Information needs and priority use cases of population health researchers to improve preparedness for future hurricanes and floods.

OBJECTIVE: Information gaps that accompany hurricanes and floods limit researchers' ability to determine the impact of disasters on population health. Defining key use cases for sharing complex disaster data with research communities and facilitators, and barriers to doing so are key to promoting population health research for disaster recovery. MATERIALS AND METHODS: We conducted a mixed-methods needs assessment with 15 population health researchers using interviews and card sorting. Interviews examined researchers' information needs by soliciting barriers and facilitators in the context of their expertise and research practices. Card sorting ranked priority use cases for disaster preparedness. RESULTS: Seven barriers and 6 facilitators emerged from interviews. Barriers to collaborative research included process limitations, collaboration dynamics, and perception of research importance. Barriers to data and technology adoption included data gaps, limitations in information quality, transparency issues, and difficulty to learn. Facilitators to collaborative research included collaborative engagement and human resource processes. Facilitators to data and technology adoption included situation awareness, data quality considerations, adopting community standards, and attractive to learn. Card sorting prioritized 15 use cases and identified 30 additional information needs for population health research in disaster preparedness. CONCLUSIONS: Population health researchers experience barriers to collaboration and adoption of data and technology that contribute to information gaps and limit disaster preparedness. The priority use cases we identified can help address information gaps by informing the design of supportive research tools and practices for disaster preparedness. Supportive tools should include information on data collection practices, quality assurance, and education resources usable during failures in electric or telecommunications systems.

Demonstrating an Integrated Antibiotic Resistance Gene Surveillance Approach in Puerto Rican Watersheds Post-Hurricane Maria.

Comprehensive surveillance approaches are needed to assess sources, clinical relevance, and mobility of antibiotic resistance genes (ARGs) in watersheds. Here, we examined metrics derived from shotgun metagenomic sequencing and relationship to human fecal markers (HFMs; crAssphage, enterococci) and anthropogenic antibiotic resistance markers (AARMs; intI1, sul1) in three distinct Puerto Rican watersheds as a function of adjacent land use and wastewater treatment plant (WWTP) input 6 months after Hurricane Maria, a category V storm. Relative abundance and diversity of total ARGs increased markedly downstream of WWTP inputs, with ARGs unique to WWTP and WWTP-impacted river samples predominantly belonging to the aminoglycoside and ß-lactam resistance classes. WWTP and other anthropogenic inputs were similarly associated with elevated resistome risk scores and mobility incidence (M%). Contig analysis indicated a wide variety of mobile ß-lactam ARGs associated with pathogens downstream of WWTP discharge that were consistent with regional clinical concern, e.g., Klebsiella pneumoniae contigs containing KPC-2 within an ISKpn6-like transposase. HFMs and AARMs correlated strongly with the absolute abundance of total ARGs, but AARMs better predicted the majority of ARGs in general (85.4 versus <2%) and ß-lactam ARGs in particular. This study reveals sensitive, quantitative, mobile, clinically relevant, and comprehensive targets for antibiotic resistance surveillance in watersheds.

Elevation distributed micro-climatology data in a coastal glaciated watershed.

Air temperature, ground temperature and relative humidity data were collected in a longitudinal transect of the Nooksack watershed at varying elevations from 500 to 1800 m above sea level. Data were collected by anchoring sensors from trees above winter snow levels and shaded from direct solar radiation. Paired sensors were also buried 3 cm under ground near each air temperature sensor to determine snow absence or presence. Select sites included relative humidity sensors to indicate whether precipitation was occurring. Data were collected every 3-4 h from December 2015 to Sept 2018 (with ongoing collection). Code for analysis of daily mean, minimum, maximum, and temperature change with elevation (lapse rates) are available on Github (https://doi.org/10.5281/zenodo.3239539). The sensor download and intermediate data products are available on HydroShare at (http://www.hydroshare.org/resource/222e832d3df24dea9bae9bbeb6f4219d) with publicly accessible visualization available from the Nooksack Observatory at data.cuahsi.org. Hydrologic models are generally structured with a single annual average lapse rate parameter which assumes a linear temperature gradient with elevation. The daily data (2016-2018) is used as part of ongoing studies on the non-linear dynamics and temporal variability of temperature with elevation to improve assessments of watershed function and salmon habitat.