30 research outputs found
Study of the effectiveness of incentive measures on Covid-19 vaccination in the United States of America
With COVID-19 having emerged as the most widespread human pandemic disease in
a century, the need to control its spread to avoid massive loss of life became
more than necessary, and extremely fast. Several vaccines were developed and
the task of policy makers was suddenly to convince the reluctant population to
be vaccinated by various means. While some countries have chosen a policy of
mandatory vaccination or punitive incentives, many states in the United States
have adopted various incentives to try to increase vaccination coverage. A
study we conducted in recent months quantified the effect of these measures on
the proportion of the population vaccinated, using the synthetic control
method, by simulating what would have happened without these measures. The aim
now is to generalize this study to smaller scales, to improve the results of
our previous study, to quantify their robustness and to provide a tool that can
be used by policy makers to adapt their behavior in light of the results
obtained
Patient diversity and author representation in clinical studies supporting the Surviving Sepsis Campaign guidelines for management of sepsis and septic shock 2021: a systematic review of citations
Background: The generalizability of the Surviving Sepsis Campaign (SSC) guidelines to various patient populations and hospital settings has been debated. A quantitative assessment of the diversity and representation in the clinical evidence supporting the guidelines would help evaluate the generalizability of the recommendations and identify strategic research goals and priorities. In this study, we evaluated the diversity of patients in the original studies, in terms of sex, race/ethnicity, and geographical location. We also assessed diversity in sex and geographical representation among study first and last authors. Methods: All clinical studies cited in support of the 2021 SSC adult guideline recommendations were identified. Original clinical studies were included, while editorials, reviews, non-clinical studies, and meta-analyses were excluded. For eligible studies, we recorded the proportion of male patients, percentage of each represented racial/ethnic subgroup (when available), and countries in which they were conducted. We also recorded the sex and location of the first and last authors. The World Bank classification was used to categorize countries. Results: The SSC guidelines included six sections, with 85 recommendations based on 351 clinical studies. The proportion of male patients ranged from 47 to 62%. Most studies did not report the racial/ ethnic distribution of the included patients; when they did so, most were White patients (68â77%). Most studies were conducted in high-income countries (77â99%), which included Europe/Central Asia (33â66%) and North America (36â55%). Moreover, most first/last authors were males (55â93%) and from high-income countries (77â99%). Conclusions: To enhance the generalizability of the SCC guidelines, stakeholders should define strategies to enhance the diversity and representation in clinical studies. Though there was reasonable representation in sex among patients included in clinical studies, the evidence did not reflect diversity in the race/ethnicity and geographical locations. There was also lack of diversity among the first and last authors contributing to the evidence
Causal inference in medical records and complementary systems pharmacology for metformin drug repurposing towards dementia.
Metformin, a diabetes drug with anti-aging cellular responses, has complex actions that may alter dementia onset. Mixed results are emerging from prior observational studies. To address this complexity, we deploy a causal inference approach accounting for the competing risk of death in emulated clinical trials using two distinct electronic health record systems. In intention-to-treat analyses, metformin use associates with lower hazard of all-cause mortality and lower cause-specific hazard of dementia onset, after accounting for prolonged survival, relative to sulfonylureas. In parallel systems pharmacology studies, the expression of two AD-related proteins, APOE and SPP1, was suppressed by pharmacologic concentrations of metformin in differentiated human neural cells, relative to a sulfonylurea. Together, our findings suggest that metformin might reduce the risk of dementia in diabetes patients through mechanisms beyond glycemic control, and that SPP1 is a candidate biomarker for metformin's action in the brain
Unsupervised learning for county-level typological classification for COVID-19 research
The analysis of county-level COVID-19 pandemic data faces computational and analytic challenges, particularly when considering the heterogeneity of data sources with variation in geographic, demographic, and socioeconomic factors between counties. This study presents a method to join relevant data from different sources to investigate underlying typological effects and disparities across typologies. Both consistencies within and variations between urban and non-urban counties are demonstrated. When different county types were stratified by age group distribution, this method identifies significant community mobility differences occurring before, during, and after the shutdown. Counties with a larger proportion of young adults (age 20â24) have higher baseline mobility and had the least mobility reduction during the lockdown.National Institutes of Health (Grant R01 EB017205
Impact of non-pharmaceutical interventions, weather, vaccination, and variants on COVID-19 transmission across departments in France: a modelling study
Background: Multiple factors shape the temporal dynamics of the COVID-19 pandemic. Quantifying their relative contributions is key to guide future control strategies. Our objective was to disentangle the individual effects of non-pharmaceutical interventions (NPIs), weather, vaccination, and variants of concern (VOC) on local SARS-CoV-2 transmission.Methods: We developed a log-linear model for the weekly reproduction number (R) of hospital admissions in 92 French metropolitan departments. We leveraged (i) the homogeneity in data collection and NPI definitions across departments, (ii) the spatial heterogeneity in the timing of NPIs, and (iii) an extensive observation period (14 months) covering different meteorological conditions, VOC proportions, and vaccine coverage levels.Results: Three lockdowns reduced R by 72.9% (95%CI: 71.4-74.2), 70.4% (69.2-71.6) and 60.4% (56.1-64.3), respectively. Curfews implemented at 6/7pm and 8/9pm reduced R by 34.5% (28.1-40.4) and 18.4% (11.4-24.8), respectively. School closures reduced R by only 4.6% (1.6-7.4). We estimated that vaccination of the entire population would have reduced R by 74.0% (59.4-83.3), whereas the emergence of VOC (mainly Alpha during the study period) increased transmission by 46.9% (38.2-56.0) compared with the historical variant. Winter weather conditions (lower temperature and absolute humidity) increased R by 41.7% (37.0-46.7) compared to summer weather conditions. Additionally, we explored counterfactual scenarios (absence of VOC or vaccination) to assess their impact on hospital admissions.Conclusions: Our study demonstrates the strong effectiveness of NPIs and vaccination and quantifies the role of meteorological factors while adjusting for other confounders. It highlights the importance of retrospective evaluation of interventions to inform future decision-making
Diversity and inclusion: A hidden additional benefit of Open Data.
The recent imperative by the National Institutes of Health to share scientific data publicly underscores a significant shift in academic research. Effective as of January 2023, it emphasizes that transparency in data collection and dedicated efforts towards data sharing are prerequisites for translational research, from the lab to the bedside. Given the role of data access in mitigating potential bias in clinical models, we hypothesize that researchers who leverage open-access datasets rather than privately-owned ones are more diverse. In this brief report, we proposed to test this hypothesis in the transdisciplinary and expanding field of artificial intelligence (AI) for critical care. Specifically, we compared the diversity among authors of publications leveraging open datasets, such as the commonly used MIMIC and eICU databases, with that among authors of publications relying exclusively on private datasets, unavailable to other research investigators (e.g., electronic health records from ICU patients accessible only to Mayo Clinic analysts). To measure the extent of author diversity, we characterized gender balance as well as the presence of researchers from low- and middle-income countries (LMIC) and minority-serving institutions (MSI) located in the United States (US). Our comparative analysis revealed a greater contribution of authors from LMICs and MSIs among researchers leveraging open critical care datasets (treatment group) than among those relying exclusively on private data resources (control group). The participation of women was similar between the two groups, albeit slightly larger in the former. Notably, although over 70% of all articles included at least one author inferred to be a woman, less than 25% had a woman as a first or last author. Importantly, we found that the proportion of authors from LMICs was substantially higher in the treatment than in the control group (10.1% vs. 6.2%, p<0.001), including as first and last authors. Moreover, we found that the proportion of US-based authors affiliated with a MSI was 1.5 times higher among articles in the treatment than in the control group, suggesting that open data resources attract a larger pool of participants from minority groups (8.6% vs. 5.6%, p<0.001). Thus, our study highlights the valuable contribution of the Open Data strategy to underrepresented groups, while also quantifying persisting gender gaps in academic and clinical research at the intersection of computer science and healthcare. In doing so, we hope our work points to the importance of extending open data practices in deliberate and systematic ways
Impact of non-pharmaceutical interventions, weather, vaccination, and variants on COVID-19 transmission across departments in France
International audienceBackground: Multiple factors shape the temporal dynamics of the COVID-19 pandemic. Quantifying their relative contributions is key to guide future control strategies. Our objective was to disentangle the individual effects of non-pharmaceutical interventions (NPIs), weather, vaccination, and variants of concern (VOC) on local SARS-CoV-2 transmission.Methods: We developed a log-linear model for the weekly reproduction number (R) of hospital admissions in 92 French metropolitan departments. We leveraged (i) the homogeneity in data collection and NPI definitions across departments, (ii) the spatial heterogeneity in the timing of NPIs, and (iii) an extensive observation period (14 months) covering different weather conditions, VOC proportions, and vaccine coverage levels.Findings: Three lockdowns reduced R by 72.7% (95% CI 71.3â74.1), 70.4% (69.2â71.6) and 60.7% (56.4â64.5), respectively. Curfews implemented at 6/7 pm and 8/9 pm reduced R by 34.3% (27.9â40.2) and 18.9% (12.04â25.3), respectively. School closures reduced R by only 4.9% (2.0â7.8). We estimated that vaccination of the entire population would have reduced R by 71.7% (56.4â81.6), whereas the emergence of VOC (mainly Alpha during the study period) increased transmission by 44.6% (36.1â53.6) compared with the historical variant. Winter weather conditions (lower temperature and absolute humidity) increased R by 42.2% (37.3â47.3) compared to summer weather conditions. Additionally, we explored counterfactual scenarios (absence of VOC or vaccination) to assess their impact on hospital admissions.Interpretation: Our study demonstrates the strong effectiveness of NPIs and vaccination and quantifies the role of weather while adjusting for other confounders. It highlights the importance of retrospective evaluation of interventions to inform future decision-making