title: Agent-Based Model Forecasts Aging of the Population of People Who Inject Drugs in Metropolitan Chicago and Changing Prevalence of Hepatitis C Infections.
abstract: People who inject drugs (PWID) are at high risk for blood-borne pathogens transmitted during the sharing of contaminated injection equipment, particularly hepatitis C virus (HCV). HCV prevalence is influenced by a complex interplay of drug-use behaviors, social networks, and geography, as well as the availability of interventions, such as needle exchange programs. To adequately address this complexity in HCV epidemic forecasting, we have developed a computational model, the Agent-based Pathogen Kinetics model (APK). APK simulates the PWID population in metropolitan Chicago, including the social interactions that result in HCV infection. We used multiple empirical data sources on Chicago PWID to build a spatial distribution of an in silico PWID population and modeled networks among the PWID by considering the geography of the city and its suburbs. APK was validated against 2012 empirical data (the latest available) and shown to agree with network and epidemiological surveys to within 1%. For the period 2010-2020, APK forecasts a decline in HCV prevalence of 0.8% per year from 44(± 2)% to 36(± 5)%, although some sub-populations would continue to have relatively high prevalence, including Non-Hispanic Blacks, 48(± 5)%. The rate of decline will be lowest in Non-Hispanic Whites and we find, in a reversal of historical trends, that incidence among non-Hispanic Whites would exceed incidence among Non-Hispanic Blacks (0.66 per 100 per years vs 0.17 per 100 person years). APK also forecasts an increase in PWID mean age from 35(± 1) to 40(± 2) with a corresponding increase from 59(± 2)% to 80(± 6)% in the proportion of the population >30 years old. Our studies highlight the importance of analyzing subpopulations in disease predictions, the utility of computer simulation for analyzing demographic and health trends among PWID and serve as a tool for guiding intervention and prevention strategies in Chicago, and other major cities.
authors.affiliation: Division of Epidemiology and Biostatistics, School of Public Health, University of Illinois at Chicago, Chicago, Illinois, United States of America; The Program for Experimental & Theoretical Modeling, Department of Medicine, Loyola University Medical Center, Maywood, Illinois, United States of America; Center for Biologics Evaluation and Research, Food and Drug Administration, Silver Spring, Maryland, United States of America.
authors.forename: Alexander
authors.initials: A
authors.lastname: Gutfraind
authors.affiliation: Division of Epidemiology and Biostatistics, School of Public Health, University of Illinois at Chicago, Chicago, Illinois, United States of America.
authors.forename: Basmattee
authors.initials: B
authors.lastname: Boodram
authors.affiliation: STI/HIV Surveillance, Chicago Department of Public Health, Chicago, Illinois, United States of America.
authors.forename: Nikhil
authors.initials: N
authors.lastname: Prachand
authors.affiliation: Department of Computational Social Science, George Mason University, Fairfax, Virginia, United States of America.
authors.forename: Atesmachew
authors.initials: A
authors.lastname: Hailegiorgis
authors.affiliation: The Program for Experimental & Theoretical Modeling, Department of Medicine, Loyola University Medical Center, Maywood, Illinois, United States of America; Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America.
authors.forename: Harel
authors.initials: H
authors.lastname: Dahari
authors.affiliation: Center for Biologics Evaluation and Research, Food and Drug Administration, Silver Spring, Maryland, United States of America.
authors.forename: Marian E
authors.initials: ME
authors.lastname: Major
mesh_terms: D000293:Adolescent; D000328:Adult; D017848:Blood-Borne Pathogens; D002641:Chicago / Q000453:epidemiology; D003198:Computer Simulation; D066264:Datasets as Topic; D005260:Female; D006526:Hepatitis C / Q000453:epidemiology*; D006801:Humans; D008297:Male; D018671:Needle-Exchange Programs / Q000706:statistics & numerical data; D011157:Population Dynamics / Q000639:trends*; D000069457:Population Forecast; D011159:Population Surveillance; D015995:Prevalence; D015819:Substance Abuse, Intravenous / Q000150:complications / Q000453:epidemiology* / Q000821:virology; D019966:Substance-Related Disorders / Q000150:complications / Q000453:epidemiology* / Q000821:virology; D055815:Young Adult