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<!DOCTYPE html>
<html>
<head>
<title>Robert Brown - Data Science | Machine Learning | Public Health</title>
<meta name="description" content="Robert Brown - Full Stack Data Scientist - Los Angeles, CA.">
<meta name="keywords" content="Robert Brown, Robert, Brown, UCD, UCB, Berkeley, San Francisco, Oakland, Los Angeles, California">
<meta name="author" content="Robert Brown">
<meta name="viewport" content="width=device-width">
<meta name="viewport" content="initial-scale=1, maximum-scale=1, user-scalable=no">
<link rel="stylesheet" href="/css/base.min.css">
<script async src="/js/base.min.js"></script>
</head>
<body>
<div class="code"></div>
<div id="prof-pic-wrap">
<img id="prof-pic" src="/img/profile-picture.jpg" alt="Robert Brown">
</div>
<header id="mainnav" role="banner" class="centered-navigation">
<div class="centered-navigation-wrapper">
<a href="javascript:void(0)" class="mobile-logo">
<img src="/img/profile-picture.jpg" alt="Logo image">
</a>
<a id="js-centered-navigation-mobile-menu" href="javascript:void(0)" class="centered-navigation-mobile-menu">MENU</a>
<nav role="navigation">
<ul id="js-centered-navigation-menu" class="centered-navigation-menu show">
<li class="nav-link">
<a href="#education">Education</a>
</li>
<li class="nav-link">
<a href="#skillset">Skillset</a>
</li>
<li class="nav-link">
<a href="#experience">Experience</a>
</li>
<li id="prof-pic-filler-li" class="nav-link">
<span id="prof-pic-filler"></span>
</li>
<li class="nav-link">
<a href="#publications">Publications</a>
</li>
<li class="nav-link">
<a href="#projects">Projects</a>
</li>
<li class="nav-link">
<a href="#awards">Awards</a>
</li>
</ul>
</nav>
</div>
</header>
<div class="container">
<div class="row row-centered">
<div class="col-md-9 col-centered">
<section id="name" class="print-header">
<div id="left">
<h1>Robert Brown</h1>
<address>123 Fake St. Oakland, CA</address>
</div>
<div id="right">
<p>[email protected](510) xxx - xxxxhttp://elreb.github.io</p>
</div>
<div class="clearfix"></div>
<hr>
</section>
<section id="name" class="one-pager-erase">
<h1>Robert Brown</h1>
<p class="subhead">Data Scientist · Epidemiologist</p>
<p>
<a href="/">http://elreb.me</a>
· San Francisco, CA ·<a href="mailto:[email protected]"> [email protected]</a>
</p>
<p>
<a href="/CV.pdf" target="_blank">
Curriculum Vitae <i class="fa fa-file-pdf-o"></i>
</a> ·
<a href="/resume.pdf" target="_blank">
Resume <i class="fa fa-file-pdf-o"></i>
</a>
</p>
</section>
<section id="education" class="row">
<div class="col-md-2">
<h2>Education</h2>
</div>
<div class="col-md-10 col-lg-9">
<h3 class="school">University of California - Berkeley</h3>
<p class="location">Berkeley, CA</p>
<div class="clearfix"></div>
<div class="clearfix"></div>
<h4 class="degree">
Epidemiology and Biostatisitics, MPH
</h4>
<p class="dates">Aug. 2015 —June 2017</p>
<div class="clearfix"></div>
<div class="clearfix"></div>
<h3 class="school">University of California, Davis</h3>
<p class="location">Davis, CA</p>
<div class="clearfix"></div>
<div class="clearfix"></div>
<h4 class="degree">
Neurophysiology and Behavior BS,
Psychology BA
</h4>
<p class="dates">Sept. 2006 —June 2011</p>
<div class="clearfix"></div>
</div>
</section>
<div class="clearfix"></div>
<br>
<section id="experience" class="row">
<div class="col-md-2">
<h2>Experience</h2>
</div>
<div class="col-md-10 col-lg-9">
<h3 class="position">Atropos Health</h3>
<p class="location">
<a target="_blank" href="https://www.atroposhealth.com/">Atropos Health</a>
</p>
<div class="clearfix"></div>
<div class="clearfix"></div>
<h4 class="company">Machine Learning Engineer</h4>
<p class="dates">December 2022-Present</p>
<div class="clearfix"></div>
<ul>
<li> Built tools to transform how RWE is generated
</li>
<li> Implemented new features and tools into production (including AI features with LLM's), including an autosummary feature that provides +20k per year savings, and optimized core production code (6x gains in speed through parallel processing and 100x memory usage efficiency)
</li>
</ul>
<div class="clearfix"></div>
<div class="clearfix"></div>
<h3 class="position">Berlin Brands Group</h3>
<p class="location">
<a target="_blank" href="https://www.berlin-brands-group.com/en">BBG</a>
</p>
<div class="clearfix"></div>
<div class="clearfix"></div>
<h4 class="company">Data Engineer</h4>
<p class="dates">October 2021 — December 2022</p>
<div class="clearfix"></div>
<ul>
<li> Created and implemented computing, reporting, and ETL infrastructure using AWS (S3, Lambda, EC2) and Snowflake
</li>
<li> Developed prospecting tool in Django for brand acquisition, monitoring product purchase behavior. Trained time
series models (ARIMA, LSTM, FB Prophet) to forecast future revenue growth using data ingested from data mining, operational and third-party data API end points
</li>
</ul>
<div class="clearfix"></div>
<div class="clearfix"></div>
<h3 class="position">Los Angeles County Public Health Department</h3>
<p class="location">
<a target="_blank" href="http://publichealth.lacounty.gov/">LAPHD</a>
</p>
<div class="clearfix"></div>
<div class="clearfix"></div>
<h4 class="company">Data Scientist</h4>
<p class="dates">January — October 2021</p>
<div class="clearfix"></div>
<ul>
<li> Created multiple interactive internal and public facing dashboards displaying infectious disease data for LA County (R and Python)
</li>
<li> Developed a whole genome sequencing (WGS) database and implemented ETL pipelines, implemented in Airflow, processing tens of inputs and 100s of GBs of epidemiologic, genomic sequence data and associated metadata
</li>
</ul>
<div class="clearfix"></div>
<h3 class="position">Insight Data Science</h3>
<p class="location">
<a target="_blank" href="https://insightfellows.com/">Insight Fellows</a>
</p>
<div class="clearfix"></div>
<h4 class="company">Health Data Science Fellow</h4>
<p class="dates">August 2020 — November 2020</p>
<div class="clearfix"></div>
<ul>
<li> Consulted with an organization to provide a churn model using client records (Python)
</li>
<li> Used advanced SQL querying to aggregate together 10+ tables, and used contextual knowledge to engineer features such as an engagement score to increase model performance
</li>
<li> Used survivor analysis to generate a probability of of client churn over time, as well as used a binary classifier using logistic regression with lasso regularization to achieve 78% ROC AUC
</li>
</ul>
<div class="clearfix"></div>
<h3 class="position">Alameda County Public Health Department</h3>
<p class="location">
<a target="_blank" href="http://www.acphd.org/">ACPHD</a>
</p>
<div class="clearfix"></div>
<div class="clearfix"></div>
<h4 class="company">Epidemiologist II</h4>
<p class="dates">August 2019 — September 2020</p>
<div class="clearfix"></div>
<ul>
<li> Lead data analyses and surveillance reporting of tuberculosis and over 70 other notifiable acute communicable diseases (in R and SAS).
</li>
<li> Conducted epidemiological investigations and evaluations of disease clusters and outbreaks, linkage-to-care projects, and quality improvement efforts.
</li>
</ul>
<div class="clearfix"></div>
<h4 class="company">HIV Data-to-Care Specialist</h4>
<p class="dates">August 2018 — August 2019 </p>
<div class="clearfix"></div>
<ul>
<li> Managed the HIV Data-to-Care program for the county, leveraging various case management and surveillance data with integrated
inter-departmental work-flows to increase the number of HIV positive residents in care
</li>
</ul>
<div class="clearfix"></div>
<h3 class="position">UC Berkeley Extension</h3>
<p class="location">
<a target="_blank" href="https://bootcamp.berkeley.edu/data/">UC Berkeley Data Analytics Boot Camp</a>
</p>
<div class="clearfix"></div>
<div class="clearfix"></div>
<h4 class="company">Data Analytics Substitute Instructor & TA</h4>
<p class="dates">July 2019 - July 2020</p>
<div class="clearfix"></div>
<ul>
<li>Assisted teaching fundamentals of computer science, programming, Git, Bash, data munging, visualization, web development, project
management, and machine learning to multiple different classes of students through the UC Berkeley Data Analytics Boot Camp
</li>
</ul>
<div class="clearfix"></div>
<h3 class="position">Method Data Science</h3>
<p class="location">
<a target="_blank" href="https://methoddatascience.com/">Method Data Science</a>
</p>
<div class="clearfix"></div>
<div class="clearfix"></div>
<h4 class="company">Resident Data Scientist</h4>
<p class="dates">November 2018 - April 2019</p>
<div class="clearfix"></div>
<ul>
<li> Utilized a complex assortment of data sources to produce data models & prediction algorithms for various clients including
Biotechnology start-up companies
</li>
</ul>
<div class="clearfix"></div>
<h3 class="position">California Rural Indian Health Board </h3>
<p class="location">
<a href="https://crihb.org/" target="_blank">CRIHB</a>
</p>
<div class="clearfix"></div>
<div class="clearfix"></div>
<h4 class="company">Epidemiologist</h4>
<p class="dates">October 2017 — August 2018 </p>
<div class="clearfix"></div>
<ul style="margin-bottom: 0px;">
<li>Developed state wide data collection and disease surveillance tools, IRB and data management, and wrote complex grants to secure future funding.</li>
<li>Developed databases, SQL Reports using advanced queries, technical reports, spatial data overlays and maps, performed data linkages, and produced
tables and figures in R and Tableau</li>
</ul>
<div class="one-pager-erase">
<div class="clearfix"></div>
<h3 class="position">UCSF School of Medicine</h3>
<p class="location">
<a href="https://medschool.ucsf.edu/" target="_blank">UCSF</a>
</p>
<div class="clearfix"></div>
<div class="clearfix"></div>
<h4 class="company">Statistician</h4>
<p class="dates">June — August 2017 </p>
<div class="clearfix"></div>
<ul>
<li>Performed database management, IRB management, data interpretation and conducted various analyses – including machine learning, and
causal, time-variant, clustered and multilevel analyses– on two multi-million dollar NIH funded international studies on multi-drug resistant tuberculosis</li>
</ul>
<div class="clearfix"></div>
<h3 class="position">UC Berkeley School of Public Health</h3>
<p class="location">
<a href="http://chl.berkeley.edu/" target="_blank">UC Berkeley CHL</a>
</p>
<h4 class="company">Center for Health Leadership Fellow</h4>
<p class="dates">Aug. 2016 — May 2017 </p>
<div class="clearfix"></div>
<ul>
<li>Selected to participate in a three-semester leadership development program </li>
<li>Learning activities include: consulting project with non-profit agency, training workshops, needs assessment, project management and meeting facilitation</li>
</ul>
<div class="clearfix"></div>
<div class="clearfix"></div>
<h3 class="position">UC Berkeley School of Public Health</h3>
<p class="location">
<a href="https://publichealth.berkeley.edu/" target="_blank">UC Berkeley</a>
</p>
<div class="clearfix"></div>
<div class="clearfix"></div>
<h4 class="company">Graduate Student Researcher</h4>
<p class="dates">Aug. 2016 — May 2017 </p>
<div class="clearfix"></div>
<ul>
<li>Conducted analysis on the accuracy of population estimates of those with a genetic disorder or genetic determinants (e.g. cancer, congenital malformation,
etc.) with those of elusive populations (e.g. drug addiction) using both non-Bayesian and Bayesian methods</li>
</ul>
<h3 class="position">Google</h3>
<p class="location">
<a href="https://www.google.com/" target="_blank">Google</a>
</p>
<div class="clearfix"></div>
<div class="clearfix"></div>
<h4 class="company">Public Health Data Specialist</h4>
<p class="dates">May-December 2016</p>
<div class="clearfix"></div>
<ul>
<li>Responsible for data curation, data analysis of existing public health and medical research</li>
<li>Curated, verified, and provided expert feedback to improve the information in the health knowledge graph and related algorithm performance</li>
</ul>
<h3 class="position">San Francisco Department of Public Health </h3>
<p class="location">
<a href="https://publichealth.berkeley.edu/" target="_blank">UC Berkeley</a>
</p>
<div class="clearfix"></div>
<div class="clearfix"></div>
<h4 class="company">Research Data Analyst</h4>
<p class="dates">May-December 2016 </p>
<div class="clearfix"></div>
<ul>
<li>Conducted various statistical analyses, mathematical modeling, data management, and data interpretation on over four studies involving randomized
control trials of pharmacologic interventions to treat substance use, HIV risk, and a prospective longitudinal study among transgender youth</li>
</ul>
<div class="clearfix"></div>
<h3 class="position">Amity Foundation</h3>
<p class="location">
<a href="https://www.amityfdn.org" target="_blank">Amity Foundation</a>
</p>
<div class="clearfix"></div>
<div class="clearfix"></div>
<h4 class="company">Program Coordinator</h4>
<p class="dates">February - August 2015</p>
<div class="clearfix"></div>
<ul>
<li>Managed reporting, program evaluation and program implementation for grant that provided mentors to formerly incarcerated individuals</li>
<li>Implemented program plan, maintained program management efforts for a financial literacy grant entitled, Financial Empowerment</li>
<li>Managed reporting, program evaluation and program implementation for grant that provided mentors to formerly incarcerated individuals</li>
</ul>
<div class="clearfix"></div>
<h3 class="position">Native American Health Center</h3>
<p class="location">
<a href="http://www.nativehealth.org/" target="_blank">Native American Health Center</a>
</p>
<div class="clearfix"></div>
<div class="clearfix"></div>
<h4 class="company">Data Coordinator</h4>
<p class="dates">February 2014 - February 2015</p>
<div class="clearfix"></div>
<ul>
<li>Managed data collection, entry and analysis for health clinic that saw over 15 thousand patients at three sites in the San Francisco Bay Area. Supervised two Data Assistants</li>
<li>Responsible for creating clinical and behavioral health reports via SQL from our EHR service to track funding grants requirements, and provide general reporting for the board of directors, clinicians, staff, and the public</li>
<li>Provided evaluation and project management on a technology integration grant that helped fund the creation of the clinics EHR</li>
</ul>
<div class="clearfix"></div>
<div class="clearfix"></div>
<h4 class="company">Data Assistant</h4>
<p class="dates">June 2012 - January 2014</p>
<div class="clearfix"></div>
<ul>
<li>Responsible for grant funded survey data collection, health behavior quantative analysis, and technical and statistical reporting of over 15 grants</li>
<li>Other roles included: consenting survey participants, determining insurance eligibility, and performing HIV rapid tests and HIV counseling</li>
</ul>
<div class="clearfix"></div>
<h3 class="position">UC Davis Department of Physiology and Biology</h3>
<p class="location">
<a href="https://www.gomeslab.net/" target="_blank">Gomes Lab</a>
</p>
<div class="clearfix"></div>
<div class="clearfix"></div>
<h4 class="company">Research Assistant</h4>
<p class="dates">September 2008 - June 2011</p>
<div class="clearfix"></div>
<ul>
<li>Collected, maintained, and evaluated data on mutations of cardiac Troponin T and cardiac Troponin C and their relation to familial cardiomyopathies. Proteins were expressed in KO mice using in
vitro techniques and structurally analyzed via homology modeling</li>
<li>Performed various molecular biology techniques such as gel electrophoresis, and western blots to determine relative levels of Ubiquitin levels and proteasome activity expressed KO mice</li>
</ul>
</div>
</div>
</section>
<section id="skillset" class="row">
<div class="col-md-2">
<h2>Skillset Overview</h2>
</div>
<div class="col-md-10 col-lg-9">
<ul>
<li>
<b>Languages/Technologies</b>
: Python (Jupyter, Pandas, Scikit-Learn, Numpy/SciPy, Matplotlib, Django, Flask/FastAPI, PySpark, Keras, Pytest), R, SAS, JavaScript (JQuery, D3.js), SQL, noSQL (MongoDB), HTML, CSS (Bootstrap, Grid), Bash, Latex, AWS (Redshift, S3, Lambda, DynamoDB, EC2, CodePipeline), Apache Airflow, Docker, CI/CD, Git, Tableau
</li>
<li>
<b>Platforms</b>
: Linux (Ubuntu Desktop & Server), OS X, Windows
</li>
</ul>
</div>
</section>
<section id="projects" class="row">
<div class="col-md-2">
<h2>Projects</h2>
</div>
<div class="col-md-10 col-lg-9">
<ul>
<li>
<a href="/data_science_prep/docs/index.html" target="_blank">
Data Science Prep Guide. <i class="fa fa-line-chart"></i>
</a>
</li>
</ul>
</div>
</section>
<section id="awards" class="row">
<div class="col-md-2">
<h2>Awards & Certs</h2>
</div>
<div class="col-md-10 col-lg-9">
<ul>
<li>
Completed +200 hours of additional online training in programming, statistics, data manipulation and machine learning, including:
<a href="https://www.datacamp.com/profile/rbrtebrwn" target="_blank">+30 Datacamp courses</a>
, certification in SAS (<a href="https://www.youracclaim.com/badges/f0261e67-d1c3-4f4d-ae0f-44b6004bce63/linked_in_profile" target="_blank">SAS Base Programmer for SAS 9</a>)
and several courses from coursera (including in <a href="https://www.coursera.org/account/accomplishments/records/MPQLUHHM47QY" target="_blank">Tableau</a>,
<a href="https://www.coursera.org/account/accomplishments/records/GN9VJ3C2YM3S" target="_blank">SQL</a> and several in
<a href="https://www.coursera.org/account/accomplishments/records/CMHCXSKM7G96" target="_blank">the data science concentration.</a>)
</li>
<li>
Received the <a target="_blank" href="https://developers.google.com/training/">Grow with Google Challenge Scholarship</a>, for training in Android application development.
</li>
<li>
Received the <a target="_blank" href="https://gspp.berkeley.edu/programs/masters-of-public-policy-mpp/tuition-and-financial-aid/gspp-departmental-fellowships">Graduate Opportunity Program
(GOP) Full Fellowship</a>, a competitive and fully-funded merrit based graduate fellowship.
</li>
</ul>
</div>
</section>
<section id="publications" class="row">
<div class="col-md-2">
<h2>Publications</h2>
</div>
<div class="col-md-10 col-lg-9">
<p>
Jackson ML., Gombar S, Manickam R, <b>Brown R</b>, Tekumalla R, Low Y. <a target="_blank" href="https://www.ohdsi.org/wp-content/uploads/2023/10/121-Jackson-BriefReport-Mike-Jackson.pdf">Validating a clinical informatics consulting service using negative control
reference sets</a>. Poster presented at: 2023 OHDSI Symposium; October 20, 2023; East Bruswick, New Jersey.
</p>
<div class="expander">
<a id="js-expander-trigger96" href="javascript:void(0)" class="expander-trigger expander-hidden">Abstract</a>
<div id="js-expander-content96" class="expander-content">
<p>
Context: Patients with culture-negative pulmonary TB (PTB) can face delays in diagnosis that worsen outcomes and lead to ongoing transmission. An understanding of current trends and characteristics of culture-negative PTB can support earlier detection and access to care.<br>
Objective: Describe epidemiology of culture-negative PTB.<br>
Design, setting, participants: We utilized Alameda County TB surveillance data from 2010 to 2019. Culture-negative PTB cases met clinical but not laboratory criteria for PTB per US National Tuberculosis Surveillance System definitions. We calculated trends in annual incidence and proportion of culture-negative PTB using Poisson and weighted linear regression, respectively. We further compared demographic and clinical characteristics among culture-negative versus culture-positive PTB cases.<br>
Results: During 2010-2019, there were 870 cases of PTB, of which 152 (17%) were culture-negative. The incidence of culture-negative PTB declined by 76%, from 1.9/100 000 to 0.46/100 000 (P for trend <.01), while the incidence of culture-positive PTB reduced by 37% (6.5/100 000 to 4.1/100 000, P for trend =.1). Culture-negative PTB case-patients were more likely than culture-positive PTB case-patients to be younger (7.9% were children <15 years old vs 1.1%; P < .01), recent immigrants within 5 years of arrival (38.2% vs 25.5%; P < .01), and have a TB contact (11.2% vs 2.9%; P < .01). Culture-negative PTB case-patients were less likely than culture-positive PTB case-patients to be evaluated because of TB symptoms (57.2% vs 74.7%; P < .01) or have cavitation on chest imaging (13.1% vs 38.8%; P < .01). At the same time culture-negative PTB case-patients were less likely to die during TB treatment (2.0% vs 9.6%; P < .01).<br>
Conclusions: The incidence of culture-negative PTB disproportionately declined compared with culture-positive TB and raises concern for gaps in detection. Expansion of screening programs for recent immigrants and TB contacts and greater recognition of risk factors may increase detection of culture-negative PTB.
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<p>
Chen J, Marusinec R, <b>Brown R</b>, Shiau R, Jaganath D, Chitnis AS. <a target="_blank" href="https://pubmed.ncbi.nlm.nih.gov/36867649/">Epidemiology of Culture-Negative Pulmonary Tuberculosis-Alameda County, 2010-2019</a>. J Public Health Manag Pract. 2023 Mar 3. doi: 10.1097/PHH.0000000000001715. Epub ahead of print. PMID: 36867649.
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Context: Patients with culture-negative pulmonary TB (PTB) can face delays in diagnosis that worsen outcomes and lead to ongoing transmission. An understanding of current trends and characteristics of culture-negative PTB can support earlier detection and access to care.<br>
Objective: Describe epidemiology of culture-negative PTB.<br>
Design, setting, participants: We utilized Alameda County TB surveillance data from 2010 to 2019. Culture-negative PTB cases met clinical but not laboratory criteria for PTB per US National Tuberculosis Surveillance System definitions. We calculated trends in annual incidence and proportion of culture-negative PTB using Poisson and weighted linear regression, respectively. We further compared demographic and clinical characteristics among culture-negative versus culture-positive PTB cases.<br>
Results: During 2010-2019, there were 870 cases of PTB, of which 152 (17%) were culture-negative. The incidence of culture-negative PTB declined by 76%, from 1.9/100 000 to 0.46/100 000 (P for trend <.01), while the incidence of culture-positive PTB reduced by 37% (6.5/100 000 to 4.1/100 000, P for trend =.1). Culture-negative PTB case-patients were more likely than culture-positive PTB case-patients to be younger (7.9% were children <15 years old vs 1.1%; P < .01), recent immigrants within 5 years of arrival (38.2% vs 25.5%; P < .01), and have a TB contact (11.2% vs 2.9%; P < .01). Culture-negative PTB case-patients were less likely than culture-positive PTB case-patients to be evaluated because of TB symptoms (57.2% vs 74.7%; P < .01) or have cavitation on chest imaging (13.1% vs 38.8%; P < .01). At the same time culture-negative PTB case-patients were less likely to die during TB treatment (2.0% vs 9.6%; P < .01).<br>
Conclusions: The incidence of culture-negative PTB disproportionately declined compared with culture-positive TB and raises concern for gaps in detection. Expansion of screening programs for recent immigrants and TB contacts and greater recognition of risk factors may increase detection of culture-negative PTB.
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<p>
Lloyd T, Bender M, Huang S, <b>Brown R</b>, Shiau R, Yette E, Shemsu M, Pandori M. <a target="_blank" href="https://pubmed.ncbi.nlm.nih.gov/32376667/">Assessing the Use of PCR To Screen for Shedding of Salmonella enterica in Infected Humans</a>. J Clin Microbiol. 2020 Jun 24;58(7):e00217-20. doi: 10.1128/JCM.00217-20. PMID: 32376667; PMCID: PMC7315023.
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Recovery from enteric bacterial illness often includes a phase of organismal shedding over a period of days to months. The monitoring of this process through laboratory testing forms the foundation of public health action to prevent further transmission. Regulations in most jurisdictions in the United States exclude individuals who continue to shed certain organisms from sensitive occupations and situations, such as food handling, providing direct patient care, or attending day care. The burden that this creates for recovering patients and their families/coworkers is great, so any effort to provide efficiency to the testing process would be of significant benefit. We sought to assess the ability of PCR for the detection of Salmonella enterica shedding and to compare that ability to culture-based testing. PCR would be faster than culture and would allow results to be generated more quickly. Herein, we show data that indicate that, while PCR and culture testing agree in the majority of cases, there are incidents of discordance between the two tests, whereupon PCR shows positive results when culture indicates lack of detectable viable organisms. Using culture-based testing as the standard, the negative predictive value of PCR was found to be 100%, while the positive predictive value was 79%. The nature of this discordance is briefly investigated. We found that it is possible that PCR may not only detect nonviable organisms in stool but also viable organisms that remain undetectable by standard culture methods.
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<p>
Allgeier D, Gebreegziabher E, Ycasas J, Murgai N, <b>Brown RE</b>, Moss N. <a target="_blank" href="http://www.acphd.org/media/532487/hiv2018_ac.pdf">HIV in Alameda County, 2015-2017</a>. 2018.
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<p>
<b>Brown RE</b>, Turner C, Hern J, Santos GM. <a target="_blank" href="https://www.sciencedirect.com/science/article/abs/pii/S0376871617301722">Partner-level substance use associated with increased sexual risk behaviors among men who have sex with men in San Francisco, CA</a>. Drug Alcohol Depend. 2017;176:176–80.
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BACKGROUND <br>
Substance use is highly prevalent among men who have sex with men (MSM) and is associated with individual-level sexual risk behaviors. However, few studies have explored the relationship between substance use and HIV risk behaviors within partnerships. <br>
METHODS <br>
We examined partner-level data between MSM participants (n = 23) and their sexual partners (n = 52). We used multivariable generalized estimating equations (GEE) logistic regression to assess the relationship between partner-level substance use during their last sexual encounter with each partner, and engaging in condomless anal intercourse (CAI) and serodiscordant CAI. <br>
RESULTS <br>
In multivariable analyses, participants had significantly higher adjusted odds ratio (AOR) of CAI when the participant (AOR = 22.2, 95%CI = 2.5-199.5) or their partners used any drugs (AOR = 21.8, 95%CI = 3.3-144.3); their partners (AOR = 5.7, 95%CI = 1.7-19.3) or both participant and partner had concordant use of methamphetamine (AOR = 10.5, 95%CI = 2.2-50.6); or when both used poppers (AOR = 11.4, 95%CI = 1.5-87). There were higher odds of SDCAI if the participant binge drank (AOR = 4, 95%CI = 1.01-15.8), used more than one substance (AOR = 15.8, 95%CI = 1.9-133), or used other drugs (AOR = 4.8, 95%CI = 1.3-18.4); if their partner used poppers (AOR = 7.6, 95%CI = 1.5-37.6), or used more than one substance (AOR = 7.9, 95%CI = 1.9-34.1); and when both participant and partner had concordant use of poppers (AOR = 4.4, 95%CI = 1.2-16.8). <br>
CONCLUSIONS <br>
This study observed significant relationship between substance use and HIV risk behaviors within partnerships. Specifically, when either the participant, the partner, or both used any drugs there was an increased odds of sexual risk behaviors. Findings suggest that partner-level substance use behaviors should be taken in account when developing sexual risk reduction interventions. <br>
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