Although the pandemic has ended, staffing shortages and employee burnout still plague U.S. nursing homes, a new government report finds.
But the problems didn’t end there: The report, issued Thursday by the Inspector General’s Office at the U.S. Department of Health and Human Services, showed that infection-control procedures were still sorely lacking at many facilities.
Not only that, COVID-19 booster vaccination rates remain far lower than they should be, with only 38% of residents and 15% of staff up-to-date on their shots, according to a recent KFF report.
COVID-19, caused by the SARS-CoV-2 virus, is a respiratory infectious disease. While most patients recover after treatment, there is growing evidence that COVID-19 may result in cognitive impairment. Recent studies reveal that some individuals experience cognitive deficits, such as diminished memory and attention, as well as sleep disturbances, suggesting that COVID-19 could have long-term effects on cognitive function. Research indicates that COVID-19 may contribute to cognitive decline by damaging crucial brain regions, including the hippocampus and anterior cingulate cortex. Additionally, studies have identified active neuroinflammation, mitochondrial dysfunction, and microglial activation in COVID-19 patients, implying that these factors may be potential mechanisms leading to cognitive impairment. Given these findings, the possibility of cognitive impairment following COVID-19 treatment warrants careful consideration. Large-scale follow-up studies are needed to investigate the impact of COVID-19 on cognitive function and offer evidence to support clinical treatment and rehabilitation practices. In-depth neuropathological and biological studies can elucidate precise mechanisms and provide a theoretical basis for prevention, treatment, and intervention research. Considering the risks of the long-term effects of COVID-19 and the possibility of reinfection, it is imperative to integrate basic and clinical research data to optimize the preservation of patients’ cognitive function and quality of life. This integration will also offer valuable insights for responding to similar public health events in the future. This perspective article synthesizes clinical and basic evidence of cognitive impairment following COVID-19, discussing potential mechanisms and outlining future research directions.
The authors of the 12 essays in this guide work through how to include equity at every step of the data collection and analysis process. They recommend that data practitioners consider the following:
Community engagement is necessary. Often, data practitioners take their population of interest as subjects and data points, not individuals and people. But not every person has the same history with research, nor do all people need the same protections. Data practitioners should understand who they are working with and what they need.
Who is not included in the data can be just as important as who is. Most equitable data work emphasizes understanding and caring for the people in the study. But for data narratives to truly have an equitable framing, it is just as important to question who is left out and how that exclusion may benefit some groups while disadvantaging others.
Conventional methods may not be the best methods. Just as it is important for data practitioners to understand who they are working with, it is also important for them to question how they are approaching the work. While social sciences tend to emphasize rigorous, randomized studies, these methods may not be the best methods for every situation. Working with community members can help practitioners create more equitable and effective research designs.
By taking time to deeply consider how we frame our data work—the definitions, questions, methods, icons, and word choices—we can create better results. As the field undertakes these new frontiers, data practitioners, researchers, policymakers, and advocates should keep front of mind who they include, how they work, and what they choose to show.
Author(s):
(editors) Jonathan Schwabish, Alice Feng, Wesley Jenkins
Of the graduates in non-college-level jobs a year after leaving college, the vast majority remained underemployed a decade later, according to researchers at labor analytics firm Burning Glass Institute and nonprofit Strada Education Foundation, which analyzed the résumés of workers who graduated between 2012 and 2021.
More than any other factor analyzed—including race, gender and choice of university—what a person studies determines their odds of getting on a college-level career track. Internships are also critical.
….
Bachelor’s degree holders in college-level jobs earn nearly 90% more than people with just a high-school diploma in their 20s, according to a Burning Glass analysis of 2022 U.S. Census Bureau data.
By comparison, underemployed college graduates earn 25% more than high-school graduates.
….
Contrary to conventional wisdom, not all degrees in science, technology, engineering and math, or STEM, disciplines are a sure bet to landing a job that reflects a college education, the study found.
Nearly half of people who majored in biology and biomedical sciences—47%—remained underemployed five years after graduating. Likewise, business majors less focused on quantitative skills, such as marketing and human resources, were twice as likely to be underemployed than those with math-intensive business degrees, such as accounting or finance. The data cover graduates who didn’t get master’s or other advanced degrees after college.
The overall mortality rate in nursing homes rose 32 percent in 2020. The pandemic had far-reaching implications for all nursing home beneficiaries, beyond those who had or likely had COVID-19. Among all Medicare beneficiaries in nursing homes, 22.5 percent died in 2020, which is an increase of one-third from 2019 when 17.0 percent of Medicare beneficiaries in nursing homes died. This 32-percent increase amounts to 169,291 more deaths in 2020 than if the mortality rate had remained the same as in 2019. Each month of 2020 had a higher mortality rate than the corresponding month a year earlier.
Almost 1,000 more beneficiaries died per day in April 2020 than in the previous year. In April 2020 alone, a total of 81,484 Medicare beneficiaries in nursing homes died. This is almost 30,000 more deaths—an average of about 1,000 per day—compared to the previous year. This increase in number occurred even though the nursing home population was smaller in April 2020. Overall, Medicare beneficiaries in nursing homes were almost twice as likely to die in April 2020 than in April 2019. In April 2020, 6.3 percent of all Medicare beneficiaries in nursing homes died, whereas 3.5 percent died in April 2019.
The mortality rates also rose at the end of 2020. In November, 5.1 percent of all Medicare beneficiaries in nursing homes died, and in December that increased to 6.2 percent. Again, these rates are markedly higher than the previous year. In November 2019, 3.6 percent of all Medicare beneficiaries in nursing homes died, and, in December 2019, 3.8 percent did.
Author(s): Jenell Clarke-Whyte and team
Publication Date: June 2021
Publication Site: Office of Inspector General, HHS
I was reminded of this student’s clever ploy when Frederik Joelving, a journalist with Retraction Watch, recently contacted me about a published paper written by two prominent economists, Almas Heshmati and Mike Tsionas, on green innovations in 27 countries during the years 1990 through 2018. Joelving had been contacted by a PhD student who had been working with the same data used by Heshmati and Tsionas. The student knew the data in the article had large gaps and was “dumbstruck” by the paper’s assertion these data came from a “balanced panel.” Panel data are cross-sectional data for, say, individuals, businesses, or countries at different points in time. A “balanced panel” has complete cross-section data at every point in time; an unbalanced panel has missing observations. This student knew firsthand there were lots of missing observations in these data.
The student contacted Heshmati and eventually obtained spreadsheets of the data he had used in the paper. Heshmati acknowledged that, although he and his coauthor had not mentioned this fact in the paper, the data had gaps. He revealed in an email that these gaps had been filled by using Excel’s autofill function: “We used (forward and) backward trend imputations to replace the few missing unit values….using 2, 3, or 4 observed units before or after the missing units.”
That statement is striking for two reasons. First, far from being a “few” missing values, nearly 2,000 observations for the 19 variables that appear in their paper are missing (13% of the data set). Second, the flexibility of using two, three, or four adjacent values is concerning. Joelving played around with Excel’s autofill function and found that changing the number of adjacent units had a large effect on the estimates of missing values.
Joelving also found that Excel’s autofill function sometimes generated negative values, which were, in theory, impossible for some data. For example, Korea is missing R&Dinv (green R&D investments) data for 1990-1998. Heshmati and Tsionas used Excel’s autofill with three years of data (1999, 2000, and 2001) to create data for the nine missing years. The imputed values for 1990-1996 were negative, so the authors set these equal to the positive 1997 value.
A paper on green innovation that drew sharp rebuke for using questionable and undisclosed methods to replace missing data will be retracted, its publisher told Retraction Watch.
Previous work by one of the authors, a professor of economics in Sweden, is also facing scrutiny, according to another publisher.
As we reported earlier this month, Almas Heshmati of Jönköping University mended a dataset full of gaps by liberally applying Excel’s autofill function and copying data between countries – operations other experts described as “horrendous” and “beyond concern.”
Heshmati and his coauthor, Mike Tsionas, a professor of economics at Lancaster University in the UK who died recently, made no mention of missing data or how they dealt with them in their 2023 article, “Green innovations and patents in OECD countries.” Instead, the paper gave the impression of a complete dataset. One economist argued in a guest post on our site that there was “no justification” for such lack of disclosure.
Elsevier, in whose Journal of Cleaner Production the study appeared, moved quickly on the new information. A spokesperson for the publisher told us yesterday: “We have investigated the paper and can confirm that it will be retracted.”
