2014 Epidemiological Update by Dr. Kathleen Brady

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Information about 2014 Epidemiological Update by Dr. Kathleen Brady
Health & Medicine

Published on February 21, 2014

Author: brianalmorgan

Source: slideshare.net

Description

Dr. Kathleen Brady (AACO)'s annual epidemiological update. This presentation was given to the Philadelphia EMA Ryan White Planning Council on Thursday, February 20, 2014.

I spy an epidemiologist Kathleen A Brady, MD Medical Director/Medical Epidemiologist AIDS Activities Coordinating Office Philadelphia Department of Public Health February 20, 2014

The HIV Care Continuum

National and Local Engagement in Care  Data  National and local HIV Surveillance System Prevalence (total, diagnosed) – number of persons living with HIV  Linkage to care   Medical Monitoring Project (MMP) Retention in care  Prescribed ART  Viral suppression 

Methods  Prevalence  HIV diagnosis data  Data adjustments at the national level  Back-calculation methods to estimate unaware  Linkage to Care  Data reported through December 2012  Percentage of persons with >1 CD4 or viral load test result within 3 months of HIV diagnosis

Medical Monitoring Project  MMP is a national probability sample of HIV-infected persons receiving care in the US in order to:     describe HIV care and support services being received and the quality of such services describe the prevalence and occurrence of co-morbidities related to HIV disease determine prevalence of ongoing risk behaviors and access to and use of prevention services among persons living with HIV identify met and unmet needs for HIV care and prevention services in order to inform community and care planning groups, health care providers and other stakeholders  Philadelphia has participated in MMP since 2005. All charts of sampled patients are abstracted for clinical information and patients are offered a voluntary interview.

MMP Population Size Estimates  States, facilities, and patients sampled with known probabilities  Analysis weights include:  Design weights Inverse of the probability of selection  Extend inference from sample to reference population   Non-response adjustment  Extend inference from respondents to sample  Sum of weights estimates number of HIV-infected adults who received at least one medical visit January-April of a calendar year

MMP Definitions  Retention in care: Number of HIV-infected adults who received at least one medical care visit between January and April of the calendar year  Prescription of antiretroviral therapy (ART): Documentation in medical record abstraction of any ART prescription in the past 12 months  Viral suppression: Documentation in medical record abstraction of most

Philadelphia Engagement in Care, 2009-2010 25000 20000 15000 10000 5000 0 20541 19188 16844 15753 13745 11894 9105 8185 9944 8751 6319 5775 2009 2010

Philadelphia Engagement in Care, 2009-2010 120% 100% 80% 60% 40% 20% 0% 100% 82% 100% 76% 63% 54% 56% 49% 38% 37% 2009 2010

For every 100 people living with HIV: Philadelphia US Number Number 100 Diagnosed 100 Diagnosed 80 Are linked to HIV care 82 Are linked to HIV care 45 Stay in HIV care 54 Stay in HIV care 40 Get antiretroviral therapy 49 Get antiretroviral therapy 30 Have a very low amount of virus in their body 38 Have a very low amount of virus in their body 2010 Data

Who is Aware?

HIV Prevalence in Philadelphia (reported thru 6/30/2013)  19,832 PLWHA (aware)   11,954 AIDS cases 7,878 HIV cases  Rates (known) vary by race   4,353 estimated to be  living with HIV and unaware  1.58% Philadelphia residents estimated to be HIV+  1.9% of blacks 1.5% of Latinos 0.7% of whites  Rates vary by sex   2.0% of males 0.7% of females

HIV Prevalence in the Philadelphia EMA (reported thru 6/30/2013)  27,063 PLWHA (aware)   15,683 AIDS cases 11,380 HIV cases  Rates (known) vary by race   5,941 estimated to be  living with HIV and unaware  0.5% Philadelphia EMA residents estimated to be HIV+  1.4% of blacks 0.9% of Latinos 0.2% of whites  Rates vary by sex   0.8% of males 0.3% of females

HIV/AIDS Cases by Date of Diagnosis AIDS 1308 1302 1178 1177 1200 1200 1001 940 918 928 897 894 895 907 861 821 898 1000 712 800 756 734 652 600 676 750 453 400 226 244 179 198 200 Year 16 2012 2011 2010 2009 2008 2007 2006 2005 2004 2003 2002 2001 2000 1999 1998 1997 1996 1995 1994 0 1993 Number of Cases 1400 HIV

HIV/AIDS Cases by Sex and Date of Diagnosis AIDS Male HIV Female HIV Male Year 17 20 11 20 09 20 07 20 05 20 03 20 01 19 99 19 97 19 95 19 93 1600 1400 1200 1000 800 600 400 200 0 19 91 Number of Cases AIDS Female

HIV Cases by Race/Ethnicity and Date of Diagnosis White AfrAm Hispanic 700 Number of Cases 600 575 597 536 517 482 500 474 107 106 400 300 200 100 0 2006 171 133 2007 143 127 211 147 2008 125 2009 2010 87 2011 Year 18 103 110 2012

HIV Cases by Mode of Transmission and Date of Diagnosis Number of Cases MSM 500 450 400 350 300 250 200 150 100 50 0 2006 457 281 IDU HetSx NIR 447 305 334 304 251 325 331 316 314 279 150 127 195 103 75 13 27 28 2007 2008 2009 2010 50 9 2011 Year 19 65 27 2012

HIV Cases by Age and Date of Diagnosis 13-19 20-29 350 40-49 50+ 306 300 Number of Cases 30-39 251 250 237 232 266 256 224 150 155 100 128 68 197 194 200 233 212 65 181 134 45 148 155 157 170 50 0 2006 220 158 124 43 161 135 109 39 2007 2008 2009 2010 32 2011 2012 Year 20

Summary  High HIV morbidity in Philadelphia  Philadelphia epidemic predominantly affects     minority populations MSM and Heterosexual transmission predominant modes of transmission Cases among MSM are increasing Growing numbers of persons living with HIV and AIDS First recent year to see an increase in AIDS cases 23

Who is getting infected?

Incidence Surveillance  Collect and STARHS test the diagnostic blood specimens from all newly diagnosed HIV infections reported from public and private laboratories and providers to HIV Surveillance Unit.  Collect the HIV testing information needed for the statistical estimates of incidence.  Calculate population-based estimates of HIV incidence.  Use these estimates to identify emerging subepidemics, monitor trends, target prevention resources and interventions to areas and populations most heavily affected, and evaluate programs.

Incidence vs. Prevalence 1981 2006 2007 HIV Incidence = the number of individuals newly infected with HIV within a given period of time (6 - 12 months). 1981 2006 2007 HIV Prevalence = the total number of HIV cases that exist at a specific time within a specific population.

What is STARHS? Serologic Testing Algorithm for Recent HIV Seroconversion

Requirements for HIV Incidence Surveillance Remnant HIV+ Serum STARHS Testing using BED Assay Supplemental Data Includes: •Race, sex, mode of transmission •Testing history & reasons for testing (Calculating weights) •Any exclusionary info (AIDS diagnosis, prior recent ART) •Adjust for LFU, QNS HIV Incidence Estimation

CDC STARHS Test Results  (+) standard test and (+) STARHS test = long-standing HIV infection  (+) standard test and (-) STARHS test = recent HIV infection

National Incidence Data  Estimated 47,500 HIV infections in 2010 in adults     and adolescents (95% CI, 42,000 – 53,000) Estimated incidence 19.0 infections per 100,000 population 44% among blacks, 21% Latinos 51% among MSM, 38% heterosexual 26% among 13-24 year olds

2011 Local Estimate of HIV Incidence  Local estimate of 872 new HIV infections in 2011 in adults and adolescents (95% CI, 575-1,169)  Rate is over 3.5 times that of the US estimate

HIV Incidence Trends by Demographic Groups 1200 1000 800 Total Age 13-24 600 Male Black 400 MSM 200 0 2006 2007 2008 2009 2011

Estimated Incidence Rates - 2011 Population Population in 2010 (13 +) ESTIMATED Incidence Estimate, 201 Estimated 95% CI Case Rate lower bound per 100,000 95% CI upper bound MSM 27,841 439 1,476 787 2,162 IDU 37,378 52 139.1 0 332 HET 254,200* 382 129.6 68 191 *Includes persons >13 living in poverty Data Source: PDPH/AACO HIV Incidence Surveillance Program

Incidence Summary  Includes people unaware of their status.  Overall, HIV incidence in Philadelphia is stable  Incidence higher than baseline 2006 data for MSM

Who is unaware?

Concurrent HIV/AIDS, 2012

Concurrent HIV/AIDS, 2012

Retention in care

Definition: Met Need for Primary Care  Met Need for Primary Care defined as measurement of at least one CD4 count and/or one Viral Load and/or receipt of antiretroviral therapy during a specified time period

Framework  Input  Population sizes of those with HIV and AIDS within the service area  Care Patterns of those with HIV and AIDS  Calculated Result  Number of persons with HIV and AIDS with unmet need

Population Sizes Population Sizes Value Data Source(s) Row A. Number of persons living with AIDS (PLWA), for the period of 12/31/2012 in the EMA 15,683 Local eHARS data Row B. Number of persons living with HIV (PLWH)/nonAIDS/aware, for the period of 12/31/2012 in the EMA 11,380 Local eHARS data Row C. Total number of HIV+/aware for the period of 12/31/2012 in the EMA 27,063 Local eHARS data

Care Patterns Value Data Source(s) Surveillance Data (Lab Data) CAREWare Row D. Number of PLWA who received the specified HIV primary medical care during the 12month period of 2012 in the EMA 13,770 Row E. Number of PLWH/non-AIDS who received the specified HIV primary medical care during the 12month period of 2012 in the EMA 8,296 Surveillance Data (Lab Data) CAREWare

Row F. Total number of HIV+/aware who received the specified HIV primary medical care during the 12-month period of 2012 in the EMA 22,066

Calculated Results Value Calculation 1,913 (12.0%) =A–D Row H. Number of PLWH/nonAIDS who did not receive primary medical services during the 12-month period of 2011 in the EMA 3,084 (27%) =B–E Row I. 4,997 (18%) =G+H Row G. Number of PLWA who did not receive primary medical services during the 12-month period of 2011 in the EMA Total of HIV+/aware not receiving specified primary medical care services (quantified estimate of unmet need in the EMA

Met need by demographic groups 100.0% 100.0% 90.0% 80.0% 70.0% 60.0% 76.2% 72.0% 71.7% 86.2% 88.4%85.9% 91.5% 86.4% 90.0% 80.0% 70.0% 77.3% 71.0% 60.0% 50.0% 50.0% 40.0% 40.0% 30.0% 30.0% 20.0% 20.0% 10.0% 10.0% 0.0% 0.0% HIV Black White AIDS Hispanic HIV Male AIDS Female

Met need by insurance status 100.0% 90.0% 80.0% 70.0% 60.0% 50.0% 40.0% 30.0% 20.0% 10.0% 0.0% 77.5% 81.2% 69.3% 93.3% 88.7% 89.9% 67.9% 80.1% 73.7% 60.6% HIV Medicaid Private AIDS Other public Unknown None

Disparities

Engagement in Care by Sex, 2010 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% 100% Male 80% 54% 50% 39% 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% 100% Female 86% 53% 45% 33%

Philadelphia Engagement in Care, 2010 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% 100% 100% 100% 100% 91% 82% 86% 79% 54% 49% 46% All Diagnosed White Linked 47% 45% 30% 55% 53% 50% Retained 52% 43% 33% Black on ART Latino Suppressed

Engagement in Care by Mode of Transmission, 2010 6000 4000 4051 3156 1000 0 4210 3153 3000 2000 4901 4855 4248 5000 1414 1984 1722 1654 1465 1465 1240 935 MSM of Color Diagnosed White MSM Linked In Care 2586 3039 2170 2365 HET male On ART 1582 HET female Suppressed

Engagement in Care by Mode of Transmission, 2010 120% 100% 80% 100% 100% 80% 20% 100% 88% 74% 60% 40% 100% 87% 83% 86% 65% 53% 63% 35% 44% 74% 49% 31% 23% 32% 0% MSM of Color Diagnosed White MSM Linked In Care HET male On ART HET female Suppressed

Engagement in Care by Age Group, 2010 120% 100% 80% 60% 100% 100% 100% 78% 82% 81% 50% 44% 82% 56% 53% 43% 51% 46% 40% 20% 4% 0% 18-24 Diagnosed 25-49 Linked In Care 50+ On ART Suppressed

Engagement in Care Summary  On ART  Higher for males than females  Higher for men who have sex with men (MSM) than for women who have sex with men (WSM)  Viral suppression  Higher for males than females  Higher for MSM than WSM  Higher for whites compared to blacks and Hispanics  Higher for those >50 compared to 18-29 year olds All P values <0.05

Geographic Disparities BEHIND THE CASCADE: ANALYZING SPATIAL PATTERNS ALONG THE HIV CARE CONTINUUM

Penn-AACO ECHPP Collaboration  Complete basic and advanced Geographic Information System (GIS) Training to staff of the AIDS Activities Coordinating Office  Developed resources and ongoing collaboration on GIS in HIV between Penn CFAR and AACO  Established a permanent Core service to provide GIS support to HIV investigators

Study objectives Using GIS analytic strategies, we sought to identify areas associated with:  not linking to care  not linking to care within 90 days  not retaining in care  not achieving viral suppression after HIV diagnosis

Methods  Retrospective cohort  Data extracted from eHARS  Inclusion/Exclusion criteria:  New HIV diagnosis in 2008 and 2009  Philadelphia address at the time of diagnosis  Persons with an invalid address or with a prison address at the time of their diagnosis were excluded

Outcomes  Linkage to Care – Defined as documentation of >1 CD4 or viral load test results after the diagnosis  Linkage to Care in 90 days – Defined as documentation of >1 CD4 or viral load test results within 90 days of HIV diagnosis  Retention in Care – Defined by NQF Medical Visit Frequency Measure. completing at least 1 medical visit with a provider with prescribing privileges in each 6-month interval of the 24-month measurement period, with a minimum of 60 days between medical visits.   Date of first linkage defined the start of the 24 month measurement period. We used CD4 and/or viral load as a proxy for HIV medical care visits  Viral Suppression – Defined as evidence of HIV-1 RNA <200 copies closest to the end of the 24 month measurement period

Variables of Interest  Age, sex at birth, race/ethnicity, HIV transmission risk, insurance status at the time of diagnosis, imprisonment, multiple care providers, distance to nearest care site  Spatial Analyses - K function      Analyze a spatial point process Multiple distance scales  e.g. clustered at small distances yet dispersed at large distances Complete spatial randomness (CSR) Utilizes all points in a given area Compare to multiple simulated random processes

Results  1,861 cases, 157 excluded (8%) due to an invalid address or imprisoned at the time of diagnosis  Excluded persons less likely to be black/Hispanic, more likely to be >45 years of age, IDU and privately insured  Among 1,704 person included:  70% male, 63% black, 30% 45 years or older  40% heterosexuals, 36% MSM  82% linked to care  Among those linked, 75% linked in 90 days and 37% were retained in care  Among those retained, 72% achieved viral suppression

Multivariate Regression Models for Involvement in Continuum of Care Characteristic Not Linked to Care Not Linked <90 Days Age at Dx Not Retained in Care Not Virally Suppressed <25 Sex at birth Male Race/ ethnicity Black Risk Group IDU Insurance Medicare Uninsured Uninsured Yes Yes Geographic Area Black Hispanic Yes Yes Prison stay Proximity to care Multiple care sites Yes Yes

Geographic Pattern Analysis of HIV Medical Care Engagement, 2008-2009

Summary  Geographic clustering was independently associated with poor outcomes at each step along the HIV Care Continuum  Geographic clusters identified were unique with no geographic overlap between steps in the Continuum  Geographic clusters identified have a greater burden of HIV disease compared to other neighborhoods  Proximity to HIV medical care was not associated with linkage to care, linkage in <90 days or retention in care

Conclusions  Community factors related to poverty and community socioeconomic status may impact HIV treatment outcomes for individuals in living in geographic clusters  We hypothesize:    Community norms and social disorder may have a greater effect on linkage to care; Access to public transportation and social services may have a greater effect on retention in care; And access to pharmacies may have a greater effect on viral suppression.  Differences in community factors that influence each step of the cascade may explain the lack of overlap in hot spots.

Next Steps  Better understand of the characteristics of places that influence access to HIV medical care and treatment outcomes—mixed methods strategies  Consistent with CDC’s High Impact Prevention program, identification of geographic clusters could help to specifically target separate linkage, retention, and adherence interventions in the areas identified with the greatest need  Philadelphia’s CDC CoRECT application – selected medical providers in the geographic cluster identified for retention  Develop new strategies for intervention based upon ecological factors of the distinct clusters

Starting Antiretroviral Therapy in 2012: A Compendium of Interactive Cases clinicaloptions.com/hiv What Will It Take to Substantially Reduce HIV Transmission in an Entire Population? Undiagnosed HIV Not linked to care Not retained in care ART not required ART not utilized Viremic on ART Undetectable HIV-1 RNA •Number of Individuals •1,200,000 •1,000,000 •800,000 •600,000 •400,000 •200,000 •0 •66% •19% •22% •Current •DX 90% •34% •28% •Engage 90% •Treat 90% •21% •VL < 50 •Dx, in 90% Engage, Tx, and VL < 50 in 90% •Answer: Treatment AND Prevention •Gardner EM, et al. Clin Infect Dis. 2011;52:793-800.

Quality of Care in Patients Living with HIV: Performance Measures in HIV Clinics

Background  As PLWH live longer, ensuring receipt of high quality care has become increasingly important  HRSA performance measures include:    HIV specific measures (clinic visits, CD4 and viral load monitoring, ART, viral suppression, PCP prophylaxis) Screenings for comorbid conditions( syphilis, GC, Chlamydia, HBV, HCV, TB, hyperlipidemia, cervical cancer) Vaccinations (influenza, HBV, pneumococcus  Little known about the patient and clinic factors that may contribute to success in meeting performance measures

Methods  Weighted abstraction data from the 2009 cycle of MMP in Philadelphia were utilized   Data included for 376 participants (94% of sampled patients) Facility attributes data included from 24 facilities  Sociodemographic variables were defined according to CDC criteria.  Clinic variables included Patient/FTE ratio, type of practice, and variables on adherence counseling, case management were dichotomized into available or not  Outcome variable – Receipt of HRSA performance measures was based on HRSA definitions

Methods - Continued  Outcome variable  described as a continuous variable  maximum score of 15, where each HRSA performance measure chosen was given one point  certain performance measures were excluded: not well-documented across clinics (ex., oral exam, substance abuse screening) or  only have applied to fewer than 50% of patients (ex., cervical cancer screening, PCP prophylaxis)   A total of 15 of the 27 HRSA performance measures were included in the outcome variable

Results – Sample Characteristics Characteristic % % Birth outside US Sex Characteristic 4.3% Male 64.6% HIV risk category Female 35.8% Heterosexual 37.2% MSM 30.9% Race White 21.3% IDU 29.5% Black 67.3% Other/Unknown 2.4% Hispanic 10.9% Other 0.5% Insurance status 23.6% Medicaid Age Private 55.6% 18-29 13.6% Medicare 12.2% 30-39 17.8% VA 3.7% 40-49 37.8% Uninsured 8.0% >50 30.9% Adherence program 89.4% 60.9% Case management 59.8% Ryan White funded 87.8% AIDS Diagnosis

Results – Multivariate Model Characteristic Female (male ref.) IRR (95% CI) Adjusted IRR (95% CI) 1.01 (0.97-1.04) 0.99 (0.89-1.09) 1.02 (0.93-1.11) 1.04 (0.97-1.10) 0.98 (0.88-1.09) 1.04 (0.96-1.09) 1.00 (0.86-1.16) 1.09 (0.96-1.25) 1.13 (0.77-1.30) 1.19 (1.05-1.35) 1.12 (0.96-1.31) 1.17 (1.01-1.35) 1.02 (0.73-1.45) 1.05 (0.73-1.50) Race/ethnicity (white ref.) Black Hispanic Age (18-29 ref.) 30-39 40-49 50+ Non-US birth (US birth ref.)

Results – Multivariate Model Characteristic IRR (95% CI) Adjusted IRR (95% CI) 1.02 (0.93-1.13) 1.05 (0.93-1.18) 0.99 (0.90-1.10) 1.00 (0.93-1.07) 0.97 (0.93-1.02) 0.98 (0.92-1.06) 0.93 (0.83-1.04) 0.97 (0.88-1.08) 0.98 (0.96-1.01) 1.01 (0.87-1.18) 0.73 (0.64-0.82) 0.91 (0.81-1.03) HIV Risk Factor (Het ref.) MSM IDU Insurance (Private ref.) Medicaid Medicare VA Uninsured

Results – Multivariate Model Characteristic Adherence counseling Case management Ryan White funded IRR (95% CI) Adjusted IRR (95% CI) 1.21 (1.13-1.28) 1.12 (1.04-1.21) 1.14 (1.03-1.25) 1.12 (1.04-1.14) 1.06 (0.96-1.17) 1.07 (0.92-1.25) 1.08 (1.00-1.17) 1.08 (0.98-1.19) 1.07 (1.02-1.13) 1.14 (1.02-1.28) 1.04 (1.03-1.25) 1.07 (0.92-1.25) Minimum CD4 Count (<200 ref) 200-349 350-499 >500

Results Summary  The mean number of performance measure met was 8.52 (standard deviation 2.39) of the 15 assessed.  Older PLWH more likely to meet performance measures  HIV-specific measures more likely to be met than for vaccination related measures and screenings for comorbid conditions  Patients who attended clinics with adherence counseling programs and that provided case management had higher summed performance scores

Limitations  By using MMP data, we could not address how clinics tried to improve performance measures  Focus on one city, results may not be generalizable.  MMP may underestimate preventative services received by patients attending other clinics (such as primary care clinics)  The performance summary measure   Some measures excluded All measure given equal weighting in the score but not necessarily equally important (VL suppression > GC screening)

Conclusions  Few patients achieved all performance measures  PLWH more likely to achieve performance measure related to HIV care (data not shown)  Future work should focus on how to improve compliance with performance measures  Investigation of barriers and facilitator to improving care of PLWH in HIV clinics  Still need a better understanding of what other clinic characteristics may be associated with meeting performance measure metrics

The End QUESTIONS?

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