HIV in Alameda County

Transcription

1 HIV in Alameda County Annual lepidemiology i Dt Data Presentation ti to the CCPC July 22, 2015 Prepared By Richard Lechtenberg & Neena Murgai HIV Epidemiology and Surveillance Unit HIV in Alameda County: An Overview 220 new diagnoses, on average, in per 100,000 population per year 70.4% 82.5% linked to care within 90 days 5,649 people living with HIV disease (PLHIV) at yearend per 100,000 population 70.1% received any HIV care in % were virally suppressed at last measurement in /17/2015 1

2 On the agenda 1. Diagnosis and Prevalence A. By Demographics B. By Social Determinants of Health 2. The Continuum of HIV Care 1. Identify Epi 101 Recap A. the population/denominator B. the sub population/numerator 7/17/2015 2

3 Most Measures = What defines the subpopulation? # # What defines the population?? Numerator Denominator May be called different names Fraction Proportion Percentage Rate May be expressed in different ways ½ % 5per 100,000 (instead of 0.005%) To understand the measure you need to understand both! Surveillance Database Right for PLHIV who are patients at Clinic A Right for all PLHIV (bigger denominator) Right for PLHIV who are patients at Clinic B 7/17/2015 3

4 1. Identify Epi 101 Recap A. the population/denominator B. the sub population/numerator 2. Beware A. the prosecutor s fallacy! The Prosecutor s Fallacy Defined The assumption that: The chances of A among B = The chances of B among A 7/17/2015 4

5 The Prosecutor s Fallacy Illustrated The fallacy: Most striped squares are grey. Most grey squares are striped. Chances that a square is grey among striped squares: 4/9 = 44.4% Chances that a square is striped among grey squares: 4/25 = 16% 1. Identify Epi 101 Recap A. the population/denominator B. the sub population/numerator 2. Beware A. the prosecutor s fallacy! B. association vs. causation 7/17/2015 5

6 Confounding: An example Question: Is Alaska s mortality rate different than Florida s? Mortality rate: 399 per 100, % 40% 30% 20% 10% 0% State < >64 Age The observed difference in mortality rates is confounded by age!? Not fair to compare them directly! 50% 40% 30% 20% 10% 0% Death < >64 Mortality rate: 1,069 per 100,000 SOURCE: Modules/EP/EP713_StandardizedRates/EP713_StandardizedRates3.html 1. Identify Epi 101 Recap A. the population/denominator B. the sub population/numerator 2. Beware A. the prosecutor s fallacy! B. association vs. causation C. small numbers 7/17/2015 6

7 Less Data Less Confidence in What the Data Says A caveat: Gender identity is not reliably captured in surveillance data because only sex assigned at birth is routinely captured in the medical record. To avoid underestimating the burden of HIV in the transgender community, breakdowns will be provided by sex assigned at birth. 7/17/2015 7

8 On the agenda 1. Diagnosis and Prevalence A. By Demographics B. By Social Determinants of Health 2. The Continuum of HIV Care 7/17/2015 8

9 HIV in Alameda County by the Numbers # new diagnoses, regardless of stage #new AIDS diagnoses # of PLHIV (at year end) , , , ,649 On the agenda 1. Diagnosis and Prevalence A. By Demographics B. By Social Determinants of Health 2. The Continuum of HIV Care 7/17/2015 9

10 Annual Diagnosis Rate per 100,000 Trends in New HIV Diagnosis Rates by Sex, Alameda County, Sex All Male Female NOTE: (1) Rates are 3 year average annual rates. (2) Sex refers to sex assigned at birth. (3) Grey areas are 95% confidence bands. Trends in New HIV Diagnosis Rates by Race/Ethnicity, Alameda County, All races African American White Race/Ethnicity Hispanic/Latino API Annual Diagnosis Rate per 100, NOTE: (1) Rates are 3 year average annual rates. (2) Grey areas are 95% confidence bands. NOT SHOWN: Other/unknown race (rates not calculable). 7/17/

11 Trends in New HIV Diagnosis Rates by Sex & Race/Ethnicity, Alameda County, Race/Ethnicity All races African American White Hispanic/Latino API Male Female Ann nual Diagnosis Rate per 100, NOTE: (1) Rates are 3 year average annual rates. (2) Sex refers to sex assigned at birth. (3) Grey areas are 95% confidence bands. NOT SHOWN: Other/unknown race (rates not calculable). Trends in New HIV Diagnosis Rates by Age & Race/Ethnicity, Alameda County, NOTES: Analysis done by Poisson regression assuming a linear effect of time (on the log scale) and allowing for all 2 way interactions. 7/17/

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13 Key takeaways: Overall, diagnosis rates have decreased since 2006 The most notable declines have occurred among African American women African Americans and whites in their 30s and 50s Although increases have been seen in API in their 20s and 40s, rates among them remain low compared to other groups On the agenda 1. Diagnosis and Prevalence A. By Demographics B. By Social Determinants of Health 2. The Continuum of HIV Care 7/17/

14 Social Determinants of Health Factors such as Poverty Unemployment Education level Can have individual as well as community effects E.g., an individual s id health may be impacted dby their own wealth as well as that of their community Diagnosis Rates by Neighborhood Poverty Level, Alameda County % of Census Tract Residents Living Below Poverty % % % % % % Annual Diagnosis Rate per 100,000 NOTE: (1) Bar widths proportional to the fraction of the underlying population in the category (with the exception categories comprising <2% of the population, for which bars are enlarged for visibility). (2) A clustering algorithm was used to determine optimal category cut points. (3) The dashed line indicates the overall rate for the county as a whole. 7/17/

15 Prevalence by Neighborhood Poverty Level, Alameda County, Year End % of Census s Tract Residents Living Below Poverty % % % % % % % % Prevalence per 100,000 NOTE: (1) Bar widths proportional to the fraction of the underlying population in the category (with the exception categories comprising <2% of the population, for which bars are enlarged for visibility). (2) A clustering algorithm was used to determine optimal category cut points. (3) The dashed line indicates the overall rate for the county as a whole. Diagnosis Rates by Neighborhood Unemployment, Alameda County % of Census Tract Residents who are Unemployed % % % % % % Annual Diagnosis Rate per 100,000 NOTE: (1) Bar widths proportional to the fraction of the underlying population in the category (with the exception categories comprising <2% of the population, for which bars are enlarged for visibility). (2) A clustering algorithm was used to determine optimal category cut points. (3) The dashed line indicates the overall rate for the county as a whole. 7/17/

16 Prevalence by Neighborhood Unemployment, Alameda County, Year End % of Census s Tract Residents who are Unemployed % % % % % % % % Prevalence per 100,000 NOTE: (1) Bar widths proportional to the fraction of the underlying population in the category (with the exception categories comprising <2% of the population, for which bars are enlarged for visibility). (2) A clustering algorithm was used to determine optimal category cut points. (3) The dashed line indicates the overall rate for the county as a whole. Diagnosis Rates by Neighborhood Education Level, Alameda County % of fcensus Tract Residents with Less than a High School Education % % % % % % % Annual Diagnosis Rate per 100,000 NOTE: (1) Bar widths proportional to the fraction of the underlying population in the category (with the exception categories comprising <2% of the population, for which bars are enlarged for visibility). (2) A clustering algorithm was used to determine optimal category cut points. (3) The dashed line indicates the overall rate for the county as a whole. 7/17/

17 Prevalence by Neighborhood Education Level, Alameda County, Year End % of Census Tract Residents with Less than a High School Education % % % % % % % 0048% % Prevalence per 100,000 NOTE: (1) Bar widths proportional to the fraction of the underlying population in the category (with the exception categories comprising <2% of the population, for which bars are enlarged for visibility). (2) A clustering algorithm was used to determine optimal category cut points. (3) The dashed line indicates the overall rate for the county as a whole. Diagnosis Rates by Neighborhood Insurance Status, Alameda County % of Census Tract Residents who are Uninsured % % % % % % Annual Diagnosis Rate per 100,000 NOTE: (1) Bar widths proportional to the fraction of the underlying population in the category (with the exception categories comprising <2% of the population, for which bars are enlarged for visibility). (2) A clustering algorithm was used to determine optimal category cut points. (3) The dashed line indicates the overall rate for the county as a whole. 7/17/

18 Prevalence by Neighborhood Insurance Status, Alameda County, Year End % of Census s Tract Residents who are Uninsured % % % % % % 4-8.4% % Prevalence per 100,000 NOTE: (1) Bar widths proportional to the fraction of the underlying population in the category (with the exception categories comprising <2% of the population, for which bars are enlarged for visibility). (2) A clustering algorithm was used to determine optimal category cut points. (3) The dashed line indicates the overall rate for the county as a whole. Prevalence by Neighborhood Poverty Level and Race/Ethnicity, Alameda County, 2011 revalence per 100,000 Pr Race/Ethnicity African American API Hispanic/Latino White 0% 10% 20% 30% 40% 50% % of Census Tract Residents Living Below Poverty NOTE: A clustering algorithm was used to determine optimal category cut points. EXCLUSIONS: N=24 PLHIV <18 years of age. NOT SHOWN: N=183 PLHIV with other or unknown race/ethnicity. 7/17/

19 Prevalence by Neighborhood Unemployment and Race/Ethnicity, Alameda County, 2011 Pr revalence per 100, Race/Ethnicity African American API Hispanic/Latino White 0 0% 10% 20% 30% % of Census Tract Residents who are Unemployed NOTE: A clustering algorithm was used to determine optimal category cut points. EXCLUSIONS: N=24 PLHIV <18 years of age. NOT SHOWN: N=183 PLHIV with other or unknown race/ethnicity. Prevalence by Neighborhood Education Level and Race/Ethnicity, Alameda County, Race/Ethnicity African American API Hispanic/Latino White revalence per 100,000 Pr % 10% 20% 30% 40% 50% % of Census Tract Residents with Less than a High School Education NOTE: A clustering algorithm was used to determine optimal category cut points. EXCLUSIONS: N=24 PLHIV <18 years of age. NOT SHOWN: N=183 PLHIV with other or unknown race/ethnicity. 7/17/

20 Prevalence by Neighborhood Insurance Status and Race/Ethnicity, Alameda County, Race/Ethnicity African American API Hispanic/Latino White revalence per 100,000 Pr % 10% 20% 30% % of Census Tract Residents who are Uninsured NOTE: A clustering algorithm was used to determine optimal category cut points. EXCLUSIONS: N=24 PLHIV <18 years of age. NOT SHOWN: N=183 PLHIV with other or unknown race/ethnicity. Key takeaways: Diagnosis rates and prevalence generally increase with increasing neighborhood poverty and unemployment and with decreasing rates of insurance and education These associations appear to vary by race/ethnicity Appear to be less prominent among Latinos 7/17/

21 On the agenda 1. Diagnosis and Prevalence A. By Demographics B. By Social Determinants of Health 2. The Continuum of HIV Care The Continuum of HIV Care in Alameda County Among N=669 new diagnoses in * Among N=5,370 PLHIV in Alameda Co. for the entirety of 2013** 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% 82.5% 70.4% 70.1% 55.7% 44.5% Linked Retained Virally Suppressed *Of 682 total diagnoses, 13 died within 90 days and are excluded from analysis **Of 5,585 PLHIV at year end 2012, 42 are known to have died and an additional 173 to have moved out of Alameda County in ) Linkage defined as having a reported CD4 or VL ordered within 90 days or less of diagnosis; 2) Retention calculated using labs ordered in 2013; 3) Viral suppression defined as most recent VL in 2013 < 200 copies/ml 7/17/

22 Linkage to HIV Care in 90 days of Diagnosis by Sex, Alameda County, Including labs on the date of diagnosis? No Yes All (N=669) 70.4% 82.5% Male (N=573) 71.0% 82.9% Female (N=96) 66.7% 80.2% 0% 25% 50% 75% 100% Percent linked in 90 days or less NOTES: (1) Linkage defined as having CD4 and viral load tests. (2) Sex refers to sex assigned at birth. EXCLUSIONS: N=13 patients who died within 90 days of diagnosis. Linkage to HIV Care in 90 days of Diagnosis by Race/Ethnicity, Alameda County, Including labs on the date of diagnosis? No Yes All races (N=669) African American (N=277) White (N=161) Hispanic/Latino (N=149) 70.4% 70.4% 67.1% 73.2% 82.5% 81.2% 80.7% 86.6% API (N=65) 70.8% 83.1% 0% 25% 50% 75% 100% Percent linked in 90 days or less NOTE: Linkage defined as having CD4 and viral load tests. EXCLUSIONS: N=13 patients who died within 90 days of diagnosis. NOT SHOWN: N=17 patients with other/unknown race. 7/17/

23 Linkage to HIV Care in 90 days of Diagnosis by Age at Diagnosis, Alameda County, All ages (N=669) (N=34) (N=188) (N=158) (N=182) (N=84) 60 & over (N=22) Including labs on the date of diagnosis? No Yes 58.8% 70.2% 67.1% 70.4% 70.6% 70.3% 72.7% 82.5% 82.4% 81% 82.4% 81% 81.8% 90.5% 0% 25% 50% 75% 100% Percent linked in 90 days or less NOTE: Linkage defined as having CD4 and viral load tests. EXCLUSIONS: N=13 patients who died within 90 days of diagnosis. NOT SHOWN: N < 5 patients aged Engagement in HIV Care in 2013 by Sex Among PLHIV at Year End 2012, Alameda County Measure 1+ visit 2+ visits 90+ days apart All (N=5,370) 44.5% 70.1% Male (N=4,402) 45.6% 70.3% Female (N=968) 39.5% 69.1% 0% 20% 40% 60% 80% NOTE: (1) Care visits defined as having CD4 and viral load tests. (2) Sex refers to sex assigned at birth. EXCLUSIONS: PLHIV at year end 2012 who died (N=42) or moved (N=173) during /17/

24 Engagement in HIV Care in 2013 by Race/Ethnicity Among PLHIV at Year End 2012, Alameda County Measure 1+ visit 2+ visits 90+ days apart All races (N=5,370) African American (N=2,280) White (N=1,787) Hispanic/Latino (N=906) 44.5% 40.6% 48.8% 44.2% 70.1% 68.2% 72.9% 67.0% 51.1% API (N=231) 78.8% 0% 20% 40% 60% 80% NOTE: Care visits defined as having CD4 and viral load tests. EXCLUSIONS: PLHIV at year end 2012 who died (N=42) or moved (N=173) during NOT SHOWN: N=166 PLHIV with other/unknown race/ethnicity. Engagement in HIV Care in 2013 by Age Among PLHIV at Year End 2012, Alameda County Measure 1+ visit 2+ visits 90+ days apart All ages (N=5 5,370) 44.5% 70.1% (N=26) 42.3% 73.1% (N=392) 41.3% 71.4% (N=782) 38.1% 65.3% (N=1,646) 43.2% 68.7% (N=1,664) 47.4% 72.7% 7% 60 & over (N=848) 48.7% 71.2% 0% 20% 40% 60% 80% NOTE: (1) Care visits defined as having CD4 and viral load tests. (2) Age is at year end EXCLUSIONS: PLHIV at year end 2012 who died (N=42) or moved (N=173) during NOT SHOWN: N=12 PLHIV aged /17/

25 Most Recent Viral Load in 2013 by Sex Among PLHIV at Year End 2012, Alameda County Virologic Status Undetectable Suppressed Unsuppressed Only CD4 reported No CD4s or VLs reported All (N=5,370) Male (N=4,402) Female (N=968) 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% NOTE: VL categories are defined as follows: Undetectable = 0 75 copies/ml, Suppressed = , Unsuppressed = 200+; Sex refers to sex assigned at birth EXCLUSIONS: PLHIV at year end 2012 who died (N=42 ) or moved (N=173) during 2012 Most Recent Viral Load in 2013 by Race/Ethnicity Among PLHIV at Year End 2012, Alameda County Virologic Status Undetectable Suppressed Unsuppressed Only CD4 reported No CD4s or VLs reported All races (N=5,370) African American (N=2,280) White (N=1,787) Hispanic/Latino (N=906) API (N=231) 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% NOTE VL categories are defined as follows: Undetectable = 0 75 copies/ml, Suppressed = , Unsuppressed = 200+ EXCLUSIONS: PLHIV at year end 2012 who died (N=42 ) or moved (N=173) during 2012 NOT SHOWN: N=166 PLHIV with other/unknown race/ethnicity 7/17/

26 Most Recent Viral Load in 2013 by Age Among PLHIV at Year End 2012, Alameda County Virologic Status Undetectable Suppressed Unsuppressed Only CD4 reported No CD4s or VLs reported All ages (N=5,370) (N=26) (N=392) (N=782) (N=1,646) (N=1,664) 60 & over (N=848) 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% NOTES: VL categories are defined as follows: Undetectable = 0 75 copies/ml, Suppressed = , Unsuppressed = 200+ EXCLUSIONS: PLHIV at year end 2012 who died (N=42 ) or moved (N=173) during 2012 NOT SHOWN: N=12 PLHIV aged 0 12 Key takeaways: Linkage Lowest among women and whites Highest among those in their 50s Retention in (any) care Lower among African Americans, Latinos, and those in their 30s in continuous care Lower among women, as well as the above groups Viral Suppression Lower among women, African Americans, and Latinos Increasingly common in older age groups 7/17/

27 Thank you! Contact withany questions or comments 7/17/