social determinates of health: A state perspective Valorie Eckert, - - PowerPoint PPT Presentation

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social determinates of health: A state perspective Valorie Eckert, - - PowerPoint PPT Presentation

Aug 16, 2022 93 likes •342 views

Addressing HIV/AIDS disparity and social determinates of health: A state perspective Valorie Eckert, MPH HIV Epidemiology Center for Infectious Disease CDPH, Office of AIDS Factors, drivers, and social determinates, oh my! Demographics: 1)

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Valorie Eckert, MPH

HIV Epidemiology Center for Infectious Disease CDPH, Office of AIDS

Addressing HIV/AIDS disparity and social determinates of health: A state perspective

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Demographics: 1) Race/ethnicity, gender 2) County/LHJ level Transmission related: 1) Risk group 2) STD prevalence/co-infection 3) Infectiousness (‘late testers’, non- adherence, etc) 4) Social networks Other measures: 1) Poverty/living environment 2) Access to services 3) Quality of care 4) Incarceration 5) Unstable housing/”homelessness”

Factors, drivers, and social determinates, oh my!

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Trends in new diagnoses

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Most dramatic decrease seen among whites, MSM

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California HIV Surveillance data (eHARS) Frozen dataset, 2/22/12

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Selected Bay Area Counties

California HIV Surveillance data (eHARS) Frozen dataset, 2/22/12

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Disparity

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How does Bay Area compare?

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Co-factors/drivers: STDs

  • STD incidence among persons living with HIV/AIDS was highest among

males, those aged 20-29 years and those living in SF.

  • Incident gonorrhea is highest among black males, particularly black MSM.
  • Syphilis incidence highest among Asian and Hispanic race/ethnicities.

Source: Nicole Olsen , Michael Samuel, et. al. STD and HIV/AIDS Case Registry Matching to Estimate California STD-HIV/AIDS Co-infection, 2009.

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HIV/AIDS concurrency or “late testing”

(i.e. diagnosed with AIDS at the time or within

  • ne year of original HIV diagnosis)

A measure of transmission risk and access to services:

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Age at diagnosis Race/ethnicity

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Emerging areas of focus

  • Use of surveillance data needed to evaluate (including

National HIV/AIDS Strategy objectives)

  • Increase electronic lab and case reporting capacity
  • Use HIV epidemiologic research data to identify

critical HIV disparities and to develop novel approaches to reducing these disparities in CA

  • Geographic presentation and analysis of data
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Examples of using existing data to measure linkage to care, treatment and retention in care

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NHAS: Goal 3

Indicator: viral load suppression among MSM/Bi, Blacks and Latinos

Note: statewide data

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ADAP

PERCENT CD4 GROUP BY RACE / ETHNICITY FOR NEW ENROLLEES, 2010 CD4 GROUP WHITE (N=1650) AFRICAN AMERICAN (N=821) HISPANIC / LATINO (1475) ASIAN (N=203) NATIVE AM / INDIAN (N=7) PACIFIC ISLANDER (N=19) MULTI- RACE / ETH (N=145) UNKNOWN (N=88) TOTAL Less than 200 16.79% 22.78% 27.80% 23.65% 0.00% 15.79% 24.83% 23.86% 22.28% 201 - 350 17.88% 19.37% 21.49% 26.11% 28.57% 31.58% 19.31% 14.77% 19.80% 351 - 500 19.58% 20.10% 19.80% 20.69% 28.57% 15.79% 17.24% 13.64% 19.60% 501 - 750 22.67% 17.17% 16.75% 20.20% 14.29% 31.58% 24.83% 13.64% 19.46% Greater than 750 12.55% 6.09% 6.44% 3.45% 0.00% 5.26% 11.72% 6.82% 8.69% Invalid date / result 4.67% 5.60% 2.51% 2.46% 28.57% 0.00% 2.07% 1.14% 3.88% Unknown 5.88% 8.89% 5.22% 3.45% 0.00% 0.00% 0.00% 26.14% 6.28% TOTAL 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%

Indicator: CD4 or VL level of clients upon enrollment in ADAP

PERCENT VL GROUP BY RACE / ETHNICITY FOR NEW ENROLLEES, 2010 VL GROUP WHITE (N=1650) AFRICAN AMERICAN (N=821) HISPANIC / LATINO (1475) ASIAN (N=203) NATIVE AM / INDIAN (N=7) PACIFIC ISLANDER (N=19) MULTI- RACE / ETH (N=145) UNKNOWN (N=88) % TOTAL Undetectable: 200 or less 50.24% 41.05% 40.41% 38.42% 42.86% 47.37% 31.72% 56.82% 44.19% 201 - 5,000 9.52% 11.33% 9.08% 10.84% 0.00% 21.05% 12.41% 9.09% 9.89% 5,001 - 100,000 21.27% 28.75% 29.83% 29.06% 14.29% 26.32% 31.72% 22.73% 26.27% 100,001 - 500,000 13.09% 13.52% 15.19% 16.26% 14.29% 0.00% 17.24% 7.95% 14.00% Greater than 500,000 2.30% 1.46% 3.46% 2.46% 0.00% 5.26% 4.14% 2.27% 2.61% Invalid date / result 3.58% 3.90% 2.03% 2.96% 28.57% 0.00% 2.76% 1.14% 3.04% TOTAL 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%

Note: statewide data

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Goal: Get clients enrolled and in care earlier (i.e. when T-cells still high)

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Geocoding project

  • Residence at diagnosis
  • Current residence
  • Homeless/marginally housed
  • Correctional populations
  • IDU networks
  • Undocumented/migrant workers/day laborers?
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Location, location, location…

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Other data sources

  • ARIES data (Ryan White)

– Unmet need

  • Counseling &Testing data

– Proposing new ways to link to EHARS & ARIES – Expanded testing grant – Rapid tests and new testing strategies

  • Medical Monitoring Project (MMP)

– Care measures

  • Nat’l HIV/AIDS Behavioral Surveillance (NHSB)

– Indicators of risk – Expanded testing – Mapping opportunities

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  • Health Disparities Framework

– “commitment” to use all resources at hand to eliminate disparities in HIV/AIDS – sustainable

  • Population profiles: Burden of disease and allocation
  • f resources
  • Minority AIDS Initiative
  • Insurance assistance programs
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Contact Information

Valorie Eckert, MPH HIV Epidemiology Phone: (916) 449-5820 Valorie.eckert@cdph.ca.gov

For more HIV Information and Statistics : http://www.cdph.ca.gov/programs/ aids/Pages/Default.aspx

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