Bridging Data Systems to Develop Interventions for Older Adults Exposed to Extreme Heat

The development of clinical tools to combat extreme heat events (EHEs) is urgently needed. However, the collection, linkage, and application of data and technology required to address the health consequences of EHEs-through individualized decision-making, population-focused interventions, and health system planning-remain in its infancy despite the wealth of data infrastructure in health care systems.

In this paper, we describe a use case for data-intensive system architecture that can enable best practices for addressing EHE-related health risks in older adults with cardiovascular disease (CVD), a population uniquely vulnerable to EHEs.

Descriptions of various data sources integrated into a modular approach are discussed that allows multilevel (ie, individual-level, population-level) evaluation of EHE-related risk. Individual data streams include batched data from personal digital health devices such as wearables, indoor temperature sensors, and electronic medical record data linked through unique identifiers. Data collection, processing, and analysis as well as related challenges (eg, data quality, processing requirements, and health care system attribution) are also discussed. How this data architecture can then address important preclinical, clinical, and related questions are then described, including: (1) which physiological signals (including cardiovascular and sleep measures) may best anticipate EHE-related health care utilization in older adults with CVD; (2) how do heat thresholds that increase EHE-related health care utilization differ by medication use and type, and comorbidities; and (3) how does indoor versus outdoor temperature measures influence these associations-all understudied aspects of EHE risk in older adults.

With considered effort and expertise, a modular data architecture that allows the combination of different elements will enable the development of clinical tools to address EHE-related health risk among older adults with CVD at multilevel scales.