A database of US state policies to mitigate COVID-19 and its economic consequences.

BACKGROUND: Since COVID-19 first appeared in the United States (US) in January 2020, US states have pursued a wide range of policies to mitigate the spread of the virus and its economic ramifications. Without unified federal guidance, states have been the front lines of the policy response. MAIN TEXT: We created the COVID-19 US State Policy (CUSP) database ( https://statepolicies.com/ ) to document the dates and components of economic relief and public health measures issued at the state level in response to the COVID-19 pandemic. Documented interventions included school and business closures, face mask mandates, directives on vaccine eligibility, eviction moratoria, and expanded unemployment insurance benefits. By providing continually updated information, CUSP was designed to inform rapid-response, policy-relevant research in the context of the COVID-19 pandemic and has been widely used to investigate the impact of state policies on population health and health equity. This paper introduces the CUSP database and highlights how it is already informing the COVID-19 pandemic response in the US. CONCLUSION: CUSP is the most comprehensive publicly available policy database of health, social, and economic policies in response to the COVID-19 pandemic in the US. CUSP documents widespread variation in state policy decisions and implementation dates across the US and serves as a freely available and valuable resource to policymakers and researchers.

Disparities Made Invisible: Gaps in COVID-19 Data for American Indian and Alaska Native Populations.

Introduction: Complete COVID-19 data for American Indian/Alaska Native (AI/AN) populations are critical to equitable pandemic response. Methods: We used the COVID-19 U.S. State Policy database to document gaps in COVID-19 data reporting for AI/AN people. Results: Sixty-four percent of states do not report AI/AN data for at least one COVID-19 health metric: cases, hospitalizations, deaths, or vaccinations. Discussion: The lack of AI/AN-specific data masks the disproportionate burden of COVID-19 and presents challenges to COVID-19 prevention, policy implementation, and health equity. Conclusions: Public-facing data disaggregated by race may facilitate rapid response COVID-19 research and policymaking to support AI/AN communities.

Quantifying Uncertainty in Ecotoxicological Risk Assessment: MUST, a Modular Uncertainty Scoring Tool.

Whether conducting a risk, hazard, or alternatives assessment, one invariably struggles with the task of reconciling multiple available values of toxicological thresholds into a single outcome. When combining multiple pieces of evidence from many different sources, it is important to consider the role of data uncertainty. Uncertainty is inherent to all scientific data. However, in toxicological assessments, controversies and uncertainties are typically understated; they lack methodological transparency; or they poorly integrate qualitative and quantitative sources of information. Similarly, in model development, data curation is rarely performed with sufficient rigor, particularly when applying big data statistics. To overcome the hurdles of a decision process that must reconcile divergent data, we developed an uncertainty scoring tool that can be trained to reproduce specific decision-making paradigms and ensure consistency in the practitioner's judgment across complex scenarios. While designed to aid with ecotoxicological assessments and predictive model development, the tool's applicability extends to any decision-making process that calls for synthesis of incongruent data. Here, we highlight the development process, as well as demonstrate the method's utility in several prototypical ecotoxicological case studies.