Future Directions for the HRS Harmonized Cognitive Assessment Protocol.
Jacqueline M TorresM Maria GlymourPublished in: Forum for health economics & policy (2022)
In the absence of effective pharmacological treatment to halt or reverse the course of Alzheimer's disease and related dementias (ADRDs), population-level research on the modifiable determinants of dementia risk and outcomes for those living with ADRD is critical. The Harmonized Cognitive Assessment Protocol (HCAP), fielded in 2016 as part of the U.S. Health and Retirement Study (HRS) and multiple international counterparts, has the potential to play an important role in such efforts. The stated goals of the HCAP are to improve our ability to understand the determinants, prevalence, costs, and consequences of cognitive impairment and dementia in the U.S. and to support cross-national comparisons. The first wave of the HCAP demonstrated the feasibility and value of the more detailed cognitive assessments in the HCAP compared to the brief cognitive assessments in the core HRS interviews. To achieve its full potential, we provide eight recommendations for improving future iterations of the HCAP. Our highest priority recommendation is to increase the representation of historically marginalized racial/ethnic groups disproportionately affected by ADRDs. Additional recommendations relate to the timing of the HCAP assessments; clinical and biomarker validation data, including to improve cross-national comparisons; dropping lower performing items; enhanced documentation; and the addition of measures related to caregiver impact. We believe that the capacity of the HCAP to achieve its stated goals will be greatly enhanced by considering these changes and additions.
Keyphrases
- cognitive impairment
- mild cognitive impairment
- quality improvement
- randomized controlled trial
- electronic health record
- healthcare
- public health
- current status
- human health
- cognitive decline
- mental health
- clinical practice
- risk factors
- type diabetes
- big data
- machine learning
- global health
- artificial intelligence
- skeletal muscle
- health information
- glycemic control