The polarization of politics and public opinion and their effects on racial inequality in COVID mortality.

Evidence from the early months of the COVID-19 pandemic in the U.S. indicated that the virus had vastly different effects across races, with black Americans faring worse on dimensions including illness, hospitalization and death. New data suggests that our understanding of the pandemic's racial inequities must be revised given the closing of the gap between black and white COVID-related mortality. Initial explanations for inequality in COVID-related outcomes concentrated on static factors-e.g., geography, urbanicity, segregation or age-structures-that are insufficient on their own to explain observed time-varying patterns in inequality. Drawing from a literature suggesting the relevance of political factors in explaining pandemic outcomes, we highlight the importance of political polarization-the partisan divide in pandemic-related policies and beliefs-that varies over time and across geographic units. Specifically, we investigate the role of polarization through two political factors, public opinion and state-level public health policies, using fine-grained data on disparities in public concern over COVID and in state containment/health policies to understand the changing pattern of inequality in mortality. We show that (1) apparent decreases in inequality are driven by increasing total deaths-mostly among white Americans-rather than decreasing mortality among black Americans (2) containment policies are associated with decreasing inequality, likely resulting from lower relative mortality among Blacks (3) as the partisan disparity in Americans who were "unconcerned" about COVID increased, racial inequality in COVID mortality decreased, generating the appearance of greater equality consistent with a "race to the bottom'' explanation as overall deaths increased and substantively swamping the effects of containment policies.

Years of life lost to COVID-19 in 81 countries.

Understanding the mortality impact of COVID-19 requires not only counting the dead, but analyzing how premature the deaths are. We calculate years of life lost (YLL) across 81 countries due to COVID-19 attributable deaths, and also conduct an analysis based on estimated excess deaths. We find that over 20.5 million years of life have been lost to COVID-19 globally. As of January 6, 2021, YLL in heavily affected countries are 2-9 times the average seasonal influenza; three quarters of the YLL result from deaths in ages below 75 and almost a third from deaths below 55; and men have lost 45% more life years than women. The results confirm the large mortality impact of COVID-19 among the elderly. They also call for heightened awareness in devising policies that protect vulnerable demographics losing the largest number of life-years.

Thyroid ultrasound and nodule malignancy risk: a "real world" assessment of ultrasound reporting and agreement of ultrasound-based malignancy risk estimates with cytology and histology findings.

AIMS: Thyroid nodule malignancy risk is increasingly estimated using ultrasound characteristics. We assessed ultrasound reports of nodules and compared ultrasound-based malignancy risk assessments with cytology and histology findings. METHODS: We identified patients with thyroid ultrasound (55% by private provider, 45% by DHB) and cytology at CMDHB over 18 months. Malignancy risk for each nodule was categorised based on the ultrasound report, then using ultrasound images with the local CMDHB approach and American Thyroid Association guidelines, and then was compared with cytology/histology results. RESULTS: 36/91 nodules (84 patients) had abnormal (Bethesda 3-6) cytology. Forty-eight patients (54 nodules) underwent thyroid surgery and 13 nodules (12 patients) had thyroid cancers. Most ultrasound reports did not mention nodule malignancy risk characteristics (range 13-98%) or a malignancy risk estimate (66/91). 12/33 nodules with benign (Bethesda 2) cytology and 18/36 nodules with abnormal (Bethesda 3-6) cytology were considered intermediate/high risk of malignancy by ultrasound; none and seven, respectively, had cancer identified subsequently. In 18 nodules considered low risk by ultrasound, four cancers were identified. CONCLUSIONS: Most ultrasound reports contained insufficient information about nodule malignancy risk to allow an independent assessment. Agreement between cytological/histological findings and malignancy risk estimates using ultrasound was not high.

Americans preferred Syrian refugees who are female, English-speaking, and Christian on the eve of Donald Trump's election.

What types of refugees do Americans prefer for admission into the United States? Scholars have explored the immigrant characteristics that appeal to Americans and the characteristics that Europeans prioritize in asylum-seekers, but we currently do not know which refugee characteristics Americans prefer. We conduct a conjoint experiment on a representative sample of 1800 US adults, manipulating refugee attributes in pairs of Syrian refugee profiles, and ask respondents to rate each refugee's appeal. Our focus on Syrian refugees in a 2016 survey experiment allows us to speak to the concurrent refugee crisis on the eve of a polarizing election, while also identifying religious discrimination, holding constant the refugee's national origin. We find that Americans prefer Syrian refugees who are female, high-skilled, English-speaking, and Christian, suggesting they prioritize refugee integration into the U.S. labor and cultural markets. We find that the preference for female refugees is not driven by the desire to exclude Muslim male refugees, casting doubt that American preferences at the time were motivated by security concerns. Finally, we find that anti-Muslim bias in refugee preferences varies in magnitude across key subgroups, though it prevails across all sample demographics.

Perspective taking can promote short-term inclusionary behavior toward Syrian refugees.

Social scientists have shown how easily individuals are moved to exclude outgroup members. Can we foster inclusion instead? This study leverages one of the most significant humanitarian crises of our time to test whether, and under what conditions, American citizens adopt more inclusionary behavior toward Syrian refugees. We conduct a nationally representative survey of over 5,000 American citizens in the weeks leading up to the 2016 presidential election and experimentally test whether a perspective-taking exercise increases inclusionary behavior in the form of an anonymous letter supportive of refugees to be sent to the 45th President of the United States. Our results indicate that the perspective-taking message increases the likelihood of writing such a positive letter by two to five percentage points. By contrast, an informational message had no significant effect on letter writing. The effect of the perspective-taking exercise occurs in the short run only, manifests as a behavioral rather than an attitudinal response, and is strongest among Democrats. However, this effect also appears in the subset of Republican respondents, suggesting that efforts to promote perspective taking may move to action a wide cross-section of individuals.

Drug treatment of scrub typhus: a systematic review and meta-analysis of controlled clinical trials.

Scrub typhus, a potentially fatal infection caused by the pathogen Orientia tsutsugamushi, has a wide geographical distribution. This systematic review analyses the evidence from prospective controlled clinical studies for the efficacy of antibiotics in the treatment of scrub typhus. PubMed, Embase, Scopus, Cochrane Library, CINAHL and clinical trial registries in China, India and Sri Lanka were searched for controlled prospective clinical trials (randomized, quasi-randomized or non-randomized) enrolling patients with confirmed scrub typhus for treatment with antibiotics. The PROSPERO registration number for this review is CRD42017071374. Eleven studies (from Southeast Asia and China) that enrolled 957 patients into 28 study groups met the inclusion criteria. Doxycycline was the most frequent comparator with other antibiotics. Chloramphenicol, tetracycline and azithromycin (limited evidence for roxithromycin, telithromycin, levofloxacin and rifampicin) had equal efficacy to doxycycline in achieving clinical cure. The cure rates in all studies varied from 64 to 100%. Of the antibiotics frequently used in current practice, azithromycin had the best evidence compared with doxycycline for equal efficacy in clinical cure (three trials, 280 participants, moderate quality evidence) with fewer gastrointestinal adverse events. The preferred choice of antibiotics for each patient depends on the adverse effect profile, personal circumstances (e.g., age, pregnancy), cost and local prescription guidelines.

Identifying regions of disease-related variants in admixed populations with the summation partition approach.

We propose a new method for identifying disease-related regions of single nucleotide variants in recently admixed populations. We use principal component analysis to derive both global and local ancestry information. We then use the summation partition approach to search for disease-related regions based on both rare variants and the local ancestral information of each region. We demonstrate this method using individuals with high systolic blood pressure from a sample of unrelated Mexican American subjects provided in the 19th Genetic Analysis Workshop.

Network-guided interaction mining for the blood pressure phenotype of unrelated individuals in genetic analysis workshop 19.

Interactions between genes are an important part of the genetic architecture of complex diseases. In this paper, we use literature-guided individual genes known to be associated with type 2 diabetes (referred to as "seed genes") to create a larger list of genes that share implied or direct networks with these seed genes. This larger list of genes are known to interact with each other, but whether they interact in ways to influence hypertension in individuals presents an interesting question. Using Genetic Analysis Workshop data on individuals with diabetes, for which only case-control labels of hypertension are known, we offer a foray into identification of diabetes-related gene interactions that are associated with hypertension. We use the approach of Lo et al. (Proc Natl Acad Sci U S A 105: 12387-12392, 2008), which creates a score to identify pairwise significant gene associations. We find that the genes GCK and PAX4, formerly known to be found within similar coexpression and pathway networks but without specific direct interactions, do, in fact, show significant joint interaction effects for hypertension.

Framework for making better predictions by directly estimating variables' predictivity.

We propose approaching prediction from a framework grounded in the theoretical correct prediction rate of a variable set as a parameter of interest. This framework allows us to define a measure of predictivity that enables assessing variable sets for, preferably high, predictivity. We first define the prediction rate for a variable set and consider, and ultimately reject, the naive estimator, a statistic based on the observed sample data, due to its inflated bias for moderate sample size and its sensitivity to noisy useless variables. We demonstrate that the [Formula: see text]-score of the PR method of VS yields a relatively unbiased estimate of a parameter that is not sensitive to noisy variables and is a lower bound to the parameter of interest. Thus, the PR method using the [Formula: see text]-score provides an effective approach to selecting highly predictive variables. We offer simulations and an application of the [Formula: see text]-score on real data to demonstrate the statistic's predictive performance on sample data. We conjecture that using the partition retention and [Formula: see text]-score can aid in finding variable sets with promising prediction rates; however, further research in the avenue of sample-based measures of predictivity is much desired.

Why significant variables aren't automatically good predictors.

Thus far, genome-wide association studies (GWAS) have been disappointing in the inability of investigators to use the results of identified, statistically significant variants in complex diseases to make predictions useful for personalized medicine. Why are significant variables not leading to good prediction of outcomes? We point out that this problem is prevalent in simple as well as complex data, in the sciences as well as the social sciences. We offer a brief explanation and some statistical insights on why higher significance cannot automatically imply stronger predictivity and illustrate through simulations and a real breast cancer example. We also demonstrate that highly predictive variables do not necessarily appear as highly significant, thus evading the researcher using significance-based methods. We point out that what makes variables good for prediction versus significance depends on different properties of the underlying distributions. If prediction is the goal, we must lay aside significance as the only selection standard. We suggest that progress in prediction requires efforts toward a new research agenda of searching for a novel criterion to retrieve highly predictive variables rather than highly significant variables. We offer an alternative approach that was not designed for significance, the partition retention method, which was very effective predicting on a long-studied breast cancer data set, by reducing the classification error rate from 30% to 8%.

Syringocoele of the bulbourethral duct with additional lower genito-urinary anomalies.

The ultrasonographic (US) appearance of a syringocoele of the bulbourethral (Cowper) duct, with correlative urethrocystoscopic images, is demonstrated. An infant boy, 5 weeks of age, who presented with E. coli infection of the urinary tract also had bilateral hydroureteronephrosis, small bilateral simple ureterocoeles, and posterior urethral valve leaflets.