1. Garrett Cox, MPH Mark Malek, MD, MPH Sonali Kulkarni, MD, MPH Los Angeles County Jail Los Angeles County Sheriff’s Department RISK-BASED SURVEILLANCE OF HIV AT THE LOS ANGELES COUNTY JAIL: A BAYESIAN APPROACH

2.  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? BIOSURVEILLANCE AND PUBLIC HEALTH IN CORRECTIONS

3.  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. RISK-BASED SURVEILLANCE

4.  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 WHY BAYESIAN METHODS FOR SURVEILLANCE?

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

6.  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 PRIOR DATA

7. HIV AND SELECTED RISK FACTORS AT THE LOS ANGELES COUNTY JAIL

8.  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 UPDATING

9.  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. LIMITATIONS

10.  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 CONCLUSIONS

11. 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.1 st Ed. 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. REFERENCES