RT Journal Article SR Electronic T1 Using Data to Keep Vaccines Cold in Kenya: Remote Temperature Monitoring With Data Review Teams for Vaccine Management JF Global Health: Science and Practice JO GLOB HEALTH SCI PRACT FD Johns Hopkins University- Global Health. Bloomberg School of Public Health, Center for Communication Programs SP 585 OP 597 DO 10.9745/GHSP-D-19-00157 VO 7 IS 4 A1 Lutukai, Mercy A1 Bunde, Elizabeth A. A1 Hatch, Benjamin A1 Mohamed, Zoya A1 Yavari, Shahrzad A1 Some, Ernest A1 Chweya, Amos A1 Kania, Caroline A1 Ross, Jesse C. A1 Keddem, Carmit A1 Chandani, Yasmin YR 2019 UL http://www.ghspjournal.org/content/7/4/585.abstract AB Using technology to make data visible to stakeholders and giving those stakeholders a framework for analyzing that data for decision making improves cold chain management of vaccines in Kenya.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.