Air Pollution and Acute Kidney Injury in the U.S. Medicare Population: A Longitudinal Cohort Study.

BACKGROUND: Recent studies have reported the association between air pollution exposure and reduced kidney function. However, it is unclear whether air pollution is associated with an increased risk of acute kidney injury (AKI). OBJECTIVES: To address this gap in knowledge, we investigated the effect estimates of long-term exposures to fine particulate matter [PM ≤2.5µm in aerodynamic diameter (PM2.5)], nitrogen dioxide (NO2), and ozone (O3) on the risk of first hospital admission for AKI using nationwide Medicare data. METHODS: This nationwide population-based longitudinal cohort study included 61,300,754 beneficiaries enrolled in Medicare Part A fee-for-service (FFS) who were ≥65 years of age and resided in the continental United States from the years 2000 through 2016. We applied Cox-equivalent Poisson models to estimate the association between air pollution and first hospital admission for AKI. RESULTS: Exposure to PM2.5, NO2, and O3 was associated with increased risk for first hospital admission for AKI, with hazard ratios (HRs) of 1.17 (95% CI: 1.16, 1.19) for a 5-µg/m3 increase in PM2.5, 1.12 (95% CI: 1.11, 1.13) for a 10-ppb increase in NO2, and 1.03 (95% CI: 1.02, 1.04) for a 10-ppb increase in summer-period O3 (June to September). The associations persisted at annual exposures lower than the current National Ambient Air Quality Standard. DISCUSSION: This study found an association between exposures to air pollution and the risk of the first hospital admission with AKI, and this association persisted even at low concentrations of air pollution. Our findings provide beneficial implications for public health policies and air pollution guidelines to alleviate health care expenditures and the disease burden attributable to AKI. https://doi.org/10.1289/EHP10729.

Nine best practices for research software registries and repositories.

Scientific software registries and repositories improve software findability and research transparency, provide information for software citations, and foster preservation of computational methods in a wide range of disciplines. Registries and repositories play a critical role by supporting research reproducibility and replicability, but developing them takes effort and few guidelines are available to help prospective creators of these resources. To address this need, the FORCE11 Software Citation Implementation Working Group convened a Task Force to distill the experiences of the managers of existing resources in setting expectations for all stakeholders. In this article, we describe the resultant best practices which include defining the scope, policies, and rules that govern individual registries and repositories, along with the background, examples, and collaborative work that went into their development. We believe that establishing specific policies such as those presented here will help other scientific software registries and repositories better serve their users and their disciplines.

A large-scale study on research code quality and execution.

This article presents a study on the quality and execution of research code from publicly-available replication datasets at the Harvard Dataverse repository. Research code is typically created by a group of scientists and published together with academic papers to facilitate research transparency and reproducibility. For this study, we define ten questions to address aspects impacting research reproducibility and reuse. First, we retrieve and analyze more than 2000 replication datasets with over 9000 unique R files published from 2010 to 2020. Second, we execute the code in a clean runtime environment to assess its ease of reuse. Common coding errors were identified, and some of them were solved with automatic code cleaning to aid code execution. We find that 74% of R files failed to complete without error in the initial execution, while 56% failed when code cleaning was applied, showing that many errors can be prevented with good coding practices. We also analyze the replication datasets from journals' collections and discuss the impact of the journal policy strictness on the code re-execution rate. Finally, based on our results, we propose a set of recommendations for code dissemination aimed at researchers, journals, and repositories.

If these data could talk.

In the last few decades, data-driven methods have come to dominate many fields of scientific inquiry. Open data and open-source software have enabled the rapid implementation of novel methods to manage and analyze the growing flood of data. However, it has become apparent that many scientific fields exhibit distressingly low rates of reproducibility. Although there are many dimensions to this issue, we believe that there is a lack of formalism used when describing end-to-end published results, from the data source to the analysis to the final published results. Even when authors do their best to make their research and data accessible, this lack of formalism reduces the clarity and efficiency of reporting, which contributes to issues of reproducibility. Data provenance aids both reproducibility through systematic and formal records of the relationships among data sources, processes, datasets, publications and researchers.