National-scale remotely sensed lake trophic state from 1984 through 2020.

Lake trophic state is a key ecosystem property that integrates a lake's physical, chemical, and biological processes. Despite the importance of trophic state as a gauge of lake water quality, standardized and machine-readable observations are uncommon. Remote sensing presents an opportunity to detect and analyze lake trophic state with reproducible, robust methods across time and space. We used Landsat surface reflectance data to create the first compendium of annual lake trophic state for 55,662 lakes of at least 10 ha in area throughout the contiguous United States from 1984 through 2020. The dataset was constructed with FAIR data principles (Findable, Accessible, Interoperable, and Reproducible) in mind, where data are publicly available, relational keys from parent datasets are retained, and all data wrangling and modeling routines are scripted for future reuse. Together, this resource offers critical data to address basic and applied research questions about lake water quality at a suite of spatial and temporal scales.

Application of the remaining vaccine vial monitor life calculation to field temperature monitoring data to improve visibility into cold chain equipment performance.

Vaccine Vial Monitors (VVM) are used to estimate if a vaccine has been exposed to excessive hot temperatures. This endpoint measurement is useful in determining if a vaccine is safe to be administered to a patient, but it does not pinpoint where in the cold chain a vaccine was exposed to excessive heat. With the expansion and technological advancement of cold chain equipment temperature monitoring, it is now possible to remotely estimate VVM status as a vaccine moves through the cold chain. In the present study, we examine the application of the mathematical principles backing VVMs on real, continuous, temperature monitoring data in Africa. Results suggest that exposure to short bursts of hot temperature or long power outages may still allow for safe distribution of affected vaccines. The remaining VVM life calculation could improve managerial visibility into cold chain equipment performance allowing for better data-driven planning and maintenance decisions.

Using Data to Keep Vaccines Cold in Kenya: Remote Temperature Monitoring with Data Review Teams for Vaccine Management.

BACKGROUND: Global vaccination coverage rates have remained around 85% for the past several years. Increasing immunization coverage rates requires an effective cold chain to maintain vaccine potency. Remote temperature monitoring (RTM) technology for vaccine refrigerators has shown promise for improving the ability of supply systems to maintain optimal temperature conditions to ensure potent vaccines reach the end users. METHODS: A pilot study of RTM technology and data use teams was implemented in 36 study sites in Kenya. Data were collected at baseline and endline points over a 3-month baseline and 7-month implementation period. Data included 44 qualitative interviews, process logs, meeting minutes from data use team meetings, and quantitative temperature and power data from the RTM devices. RESULTS: The ability of cold chain equipment to maintain World Health Organization-recommended temperatures in study sites improved markedly between the baseline and implementation periods, resulting in an improvement in total time spent in the correct range from 83.9% in the baseline period to 90.9% in the intervention period and an improvement in time spent in the too cold range from 6.5% to 1.5%. Friedman tests revealed that differences in time spent in the correct range and time spent in the too cold range during the course of the study were statistically significant (P<.001 and P=.04, respectively). Qualitative and quantitative data suggest that this improvement was due to a combination of improved responsiveness to temperature excursions at the facility level, resulting from SMS alarms for temperature excursion periods, and improved ability at the management level to recognize and address recurring problems. CONCLUSION: The combination of using RTM technology with a structured data review process by a management team is a promising approach for improving cold chain outcomes. Future research examining the added value of each of the technological and behavioral components separately is needed.