Dimensions of Uncertainty: A spatiotemporal review of five COVID-19 datasets.

COVID-19 surveillance across the U.S. 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.

Social-spatial network structures among young urban and suburban persons who inject drugs in a large metropolitan area.

BACKGROUND: Recent studies underscore the significance of adopting a syndemics approach to study opioid misuse, overdose, hepatitis C (HCV) and HIV infections, within the broader context of social and environmental contexts in already marginalized communities. Social interactions and spatial contexts are crucial structural factors that remain relatively underexplored. This study examines the intersections of social interactions and spatial contexts around injection drug use. More specifically, we investigate the experiences of different residential groups among young (aged 18-30) people who inject drugs (PWID) regarding their social interactions, travel behaviors, and locations connected to their risk behaviors. By doing so, we aim to achieve a more comprehensive understanding of the multidimensional risk environment, thereby facilitating the development of informed policies. METHODS: We collected and examined data regarding young PWID's egocentric injection network and geographic activity spaces (i.e., where they reside, inject drugs, purchase drugs, and meet sex partners). Participants were stratified based on the location of all place(s) of residence in the past year i.e., urban, suburban, and transient (both urban and suburban) to i) elucidate geospatial concentration of risk activities within multidimensional risk environments based on kernel density estimates; and ii) examine spatialized social networks for each residential group. RESULTS: Participants were mostly non-Hispanic white (59%); 42% were urban residents, 28% suburban, and 30% transient. We identified a spatial area with concentrated risky activities for each residential group on the West side of Chicago in Illinois where a large outdoor drug market area is located. The urban group (80%) reported a smaller concentrated area (14 census tracts) compared to the transient (93%) and suburban (91%) with 30 and 51 tracts, respectively. Compared to other areas in Chicago, the identified area had significantly higher neighborhood disadvantages. Significant differences were observed in social network structures and travel behaviors: suburban participants had the most homogenous network in terms of age and residence, transient participants had the largest network (degree) and more non-redundant connections, while the urban group had the shortest travel distance for all types of risk activities. CONCLUSION: Distinct residential groups exhibit varying patterns of network interaction, travel behaviors, and geographical contexts related to their risk behaviors. Nonetheless, these groups share common concentrated risk activity spaces in a large outdoor urban drug market area, underscoring the significance of accounting for risk spaces and social networks in addressing syndemics within PWID populations.

Spatial inequities in access to medications for treatment of opioid use disorder highlight scarcity of methadone providers under counterfactual scenarios.

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.

How do unobserved confounding mediators and measurement error impact estimated mediation effects and corresponding statistical inferences? Introducing the R package ConMed for sensitivity analysis.

Empirical studies often demonstrate multiple causal mechanisms potentially involving simultaneous or causally related mediators. However, researchers often use simple mediation models to understand the processes because they do not or cannot measure other theoretically relevant mediators. In such cases, another potentially relevant but unobserved mediator potentially confounds the observed mediator, thereby biasing the estimated direct and indirect effects associated with the observed mediator and threatening corresponding inferences. Additionally, researchers may not know the extent to which their measures are reliable, and accordingly, measurement error may bias estimated effects and mislead statistical inferences. Given these threats, we explore how the omission of an unobserved mediator and/or using variables with measurement error biases estimates and affects inferences associated with the observed mediator. Then, building off Frank's impact threshold for a confounding variable (ITCV), we propose a correlation-based sensitivity analysis. Lastly, we provide an R package ConMed to assess the robustness of mediation inferences given the omission of an unobserved, confounding mediator and/or measurement error. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Social-spatial network structures among young urban and suburban persons who inject drugs in a large metropolitan area.

Background: It is estimated that there are 1.5% US adult population who inject drugs in 2018, with young adults aged 18-39 showing the highest prevalence. PWID are at a high risk of many blood-borne infections. Recent studies have highlight the importance of employing the syndemic approach to study opioid misuse, overdose, HCV and HIV, along with the social and environmental contexts where these interrelated epidemics occur in already marginalized communities. Social interactions and spatial contexts are important structural factors that are understudied. Methods: Egocentric injection network and geographic activity spaces for young (aged 18-30) PWID and their injection, sexual, and social support network members (i.e., where reside, inject drugs, purchase drugs, and meet sex partners) were examined using baseline data from an ongoing longitudinal study (n=258). Participants were stratified based on the location of all place(s) of residence in the past year i.e., urban, suburban, and transient (both urban and suburban) to i) elucidate geospatial concentration of risk activities within multi-dimensional risk environments based on kernel density estimates; and ii) examine spatialized social networks for each residential group. Results: Participants were mostly non-Hispanic white (59%); 42% were urban residents, 28% suburban, and 30% transient. We identified a spatial area with concentrated risky activities for each residence group on the West side of Chicago where a large outdoor drug market area is located. The urban group (80%) reported a smaller concentrated area (14 census tracts) compared to the transient (93%) and suburban (91%) with 30 and 51 tracts, respectively. Compared to other areas in Chicago, the identified area had significantly higher neighborhood disadvantages (e.g., higher poverty rate, p <0.001). Significant ( p <0.01 for all) differences were observed in social network structures: suburban had the most homogenous network in terms of age and residence, transient participants had the largest network (degree) and more non-redundant connections. Conclusion: We identified concentrated risk activity spaces among PWID from urban, suburban, and transient groups in a large outdoor urban drug market area, which highlights the need for considering the role of risk spaces and social networks in addressing the syndemics in PWID populations.

Quantifying the robustness of causal inferences: Sensitivity analysis for pragmatic social science.

Social scientists seeking to inform policy or public action must carefully consider how to identify effects and express inferences because actions based on invalid inferences may not yield the intended results. Recognizing the complexities and uncertainties of social science, we seek to inform inevitable debates about causal inferences by quantifying the conditions necessary to change an inference. Specifically, we review existing sensitivity analyses within the omitted variables and potential outcomes frameworks. We then present the Impact Threshold for a Confounding Variable (ITCV) based on omitted variables in the linear model and the Robustness of Inference to Replacement (RIR) based on the potential outcomes framework. We extend each approach to include benchmarks and to fully account for sampling variability represented by standard errors as well as bias. We exhort social scientists wishing to inform policy and practice to quantify the robustness of their inferences after utilizing the best available data and methods to draw an initial causal inference.

Methadone prescribing by addiction specialists likely to leave communities without available methadone treatment.

Methadone treatment for opioid use disorder is not available in most suburban and rural US communities. We examined 2 options to expand methadone availability: (1) addiction specialty physician or (2) all clinician prescribing. Using 2022 Health Resources and Services Administration data, we used mental health professional shortage areas to indicate the potential of addiction specialty physician prescribing and the location of federally qualified health centers (ie, federally certified primary care clinics) to indicate the potential of all clinician prescribing. We examined how many census tracts without an available opioid treatment program (ie, methadone clinic) are (1) located within a mental health professional shortage area and (2) are also without an available federally qualified health center. Methadone was available in 49% of tracts under current regulations, 63% of tracts in the case of specialist physician prescribing, and 86% of tracts in the case of all clinician prescribing. Specialist physician prescribing would expand availability to an additional 12% of urban, 18% of suburban, and 16% of rural tracts, while clinician prescribing would expand to an additional 30% of urban, 53% of suburban, and 58% of rural tracts relative to current availability. Results support enabling broader methadone prescribing privileges to ensure equitable treatment access, particularly for rural communities.

Individual, interpersonal, and neighborhood measures associated with opioid use stigma: Evidence from a nationally representative survey.

Despite growing awareness of opioid use disorder (OUD), fatal overdoses and downstream health conditions (e.g., hepatitis C and HIV) continue to rise in some populations. Various interrelated structural forces, together with social and economic determinants, contribute to this ongoing crisis; among these, access to medications for opioid use disorder (MOUD) and stigma towards people with OUD remain understudied. We combined data on methadone, buprenorphine, and naltrexone providers from SAMHSA's 2019 directory, additional naltrexone providers from Vivitrol's location finder service, with a nationally representative survey called "The AmeriSpeak survey on stigma toward people with OUD." Integrating the social-ecological framework, we focus on individual characteristics, personal and family members' experience with OUD, and spatial access to MOUD at the community level. We use nationally representative survey data from 3008 respondents who completed their survey in 2020. Recognizing that stigma is a multifaceted construct, we also examine how the process varies for different types of stigma, specifically perceived dangerousness and untrustworthiness, as well as social distancing measures under different scenarios. We found a significant association between stigma and spatial access to MOUD - more resources are related to weaker stigma. Respondents had a stronger stigma towards people experiencing current OUD (versus past OUD), and they were more concerned about OUD if the person would marry into their family (versus being their coworkers). Additionally, respondents' age, sex, education, and personal experience with OUD were also associated with their stigma, and the association can vary depending on the specific type of stigma. Overall, stigma towards people with OUD was associated with both personal experiences and environmental measures.

Assessment of Community-Level Vulnerability and Access to Medications for Opioid Use Disorder.

Importance: Given that COVID-19 and recent natural disasters exacerbated the shortage of medication for opioid use disorder (MOUD) services and were associated with increased opioid overdose mortality, it is important to examine how a community's ability to respond to natural disasters and infectious disease outbreaks is associated with MOUD access. Objective: To examine the association of community vulnerability to disasters and pandemics with geographic access to each of the 3 MOUDs and whether this association differs by urban, suburban, or rural classification. Design, Setting, and Participants: This cross-sectional study of zip code tabulation areas (ZCTAs) in the continental United States excluding Washington, DC, conducted a geospatial analysis of 2020 treatment location data. Exposures: Social vulnerability index (US Centers for Disease Control and Prevention measure of vulnerability to disasters or pandemics). Main Outcomes and Measures: Drive time in minutes from the population-weighted center of the ZCTA to the ZCTA of the nearest treatment location for each treatment type (buprenorphine, methadone, and extended-release naltrexone). Results: Among 32 604 ZCTAs within the continental US, 170 within Washington, DC, and 20 without an urban-rural classification were excluded, resulting in a final sample of 32 434 ZCTAs. Greater social vulnerability was correlated with longer drive times for methadone (correlation, 0.10; 95% CI, 0.09 to 0.11), but it was not correlated with access to other MOUDs. Among rural ZCTAs, increasing social vulnerability was correlated with shorter drive times to buprenorphine (correlation, -0.10; 95% CI, -0.12 to -0.08) but vulnerability was not correlated with other measures of access. Among suburban ZCTAs, greater vulnerability was correlated with both longer drive times to methadone (correlation, 0.22; 95% CI, 0.20 to 0.24) and extended-release naltrexone (correlation, 0.15; 95% CI, 0.13 to 0.17). Conclusions and Relevance: In this study, communities with greater vulnerability did not have greater geographic access to MOUD, and the mismatch between vulnerability and medication access was greatest in suburban communities. Rural communities had poor geographic access regardless of vulnerability status. Future disaster preparedness planning should match the location of services to communities with greater vulnerability to prevent inequities in overdose deaths.

Assessment of Structural Barriers and Racial Group Disparities of COVID-19 Mortality With Spatial Analysis.

IMPORTANCE: Although social determinants of health (SDOH) are important factors in health inequities, they have not been explicitly associated with COVID-19 mortality rates across racial and ethnic groups and rural, suburban, and urban contexts. OBJECTIVES: To explore the spatial and racial disparities in county-level COVID-19 mortality rates during the first year of the pandemic. DESIGN, SETTING, AND PARTICIPANTS: This cross-sectional study analyzed data for all US counties in 50 states and the District of Columbia for the first full year of the COVID-19 pandemic (January 22, 2020, to February 28, 2021). Counties with a high concentration of a single racial and ethnic population and a high level of COVID-19 mortality rate were identified as concentrated longitudinal-impact counties. The SDOH that may be associated with mortality rate across these counties and in urban, suburban, and rural contexts were examined. The 3 largest racial and ethnic groups in the US were selected: Black or African American, Hispanic or Latinx, and non-Hispanic White populations. EXPOSURES: County-level characteristics and community health factors (eg, income inequality, uninsured rate, primary care physicians, preventable hospital stays, severe housing problems rate, and access to broadband internet) associated with COVID-19 mortality. MAIN OUTCOMES AND MEASURES: Data on county-level COVID-19 mortality rates (deaths per 100 000 population) reported by the US Centers for Disease Control and Prevention were analyzed. Four indexes were used to measure multiple dimensions of SDOH: socioeconomic advantage index, limited mobility index, urban core opportunity index, and mixed immigrant cohesion and accessibility index. Spatial regression models were used to examine the associations between SDOH and county-level COVID-19 mortality rate. RESULTS: Of the 3142 counties included in the study, 531 were identified as concentrated longitudinal-impact counties. Of these counties, 347 (11.0%) had a large Black or African American population compared with other counties, 198 (6.3%) had a large Hispanic or Latinx population compared with other counties, and 33 (1.1%) had a large non-Hispanic White population compared with other counties. A total of 489 254 COVID-19-related deaths were reported. Most concentrated longitudinal-impact counties with a large Black or African American population compared with other counties were spread across urban, suburban, and rural areas and experienced numerous disadvantages, including higher income inequality (297 of 347 [85.6%]) and more preventable hospital stays (281 of 347 [81.0%]). Most concentrated longitudinal-impact counties with a large Hispanic or Latinx population compared with other counties were located in urban areas (114 of 198 [57.6%]), and 130 (65.7%) of these counties had a high percentage of people who lacked health insurance. Most concentrated longitudinal-impact counties with a large non-Hispanic White population compared with other counties were in rural areas (23 of 33 [69.7%]), included a large group of older adults (26 of 33 [78.8%]), and had limited access to quality health care (24 of 33 [72.7%]). In urban areas, the mixed immigrant cohesion and accessibility index was inversely associated with COVID-19 mortality (coefficient [SE], -23.38 [6.06]; P < .001), indicating that mortality rates in urban areas were associated with immigrant communities with traditional family structures, multiple accessibility stressors, and housing overcrowding. Higher COVID-19 mortality rates were also associated with preventable hospital stays in rural areas (coefficient [SE], 0.008 [0.002]; P < .001) and higher socioeconomic status vulnerability in suburban areas (coefficient [SE], -21.60 [3.55]; P < .001). Across all community types, places with limited internet access had higher mortality rates, especially in urban areas (coefficient [SE], 5.83 [0.81]; P < .001). CONCLUSIONS AND RELEVANCE: This cross-sectional study found an association between different SDOH measures and COVID-19 mortality that varied across racial and ethnic groups and community types. Future research is needed that explores the different dimensions and regional patterns of SDOH to address health inequity and guide policies and programs.

National variability in Americans' COVID-19 protective behaviors: Implications for vaccine roll-out.

Protective behaviors such as mask wearing and physical distancing are critical to slow the spread of COVID-19, even in the context of vaccine scale-up. Understanding the variation in self-reported COVID-19 protective behaviors is critical to developing public health messaging. The purpose of the study is to provide nationally representative estimates of five self-reported COVID-19 protective behaviors and correlates of such behaviors. In this cross-sectional survey study of US adults, surveys were administered via internet and telephone. Adults were surveyed from April 30-May 4, 2020, a time of peaking COVID-19 incidence within the US. Participants were recruited from the probability-based AmeriSpeak® national panel. Brief surveys were completed by 994 adults, with 73.0% of respondents reported mask wearing, 82.7% reported physical distancing, 75.1% reported crowd avoidance, 89.8% reported increased hand-washing, and 7.7% reported having prior COVID-19 testing. Multivariate analysis (p critical value .05) indicates that women were more likely to report protective behaviors than men, as were those over age 60. Respondents who self-identified as having low incomes, histories of criminal justice involvement, and Republican Party affiliation, were less likely to report four protective behaviors, though Republicans and individuals with criminal justice histories were more likely to report having received COVID-19 testing. The majority of Americans engaged in COVID-19 protective behaviors, with low-income Americans, those with histories of criminal justice involvement, and self-identified Republicans less likely to engage in these preventive behaviors. Culturally competent public health messaging and interventions might focus on these latter groups to prevent future infections. These findings will remain highly relevant even with vaccines widely available, given the complementarities between vaccines and protective behaviors, as well as the many challenges in delivering vaccines.

Social-spatial network structures and community ties of egocentric sex and confidant networks: A Chicago case study.

Exploring how sexual and confidant networks overlap spatially and socially could facilitate a better understanding of sexually transmitted infection risk, as well as help identify areas for interventions. This study aims to examine how a sexual and peer-affiliate network is impacted or shaped by interconnected social relationships and spatial patterns. We used data collected from a sample of 618 young black men who have sex with men (YBMSM) and transgender women in Chicago (2013-2014) that includes partner and confidant links, geolocations, and pre-exposure prophylaxis (PrEP) awareness. We spatialize different types of social networks and examine joint social-spatial community ties to both identify and differentiate social-spatial behavioral patterns. We explore the spatial structures of the social network by comparing ego-alter network residence patterns, visualizing ego-alter community ties in aggregate, and grouping different types of dyad relationships based on their spatial structure. Findings showed overlapping social and sexual networks. Egos with partners residing in more resourced communities furthest away, with wider alter-ego power differentials, also tended to be at greatest risk. Identifying the social-spatial structures of community ties is critical to enhance our understanding of the spatial context of social relationships, and further distill risk heterogeneity in vulnerable populations within an equitable health framework.

The US COVID Atlas: A dynamic cyberinfrastructure surveillance system for interactive exploration of the pandemic.

Distributed spatial infrastructures leveraging cloud computing technologies can tackle issues of disparate data sources and address the need for data-driven knowledge discovery and more sophisticated spatial analysis central to the COVID-19 pandemic. We implement a new, open source spatial middleware component (libgeoda) and system design to scale development quickly to effectively meet the need for surveilling county-level metrics in a rapidly changing pandemic landscape. We incorporate, wrangle, and analyze multiple data streams from volunteered and crowdsourced environments to leverage multiple data perspectives. We integrate explorative spatial data analysis (ESDA) and statistical hotspot standards to detect infectious disease clusters in real time, building on decades of research in GIScience and spatial statistics. We scale the computational infrastructure to provide equitable access to data and insights across the entire USA, demanding a basic but high-quality standard of ESDA techniques. Finally, we engage a research coalition and incorporate principles of user-centered design to ground the direction and design of Atlas application development.

Hypothetical case replacement can be used to quantify the robustness of trial results.

OBJECTIVES: We apply a general case replacement framework for quantifying the robustness of causal inferences to characterize the uncertainty of findings from clinical trials. STUDY DESIGN AND SETTING: We express the robustness of inferences as the amount of data that must be replaced to change the conclusion and relate this to the fragility of trial results used for dichotomous outcomes. We illustrate our approach in the context of an RCT of hydroxychloroquine on pneumonia in COVID-19 patients and a cumulative meta-analysis of the effect of antihypertensive treatments on stroke. RESULTS: We developed the Robustness of an Inference to Replacement (RIR), which quantifies how many treatment cases with positive outcomes would have to be replaced with hypothetical patients who did not receive a treatment to change an inference. The RIR addresses known limitations of the Fragility Index by accounting for the observed rates of outcomes. It can be used for varying thresholds for inference, including clinical importance. CONCLUSION: Because the RIR expresses uncertainty in terms of patient experiences, it is more relatable to stakeholders than P-values alone. It helps identify when results are statistically significant, but conclusions are not robust, while considering the rareness of events in the underlying data.