1. Epidemiology of HIV in California: Hot spots, cold spots, and program improvement Juliana Grant, MD MPH Chief, Surveillance, Research, and Evaluation Branch February 2, 2016

2. USING DATA AT THE STATE LEVEL Where are the hot spots?

3. HIV/AIDS Diagnoses, Deaths, & Persons Living with HIV/AIDS in California, 1982-2013

4. Geography Living HIV/AIDS Cases and Newly Diagnosed HIV Infection, 2010

5. Gender: Living with HIV/AIDS, 2013

6. Gender: Newly Diagnosed HIV, 2013

7. Risk behavior: Living with HIV & Newly Diagnosed HIV, 2013 HRH: High risk heterosexual contact; IDU: Intravenous drug use; MSM: Men who have sex with men; Unk/Oth: Unknown/other

8. Age: Living with HIV & Newly Diagnosed HIV, 2013

9. Race/Ethnicity: Living with HIV/AIDS, 2013

10. Race/Ethnicity Rate per 100,000 Living with HIV/AIDS, 2013

11. Race/Ethnicity: Newly Diagnosed HIV, 2013

12. Race/Ethnicity Rate per 100,000 Newly Diagnosed HIV Infection, 2013

13. Hot Spots Statewide Generally stable over time Urban areas MSM Younger adults Blacks –Substantial health disparities Latinos

14. USING DATA AT THE LOCAL LEVEL

15. Data to Care New public health strategy Use surveillance data to identify HIV- diagnosed individuals not in care Link patients to care and offer partner services https://effectiveinterventions.cdc.gov/en/hi ghimpactprevention/publichealthstrategies/ DatatoCare.aspxhttps://effectiveinterventions.cdc.gov/en/hi ghimpactprevention/publichealthstrategies/ DatatoCare.aspx

16. Data to Care in California Multiple efforts to implement at state and local levels –Rapid acute HIV reporting –Partner services training –Building data systems –Developing processes and procedures Breakout session 10-11:30 am on Frontline Models Using Data to Care

17. Data to Program Use HIV data to guide program planning and implementation –Surveillance –LEO –ARIES (Ryan White) Statewide Needs Assessment and Integrated Plan

18. DATA TO PROGRAM A case study

19. County A and County B County A –Urban –Population ~1 million –25% Asian, 20% Latino, 15% Black –Median household income ~$70,000 County B –Rural –Population ~1 million –50% Latino/a, 10% Native American –Median household income ~$50,000

20. County A and County B: HIV Epidemic County ACounty B Number PLWH70002000

21. County A and County B: HIV Epidemic County ACounty B Number PLWH70002000 Age group (years)Percent (%) 13-244%4%4% 25-3412%17% 35-4421% 45-5435%33% 55-6421%19% >=657%7%5%5%

22. County A and County B: HIV Epidemic County ACounty B Number PLWH70002000 Race/ethnicityPercent (%) American Indian/Alaska Native0%1% Asian5%3% Black/African American41%17% Hispanic/Latino17%44% Native Hawaiian/Pacific Islander1%0% White34%35% Multiple races2%1%

23. County A and County B: HIV Epidemic County ACounty B Number PLWH70002000 Risk groupPercent (%) MSM60%61% IDU9%15% MSM and IDU6%10% Heterosexual contact18%11% Other1%

24. County A versus County B Similar age distribution Predominantly MSM County A has more PLWH (n=7000) –More blacks/African Americans –More heterosexual contact with high risk persons County B has fewer PLWH (n=2000) –More Latinos/Hispanics –More IDU

25. County A and County B: Continuum of Care County ACounty B Number PLWH70002000 % in care70%65% In care statewide: ~65%

26. County A and County B: Continuum of Care County ACounty B Number PLWH70002000 % in care70%65% % virally suppressed55%40% Virally suppressed statewide: ~45%

27. County A and County B: PLWH Age group (years) % virally suppressed County ACounty B 13-2443%42% 25-3446%39% 35-4451%42% 45-5457%43% 55-6462%40% >=6562%37%

28. County A and County B: PLWH Race/ethnicity Total number and % virally suppressed County ACounty B American Indian/Alaska Native2050%1060% Asian34060%6045% Black/African American280051%35042% Hispanic/Latino120056%90044% Native Hawaiian/Pacific Islander4055%1030% White230060%65038% Multiple races10058%2040%

29. County A and County B: PLWH Risk group Total number and % virally suppressed County ACounty B MSM450060124041 IDU6505031038 MSM and IDU4505320046 Heterosexual contact13005223042 Other100462050

30. County A and County B: Viral Suppression County A –Doing well overall –Disparities among Younger PLWH (<35 years old) Blacks (primarily) High risk heterosexuals County B –Broad improvements needed –Surveillance issue?

31. County A and County B: Continuum of Care County ACounty B Number new HIV cases/year250100 % linked to care within 90 days90%75% Linked w/i 90 days statewide: ~80%

32. County A and County B: Continuum of Care County ACounty B Number new HIV cases/year250100 % linked to care within 90 days90%75% % linked to care within 1 year95%82%

33. County A and County B: Newly Diagnosed Age group (years) Total number and % linked to care in 90 days County ACounty B 13-243093%2085% 25-348088%3569% 35-445589%2070% 45-545589%2075% 55-642588%367% >=65580%2100%

34. County A and County B: Newly Diagnosed Race/ethnicity Total number and % linked to care in 90 days County ACounty B American Indian/Alaska Native0-1100% Asian3090%3100% Black/African American10090%785% Hispanic/Latino5084%6068% Native Hawaiian/Pacific Islander0-10%0% White6090%2575% Multiple races10100%367%

35. County A and County B: Newly Diagnosed Risk group Total number and % linked to care in 90 days County ACounty B MSM16088%8970% IDU15100%4 MSM and IDU1080%3100% Heterosexual contact5100%1090% Other5090%475%

36. County A and County B: Linkage to Care County A –Generally doing well –Disparities among older age groups, Latinos, and MSM/IDU County B –Disparities among older age groups, Latinos, Whites, and MSM (most groups)

37. County A and County B: Summary County A –Disparities Viral suppression: Blacks, younger age groups, HRH Linkage to care: Latinos, older age groups –Targeted program assessment and changes County B –Disparities Viral suppression: Everyone Linkage to care: Almost everyone (bright spots younger age groups, IDU) –Holistic look at program

38. How to Find Your ‘Hot Spots’ Don’t need maps Do need good surveillance data –Complete, timely, accurate Look at aggregate numbers, not just individuals –Both total number and percentage Collaborate among surveillance, prevention, Ryan White, STD, private providers

39. How to Find Your ‘Hot Spots’ Start with newly diagnosed/linkage to care –Surveillance data are likely accurate –Who are the people newly diagnosed with HIV in your area? Are programs succeeding with getting them linked to care? Which groups aren’t they succeeding with? –What could be changed to improve linkage? Re-engagement and retention –Who is out of care? Who is not virally suppressed? –Do your programs serve these groups? Do your Ryan White partners serve these groups?

40. Hot Spots, Cold Spots, Program Improvements We have data Let’s use it

41. Acknowledgments Thank you to the following for their assistance in creating these slides –Scott Masten –Valorie Eckert –Sunitha Gurusinghe –Eric Chapman –All OA Surveillance Section and local health jurisdiction surveillance staff for their efforts collecting, cleaning, and managing the surveillance data

42. Questions?

44. Extra slides

45. Continuum of HIV Care California, 2013