Public health implications of opening National Football League stadiums during the COVID-19 pandemic

Using attendance data from the 2020 National Football League (NFL) regular season and local COVID-19 case counts, we estimate the public health impact of opening NFL stadiums to fans during the COVID-19 pandemic. Data are analyzed using robust synthetic control, a statistical method that is employed to obtain counterfactual estimates from observational data. Unlike previous studies [J. Kurland et al., SSRN, 2021], which do not consider confounding factors such as evolving policy landscapes in different states, the synthetic control methodology allows us to account for effects that are county specific and may be changing over time. We find it is likely that opening stadiums had no impact on local COVID-19 case counts; this suggests that, for the 2020 NFL season, the benefits of providing a tightly controlled outdoor spectating environment—including masking and distancing requirements—counterbalanced the risks associated with opening. These results are specific to the 2020 NFL season, and care should be taken in generalizing our conclusions. In particular, 1) these data reflect a period during which earlier strains of COVID-19 were dominant prior to the emergence of more-transmissive strains such as the Delta and Omicron variants, and 2) the data are restricted to outdoor environments; hence our results cannot be applied to small indoor spaces where transmission-restricting controls are essential.

A year and a half into the global COVID-19 pandemic, we have an opportunity to analyze and reflect upon the policies and decisions enacted over the past 18 mo. Given the distributed nature of policy decisions in the United States, we find ourselves in a unique position in which states and municipalities have explored different strategies to combat the virus, and the efficacy of those policies has been imprinted in the local case counts, hospitalizations, and death records. In particular, these data contain a wealth of information about which policies have proven to be effective in preserving the health and safety of our communities.One activity that one may wish to consider is the opening of outdoor sporting events to spectators. This question has recently generated quite a bit of interest as ballparks across the nation open for summer and events such as the 2021 Summer Olympics in Japan take place.^* On the one hand, governing bodies are naturally wary of opening stadiums given the well-documented importance of avoiding large gatherings. On the other hand, sporting events are often held outdoors, where airflow is largely unobstructed (1), and in venues where crowd density can be carefully controlled if the event is properly managed. In the absence of a detailed analysis, it is not immediately obvious which of these effects dominates.Data from the National Football League (NFL) may provide an answer to this question. During the 2020 regular season, teams in the NFL collaborated with local communities to determine whether or not to allow fans in the stadiums during the pandemic. In general, stadiums that opened their doors to fans adopted pandemic requirements for all in attendance (1), which typically include some combination of staggered entry, required masking, health questionnaires, temperature checks for staff, deployment of compliance officers, modified concessions, social distancing in seating and lines, mobile ticketing, enhanced cleaning protocols, amplified health and safety communications, and capacity limitations. The highest capacity that any NFL stadium allowed during the 2020 regular season was 30% (Dallas), with most other stadiums considerably below that limit (2). These policy decisions were made based on local guidelines, local prevalence, community risk tolerance, and other localized considerations; some stadiums ultimately decided to allow fans at the games, while others remained closed, providing perhaps the first set of natural experiments that can be analyzed to investigate the impact of opening stadiums on COVID-19 case rates. In the words of Kurland et al. (3), who recently provided a first look at this data, “Scant evidence has been gathered in the extant literature on the impact of sport venues on local public health, influenza-related mortality rates, or disease contagion more generally. There is a complete absence of any evidence related to the impact of fans gathering at sporting events, or mass gatherings more generally, on incidence of COVID-19 at the local-level.” The natural experiments from the 2020 NFL season and other sports leagues present a golden opportunity to address these questions in the context of the original 2020 COVID-19 strain (4, 5).In the Kurland et al. (3) study, the authors compared COVID-19 case data from NFL stadium counties that allowed fans in the stadium to counties that did not allow fans, and looked for spikes in the data in the weeks following a game; the authors concluded, from this analysis, that the presence of large numbers of fans at NFL games led to “tangible increases” in the local incidence of COVID-19 cases. However, this type of analysis may be problematic: In this context, the control stadiums (i.e., those without fans) tend to be embedded in states with stricter COVID-19 policies—rather than a random control—so the sample of control counties is strongly biased. New York and Dallas, for example, are immersed in very different environments with different pandemic policies, and it is not at all obvious that one can attribute the differences in case spikes to the stadiums, given the enormous number of confounding factors.Fortunately, there exists a rich literature of techniques—longitudinal methods, hierarchical methods, factor model methods, synthetic control, etc.—that we can draw upon to account for these confounding factors. In this particular analysis, we turn to synthetic control (6–9), which has been applied in a diversity of fields—criminology (10), healthcare (11), sports (12), and political science and policy evaluation (13–15), to name a few. At its heart, synthetic control is a method for estimating a counterfactual in the absence of an intervention, in this case, what would have happened if stadiums had not opened. The method provides a systematic way to choose relevant comparison units when randomized controls are not available.To illustrate the power of synthetic control, imagine the ideal experiment one would like to run in order to quantify the impact of opening the Dallas stadium to fans. In principle, we would like to have COVID-19 case counts from Dallas County throughout the season with the stadium open to fans and case counts from a Dallas twin—with identical people and policies to the first Dallas—in which the stadium did not open for comparison. The first set of data (Dallas open to fans) is readily available. The second set of data can be constructed from information from other counties in Texas—hereafter referred to as donor counties—which have policies and characteristics similar to Dallas. Synthetic control provides a methodology to build a weighted combination of these Dallas-like counties, which can then be used as a control group, that is, a “synthetic” Dallas twin. In particular, we seek the linear combination of case counts from other Texas counties that most closely mirrors the Dallas case counts prior to the stadium opening. Given that none of these non-Dallas counties have a stadium, this linear combination can be extended postintervention (i.e., after opening the stadium) to estimate what would have happened in the synthetic Dallas in which no stadium opened. Once it has been established that the stadium county and the synthetically generated county have similar behavior over extended periods of time prior to the intervention, a discrepancy in the number of COVID-19 cases following the intervention may be interpreted as a result of allowing fans in the stadium. One of the advantages of this method is that it can account for the effects of confounding factors that are county specific and may be changing over time, which is crucial in the ever-evolving policy landscape of a pandemic (16). In particular, our methodology allows for correlation between the decision to open the stadium and characteristics that define the county (cultural or political leaning, population density, demographics, etc.), but cannot account for correlations between the decision and exogenous noise.At this point, it is reasonable to speculate whether one should expect linear combinations of donor counties to accurately represent stadium counties (both observed and counterfactual). In general, assuming linearity is appropriate provided there exists an underlying low-dimensional structure to the case count data, that is, if the matrix containing discretized time series of donor county case counts is approximately low rank. Under a such a setting, linearity between counties is an almost immediate consequence (see Materials and Methods for details). This low-rank assumption is common in the matrix completion literature; notably, low-rank matrices have also been shown to naturally arise in modern datasets and emerge from “well-behaved” generative models (e.g., Lipschitz functions) (17–20). This point will be revisited in Results, where we test for low rankedness empirically in the context of our dataset.Finally, the selection of donor units is a critical step in the successful implementation of creating a synthetic control. In particular, donor units (in our case, counties) should have the following characteristics:

• 1)Counties affected by the intervention or by events of a similar nature should be excluded from the donor pool.
• 2)Counties that may have suffered large “idiosyncratic shocks” (7, 21) during the preintervention period should be excluded.
• 3)The donor pool should be restricted to counties with characteristics similar to the stadium county; in this case, we restrict our pool to counties from the same state to maintain some consistency in COVID-19 policies.
• 4)Case counts that cover an extended period of time prior to the intervention are required for both stadium counties and donor counties.

In order to establish which counties satisfy these constraints, the NFL provided us with aggregate attendance data indicating the percentage of fans from each county in each state (2). In general, 10% or more of the fans come from the county in which the stadium is located. Hence, we designate counties that provided more than 10% of the fan base as stadium counties. In addition, there are a number of counties that are home to many fans but not to the same extent as that of the stadium counties. Since there is some ambiguity as to whether these counties should be counted as stadium counties or donor counties, we designate counties that supply between 1% and 10% of the fan base as buffer counties and, in light of the first criterion above, do not include them as either stadium or donor counties. Second, to address criterion 3, we only include counties in the donor pool that come from the same state as the stadium county. Although there is variation at the county level, overarching COVID-19 guidance, in general, comes from the states; hence, we assume that policies are relatively consistent within states and allow that they may vary dramatically from state to state. In addition, we only retain counties in which at least 200 cases have been recorded, in order to eliminate donor counties that are either markedly underreporting or undertesting. Finally, we are fortunate that football season starts in September, which allows us to address criterion 4; given that relatively reliable COVID-19 case count data have been available since approximately April 2020, we have 4 mo of training data at our disposal to learn the weights for the synthetic counties. Criterion 2 is trickier, given that we do not necessarily know, a priori, all events that could cause a shock to the system; however, a posteriori, we can investigate the outcomes and look for signs of such a shock.^†     

Prevalence of drug interactions in hospital healthcare

Aim of the review To study the prevalence of drug interactions in hospital healthcare by reviewing literature. Method A review was carried out of studies written in Spanish and English on the prevalence of drug interactions in hospital care published in Pubmed between January 1990 and September 2008. The search strategy combined free text and MeSH terms, using the following keywords: ??Drug interaction??, ??prevalence?? and ??hospital??. For each article, we classified independent variables (pathology, age of population, whether patients were hospitalized or not, geographical location, etc.) and dependent variables (number of interactions per 100 patients studied, prevalence of patients with interactions, most common drug interactions, and others). Results The search generated 436 articles. Finally, 47 articles were selected for the study, 3 provided results about drug interactions with real clinical consequences, 42 about potential interactions, and 2 described both. The prevalence of patients with interactions was between 15 and 45?% and the number of interactions per 100 patients was between 37 and 106, depending on the group of studies analyzed. There was a considerable increase in these rates in patients with heart diseases and elderly persons. Conclusion There is a large number of studies on the prevalence of drug interactions in hospitals but they report widely varying results. The prevalence is higher in patients with heart diseases and elderly people.     

Actualización en cardiopatía isquémica y cuidados críticos cardiológicos

This article summarizes the main developments reported during the year 2012 concerning ischemic heart disease, together with the most relevant innovations in the management of acute cardiac patients.     

Proton magnetic resonance spectroscopy of the thalamus in patients with chronic neuropathic pain after spinal cord injury

BACKGROUND AND PURPOSE: Spinal cord injury (SCI) results in a number of consequences; one of the most difficult to manage is chronic neuropathic pain. Thus, defining the potential neural and biochemical changes associated with chronic pain after SCI is important because this may lead to development of new treatment strategies. Prior studies have looked at the thalamus, because it is a major sensory relay station. The purpose of our study was to define alterations in metabolites due to injury-induced functional changes in thalamic nuclei by using single-voxel stimulated echo acquisition mode MR spectroscopy. METHODS: Twenty-six men were recruited: 16 patients with SCI and paraplegia (seven with pain, nine without pain) and 10 healthy control subjects. Pain was evaluated in an interview, which included the collection of information concerning the location, quality, and intensity of pain, carefully identifing the dysesthetic neuropathic pain often seen in SCI. Localized single-voxel (8-cm(3) volume) proton spectra were acquired from the left and right thalami. RESULTS: The concentration of N-acetyl (NA) was negatively correlated with pain intensity (r = -0.678), and the t test showed that NA was significantly different between patients with pain and patients without pain (P =.006). Myo-inositol was positively correlated with pain intensity (r = 0.520); difference between patients with pain and those without pain was almost significant (P =.06). CONCLUSION: The observed differences in metabolites in SCI patients with and pain and in those without pain suggest anatomic, functional, and biochemical changes in the thalamic region.