Advancing Spatiotemporal Modeling of Access to Healthcare – A Methodological Perspective

Modelers apply system dynamics (SD) modeling in various fields for different purposes including policy analysis, however, they need to integrate SD with other methodologies to facilitate the inclusion of spatial factors and study their influence on the system’s behavior. We investigate the combination of SD modeling with Geographic Information Systems using healthcare data to facilitate the study of both spatial and systemic factors for more effective policy design. We propose an algorithm for integrating these methodologies and explain one of its applications in the complex health systems—Medicaid beneficiaries’ access to primary care (PC). Our results reveal insights and information that were not available through merely SD modeling; this approach provides the opportunity for policymakers to learn about the influence of spatiotemporal factors on health outcomes in a complex health system, and identify the areas with a high need for PC providers

Trends and Insights from Transportation Congestion Pricing Policy Research: A Bibliometric Analysis

Toll-based congestion pricing (CP) policies are increasingly implemented globally for alleviating road traffic congestion. Several interconnected factors affecting or induced by CP implementation include air quality/emissions, travel time, and road user safety. We sought to examine and characterize research output and patterns across several domains (e.g., health, policy acceptability) surrounding toll-based CP policies, in order to identify where research has focused and where gaps exist. We conducted a structured review and identified 2333 relevant publications, using semi-supervised and machine learning strategies combined with manual review. Annual publication counts peaked in 2015 (n = 122). Themes identified from title and abstract terms included policy implementation characteristics, advanced transportation modeling methods and approaches, and public perception and acceptability. Authorship networks indicated a lack of interdisciplinary research. Country analyses identified the US, China, and the UK as the most frequently represented countries, and underrepresentation from low-income countries. Findings indicate that research focused on specific road user types (e.g., pedestrians) and safety impacts, and equity considerations were relatively sparse compared to other topics (e.g., policy economics, public perception). Additional research on these critical topics is necessary to ensure that such policies are designed to promote positive and equitable effects on road user health and safety

Hepatitis C virus risk among young people who inject drugs

BackgroundInjection drug use (IDU) is the leading risk factor for hepatitis C virus (HCV) transmission in the U.S. While the general risk factors for HCV transmission are known, there is limited work on how these factors interact and impact young people who inject drugs (YPWID).MethodsProject data were drawn from a study of 539 New York City (NYC) residents ages 18-29 who were recruited via Respondent-Driven Sampling and, reported past-month non-medical use of prescription opioids and/or heroin. Analyses are based on a subsample of 337 (62%) who reported injecting any drug in the past 12 months. All variables were assessed via self-report, except HCV status, which was established via rapid antibody testing. Integrating the observed statistical associations with extant literature on HCV risk, we also developed a qualitative system dynamics (SD) model to use as a supplemental data visualization tool to explore plausible pathways and interactions among key risk and protective factors for HCV.ResultsResults showed a 31% HCV antibody prevalence with an overall incidence of 10 per 100 person-years. HCV status was independently correlated with having shared cookers with two or more people (AOR = 2.17); injected drugs 4–6 years (AOR = 2.49) and 7 or more years (AOR = 4.95); lifetime homelessness (AOR = 2.52); and having been incarcerated two or more times (AOR = 1.99). These outcomes along with the extant literature on HCV risk were used to develop the qualitative SD model, which describes a causal hypothesis around non-linearities and feedback loop structures underlying the spread of HCV among YPWID.ConclusionsDespite ongoing harm reduction efforts, close to a third of YPWID in the community sample have been exposed to HCV, have risks for injection drug use, and face challenges with structural factors that may be preventing adequate intervention. The qualitative SD model explores these issues and contributes to a better understanding of how these various risk factors interact and what policies could potentially be effective in reducing HCV infections

A Systems Dynamic Approach to Alzheimer’s Disease Prevention

ABSTRACT Objectives As estimated there are about 5.3 million who suffer from Alzheimer’s disease in United States. The incidence is increasing as the population is aging. Due to the increasing trend of Alzheimer’s disease, there is a lot of discussion on prevention efforts or slowing the incidence. Also, models that could predict individual risk of cognitive impairment are needed to assist in prevention efforts.  In general dementia development has been associated with growth in various vascular, lifestyle and other risk factors. Epidemiological research provides evidence of some vascular, lifestyle and psychological risk factors that are modifiable and protective of disease incidence either independently or while interacting with other factors. However, as reported by National Institute of Aging, it is not yet clear whether health or lifestyle factors can prevent Alzheimer’s disease. The objective of this research project is to adopt a system dynamics modeling approach to study the interaction of several key factors including vascular, lifestyle and psychological aspects over the life course of individuals, to gain further understanding of Alzheimer’s disease incidence and evaluate prevention strategies. Both datasets of ‘Alzheimer's Disease Neuroimaging Initiative (ADNI)’ and ‘Health and Retirement Study (HRS)’ will be used for model development and validation. Approach A system dynamics approach is an optimal choice for addressing the goal of this proposal because different key factors interact over time and make Alzheimer’s disease incidence a complex problem. Furthermore, system dynamics approaches focus on understanding the relationship between the structure of a system and the resulting dynamic behaviors generated through multiple interacting feedback loops. Such an approach could be invaluable in studying dynamic problems arising in complex health, social, economic, or ecological systems. Results For the purpose of the proposal, the following stages are planned: 1. Develop a system dynamics simulation model at individual level that predicts the Alzheimer’s disease incidence over the life course, and aggregates individual level models to predict population level trends 2. Calibrate the resulting simulation model based upon longitudinal data trends employed from Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Both cohorts with Alzheimer’s disease and control subjects from this database will be used to fine-tune the simulation model. Conclusion The final validated model would be used to test different hypotheses and evaluate various strategies and/or their combinations to help evaluate the efficacy of prevention strategies on Alzheimer’s disease incidence and its growth

Risk Factors of Lyme Disease: An Intersection of Environmental Ecology and Systems Science

Lyme disease (LD) cases have been on the rise throughout the United States, costing the healthcare system up to $1.3 billion per year, and making LD one of the greatest threats to public health. Factors influencing the number of LD cases range from environmental to system-level variables, but little is known about the influence of vegetation (canopy, understory, and ground cover) and human behavioral risk on LD cases and exposure to infected ticks. We determined the influence of various risk factors on the risk of exposure to infected ticks on 22 different walkways using multinomial logistic regression. The model classifies the walkways into high-risk and low-risk categories with 90% accuracy, in which the understory, human risk, and number of rodents are significant indicators. These factors should be managed to control the risk of transmission of LD to humans

Trends and Insights from Transportation Congestion Pricing Policy Research: A Bibliometric Analysis

Toll-based congestion pricing (CP) policies are increasingly implemented globally for alleviating road traffic congestion. Several interconnected factors affecting or induced by CP implementation include air quality/emissions, travel time, and road user safety. We sought to examine and characterize research output and patterns across several domains (e.g., health, policy acceptability) surrounding toll-based CP policies, in order to identify where research has focused and where gaps exist. We conducted a structured review and identified 2333 relevant publications, using semi-supervised and machine learning strategies combined with manual review. Annual publication counts peaked in 2015 (n = 122). Themes identified from title and abstract terms included policy implementation characteristics, advanced transportation modeling methods and approaches, and public perception and acceptability. Authorship networks indicated a lack of interdisciplinary research. Country analyses identified the US, China, and the UK as the most frequently represented countries, and underrepresentation from low-income countries. Findings indicate that research focused on specific road user types (e.g., pedestrians) and safety impacts, and equity considerations were relatively sparse compared to other topics (e.g., policy economics, public perception). Additional research on these critical topics is necessary to ensure that such policies are designed to promote positive and equitable effects on road user health and safety

The Underlying Formula of our System Dynamics Model from Model-based risk assessment and public health analysis to prevent Lyme disease

Supplementary Material

Qualitative System Dynamics Modeling to Support Community Planning in Opioid Overdose Prevention

Purpose: We aimed to help community stakeholders develop a shared understanding of the opioid crisis through qualitative system dynamics (SD) modeling to inform local strategies for prevention and treatment. Methods: As part of the HEALing Communities Study-New York State, we used secondary qualitative data from community stakeholder interviews and coalition meeting notes to develop qualitative SD models that elucidate the interdependencies and feedback structures underlying the opioid epidemic in each community. Results: The synthesized model revealed multiple balancing and reinforcing feedback loops that influenced the adoption and reach of evidence-based practices to reduce opioid overdose and fatality. Conclusion: SD modeling is a novel approach to helping community stakeholders to see the inter-connectedness of actors, factors and sectors and the need for multiple mutually reinforcing strategies to avert opioid overdose and fatality. Social workers could play a key role in linking actions across sectors in such a complex system