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With the rapid increase in technologies and innovations to support a growing aging population in many countries, health technology assessment (HTA) of technologies for the aging populace warrants special consideration. Building on our efforts at Health Technology Assessment international (HTAi) conferences in 2016 and 2017, this presentation will highlight themes generated from two previous HTAi collaborations, with an aim of continuing to build interest and capacity in HTA for aging-related technologies in an international ecosystem that is responsive to local needs and global opportunities.
Methods:
Researchers from Canada's technology and aging network (AGE-WELL) collaborated with international panelists at HTAi conferences in 2016 and 2017 to explore interest in HTA focused on aging. International panelists shared the current state of aging and HTA in their respective countries. At both sessions, opportunities were provided for participants to rate the importance of themes identified by the panelists.
Results:
At the 2016 session, the two most highly ranked themes were: (i) how HTA can help identify the unmet needs of older adults in society that could be met by technology; and (ii) engagement of older adults and caregivers. These two themes became the starting point for the panel discussion in 2017. At this session, the highest ranked themes were: (i) identification of challenges in HTA and aging; (ii) approaches to advancing the effectiveness of HTA in addressing technology and aging; and (iii) development of an aging-related interest group in HTAi.
Conclusions:
International collaborations have identified a number of recommendations to consider for HTA and aging-related work including: developing a good mutual awareness and understanding of barriers and opportunities; the importance of co-creating solutions with patients, healthcare providers, researchers, innovators, and funders; and the identification of a suite of methods and tools that can help accelerate technological innovation in care delivery.
With the increase in technologies to support an aging population, health technology assessment (HTA) of aging-related technologies warrants special consideration. At Health Technology Assessment international (HTAi) 2016 and HTAi 2017, an international panel explored interests in HTA focused on aging.
Methods:
Panelists from five countries shared the state of aging and HTA in their countries. Opportunities were provided for participants to discuss and rate the themes identified by the panelists.
Results:
In 2016, the highest ranked themes were: (i) identifying unmet needs of older adults that could be met by technology—how can HTA help?; (ii) differences in assessment of aging-related technologies—what is the scope?; and (iii) involvement of older adults and caregivers. These themes became the starting point for discussion in 2017, for which the highest ranked themes were: (i) identification of challenges in HTA and aging; and (ii) approaches to advancing effectiveness of HTA for aging.
Conclusion:
These discussions allowed for examination of future directions for HTA and aging: engagement of older adults to inform the agenda of HTA and the broader public policy enterprise; a systems approach to thinking about needs of older persons should support the type and level of care desired by the individual rather than the health institutions, and HTA should reflect these desires when evaluating technological aides; and there is potential for health information systems and “big data” to support HTA activities that assess usability of technologies for older adults. We hope to build on the momentum of this community to continue exploring opportunities for aging and HTA.
Some inadequacies of both the traditional (exponential smoothing) and Box-Jenkins approaches to time series forecasting of economic data are investigated. An approach is suggested which integrates these two methodologies. It is based on smoothing the data using straight line segments instead of differencing to obtain stationarity, and forecasting using an autoregressive-moving-average model for the residuals from the most recent linear segment. The efficiency of this approach is calculated theoretically using a series comprising integrated white noise.