1 / 11

Risk-Based Surveillance of HIV at the Los Angeles County Jail: a Bayesian Approach

Risk-Based Surveillance of HIV at the Los Angeles County Jail: a Bayesian Approach. Garrett Cox, MPH Mark Malek , MD, MPH Sonali Kulkarni , MD, MPH Los Angeles County Jail Los Angeles County Sheriff’s Department. Biosurveillance AND PUBLIC HEALTH IN CORRECTIONS.

taber
Download Presentation

Risk-Based Surveillance of HIV at the Los Angeles County Jail: a Bayesian Approach

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Risk-Based Surveillance of HIV at the Los Angeles County Jail: a Bayesian Approach Garrett Cox, MPH Mark Malek, MD, MPH SonaliKulkarni, MD, MPH Los Angeles County Jail Los Angeles County Sheriff’s Department

  2. Biosurveillance AND PUBLIC HEALTH IN CORRECTIONS • SURVEILLANCE OF DISEASE IS A PRIMARY PUBLIC HEALTH FUNCTION • Estimating disease occurrence • Identifying risk factors • Detecting outbreaks • POPULATION SCREENING • Identification of new cases • Early detection of disease improves outcomes • CORRECTIONAL POPULATIONS ARE ALREADY HIGH RISK • Who do we screen?

  3. RISK-BASED SURVEILLANCE • HIV RISK FACTORS ARE WELL ESTABLISHED • Sexual behavior: MSM, previous or current STI’s • Mental Health and substance abuse • OBJECTIVE DATA FOR RISK ASSESSMENT IS AVAILABLE • Electronic medical records • Custody related data • SCREENING SHOULD FOCUS ON INDIVIDUALS MOST AT RISK MONITORING OF ROUTINE SCREENING COMBINED WITH RISK-BASED SCREENING CAN BOTH ESTIMATE RATES AND DETECT RATE INCREASES.

  4. WHY BAYESIAN METHODS FOR SURVEILLANCE? • BAYESIAN METHODS TAKE INTO ACCOUNT PRIOR INFORMATION • Prior HIV rates • Prior population rates of risk factors • POSTERIOR RESULTS VERIFY OR UPDATE THE PRIOR ESTIMATION • New data is evaluated based on prior estimations • Changes update or refine prior probabilities • MODERN ADVANCES IN COMPUTING AND SIMULATION HAVE MADE BAYESIAN ANALYTICS PRACTICAL

  5. DIFFERENCES BETWEEN BAYESIAN AND TRADITIONAL APPROACHs BAYESIAN TRADITIONAL Makes statements based on long-run repetition Probabilities are objective and prior knowledge or data has no bearing • Makes direct statements based on observed data • Probabilities are subjective and based on prior knowledge or data

  6. Prior data • ESTABLISHING PRIOR PROBABILITIES FOR A RISK-BASED SURVEILLANCE PROGRAM • 1.) Establish the prevalence of risk factors in a population • 2.) Calculate the HIV prevalence based for each risk factor • 3.) Use these probability distributions as the priors for establishing a risk-based approach

  7. HIV and selected risk factors at the los Angeles county jail

  8. Updating • NEW INCARCERATIONS WITH RISK FACTORS ARE IDENTIFIED • USING STRUCTURES ALREADY IN PLACE, OPT-OUT HIV TESTING IS ORDERED FOR EACH INDIVIDUAL • DATA ARE COLLECTED AND ANALYZED • PRIORS ARE UPDATED WITH POSTERIOR PROBABILITIES BASED ON NEW DATA: CHANGES TO PRIOR PROBABILTIES CAN INDICATE AN INCREASE IN DISEASE RATES • REPEAT

  9. LIMITATIONS • POSTERIOR ODDS CAN BE HEAVILY INFLUENCED BY PRIORS • Priors should be supported by good preliminary data or knowledge • DATA ANALYSIS IS COMPUTATIONALLY INTENSIVE AND REQUIRES KNOWLEDGE STATISTICAL PROGRAMMING • BAYESIAN METHODS ARE NOT AS WIDELY KNOWN OR UTILIZED • ASCERTAINING THE PREVALENCE RATE AMONG INMATES WITHOUT KNOWN RISK FACTORS CAN BE PROBLEMATIC.

  10. CONCLUSIONS • BAYESIAN METHODS PROVIDE AN INTUITIVE WAY OF COMBINING PRIOR INFORMATION WITH NEW DATA USING A SYSTEMATIC AND FLEXIBLE THEORETICAL APPROACH. • BAYESIAN METHODOLOGY IS IDEAL FOR IMPLEMENTATION IN CORRECTIONAL SETTINGS. • BAYESIAN UPDATING PROVIDES THE ANALYTIC FRAMEWORK FOR DESCRIBING DISEASE RATES AND FOR DETECTING CHANGES. • BY FOCUSING ON RISK FACTORS WE CAN PINPOINT SPECIFIC CHANGES WITHIN A LARGER POPULATION

  11. REFERENCES Abbas, K, Mikler A, Ramezani A, and Menezes S. Computational Epidemiology: Bayesian Disease Surveillance, 09/01/2003-05/31/2005, Proceedings of the International Conference on Bioinformatics and its Applications (ICBA'04), Fort Lauderdale, FL, December, 2004,  2004 Lesaffe E, Lawson A. Bayesian Biostatistics.1stEd. New York: Wiley & Sons. 2012 Malek M, Bazazi AR, Cox G, Rival G, Baillargeon J, Miranda A, Rich JD. Implementing opt-out programs at Los Angeles county jail: a gateway to novel research and interventions. Journal of Correctional Health Care. 2011 Jan;17(1):69-76. O’Hagen A, Luce B. A primer on Bayesian Statistics in Health and Outcomes Research. MEDTAP: 2003.

More Related