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Using synthetic populations to understand geospatial patterns in opioid related overdose and predicted opioid misuse

DOI:10.1007/s10588-018-09281-2
Authors:
Savannah Bates
Savannah Bates
Savannah Bates
  • This person is not on ResearchGate, or hasn't claimed this research yet.
Vasiliy Leonenko at ITMO University
Vasiliy Leonenko
James Rineer at RTI International
James Rineer
Georgiy V Bobashev at University of North Carolina at Chapel Hill
Georgiy V Bobashev
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Abstract and Figures

Ohio is leading the nation in an epidemic of overdose deaths, most of which are caused by opioids. Through this study we estimate associations between opioid drug overdoses measured as EMS calls and model-predicted drug misuse. The RTI-developed synthetic population statistically represents every household in Cincinnati and allows one to develop a geographically explicit model that links Cincinnati EMS data, and other datasets. From the publicly available National Survey on Drug Use and Health (NSDUH), we developed a model of opioid misuse and assigned probability of misuse to each synthetic individual. We then analyzed EMS overdose data in the context of local level misuse and demographic characteristics. The main results show locations where there is a dramatic variation in ratio values between overdose events and the number of misusers. We concluded that, for optimal efficacy, intervention strategies should consider the existence of exceptional geographic locations with extremely high or low values of this ratio.
Venn diagram of cell data types
Venn diagram of cell data types
… 
Categorical depiction of cell level data using the zero threshold. White cells are those outside the Cincinnati city limits
Categorical depiction of cell level data using the zero threshold. White cells are those outside the Cincinnati city limits
… 
Heat map of r1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$r_1$$\end{document}—the modified ratio of calls per misuser from January 2015 to April 29, 2018. The r1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$r_1$$\end{document} values for cells are plotted on a log scale. Cells in red have a higher number of calls per misuser and conversely cells in blue have lower number of calls per misuser. Cells which were hard to interpret by the misuse model based on r1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$r_1$$\end{document} and r2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$r_2$$\end{document} are shaded in grey. (Color figure online)
Heat map of r1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$r_1$$\end{document}—the modified ratio of calls per misuser from January 2015 to April 29, 2018. The r1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$r_1$$\end{document} values for cells are plotted on a log scale. Cells in red have a higher number of calls per misuser and conversely cells in blue have lower number of calls per misuser. Cells which were hard to interpret by the misuse model based on r1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$r_1$$\end{document} and r2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$r_2$$\end{document} are shaded in grey. (Color figure online)
… 
Heat map of log10(r1)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\log _{10}(r_1)$$\end{document} plotted on the map of Cincinnati with uninterpreted cells shaded in gray. Regions containing the highest and lowest ratios have been highlighted. The locations of a certain fast food restaurant are shown by red stars. (Color figure online)
Heat map of log10(r1)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\log _{10}(r_1)$$\end{document} plotted on the map of Cincinnati with uninterpreted cells shaded in gray. Regions containing the highest and lowest ratios have been highlighted. The locations of a certain fast food restaurant are shown by red stars. (Color figure online)
… 
On the left, a heat map of log10(r1)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\log _{10}(r_1)$$\end{document} with uninterpreted cells shaded in gray is plotted with the Cincinnati zoning borders. On the right, we show a simplified zoning map of Cincinnati. Red areas represent commercial zones, yellow represent industrial areas and parks, and blue represents residential areas. (Color figure online)
On the left, a heat map of log10(r1)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\log _{10}(r_1)$$\end{document} with uninterpreted cells shaded in gray is plotted with the Cincinnati zoning borders. On the right, we show a simplified zoning map of Cincinnati. Red areas represent commercial zones, yellow represent industrial areas and parks, and blue represents residential areas. (Color figure online)
… 
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Vol:.(1234567890)
Computational and Mathematical Organization Theory (2019) 25:36–47
https://doi.org/10.1007/s10588-018-09281-2
1 3
S.I. : SBP-BRIMS 2018
Using synthetic populations tounderstand geospatial
patterns inopioid related overdose andpredicted opioid
misuse
SavannahBates1 · VasiliyLeonenko2· JamesRineer3· GeorgiyBobashev3
Published online: 5 December 2018
© Springer Science+Business Media, LLC, part of Springer Nature 2018
Abstract
Ohio is leading the nation in an epidemic of overdose deaths, most of which are
caused by opioids. Through this study we estimate associations between opioid
drug overdoses measured as EMS calls and model-predicted drug misuse. The RTI-
developed synthetic population statistically represents every household in Cincin-
nati and allows one to develop a geographically explicit model that links Cincinnati
EMS data, and other datasets. From the publicly available National Survey on Drug
Use and Health (NSDUH), we developed a model of opioid misuse and assigned
probability of misuse to each synthetic individual. We then analyzed EMS overdose
data in the context of local level misuse and demographic characteristics. The main
results show locations where there is a dramatic variation in ratio values between
overdose events and the number of misusers. We concluded that, for optimal effi-
cacy, intervention strategies should consider the existence of exceptional geographic
locations with extremely high or low values of this ratio.
Keywords Opioids· Synthetic populations· Data linkage· Overdose
Savannah Bates received support from the Research Training Group in Mathematical Biology,
funded by a National Science Foundation Grant RTG/DMS—1246991. Vasiliy Leonenko was
supported by the Fullbright Visiting Scholar Program.
* Georgiy Bobashev
bobashev@rti.org
Savannah Bates
svbates@ncsu.edu
1 Department ofBiomathematics, North Carolina State University, 2620 Yarbrough Dr, Raleigh,
NC27607, USA
2 Institute ofDesign & Urban Studies, ITMO University, 49 Kronverksky Pr., St.Petersburg,
Russia197101
3 RTI International, 3040 E Cornwallis Rd, Durham, NC27709, USA
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
... In order to understand the relationship between the numbers of AH+ users and the number of EMS calls, we follow our earlier research [2], where the ratio r 1 between the overdose-related EMS calls and the assessed number of opioid drug users was studied. In this paper, we compare r 1 with the alternative indicator r 2 which depends on the cumulative number of people in the cell under study instead of the assessed quantity of AH+ individuals. ...
... Despite the fact that we cannot draw any definite and final conclusions, in the author's opinion, the study successfully introduces the application of the concept of using synthesized data for health conditions of unknown prevalence (arterial hypertension) to categorize spatial distribution of their acute repercussions (acute coronary syndrome). As it was demonstrated by the authors before [2], the same approach can be successfully used in case of opioid drug usage, and we expect to broaden the scope of its application by applying it in other domains. ...
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In the current study, the authors demonstrate the method aimed at analyzing the distribution of acute coronary syndrome (ACS) cases in Saint Petersburg. The employed approach utilizes a synthetic population of Saint Petersburg and a statistical model for arterial hypertension prevalence. The number of ACS–related emergency services calls in an area is matched with the population density and the prospected number of individuals with arterial hypertension, which makes it possible to find locations with excessive ACS incidence. Three categories of locations, depending on the joint distribution of the above-mentioned indicators, are proposed as a result of data analysis. The method is implemented in Python programming language, the visualization is made using QGIS open software. The proposed method can be used to assess the prevalence of certain health conditions in the population and to match them with the corresponding severe health outcomes.
... Homicide rates across the US are also correlated with opioid overdoses, suggesting a close nexus between violence and the drug markets that fuel the opioid crisis (Rosenfeld et al., 2021), with particular emphasis on gun violence (Johnson et al., 2020). This is in part because users prefer to ingest opioids near the location of drug markets (Bates et al., 2019). ...
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... In addition to the mentioned framework modification, we consider another way of framework development which includes the changing of the modeling scale. We propose a framework evolution towards a geographically explicit prediction of dynamics of PJI cases in time at a city level, using synthesized populations as a model input [33,34]. The modified modeling framework for the PJI treatment can be fed by synthetic data coming from a demographic model for the urban population and a statistical model of PJI occurrence in that population. ...
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... One of the possible framework applications consists in forecasting disease trajectories for patients using their individual characteristics (age, gender, body mass index) as parameters affecting the transition probabilities. Another one is to consider the prediction of PJI cases at the city level, using synthesized populations as a model input [9,10]. The framework coupled with the statistical model of PJI occurrence probability and the synthetic demographic data will make it possible to assess the middle-and long-term expenses of PJI treatment, the hospital occupancy and the prospected potential years of life lost in the urban population depending on the prevalent treatment methods. ...
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In this paper, we present a framework, which aims at facilitating the choice of the best strategy related to the treatment of periprosthetic joint infection (PJI). The framework includes two models: a detailed non-Markovian model based on the decision tree approach, and a general Markov model, which captures the most essential states of a patient under treatment. The application of the framework is demonstrated on the dataset provided by Russian Scientific Research Institute of Traumatology and Orthopedics “R.R. Vreden”, which contains records of patients with PJI occurred after total hip arthroplasty. The methods of cost-effectiveness analysis of treatment strategies and forecasting of individual treatment outcomes depending on the selected strategy are discussed.
... Examples include predicting the effect of new cancer screening on the US population, identifying obesity hotspots (i.e., geographic areas with unusually high prevalence of high BMI individuals), and predicting the effect of interventions aimed to reduce opioid-related deaths. [1][2][3][4][5][6] In each case, predictive models need to use data on population demographics and geographic locations, along with the natural history of the disease, administrative data, etc. We can thus formulate the main question as follows: ...
Universal Mechanism to Link Multiple Datasets with Synthetic Populations for Clinical and Epidemiological Research
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... In some areas, accidental deaths from overdose may be co-located with outdoor drug markets. Recent research suggests that many opioid misusers, in particular, use drugs near locations of drug purchase rather than at their homes (Bates et al. 2019;Metraux et al. 2019). Law enforcement reports and anecdotal evidence also suggest a contemporary link between drug market locations and deaths from drug overdose given the powerful lure of inexpensive drugs and high-quality heroin in some jurisdictions. ...
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Attribute Assignment to a Synthetic Population in Support of Agent-Based Disease Modeling
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  • Sep 2010
Communicable-disease transmission models are useful for the testing of prevention and intervention strategies. Agent-based models (ABMs) represent a new and important class of the many types of disease transmission models in use. Agent-based disease models benefit from their ability to assign disease transmission probabilities based on characteristics shared by individual agents. These shared characteristics allow ABMs to apply transmission probabilities when agents come together in geographic space. Modeling these types of social interactions requires data, and the results of the model largely depend on the quality of these input data. We initially generated a synthetic population for the United States, in support of the Models of Infectious Disease Agent Study. Subsequently, we created shared characteristics to use in ABMs. The specific goals for this task were to assign the appropriately aged populations to schools, workplaces, and public transit. Each goal presented its own challenges and problems; therefore, we used different techniques to create each type of shared characteristic. These shared characteristics have allowed disease models to more realistically predict the spread of disease, both spatially and temporally.
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Full-text available
  • May 2009
Agent-based models simulate large-scale social systems. They assign behaviors and activities to "agents" (individuals) within the population being modeled and then allow the agents to interact with the environment and each other in complex simulations. Agent-based models are frequently used to simulate infectious disease outbreaks, among other uses.RTI used and extended an iterative proportional fitting method to generate a synthesized, geospatially explicit, human agent database that represents the US population in the 50 states and the District of Columbia in the year 2000. Each agent is assigned to a household; other agents make up the household occupants.For this database, RTI developed the methods for generating synthesized households and personsassigning agents to schools and workplaces so that complex interactions among agents as they go about their daily activities can be taken into accountgenerating synthesized human agents who occupy group quarters (military bases, college dormitories, prisons, nursing homes).In this report, we describe both the methods used to generate the synthesized population database and the final data structure and data content of the database. This information will provide researchers with the information they need to use the database in developing agent-based models.Portions of the synthesized agent database are available to any user upon request. RTI will extract a portion (a county, region, or state) of the database for users who wish to use this database in their own agent-based models.
Ohio’s Overdose Deaths Soar Again, as State Ranks 2nd Worst in the Nation
  • T Demio
DeMio T (2017) Ohio's Overdose Deaths Soar Again, as State Ranks 2nd Worst in the Nation. Cincinnati Enquirer. www.cinci nnati.com/story /news/2017/12/21/ohios -overd ose-death s-soar-again -state -ranks -2nd-worst -natio n/97322 0001/. Accessed 7 May 2018
Deters: Man Who Kidnapped 84-Year-Old ‘Deserves to Stay in Prison for a Long Time
  • C Knight
  • S Brookbank
Knight C, Brookbank S (2018) Deters: Man Who Kidnapped 84-Year-Old 'Deserves to Stay in Prison for a Long Time'. Cincinnati Enquirer. www.cinci nnati.com/story /news/crime /crime -and-court s/2018/02/19/ polic e-man-made-woman -buy-him-wendy s-drive -him-drug-deal-knife point /35295 4002/. Accessed 10 May 2018
Ohio Parents Overdose on Heroin at White Castle While Kids Are with Them
  • M Rotuno-Johnson
Rotuno-Johnson M (2017) Ohio Parents Overdose on Heroin at White Castle While Kids Are with Them. WCMH
How Can Ohio Cities Step Up Their Response To Overdoses? Make The Data Public. WOSU 89.7 NPR News. radio.wosu.org/post/how-can-ohio-cities-step-their-response-overdoses-make-data-public#stream/0
  • L Walinchus
Walinchus L (2018) How Can Ohio Cities Step Up Their Response To Overdoses? Make The Data Public. WOSU 89.7 NPR News. radio.wosu.org/post/how-can-ohio-citie s-step-their -respo nse-overd oses-makedata-publi c#strea m/0. Accessed 7 May 2018