implement professional management of STD and HIV databases

  8. Databases • STD/MIS • Chlamydia, Gonorrhea, Syphilis incidents in Indiana residents and DIS interview records of HIV • 1999 through 2001 incidents • 64,000+ records • HARS (HIV/AIDS Reporting System) • contains individual records all Indiana residents with HIV disease • cumulative since 1982 • 11,000+ records

  9. ISDH Program Areas • Division of HIV/STD • Epidemiologic Resource Center to activate a hiring contract for programmer • Information Technology Services • all 3 for concurrence for selection of programmer • LAN administrator to allow access to databases

  10. Pre-Integration Activities • Determine required match criteria • Identify common fields • Assign common codes to each field • Standardize HIV names and street addresses • Clean STD database of duplicates and data entry errors and omissions

  11. Key Construction • A weighted combination of variables • 18 keys developed • Matches on keys were assigned points • Match on every key worth 100 points

  12. Variables Matched • Name • last, first, middle initial, alias, maiden name • Single variable ‘address’ split into 10 standard variables • number, direction, street, street type, post office box, city, state • Date of birth • day, month, and year

  13. Variables Matched • Telephone number • area code, prefix, last four digits • Sex • Race • County • 5-digit zip code

  14. Scoring Variable Comparison after Key Match • Good quality score was valued at 78-82 • Birth date data high score = 25 • Good score without birth date match = 53-57 • Our cutoff set at 65 point match so that a wider net would catch potential matches • Individual review of low scoring matches would determine accuracy of the match

  15. Data Observations • The HIV surveillance records from 1982 through 2001 were compared to STD morbidity reports from the past three years: • …Chlamydia matches 74 • …Gonorrhea matches 132 • …Syphilis (any stage) matches 47 (15 in 1999, 16 in 2000 and 16 in 2001)

  16. When HIV data (1991-2001) is compared to STD (1999-2001: • There were 254 matches for patients with dual infections ( HIV and another STD) in the past three years (1999-2001). • The number of matches for early syphilis cases remained the same each year, while syphilis morbidity increased then decreased. • 64% (161) of all matches occurred with patients residing in Marion County.

  17. 2001: • The data indicate that most dual infections involve patients who become infected with an STD after HIV diagnosis ( 80%) • 24% Chlamydia acquired before HIV • 25% Gonorrhea acquired before HIV • 10% of Syphilis acquired before HIV

  18. Marion County Matches • 35 of 47 syphilis/HIV matches were in Marion County • Risk factors were only collected in Marion County for syphilis

  19. Marion County ResultsThrough August 31, 2001 • 18 did not identify a risk factor in STD/MIS • 4 of these did not identify in HARS • 23 did not identify a risk factor in HARS • 4 of these did not identify in STD/MIS

  20. Marion County ResultsThrough August 31, 2001 • 1 male had sex with a male in STD/MIS • 7 males had sex with males in HARS • implies we are not able to identify and provide appropriate prevention tools

  21. Marion County ResultsThrough August 31, 2001 • 5 STD/MIS patients with more than 1 sex partner in last 90 days were identified in HARS as: 1 IDU 2 heterosexual contact with HIV infected person 2 no identified risk

  22. Marion County ResultsThrough August 31, 2001 • 12 diagnosed with HIV after syphilis • 6 white females in same zip codes and not in “hot zone” • 4 black males and 6 of 7 females were white • 10 concurrent diagnoses were related to non-injection drugs • 6 MSM in HARS had no risk identified in STD/MIS • 1 MSM in STD/MIS had no risk identified in HARS

  23. Risk Factors of 6 White Femaleswith concurrent diagnoses • >1 sex partner in last 90 days • In county jail lock-up while infectious • Used condoms with pickups only • Sex with a ‘crack’ user • Sex with a male • “hot zone” linked Do they work in the ‘hot zone’?

  24. Recommendations • Not include street address • population moves too frequently • Expand STD/MIS years of data to 1993 (morbidity only) • Repeat match each year to examine effect of syphilis outbreak on HIV • to identify subsequent infections with HIV • Include all required fields in new database structures of STDMIS upgrades and HIV Surveillance software

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