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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,more » 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. As a result, 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.« less

Authors:
 [1];  [2];  [3];  [4];  [5];  [6];  [7]
  1. Univ. of Illinois at Chicago, Chicago, IL (United States); Loyola Univ. Medical Center, Maywood, IL (United States); Food and Drug Administration, Silver Springs, MD (United States)
  2. Univ. of Illinois at Chicago, Chicago, IL (United States)
  3. Chicago Dept. of Public Health, Chicago, IL (United States)
  4. George Mason Univ., Fairfax, VA (United States)
  5. Loyola Univ. Medical Center, Maywood, IL (United States); Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
  6. Food and Drug Administration, Silver Springs, MD (United States)
  7. Univ. Medicine Greifswald (Germany)
Publication Date:
Research Org.:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1223795
Alternate Identifier(s):
OSTI ID: 1235895
Report Number(s):
LA-UR-15-20358
Journal ID: ISSN 1932-6203
Grant/Contract Number:  
AC52-06NA25396
Resource Type:
Accepted Manuscript
Journal Name:
PLoS ONE
Additional Journal Information:
Journal Volume: 10; Journal Issue: 9; Journal ID: ISSN 1932-6203
Publisher:
Public Library of Science
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES; Hepatitis C virus; careers; behavioral geography; social networks; demography; equipment; ethnic epidemiology; Hispanic people; Hepatitis C

Citation Formats

Gutfraind, Alexander, Boodram, Basmattee, Prachand, Nikhil, Hailegiorgis, Atesmachew, Dahari, Harel, Major, Marian E., and Kaderali, Lars. Agent-based model forecasts aging of the population of people who inject drugs in metropolitan Chicago and changing prevalence of hepatitis C infections. United States: N. p., 2015. Web. doi:10.1371/journal.pone.0137993.
Gutfraind, Alexander, Boodram, Basmattee, Prachand, Nikhil, Hailegiorgis, Atesmachew, Dahari, Harel, Major, Marian E., & Kaderali, Lars. Agent-based model forecasts aging of the population of people who inject drugs in metropolitan Chicago and changing prevalence of hepatitis C infections. United States. doi:10.1371/journal.pone.0137993.
Gutfraind, Alexander, Boodram, Basmattee, Prachand, Nikhil, Hailegiorgis, Atesmachew, Dahari, Harel, Major, Marian E., and Kaderali, Lars. Wed . "Agent-based model forecasts aging of the population of people who inject drugs in metropolitan Chicago and changing prevalence of hepatitis C infections". United States. doi:10.1371/journal.pone.0137993. https://www.osti.gov/servlets/purl/1223795.
@article{osti_1223795,
title = {Agent-based model forecasts aging of the population of people who inject drugs in metropolitan Chicago and changing prevalence of hepatitis C infections},
author = {Gutfraind, Alexander and Boodram, Basmattee and Prachand, Nikhil and Hailegiorgis, Atesmachew and Dahari, Harel and Major, Marian E. and Kaderali, Lars},
abstractNote = {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. As a result, 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.},
doi = {10.1371/journal.pone.0137993},
journal = {PLoS ONE},
number = 9,
volume = 10,
place = {United States},
year = {2015},
month = {9}
}

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