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Access to treatment and medication for opioid use disorder (MOUD) is essential in reducing opioid use and associated behavioral risks, such as syringe sharing among persons who inject drugs (PWID). Syringe sharing among PWID carries high risk of transmission of serious infections such as hepatitis C and HIV. MOUD resources, such as methadone provider clinics, however, are often unavailable to PWID due to barriers like long travel distance to the nearest methadone provider and the required frequency of clinic visits. The goal of this study is to examine the uncertainty in the effects of travel distance in initiating and continuing methadone treatment and how these interact with different spatial distributions of methadone providers to impact co-injection (syringe sharing) risks. A baseline scenario of spatial access was established using the existing locations of methadone providers in a geographical area of metropolitan Chicago, Illinois, USA. Next, different counterfactual scenarios redistributed the locations of methadone providers in this geographic area according to the densities of both the general adult population and according to the PWID population per zip code. We define different reasonable methadone access assumptions as the combinations of short, medium, and long travel distance preferences combined with three urban/suburban travel distance preference. Our modeling results show that when there is a low travel distance preference for accessing methadone providers, distributing providers near areas that have the greatest need (defined by density of PWID) is best at reducing syringe sharing behaviors. However, this strategy also decreases access across suburban locales, posing even greater difficulty in regions with fewer transit options and providers. As such, without an adequate number of providers to give equitable coverage across the region, spatial distribution cannot be optimized to provide equitable access to all PWID. Our study has important implications for increasing interest in methadone as a resurgent treatment for MOUD in the United States and for guiding policy toward improving access to MOUD among PWID. Author summary: Persons who inject drugs (PWID) such as heroin or other illicit substances are at increased risk of getting or transmitting HIV or hepatitis C (HCV) infection, because limited access to clean syringes and drug injection equipment means that PWID often share contaminated syringes. Public health interventions aimed at reducing injection drug use include medications for opioid use disorder (MOUD) that help to reduce cravings for opioids like heroin and fentanyl. However, resources like MOUD provider clinics are often unavailable to PWID due to factors such as the travel distance from home to clinic, frequent clinic visits, along with social phenomena such as degree of medical mistrust and stigma towards treatment. We use a computational simulation model of PWID social networks and drug use behaviors in Chicago, IL and suburbs to show how the geographic distribution of clinical providers of methadone, an MOUD, impacts the injection frequency of PWID. Our modeling results show that when PWID prefer a low travel distance to methadone providers, distributing providers near areas that have the greatest need is best at reducing syringe sharing behaviors. However, this strategy also decreases access across suburban locales, posing even greater difficulty in regions with fewer transit options and providers. [ABSTRACT FROM AUTHOR]

COVID-19 surveillance across the United States is essential to tracking and mitigating the pandemic, but data representing cases and deaths may be impacted by attribute, spatial, and temporal uncertainties. COVID-19 case and death data are essential to understanding the pandemic and serve as key inputs for prediction models that inform policy-decisions; consistent information across datasets is critical to ensuring coherent findings. We implement an exploratory data analytic approach to characterize, synthesize, and visualize spatial-temporal dimensions of uncertainty across commonly used datasets for case and death metrics (Johns Hopkins University, the New York Times, USAFacts, and 1Point3Acres). We scrutinize data consistency to assess where and when disagreements occur, potentially indicating underlying uncertainty. We observe differences in cumulative case and death rates to highlight discrepancies and identify spatial patterns. Data are assessed using pairwise agreement (Cohen's kappa) and agreement across all datasets (Fleiss' kappa) to summarize changes over time. Findings suggest highest agreements between CDC, JHU, and NYT datasets. We find nine discrete type-components of information uncertainty for COVID-19 datasets reflecting various complex processes. Understanding processes and indicators of uncertainty in COVID-19 data reporting is especially relevant to public health professionals and policymakers to accurately understand and communicate information about the pandemic. [ABSTRACT FROM AUTHOR]