HomeMy WebLinkAbouthiv-report-2017-2019-archiveHIV in Alameda County, 2017-2019 i
HIV in Alameda County, 2017-2019 ii
HIV in Alameda County, 2017-2019
December 2020
HIV Epidemiology and Surveillance Unit
HIV STD Section
Division of Communicable Disease Control and Prevention
Alameda County Public Health Department
HIV in Alameda County, 2017-2019 iii
Alameda County Public Health Department
Director
Health Officer
Division of Communicable Disease Control and Prevention
Acting Director
HIV STD Section
Director
HIV Epidemiology and Surveillance Unit
Director
Epidemiologists
Management Associate
Public Health Investigators
Kimi Watkins-Tartt
Nicholas J. Moss, MD, MPH
Darlene Fuji, RD, EdM
Nicholas J. Moss, MD, MPH
Neena Murgai, PhD, MPH
Daniel Allgeier, MPH
William Luong, MPH
Melody Yu, MPH
Nicholas Phelps
George Banks, MD
Oliver Heitkamp
Maria Hernandez
HIV in Alameda County, 2017-2019 iv
Alameda County Public Health Department
HIV Epidemiology and Surveillance Unit
1000 Broadway, Suite 310
Oakland, CA 94607
Phone: (510) 268-2372
Fax: (510) 208-1278
Email: Neena.Murgai@acgov.org
Acknowledgements
This report was produced by the HIV Epidemiology and Surveillance Unit. Neena Murgai, PhD, MPH,
Director, provided oversight of surveillance; analysis and content; and contributed to writing.
Epidemiologists Daniel Allgeier, MPH, Melody Yu, MPH, and William Luong, MPH were the major
contributors to analysis, graphics, and writing. The HIV surveillance team collected and documented the case
surveillance data included in this report. Melody Yu led design and layout of this report.
Front Cover Photo by David Leo Veksler: https://flic.kr/p/eN2NaV
Back Cover Photo by George Sing Jr: https://flic.kr/p/2hXmNPL
This report is available online at http://www.acphd.org/data-reports/reports-by-topic/communicable-
disease.aspx#HIV
Suggested citation for this report:
Alameda County Public Health Department. HIV in Alameda County, 2017-2019.
http://www.acphd.org/data-reports/reports-by-topic/communicable-disease.aspx#HIV. Published
December 2020. Accessed [date].
HIV in Alameda County, 2017-2019 v
Table of Contents
1. Background 1
Overview of this Report 1
HIV/AIDS 1
Definitions Used in this Report 2
Other Conventions Used 3
Data to Care 3
2. New Diagnoses 5
Characteristics of New Diagnoses 6
Diagnosis Rates 8
Timeliness of Diagnosis 12
3. People Living with HIV 21
Characteristics of PLHIV 22
Prevalence Rates 23
Deaths Among Alameda County Residents Ever Diagnosed with AIDS 25
4. Continuum of Care 30
Data to Care 31
The Overall Continuum of Care 32
Linkage to Care 32
D2C: Partner Services and Linkage to Care Among Newly Diagnosed 34
Retention in Care 34
D2C: Re-Engagement Among PLHIV 36
Virologic Status 37
D2C: Viral Suppression Among Out of Care 38
5. Key Populations 52
Transgender 52
People Who Inject Drugs 53
HIV in Alameda County, 2017-2019 vi
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Non-US-Born 55
Men Who Have Sex With Men 58
Young People of Color 62
6. Social Determinants of Health and HIV 64
Appendix A: Technical Notes 67
Data Sources 67
Statistical Analysis 67
Data Suppression Rules 68
Appendix B: Reporting Requirements 69
Health Care Providers 69
Laboratories 69
Appendix C: Surveillance in Alameda County 72
Security and Confidentiality of Data 72
HIV Surveillance Workflow 73
Limitations of Surveillance Data and of County Analysis 74
Bibliography 76
HIV in Alameda County, 2017-2019 vii
List of Figures
1.1: Regions of Alameda County 4
1.2: Neighborhoods in the City of Oakland 4
2.1: New Diagnosis by Sex and Year, Alameda County, 2006-2019 6
2.2: New Diagnoses by Mode of Transmission Among Males, Alameda County, 2017-2019 6
2.3: New Diagnoses by Mode of Transmission Among Females, Alameda County, 2017-
2019 6
2.4: New Diagnoses by Race/Ethnicity, Alameda County, 2017-2019 7
2.5: Age of New Diagnoses, Alameda County, 2017-2019 7
2.6: Geographic Distribution of New HIV Cases by Residence at HIV Diagnosis, Alameda
County, 2017-2019 7
2.7: Residence at HIV Diagnosis, Oakland and Surrounding Area, 2017-2019 8
2.8: Rates of New Diagnoses by Sex, Alameda County, 2017-2019 8
2.9: Trends in Rates of New Diagnoses by Sex, Alameda County, 2006-2019 9
2.10: Rates of New Diagnoses by Race/Ethnicity, Alameda County, 2017-2019 9
2.11: Trends in Rates of New Diagnoses by Race/Ethnicity, Alameda County, 2006-2019 10
2.12: Rates of New Diagnoses by Age, Alameda County, 2017-2019 11
2.13: Trends in Rates of New Diagnoses by Age, Alameda County, 2006-2019 11
2.14: Late Diagnosis by Race/Ethnicity, Alameda County, 2016-2018 12
2.15: Late Diagnosis by Sex, Alameda County, 2016-2018 13
2.16: Late Diagnosis by Age, Alameda County, 2016-2018 13
2.17: First CD4 Count at Diagnosis by Race/Ethnicity, Alameda County, 2016-2018 13
2.18: First CD4 Count at Diagnosis by Sex, Alameda County, 2016-2018 14
HIV in Alameda County, 2017-2019 viii
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2.19: First CD4 Count at Diagnosis by Age, Alameda County, 2016-2018 14
3.1: PLHIV by Sex, Alameda County, Year-End 2019 22
3.2: PLHIV by Race/Ethnicity, Alameda County, Year-End 2019 22
3.3: Age of PLHIV, Alameda County, Year-End 2019 22
3.4: Prevalence of HIV by Sex, Alameda County, Year-End 2019 23
3.5: Prevalence of HIV by Race/Ethnicity, Alameda County, Year-End 2019 23
3.6: Prevalence of HIV by Age, Alameda County, Year-End 2019 23
3.7: Prevalence of HIV by Census Tract of Residence, Alameda County, Year-End 2019 24
3.8: Prevalence of HIV by Census Tract of Residence, Oakland and Surrounding Area,
Year-End 2019 24
3.9: Death Rate Among Alameda County Residents Ever Diagnosed with AIDS, 1985-
2019 25
4.1: The Continuum of HIV Care in Alameda County, 2016-2018 32
4.2: Median and Mean Days Between Diagnosis and Linkage to Care, Alameda County,
2016-2018 32
4.3 Linkage to HIV Care Within 30 Days of Diagnosis by Sex, Alameda County, 2016-
2018 33
4.4: Linkage to HIV Care Within 30 Days of Diagnosis by Race/Ethnicity, Alameda
County, 2016-2018 33
4.5: Linkage to HIV Care Within 30 Days of Diagnosis by Age, Alameda County, 2016-
2018 33
4.6: Number of HIV Care Visits per PLHIV, Alameda County, 2018 34
4.7: Retention in HIV Care by Sex, Alameda County, 2018 35
4.8: Retention in HIV Care by Race/Ethnicity, Alameda County, 2018 35
4.9: Retention in HIV Care by Age, Alameda County, 2018 35
4.10: Virologic Status by Sex, Alameda County, 2018 37
4.11: Virologic Status by Race/Ethnicity, Alameda County, 2018 37
4.12: Virologic Status by Age, Alameda County, 2018 37
HIV in Alameda County, 2017-2019 ix
4.13: Progression Through the Continuum of HIV Care Among PLHIV, Alameda County,
2018 38
5.1: Linkage to HIV Care Among Transgender, Alameda County, 2016-2018 53
5.2: Engagement in HIV Care and Virologic Status Among Transgender PLHIV, Alameda
County, 2018 53
5.3: New Diagnoses Among PWID, Alameda County, 2006-2019 54
5.4: Retention in HIV Care and Virologic Status Among PWID, Alameda County, 2018 54
5.5: Nativity Status and Region of Origin Among Newly Diagnosed, Alameda County,
2017-2019 55
5.6: Nativity Status and Region of Origin Among PLHIV, Alameda County, 2017-2019 55
5.7: Race/Ethnicity Among Non-US-Born Newly Diagnosed in Alameda County, 2017-
2019 56
5.8: Race/Ethnicity Among Non-US-Born PLHIV in Alameda County, 2017-2019 56
5.9: Age at HIV Diagnosis Among Non-US-Born New Diagnoses, Alameda County, 2017-
2019 57
5.10: Age Among Non-US-Born PLHIV, Alameda County, 2017-2019 57
5.11: Transmission Category Among Newly Diagnosed, Non-US-Born Males, Alameda
County, 2017-2019 57
5.12: Transmission Category Among Newly Diagnosed, Non-US-Born Females, Alameda
County, 2017-2019 57
5.13: Linkage Within 30 Days Among Non-US-Born, Alameda County, 2016-2018 58
5.14: Retention in Care and Viral Suppression for US-Born and Non-US-Born, Alameda
County, 2016-2018 58
5.15: Race/Ethnicity of MSM and Non-MSM Among New Diagnoses, Alameda County,
2017-2019 59
5.16: Age at Diagnosis of MSM and Non-MSM Among New Diagnoses, Alameda County,
2017-2019 59
5.17: Late Diagnosis Rates of MSM and Non-MSM Among Newly Diagnosed, Alameda
County 2016-2018 59
5.18: Race/Ethnicity of MSM and Non-MSM Among PLHIV, Alameda County, Year-End
2019 60
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5.19: Age of MSM and Non-MSM Among PLHIV, Alameda County, Year-End 2019 60
5.20: Race/Ethnicity and Linkage to Care in 30 Days of MSM and Non-MSM Among
Newly Diagnosed, Alameda County, 2016-2018 60
5.21: Age Group and Linkage to Care in 30 Days of MSM and Non-MSM Among Newly
Diagnosed, Alameda County, 2016-2018 61
5.22: Evidence of Care and Retention in Care of MSM and Non-MSM Among PLHIV
Alameda County, Year-End 2018 61
5.23: Viral Suppression of MSM and Non-MSM Among PLHIV, Alameda County, Year-
End 2018 61
5.24: Birth Sex Among Young POC and Whites, Newly Diagnosed, Alameda County 2017
-2019 62
5.25: Diagnosis Rate Among Young POC and Whites, Newly Diagnosed, Alameda County
2017-2019 62
5.26: Late Diagnosis Among Young POC and Whites, Newly Diagnosed, Alameda County
2016-2018 62
5.27: Linkage to Care Among Young POC and Whites, Newly Diagnosed, Alameda
County 2016-2018 62
5.28: Retention in Care Among Young POC and Whites, PLHIV, Alameda County, Year-
End 2018 63
5.29: Viral Suppression Among Young POC and Whites, PLHIV, Alameda County Year-
End 2018 63
6.1: HIV Prevalence by Educational Attainment Quintile, Alameda County, Year-End
2016 65
6.2: HIV Prevalence by Nativity Quintile, Alameda County, Year-End 2016 65
6.3: HIV Prevalence by Poverty Quintile, Alameda County, Year-End 2016 65
6.4: HIV Prevalence by Unemployment Quintile, Alameda County, Year-End 2016 66
6.5: HIV Prevalence by Lack of Health Insurance Coverage Quintile, Alameda County,
Year-End 2016 66
A.1: The HIV Surveillance System in Alameda County 73
A.2: Timeline of Mandated HIV Reporting in California 74
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HIV in Alameda County, 2017-2019 xi
List of Tables
2.1: New HIV Diagnoses, Alameda County, 2017-2019 15
2.2: HIV Diagnosis Rates by Sex and Age, Alameda County, 2017-2019 16
2.3: HIV Diagnosis Rates by Sex and Race/Ethnicity, Alameda County, 2017-2019 17
2.4: HIV Diagnosis Rates by Race/Ethnicity and Age, Alameda County, 2017-2019 18
2.5: Late Diagnosis by Sex and Age, Alameda County, 2016-2018 19
2.6: Late Diagnosis by Sex and Race/Ethnicity, Alameda County, 2016-2018 19
2.7: Late Diagnosis by Race/Ethnicity and Age, Alameda County, 2016-2018 20
3.1: People Living with HIV Disease and Prevalence Rates, Alameda County, Year-End
2019 26
3.2: HIV Prevalence by Sex and Age, Alameda County, Year-End 2019 27
3.3: HIV Prevalence by Sex and Race/Ethnicity, Alameda County, Year-End 2019 28
3.4: HIV Prevalence by Race/Ethnicity and Age, Alameda County, Year-End 2019 29
4.1: Linkage to HIV Care Within 30 Days Among New Diagnoses by Sex and Age,
Alameda County, 2016-2018 39
4.2: Linkage to HIV Care Within 30 Days Among New Diagnoses by Sex and Race/
Ethnicity, Alameda County, 2016-2018 40
4.3: Linkage to HIV Care Within 30 Days Among New Diagnoses by Race/Ethnicity and
Age, Alameda County, 2016-2018 41
4.4: Linkage to HIV Care Within 90 Days Among New Diagnoses, Alameda County, 2016
-2018 42
4.5: Any Evidence of Care in 2018 Among PLHIV at Year-End 2017 by Sex and Age,
Alameda County 42
4.6: Any Evidence of Care in 2018 Among PLHIV at Year-End 2017 by Sex and Race/
Ethnicity, Alameda County 43
4.7: Any Evidence of Care in 2018 Among PLHIV at Year-End 2017 by Race/Ethnicity
and Age, Alameda County 44
HIV in Alameda County, 2017-2019 xii
4.8: Retention in Continuous HIV Care in 2018 Among PLHIV at Year-End 2017 by Sex
and Age, Alameda County 45
4.9: Retention in Continuous HIV Care in 2018 Among PLHIV at Year-End 2017 by Sex
and Race/Ethnicity, Alameda County 46
4.10: Retention in Continuous HIV Care in 2018 Among PLHIV at Year-End 2017 by
Race/Ethnicity and Age, Alameda County 47
4.11: Viral Suppression in 2018 Among PLHIV at Year-End 2017 by Sex and Age,
Alameda County 48
4.12: Viral Suppression in 2018 Among PLHIV at Year-End 2017 by Sex and Race/
Ethnicity, Alameda County 49
4.13: Viral Suppression in 2018 Among PLHIV at Year-End 2017 by Race/Ethnicity and
Age, Alameda County 50
4.14: Viral Suppression in 2018 Among PLHIV at Year-End 2017 and In Care in 2017 by
Sex, Alameda County 51
4.15: Viral Suppression in 2018 Among PLHIV at Year-End 2017 and In Care in 2017 by
Race/Ethnicity, Alameda County 51
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HIV in Alameda County, 2017-2019 1
Overview of this Report
This report is based on human immunodeficiency virus (HIV) case surveillance in Alameda County. It
summarizes data on HIV in 5 chapters as described below.
1. New Diagnoses: This chapter describes patterns of HIV diagnosis in Alameda County, characterizing
those who were recently diagnosed according to demographic factors, risk factors and stage of disease.
2. People Living with HIV: The second chapter of the report describes the characteristics of all people
known to be living with HIV disease (PLHIV) in Alameda County. This chapter describes the total
burden of HIV disease in the county and how it varies by demographic factors as well as by geography.
It also describes changes in mortality rates (deaths) over time among those ever diagnosed with
Acquired Immune Deficiency Syndrome (AIDS).
3. The Continuum of HIV Care: This chapter presents the continuum of HIV care in Alameda County.
Modern medical treatments for HIV can halt the progression of the disease and prevent its spread, but
not all persons living with HIV receive effective treatment. The continuum of HIV care (also known as
the “HIV care cascade”) is a framework that presents different indicators of engagement in HIV care
among PLHIV, including linkage to care, retention in care, and viral suppression. Summarized metrics
for the Data to Care program (D2C)—which focuses on targeting HIV prevention and care services
along the continuum of care using surveillance data—is described with the relevant continuum of care
metrics in this chapter.
4. Key Populations: This chapter highlights HIV/AIDS as pertaining to specific populations of
transgender people, young people of color, men who have sex with men (MSM), non-US-born, and
people who inject drugs (PWID).
5. Social Determinants of Health and HIV: This chapter describes the associations between the social and
structural factors affecting health and HIV. Neighborhood metrics of educational attainment, poverty,
nativity, unemployment, and health insurance are examined in relation to prevalence of HIV.
HIV/AIDS
HIV attacks the immune system, weakening it over time such that people living with HIV become
increasingly susceptible to opportunistic infections and other medical conditions. The most advanced stage
of infection, when the immune system is weakest, is called AIDS. Medical treatments can inhibit HIV’s
ability to replicate and greatly temper its effect, but the human body cannot clear HIV. HIV is typically
transmitted through sex, contaminated needles, or spread from mother to fetus during pregnancy.
Background
HIV in Alameda County, 2017-2019 2
Definitions Used in this Report
Stages of HIV Infection
For surveillance purposes, HIV disease progression is classified into 4 stages, from acute infection (Stage 0)
to AIDS (Stage 3). In this report, we use “HIV” to refer to HIV disease at any stage (including Stage 3/
AIDS) and AIDS to refer specifically to Stage 3 HIV disease. We use the acronym “PLHIV” to refer to all
people living with HIV disease, regardless of stage.
Case Definition
All reported HIV cases must meet the Centers for Disease Control and Prevention (CDC) case definition
based on laboratory or clinical criteria.1 Clinical criteria include a medical provider diagnosis and evidence of
HIV treatment, unexplained low CD4 count, or opportunistic infection. The full criteria may be found at
http://www.cdc.gov/mmwr/preview/mmwrhtml/rr6303a1.htm.
Transmission Category
For surveillance purposes, each reported HIV case must be classified according to their risk factors for
acquiring HIV. Cases with multiple risk factors are assigned a transmission category, the risk factor most
likely to have resulted in HIV transmission according to a hierarchy developed by the CDC. In this context,
“heterosexual contact” refers to sexual contact with a partner of the opposite sex with a known risk factor
for HIV. In some cases, partners’ risk factors are unknown, leaving some heterosexual cases without known
HIV risk factors. Such cases are assigned to the “unknown” transmission category. The only exception is
when a case’s sex at birth is female and she reported sex with males, in which case she is presumed to have
been infected through heterosexual contact in accordance with CDC-accepted guidance set by the Council
of State and Territorial Epidemiologists.2
Demographics
Demographic data in this report are based on investigations of medical records. Although the transgender
community is highly impacted by HIV, data on current gender identity are not reliably captured in medical
records. For this reason, all analyses are presented by sex assigned at birth, for which we use “sex” as
shorthand.
Data from racial/ethnic groups in which there were very small numbers were combined for these analyses.
Asians and Pacific Islanders are combined into a single category. American Indians, Alaskan Natives, and
those identifying with multiple races are combined along with those of unknown race into another group
(“Other/Unk”). In tables and charts, the category “Asians and Pacific Islanders” is abbreviated “API” and
“African American” is abbreviated “AfrAmer”.
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HIV in Alameda County, 2017-2019 3
Geographic Area
Residential addresses are geocoded to census tract and city/Census-designated place. Region and
neighborhood boundaries established by the Alameda County Community Assessment, Planning, and
Evaluation (CAPE) unit based on census tract aggregates are used. These geographic areas are shown in
Figures 1.1 and 1.2.
Other Conventions Used
Analyses that are broken out by subgroup (e.g., race/ethnicity) are presented along with the overall group
total (e.g., all races) for comparison.
Where rates are presented, they are often accompanied by error bars to convey their degree of statistical
variability. These error bars depict 95% confidence intervals (a “margin of error”) for the estimates. (In the
case of trends, error bands formed by connecting the ends of these margins of error are shown.) Confidence
intervals are displayed in select subgroup analyses of indicators. Confidence intervals that do not overlap are
considered “statistically significant” and generally represent true differences that are not attributed to chance
alone, though it is still possible. Details regarding how these confidence intervals are calculated can be found
in the technical notes (see “Calculation of Confidence Intervals” on page 67).
Tables showing breakdowns of populations (e.g., new diagnoses, people living with HIV) for indicators (e.g.,
diagnosis rates, viral suppression) by demographic or other subgroup are included at the end of each
chapter. Note that in each table the length of the orange bar is proportional to the fraction of the total
population in that subgroup. Additionally, estimates of each indicator and lines depicting 95% confidence
intervals for the estimate are also shown for absolute comparisons between subgroups. Relative comparisons
of subgroups (e.g., “Late diagnosis is three times as common in group A as it is in group B”) may be made
by comparing estimates, when shown. Unreliable estimates are not shown in tables, although their
confidence intervals may be. Details on data suppression can be found in the technical notes (see “Data
Suppression Rules” on page 68). Lastly, in order to protect privacy, case counts less than five are not
presented in this report.
Data to Care
Data to Care (D2C) is an Alameda County Public Health Department (ACPHD) program aimed at
improving outcomes along the continuum of HIV care. It supports and targets HIV prevention services in
the county by identifying persons newly diagnosed with HIV as well as those living with HIV who have
fallen out of care, using surveillance data. A description of D2C services and program metrics are included in
Chapter 4.
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Figure 1.1: Regions of Alameda County
Figure 1.2: Neighborhoods in the City of Oakland
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HIV in Alameda County, 2017-2019 5
ACPHD monitors the HIV epidemic through mandated reports of new diagnoses and laboratory results.
Estimating the true incidence rate of new HIV transmissions is complex due to the variable time interval
between when a person becomes infected and when their infection is diagnosed. However, surveillance data
reliably describe all new HIV diagnoses and diagnosis rates. In 2018, there were an estimated 37,515 new
diagnoses of HIV infection in the US for an overall diagnosis rate of 11.5 per 100,000 persons. Nationally,
rates were highest among males as compared to females (22.6 vs. 5.1 diagnoses per 100,000, respectively),
those aged 20 to 24 or 25 to 29 (27.8 and 32.6 per 100,000, respectively), African Americans and Latinos
(39.2 and 16.4 per 100,000), and in the South and Northeast (15.6 and 9.9 per 100,000). Men who have sex
with men, including those who inject drugs, accounted for 66% of all new diagnoses, heterosexual contact
accounted for 24%, and other modes of transmission accounted for the remaining 10%.3
In California, there were an estimated 4,747 new diagnoses for an overall statewide rate of 11.9 diagnoses
per 100,000 in 2018. The epidemiology of HIV in Alameda County largely mirrored that of the nation, with
the exception that heterosexual contact is estimated to account for only 14.3% of all new diagnoses among
Alameda County residents.4 In Alameda County the average annual diagnosis rate calculated over the 3-year
period of 2017 to 2019 was 12.7 diagnoses per 100,000.
This chapter describes HIV in Alameda County by examining characteristics of new diagnoses, new
diagnosis rates, and the timeliness of diagnoses by demographic characteristics. Stratified data on newly
diagnosed cases from 2017 to 2019 by sex, age and race/ethnicity are provided in Tables 2.1 to 2.4 at the
end of this chapter.
New Diagnoses
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NOTE: “Sex” here refers to sex assigned at birth.
Figure 2.1: New Diagnosis by Sex and Year, Alameda County, 2006-2019
Characteristics of New Diagnoses
Since HIV became reportable by name in California in 2006, between 200 and 300 new cases of HIV
disease have been reported each year among Alameda County residents. In 2019, there were 227 new
diagnoses of HIV in the county.
Among the 546 men diagnosed with HIV from 2017 to 2019, the overwhelming majority (73.3%) were
MSM. More than seven in ten (78.0%) newly diagnosed women were reported to or presumed to have
acquired HIV by heterosexual contact with a partner with known or unknown HIV status; most of the
remaining women with a known transmission category were infected through injection drug use (IDU).
NOTES: 1) N=546
2) “Sex” here refers to sex assigned at birth.
Figure 2.2: New Diagnoses by Mode of Transmission
Among Males, Alameda County, 2017-2019
NOTES: 1) N=87
2) “Sex” here refers to sex assigned at birth.
Figure 2.3: New Diagnoses by Mode of Transmission
Among Females, Alameda County, 2017-2019
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In Alameda County, newly diagnosed HIV cases were overwhelmingly male. The proportion of new
diagnoses that were among males increased from 76.2% in 2006 to 84.6% in 2019.
HIV in Alameda County, 2017-2019 7
Figure 2.6: Geographic Distribution of New HIV Cases by Residence at HIV Diagnosis, Alameda County, 2017-2019
NOTES: 1) N=587
2) An additional 46 new diagnoses (7.2% of all) are not represented due to incomplete street address.
New diagnoses of HIV were most concentrated in the Oakland area and central county regions (as defined
in Figure 1.1 on page 4).
From 2017 to 2019, African Americans comprised the largest proportion (36.3%) of new HIV diagnoses
among all racial/ethnic groups. Latinos had the next largest proportion (31.0%) of new HIV diagnoses,
followed by whites (19.7%), and API (10.7%). The median age among Alameda County residents diagnosed
with HIV disease from 2017 to 2019 was 33 years and the mean age was 36.4 years. Most diagnoses were
among those in their twenties to forties.
Figure 2.4: New Diagnoses by Race/Ethnicity,
Alameda County, 2017-2019
NOTE: The dashed lines indicate the 25th, 50th, and 75th percentile
values for age among new diagnoses.
Figure 2.5: Age of New Diagnoses,
Alameda County, 2017-2019
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HIV in Alameda County, 2017-2019 8
Figure 2.7: Residence at HIV Diagnosis, Oakland and Surrounding Area, 2017-2019
Within Oakland and the surrounding area, new diagnoses were less concentrated in the Oakland hills
(Northwest Hills, Southeast Hills, and Lower Hills neighborhoods) than in the rest of the region.
Diagnosis Rates
This section examines trends in HIV diagnosis rates. Diagnosis rates are not equivalent to HIV
incidence rates. Trends in diagnosis rates may reflect changes in HIV incidence over time, but may also
reflect changes in HIV testing practices. For example, HIV incidence could decrease while HIV diagnosis
rates increase if more HIV-unaware persons are tested and diagnosed. Due to the relatively small numbers
of diagnoses occurring in Alameda County in any given year, annual diagnosis rates are statistically unstable.
NOTE: “Sex” here refers to sex assigned at birth.
Figure 2.8: Rates of New Diagnoses by Sex,
Alameda County, 2017-2019
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We performed statistical analyses to identify trends
that are least likely to reflect random year-to-year
HIV variability. Apparent trends do not indicate
statistical significance unless specified in the text.
From 2017 to 2019, there were 633 new HIV
diagnoses in Alameda County for an average
annual rate of 12.7 per 100,000 residents. New
diagnosis rates were six times as high among males
as among females between 2017 and 2019.
HIV in Alameda County, 2017-2019 9
From 2017 to 2019, the highest diagnosis rate was
among African Americans, which was almost three
times as high as the second most impacted group—
Latinos. The lowest diagnosis rate was seen among
API.
Figure 2.10: Rates of New Diagnoses by Race/Ethnicity,
Alameda County, 2017-2019
NOTE: “Sex” here refers to sex assigned at birth.
NOTE: “Sex” here refers to sex assigned at birth
Figure 2.9: Trends in Rates of New Diagnoses by Sex, Alameda County, 2006-2019
New diagnosis rates declined steadily and significantly between 2006 and 2019, decreasing by an average of
2.8% annually overall and 2.1% annually among males. In contrast, the same period, rates among females
dropped significantly by 6.4% annually. Rates were consistently higher in men between 2006 and 2019.
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Diagnosis rates have been relatively constant since 2006 in most racial/ethnic groups. However, the average
annual decline in diagnosis rate was statistically significant among African Americans (3.4%) and whites
(3.2%). The overall decline in the diagnosis rate in the county since 2006 was driven largely by decreases in
diagnoses among African Americans—particularly African American women—amongst whom rates
decreased by 6.8% per year on average. While there were 42.1 new diagnoses per 100,000 African American
women from 2006 to 2008, that rate declined to 17.4 new diagnoses per 100,000 from 2017 to 2019. Rates
also declined among Latino women, by an average of 4.8% per year.
Among all males, the only significant trends were declines in diagnosis rates among African Americans and
whites (2.2% and 3.9%, respectively per year on average).
Figure 2.11: Trends in Rates of New Diagnoses by Race/Ethnicity, Alameda County, 2006-2019
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HIV in Alameda County, 2017-2019 11
Figure 2.12: Rates of New Diagnoses by Age,
Alameda County, 2017-2019
By age, diagnosis rates have decreased significantly from 2006 to 2019 at an average rate of 5.5% per year
among those 40 to 49 and 4.2% per year among those 50 and older. While the rate among those 20 to 29
has increased and among those 30 to 39 has decreased since 2006, these were not statistically significant
trends.
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From 2017 to 2019, new HIV diagnoses were most
common among those in their twenties, thirties, and
forties, with 30.2, 25.5, and 16.7 diagnoses per
100,000, respectively. New HIV diagnoses were
somewhat less common among those in their fifties
and least common among those at the extremes of
the age spectrum (i.e., teens and those aged 60 and
over).
Figure 2.13: Trends in Rates of New Diagnoses by Age, Alameda County, 2006-2019
HIV in Alameda County, 2017-2019 12
Among African Americans, there were significant declines in diagnosis rates between 2006 and 2019 in
several age groups. There was an average annual decline of 3.9% among those aged 30 to 39 years, 6.8%
among those 40 to 49 years, and 4.2% among those 50 to 59 years. Whites aged 40 to 49 years old saw an
average annual decline of 6.3% while those 60 and older saw a decline of 7.2%. Among Latinos, there was
an 8.9% decline among those aged 13 to 19 years; in contrast there was a 4.3% increase among those aged
20 to 29 years. There were not statistically significant trends among API by age.
Stratified diagnosis rates by sex, age and race/ethnicity are provided in tables at the end of this chapter
(Table 2.1 to 2.4 on pages 15 to 18). The disparity in diagnosis rates between African Americans and whites
was more pronounced among females than males. African American males had 5.2 times the diagnosis rates
as white males diagnosed from 2017 to 2019; African American females had 8.3 times the diagnosis rates of
white females (Table 2.3 on page 17).
Timeliness of Diagnosis
Diagnosis of HIV early in the course of infection is an important component of effective HIV prevention
and treatment as early intervention generally reduces both the risk of transmission to others and the impact
of HIV infection on a person's health.
Late Diagnosis
A key indicator of late HIV diagnosis is the time to progression to AIDS (stage 3 HIV infection). A
diagnosis is deemed late if AIDS is diagnosed at the same time as a person's initial HIV diagnosis or if the
person progresses to AIDS within one year of the initial HIV diagnosis. The analyses presented in this
section are for the years 2016 to 2018 to allow a full year of follow-up from initial HIV diagnosis. Stratified
analyses of late diagnosis by sex, age, and race/ethnicity are provided in tables at the end of this chapter.
Apparent differences should be interpreted with caution due to the small numbers of diagnoses seen in
some subgroups, resulting in statistical instability.
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Figure 2.14: Late Diagnosis by Race/Ethnicity,
Alameda County, 2016-2018
In Alameda County, 20.5% of new diagnoses
between 2016 and 2018 were late. Whites and
African Americans had the lowest rate and Latinos
and API the highest; however, differences by race/
ethnicity were not statistically significant.
HIV in Alameda County, 2017-2019 13
Figure 2.16: Late Diagnosis by Age,
Alameda County, 2016-2018
There was no difference in late diagnosis by sex.
The proportion of late diagnoses generally
increased with age; over a third of HIV diagnoses
among those aged 60 and over were late. Late
diagnosis was less common among those aged 20
to 29; 1 in 8 were diagnosed late in this age
group. The increase in rate of late diagnosis with
increasing age was statistically significant.
First CD4 Count*
CD4 cell count at the time of diagnosis is
another indicator of the timeliness of HIV
diagnosis. CD4+ T-cells, an important
component of the human immune system, are
infected and killed by HIV. Anti-retroviral
therapy (ART) allows the body to preserve or
increase the CD4 count. However, the longer a
person goes without taking ART, which controls
the level of HIV in their body, the lower their
CD4 count will drop and the more susceptible
the person will be to opportunistic infections and
other health problems. Once a person's CD4
count falls below 200 cells/mm3, the person is
considered to have stage 3 HIV disease, or
AIDS.
Among those diagnosed with HIV between 2016
and 2018 and for whom a CD4 count was
conducted within 90 days, the median CD4
count at the time of diagnosis was 437 cells/
mm3. Whites had the highest median CD4 count
at diagnosis among all racial/ethnic groups and
API had the lowest.
—–
*These analyses exclude 111 cases (16.4% of all diagnoses) with a
first CD4 count more than 90 days after diagnosis.
Figure 2.15: Late Diagnosis by Sex,
Alameda County, 2016-2018
NOTE: “Sex” here refers to sex assigned at birth.
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Figure 2.17: First CD4 Count at Diagnosis by Race/
Ethnicity, Alameda County, 2016-2018
HIV in Alameda County, 2017-2019 14
Median CD4 within 90 days of diagnosis was
higher among females than males.
Those aged 13 to 19 and 20 to 29 had higher
median CD4 counts at diagnosis than other age
groups. Median CD4 count was generally lower in
successively older age groups. Those 50 to 59 had
the lowest median CD4 count at diagnosis.
However, data among those aged 13 to 19 should
be interpreted with caution due to small numbers.
Figure 2.19: First CD4 Count at Diagnosis by Age,
Alameda County, 2016-2018
Figure 2.18: First CD4 Count at Diagnosis by Sex,
Alameda County, 2016-2018
NOTE: “Sex” here refers to sex assigned at birth.
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HIV in Alameda County, 2017-2019 15
Table 2.1: New HIV Diagnoses, Alameda County, 2017-2019
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HIV in Alameda County, 2017-2019 16
Table 2.2: HIV Diagnosis Rates by Sex and Age, Alameda County, 2017-2019
Sexa Age Average
Annual
Count
Percent Average Annual
Diagnosis Rate
per 100,000
95%
Confidence
Interval
All All ages 211.0 100.0%12.7 11.0 - 14.4
0-4 ****
5-12 ****
13-19 3.7 1.7%****
20-24 30.0 14.2%26.0 20.9 - 32.0
25-29 42.0 19.9%34.1 23.8 - 44.5
30-39 62.0 29.4%25.5 19.1 - 31.8
40-49 37.3 17.7%16.7 11.4 - 22.1
50 & older 35.3 16.7%6.4 4.3 - 8.6
Male All ages 182.0 86.3%22.3 19.1 - 25.6
0-4 0.0 0.0%****
5-12 ****
13-19 ****
20-24 25.7 44.3%44.3 34.9 - 55.3
25-29 38.3 61.9%61.9 42.3 - 81.4
30-39 56.3 46.5%46.5 34.4 - 58.7
40-49 29.7 27.0%27.0 21.7 - 33.2
50 & older 28.3 11.0%11.0 8.8 - 13.7
Female All ages 29.0 3.4%3.4 2.7 - 4.2
0-4 ****
5-12 ****
13-19 ****
20-24 4.3 2.1%7.6 4.0 - 12.9
25-29 3.7 1.7%****
30-39 5.7 2.7%4.6 2.7 - 7.4
40-49 7.7 3.6%6.8 4.3 - 10.1
50 & older 7.0 3.3%2.4 1.5 - 3.7
Source: Alameda County eHARS, 2020 Q2
[a] Refers to sex assigned at birth
[*] Some cells suppressed to protect confidentiality
[**] Unstable estimates not shown
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HIV in Alameda County, 2017-2019 17
Table 2.3: HIV Diagnosis Rates by Sex and Race/Ethnicity, Alameda County, 2017-2019
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Sexa Race/Ethnicityb Average
Annual
Count
Percent Average Annual
Diagnosis Rate
per 100,000
95%
Confidence
Interval
All All races 211.0 100.0%12.7 11.0 - 14.4
AfrAmer 76.7 36.3%45.4 35.2 - 55.5
White 41.7 19.7%7.9 5.5 - 10.3
Latino 65.3 31.0%17.5 13.2 - 21.7
API 22.7 10.7%4.5 3.5 - 5.6
Other/Unk 4.7 2.2%--
Male All races 182.0 86.3%22.3 19.1 - 25.6
AfrAmer 61.0 28.9%77.0 57.7 - 96.4
White 36.0 17.1%13.7 9.2 - 18.2
Latino 60.3 28.6%31.7 23.7 - 39.7
API ****
Other/Unk **--
Female All races 29.0 13.7%3.4 2.7 - 4.2
AfrAmer 15.7 7.4%17.4 12.8 - 23.2
White 5.7 2.7%2.1 1.2 - 3.4
Latino 5.0 2.4%2.7 1.5 - 4.5
API ****
Other/Unk **--
Source: Alameda County eHARS, 2020 Q2
[a] Refers to sex assigned at birth
[b] 'Other/Unk' = American Indians and Alaskan Natives, multiple race, unknown race
[*] Some cells suppressed to protect confidentiality
[-] Rate not calculable for lack of a denominator
HIV in Alameda County, 2017-2019 18
Table 2.4: HIV Diagnosis Rates by Race/Ethnicity and Age, Alameda County, 2017-2019
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Race/Ethnicitya Age Average
Annual
Count
Percent Average Annual
Diagnosis Rate
per 100,000
95%
Confidence
Interval
All races All ages 211.0 100.0%12.7 11.0 - 14.4
0-4 ****
5-12 ****
13-19 3.7 1.7%****
20-24 30.0 14.2%26.0 20.9 - 32.0
25-29 42.0 19.9%34.1 23.8 - 44.5
30-39 62.0 29.4%25.5 19.1 - 31.8
40-49 37.3 17.7%16.7 11.4 - 22.1
50 & older 35.3 16.7%6.4 4.3 - 8.6
AfrAmer All ages 76.7 36.3%45.4 35.2 - 55.5
0-4 ****
5-12 ****
13-19 1.7 0.8%****
20-24 10.7 5.1%95.4 65.3 - 134.7
25-29 17.0 8.1%154.2 114.8 - 202.8
30-39 18.7 8.8%84.8 64.1 - 110.2
40-49 12.3 5.8%52.5 37.0 - 72.4
50 & older 16.0 7.6%26.4 19.5 - 35.1
White All ages 41.7 19.7%7.9 5.5 - 10.3
0-4 0.0 0.0%****
5-12 0.0 0.0%****
13-19 0.0 0.0%****
20-24 3.7 1.7%****
25-29 6.3 3.0%18.6 11.2 - 29.0
30-39 12.0 5.7%18.5 13.0 - 25.7
40-49 8.7 4.1%11.9 7.8 - 17.5
50 & older 11.0 5.2%4.6 3.1 - 6.4
Latino All ages 65.3 31.0%17.5 13.2 - 21.7
0-4 ****
5-12 0.0 0.0%****
13-19 ****
20-24 12.0 5.7%37.5 26.3 - 51.9
25-29 13.7 6.5%39.2 28.1 - 53.2
30-39 22.7 10.7%35.0 27.2 - 44.4
40-49 11.7 5.5%25.2 17.6 - 35.1
50 & older 4.3 2.1%6.4 3.4 - 5.6
API All ages 22.7 10.7%4.5 3.5 - 5.6
0-4 0.0 0.0%****
5-12 ****
13-19 ****
20-24 ****
25-29 ****
30-39 ****
40-49 ****
50 & older 4.0 1.9%2.4 1.3 - 4.2
Other/Unk All ages 4.7 2.2%--
0-4 0.0 0.0%--
5-12 0.0 0.0%--
13-19 0.0 0.0%--
20-24 **--
25-29 **--
30-39 **--
40-49 **--
50 & older 0.0 0.0%--
Source: Alameda County eHARS, 2020 Q2
[a] 'Other/Unk' = American Indians and Alaskan Natives, multiple race, unknown race
[*] Some cells suppressed to protect confidentiality
[**] Unstable estimates not shown
[-] Rate not calculable for lack of a denominator
HIV in Alameda County, 2017-2019 19
Table 2.5: Late Diagnosis by Sex and Age, Alameda County, 2016-2018
Table 2.6: Late Diagnosis by Sex and Race/Ethnicity, Alameda County, 2016-2018
Sexa Age at Diagnosis Average Annual
Count
Column Percent Average Annual
Count
Row Percent
All All ages 225.7 100.0%46.3 20.5%
5-12 **0.0 *
13-19 **0.0 *
20-24 29.3 13.0%2.7 **
25-29 45.7 20.2%6.3 13.9%
30-39 62.3 27.6%15.3 24.6%
40-49 44.3 19.6%10.3 23.3%
50 & older 39.0 17.3%11.7 29.9%
Male All ages 192.3 85.2%39.0 20.3%
5-12 **0.0 *
13-19 **0.0 *
20-24 24.7 10.9%2.7 **
25-29 41.3 18.3%5.7 13.7%
30-39 56.0 24.8%12.7 22.6%
40-49 35.3 15.7%8.7 24.5%
50 & older 31.0 13.7%9.3 30.1%
Female All ages 33.3 14.8%7.3 22.0%
5-12 **0.0 *
13-19 **0.0 *
20-24 4.7 2.1%0.0 0.0%
25-29 4.3 1.9%0.7 **
30-39 6.3 2.8%2.7 **
40-49 9.0 4.0%1.7 **
50 & older 8.0 3.5%2.3 **
Source: Alameda County eHARS, 2020 Q2
[a] Refers to sex assigned at birth
[*] Some cells suppressed to protect confidentiality
[**] Unstable estimates not shown
New Diagnoses Late Diagnoses
Sexa Race/Ethnicityb Average Annual
Count
Column Percent Average Annual
Count
Row Percent
All All races 225.7 100.0%46.3 20.5%
AfrAmer 82.3 36.5%16.7 20.2%
White 43.3 19.2%7.3 16.9%
Latino 71.7 31.8%16.7 23.3%
API 21.7 9.6%4.7 21.5%
Other/Unk 6.7 3.0%1.0 **
Male All races 192.3 85.2%39.0 20.3%
AfrAmer 61.3 27.2%13.0 21.2%
White 38.7 17.1%6.3 16.4%
Latino 66.7 29.5%15.3 23.0%
API **3.7 *
Other/Unk **0.7 *
Female All races 33.3 14.8%7.3 22.0%
AfrAmer 21.0 9.3%3.7 17.5%
White 4.7 2.1%1.0 **
Latino 5.0 2.2%1.3 **
API **1.0 *
Other/Unk **0.3 *
Source: Alameda County eHARS, 2020 Q2
[a] Refers to sex assigned at birth
[b] 'Other/Unk' = American Indians and Alaskan Natives, multiple race, unknown race
[*] Some cells suppressed to protect confidentiality
[**] Unstable estimates not shown
New Diagnoses Late Diagnoses
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HIV in Alameda County, 2017-2019 20
Table 2.7: Late Diagnosis by Race/Ethnicity and Age, Alameda County, 2016-2018
Race/Ethnicitya Age at Diagnosis Average Annual
Count
Column Percent Average Annual
Count
Row Percent
All races All ages 225.7 100.0%46.3 20.5%
5-12 **0.0 *
13-19 **0.0 *
20-24 29.3 13.0%2.7 **
25-29 45.7 20.2%6.3 13.9%
30-39 62.3 27.6%15.3 24.6%
40-49 44.3 19.6%10.3 23.3%
50 & older 39.0 17.3%11.7 29.9%
AfrAmer All ages 82.3 36.5%16.7 20.2%
5-12 **0.0 *
13-19 **0.0 *
20-24 13.3 5.9%1.3 **
25-29 15.3 6.8%2.3 **
30-39 17.3 7.7%4.0 **
40-49 14.0 6.2%3.3 **
50 & older 19.3 8.6%5.7 **
White All ages 43.3 19.2%7.3 16.9%
5-12 0.0 0.0%0.0 **
13-19 0.0 0.0%0.0 **
20-24 3.0 1.3%0.0 0.0%
25-29 7.7 3.4%0.7 **
30-39 14.7 6.5%2.7 **
40-49 9.0 4.0%2.3 **
50 & older 9.0 4.0%1.7 **
Latino All ages 71.7 31.8%16.7 23.3%
5-12 **0.0 *
13-19 **0.0 *
20-24 9.3 4.1%1.3 **
25-29 16.7 7.4%1.7 **
30-39 22.7 10.0%6.0 **
40-49 16.0 7.1%4.3 **
50 & older 5.7 2.5%3.3 **
API All ages 21.7 9.6%4.7 21.5%
5-12 0.0 0.0%0.0 **
13-19 **0.0 *
20-24 **0.0 *
25-29 4.0 1.8%1.3 **
30-39 5.7 2.5%2.3 **
40-49 **0.0 *
50 & older **1.0 *
Other/Unk All ages 6.7 3.0%1.0 **
5-12 0.0 0.0%0.0 **
13-19 0.0 0.0%0.0 **
20-24 **0.0 *
25-29 2.0 0.9%0.3 **
30-39 2.0 0.9%0.3 **
40-49 **0.3 *
50 & older **0.0 *
Source: Alameda County eHARS, 2020 Q2
[a] 'Other/Unk' = American Indians and Alaskan Natives, multiple race, unknown race
[*] Some cells suppressed to protect confidentiality
[**] Unstable estimates not shown
New Diagnoses Late Diagnoses
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HIV in Alameda County, 2017-2019 21
In the United States, there were an estimated 1,040,352 persons aged 13 years or older living with diagnosed
HIV at the end of 2018. Prevalence was highest among men, those aged 50 to 54 and 55 to 59 (757.9 and
699.5 per 100,000 respectively), African Americans and Latinos (1,034.2 and 386.4 per 100,000 respectively),
and in the Northeast and South (420.5 and 371.6 per 100,000 respectively).3 At year-end 2018, California
had an estimated 136,566 PLHIV for a statewide prevalence of 342.9 per 100,000 population. HIV
prevalence among women in California (79.5 per 100,000) was less than half that of women nationally.4 At
year-end 2019 in Alameda County, the prevalence of HIV was 380.6 per 100,000 residents.
This chapter examines prevalence, or the proportion of people with HIV infection living in Alameda
County, reflecting the overall burden of HIV in the population. Data presented do not include PLHIV with
undiagnosed infection but include all those with diagnosed HIV (including newly diagnosed), regardless of
the stage of HIV infection. First, characteristics of PLHIV in the county are presented. Then, the prevalence
of HIV disease in different subpopulations is described. Finally, mortality (deaths) among PLHIV ever
diagnosed with AIDS is described. Table 3.1 summarizes data presented in this chapter. Stratified
prevalence rates by sex, age and race/ethnicity are provided in Tables 3.2 to 3.4 at the end of this chapter.
People Living with HIV
HIV in Alameda County, 2017-2019 22
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Characteristics of PLHIV
At the end of 2019, there were an estimated
6,350 PLHIV in Alameda County.
As with the distribution by sex among new
diagnoses of HIV, PLHIV in Alameda County at
year-end 2019 were predominantly male (83.8%).
PLHIV in Alameda County were predominantly
African American (38.4%) or white (29.5%).
Latinos and API each comprised a smaller
proportion of PLHIV. Racial/ethnic disparities
among PLHIV were more apparent among
women compared to men (Table 3.3). Among
men there was a similar number of PLHIV who
were African American and white; however,
among women there were nearly four times as
many PLHIV who were African American
compared to those who were white.
Half of PLHIV were in their fifties or older.
Only about a quarter were in their thirties or
younger at year-end 2019.
Figure 3.1: PLHIV by Sex, Alameda County, Year-End 2019
NOTE: “Sex” refers to sex assigned at birth.
Figure 3.2: PLHIV by Race/Ethnicity,
Alameda County, Year-End 2019
Figure 3.3: Age of PLHIV, Alameda County, Year-End 2019
HIV in Alameda County, 2017-2019 23
Prevalence Rates
At the end of 2019 there were 6,350 people
living with HIV in Alameda County for a
prevalence rate of 380.6 per 100,000 or 0.4% of
residents.
HIV prevalence was more than five times higher
among males than females at year-end 2019.
African Americans had a four times higher
burden of HIV prevalence compared to the next
most impacted racial group, whites. Prevalence
was lowest among API.
HIV prevalence was higher in each successive
age group, ranging from 15.8 per 100,000 youth
aged 13 to 19 to a high of 855.1 per 100,000
people aged 50 to 59 years. The number of
children aged 0 to 12 living with HIV was too
low to estimate a statistically reliable prevalence
rate. Prevalence among those aged 60 and over
differed only slightly from those in their thirties.
Increasing prevalence of HIV with age is
consistent with the greatly improved survival of
PLHIV in the post-ART era.
Disparities in prevalence rates by race/ethnicity
were more pronounced among females than
males. While prevalence was more than three
times higher among African American males
compared to white males, it was 10 times higher
among African American females compared to
white females (Table 3.3). Additionally, although
HIV prevalence was higher among white males
compared to Latino males, prevalence was lower
among white females compared to Latino
females.
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Figure 3.4: Prevalence of HIV by Sex,
Alameda County, Year-End 2019
NOTE: “Sex” refers to sex assigned at birth.
Figure 3.5: Prevalence of HIV by Race/Ethnicity,
Alameda County, Year-End 2019
Figure 3.6: Prevalence of HIV by Age,
Alameda County, Year-End 2019
HIV in Alameda County, 2017-2019 24
Figure 3.7: Prevalence of HIV by Census Tract of Residence, Alameda County, Year-End 2019
The city of Emeryville had the highest HIV prevalence within Alameda County, followed by Oakland,
Ashland, and Fairview. Among the Oakland neighborhoods, West Oakland, Downtown, and Chinatown
had the highest HIV prevalence, ranging between 1 to 2% of residents.
Figure 3.8: Prevalence of HIV by Census Tract of Residence, Oakland and Surrounding Area, Year-End 2019
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HIV in Alameda County, 2017-2019 25
Figure 3.9: Death Rate Among Alameda County Residents Ever Diagnosed with AIDS, 1985-2019
Deaths Among Alameda County Residents Ever Diagnosed with AIDS
Although HIV infection without AIDS has been reportable by name in California only since 2006, AIDS
has been a reportable disease since the early 1980s, allowing examination of long-term trends in death rates
among the subset of PLHIV ever diagnosed with AIDS. In 1985, there were 38.7 deaths (from any cause,
whether HIV-related or not) per 100 Alameda County residents ever diagnosed with AIDS. This rate
dropped to 7.5 deaths per 100 by 1997 and has declined slowly but steadily since then. In 2019, there were
59 deaths among the 3,733 residents living with AIDS for a rate of 1.5 deaths per 100 residents living with
AIDS.
NOTE: Death rates calculated among persons ever diagnosed with AIDS while a resident of Alameda County, regardless of
county of residence at death. Deaths in PLHIV without AIDS are not reported here.
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HIV in Alameda County, 2017-2019 26
Table 3.1: People Living with HIV Disease and Prevalence Rates, Alameda County, Year-End 2019
PE
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H
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Characteristic Category Count Percent Prevalence per
100,000
95% Confidence
Interval
All PLHIV 6,350 100.0%380.6 371.3 - 390.0
Sexa Male 5,321 83.8%649.2 631.8 - 666.7
Female 1,029 16.2%121.2 113.8 - 128.7
Race/Ethnicityb AfrAmer 2,438 38.4%1,467.0 1,408.8 - 1,525.3
White 1,872 29.5%359.8 343.5 - 376.1
Latino 1,338 21.1%347.9 329.2 - 366.5
API 455 7.2%88.0 79.9 - 96.0
Other/Unk 247 3.9%----
Age (years)c 0-12 5 0.1%****
13-19 23 0.4%15.8 10.0 - 23.7
20-29 461 7.3%194.0 176.3 - 211.7
30-39 1,100 17.3%460.3 433.1 - 487.5
40-49 1,242 19.6%555.8 524.9 - 586.7
50-59 1,948 30.7%855.1 817.1 - 893.1
60 & older 1,571 24.7%465.8 445.6 - 492.0
Residence North County 500 7.9%352.6 321.7 - 383.5
Oakland Area 3,790 59.7%717.4 649.6 - 740.3
Central County 1,271 20.0%321.4 303.7 - 339.1
South County 424 6.7%118.5 107.3 - 129.8
Tri-Valley 332 5.2%141.2 126.0 - 156.4
Remainder of county 23 0.4%257.8 163.4 - 386.9
Unknown 10 0.2%****
Source: Alameda County eHARS, 2020 Q2
[a] Refers to sex assigned at birth
[b] 'Other/Unk' = American Indians and Alaskan Natives, multiple race, unknown race
[c] Age at year-end 2019
[**] Unstable estimates not shown
[--] Rate not calculable for lack of a denominator
HIV in Alameda County, 2017-2019 27
Table 3.2: HIV Prevalence by Sex and Age, Alameda County, Year-End 2019
PE
O
P
L
E
L
I
V
I
N
G
W
I
T
H
H
I
V
Sexa Age Count Percent Prevalence per
100,000
95% Confidence
Interval
All All ages 6,350 100.0%380.6 371.3 - 390.0
0-12 5 0.1%****
13-19 23 0.4%15.8 10.0 - 23.7
20-29 461 7.3%194.0 176.3 - 211.7
30-39 1,100 17.3%460.3 433.1 - 487.5
40-49 1,242 19.6%555.8 524.9 - 586.7
50-59 1,948 30.7%855.1 817.1 - 939.1
60 & older 1,571 24.7%468.8 445.6 - 492.0
Male All ages 5,321 83.8%649.2 631.8 - 666.7
0-12 5 0.1%****
13-19 15 0.2%20.2 11.3 - 33.4
20-29 409 6.4%340.7 307.7 - 373.7
30-39 972 15.3%821.9 770.3 - 873.6
40-49 1,013 16.0%919.8 863.1 - 976.4
50-59 1,620 25.5%1,450.1 1,379.5 - 1,520.7
60 & older 1,287 20.3%846.6 800.3 - 892.8
Female All ages 1,029 16.2%121.2 113.8 - 128.7
0-12 0 0.0%****
13-19 8 0.1%****
20-29 52 0.8%44.2 33.0 - 58.0
30-39 128 2.0%108.0 87.7 - 124.4
40-49 229 3.6%202.1 175.9 - 228.3
50-59 328 5.2%282.5 251.9 - 313.1
60 & older 284 4.5%155.1 137.1 - 173.2
Source: Alameda County eHARS, 2020 Q2
[a] Refers to sex assigned at birth
[**] Unstable estimates not shown
HIV in Alameda County, 2017-2019 28
Table 3.3: HIV Prevalence by Sex and Race/Ethnicity, Alameda County, Year-End 2019
PE
O
P
L
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Sexa Race/Ethnicityb Count Percent Prevalence per
100,000
95% Confidence
Interval
All All races 6,350 100.0%380.6 371.3 - 390.0
AfrAmer 2,438 38.4%1,467.0 1,408.8 - 1,525.3
White 1,872 29.5%359.8 343.5 - 376.1
Latino 1,338 21.1%347.9 329.2 - 366.5
API 455 7.2%88.0 79.9 - 96.0
Other/Unk 247 3.9%--
Male All races 5,321 83.8%649.2 631.8 - 666.7
AfrAmer 1,829 28.8%2,343.2 2,235.8 - 2,450.6
White 1,704 26.8%656.9 625.7 - 688.0
Latino 1,178 18.6%602.1 567.7 - 636.5
API 382 6.2%158.2 142.6 - 173.9
Other/Unk 218 3.4%--
Female All races 1,029 16.2%121.2 113.8 - 128.7
AfrAmer 609 9.6%691.0 636.1 - 745.9
White 168 2.6%64.4 54.6 - 74.1
Latino 160 2.5%84.7 71.6 - 97.8
API 63 1.0%23.4 18.0 - 29.9
Other/Unk 29 0.5%--
Source: Alameda County eHARS, 2020 Q2
[a] Refers to sex assigned at birth
[b] 'Other/Unk' = American Indians and Alaskan Natives, multiple race, unknown race
[**] Unstable estimates not shown
[-] Rate not calculable for lack of a denominator
HIV in Alameda County, 2017-2019 29
Table 3.4: HIV Prevalence by Race/Ethnicity and Age, Alameda County, Year-End 2019
PE
O
P
L
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V
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H
H
I
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Race/Ethnicitya Age Count Percent Prevalence per
100,000
95% Confidence
Interval
All races All ages 6,350 100.0%380.6 371.3 - 390.0
0-12 5 0.1%****
13-19 23 0.4%15.8 10.0 - 23.7
20-29 461 7.3%194.0 176.3 - 211.7
30-39 1,100 17.3%460.3 433.1 - 487.5
40-49 1,242 19.6%555.8 524.9 - 586.7
50-59 1,948 30.7%855.1 817.1 - 893.1
60 & older 1,571 24.7%468.8 445.6 - 492.0
AfrAmer All ages 2,438 38.4%1,467.0 1,408.8 - 1,525.3
0-12 ****
13-19 ****
20-29 201 3.2%926.4 798.3 - 1054.5
30-39 423 6.7%2,037.2 1,843.1 - 2,231.4
40-49 417 6.6%1,818.1 1,643.6 - 1,992.6
50-59 734 11.6%2,904.8 2,694.7 - 3,115.0
60 & older 645 10.2%1,768.6 1,632.1 - 1,905.1
White All ages 1,872 29.5%349.8 343.5 - 376.1
0-12 ****
13-19 ****
20-29 61 1.0%96.5 73.8 - 124.0
30-39 212 3.3%350.7 303.5 - 398.0
40-49 306 4.8%439.0 389.8 - 488.2
50-59 699 11.0%760.8 704.4 - 817.2
60 & older 592 9.3%386.5 355.3 - 417.6
Latino All ages 1,338 21.1%347.9 329.2 - 366.5
0-12 ****
13-19 ****
20-29 134 2.1%197.7 164.2 - 231.2
30-39 319 5.0%482.6 429.7 - 535.6
40-49 325 5.1%673.3 600.1 - 746.5
50-59 345 5.4%1,038.2 928.6 - 1,147.7
60 & older 211 3.3%564.9 488.7 - 641.1
API All ages 455 7.2%88.0 79.9 - 96.0
0-12 ****
13-19 ****
20-29 46 0.7%61.0 44.3 - 81.9
30-39 96 1.5%117.2 94.9 - 143.1
40-49 132 2.1%177.2 147.0 - 207.4
50-59 103 1.6%146.2 118.0 - 174.4
60 & older 76 1.2%75.9 59.8 - 95.0
Other/Unk All ages 247 3.9%--
0-12 0 0.0%--
13-19 0 0.0%--
20-29 21 0.3%--
30-39 50 0.8%--
40-49 62 1.0%--
50-59 67 1.1%--
60 & older 47 0.7%--
Source: Alameda County eHARS, 2020 Q2
[a] 'Other/Unk' = American Indians and Alaskan Natives, multiple race, unknown race
[*] Some cells suppressed to protect confidentiality
[**] Unstable estimates not shown
[-] Rate not calculable for lack of a denominator
HIV in Alameda County, 2017-2019 30
Continuum of Care
Anti-retroviral therapy (ART), when taken regularly, can suppress HIV, preventing disease progression as
well as preventing the transmission of HIV entirely. Thus, ART benefits PLHIV as well as the larger
community. In order to maximize these benefits, it is crucial that PLHIV be diagnosed, linked to and
retained in regular HIV care, and be prescribed and adhere to ART. These steps—diagnosis, linkage,
retention, and prescription of and adherence to ART—are all pre-requisites for achieving virologic
suppression. Together, these steps comprise the continuum of HIV care, also called the HIV care cascade
or the stages of HIV care. The continuum is also a framework for conceptualizing HIV care and prevention
efforts.
One goal put forth by the National HIV/AIDS Strategy is to increase the percentage of newly diagnosed
persons linked to care within one month of their diagnosis to 85%.5 Alameda County previously reported
linkage within 90 days; however data on 30-day linkage is presented in this year’s report to reflect currently
relevant metrics. Evaluation of care for PLHIV is shown through two measures: any evidence of care or
being in care—defined as at least one provider visit in a year, and retention—defined as two or more visits
at least 90 days apart.
In the United States, the CDC estimated that 87.8% of persons diagnosed in 2018 were linked to care within
3 months. Additionally, the CDC estimated that, at the end of 2018, 86% of all PLHIV had been diagnosed
and that, among those still alive and who had been diagnosed by the end of the previous year, 75.7%
received any HIV care, 57.9% were retained in continuous care, and 69.6% were virally suppressed.6
In California, 86.1% of those diagnosed in 2018 were estimated to have linked to care within three months.
By the end of 2018, among those living with diagnosed HIV in California, 73.8% were estimated to have
received any HIV care in 2018, 57.9% were estimated to have been retained in continuous care, and 64.2%
were estimated to have been virally suppressed at last test.7
This chapter examines the continuum of HIV care in Alameda County and select metrics for the Data to
Care program. Care outcomes are described by demographics such as race/ethnicity, age, and sex at birth.
The continuum measures look at data one year earlier than what is available in the New Diagnoses and
People Living with HIV chapters to allow for more complete laboratory records to be included in the
analyses.
HIV in Alameda County, 2017-2019 31
Data to Care
Data to Care (D2C) is a high-impact prevention strategy that aims to positively impact outcomes
along the HIV care continuum using surveillance data. The Alameda County D2C program aims to
target HIV prevention services to persons newly diagnosed with HIV and PLHIV who appear to
have fallen out of care with an HIV provider. Prevention services supported by D2C include Partner
Services (PS), linkage to care (LTC), and re-engagement. LTC is provided by Alameda County field
staff who contact newly diagnosed clients to ensure that they have initiated HIV care with a
provider. If the client is not linked to care, field staff assist the client in contacting and initiating care.
During outreach, field staff also initiate PS, which is a broad array of services that include prevention
counseling, referrals, and identifying and notifying the client’s intimate partners of their potential
HIV exposure. Inclusion criteria for LTC and PS are new HIV diagnosis within the last six months
and residence at diagnosis in Alameda County.
D2C also aims at improving retention in HIV care by re-engaging PLHIV. Re-engagement is
focused on PLHIV who appear to have fallen out of care with an HIV provider. Being retained in
care refers to having two or more visits at least 90 days apart in a given year; being out of care
(OOC) refers to not receiving regular visits or care from a provider to manage HIV. D2C inclusion
criteria for re-engagement are previous HIV diagnosis and being determined to be OOC, either by
their provider or through analysis of surveillance data showing lack of laboratory results for the
client. After they are identified as being OOC, D2C field staff contact and re-engage clients with an
HIV care provider.
The D2C program helps ensure that clients initiate and stay engaged in HIV care, which consists of
HIV viral load testing at regular intervals as well as adherence to antiretroviral therapy. These two
components of HIV care can result in lowered HIV viral loads for the client, leading to better health
outcomes for the client and decreased transmission of HIV in the community.
A key component of the Alameda County D2C program is a client database known as Prevention
and Care Engagement (PACE) which is used to document client characteristics and program
services and is managed by an epidemiologist. Ongoing data management of PACE is key to
measuring D2C outcomes and identifying opportunities for continual improvement of the program.
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Figure 4.1: The Continuum of HIV Care in Alameda County, 2016-2018
NOTES:1) Of 677 total diagnoses, 2 died within 30 days and were excluded from analysis
2) Of 6,312 PLHIV at year-end 2017, 79 were known to have died and an additional 483 to have moved out
of Alameda County in 2018.
The Overall Continuum of Care
Figure 4.2: Median and Mean Days Between Diagnosis
and Linkage to Care, Alameda County, 2016-2018
In Alameda County, 72.3% of new diagnoses between 2016 and 2018 were linked to care within 30 days if
HIV-related labs done on the date of diagnosis were excluded; 86.4% were linked to care if labs done on the
date of diagnosis were included. Approximately 57.8% of PLHIV who resided in Alameda County for the
entirety of 2018 had two or more visits 90 or more days apart, and were considered retained in care. Viral
suppression was estimated to be 71.6% that same year.
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Linkage to Care
Here we present linkage to care estimates for Alameda County. It should be noted that receipt of a CD4
count or viral load test is not a definitive indicator of linkage to care. For example, a health care provider may
order these tests concurrently with a confirmatory HIV test or before a patient even knows the diagnosis.
Labs ordered after the date of diagnosis provide an alternative method for estimating linkage to care.
We present both estimates of linkage—one that
includes labs done on the date of diagnosis and
another that excludes them—providing a range of
what might be considered linked to care.
The median time from diagnosis to first CD4 or
viral load among Alameda County residents
diagnosed within 2016 to 2018 was four days.
Excluding labs ordered on the date of diagnosis, the
median time from diagnosis was 10 days.
HIV in Alameda County, 2017-2019 33
Overall, 86.4% of those diagnosed with HIV in
Alameda County from 2016 to 2018 were linked
to HIV care within 30 days of their diagnosis.
Excluding labs ordered on date of diagnosis,
72.3% of newly diagnosed cases were linked.
Differences by sex were not statistically
significant.
Differences in linkage to care by race/ethnicity
were not statistically significant.
Linkage was generally consistent across age
groups except for the youngest group, 13 to 19
years of age. This estimate is less reliable due to a
small number of cases.
Figure 4.3: Linkage to HIV Care Within 30 Days of Diagnosis by
Sex, Alameda County, 2016-2018
NOTES: 1) “Sex” refers to sex assigned at birth
2) Excludes persons who died within 30 days of diagnosis (N=2).
Figure 4.4: Linkage to HIV Care Within 30 Days of Diagnosis by
Race/Ethnicity, Alameda County, 2016-2018
Figure 4.5: Linkage to HIV Care within 30 Days of Diagnosis
by Age, Alameda County, 2016-2018
NOTE: Excludes persons who died within 30 days of diagnosis (N=2).
NOTE: Excludes persons who died within 30 days of diagnosis (N=2).
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D2C: Partner Services and Linkage to Care Among Newly Diagnosed
Referral to the D2C program follows a series of steps. Using laboratory surveillance data, HIV
surveillance staff investigate cases to identify and confirm HIV diagnoses that have not been
previously reported in another jurisdiction. Cases determined to be a new HIV diagnosis in Alameda
County are referred to the D2C program. The average time between diagnosis of a client and the
completion of the surveillance investigation was 27 days. The average time between completion of
the surveillance investigation and referral to the D2C program was 3.7 days. The average time
between diagnosis of a client and referral to the D2C program was approximately 33 days. The
average time between referral to the D2C program and delivery of PS was 21.2 days.
In 2018 and 2019, the D2C program identified 431 clients who were presumed to be new diagnoses.
D2C field staff determined that 55 were cases and were previously diagnosed more than six months
ago in another jurisdiction. Thus, these clients were not eligible for D2C services; the remainder—
376 clients—were eligible. Of eligible clients, 136 (36.1%) were African American, 124 (32.9%) were
Latino, 76 (20.2%) were white, 37 (9.8%) were API, and 3 (0.7%) were multiracial or American
Indian/Alaska Native.
PS is a key prevention strategy supported within the Alameda County D2C program. Assisting newly
diagnosed clients in disclosing to their partners anonymously gives them the opportunity to get tested
for HIV, initiate PrEP, or receive other prevention services as appropriate. Overall, D2C field staff
were able to offer PS to 266 (70.7%) of the 376 eligible clients. Among the 266 clients offered PS, 52
(19.5%) accepted PS, 94 (35.3%) were found to have already disclosed to their partner and 64
(24.0%) were offered PS by an alternative source. D2C field staff were not able to provide PS to 110
(29.6%) of the 376 clients for several reasons. These included: 46 (41.8%) clients who could not be
located, 20 (18.1%) who moved out of Alameda County, 28 (25.4%) clients with social and medical
factors that made outreach unattainable, 13 (11.8%) who were administratively closed, and 3 (2.7%)
who were being assessed.
Of the 376 eligible clients, 267 (71.0%) were confirmed to have been be linked to care at the time of
D2C referral and 20 (5.3%) were linked to care by D2C field staff. Field staff were still investigating
43 (11.4%) clients at time of analysis. Of the remaining 46 clients, D2C field staff referred 19 out of
Alameda County, 11 clients could not be located, 10 moved out of Alameda County, 4 refused
services, and 2 were determined to not be a new case.
Retention in Care
In 2018, 79.1% of PLHIV* were in care, i.e., had
one or more visits to an HIV care provider as
indicated by a new lab result. The proportion of all
PLHIV who had only a single visit resulting in a lab
was 16.9%. However, it is possible that some had
additional visits in which no lab tests were done.
—–
*PLHIV that died or moved in 2018 were excluded from all analyses of
retention in care.
Figure 4.6: Number of HIV Care Visits per PLHIV,
Alameda County, 2018
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HIV in Alameda County, 2017-2019 35
In 2018, 57.9% of PLHIV were retained in care,
i.e., had two or more visits 90 or more days
apart. Differences by sex were not statistically
significant.
Rates of retention in HIV care were highest
among API (63.1%) and white (60.4%) PLHIV
in 2018. Only 56.0% of Latino and African
American PLHIV were retained in care.
Differences by race/ethnicity were statistically
significant.
PLHIV aged 30 to 39 at the end of 2018 had the
lowest rates of retention in care; younger and
successively older age groups had higher
retention rates. Retention was highest among
those aged 13 to 19 and 60 and over; however,
the number of PLHIV aged 13 to 19 was small.
The general trend of higher retention in older
age groups was statistically significant.
Figure 4.7: Retention in HIV Care by Sex, Alameda County, 2018
NOTE: “Sex” refers to sex assigned at birth.
Figure 4.8: Retention in HIV Care by Race/Ethnicity,
Alameda County, 2018
Figure 4.9: Retention in HIV Care by Age, Alameda County, 2018
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D2C: Re-Engagement Among PLHIV
Retention of PLHIV in HIV care is critical in preventing ongoing transmission of HIV in the
community. A key component of the Alameda County D2C program is identifying PLHIV who
have fallen out of care and re-engaging them in care. One approach to identifying PLHIV who are
OOC is through referrals from their providers who can contact D2C field staff about clients whom
they believe to be OOC based on their own observations. Another approach to identifying OOC
clients is through an analysis of HIV lab and case surveillance data, referred to as the Surveillance
Based List (SBL). Clients who meet specific criteria, which includes a lack of HIV laboratory results
for one year, are periodically identified and assessed for eligibility for D2C re-engagement services.
In 2018 and 2019, the D2C program identified 257 referrals who were deemed OOC either by
provider referral, through the SBL, or other means. These referrals include clients who are first-time
referrals, or those referred previously and found to be OOC again. Of the 257 OOC referrals, 198
were first-time referrals and 59 had been previously referred for re-engagement.
Of these 257 referrals, the D2C program identified 47 (18.2%) clients through the SBL method.
Another 141 (54.8%) PLHIV were referred by a provider who considered them to be OOC. D2C
staff referred 23 (8.9%) as OOC after investigation, surveillance staff referred 5 (1.9%), 14 (5.4%)
clients had another referral source, 5 (1.9%) were referred from out of county, and 5 (1.9%) self-
referred as OOC.
Of the 198 unique, first-time referrals, 101 (51.0%) were African American, 43 (21.7%) were Latino,
32 (16.1%) were white, 12 (6.0%) were API, and 10 (5.0%) were Other/Unknown (multiracial,
American Indian/Alaska Native or unknown race/ethnicity). The most common barriers to
retention in HIV care identified were homelessness (17.7%), mental health issues (14.6%), lack of
health insurance (7.0%), and incarceration (6.0%). Among the 198 unique OOC clients, 59 were
repeat OOC. Barriers to care among repeat OOC included mental health issues (27.1%),
experiencing homelessness (15.25%) and alcohol or drug use (11.8%).
Of the 257 OOC referrals to the D2C program, 133 (51.7%) were re-engaged into care by D2C staff
or found to be in care. D2C staff confirmed that 16 (6.2%) moved out of Alameda County and
referred 17 (6.6%) out of county. There were 16 (6.2%) clients who refused services and 29 (11.2%)
clients who could not be located. Staff found 8 (3.1%) to be deceased, 1 (0.3%) to be not eligible;
and 35 (13.6%) were still being investigated. Of the 47 SBL clients, 5 (10.6%) were found to be in
care by D2C staff; of the 141 provider referrals, 33 (23.4%) were found to be in care and confirmed
to be in care by D2C staff.
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Virologic Status
The final measure along the care continuum is
virologic suppression, defined as a viral load
under 200 copies/mL. For the purposes of these
analyses, an undetectable viral load is defined as
75 copies/mL or less. PLHIV that died or
moved in 2018 were excluded. Disparities in
virologic suppression among PLHIV in care can
suggest possible differences in ART use or
access to care.
Approximately 71.6% of PLHIV were virally
suppressed at their most recent test in 2018, with
the majority being undetectable. Virologic
suppression was not statistically different
between male and female PLHIV.
In 2018, 76.3% and 77.2% of API and white
PLHIV, respectively, were virally suppressed.
Viral suppression was about 7 to 10% lower in
all other racial/ethnic groups. The differences
between racial/ethnic groups were significant.
Similar disparities were seen among those
retained in care (Table 4.9).
Viral suppression rates generally increased as age
increased, ranging from 64.4% among those ages
30 to 39 to 77.0% among those ages 60 and
over. A similar pattern was seen among those
retained in care (Table 4.8).
Figure 4.10: Virologic Status by Sex, Alameda County, 2018
NOTE: “Sex” refers to sex assigned at birth.
Figure 4.11: Virologic Status by Race/Ethnicity,
Alameda County, 2018
Figure 4.12: Virologic Status by Age, Alameda County, 2018
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HIV in Alameda County, 2017-2019 38
A Sankey diagram is useful for showing how PLHIV progressed through the care continuum and reached
viral suppression (Figure 4.13). The width of each bar is proportional to the number of PLHIV represented
by the identified outcome. Starting with all PLHIV at year-end 2017, most were still living in Alameda
County at the end of 2018. A majority of those living in Alameda County for all of 2018 were either engaged
in or retained in care during in 2018 (green) while some were considered out of care (orange). The diagram
shows the proportion of PLHIV engaged in or retained in care that were virally suppressed in 2018 (blue).
Most PLHIV identified as virally unsuppressed were considered out of care, i.e., did not have a viral load or
CD4 test in 2018. Only 17.3% of PLHIV engaged in care and 6.6% of those retained in care were
unsuppressed.
Figure 4.13: Progression Through the Continuum of HIV Care Among PLHIV, Alameda County, 2018
D2C: Viral Suppression Among Out of Care
Virologic suppression is an important metric to monitor clinical outcomes among PLHIV. When
PLHIV fall out of care, they are at risk of discontinuing ART, resulting in increased viral load over
time. The Alameda County D2C program aims to actively identify and re-engage these clients with a
provider who can help the client achieve and maintain viral load suppression. Among PLHIV who
were referred to D2C as OOC, many clients had a viral load that suggested they were not adherent
to their antiretroviral therapy. Among 562 clients who were referred to D2C and who had a viral
load test available, 190 (33.8%) were virally unsuppressed, i.e., had a viral load of 200 copies/mL or
higher. There were 205 (36.4%) clients who were suppressed, i.e. had a viral load lower than 200
copies/mL but higher than 75 copies/mL and there were 146 (25.9%) who were undetectable, i.e.,
had a viral load under 75 copies/mL. These findings indicate that those identified as OOC by the
D2C program have elevated viral load compared to PLHIV in the county as a whole and could be
contributing to ongoing transmission of HIV in the community.
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Table 4.1: Linkage to HIV Care Within 30 Days Among New Diagnoses by Sex and Age,
Alameda County, 2016-2018
Sexa Age at Diagnosis Average Annual
Count
Column Percent Average Annual
Count
Row Percent
All All ages 224.7 100.0%194.3 86.4%
5-12 **0.3 *
13-19 **3.0 *
20-24 29.3 13.1%25.3 **
25-29 45.7 20.3%39.7 86.8%
30-39 62.3 27.7%55.7 89.4%
40-49 44.0 19.6%37.0 84.1%
50 & older 38.3 17.1%33.3 87.0%
Male All ages 191.7 85.3%165.7 86.3%
5-12 **0.0 *
13-19 **2.3 *
20-24 24.7 11.0%21.3 **
25-29 41.3 18.4%36.0 87.2%
30-39 56.0 24.9%49.7 88.7%
40-49 35.0 15.6%29.7 84.8%
50 & older 30.7 13.6%26.7 **
Female All ages 33.0 14.7%28.7 86.9%
5-12 **0.3 *
13-19 **0.7 *
20-24 4.7 2.1%4.0 **
25-29 4.3 1.9%3.7 **
30-39 6.3 2.8%6.0 **
40-49 9.0 4.0%7.3 **
50 & older 7.7 3.4%6.7 **
Source: Alameda County eHARS, 2020 Q2
NOTE: Excludes N=2 persons who died within 30 days of diagnosis
[a]Refers to sex assigned at birth
[*] Some cells suppressed to protect confidentiality
[**] Unstable estimates not shown
New Diagnoses Linked to Care ≤ 30 Days
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Table 4.2: Linkage to HIV Care Within 30 Days Among New Diagnoses by Sex and Race/
Ethnicity, Alameda County, 2016-2018
Sexa Race/Ethnicityb Average Annual
Count
Column Percent Average Annual
Count
Row Percent
All All races 224.7 100.0%194.3 86.4%
AfrAmer 82.0 36.5%69.3 84.6%
White 43.0 19.1%37.3 86.2%
Latino 71.7 31.9%63.7 88.8%
API 21.3 9.5%19.0 **
Other/Unk 6.7 3.0%5.0 **
Male All races 191.7 85.3%165.7 86.3%
AfrAmer 61.3 27.3%51.7 84.3%
White 38.3 17.1%33.3 **
Latino 66.7 29.7%59.3 89.0%
API **17.0 *
Other/Unk **4.3 *
Female All races 33.0 14.7%28.7 86.9%
AfrAmer 20.7 9.2%17.7 85.3%
White 4.7 2.1%4.0 **
Latino 5.0 2.2%4.3 **
API **2.0 *
Other/Unk **0.7 *
Source: Alameda County eHARS, 2020 Q2
NOTE: Excludes N=2 persons who died within 30 days of diagnosis
[a] Refers to sex assigned at birth
[b] 'Other/Unk' = American Indians and Alaskan Natives, multiple race, unknown race
[*] Some cells suppressed to protect confidentiality
[**] Unstable estimates not shown
New Diagnoses Linked to Care ≤ 30 Days
CO
N
T
I
N
U
U
M
O
F
C
A
R
E
HIV in Alameda County, 2017-2019 41
Table 4.3: Linkage to HIV Care Within 30 Days Among New Diagnoses by Race/Ethnicity
and Age, Alameda County, 2016-2018
Race/Ethnicitya Age at Diagnosis Average Annual
Count
Column Percent Average Annual
Count
Row Percent
All races All ages 224.7 100.0%194.3 86.5%
5-12 **0.3 *
13-19 **3.0 *
20-24 29.3 13.1%25.3 86.4%
25-29 45.7 20.3%39.7 86.9%
30-39 62.3 27.7%55.7 89.3%
40-49 44.0 19.6%37.0 83.5%
50 & older 38.3 17.1%33.3 87.0%
AfrAmer All ages 82.0 36.5%69.3 84.6%
5-12 **0.3 *
13-19 **1.7 *
20-24 13.3 5.9%11.3 **
25-29 15.3 6.8%13.3 **
30-39 17.3 7.7%15.0 **
40-49 14.0 6.2%11.3 **
50 & older 19.0 8.5%16.3 **
White All ages 43.0 19.1%37.3 100.0%
5-12 0.0 0.0%0.0 **
13-19 0.0 0.0%0.0 **
20-24 3.0 1.3%2.0 **
25-29 7.7 3.4%6.7 **
30-39 14.7 6.5%13.3 **
40-49 8.7 3.9%7.0 **
50 & older 9.0 4.0%8.3 92.6%
Latino All ages 71.7 31.9%63.7 88.8%
5-12 **0.0 *
13-19 **1.0 *
20-24 9.3 4.2%8.7 **
25-29 16.7 7.4%14.7 **
30-39 22.7 10.1%20.3 **
40-49 16.0 7.1%14.3 **
50 & older 5.7 2.5%4.7 **
API All ages 21.3 9.5%19.0 **
5-12 0.0 0.0%0.0 **
13-19 **0.3 *
20-24 **2.7 *
25-29 4.0 1.8%3.3 **
30-39 5.7 2.5%5.0 **
40-49 **4.0 *
50 & older **3.7 *
Other/Unk All ages 6.7 3.0%5.0 **
5-12 0.0 0.0%0.0 **
13-19 0.0 0.0%0.0 **
20-24 **0.7 *
25-29 2.0 0.9%1.7 **
30-39 2.0 0.9%2.0 100.0%
40-49 **0.3 *
50 & older **0.3 *
Source: Alameda County eHARS, 20120 Q2
NOTE: Excludes N=2 persons who died within 30 days of diagnosis
[a] 'Other/Unk' = American Indians and Alaskan Natives, multiple race, unknown race
[*] Some cells suppressed to protect confidentiality
[**] Unstable estimates not shown
New Diagnoses Linked to Care ≤ 30 Days
CO
N
T
I
N
U
U
M
O
F
C
A
R
E
HIV in Alameda County, 2017-2019 42
Table 4.4: Linkage to HIV Care Within 90 Days Among New Diagnoses, Alameda County, 2016-2018
Table 4.5: Any Evidence of Care in 2018 Among PLHIV at Year-End 2017 by Sex and Age, Alameda County
Characteristic Category Average Annual
Count
Column Percent Average Annual
Count
Row Percent
All newly diagnosed 224.7 100.0%202 89.9%
Sexa Male 191.7 85.3%174.0 90.8%
Female 33.0 14.7%28.0 84.8%
Race/Ethnicityb AfrAmer 82.0 36.5%71.0 86.6%
White 43.0 19.1%39.3 91.5%
Latino 71.7 31.9%67.3 94.0%
API 21.3 9.5%18.7 **
Other/Unk 6.7 3.0%5.7 **
Age (years)c 5-12 **0.3 *
13-19 **4.3 *
20-24 29.3 13.1%27.7 85.9%
25-29 45.7 20.3%41.0 98.8%
30-39 62.3 27.7%57.0 91.4%
40-49 44.0 19.6%37.7 85.6%
50 & older 38.3 17.1%34.0 88.7%
Source: Alameda County eHARS, 2020 Q2
NOTE: Excludes N=3 persons who died within 90 days of diagnosis
[a] Refers to sex assigned at birth
[b] 'Other/Unk' = American Indians and Alaskan Natives, multiple race, unknown race
[*] Some cells suppressed to protect confidentiality
[**] Unstable estimates not shown
New Diagnoses Linked to Care ≤ 90 Days
CO
N
T
I
N
U
U
M
O
F
C
A
R
E
Sexa Age Count Column Percent Count Row Percent
All All ages 5,750 100.0%4,549 79.1%
0-12 ****
13-19 ****
20-29 339 5.9%272 80.2%
30-39 876 15.2%656 74.9%
40-49 1,205 21.0%931 77.3%
50-59 1,896 33.0%1,523 80.3%
60 & older 1,415 24.6%1,149 81.2%
Female All ages 947 16.5%729 77.0%
0-12 ****
13-19 ****
20-29 39 0.7%28 71.8%
30-39 120 2.1%87 72.5%
40-49 226 3.9%169 74.8%
50-59 302 5.3%245 81.1%
60 & older 251 4.4%191 76.1%
Male All ages 4,803 83.5%3,820 79.5%
0-12 ****
13-19 ****
20-29 300 5.2%244 81.3%
30-39 756 13.2%569 75.3%
40-49 979 17.0%762 77.8%
50-59 1,594 27.7%1,278 80.2%
60 & older 1,164 20.2%958 82.3%
Source: Alameda County eHARS, 2020 Q2
NOTE: Excludes PLHIV at year-end 2017 who died (N=79) or moved out of the county (N=483) in 2018.
[a] Refers to sex assigned at birth.
[*] Some cells suppressed to protect confidentiality
All PLHIV Any Visit in 2018
HIV in Alameda County, 2017-2019 43
Table 4.6: Any Evidence of Care in 2018 Among PLHIV at Year-End 2017 by Sex and
Race/Ethnicity, Alameda County
CO
N
T
I
N
U
U
M
O
F
C
A
R
E
Sexa Race/Ethnicityb Count Column Percent Count Row Percent
All All races 5,750 100.0%4,549 79.1%
AfrAmer 2,227 38.7%1,731 77.7%
White 1,763 30.7%1,443 81.8%
Latino 1,134 19.7%863 76.1%
API 396 6.9%322 81.3%
Other/Unk 230 4.0%190 82.6%
Male All races 4,803 83.5%3,820 79.5%
AfrAmer 1,662 28.9%1,292 77.7%
White 1,616 28.1%1,327 82.1%
Latino 987 17.2%758 76.8%
API 340 5.9%275 80.9%
Other/Unk 198 3.4%168 84.8%
Female All races 947 16.5%729 77.0%
AfrAmer 565 9.8%439 77.7%
White 147 2.6%116 78.9%
Latino 147 2.6%105 71.4%
API 56 1.0%47 **
Other/Unk 32 0.6%22 **
Source: Alameda County eHARS, 2020 Q2
NOTE: Excludes PLHIV at year-end 2017 who died (N=79) or moved out of the county (N=483) in 2018.
[a] Refers to sex assigned at birth.
[b] 'Other/Unk' = American Indians and Alaskan Natives, multiple race, unknown race.
[**] Unstable estimates not shown.
All PLHIV Any Visit in 2018
HIV in Alameda County, 2017-2019 44
Table 4.7: Any Evidence of Care in 2018 Among PLHIV at Year-End 2017 by Race/
Ethnicity and Age, Alameda County
CO
N
T
I
N
U
U
M
O
F
C
A
R
E
Race/Ethnicitya Age Count Column Percent Count Row Percent
All races All ages 5,750 100.0%4,549 79.1%
0-12 ****
13-19 ****
20-29 339 5.9%272 80.2%
30-39 876 15.2%656 74.9%
40-49 1,205 21.0%931 77.3%
50-59 1,896 33.0%1,523 80.3%
60 & older 1,415 24.6%1,149 81.2%
AfrAmer All ages 2,227 38.7%1,731 77.7%
0-12 ****
13-19 ****
20-29 169 2.9%135 79.9%
30-39 347 6.0%253 72.9%
40-49 408 7.1%316 77.5%
50-59 718 12.5%571 79.5%
60 & older 573 10.0%444 77.5%
White All ages 1,763 30.7%1,443 81.8%
0-12 ****
13-19 ****
20-29 43 0.8%33 76.7%
30-39 173 3.0%132 76.3%
40-49 303 5.3%243 80.2%
50-59 685 11.9%566 82.6%
60 & older 557 9.7%468 84.0%
Latino All ages 1,134 19.7%863 76.1%
0-12 ****
13-19 ****
20-29 78 1.4%64 82.1%
30-39 232 4.0%174 75.0%
40-49 318 5.5%230 72.3%
50-59 328 5.7%250 76.2%
60 & older 174 3.0%141 81.0%
API All ages 396 6.9%322 81.3%
0-12 ****
13-19 ****
20-29 33 0.6%28 84.8%
30-39 78 1.4%62 79.5%
40-49 121 2.1%95 78.5%
50-59 99 1.7%82 82.8%
60 & older 64 1.1%54 84.4%
Other/Unk All ages 230 4.0%190 82.6%
0-12 ****
13-19 ****
20-29 16 0.3%12 75.0%
30-39 46 0.8%35 76.1%
40-49 55 1.0%47 85.5%
50-59 66 1.2%54 81.8%
60 & older 47 0.8%42 89.4%
Source: Alameda County eHARS, 2020 Q2
NOTE: Excludes PLHIV at year-end 2017 who died (N=79) or moved out of the county (N=483) in 2018.
[a] 'Other/Unk' = American Indians and Alaskan Natives, multiple race, unknown race.
[*] Some cells suppressed to protect confidentiality
All PLHIV Any Visit in 2018
HIV in Alameda County, 2017-2019 45
Table 4.8: Retention in Continuous HIV Care in 2018 Among PLHIV at Year-End 2017 by
Sex and Age, Alameda County
CO
N
T
I
N
U
U
M
O
F
C
A
R
E
Sexa Age Count Column Percent Count Row Percent
All All ages 5,750 100.0%3,326 57.8%
0-12 ****
13-19 ****
20-29 417 6.6%221 53.0%
30-39 900 13.4%446 49.6%
40-49 1,273 21.1%700 55.0%
50-59 1,883 33.6%1,117 59.3%
60 & older 1,253 24.8%824 65.8%
Female All ages 947 16.5%534 56.4%
0-12 ****
13-19 ****
20-29 46 0.8%25 54.4%
30-39 124 2.2%69 56.7%
40-49 246 4.3%133 54.1%
50-59 290 5.0%164 56.6%
60 & older 231 4.0%136 58.9%
Male All ages 4,803 83.5%2,792 58.1%
0-12 ****
13-19 ****
20-29 371 6.5%196 52.8%
30-39 776 13.5%377 48.6%
40-49 1,027 17.9%567 55.2%
50-59 1,593 27.7%953 59.8%
60 & older 1,022 17.8%688 67.3%
Source: Alameda County eHARS, 2020 Q2
NOTE: Excludes PLHIV at year-end 2017 who died (N=79) or moved out of the county (N=483) in 2018.
[a] Refers to sex assigned at birth.
[*] Some cells suppressed to protect confidentiality
All PLHIV Retained 2018
HIV in Alameda County, 2017-2019 46
Table 4.9: Retention in Continuous HIV Care in 2018 Among PLHIV at Year-End 2017 by
Sex and Race/Ethnicity, Alameda County
CO
N
T
I
N
U
U
M
O
F
C
A
R
E
Sexa Race/Ethnicityb Count Column Percent Count Row Percent
All All races 5,750 100.0%3,326 57.8%
AfrAmer 2,227 38.7%1,244 55.9%
White 1,763 30.7%1,065 60.4%
Latino 1,134 19.7%635 56.0%
API 396 6.9%250 63.1%
Other/Unk 230 4.0%132 57.4%
Male All races 4,803 83.5%2,792 58.1%
AfrAmer 1,662 28.9%931 56.0%
White 1,616 28.1%983 60.8%
Latino 987 17.2%550 55.7%
API 340 5.9%213 62.6%
Other/Unk 198 3.4%115 58.1%
Female All races 947 16.5%534 56.4%
AfrAmer 565 9.8%313 55.4%
White 147 2.6%82 55.8%
Latino 147 2.6%85 57.8%
API 56 1.0%37 **
Other/Unk 32 0.6%17 **
Source: Alameda County eHARS, 2020 Q2
NOTE: Excludes PLHIV at year-end 2017 who died (N=79) or moved out of the county (N=483) in 2018.
[a] Refers to sex assigned at birth.
[b] 'Other/Unk' = American Indians and Alaskan Natives, multiple race, unknown race.
[**] Unstable estimates not shown.
All PLHIV Retained 2018
HIV in Alameda County, 2017-2019 47
Table 4.10: Retention in Continuous HIV Care in 2018 Among PLHIV at Year-End 2017 by
Race/Ethnicity and Age, Alameda County
CO
N
T
I
N
U
U
M
O
F
C
A
R
E
Race/Ethnicitya Age Count Column Percent Count Row Percent
All races All ages 5,750 100.0%3,326 57.8%
0-12 ****
13-19 ****
20-29 417 6.6%221 53.0%
30-39 900 13.4%446 49.6%
40-49 1,273 21.1%700 55.0%
50-59 1,883 33.6%1,117 59.3%
60 & older 1,253 24.8%824 65.8%
AfrAmer All ages 2,227 38.7%1,244 55.9%
0-12 ****
13-19 ****
20-29 199 3.5%97 48.7%
30-39 353 6.1%171 48.4%
40-49 438 7.6%235 53.7%
50-59 715 12.4%418 58.5%
60 & older 507 8.8%311 61.3%
White All ages 1,763 30.7%1,065 60.4%
0-12 ****
13-19 ****
20-29 59 1.0%29 49.2%
30-39 183 3.2%77 42.1%
40-49 330 5.7%204 61.8%
50-59 696 12.1%418 60.1%
60 & older 493 8.6%337 68.4%
Latino All ages 1,134 19.7%635 56.0%
0-12 ****
13-19 ****
20-29 100 1.7%60 60.0%
30-39 238 4.1%126 52.9%
40-49 322 5.6%159 49.4%
50-59 311 5.4%176 56.6%
60 & older 157 2.7%109 69.4%
API All ages 396 6.9%250 63.1%
0-12 ****
13-19 ****
20-29 37 0.6%27 73.0%
30-39 82 1.4%48 58.5%
40-49 122 2.1%68 55.7%
50-59 95 1.7%65 68.4%
60 & older 59 1.0%41 69.5%
Other/Unk All ages 230 4.0%132 57.4%
0-12 ****
13-19 ****
20-29 22 0.4%8 36.4%
30-39 44 0.8%24 54.5%
40-49 61 1.1%34 55.7%
50-59 66 1.1%40 60.6%
60 & older 37 0.6%26 70.3%
Source: Alameda County eHARS, 2020 Q2
NOTE: Excludes PLHIV at year-end 2017 who died (N=79) or moved out of the county (N=483) in 2018.
[a] 'Other/Unk' = American Indians and Alaskan Natives, multiple race, unknown race.
[*] Some cells suppressed to protect confidentiality
All PLHIV Retained 2018
HIV in Alameda County, 2017-2019 48
Table 4.11: Viral Suppression in 2018 Among PLHIV at Year-End 2017 by Sex and Age, Alameda County
CO
N
T
I
N
U
U
M
O
F
C
A
R
E
Sexa Age Count Column Percent Count Row Percent
All All ages 5,750 100.0%4,117 71.6%
0-12 ****
13-19 ****
20-29 417 6.6%279 66.9%
30-39 900 13.4%580 64.4%
40-49 1,273 21.1%881 69.2%
50-59 1,883 33.6%1,393 74.0%
60 & older 1,253 24.8%965 77.0%
Female All ages 947 16.5%650 68.6%
0-12 ****
13-19 ****
20-29 46 0.8%22 47.8%
30-39 124 2.2%77 62.1%
40-49 246 4.3%163 66.3%
50-59 290 5.0%208 71.7%
60 & older 231 4.0%172 74.5%
Male All ages 4,803 83.5%3,467 72.2%
0-12 ****
13-19 ****
20-29 371 6.5%257 69.3%
30-39 776 13.5%503 64.8%
40-49 1,027 17.9%718 69.9%
50-59 1,593 27.7%1,185 74.4%
60 & older 1,022 17.8%793 77.6%
Source: Alameda County eHARS, 2020 Q2
NOTE: Excludes PLHIV at year-end 2017 who died (N=78) or moved out of the county (N=428) in 2018
[a] Refers to sex assigned at birth
[*] Some cells suppressed to protect confidentiality
All PLHIV Suppressed at Last Viral
Load in 2018
HIV in Alameda County, 2017-2019 49
CO
N
T
I
N
U
U
M
O
F
C
A
R
E
Table 4.12: Viral Suppression in 2018 Among PLHIV at Year-End 2017 by Sex
and Race/Ethnicity, Alameda County
Sexa Race/Ethnicityb Count Column Percent Count Row Percent
All All races 5,750 100.0%4,117 71.6%
AfrAmer 2,227 38.7%1,506 67.6%
White 1,763 30.7%1,361 77.2%
Latino 1,134 19.7%782 69.0%
API 396 6.9%302 76.3%
Other/Unk 230 4.0%166 72.2%
Male All races 4,803 83.5%3,467 72.2%
AfrAmer 1,662 28.9%1,118 67.3%
White 1,616 28.1%1,256 77.7%
Latino 987 17.2%687 69.6%
API 340 5.9%259 76.2%
Other/Unk 198 3.4%147 74.2%
Female All races 947 16.5%650 68.6%
AfrAmer 565 9.8%388 68.7%
White 147 2.6%105 71.4%
Latino 147 2.6%95 64.6%
API 56 1.0%43 **
Other/Unk 32 0.6%19 **
Source: Alameda County eHARS, 2020 Q2
NOTE: Excludes PLHIV at year-end 2017 who died (N=79) or moved out of the county (N=483) in 2018.
[a] Refers to sex assigned at birth.
[b] 'Other/Unk' = American Indians and Alaskan Natives, multiple race, unknown race.
[**] Unstable estimates not shown.
All PLHIV Suppressed at Last Viral
Load in 2018
HIV in Alameda County, 2017-2019 50
CO
N
T
I
N
U
U
M
O
F
C
A
R
E
Table 4.13: Viral Suppression in 2018 Among PLHIV at Year-End 2017 by Race/
Ethnicity and Age, Alameda County
Race/Ethnicitya Age Count Column Percent Count Row Percent
All race All ages 5,750 100.0%4,117 71.6%
0-12 ****
13-19 ****
20-29 417 6.6%279 66.9%
30-39 900 13.4%580 64.4%
40-49 1,273 21.1%881 69.2%
50-59 1,883 33.6%1,393 74.0%
60 & older 1,253 24.8%965 77.0%
AfrAmer All ages 2,227 38.7%1,506 67.6%
0-12 ****
13-19 ****
20-29 199 3.5%120 60.3%
30-39 353 6.1%216 61.2%
40-49 438 7.6%288 65.8%
50-59 715 12.4%503 70.3%
60 & older 507 8.8%367 72.4%
White All ages 1,763 30.7%1,361 77.2%
0-12 ****
13-19 ****
20-29 59 1.0%42 71.2%
30-39 183 3.2%122 66.7%
40-49 330 5.7%252 76.4%
50-59 696 12.1%546 78.4%
60 & older 493 8.6%398 80.7%
Latino All ages 1,134 19.7%782 69.0%
0-12 ****
13-19 ****
20-29 100 1.7%74 74.0%
30-39 238 4.1%153 64.3%
40-49 322 5.6%205 63.7%
50-59 311 5.4%222 71.4%
60 & older 157 2.7%123 78.3%
API All ages 396 6.9%302 76.3%
0-12 ****
13-19 ****
20-29 37 0.6%31 83.8%
30-39 82 1.4%58 70.7%
40-49 122 2.1%91 74.6%
50-59 95 1.7%74 77.9%
60 & older 59 1.0%47 79.7%
Other/Unknown All ages 230 4.0%166 72.2%
0-12 ****
13-19 ****
20-29 22 0.4%12 54.5%
30-39 44 0.8%31 70.5%
40-49 61 1.1%45 73.8%
50-59 66 1.1%48 72.7%
60 & older 37 0.6%30 81.1%
Source: Alameda County eHARS, 2020 Q2
NOTE: Excludes PLHIV at year-end 2017 who died (N=79) or moved out of the county (N=483) in 2018.
[a] 'Other/Unk' = American Indians and Alaskan Natives, multiple race, unknown race.
[*] Some cells suppressed to protect confidentiality
All PLHIV Suppressed at Last Viral Load in
2018
HIV in Alameda County, 2017-2019 51
Table 4.14: Viral Suppression in 2018 Among PLHIV at Year-End 2017 and In Care in 2017
by Sex, Alameda County
Table 4.15: Viral Suppression in 2018 Among PLHIV at Year-End 2017 and In Care in 2017
by Race/Ethnicity, Alameda County
CO
N
T
I
N
U
U
M
O
F
C
A
R
E
Sexa Count Column Percent Count Row Percent
All 4,549 100.0%4,117 90.5%
Male 3,820 84.0%3,467 90.8%
Female 729 16.0%650 89.2%
Source: Alameda County eHARS, 2020 Q2
[a] Refers to sex assigned at birth
All PLHIV Suppressed at Last Viral Load in
2018
NOTE: Excludes PLHIV at year-end 2017 who died (N=78) or moved out of the county (N=428), or did not have any
HIV labs reported (N=1201) in 2018
Race/Ethnicitya Count Column Percent Count Row Percent
All races 4,549 100.0%4,117 90.5%
AfrAmer 1,731 38.1%1,506 87.0%
White 1,443 31.7%1,361 94.3%
Latino 863 19.0%782 90.6%
API 322 7.1%302 93.8%
Other/Unk 190 4.2%166 87.4%
Source: Alameda County eHARS, 2020 Q2
[a] 'Other/Unk' = American Indians and Alaskan Natives, multiple race, unknown race
All PLHIV Suppressed at Last Viral Load in
2018
NOTE: Excludes PLHIV at year-end 2017 who died (N=78) or moved out of the county (N=428), or did not have any
HIV labs reported (N=1201) in 2018
HIV in Alameda County, 2017-2019 52
Transgender
Transgender is an umbrella term used to describe a population whose gender identity differs from their sex
at birth. Transgender people face high levels of discrimination, exclusion from employment, and social
marginalization, resulting in increased rates of poverty, substance use, and barriers to healthcare. As a result
of these intersecting factors that influence all stages of HIV diagnosis, treatment, and the care continuum,
transgender people experience unique vulnerability to HIV.
Epidemiologic data shows that the transgender community carries a disproportionately high HIV burden
compared to other groups.8 However, attempts to characterize the specifics of such burden is often
hindered by the lack of accurate transgender data in healthcare.9 Historically, systems for collecting and
sharing medical data did not always have distinct fields to describe birth sex, current gender, or transgender
status. In addition, risk of stigmatization and discrimination may prevent transgender people from seeking
out healthcare or accurately disclosing their gender to providers. Transgender PLHIV is a critical population
that deserves more visibility as they are likely to be underestimated in routine surveillance and experience a
significant HIV burden.
A national systemic review in 2019 estimated 14% of transwomen and 3% of transmen are living with
HIV.10 Based on testing reported to CDC, the percentage of transgender people who received a new HIV
diagnosis was three times the national average in 2017.11 Transgender people of color make up the majority
of HIV diagnoses among all transgender people in the United States. In California, transgender PLHIV
report lower rates of linkage, retention, and viral suppression compared to newly diagnosed PLHIV overall.
Based on 2017 data, 75% of transgender PLHIV were linked to care within 12 months, 58% retained in
care, and 59% achieved viral suppression12 compared to 90% linked in 12 months, 74% retained, and 72%
suppressed overall.13
In Alameda County, surveillance data showed 131 transgender PLHIV at year-end 2019; the true count is
likely higher due to reasons outlined above. Over half were African American, 22.1% identified as Latino,
13% white, and 3.8% API. Ninety-two percent identified as male-to-female and 8% identified as female-to-
Key Populations
• Transgender
• People Who Inject Drugs
• Non-US-Born
• Men Who Have Sex with Men
• Young People of Color
HIV in Alameda County, 2017-2019 53
male. Among transgender PLHIV diagnosed between 2016 to 2018, 83.3% were linked to care within 30
days and 91.7% were linked to care within 90 days. Linkage rates were lower compared to the overall newly
diagnosed population in Alameda County during the same period. Retention and viral suppression
outcomes among transgender PLHIV were also poorer compared to overall PLHIV. Seventy-one percent
of transgender PLHIV had evidence of care in 2018, 51% were retained in care, and 60% were virally
suppressed. In comparison, among Alameda County PLHIV at year-end 2018, 79.1% had evidence of care,
57.9% were retained in care, and 71.6% were virally suppressed.
People Who Inject Drugs
People who inject drugs (PWID) experience a greater burden of HIV compared to other groups as they
have a greater risk for acquiring HIV and limited access to treatment or prevention services. Risk for HIV is
increased through the practices of sharing needles, syringes, and other drug use equipment, and higher
likelihood to engage in unsafe sexual practices including condomless sex, sex with multiple partners, and
exchanging sex for drugs. All these practices can also result in elevated risk for acquiring and transmitting
hepatitis B, hepatitis C, and other bloodborne infections in addition to HIV. The common overlap between
PWID populations and people who experience homelessness or incarceration brings into play social
obstacles such as stigma and legal barriers that further hinder access to services for these marginalized
groups. For all these reasons, PWID is a key population for HIV prevention.
Since 2013, the opioid and heroin epidemic has resulted in increased numbers of PWID throughout the
country, increasing the population at risk for HIV. Furthermore, the rise in PWID in nonurban areas has
generated new prevention challenges—fewer specialty providers practice in rural settings and both patients
and providers may be less familiar with advances in ART.14
According to the CDC, there are more than 122,000 PWID living with HIV in 2018, of which 46% are
Black, 27% are Latino, and 21% are white.15 PWID account for about 1 in 15 new HIV diagnoses in the
US. Within California, PWID made up 5.9% of an estimated 153,000 PLHIV in year-end 2017. Although
linkage rates do not differ significantly from the statewide average, viral suppression in six months among
PWID is the lowest of all transmission categories.16
NOTE: Excludes persons who died within 30 days of diagnosis (N=2).
Figure 5.1: Linkage to HIV Care Among Transgender,
Alameda County, 2016-2018
Figure 5.2: Engagement in HIV Care and Virologic Status
Among Transgender PLHIV, Alameda County, 2018
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Alameda County was alerted of a time-space HIV cluster among PWID occurring across 2019. This type of
cluster is defined as an increase in the number of diagnoses in a geographic area to levels above expected
based on previous patterns, meaning that a significant rise in HIV diagnosis among PWID was detected at
the local level. With a growing population both nationally and locally, worse reported health outcomes, and
complex social and structural barriers, PWID is a crucial subpopulation of PLHIV in the county.
Over the years 2010 to 2018, the number of new HIV diagnoses among PWID in Alameda County hovered
around an average of 10 per year. However, 2019 saw a spike of 19 new cases, prompting a specific
examination of the epidemic among the local PWID population. Prominent characteristics of newly
diagnosed PWID from 2017 to 2019 were: male (57.1%), African American (45.7%), and aged 30 to 39
(31.4%).
Figure 5.3: New Diagnoses Among PWID, Alameda County, 2006-2019
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In the years 2016 to 2018, 65.2% of newly diagnosed
PWID were linked to care within 30 days and 78.3%
were linked within 90 days, which is significantly lower
than the overall newly diagnosed population during the
same period. Fifty-one percent of PLHIV who inject
drugs and resided in Alameda County for the entirety
of 2018 had two or more visits 90 or more days apart
in that year and were considered retained in care,
compared to 57.9% of the overall PLHIV population.
Viral suppression among PLHIV who inject drugs was
60% compared to 71.6% of PLHIV in the county.
Figure 5.4: Retention in HIV Care and Virologic Status
Among PWID, Alameda County, Year-End 2018
HIV in Alameda County, 2017-2019 55
PLHIV* who inject drugs showed lower levels of engagement in care on all measures along the continuum
of care compared to PLHIV in the county.
Non-US-Born
Non-US-born persons face a variety of challenges that put them at risk of developing HIV or facing barriers
to receiving appropriate HIV care. The challenges experienced by non-US-born persons include lack of
acculturation, discrimination, and language barriers. All these issues combined may negatively impact or
obstruct their ability to access affordable and culturally competent health care, employment, education, and
housing. Some studies show that non-US-born persons are more likely to hold lower wage jobs and are less
likely to have health insurance through their employer. Further, 23% of documented immigrants were
uninsured and 45% of undocumented immigrants were uninsured.17
According to the US Census, non-US-born persons made up 13% of the US population in 2010. In the
same year, non-US-born persons comprised 16% of all persons living with HIV.18 In Alameda County, there
were approximately 541,000 non-US-born persons, which made up nearly one-third of its population of 1.6
million people in 2019.19 Among the 6,350 people living with HIV at year-end 2019 in Alameda County, one
-fifth were non-US-born. Thus non-US-born persons are a key population with regards to risk and burden
of HIV. Data on nativity status can help in describing the need for culturally appropriate HIV services for
non-US-born persons.
Among 633 new HIV diagnoses from 2017 to 2019 in Alameda County, a quarter (25.3%) were born in
another country. US-born persons comprised 48.6% and persons with unknown country of birth comprised
25.9%. Of the 161 non-US-born new HIV diagnoses, 57.1% came from Central or South America, 24.2%
——
*Those who met the criteria of injection drug use as a risk factor for transmission at the time of HIV diagnosis were considered PWID.
Transmission risk factors such as MSM, heterosexual contact, perinatal exposure, and injection drug use were assessed at the time of diagnosis.
Analysis of PWID as a risk factor among PLHIV should be interpreted with caution as it may not represent current risk—which is not assessed in
routine case surveillance and could potentially be a more reliable indicator of transmission risk. Consequently, those in the PWID category may not
have consistently met or be currently meeting the definition of IDU as a risk factor, but this nuance is not distinguishable in the presented analyses.
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Figure 5.5: Nativity Status and Region of Origin Among
Newly Diagnosed, Alameda County, 2017-2019
NOTE: N= 633 newly diagnosed.
Figure 5.6: Nativity Status and Region of Origin
Among PLHIV, Alameda County, 2017-2019
NOTE: N= 6,350 PLHIV.
HIV in Alameda County, 2017-2019 56
came from Asia, followed by 17.3% from Africa and 1.2% from Oceania. The top three countries of birth
were Mexico, the Philippines, and India with 33.5%, 6.2%, and 5.5% of non-US-born new diagnoses
respectively.
Between 2017 and 2019 there were 6,350 PLHIV in Alameda County. Of these, 4,399 (69.2%) were US-
born, 1,292 (20.3%) were non-US-born and 659 (10.3%) had unknown country of birth. Non-US-born
PLHIV were primarily from Central or South America (52.6%), followed by Asia (24.1%), Africa (17.5%),
Europe (4.7%) and Oceania (0.8%) regions. Among non-US-born PLHIV, Mexico (32.3%), the Philippines
(6.5%) and Ethiopia (4.9%) were the top three countries of birth.
Latinos comprised 57.1% of all non-US-born persons newly diagnosed with HIV. The next largest racial/
ethnic group was API (24.2%), followed by Blacks originating from Africa and other regions (16.7%). Non-
US-born PLHIV had a similar racial/ethnic distribution—the largest group was Latino (51.3%) followed by
API (20.6%) and Blacks originating from Africa and other regions (18.8%).
Figure 5.7: Race/Ethnicity Among Non-US-Born Newly
Diagnosed in Alameda County, 2017-2019
Figure 5.8: Race/Ethnicity Among Non-US-Born PLHIV in
Alameda County, 2017-2019
NOTES: 1) N=161 newly diagnosed
2) “AfrAmer” refers to Blacks originating from Africa and
other regions for non-US-born.
NOTES: 1) N=1,292 PLHIV
2) “AfrAmer” refers to Blacks originating from Africa and
other regions for non-US-born.
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Those aged 30 to 39 comprised 39.7% of newly diagnosed non-US-born persons followed by those 40 to 49
(23.6%), and those aged 20 to 29 (21.7%). Non-US-born PLHIV had a similar age distribution. Persons
aged 30 to 39 (38.6%) were the largest group, followed by those aged 20 to 29 (28.5%) and 40 to 49
(19.3%).
Figure 5.9: Age at HIV Diagnosis Among Non-US-Born
New Diagnoses, Alameda County, 2017-2019
NOTE: N=161 newly diagnosed.
Figure 5.10: Age Among Non-US-Born PLHIV,
Alameda County, 2017-2019
Figure 5.11: Transmission Category Among Newly Diagnosed,
Non-US-Born Males, Alameda County, 2017-2019
NOTE: N=1,292 PLHIV.
Figure 5.12: Transmission Category Among Newly Diagnosed,
Non-US-Born Females, Alameda County, 2017-2019
NOTE: N=136 Non-US-born males. NOTE: N=25 Non-US-born females.
From 2017 to 2019, the most common mode of transmission for new HIV diagnoses among non-US-born
males was MSM (78.7%). For new diagnoses among non-US-born females, presumed (48.0%) or reported
heterosexual contact (36.0%) were the predominant modes of transmission.
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From 2016 to 2018, 88.8% of US-born newly diagnosed persons were linked to care within 90 days of
diagnosis, including labs done on the diagnosis date. During the same period, 91.7% of non-US-born newly
diagnosed persons were linked to care within 90 days of diagnosis, including labs done on the diagnosis
date.
Of newly diagnosed US-born persons 85.1% were linked to care within 30 days of diagnosis including labs
done on the diagnosis date compared to 87.5% of newly diagnosed non-US-born persons. Excluding labs
done on diagnosis date, 75.5% of US-born persons were linked to care within 30 days compared to 64.5%
of non-US-born persons.
Among PLHIV, 58.4% of US-born persons were retained in care, compared to 54.8% of non-US-born
persons. With regards to viral suppression, 72.5% of US-born persons were virally suppressed, compared to
68.2% of non-US-born persons.
Men Who Have Sex With Men
Local, state, and national data indicate that men who have sex with men are at an increased risk of acquiring
HIV. A recent study has shown that overall incidence of HIV has decreased between 2008 and 2015 in all
transmission risk groups except for MSM.20 In 2013, the US Preventative Services Task Force
recommended annual screening for MSM in contrast to just once for all persons aged 13 to 64 who were
not considered at higher risk. In Alameda County from 2017 to 2019, 76% of newly diagnosed cases had a
transmission risk category of MSM.
Figure 5.13: Linkage Within 30 Days Among Non-US-
Born, Alameda County, 2016-2018
Figure 5.14: Retention in Care and Viral Suppression for
US-Born and Non-US-Born, Alameda County, 2016-2018
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Among the 633 new diagnoses from 2017 to
2019, 391 had a risk category of MSM. Among
those identified as MSM, 37.9% were Latino and
28.1% were African American. This contrasts
with other transmission risk categories among
men which were 19.2% Latino and 48.6%
African American.
The age distribution among newly diagnosed
MSM was much younger with 76% under the
age of 40. In contrast among newly diagnosed
males not identified as MSM only 45.9% were
under the age of 40 at diagnosis.
The rate of late diagnosis was higher among
newly diagnosed non-MSM males (26.4%) than
MSM males (18.5%).
Figure 5.15: Race/Ethnicity of MSM and Non-MSM Among New
Diagnoses, Alameda County, 2017-2019
NOTE: Male as defined by sex assigned at birth.
Figure 5.16: Age at Diagnosis of MSM and Non-MSM
Among New Diagnoses, Alameda County, 2017-2019
Figure 5.17: Late Diagnosis Rates of MSM and Non-MSM Among
Newly Diagnosed, Alameda County, 2016-2018
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NOTE: Male as defined by sex assigned at birth.
NOTE: Male as defined by sex assigned at birth.
HIV in Alameda County, 2017-2019 60
The racial/ethnic distribution among male
PLHIV largely mirrored that for those newly
diagnosed. However, while the proportion of
newly diagnosed males who were white was
approximately equal for MSM and non-MSM
(19.7% and 20.5%, respectively), that proportion
diverged among PLHIV—35.5% of MSM were
white compared to 23.5% of non-MSM males.
Among males living with HIV, a greater portion
of MSM (28.5%) were under the age of 40 than
non-MSM (17.1%).
MSM in Alameda County were linked to care
within 30 days of diagnosis at higher rates than
non-MSM males. This was consistent across
racial/ethnic groups except in the case of
African Americans, where 84.9% of non-MSM
African American males were linked to care
compared to 83.9% who of MSM African
American males.
Figure 5.18: Race/Ethnicity of MSM and Non-MSM Among PLHIV,
Alameda County, Year-End 2019
Figure 5.19: Age of MSM and Non-MSM Among PLHIV,
Alameda County, Year-End 2019
Figure 5.20: Race/Ethnicity and Linkage to Care in 30 Days of MSM and
Non-MSM Among Newly Diagnosed, Alameda County, 2016-2018
NOTES: 1) Male as defined by sex assigned at birth
2) Includes labs at diagnosis date.
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NOTE: Male as defined by sex assigned at birth.
NOTE: Male as defined by sex assigned at birth.
HIV in Alameda County, 2017-2019 61
MSM were linked to care at higher rates than non-
MSM males in all age groups with the exception of
the 20 to 29 year age group.
Figure 5.21: Age Group and Linkage to Care in 30 Days of MSM and
Non-MSM Among Newly Diagnosed, Alameda County, 2016-2018
Figure 5.22: Evidence of Care and Retention in Care of MSM
and Non-MSM Among PLHIV, Alameda County, Year-End 2018
Evidence of being in care and retention in care were
slightly higher among MSM than non-MSM males in
2018.
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Figure 5.23: Viral Suppression of MSM and Non-MSM Among PLHIV,
Alameda County, Year-End 2018
Viral suppression was higher among MSM (74.3%)
than non-MSM males (66.2%).
NOTE: Male as defined by sex assigned at birth.
NOTE: Male as defined by sex assigned at birth.
NOTE: Male as defined by sex assigned at birth.
HIV in Alameda County, 2017-2019 62
Young People of Color
As discussed in Chapter 2, African Americans and Latinos experience higher HIV diagnosis rates than
whites. Diagnosis rates are also higher among younger age groups such as those aged 20 to 29. Between
2006 and 2019, Latinos aged 20 to 29 experienced a statistically significant increase in diagnosis rate (4.3%
increase annually, on average).
For this analysis “young” is defined as those age 13 to 29 years at the time of diagnosis when discussing
those newly diagnosed or at a specific year-end when looking at PLHIV. The term “people of color (POC)”
refers to individuals not identified as white or of unknown race/ethnicity.
Figure 5.24: Birth Sex Among Young POC and Whites,
Newly Diagnosed, Alameda County, 2017-2019 Figure 5.25: Diagnosis Rate Among Young POC and
Whites, Newly Diagnosed, Alameda County, 2017-2019
From 2017 to 2019, the proportion of young people who were male and female was similar among whites and
POC. Diagnosis rates were more than twice as high for young POC than young whites in Alameda County.
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Figure 5.26: Late Diagnosis Among Young POC and Whites,
Newly Diagnosed, Alameda County, 2016-2018
Figure 5.27: Linkage to Care Among Young POC and Whites,
Newly Diagnosed, Alameda County, 2016-2018
Late diagnoses were more common among young POC (11.0%) than among young whites (7.7%). This finding
is consistent with higher rates of late diagnoses among the non-US born population, which is
disproportionately comprised of POC.
Young POC were linked to care at higher rates than young whites. The rate of linkage to care within 90 days
including labs on the date of diagnosis was 94.1% among young POC and 78.3% among young whites. For
linkage to care within 30 days the difference narrowed to 85.9% and 78.3% for POC and young whites,
respectively.
HIV in Alameda County, 2017-2019 63
At year-end 2018, young POC had higher rates
of being in care and retention in care than young
white PLHIV. While 57.0% of young POC were
retained in care only 48.9% of young white
PLHIV were.
Figure 5.28: Retention in Care Among Young POC and Whites, PLHIV,
Alameda County, Year-End 2018
Figure 5.29: Viral Suppression Among Young POC and Whites, PLHIV, Alameda County, Year-End 2018
Overall viral suppression was similar between both groups with 67.0% of young POC virally suppressed and
66.7% of young white PLHIV suppressed. However, among the unsuppressed, 24.4% of young white
PLHIV had no CD4 or viral load tests reported in 2018 compared to just 18.5% of young POC—a finding
consistent with the higher retention rates among young POC.
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Social determinants of health (SDOH) refer to the complex and overlapping economic and social structures
that contribute to health inequities and disparities. Five core dimensions that are assessed by SDOH are: the
physical neighborhood and built environment, healthcare services, social and community context, education,
and economic stability.21 SDOH are the social and physical conditions in which people grow, work, learn,
and age, as well as the effects that those conditions have on community and individual health outcomes.22
These are factors largely outside the realm of individual characteristics related to behavioral risk factors. For
example, low income neighborhoods that lack affordable, fresh produce or safe recreational areas such as
parks and playgrounds are associated with less physical activity and poor nutrition which may contribute to
increased risk of chronic health conditions like heart disease and diabetes.23 SDOH are mostly responsible
for health inequities—the unfair and avoidable differences in health status in a community.24 Adverse social
conditions can potentially increase the risks for a person acquiring HIV or progressing to stage 3 HIV
disease (AIDS). Research has indicated that HIV diagnosis rates increased as the number of unemployed
persons in a census tract increased, as well as the number of adults with only a high school diploma
increased.25
In this chapter we present analyses to examine HIV burden by select SDOH, using American Community
Survey data. The SDOH examined include educational attainment, poverty, nativity, unemployment, and
lack of health insurance. SDOH measures are presented as neighborhood measures in quintiles that
represent groups of census tracts by percentage of each SDOH measure. These analyses illustrate the
association of HIV prevalence with select social conditions and can help guide policies to address the
underlying needs of communities disproportionately impacted by HIV in Alameda County.
Social Determinants of Health and HIV
HIV in Alameda County, 2017-2019 65
Educational attainment at the neighborhood level
is measured by percentage of people over age 25
with a high school diploma or higher in a census
tract. In census tracts with lower educational
attainment (i.e. in the lower quintiles) the
prevalence of HIV is higher. As educational
attainment in a neighborhood increases (i.e. in the
higher quintiles), the prevalence of HIV decreases.
Figure 6.1: HIV Prevalence by Educational Attainment
Quintile, Alameda County, Year-End 2016
Figure 6.2: HIV Prevalence by Nativity Quintile, Alameda
County, Year-End 2016
Nativity is measured by percentage of non-US-
born residents in a census tract and is one
indicator of the concentration of immigrants in a
neighborhood or community. As the percentage
of non-US-born residents in a census tract
increases from lowest to highest quintile,
prevalence of HIV decreases.
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Figure 6.3: HIV Prevalence by Poverty Quintile,
Alameda County, Year-End 2016
Neighborhood poverty is measured by percentage
of residents living below the federal poverty level
in a census tract. Higher poverty neighborhoods
experience higher prevalence of HIV.
HIV in Alameda County, 2017-2019 66
Figure 6.4: HIV Prevalence by Unemployment Quintile,
Alameda County, Year-End 2016
Unemployment is measured by percentage of
residents experiencing unemployment in a census
tract and is an indicator of economic stability in a
neighborhood. Higher neighborhood
unemployment is associated with higher HIV
prevalence.
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Figure 6.5: HIV Prevalence by Lack of Health Insurance
Coverage Quintile, Alameda County, Year-End 2016
Lack of health insurance coverage is measured by
percentage of residents without health insurance in
a census tract and is an indicator of access to health
care in a neighborhood. Census tracts with a
greater percentage of residents lacking health
insurance have higher prevalence of HIV.
HIV in Alameda County, 2017-2019 67
Data Sources
All counts and proportions in this report were calculated using data from the Enhanced HIV/AIDS
Reporting System (eHARS). Numerators of rates were also obtained from eHARS; denominators were
derived using data from the United States Census (2000 and 2010) and Environmental Systems Research
Institute (2012 and later). Mid-year population estimates for intercensal years prior to 2012 as well as all year
-end estimates were obtained through linear interpolation. To calculate prevalence of HIV among non-US-
born and US-born individuals, estimates of the proportions of non-US-born and US-born in Alameda
County were obtained from American Community Survey (ACS) and applied to the Community
Assessment, Planning, and Evaluation (CAPE) mid-year population estimates of all people living in
Alameda County. PLHIV at the end of 2019 were identified from eHARS. Census tract level statistics such
as poverty, educational attainment, percent living without health insurance, and percent non-US-born were
obtained from the ACS 5-year survey estimates. Estimates were attributed to the middle year: for a 5-year
estimate ending in 2018, 2016 is considered the middle year.
Statistical Analysis
Calculation of Confidence Intervals
All confidence intervals (CI) depicted in the report are at the 95% confidence level. CIs for proportions are
calculated on the log odds (“logit”) scale and then antilogit-transformed in order to preclude lower limits
less than 0% and upper limits greater than 100%. Confidence limits for rates are calculated using a Poisson
distribution for counts less than 100 and a binomial distribution for counts of 100 or greater.
Significance Testing and Statistical Modeling
The statistical significance of associations between categorical variables was tested by Pearson's chi square
test or Fisher's exact test, as appropriate. Differences in CD4 count at diagnosis were assessed using
ANOVA unless Levene's Test for Homogeneity of Variances yielded a significant result (at alpha = 0.05), in
which case Welch's ANOVA was used. Trend analyses were performed using Join Point26 to model crude
rates as a log-linear function of year separately for each stratum of the categorical variable(s); errors were
assumed to have Poisson variance and to be independent. Grid search and the modified Bayesian
Information Criterion were used to select the best fitting model from among those with zero to four join
points at least 2 years apart between 2007 and 2018 (the second and second-to-last years examined).
Appendix A
Technical Notes
HIV in Alameda County, 2017-2019 68
Data Suppression Rules
0.0.1 Proportions
In accordance with draft guidelines released by the National Center for Health Statistics27,
proportions are considered to be statistically unreliable and are not presented if they meet either of
the following criteria:
1. The absolute CI width exceeds 20%.
2. The absolute CI width does not exceed 20%, but the relative CI width (the absolute CI
width divided by the lesser of the proportion and its complement) exceeds 120%.
Rates
Rates for subpopulations with fewer than 12 cases are considered to be statistically unreliable and
were not presented. In these instances, the relative standard error of the rate exceeds 30%.
Death Ascertainment
Alameda County HIV surveillance officials are notified by the local Office of Vital Registration
whenever HIV is documented on a death certificate filed in Alameda County. Additionally, the
California Office of AIDS periodically matches state HIV registry data to national death databases
such as the National Death Index and the Social Security Administration’s Death Master File.
PLHIV who died outside of Alameda County and were ever associated with Alameda County or
whose HIV was not documented on their death certificate are thus generally captured through this
process with some delay.
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The representativeness and accuracy of HIV surveillance data depend on the reliable, complete, and timely
reporting of data by health care providers and laboratories in accordance with California law.
Health Care Providers
Title 17, Section 2643.5, “HIV Reporting by Health Care Providers,” requires health care providers to
report cases of HIV disease (at any stage) to the local health department in the jurisdiction of their practice:
a) Each health care provider that orders a laboratory test used to identify HIV, a component of HIV, or
antibodies to or antigens of HIV shall submit to the laboratory performing the test a pre-printed
laboratory requisition form which includes all documentation as specified in 42 CFR 493.1105 (57 FR
7162, Feb. 28, 1992, as amended at 58 FR 5229, Jan. 19, 1993) and adopted in Business and Professions
Code, Section 1220.
b) The person authorized to order the laboratory test shall include the following when submitting
information to the laboratory:
1. Complete name of patient; and
2. Patient date of birth (2-digit month, 2-digit day, 4-digit year); and
3. Patient gender (male, female, transgender male-to-female, or transgender female-to-male); and
4. Date biological specimen was collected; and
5. Name, address, telephone number of the health care provider and the facility where services
were rendered, if different.
c) Each health care provider shall, within seven calendar days of receipt from a laboratory of a patient's
confirmed HIV test or determination by the health care provider of a patient's confirmed HIV test,
report the confirmed HIV test to the local Health Officer for the jurisdiction where the health care
provider facility is located. The report shall consist of a completed copy of the HIV/AIDS Case Report
form.
1. All reports containing personal information, including HIV/AIDS Case Reports, shall be sent
to the local Health Officer or his or her designee by:
A. courier service, US Postal Service Express or Registered mail, or other traceable mail;
or
B. person-to-person transfer with the local Health Officer or his or her designee.
2. The health care provider shall not submit reports containing personal information to the local
Health Officer or his or her designee by electronic facsimile transmission or by electronic mail
or by non-traceable mail.
d) HIV reporting by name to the local Health Officer, via submission of the HIV/AIDS Case Report,
shall not supplant the reporting requirements in Article 1 of this Subchapter when a patient's medical
Reporting Requirements
Appendix B
HIV in Alameda County, 2017-2019 70
condition progresses from HIV infection to an Acquired Immunodeficiency Syndrome (AIDS)
diagnosis.
e) A health care provider who receives notification from an out-of-state laboratory of a confirmed HIV
test for a California patient shall report the findings to the local Health Officer for the jurisdiction
where the health care provider facility is located.
f) When a health care provider orders multiple HIV-related viral load tests for a patient or receives
multiple laboratory reports of a confirmed HIV test, the health care provider shall be required to submit
only one HIV/AIDS Case Report, per patient, to the local Health Officer.
g) Nothing in this Subchapter shall prohibit the local health department from assisting health care
providers to report HIV cases.
h) Information reported pursuant to this Article is acquired in confidence and shall not be disclosed by the
health care provider except as authorized by this Article, other state or federal law, or with the written
consent of the individual to whom the information pertains or the legal representative of that individual.
Note: Authority cited: Sections 120125, 120130, 120140, 121022, 131080 and 131200, Health and Safety
Code. Reference: Sections 1202.5, 1206, 1206.5, 1220, 1241, 1265 and 1281, Business and Professions Code;
and Sections 1603.1, 101160, 120175, 120250, 120775, 120885-120895, 120917, 120975, 120980, 121015,
121022, 121025, 121035, 121085, 131051, 131052, 131056 and 131080, Health and Safety Code.
Laboratories
Title 17, Section 2643.10, “HIV Reporting by Laboratories,” requires laboratories to report all HIV-related
laboratory tests to the local health department in the jurisdiction of the ordering provider:
a) The laboratory director or authorized designee shall, within seven calendar days of determining a
confirmed HIV test, report the confirmed HIV test to the Health Officer for the local health
jurisdiction where the health care provider facility is located. The report shall include the
1. Complete name of patient; and
2. Patient date of birth (2-digit month, 2-digit day, 4-digit year); and
3. Patient gender (male, female, transgender male-to-female, or transgender female-to-male); and
4. Name, address, and telephone number of the health care provider and the facility that
submitted the biological specimen to the laboratory, if different; and
5. Name, address, and telephone number of the laboratory; and
6. Laboratory report number as assigned by the laboratory; and
7. Laboratory results of the test performed; and
8. Date the biological specimen was tested in the laboratory; and
9. Laboratory Clinical Laboratory Improvement Amendments (CLIA) number.
b)
1. All reports containing personal information, including laboratory reports, shall be sent to the
local Health Officer or his or her designee by:
A. courier service, US Postal Service Express or Registered mail, or other traceable mail;
or
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B. person-to-person transfer with the local Health Officer or his or her designee.
2. The laboratory shall not submit reports containing personal information to the local Health
Officer or his or her designee by electronic facsimile transmission or by electronic mail or by
non-traceable mail.
c) A laboratory that receives incomplete patient data from a health care provider for a biological specimen
with a confirmed HIV test, shall contact the submitting health care provider to obtain the information
required pursuant to Section 2643.5(b)(1)-(5), prior to reporting the confirmed HIV test to the local
Health Officer.
d) If a laboratory transfers a biological specimen to another laboratory for testing, the laboratory that first
receives the biological specimen from the health care provider shall report confirmed HIV tests to the
local Health Officer.
e) Laboratories shall not submit reports to the local health department for confirmed HIV tests for
patients of an Alternative Testing Site or other anonymous HIV testing program, a blood bank, a
plasma center, or for participants of a blinded and/or unlinked seroprevalence study.
f) When a California laboratory receives a biological specimen for testing from an out-of-state laboratory
or health care provider, the California director of the laboratory shall ensure that a confirmed HIV test
is reported to the state health department in the state where the biological specimen originated.
g) When a California laboratory receives a report from an out of state laboratory that indicates evidence of
a confirmed HIV test for a California patient, the California laboratory shall notify the local Health
Officer and health care provider in the same manner as if the findings had been made by the California
laboratory.
h) Information reported pursuant to this Article is acquired in confidence and shall not be disclosed by the
laboratory except as authorized by this Article, other state or federal law, or with the written consent of
the individual to whom the information pertains or the legal representative of the individual.
Note: Authority cited: Section 1224, Business and Professions Code; and Sections 120125, 120130, 120140,
121022, 131080 and 131200, Health and Safety Code. Reference: Sections 1206, 1206.5, 1209, 1220, 1241,
1265, 1281 and 1288, Business and Professions Code; and Sections 101150, 120175, 120775, 120885-
120895, 120975, 120980, 121022, 121025, 121035, 131051, 131052, 131056 and 131080, Health and Safety
Code.
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California Code of Regulations (CCR) Title 17, Section 2643.5 requires all health care providers (HCP) to
report all cases of HIV disease they encounter in their clinical practice to the county/local health jurisdiction
in which the encounter occurs. Additionally, CCR Title 17, Section 2643.10 requires all commercial
laboratories to report all HIV-related laboratory tests they conduct to the local health jurisdiction of the
HCP who ordered the test, providing an additional means by which local health departments may learn of a
case of HIV disease.
In November 2015, California adopted the Electronic Laboratory Reporting (ELR) system for laboratories
performing HIV testing. HIV test results delivered through ELR meet the statutory and regulatory reporting
requirements for HIV test results. HIV-related laboratory results are submitted to the California
Department of Public Health (CDPH) and routed to Alameda County for investigation. Establishment of
ELR resulted in major changes in the local processing and management of laboratory results for HIV
surveillance. Figure A.1 on page 73 illustrates the steps involved in processing lab results, including ELR, for
HIV surveillance in Alameda County. As shown in the figure, reported labs are checked against a local
database to identify cases not previously reported. Potential new cases are investigated by trained field staff,
who visit the office of the HCP that ordered the laboratory test(s) or submitted the lab report and complete
a case report using information abstracted from the patient’s medical record and obtained from the HCP.
For adult cases, standardized case report forms are completed and submitted in the California Reportable
Disease Information Exchange (CalREDIE)—the secure CDPH system for electronic disease reporting and
surveillance. Hard copies of the Adult Case Report Form have largely been replaced by entry into
CalREDIE, but are sometimes used by HCPs to notify the local health jurisdiction. A copy of the Adult
Case Report form can be found here: https://www.sccgov.org/sites/phd-p/programs/hiv-prev/
Documents/HIV%20Forms/adults-aids-case-form.pdf.28 Hard copies of death certificates and pediatric
HIV cases documented on a paper case report form found here: https://www.sccgov.org/sites/phd-p/
programs/hiv-prev/Documents/HIV%20Forms/HIV_Pediatric_Report_Form_DHS_8641_P.pdf29, are
mailed to the CDPH Office of AIDS. All case reports submitted to CDPH are routinely de-identified and
transmitted to CDC. When cases reported by different states appear to be the same person, CDC notifies
the appropriate states to contact each other directly and determine whether the cases are duplicates.
Security and Confidentiality of Data
In accordance with the county’s data use and disclosure agreement with CDPH, all data collected in the
course of conducting HIV surveillance are used solely for public health purposes. Additionally,
administrative, technical, and physical safeguards are in place to ensure the security and confidentiality of
these data. All paper records are stored in locked file cabinets in an office with restricted access. Electronic
Surveillance in Alameda County
Appendix C
HIV in Alameda County, 2017-2019 73
Figure A.1: The HIV Surveillance System in Alameda County
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HIV Surveillance Workflow
data transmissions are encrypted and occur over a secure file transfer network. All electronic data are stored
in a restricted access directory on a protected server.
HIV in Alameda County, 2017-2019 74
Limitations of Surveillance Data and of County Analysis
A major strength of HIV surveillance data is that it captures and reflects the entire population of HIV
diagnosed individuals. HIV surveillance data are not without their limitations however, which limit the
analyses that can be done. These limitations include, but are not limited to:
• Data quality: Public health investigators extract required information from medical records for HIV
reporting. Some information, such as risk factors or identification as transgender may not have been
available in the medical record, elicited from the patient by the HCP, or adequately described. STDs are
recognized to be widely under-reported, which may affect the figures reported here.
• Data quantity: In small subpopulations, the number of new diagnoses or PLHIV was not large enough
to allow certain analyses. Statistical analyses based on small numbers may result in unstable estimates
which can be misleading.
• Timeliness of reporting: Surveillance data are the product of a long process triggered by a visit to a
HCP by an HIV-infected individual and culminating in the entry of case data into the statewide HIV
surveillance database at the California Department of Public Health. Intermediate steps include, but are
not limited to, laboratory testing, submission of case reports and lab results to the local health
department, and investigation of each report. Data preparation, analysis and interpretation take
additional time. For these reasons, there can be a 6 to 12-month delay in estimating numbers of
diagnoses or PLHIV and in estimating any measures dependent on laboratory test results.
• History of reporting laws: The laws mandating the reporting of HIV-related laboratory test results and
of cases of HIV disease at its different stages have changed over time, and this impacts our ability to
characterize the epidemic at different points in the past. Although AIDS has been reportable since 1983,
HIV disease at its earlier stages was not reportable until mid-2002 and even then only by a non-name
code. More reliable, name-based data on HIV non-AIDS cases became mandated in 2006, and HIV-
related labs became reportable in California in 2009. Consequently, most of analyses are limited to 2006
and later, and analyses relying on laboratory reporting are limited to 2010 and later.
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Figure A.2: Timeline of Mandated HIV Reporting in California
HIV in Alameda County, 2017-2019 75
• Diagnosis date assigned to non-US-born cases: A small number of non-US-born PLHIV may have
been initially diagnosed with HIV in another country before arriving in the US, but due to the absence
of verified information on date of initial diagnosis, their diagnosis date in the surveillance data reflects
the earliest date of HIV diagnosis in the US. As a consequence new diagnoses and late diagnoses may be
overestimated in our data.
• Social Determinants of Health: Analyses of social determinants of health primarily used census tract
level data provided by the American Community Survey and not individual level data. As is the case
with ecological methods, a person’s assigned category regarding household poverty, educational
attainment, or other variables related to geographic location of residence may not accurately reflect their
individual situation.
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