Comparing measures of centrality in bipartite patient-prescriber networks: A study of drug seeking for opioid analgesics.

Visiting multiple prescribers is a common method for obtaining prescription opioids for nonmedical use and has played an important role in fueling the United States opioid epidemic, leading to increased drug use disorder and overdose. Recent studies show that centrality of the bipartite network formed by prescription ties between patients and prescribers of opioids is a promising indicator for drug seeking. However, node prominence in bipartite networks is typically estimated with methods that do not fully account for the two-mode topology of the underlying network. Although several algorithms have been proposed recently to address this challenge, it is unclear how these algorithms perform on real-world networks. Here, we compare their performance in the context of identifying opioid drug seeking behaviors by applying them to massive bipartite networks of patients and providers extracted from insurance claims data. We find that two variants of bipartite centrality are significantly better predictors of subsequent opioid overdose than traditional centrality estimates. Moreover, we show that incorporating non-network attributes such as the potency of the opioid prescriptions into the measures can further improve their performance. These findings can be reproduced on different datasets. Our results demonstrate the potential of bipartiteness-aware indices for identifying patterns of high-risk behavior.

New means, new measures: assessing prescription drug-seeking indicators over 10 years of the opioid epidemic.

BACKGROUND AND AIMS: Prescription drug-seeking (PDS) from multiple prescribers is a primary means of obtaining prescription opioids; however, PDS behavior has probably evolved in response to policy shifts, and there is little agreement about how to operationalize it. We systematically compared the performance of traditional and novel PDS indicators. DESIGN: Longitudinal study using a de-identified commercial claims database. SETTING: United States, 2009-18. PARTICIPANTS: A total of 318 million provider visits from 21.5 million opioid-prescribed patients. MEASUREMENTS: We applied binary classification and generalized linear models to compare predictive accuracy and average marginal effect size predicting future opioid use disorder (OUD), overdose and high morphine milligram equivalents (MME). We compared traditional indicators of PDS to a network centrality measure, PageRank, that reflects the prominence of patients in a co-prescribing network. Analyses used the same data and adjusted for patient demographics, region, SES, diagnoses and health services. FINDINGS: The predictive accuracy of a widely used traditional measure (N + unique doctors and N + unique pharmacies in 90 days) on OUD, overdose and MME decreased between 2009 and 2018, and performed no better than chance (50% accuracy) after 2015. Binarized PageRank measures however exhibited higher predictive accuracy than the traditional binary measures throughout 2009-2018. Continuous indicators of PDS performed better than binary thresholds, with days of Rx performing best overall with 77-93% predictive accuracy. For example, days of Rx had the highest average marginal effects on overdose and OUD: a 1 standard deviation increase in days of Rx was associated with a 6-8% [confidence intervals (CIs) = 0.058-0.061 and 0.078-0.082] increase in the probability of overdose and a 4-5% (CIs = 0.038-0.043 and 0.047-0.053) increase in the probability of OUD. PageRank performed nearly as well or better than traditional indicators of PDS, with predictive performance increasing after 2016. CONCLUSIONS: In the United States, network-based measures appear to have increasing promise for identifying prescription opioid drug-seeking behavior, while indicators based on quantity of providers or pharmacies appear to have decreasing utility.

Provider Bias in prescribing opioid analgesics: a study of electronic medical Records at a Hospital Emergency Department.

BACKGROUND: Physicians do not prescribe opioid analgesics for pain treatment equally across groups, and such disparities may pose significant public health concerns. Although research suggests that institutional constraints and cultural stereotypes influence doctors' treatment of pain, prior quantitative evidence is mixed. The objective of this secondary analysis is therefore to clarify which institutional constraints and patient demographics bias provider prescribing of opioid analgesics. METHODS: We used electronic medical record data from an emergency department of a large U.S hospital during years 2008-2014. We ran multi-level logistic regression models to estimate factors associated with providing an opioid prescription during a given visit while controlling for ICD-9 diagnosis codes and between-patient heterogeneity. RESULTS: A total of 180,829 patient visits for 63,513 unique patients were recorded during the period of analysis. Overall, providers were significantly less likely to prescribe opioids to the same individual patient when the visit occurred during higher rates of emergency department crowding, later times of day, earlier in the week, later years in our sample, and when the patient had received fewer previous opioid prescriptions. Across all patients, providers were significantly more likely to prescribe opioids to patients who were middle-aged, white, and married. We found no bias towards women and no interaction effects between race and crowding or between race and sex. CONCLUSIONS: Providers tend to prescribe fewer opioids during constrained diagnostic situations and undertreat pain for patients from high-risk and marginalized demographic groups. Potential harms resulting from previous treatment decisions may accumulate by informing future treatment decisions.

If you build it, will they come? Social, economic, and psychological determinants of COVID-19 testing decisions.

BACKGROUND: The efficacy of testing and tracing programs to reduce COVID-19 transmission hinges not only on widespread access to testing, but also on the public's willingness to participate in them. To the extent that testing intentions are patterned by social determinants of health, this constitutes an understudied mechanism of disparities in COVID-19 morbidity and mortality. DESIGN: Using data from a representative household probability sample, the Person to Person Health Interview Study (n = 935), sociodemographic, economic, and psychological determinants of testing considerations were evaluated across six domains: treatment affordability, ability to work if positive, hospital effectiveness, symptom severity, proximity to infected, and risk of transmitting to others. RESULTS: Findings demonstrated significant differences in testing motivations across race/ethnicity, education level, socioeconomic status, and worry about self and loved ones. Notably, Black (p<0.01) and Latino (p<0.05) respondents and those experiencing financial strain (p<0.001) were disproportionately likely to indicate that resource factors would influence their decision to get tested. Desire to reduce transmission and concern about proximity to the infected were reported among those who expressed COVID-19 worries (p<0.001). CONCLUSION: Public health efforts to combat the COVID-19 pandemic must address social, economic, and psychological factors that enable and constrain individual behavior. Increasing access to preventative interventions and technologies, including vaccines, is unlikely to markedly reduce morbidity and mortality without effective messaging and economic support to improve uptake in vulnerable populations.

Pandemic precarity: COVID-19 is exposing and exacerbating inequalities in the American heartland.

Crises lay bare the social fault lines of society. In the United States, race, gender, age, and education have affected vulnerability to COVID-19 infection. Yet, consequences likely extend far beyond morbidity and mortality. Temporarily closing the economy sent shock waves through communities, raising the possibility that social inequities, preexisting and current, have weakened economic resiliency and reinforced disadvantage, especially among groups most devastated by the Great Recession. We address pandemic precarity, or risk for material and financial insecurity, in Indiana, where manufacturing loss is high, metro areas ranked among the hardest hit by the Great Recession nationally, and health indicators stand in the bottom quintile. Using longitudinal data (n = 994) from the Person to Person Health Interview Study, fielded in 2019-2020 and again during Indiana's initial stay-at-home order, we provide a representative, probability-based assessment of adverse economic outcomes of the pandemic. Survey-weighted multivariate regressions, controlling for preexisting inequality, find Black adults over 3 times as likely as Whites to report food insecurity, being laid off, or being unemployed. Residents without a college degree are twice as likely to report food insecurity (compared to some college), while those not completing high school (compared to bachelor's degree) are 4 times as likely to do so. Younger adults and women were also more likely to report economic hardships. Together, the results support contentions of a Matthew Effect, where pandemic precarity disproportionately affects historically disadvantaged groups, widening inequality. Strategically deployed relief efforts and longer-term policy reforms are needed to challenge the perennial and unequal impact of disasters.

Beyond Net Worth: Racial Differences in Wealth Portfolios and Black-White Health Inequality across the Life Course.

A large body of research links wealth and health, but most previous work focuses on net worth. However, the assets and debts that comprise wealth likely relate to health in different and meaningful ways. Furthermore, racial differences in wealth portfolios may contribute to racial health gaps. Using longitudinal data from the Panel Study of Income Dynamics (PSID) and mixed effects growth curve models, we examined the associations between various wealth components and multiple health outcomes. We also investigated whether black-white differences in wealth portfolios contributed to racial health inequality. We found that savings, stock ownership, and homeownership consistently improve health, but debt is associated with worse health, even after adjusting for total net worth. We found little evidence that home equity is associated with health. Findings also revealed differential health returns to assets by race. These findings provide new insights into the complex relationship among race, wealth, and health.

Immigrants in the one percent: The national origin of top wealth owners.

BACKGROUND: Economic inequality in the United States is extreme, but little is known about the national origin of affluent households. Households in the top one percent by total wealth own vastly disproportionate quantities of household assets and have correspondingly high levels of economic, social, and political influence. The overrepresentation of white natives (i.e., those born in the U.S.) among high-wealth households is well-documented, but changing migration dynamics suggest that a growing portion of top households may be immigrants. METHODS: Because no single survey dataset contains top wealth holders and data about country of origin, this paper uses two publicly-available data sets: the Survey of Consumer Finances (SCF) and the Survey of Income and Program Participation (SIPP). Multiple imputation is used to impute country of birth from the SIPP into the SCF. Descriptive statistics are used to demonstrate reliability of the method, to estimate the prevalence of immigrants among top wealth holders, and to document patterns of asset ownership among affluent immigrants. RESULTS: Significant numbers of top wealth holders who are usually classified as white natives may be immigrants. Many top wealth holders appear to be European and Canadian immigrants, and increasing numbers of top wealth holders are likely from Asia and Latin America as well. Results suggest that of those in the top one percent of wealth holders, approximately 3% are European and Canadian immigrants, .5% are from Mexico or Cuban, and 1.7% are from Asia (especially Hong Kong, Taiwan, Mainland China, and India). Ownership of key assets varies considerably across affluent immigrant groups. CONCLUSION: Although the percentage of top wealth holders who are immigrants is relatively small, these percentages represent large numbers of households with considerable resources and corresponding social and political influence. Evidence that the propensity to allocate wealth to real and financial assets varies across immigrant groups suggests that wealth ownership is more global than previous research suggests and that immigrant groups are likely to become more prevalent in top wealth positions in the U.S. As the representation of immigrants in top wealth positions grows, their economic, social, and political influence is likely to increase as well.

Chinese Immigrant Wealth: Heterogeneity in Adaptation.

Chinese immigrants are a diverse and growing group whose members provide a unique opportunity to examine within-immigrant group differences in adaptation. In this paper, we move beyond thinking of national-origin groups as homogenous and study variation among Chinese immigrants in wealth ownership, a critical indicator of adaptation that attracts relatively little attention in the immigration literature. We develop an analytical approach that considers national origin, tenure in the U.S., and age to examine heterogeneity in economic adaptation among the immigrant generation. Our results show that variations among Chinese immigrants explain within-group differences in net worth, asset ownership, and debt. These differences also account for important variation between Chinese immigrants, natives, and other immigrant groups and provide important, new insight into the processes that lead to immigrant adaptation and long-term class stability.

Peer influence as a potential magnifier of ADHD diagnosis.

The prevalence of Attention Deficit and Hyperactivity Disorder (ADHD) is growing in America, but its cause is unclear. Scholars have identified many environmental factors that can cause or confound ADHD diagnosis, but epidemiological studies that try to control for confounding factors still find evidence that rates of ADHD diagnosis are increasing. As a preliminary explanation to ADHD's increasing prevalence, this article examines whether core ADHD diagnostic traits are subject to peer influence. If ADHD diagnosis can be confounded by peer influence, there are several mechanisms that could have caused increased rates of diagnosis. With data drawn from two schools across three waves in the National Longitudinal Survey of Adolescent Health (n = 2193), the author uses a stochastic actor oriented model to estimate the effect of peer influence on inattention, controlling for alternative network and behavioral causes. Results indicate that respondents have a strong likelihood to modify their self-reports of inattention, a core ADHD trait, to resemble that of their friends.

Rare case of left upper lobe partial anomalous pulmonary venous connection.

Partial anomalous pulmonary venous connection is a rare abnormality with failure of connection between the initial draining system of the lungs and the common pulmonary vein. Right sided anomalous return is the most common form of anomalous connection, with left sided anomalous return uncommon. Presented is a case of left upper lobe partial anomalous pulmonary venous connection that was diagnosed incidentally on computed tomography (CT). This is an example of the utility of CT, in particular coronal, sagittal and 3-d reconstructions, in assessment of cardiopulmonary anatomy.

Software-annotated, digitally photographed, and printed MR images: suitability for publication.

RATIONALE AND OBJECTIVES: The authors performed this study to evaluate whether digitally photographed, computer-annotated MR images produced by clinical radiologists and printed with an inexpensive photo printer are suitable for publication. MATERIALS AND METHODS: Laser prints of 20 magnetic resonance images of the brain were photographed with a 3-megapixel digital camera and annotated with arrows, arrowheads, and asterisks by using graphics software that incorporates vector support. Then, 5 x 7-inch glossy prints with white borders were made by using an inexpensive photo printer. These prints were compared with those produced of the same 20 images by members of the medical center's graphics department with professional scanning and printing equipment and annotated with conventional rub-on symbols. Eight radiologists evaluated image and annotation quality and overall suitability for publication. RESULTS: In all three categories, the images produced by radiologists outscored those produced by the graphics department. CONCLUSION: Digitally photographed, software-annotated MR images printed with an inexpensive photo printer are suitable for publication.