RT Journal Article
SR Electronic
T1 Innovations to maximise impact of a data for decision-making training programme in the Federated States of Micronesia
JF BMJ Global Health
JO BMJ Global Health
FD BMJ Publishing Group Ltd
SP e005855
DO 10.1136/bmjgh-2021-005855
VO 6
IS 10
A1 A Mark Durand
A1 W Thane Hancock
A1 Haley L Cash
A1 Ian Rouse
A1 Emi Chutaro
A1 Livinson Taulung
A1 Mahomed Patel
YR 2021
UL http://gh.bmj.com/content/6/10/e005855.abstract
AB Accurate and timely health information is an essential foundation for strengthening health systems. Data for decision making (DDM) is a training curriculum designed to enhance capacity of health department staff to capture and use high-quality data to address priority health issues. In 2013, the Pacific Public Health Surveillance Network adapted and piloted the DDM curriculum as an ‘at work, from work, for work’ field epidemiology training programme component for low-income and middle-income Pacific Island jurisdictions. Based on lessons learned from the pilot, we made several innovations, including delivery on-site at each district (rather than bringing trainees to a central location), conducting pre-DDM consultations and ongoing contact with health leaders across the programme, taking more care in selecting trainees and enrolling a larger cohort of students from within each health department. The decentralised programme was delivered in-country at four sites (both at national and state levels) in the Federated States of Micronesia. Following delivery, we performed an external evaluation of the programme to assess student outcomes, benefits to the health department and general programme effectiveness. Of the 48 trainees who completed all four classroom modules, 40 trainees participated in the evaluation. Thirty-two of these trainees completed the programme’s capstone field project. Eighteen of these projects directly contributed to changes in legislation, revised programme budgets, changes in programme strategy to augment outreach and to target disease and risk factor ‘hot spots’.Data are available upon reasonable request.