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

Bates, Savannah; Leonenko, Vasiliy N.; Rineer, James; Bobashev, Georgiy

doi:10.1007/s10588-018-09281-2

Cited by 16 publications

(13 citation statements)

References 2 publications

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“…For each block group, we calculated measures of accessibility to health facilities, including minimum distance from the the geographic centers of each block group to hospitals [31], pharmacies [31], federally qualified health centers (FQHCs) [31], buprenorphine prescribing physicians, and Substance Abuse and Mental Health Services Administration (SAMHSA) Opioid Treatment Programs (OTPs) [32]; and the distance to the closest fire department [33]. Environmental variables have also been found to be associated with drug use and overdose [34–36]. In order to characterize block groups by features of their built environment relevant to the risk of overdose, we calculated the proportion of areas covered by quarter-mile buffer areas from bus stops, the proportion of park areas using the publicly available GIS data set from the city of Cincinnati [37], and the number of fast food restaurants from the Cincinnati Bell directory [38].…”

Section: Methodsmentioning

confidence: 99%

Suspected heroin-related overdoses incidents in Cincinnati, Ohio: A spatiotemporal analysis

Xie

Crawford

et al. 2019

PLoS Med

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Section: Methodsmentioning

confidence: 99%

Suspected heroin-related overdoses incidents in Cincinnati, Ohio: A spatiotemporal analysis

Xie

Crawford

et al. 2019

PLoS Med

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“…Since ACS is one of the most widespread consequences of AH [1], our idea is to compare different neighborhoods by the value of the ratio of total number of ACS cases to the assessed number of AH+ individuals. The same technique was earlier developed and applied by the authors to the ratio between the number of calls to emergency services related to opioid overdoses and the assessed number of opioid drug users in Cincinnatti [5].…”

Section: Resultsmentioning

confidence: 99%

“…It was used by various research groups to create populations for 50 US states, along with another regions and countries. The authors have experience of working with the synthetic populations from RTI, using it for statistical analysis of opioid-related overdoses in Cincinnatti [5] and for the investigation of the dynamics of influenza in Russian cities [6].…”

Section: Introductionmentioning

confidence: 99%

Analyzing the spatial distribution of individuals predisposed to arterial hypertension in Saint Petersburg using synthetic populations

Leonenko

Kovalchuk

2020

ITM Web Conf.

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Arterial hypertension (AH) is one of the most common cardiovascular diseases, and it can lead to serious complications. To optimize the delivery of patients exposed to AH to medical institutions and thus to curtail mortality in Russian cities caused by the consequences of hypertension, it is necessary to estimate the number of potential patients, along with their spatial distribution. This paper presents a method which uses synthetic population data to assess the spatial distribution of individuals potentially prone to arterial hypertension. The risk of arterial hypertension of an individual is calculated based on its demographic characteristics (age and gender). Using Saint Petersburg as a case study, we demonstrate that the mentioned approach makes it possible to perform predictions of AH cases distribution in absence of real data on hypertension status of the individuals. The results of the study will be used to assess the input flows of patients to healthcare facilities and optimize their workflow. Synthetic populationA "synthetic population" is a synthesized, spatially explicit human agent database (essentially, a simulated census) representing the population of a city, region or country. By its *

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“…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:…”

Section: Introductionmentioning

confidence: 99%

Universal Mechanism to Link Multiple Datasets with Synthetic Populations for Clinical and Epidemiological Research

Bobashev

Hadley

Karr³

et al. 2021

Preprint

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Many modern predictive models require knowledge acquired from multiple datasets, yet data linkage can be a substantial challenge. Datasets exist in a wide variety of formats and linking them directly is often infeasible or restricted by privacy requirements. One solution is to map variables from different datasets onto a synthetic population, producing a dataset that contains information from multiple sources sufficient for reliable statistical inference with quantifiable uncertainty. This approach is universal because it is applicable to a broad range of datasets and has good potential for privacy protection.We consider datasets with information about individuals and describe three methods for building linked synthetic data: resampling, modeling predictors independently, and modeling predictors sequentially. We apply these methods to the prediction of the prevalence of Florida youth vaping by state, county, and census tract using the 2018 Florida Youth Substance Abuse Survey (FYSAS) and the 5-Year American Community Survey (ACS). We find that resampling and sequential modeling most closely approximate the 2018 survey results, and that the sequential model captures more variability. We find that the order of predicting the variables in sequential modeling does not substantially impact the outcome.

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

Cited by 16 publications

References 2 publications

Suspected heroin-related overdoses incidents in Cincinnati, Ohio: A spatiotemporal analysis

Suspected heroin-related overdoses incidents in Cincinnati, Ohio: A spatiotemporal analysis

Analyzing the spatial distribution of individuals predisposed to arterial hypertension in Saint Petersburg using synthetic populations

Universal Mechanism to Link Multiple Datasets with Synthetic Populations for Clinical and Epidemiological Research

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