Leveraging the E-commerce footprint for the surveillance of healthcare utilization.

The utilization of healthcare services serves as a barometer for current and future health outcomes. Even in countries with modern healthcare IT infrastructure, however, fragmentation and interoperability issues hinder the (short-term) monitoring of utilization, forcing policymakers to rely on secondary data sources, such as surveys. This deficiency may be particularly problematic during public health crises, when ensuring proper and timely access to healthcare acquires special importance. We show that, in specific contexts, online pharmacies' digital footprint data may contain a strong signal of healthcare utilization. As such, online pharmacy data may enable utilization surveillance, i.e., the monitoring of short-term changes in utilization levels in the population. Our analysis takes advantage of the scenario created by the first wave of the Covid-19 pandemic in Mainland China, where the virus' spread lead to pervasive and deep reductions of healthcare service utilization. Relying on a large sample of online pharmacy transactions with full national coverage, we first detect variation that is strongly consistent with utilization reductions across geographies and over time. We then validate our claims by contrasting online pharmacy variation against credit-card transactions for medical services. Using machine learning methods, we show that incorporating online pharmacy data into the models significantly improves the accuracy of utilization surveillance estimates.

Propelling consumer engagement via entrepreneurs' live streaming?

Entrepreneurs' live streaming (ELS) is an important tool for marketing, and it can increase consumer engagement, especially during the COVID-19 pandemic. Previous live streaming literature mainly focused on third-party live streaming, targeted at professional streamers and online celebrities. This study aims to discuss the factors underlying consumer engagement in the ELS. Using a mixed method of a quasi-experiment and an online survey, we analyzed the impact of the ELS on consumer engagement and the factors that drive consumer engagement in the ELS in each of 231 samples. In the enterprises' live streaming, the ELS has a significantly higher influence on consumer engagement compared with the employees' live streaming. In the ELS, based on source credibility theory and signaling theory, this study concludes that factors of ELS's credibility consist of internal factors (reputation, expertise, and interactivity) and external factors (guarantee, authenticity, and money-saving). The authors demonstrate that both internal and external factors positively affect trust in activities. Trust in activities positively affects consumer engagement and mediates the effects of reputation, expertise, interactivity, guarantee, and authenticity on consumer engagement. Moreover, reputation and expertise positively improve consumers' admiration toward the entrepreneur streamer and in turn, positively increase consumer engagement. Interactivity and expertise shorten the psychological distance. Psychological distance negatively affects consumer engagement and only helps increase the positive effect of interactivity on consumer engagement. These findings have theoretical and practical implications for live streaming e-commerce.

An integrative agent-based vertical evacuation risk assessment model for near-field tsunami hazards.

This study couples FN-curves with Agent-based Modeling and Simulation (ABMS) to assess risk for tsunamis with various recurrence intervals . By considering both expected number of casualties and the likelihood of tsunami events, multiple series of simulations and in-depth analyses determine (1) how vertical evacuation structure (VES) placement impacts mortality rate; (2) what the best evacuation strategies VES locations are; and (3) where evacuees are likely to be caught by tsunami waves. The results from utilizing FN-curves to conduct disaggregative analyses based on six tsunami scenarios indicate that choosing one tsunami scenario or averaging the risk of different scenarios may not fully articulate VES impacts due to the "levee effect," which potentially leads to false positives. Findings show that placing VESs close to shorelines saves nearby at-risk populations, but also results in two risk increasing phenomena: "exposure to risk" (i.e., evacuees being attracted to high risk roads by a VES when evacuating) and "blind zones" (i.e., locations near a VES where evacuees increase their risk by evacuating to that VES). When limited to one VES, placement near a population's centroid results in the lowest mortality rate. More than one VES may lower mortality rate further if VESs are spreading out according to community's topography. In addition to the analysis of tsunamis, the approach of coupling FN-curves with ABMS can be used by local authorities and engineers to determine tailored hard-adaptive measures and evacuation strategies, which helps to avoid maladaptive actions in different hazardous events.

Building an Interdisciplinary Team for Disaster Response Research: A Data-Driven Approach.

Building an interdisciplinary team is critical to disaster response research as it often deals with acute onset events, short decision horizons, constrained resources, and uncertainties related to rapidly unfolding response environments.  This article examines three teaming mechanisms for interdisciplinary disaster response research, including ad hoc and/or grant proposal driven teams, research center or institute based teams, and teams oriented by matching expertise toward long-term collaborations. Using hurricanes as the response context, it further examines several types of critical data that require interdisciplinary collaboration on collection, integration, and analysis. Last, suggesting a data-driven approach to engaging multiple disciplines, the article advocates building interdisciplinary teams for disaster response research with a long-term goal and an integrated research protocol.

Critical Time, Space, and Decision-Making Agent Considerations in Human-Centered Interdisciplinary Hurricane-Related Research.

In hazard and disaster contexts, human-centered approaches are promising for interdisciplinary research since humans and communities feature prominently in many definitions of disaster and the built environment is designed and constructed by humans to serve their needs. With a human-centered approach, the decision-making agent becomes a critical consideration. This article discusses and illustrates the need for alignment of decision-making agents, time, and space for interdisciplinary research on hurricanes, particularly evacuation and the immediate aftermath. We specifically consider the fields of sociobehavioral science, transportation engineering, power systems engineering, and decision support systems in this context. These disciplines have historically adopted different decision-making agents, ranging from individuals to households to utilities and government agencies. The fields largely converged to the local level for studies' spatial scales, with some extensions based on the physical construction and operation of some systems. Greater discrepancy across the fields is found in the frequency of data collection, which ranges from one time (e.g., surveys) to continuous monitoring systems (e.g., sensors). Resolving these differences is important for the success of interdisciplinary teams in protective-action-related disaster research.

Collaborative multicenter logistics delivery network optimization with resource sharing.

Collaboration among logistics facilities in a multicenter logistics delivery network can significantly improve the utilization of logistics resources through resource sharing including logistics facilities, vehicles, and customer services. This study proposes and tests different resource sharing schemes to solve the optimization problem of a collaborative multicenter logistics delivery network based on resource sharing (CMCLDN-RS). The CMCLDN-RS problem aims to establish a collaborative mechanism of allocating logistics resources in a manner that improves the operational efficiency of a logistics network. A bi-objective optimization model is proposed with consideration of various resource sharing schemes in multiple service periods to minimize the total cost and number of vehicles. An adaptive grid particle swarm optimization (AGPSO) algorithm based on customer clustering is devised to solve the CMCLDN-RS problem and find Pareto optimal solutions. An effective elite iteration and selective endowment mechanism is designed for the algorithm to combine global and local search to improve search capabilities. The solution of CMCLDN-RS guarantees that cost savings are fairly allocated to the collaborative participants through a suitable profit allocation model. Compared with the computation performance of the existing nondominated sorting genetic algorithm-II and multi-objective evolutionary algorithm, AGPSO is more computationally efficient. An empirical case study in Chengdu, China suggests that the proposed collaborative mechanism with resource sharing can effectively reduce total operational costs and number of vehicles, thereby enhancing the operational efficiency of the logistics network.

Robust component: a robustness measure that incorporates access to critical facilities under disruptions.

The objective of this paper is to integrate the post-disaster network access to critical facilities into the network robustness assessment, considering the geographical exposure of infrastructure to natural hazards. Conventional percolation modelling that uses generating function to measure network robustness fails to characterize spatial networks due to the degree correlation. In addition, the giant component alone is not sufficient to represent the performance of transportation networks in the post-disaster setting, especially in terms of the access to critical facilities (i.e. emergency services). Furthermore, the failure probability of various links in the face of different hazards needs to be encapsulated in simulation. To bridge this gap, this paper proposed the metric robust component and a probabilistic link-removal strategy to assess network robustness through a percolation-based simulation framework. A case study has been conducted on the Portland Metro road network during an M9.0 earthquake scenario. The results revealed how the number of critical facilities severely impacts network robustness. Besides, earthquake-induced failures led to a two-phase percolation transition in robustness performance. The proposed robust component metric and simulation scheme can be generalized into a wide range of scenarios, thus enabling engineers to pinpoint the impact of disastrous disruption on network robustness. This research can also be generalized to identify critical facilities and sites for future development.

Error Measures for Trajectories Estimations with Geo-tagged Mobility Sample Data.

Although geo-tagged mobility data (e.g., cell phone data and social media data) can be potentially used to estimate individual space-time travel trajectories, they often have low sample rates that only tell travelers' whereabouts at the sparse sample times while leaving the remaining activities to be estimated with interpolation. This study proposes a set of time geography-based measures to quantify the accuracy of the trajectory estimation in a robust manner. A series of measures including activity bandwidth and normalized activity bandwidth are proposed to quantify the possible absolute and relative error ranges between the estimated and the ground truth trajectories that cannot be observed. These measures can be used to evaluate the suitability of the estimated individual trajectories from sparsely sampled geo-tagged mobility data for travel mobility analysis. We suggest cutoff values of these measures to separate useful data with low estimation errors and noisy data with high estimation errors. We conduct theoretical analysis to show that these error measures decrease with sample rates and people's activity ranges. We also propose a lookup table-based interpolation method to expedite the computational time. The proposed measures have been applied to 2013 geo-tagged tweet data in New York City and 2014 cell-phone data in Shenzhen, China. The results illustrate that the proposed measures can provide estimation error ranges for exceptionally large datasets in much shorter times than the benchmark method without using lookup tables. These results also reveal managerial results into the quality of these data for human mobility studies, including their distribution patterns.

A user exposure based approach for non-structural road network vulnerability analysis.

Aiming at the dense urban road network vulnerability without structural negative consequences, this paper proposes a novel non-structural road network vulnerability analysis framework. Three aspects of the framework are mainly described: (i) the rationality of non-structural road network vulnerability, (ii) the metrics for negative consequences accounting for variant road conditions, and (iii) the introduction of a new vulnerability index based on user exposure. Based on the proposed methodology, a case study in the Sioux Falls network which was usually threatened by regular heavy snow during wintertime is detailedly discussed. The vulnerability ranking of links of Sioux Falls network with respect to heavy snow scenario is identified. As a result of non-structural consequences accompanied by conceivable degeneration of network, there are significant increases in generalized travel time costs which are measurements for "emotionally hurt" of topological road network.

How bicycle level of traffic stress correlate with reported cyclist accidents injury severities: A geospatial and mixed logit analysis.

Transportation agencies need efficient methods to determine how to reduce bicycle accidents while promoting cycling activities and prioritizing safety improvement investments. Many studies have used standalone methods, such as level of traffic stress (LTS) and bicycle level of service (BLOS), to better understand bicycle mode share and network connectivity for a region. However, in most cases, other studies rely on crash severity models to explain what variables contribute to the severity of bicycle related crashes. This research uniquely correlates bicycle LTS with reported bicycle crash locations for four cities in New Hampshire through geospatial mapping. LTS measurements and crash locations are compared visually using a GIS framework. Next, a bicycle injury severity model, that incorporates LTS measurements, is created through a mixed logit modeling framework. Results of the visual analysis show some geospatial correlation between higher LTS roads and "Injury" type bicycle crashes. It was determined, statistically, that LTS has an effect on the severity level of bicycle crashes and high LTS can have varying effects on severity outcome. However, it is recommended that further analyses be conducted to better understand the statistical significance and effect of LTS on injury severity. As such, this research will validate the use of LTS as a proxy for safety risk regardless of the recorded bicycle crash history. This research will help identify the clustering patterns of bicycle crashes on high-risk corridors and, therefore, assist with bicycle route planning and policy making. This paper also suggests low-cost countermeasures or treatments that can be implemented to address high-risk areas. Specifically, with the goal of providing safer routes for cyclists, such countermeasures or treatments have the potential to substantially reduce the number of fatalities and severe injuries.

Heterogeneous impacts of gender-interpreted contributing factors on driver injury severities in single-vehicle rollover crashes.

In this study, a mixed logit model is developed to identify the heterogeneous impacts of gender-interpreted contributing factors on driver injury severities in single-vehicle rollover crashes. The random parameter of the variables in the mixed logit model, the heterogeneous mean, is elaborated by driver gender-based linear regression models. The model is estimated using crash data in New Mexico from 2010 to 2012. The percentage changes of factors' predicted probabilities are calculated in order to better understand the model specifications. Female drivers are found more likely to experience severe or fatal injuries in rollover crashes than male drivers. However, the probability of male drivers being severely injured is higher than female drivers when the road surface is unpaved. Two other factors with fixed parameters are also found to significantly increase driver injury severities, including Wet and Alcohol Influenced. This study provides a better understanding of contributing factors influencing driver injury severities in rollover crashes as well as their heterogeneous impacts in terms of driver gender. Those results are also helpful to develop appropriate countermeasures and policies to reduce driver injury severities in single-vehicle rollover crashes.

Apoptosis-induction is a novel therapeutic strategy for gastrointestinal and liver cancers.

Gastrointestinal and liver cancers are among the most frequently encountered cancers worldwide. Though great strides have been made in improving the early detection and overall patient survival in the last decade, the currently available treatment approaches remain suboptimal due to drug resistance and side effects. Apoptosis is the most desirable type of programmed cell death in cancer therapy. By eliminating unnecessary and unwanted cells, apoptosis plays a critical role in development, physiology and homeostasis. Recent studies have shown that apoptosis-inducing therapy is a safe and effective anti-cancer approach, which thus may become one of the most important areas in cancer treatment. In this review, the authors provide an overview on the apoptosis system in multicellular organisms and summarize the recent advances of apoptosis-inducing therapeutic strategy for gastrointestinal and liver cancers.

Bayesian analysis for finite mixture in non-recursive non-linear structural equation models.

This paper considers finite mixtures of structural equation models with non-linear effects of exogenous latent variables and non-recursive relations among endogenous latent variables. A Bayesian approach is developed to analyse this kind of model. In order to cope with the label switching problem, the permutation sampler is used to choose an appropriate identification constraint. Furthermore, a hybrid Markov chain Monte Carlo method that combines the Gibbs sampler, Metropolis-Hastings algorithm, and Langevin-Hastings algorithm is implemented to produce the Bayesian outputs. Finally, the proposed approach is illustrated by a simulation study and a real example.

[Combination of gemcitabine and cisplatin in the treatment of advanced non-small cell lung cancer].

BACKGROUND: The platinum-based combined chemotherapy is effective for advanced non-small cell lung cancer (NSCLC). This study is to observe the clinical effect and toxicity of combination of gemcitabine with cisplatin for advanced NSCLC. METHODS: All of 32 patients were pathologically confirmed as stage III or IV NSCLC who lost chance to receive operation. Gemcitabine was given on days 1, 8, 15 at a dose of 1000mg/m² and cisplatin on days 1-5 at a dose of 20mg. The chemotherapy was repeated every 28 days up to 3-4 cycles. RESULTS: There was no patient who got complete response, and the overall response rate was 34.4% (11/32). The median survival duration was 329 days and the 1-year survival rate was 32.7%. The main toxicities were myelosuppression, nausea and vomiting, however, there was no severe grade IV damage or obvious liver and kidney damage. No one was delayed for chemotherapy due to adverse effect. CONCLUSIONS: The combination of gemcitabine and cisplatin is effective and well tolerated in the treatment of advanced NSCLC.

[Supracricoid subtotal laryngectomy with laryngeal reconstruction for laryngeal cancers].

OBJECTIVE: To assess the effectiveness of supracricoid subtotal laryngectomy in the treatment of laryngeal cancer and the reconstructed laryngeal functions. METHOD: A retrospective review was made on 411 laryngeal cancer patients in the stage T2 to T4 treated by this surgical procedure form 1985 to 2000. In these cases, 35 were glottic cancers, 6 were supraglottic cancers, 23 patients were performed by supracricoid subtotal Laryngectomy with cricohyoidoepiglottopexy, 18 were performed with cricohyoidopexy. RESULT: Over 3 year and 5 year survival rates were 83.3%(30/36) and 71.4% (20/28). Decannulation rate was 92.7% (38/41). 40 patients resumed normal oral feeding but one was still performed an operation to separate the tracheal from pharynx because of the severe aspiration. All cases were able to speak by the new larynx. CONCLUSION: Supracricoid subtotal laryngectomy with laryngeal reconstruction was not only completely removal of tumors, but also restored the three physiological functions of larynx. It is an effective treatment for laryngeal cancer.