Importance Many medical journals, including JAMA, restrict the use of causal language to the reporting of randomized clinical trials. Although well-conducted randomized clinical trials remain the preferred approach for answering causal questions, methods for observational studies have advanced such that causal interpretations of the results of well-conducted observational studies may be possible when strong assumptions hold. Furthermore, observational studies may be the only practical source of information for answering some questions about the causal effects of medical or policy interventions, can support the study of interventions in populations and settings that reflect practice, and can help identify interventions for further experimental investigation. Identifying opportunities for the appropriate use of causal language when describing observational studies is important for communication in medical journals.
Observations A structured approach to whether and how causal language may be used when describing observational studies would enhance the communication of research goals, support the assessment of assumptions and design and analytic choices, and allow for more clear and accurate interpretation of results. Building on the extensive literature on causal inference across diverse disciplines, we suggest a framework for observational studies that aim to provide evidence about the causal effects of interventions based on 6 core questions: what is the causal question; what quantity would, if known, answer the causal question; what is the study design; what causal assumptions are being made; how can the observed data be used to answer the causal question in principle and in practice; and is a causal interpretation of the analyses tenable?
Conclusions and Relevance Adoption of the proposed framework to identify when causal interpretation is appropriate in observational studies promises to facilitate better communication between authors, reviewers, editors, and readers. Practical implementation will require cooperation between editors, authors, and reviewers to operationalize the framework and evaluate its effect on the reporting of empirical research.
Disruptions in mental-health care during the pandemic left many Americans vulnerable. Among people ages 18 to 44, insurance claims related to psychotic episodes rose 30% to 2 million in 2023 from 2019, according to LexisNexis Risk Solutions, a data-analytics company. Around the U.S., hospitals are overwhelmed. Emergency rooms are adding security guards. Jails serve as a last resort for those unable to care for themselves.
Why women are at greater risk of autoimmune disease such as multiple sclerosis, lupus and rheumatoid arthritis is a long-standing medical mystery, and a team of researchers at Stanford University may now be a step closer to unraveling it.
How the female body handles its extra X chromosome (the male body has just one plus a Y chromosome) might be a factor that helps explain why women are more susceptible to these types of disorders, a new study has suggested. The predominantly chronic conditions involve an off-kilter immune system attacking its own cells and tissues.
While the research involving experiments on mice is preliminary, the observation, after further study, may help inform new treatments and ways to diagnose the diseases, said Dr. Howard Chang, seniorauthor of the paper published in the journal Cell on February 1.
….
Other researchers had focused on the disorders’ “female bias” by analyzing sex hormones or chromosome counts. Chang instead zoned in on the role played by a molecule called Xist (pronounced exist) that is not present in male cells.
The Xist molecule’s main job is to deactivate the second female X chromosome in embryos, ensuring that the body’s cells don’t get a potentially toxic double whammy of the chromosome’s protein-coding genes.
“Xist is a very long RNA, 17,000 nucleotides long, or letters, and it associates with approximately almost 100 proteins,” Chang said. Xist molecules work with those proteins to shut down gene expression in the second X chromosome.
Importance Antivaccine sentiment is increasingly associated with conservative political positions. Republican-inclined states exhibit lower COVID-19 vaccination rates, but the association between political inclination and reported vaccine adverse events (AEs) is unexplored.
Objective To assess whether there is an association between state political inclination and the reporting rates of COVID-19 vaccine AEs.
Design, Setting, and Participants This cross-sectional study used the AE reports after COVID-19 vaccination from the Vaccine Adverse Event Reporting System (VAERS) database from 2020 to 2022, with reports after influenza vaccines from 2019 to 2022 used as a reference. These reports were examined against state-level percentage of Republican votes in the 2020 US presidential election.
Exposure State-level percentage of Republican votes in the 2020 US presidential election.
Main Outcomes and Measures Rates of any AE among COVID-19 vaccine recipients, rates of any severe AE among vaccine recipients, and the proportion of AEs reported as severe.
Results A total of 620 456 AE reports (mean [SD] age of vaccine recipients, 51.8 [17.6] years; 435 797 reports from women [70.2%]; a vaccine recipient could potentially file more than 1 report, so reports are not necessarily from unique individuals) for COVID-19 vaccination were identified from the VAERS database. Significant associations between state political inclination and state AE reporting were observed for all 3 outcomes: a 10% increase in Republican voting was associated with increased odds of AE reports (odds ratio [OR], 1.05; 95% CI, 1.05-1.05; P < .001), severe AE reports (OR, 1.25; 95% CI, 1.24-1.26; P < .001), and the proportion of AEs reported as severe (OR, 1.21; 95% CI, 1.20-1.22; P < .001). These associations were seen across all age strata in stratified analyses and were more pronounced among older subpopulations.
Conclusions and Relevance This cross-sectional study found that the more states were inclined to vote Republican, the more likely their vaccine recipients or their clinicians reported COVID-19 vaccine AEs. These results suggest that either the perception of vaccine AEs or the motivation to report them was associated with political inclination.
Now let’s turn to the paper. Here is what the authors find (weak correlation btw voting and vaccine injuries) , and here are the issues.
These data are ecological. It doesn’t prove that republicans themselves are more likely to report vaccine injuries. It would not be difficult to pair voting records with vaccine records at an individual patient level if the authors wished to do it right— another example of research laziness.
What if republicans actually DO have more vaccine injuries? The authors try to correct for the fact by adjusting for influenza adverse events.
Let me explain why this is a poor choice. The factors that predict whether someone has an adverse event to influenza vaccine may not be the same as those that predict adverse events from covid shots. It could be that there are actually more covid vaccine injuries in one group than another— even though both had equal rates of influenza injuries.
Another way to think of it is, there can be two groups of people and you can balance them by the rate with which they get headaches from drinking wine, but one group can be more likely to get headaches from reading without glasses because more people in that group wear glasses. In other words, states with more republicans might be states with specific co-morbidities that predict COVID vaccine adverse side effects but not influenza vaccine side effects. We already know that COVID vaccine injuries do affect different groups (young men, for e.g.).
Author(s): Vinay Prasad
Publication Date: 2 Apr 2024
Publication Site: Vinay Prasad’s Thoughts and Observations at substack
In this paper I use the 1973 cross-sectional Current Population Survey (CPS) matched to longitudinal Social Security administrative data (through 1998) to examine the relationship between retirement age and mortality for men who have lived to at least age 65 by year 1997 or earlier.1 Logistic regression results indicate that controlling for current age, year of birth, education, marital status in 1973, and race, men who retire early die sooner than men who retire at age 65 or older. A positive correlation between age of retirement and life expectancy may suggest that retirement age is correlated with health in the 1973 CPS; however, the 1973 CPS data do not provide the ability to test that hypothesis directly.
Regression results also indicate that the composition of the early retirement variable matters. I represent early retirees by four dummy variables representing age of entitlement to Social Security benefits—exactly age 62 to less than 62 years and 3 months (referred to as exactly age 62 in this paper), age 62 and 3 months to 62 and 11 months, age 63, and age 64. The reference variable is men taking benefits at age 65 or older. I find that men taking benefits at exactly age 62 have higher mortality risk than men taking benefits in any of the other four age groups. I also find that men taking benefits at age 62 and 3 months to 62 and 11 months, age 63, and age 64 have higher mortality risk than men taking benefits at age 65 or older. Estimates of mortality risk for “early” retirees are lowered when higher-risk age 62 retirees are combined with age 63 and age 64 retirees and when age 62 retirees are compared with a reference variable of age 63 and older retirees. Econometric models may benefit by classifying early retirees by single year of retirement age—or at least separating age 62 retirees from age 63 and age 64 retirees and age 63 and age 64 retirees from age 65 and older retirees—if single-year breakdowns are not possible.
The differential mortality literature clearly indicates that mortality risk is higher for low-educated males relative to high-educated males. If low-educated males tend to retire early in relatively greater numbers than high-educated males, higher mortality risk for such individuals due to low educational attainment would be added to the higher mortality risk I find for early retirees relative to that for normal retirees. Descriptive statistics for the 1973 CPS show that a greater proportion of age 65 retirees are college educated than age 62 retirees. In addition, a greater proportion of age 64 retirees are college educated than age 62 retirees, and a lesser proportion of age 64 retirees are college educated than age 65 or older retirees. Age 63 retirees are only slightly more educated than age 62 retirees.
Despite a trend toward early retirement over the birth cohorts in the 1973 CPS, I do not find a change in retirement age differentials over time. However, I do find a change in mortality risk by education over time. Such a change may result from the changing proportion of individuals in each education category over time, a trend toward increasing mortality differentials by socioeconomic status, or a combination of the two.
This paper does not directly explore why a positive correlation between retirement age and survival probability exists. One possibility is that men who retire early are relatively less healthy than men who retire later and that these poorer health characteristics lead to earlier deaths. One can interpret this hypothesis with a “quasidisability” explanation and a benefit optimization explanation. Links between these interpretations and my analysis of the 1973 CPS are fairly speculative because I do not have the appropriate variables needed to test these interpretations.
A quasi-disability explanation, following Kingson (1982), Packard (1985), and Leonesio, Vaughan, and Wixon (2000), could be that a subgroup of workers who choose to take retired-worker benefits at age 62 is significantly less healthy than other workers but unable to qualify for disabled-worker benefits. An econometric model with a mix of both these borderline individuals and healthy individuals retiring at age 62 and with almost no borderline individuals retiring at age 65 could lead to a positive correlation between retirement and mortality, even if a greater percentage of individuals who retire at age 62 are healthy than unhealthy. Evidence for this hypothesis can be inferred from the finding that retiring at exactly age 62 increases the odds of dying in a unit age interval by 12 percent relative to men retiring at 62 and 3 months to 62 and 11 months for men in the 1973 CPS. In addition, retiring exactly at age 62 increases the odds of dying by 23 percent relative to men retiring at age 63 and by 24 percent relative to men retiring at age 64. A group with relatively severe health problems waiting for their 62nd birthday to take benefits could create this result.
An explanation based on benefit optimization follows Hurd and McGarry’s research (1995, 1997) in which they find that individuals’ subjective survival probabilities roughly predict actual survival. If men in the 1973 CPS choose age of benefit receipt based on expectations of their own life expectancy, then perhaps a positive correlation between age of retirement and life expectancy implies that their expectations are correct on average. If actuarial reductions for retirement before the normal retirement age are linked to average life expectancy and an individual’s life expectancy is below average, it may be rational for that individual to retire before the normal retirement age. Evidence for this hypothesis can be inferred from the fact that men retiring at age 62 and 3 months to age 62 and 11 months, age 63, and age 64 all experience greater mortality risk than men retiring at age 65 or older. If only men with severe health problems who are unable to qualify for disability benefits are driving the results, we probably would not expect to see this result. We might expect most of these individuals to retire at the earliest opportunity (exactly age 62).2
Author(s): Hilary Waldron
Publication Date: August 2001
Publication Site: Social Security Office of Policy, ORES Working Paper No 93
Brain fog is a colloquial term that describes a state of mental sluggishness or lack of clarity and haziness that makes it difficult to concentrate, remember things and think clearly.
Fast-forward four years and there is now abundant evidence that being infected with SARS-CoV-2 – the virus that causes COVID-19 – can affect brain health in many ways.
In addition to brain fog, COVID-19 can lead to an array of problems, including headaches, seizure disorders, strokes, sleep problems, and tingling and paralysis of the nerves, as well as several mental health disorders.
Making those timelines — 2020, 2021, 2022, 2023, 2024 — really brought home to me how long this pandemic has been going on; I lost track in the daily grind (though the daily grind is also my form of coping). And it’s a bit discouraging to see the most solidarity our society seems capable of fizzle out after 2020, followed by a struggle to return to business as usual, a struggle that failed by 2024, in that a once-essential part of touring — contact with the fans — has now gone missing.
We can, of course, moralize about what how these artists have gone about their business:
To be fair, though, when CDC Director Mandy Cohen is swanning about with no mask, modeling how to infect everybody she breathes on, what’s a poor celebrity to do? Restoring social norms that support non-pharmaceutical interventions will probably take a whole-of-society approach (which could happen when those Tiktokers start doing their research).
Here, however, are two small steps artists like Adele and Taylor Swift could do to improve the Covid pandemic situation.
First, big acts could really help out smaller acts by supporting organizations like this one: [Clean Air Club]
Second, sell N95s at your concerts and on your websites as branded merch. K-Pop powerhouse Twice already does this (though KN94s, not N95s):
And if, by some happy chance, some intern from either organization reads this post, please champion these ideas!
Oh, and champion clean air, too. Who could be against that? Miasma delenda est!
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 United States is knee-deep in what some experts call the opioid epidemic’s “fourth wave,” which is not only placing drug users at greater risk but is also complicating efforts to address the nation’s drug problem.
These waves, according to a report out today from Millennium Health, began with the crisis in prescription opioid use, followed by a significant jump in heroin use, then an increase in the use of synthetic opioids like fentanyl.
The latest wave involves using multiple substances at the same time, combining fentanyl mainly with either methamphetamine or cocaine, the report found. “And I’ve yet to see a peak,” said one of the co-authors, Eric Dawson, vice president of clinical affairs at Millennium Health, a specialty laboratory that provides drug testing services to monitor use of prescription medications and illicit drugs.
The report, which takes a deep dive into the nation’s drug trends and breaks usage patterns down by region, is based on 4.1 million urine samples collected from January 2013 to December 2023 from people receiving some kind of drug addiction care.
Its findings offer staggering statistics and insights. Its major finding: how common polysubstance use has become. According to the report, an overwhelming majority of fentanyl-positive urine samples — nearly 93% — contained additional substances. “And that is huge,” said Nora Volkow, director of the National Institute on Drug Abuse at the National Institutes of Health.
