Elastomeric Respirators for COVID-19 and the Next Respiratory Virus Pandemic: Essential Design Elements.

Respiratory viruses are transmitted via respiratory particles that are emitted when people breath, speak, cough, or sneeze. These particles span the size spectrum from visible droplets to airborne particles of hundreds of nanometers. Barrier face coverings ("cloth masks") and surgical masks are loose-fitting and provide limited protection from airborne particles since air passes around the edges of the mask as well as through the filtering material. Respirators, which fit tightly to the face, provide more effective respiratory protection. Although healthcare workers have relied primarily on disposable filtering facepiece respirators (such as N95) during the COVID-19 pandemic, reusable elastomeric respirators have significant potential advantages for the COVID-19 and future respiratory virus pandemics. However, currently available elastomeric respirators were not designed primarily for healthcare or pandemic use and require further development to improve their suitability for this application. The authors believe that the development, implementation, and stockpiling of improved elastomeric respirators should be an international public health priority.

Clinician feedback on using episode groupers with Medicare claims data.

CMS is investigating techniques that might help identify costly physician practice patterns. One method presently under evaluation is to compare resource use for certain episodes of care using commercially available episode grouping software. Although this software has been used by the private sector to classify insured individuals' medical claims into episodes of care, it has never been used with fee-for-service Medicare claims except in the studies by the Medicare Payment Advisory Commission (MedPAC) and CMS. This study reviews and reports on clinician feedback on the most obvious and important decisions that must be faced by Medicare to use grouped claims data as the foundation for a physician performance measurement system. The panel reactions show the importance of bringing persons with clinical knowledge into the development process. The clinician feedback confirms that additional research is needed.

Intercity transferability of land use regression models for estimating ambient concentrations of nitrogen dioxide.

Land use regression (LUR) is a method for predicting the spatial distribution of traffic-related air pollution. To facilitate risk and exposure assessment, and the design of future monitoring networks and sampling campaigns, we sought to determine the extent to which LUR can be used to predict spatial patterns in air pollution in the absence of dedicated measurements. We evaluate the transferability of one LUR model to two other geographically comparable areas with similar climates and pollution types. The source model, developed in 2003 to estimate ambient nitrogen dioxide (NO(2)) concentrations in Vancouver (BC, Canada) was applied to Victoria (BC, Canada) and Seattle (WA, USA). Model estimates were compared with measurements made with Ogawa passive samplers in both cities. As part of this study, 42 locations were sampled in Victoria for a 2-week period in June 2006. Data obtained for Seattle were collected for a different project at 26 locations in March 2005. We used simple linear regression to evaluate the fit of the source model under three scenarios: (1) using the same variables and coefficients as the source model; (2) using the same variables as the source model, but calculating new coefficients for local calibration; and (3) developing site-specific equations with new variables and coefficients. In Scenario 1, we found that the source model had a better fit in Victoria (R(2)=0.51) than in Seattle (R(2)=0.33). Scenario 2 produced improved R(2)-values in both cities (Victoria=0.58, Seattle=0.65), with further improvement achieved under Scenario 3 (Victoria=0.61, Seattle=0.72). Although it is possible to transfer LUR models between geographically similar cities, success may depend on the between-city consistency of the input data. Modest field sampling campaigns for location-specific model calibration can help to produce transfer models that are equally as predictive as their sources.