This paper contributes an analysis of natural resource management tools for planning for climate change, including a case study on scenario planning, to the Climate Vulnerability and Adaptation Study for California. It presents the findings of a literature review on decision‐ making tools for climate change planning and consultations with adaptation planners, resource managers, and scientists. In addition, it discusses lessons learned from a one‐day climate change scenario planning workshop with resource managers and scientists working in Marin County that focused on analyzing the scenario approach in the context of other approaches. It also describes cases of public agency and private conservation non‐profit organizations working on resource management problems under climate change.

Introduction

Delayed notification and lack of early information hinder timely hospital based activations in large scale multiple casualty events. We hypothesized that Twitter real-time data would produce a unique and reproducible signal within minutes of multiple casualty events and we investigated the timing of the signal compared with other hospital disaster notification mechanisms.

Methods

Using disaster specific search terms, all relevant tweets from the event to 7 days post-event were analyzed for 5 recent US based multiple casualty events (Boston Bombing [BB], SF Plane Crash [SF], Napa Earthquake [NE], Sandy Hook [SH], and Marysville Shooting [MV]). Quantitative and qualitative analysis of tweet utilization were compared across events.

Results

Over 3.8 million tweets were analyzed (SH 1.8 m, BB 1.1m, SF 430k, MV 250k, NE 205k). Peak tweets per min ranged from 209-3326. The mean followers per tweeter ranged from 3382-9992 across events. Retweets were tweeted a mean of 82-564 times per event. Tweets occurred very rapidly for all events (<2 mins) and represented 1% of the total event specific tweets in a median of 13 minutes of the first 911 calls. A 200 tweets/min threshold was reached fastest with NE (2 min), BB (7 min), and SF (18 mins). If this threshold was utilized as a signaling mechanism to place local hospitals on standby for possible large scale events, in all case studies, this signal would have preceded patient arrival. Importantly, this threshold for signaling would also have preceded traditional disaster notification mechanisms in SF, NE, and simultaneous with BB and MV.

Conclusions

Social media data has demonstrated that this mechanism is a powerful, predictable, and potentially important resource for optimizing disaster response. Further investigated is warranted to assess the utility of prospective signally thresholds for hospital based activation.

Background

Acute respiratory distress syndrome (ARDS) following trauma is historically associated with crystalloid and blood product exposure. Advances in resuscitation have occurred over the last decade, but their impact on ARDS is unknown. We sought to investigate predictors of postinjury ARDS in the era of hemostatic resuscitation.

Methods

Data were prospectively collected from arrival to 28 days for 914 highest-level trauma activations who required intubation and survived more than 6 hours from 2005 to 2016 at a Level I trauma center. Patients with ratio of partial pressure of oxygen to fraction of inspired oxygen of 300 mmHg or less during the first 8 days were identified. Two blinded expert clinicians adjudicated all chest radiographs for bilateral infiltrates in the first 8 days. Those with left-sided heart failure detected were excluded. Multivariate logistic regression was used to define predictors of ARDS.

Results

Of the 914 intubated patients, 63% had a ratio of partial pressure of oxygen to fraction of inspired oxygen of 300 or less, and 22% developed ARDS; among the ARDS cases, 57% were diagnosed early (in the first 24 hours), and 43% later. Patients with ARDS diagnosed later were more severely injured (ISS 32 vs. 20, p = 0.001), with higher rates of blunt injury (84% vs. 72%, p = 0.008), chest injury (58% vs. 36%, p < 0.001), and traumatic brain injury (72% vs. 48%, p < 0.001) compared with the no ARDS group. In multivariate analysis, head/chest Abbreviated Injury Score scores, crystalloid from 0 to 6 hours, and platelet transfusion from 0 to 6 hours and 7 to 24 hours were independent predictors of ARDS developing after 24 hours.

Conclusions

Blood and plasma transfusion were not independently associated with ARDS. However, platelet transfusion was a significant independent risk factor. The role of platelets warrants further investigation but may be mechanistically explained by lung injury models of pulmonary platelet sequestration with peripheral thrombocytopenia.

Level of evidence

Prognostic study, level IV.