Explore the vast range of titles available.

Death certificates are commonly used in epidemiological studies investigating the relationship between exposure and health outcomes. It is known that death certificates may misclassify the underlying causes of death, and it is commonly understood that if misclassification is non-differential, it will bias dose-response relationships toward the null or underestimate the association. This simulation study explores the probability that results of an individual study may contradict the general understanding by addressing two key questions: (1) what is the probability that misclassification of disease mortality moves measures of dose-response associations away from the null? and (2) what is the probability that misclassification moves measures of dose-response associations away from the null sufficiently to change the conclusion of a study from statistically non-significant to significant? As the starting point, this simulation study used a small group of radiation-exposed nuclear workers for whom both death certificates and autopsy reports were available. Results suggest that nominally non-differential misclassification can lead to an odds ratio that moves away from the null. For datasets where the initial p -values were slightly non-significant, the percentage of odds ratios that moved away from the null generally decreased with higher levels of misclassification, and the probability that the p -values associated with these odds ratios would change to significant decreased with increasing misclassification rates. The traditional heuristic is more likely to be true when: (1) there is a larger misclassification rate, and (2) there is a high association between dose and disease mortality. This has implications for environmental epidemiology, such as low-dose radiation epidemiology, where estimated effects are often small and conclusions may hinge on marginal statistical significance. As another implication, these findings apply broadly to various health outcomes, even if the outcome misclassification rate is low.

This paper analyzes the discrepancies between the historical reality of certain poems written decades or even centuries ago, and the results provided by modern programs designed to detect AI (Artificial Intelligence) generated content. These discrepancies are important because they can lead to errors in the authentic evaluation of literary texts affecting both the recognition of the cultural value of these works and the editorial process. Such a phenomenon can contribute to a distorted perception of classical authors and a lack of confidence in the validity of literature transmitted over the centuries.In the academic environment, some classical texts analyzed using such AI detectors have been erroneously marked as artificially generated, which has led to unnecessary re-evaluations of their authenticity or even the rejection of academic work. Through a case study on several classical and contemporary poems scanned with such a tool, we observe how AI detectors can incorrectly classify authentic texts as AI-generated. These errors raise issues concerning the use of such instruments in evaluating the originality of literary content especially in academic and editorial contexts.

The Klebsiella pneumoniae complex comprises seven K. pneumoniae-related species, including K. variicola. K. variicola is a versatile bacterium capable of colonizing different hosts such as plants, humans, insects and animals. Currently, K. variicola is gaining recognition as a cause of several human infections; nevertheless, its virulence profile is not fully characterized. The clinical significance of K. variicola infection is hidden by imprecise detection methods that underestimate its real prevalence; however, several methods have been developed to correctly identify this species. Recent studies of carbapenemase-producing and colistin-resistant strains demonstrate a potential reservoir of multidrug-resistant genes. This finding presents an imminent scenario for spreading antimicrobial resistant genes among close relatives and, more concerningly, in clinical and environmental settings. Since K. variicola was identified as a novel bacterial species, different research groups have contributed findings elucidating this pathogen; however, important details about its epidemiology, pathogenesis and ecology are still missing. This review highlights the most significant aspects of K. variicola, discussing its different phenotypes, mechanisms of resistance, and virulence traits, as well as the types of infections associated with this pathogen.

Objective: The primary purpose of this study was to compare age-adjusted mortality rates before and after linkage with Indian Health Service records, adjusting for racial misclassification. We focused on differences in racial misclassification by gender, age, geographic differences, substate planning districts, and cause of death. Our secondary purpose was to evaluate time trends in misclassification from 1991 to 2015. Design: Retrospective, descriptive study. Setting: Oklahoma. Participants: Persons contained in the Oklahoma State Health Department Vital Records. Main Outcome Measures: To evaluate the age-adjusted mortality ratio pre- and post-Indian Health Service record linkage (misclassification rate ratio) and to evaluate the overall trend of racial misclassification on mortality records measured through annual percent change (APC) and average annual percent change (AAPC). Results: We identified 2 stable trends of racial misclassification upon death for American Indians/Alaska Natives (AI/ANs) from 1991 to 2001 (APC: −0.2%; 95% confidence interval: −1.4% to 1.0%) and from 2001 to 2005 (APC: −6.9%; 95% confidence interval: −13.7% to 0.4%). However, the trend identified from 2005 to 2015 decreased significantly (APC: −1.4%; 95% confidence interval: −2.5% to −0.2%). For the last 5 years available (2011-2015), the racial misclassification adjustment resulted in higher mortality rates for AI/ANs reflecting an increase from 1008 per 100 000 to 1305 per 100 000 with the linkage process. There were an estimated 3939 AI/ANs in Oklahoma who were misclassified as another race upon death in those 5 years, resulting in an underestimation of actual AI/AN deaths by nearly 29%. Conclusions: An important result of this study is that misclassification is improving; however, this effort needs to be maintained and further improved. Continued linkage efforts and public access to linked data are essential throughout the United States to better understand the burden of disease in the AI/AN population.

Misclassification of Medicare beneficiaries' race/ethnicity in administrative data sources is frequently overlooked and a limitation in health disparities research. To compare the validity of 2 race/ethnicity variables found in Medicare administrative data [enrollment database (EDB) and Research Triangle Institute (RTI) race] against a gold-standard source also available in the Medicare data warehouse: the self-reported race/ethnicity variable on the home health Outcome and Assessment Information Set (OASIS). Medicare beneficiaries over the age of 18 who received home health care in 2015 (N=4,243,090). Percent agreement, sensitivity, specificity, positive predictive value, and Cohen κ coefficient. The EDB and RTI race variable have high validity for black race and low validity for American Indian/Alaskan Native race. Although the RTI race variable has better validity than the EDB race variable for other races, κ values suggest room for future improvements in classification of whites (0.90), Hispanics (0.87), Asian/Pacific Islanders (0.77), and American Indian/Alaskan Natives (0.44). The status quo of using \"good-enough for government\" race/ethnicity variables contained in Medicare administrative data for minority health disparities research can be improved through the use of self-reported race/ethnicity data, available in the Medicare data warehouse. Health services and policy researchers should critically examine the source of race/ethnicity variables used in minority health and health disparities research. Future work to improve the accuracy of Medicare beneficiaries' race/ethnicity data should incorporate and augment the self-reported race/ethnicity data contained in assessment and survey data, available within the Medicare data warehouse.

AbstractObjectivesTo use the estimates from the Global Burden of Disease Study 2016 to describe patterns of suicide mortality globally, regionally, and for 195 countries and territories by age, sex, and Socio-demographic index, and to describe temporal trends between 1990 and 2016.DesignSystematic analysis.Main outcome measuresCrude and age standardised rates from suicide mortality and years of life lost were compared across regions and countries, and by age, sex, and Socio-demographic index (a composite measure of fertility, income, and education).ResultsThe total number of deaths from suicide increased by 6.7% (95% uncertainty interval 0.4% to 15.6%) globally over the 27 year study period to 817 000 (762 000 to 884 000) deaths in 2016. However, the age standardised mortality rate for suicide decreased by 32.7% (27.2% to 36.6%) worldwide between 1990 and 2016, similar to the decline in the global age standardised mortality rate of 30.6%. Suicide was the leading cause of age standardised years of life lost in the Global Burden of Disease region of high income Asia Pacific and was among the top 10 leading causes in eastern Europe, central Europe, western Europe, central Asia, Australasia, southern Latin America, and high income North America. Rates for men were higher than for women across regions, countries, and age groups, except for the 15 to 19 age group. There was variation in the female to male ratio, with higher ratios at lower levels of Socio-demographic index. Women experienced greater decreases in mortality rates (49.0%, 95% uncertainty interval 42.6% to 54.6%) than men (23.8%, 15.6% to 32.7%).ConclusionsAge standardised mortality rates for suicide have greatly reduced since 1990, but suicide remains an important contributor to mortality worldwide. Suicide mortality was variable across locations, between sexes, and between age groups. Suicide prevention strategies can be targeted towards vulnerable populations if they are informed by variations in mortality rates.

As with other fields, medical sciences are subject to different sources of bias. While understanding sources of bias is a key element for drawing valid conclusions, bias in health research continues to be a very sensitive issue that can affect the focus and outcome of investigations. Information bias, otherwise known as misclassification, is one of the most common sources of bias that affects the validity of health research. It originates from the approach that is utilized to obtain or confirm study measurements. This paper seeks to raise awareness of information bias in observational and experimental research study designs as well as to enrich discussions concerning bias problems. Specifying the types of bias can be essential to limit its effects and, the use of adjustment methods might serve to improve clinical evaluation and health care practice.

Objective: To evaluate the performance of gender detection tools that allow the uploading of files (e.g., Excel or CSV files) containing first names, are usable by researchers without advanced computer skills, and are at least partially free of charge.Methods: The study was conducted using four physician datasets (total number of physicians: 6,131; 50.3% female) from Switzerland, a multilingual country. Four gender detection tools met the inclusion criteria: three partially free (Gender API, NamSor, and genderize.io) and one completely free (Wiki-Gendersort). For each tool, we recorded the number of correct classifications (i.e., correct gender assigned to a name), misclassifications (i.e., wrong gender assigned to a name), and nonclassifications (i.e., no gender assigned). We computed three metrics: the proportion of misclassifications excluding nonclassifications (errorCodedWithoutNA), the proportion of nonclassifications (naCoded), and the proportion of misclassifications and nonclassifications (errorCoded).Results: The proportion of misclassifications was low for all four gender detection tools (errorCodedWithoutNA between 1.5 and 2.2%). By contrast, the proportion of unrecognized names (naCoded) varied: 0% for NamSor, 0.3% for Gender API, 4.5% for Wiki-Gendersort, and 16.4% for genderize.io. Using errorCoded, which penalizes both types of error equally, we obtained the following results: Gender API 1.8%, NamSor 2.0%, Wiki-Gendersort 6.6%, and genderize.io 17.7%.Conclusions: Gender API and NamSor were the most accurate tools. Genderize.io led to a high number of nonclassifications. Wiki-Gendersort may be a good compromise for researchers wishing to use a completely free tool. Other studies would be useful to evaluate the performance of these tools in other populations (e.g., Asian). 

In response to the coronavirus disease (COVID-19) pandemic, public health scientists have produced a large and rapidly expanding body of literature that aims to answer critical questions, such as the proportion of the population in a geographic area that has been infected; the transmissibility of the virus and factors associated with high infectiousness or susceptibility to infection; which groups are the most at risk of infection, morbidity and mortality; and the degree to which antibodies confer protection to re-infection. Observational studies are subject to a number of different biases, including confounding, selection bias, and measurement error, that may threaten their validity or influence the interpretation of their results. To assist in the critical evaluation of a vast body of literature and contribute to future study design, we outline and propose solutions to biases that can occur across different categories of observational studies of COVID-19. We consider potential biases that could occur in five categories of studies: (1) cross-sectional seroprevalence, (2) longitudinal seroprotection, (3) risk factor studies to inform interventions, (4) studies to estimate the secondary attack rate, and (5) studies that use secondary attack rates to make inferences about infectiousness and susceptibility.

Research summary: This article applies data from Linkedln to advance strategy research into the effect of human capital on mobility of engineers and scientists. Through an inventor survey, we show that Linkedln provides more accurate career histories than patents. Compared to Linkedln, patent measures of mobility generate 12 percent false positives and 83 percent false negatives. Using Linkedln, we review findings from previous research using patents to track the effect of human capital on mobility. One previous finding is robust: that mobility is higher in Silicon Valley than elsewhere. Other findings are possibly sensitive to the measure of mobility or sample selection. We interpret our results as the outcome of targeted retention of human capital. Data for this study may be accessed at FIVE, five.dartmouth.edu. Managerial summary: How does the mobility of engineers and scientists depend on their human capital? Previous research used patents to track inventor mobility and concluded that employers targeted inventors for recruitment by their human capital. Here, we introduce data from Linkedln to review the previous research. Through an inventor survey, we show that Linkedln provides more accurate career histories than patents. Compared to Linkedln, patent measures of mobility generate 12 percent false positives and 82 percent false negatives. Among the previous findings, we show that one is robust: mobility is higher among inventors in Silicon Valley than elsewhere. Other findings are possibly sensitive to the measure of mobility or sample selection. Our results suggest that current employers target engineers and scientists for retention according to their human capital.