Novel algorithms incorporating multiple streams of electronic health data can reasonably detect herpes zoster and PHN. These algorithms could facilitate meaningful public health surveillance using electronic health data. The incidence of PHN may be increasing.

Introduction:Patient privacy and data security concerns often limit the feasibility of pooling patient-level data from multiple sources for analysis. Distributed data networks (DDNs) that employ privacy-protecting analytical methods, such as distributed regression analysis (DRA), can mitigate these concerns. However, DRA is not routinely implemented in large DDNs.Objective:We describe the design and implementation of a process framework and query workflow that allow automatable DRA in real-world DDNs that use PopMedNet™, an open-source distributed networking software platform.Methods:We surveyed and catalogued existing hardware and software configurations at all data partners in the Sentinel System, a PopMedNet-driven DDN. Key guiding principles for the design included minimal disruptions to the current PopMedNet query workflow and minimal modifications to data partners’ hardware configurations and software requirements.Results:We developed and implemented a three-step process framework and PopMedNet query workflow that enables automatable DRA: 1) assembling a de-identified patient-level dataset at each data partner, 2) distributing a DRA package to data partners for local iterative analysis, and 3) iteratively transferring intermediate files between data partners and analysis center. The DRA query workflow is agnostic to statistical software, accommodates different regression models, and allows different levels of user-specified automation.Discussion:The process framework can be generalized to and the query workflow can be adopted by other PopMedNet-based DDNs.Conclusion:DRA has great potential to change the paradigm of data analysis in DDNs. Successful implementation of DRA in Sentinel will facilitate adoption of the analytic approach in other DDNs.

Porous tin dioxide is an important low-cost semiconductor applied in electronics, gas sensors, and biosensors. Here, we present a versatile template-assisted synthesis of nanostructured tin dioxide thin films using cellulose nanocrystals (CNCs). We demonstrate that the structural features of CNC-templated tin dioxide films strongly depend on the precursor composition. The precursor properties were studied by using low-temperature nuclear magnetic resonance spectroscopy of tin tetrachloride in solution. We demonstrate that it is possible to optimize the precursor conditions to obtain homogeneous precursor mixtures and therefore highly porous thin films with pore dimensions in the range of 10–20 nm (A BET = 46–64 m2 g–1, measured on powder). Finally, by exploiting the high surface area of the material, we developed a resistive gas sensor based on CNC-templated tin dioxide. The sensor shows high sensitivity to carbon monoxide (CO) in ppm concentrations and low cross-sensitivity to humidity. Most importantly, the sensing kinetics are remarkably fast; both the response to the analyte gas and the signal decay after gas exposure occur within a few seconds, faster than in standard SnO2-based CO sensors. This is attributed to the high gas accessibility of the very thin porous film.

In this population, the proportion of patients with uncontrolled asthma, particularly as indicated by high SABA fills, decreased over a 5-year period. Several individual- and neighborhood-level characteristics were associated with uncontrolled asthma events. Clinicians and health plans can identify higher-risk patients in order to target asthma management strategies and reduce asthma-related morbidity and its associated costs.