Government responses to COVID-19 are among the most globally impactful events of the 21st century. The extent to which responses—such as school closures—were associated with changes in COVID-19 ...outcomes remains unsettled. Multiverse analyses offer a systematic approach to testing a large range of models. We used daily data on 16 government responses in 181 countries in 2020–2021, and 4 outcomes—cases, infections, COVID-19 deaths, and all-cause excess deaths—to construct 99,736 analytic models. Among those, 42% suggest outcomes improved following more stringent responses (“helpful”). No subanalysis (e.g. limited to cases as outcome) demonstrated a preponderance of helpful or unhelpful associations. Among the 14 associations with P values < 1 × 10 −30 , 5 were helpful and 9 unhelpful. In summary, we find no patterns in the overall set of models that suggests a clear relationship between COVID-19 government responses and outcomes. Strong claims about government responses’ impacts on COVID-19 may lack empirical support.
Globally, COVID-19 government responses were followed by increases and decreases in COVID-19, in equal measures.
Abstract
Massive data sets are often regarded as a panacea to the underpowered studies of the past. At the same time, it is becoming clear that in many of these data sets in which thousands of ...variables are measured across hundreds of thousands or millions of individuals, almost any desired relationship can be inferred with a suitable combination of covariates or analytic choices. Inspired by the genome-wide association study analysis paradigm that has transformed human genetics, X-wide association studies or “XWAS” have emerged as a popular approach to systematically analyzing nongenetic data sets and guarding against false positives. However, these studies often yield hundreds or thousands of associations characterized by modest effect sizes and miniscule P values. Many of these associations will be spurious and emerge due to confounding and other biases. One way of characterizing confounding in the genomics paradigm is the genomic inflation factor. An analogous “X-wide inflation factor,” denoted λX, can be defined and applied to published XWAS. Effects that arise in XWAS may be prioritized using replication, triangulation, quantification of measurement error, contextualization of each effect in the distribution of all effect sizes within a field, and pre-registration. Criteria like those of Bradford Hill need to be reconsidered in light of exposure-wide epidemiology to prioritize signals among signals.
The National Health and Nutrition Examination Survey (NHANES) is a population survey implemented by the Centers for Disease Control and Prevention (CDC) to monitor the health of the United States ...whose data is publicly available in hundreds of files. This Data Descriptor describes a single unified and universally accessible data file, merging across 255 separate files and stitching data across 4 surveys, encompassing 41,474 individuals and 1,191 variables. The variables consist of phenotype and environmental exposure information on each individual, specifically (1) demographic information, physical exam results (e.g., height, body mass index), laboratory results (e.g., cholesterol, glucose, and environmental exposures), and (4) questionnaire items. Second, the data descriptor describes a dictionary to enable analysts find variables by category and human-readable description. The datasets are available on DataDryad and a hands-on analytics tutorial is available on GitHub. Through a new big data platform, BD2K Patient Centered Information Commons (http://pic-sure.org), we provide a new way to browse the dataset via a web browser (https://nhanes.hms.harvard.edu) and provide application programming interface for programmatic access.
A nutrient-wide approach may be useful to comprehensively test and validate associations between nutrients (derived from foods and supplements) and blood pressure (BP) in an unbiased manner.
Data ...from 4680 participants aged 40 to 59 years in the cross-sectional International Study of Macro/Micronutrients and Blood Pressure (INTERMAP) were stratified randomly into training and testing sets. US National Health and Nutrition Examination Survey (NHANES) four cross-sectional cohorts (1999-2000, 2001-2002, 2003-2004, 2005-2006) were used for external validation. We performed multiple linear regression analyses associating each of 82 nutrients and 3 urine electrolytes with systolic and diastolic BP in the INTERMAP training set. Significant findings were validated in the INTERMAP testing set and further in the NHANES cohorts (false discovery rate <5% in training, P<0.05 for internal and external validation). Among the validated nutrients, alcohol and urinary sodium-to-potassium ratio were directly associated with systolic BP, and dietary phosphorus, magnesium, iron, thiamin, folacin, and riboflavin were inversely associated with systolic BP. In addition, dietary folacin and riboflavin were inversely associated with diastolic BP. The absolute effect sizes in the validation data (NHANES) ranged from 0.97 mm Hg lower systolic BP (phosphorus) to 0.39 mm Hg lower systolic BP (thiamin) per 1-SD difference in nutrient variable. Inclusion of nutrient intake from supplements in addition to foods gave similar results for some nutrients, though it attenuated the associations of folacin, thiamin, and riboflavin intake with BP.
We identified significant inverse associations between B vitamins and BP, relationships hitherto poorly investigated. Our analyses represent a systematic unbiased approach to the evaluation and validation of nutrient-BP associations.
Diseases such as type 2 diabetes (T2D) result from environmental and genetic factors, and risk varies considerably in the population. T2D-related genetic loci discovered to date explain only a small ...portion of the T2D heritability. Some heritability may be due to gene–environment interactions. However, documenting these interactions has been difficult due to low availability of concurrent genetic and environmental measures, selection bias, and challenges in controlling for multiple hypothesis testing. Through genome-wide association studies (GWAS), investigators have identified over 90 single nucleotide polymorphisms (SNPs) associated to T2D. Using a method analogous to GWAS environment-wide association study (EWAS), we found five environmental factors associated with the disease. By focusing on risk factors that emerge from GWAS and EWAS, it is possible to overcome difficulties in uncovering gene–environment interactions. Using data from the National Health and Nutrition Examination Survey (NHANES), we screened 18 SNPs and 5 serum-based environmental factors for interaction in association to T2D. We controlled for multiple hypotheses using false discovery rate (FDR) and Bonferroni correction and found four interactions with FDR <20 %. The interaction between rs13266634 (SLC30A8) and trans-β-carotene withstood Bonferroni correction (corrected
p
= 0.006, FDR <1.5 %). The per-risk-allele effect sizes in subjects with low levels of trans-β-carotene were 40 % greater than the marginal effect size odds ratio (OR) 1.8, 95 % CI 1.3–2.6. We hypothesize that impaired function driven by rs13266634 increases T2D risk when combined with serum levels of nutrients. Unbiased consideration of environmental and genetic factors may help identify larger and more relevant effect sizes for disease associations.
Quantify the impact of genetic and socioeconomic factors on risk of type 2 diabetes (T2D) and obesity.
Among participants in the Mass General Brigham Biobank (MGBB) and UK Biobank (UKB), we used ...logistic regression models to calculate cross-sectional odds of T2D and obesity using 1) polygenic risk scores for T2D and BMI and 2) area-level socioeconomic risk (educational attainment) measures. The primary analysis included 26,737 participants of European genetic ancestry in MGBB with replication in UKB (N = 223,843), as well as in participants of non-European ancestry (MGBB N = 3,468; UKB N = 7,459).
The area-level socioeconomic measure most strongly associated with both T2D and obesity was percent without a college degree, and associations with disease prevalence were independent of genetic risk (P < 0.001 for each). Moving from lowest to highest quintiles of combined genetic and socioeconomic burden more than tripled T2D (3.1% to 22.2%) and obesity (20.9% to 69.0%) prevalence. Favorable socioeconomic risk was associated with lower disease prevalence, even in those with highest genetic risk (T2D 13.0% vs. 22.2%, obesity 53.6% vs. 69.0% in lowest vs. highest socioeconomic risk quintiles). Additive effects of genetic and socioeconomic factors accounted for 13.2% and 16.7% of T2D and obesity prevalence, respectively, explained by these models. Findings were replicated in independent European and non-European ancestral populations.
Genetic and socioeconomic factors significantly interact to increase risk of T2D and obesity. Favorable area-level socioeconomic status was associated with an almost 50% lower T2D prevalence in those with high genetic risk.
To assess how the results of published national registry-based pharmacoepidemiology studies (where select associations are of interest) compare with an agnostic medication-wide approach (where all ...possible drug associations are tested).
We systematically searched for publications that reported drug associations with any, breast, colon/colorectal, or prostate cancer in the Swedish Prescribed Drug Registry. Results were compared against a previously performed agnostic medication-wide study on the same registry. Protocol: https://osf.io/kqj8n.
Most published studies (25/32) investigated previously reported associations. 421/913 (46%) associations had statistically significant results. 134 of the 162 unique drug-cancer associations could be paired with 70 associations in the agnostic study (corresponding drug categories and cancer types). Published studies reported smaller effect sizes and absolute effect sizes than the agnostic study, and generally used more adjustments. Agnostic analyses were less likely to report statistically significant protective associations (based on a multiplicity-corrected threshold) than their paired associations in published studies (McNemar odds ratio 0.13, P = 0.0022). Among 162 published associations, 36 (22%) showed increased risk signal and 25 (15%) protective signal at P < 0.05, while for agnostic associations, 237 (11%) showed increased risk signal and 108 (5%) protective signal at a multiplicity-corrected threshold. Associations belonging to drug categories targeted by individual published studies vs. nontargeted had smaller average effect sizes; smaller P values; and more frequent risk signals.
Published pharmacoepidemiology studies using a national registry addressed mostly previously proposed associations, were mostly “negative”, and showed only modest concordance with their respective agnostic analyses in the same registry.
Abstract
Background
The results of studies on observational associations may vary depending on the study design and analysis choices as well as due to measurement error. It is important to understand ...the relative contribution of different factors towards generating variable results, including low sample sizes, researchers’ flexibility in model choices, and measurement error in variables of interest and adjustment variables.
Methods
We define sampling, model and measurement uncertainty, and extend the concept of vibration of effects in order to study these three types of uncertainty in a common framework. In a practical application, we examine these types of uncertainty in a Cox model using data from the National Health and Nutrition Examination Survey. In addition, we analyse the behaviour of sampling, model and measurement uncertainty for varying sample sizes in a simulation study.
Results
All types of uncertainty are associated with a potentially large variability in effect estimates. Measurement error in the variable of interest attenuates the true effect in most cases, but can occasionally lead to overestimation. When we consider measurement error in both the variable of interest and adjustment variables, the vibration of effects are even less predictable as both systematic under- and over-estimation of the true effect can be observed. The results on simulated data show that measurement and model vibration remain non-negligible even for large sample sizes.
Conclusion
Sampling, model and measurement uncertainty can have important consequences for the stability of observational associations. We recommend systematically studying and reporting these types of uncertainty, and comparing them in a common framework.
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•Diesel exhaust pollutants were evaluated for workers in the US trucking industry.•Metabolomic alterations were associated with elemental carbon and organic carbon.•No metabolites ...were associated with PM2.5 exposure.•Exposure-associated metabolites were related to oxidative stress and nitric oxide production.•Gene and metabolite networks support associations with cardiopulmonary disease.
Exposure to traffic-related pollutants, including diesel exhaust, is associated with increased risk of cardiopulmonary disease and mortality; however, the precise biochemical pathways underlying these effects are not known. To investigate biological response mechanisms underlying exposure to traffic related pollutants, we used an integrated molecular response approach that included high-resolution metabolomic profiling and peripheral blood gene expression to identify biological responses to diesel exhaust exposure. Plasma samples were collected from 73 non-smoking males employed in the US trucking industry between February 2009 and October 2010, and analyzed using untargeted high-resolution metabolomics to characterize metabolite associations with shift- and week-averaged levels of elemental carbon (EC), organic carbon (OC) and particulate matter with diameter ≤ 2.5 μm (PM2.5). Metabolic associations with EC, OC and PM2.5 were evaluated for biochemical processes known to be associated with disease risk. Annotated metabolites associated with exposure were then tested for relationships with the peripheral blood transcriptome using multivariate selection and network correlation. Week-averaged EC and OC levels, which were averaged across multiple shifts during the workweek, resulted in the greatest exposure-associated metabolic alterations compared to shift-averaged exposure levels. Metabolic changes associated with EC exposure suggest increased lipid peroxidation products, biomarkers of oxidative stress, thrombotic signaling lipids, and metabolites associated with endothelial dysfunction from altered nitric oxide metabolism, while OC exposures were associated with antioxidants, oxidative stress biomarkers and critical intermediates in nitric oxide production. Correlation with whole blood RNA gene expression provided additional evidence of changes in processes related to endothelial function, immune response, inflammation, and oxidative stress. We did not detect metabolic associations with PM2.5. This study provides an integrated molecular assessment of human exposure to traffic-related air pollutants that includes diesel exhaust. Metabolite and transcriptomic changes associated with exposure to EC and OC are consistent with increased risk of cardiovascular diseases and the adverse health effects of traffic-related air pollution.
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