Mapping the Climate-Health Evidence Base (2007-2023): A Bibliometric, Statistical, and NLP Multi-Label Text Analysis of 22,695 Records
We analyzed a curated climate--health bibliographic corpus of 22,695 multi-labeled records from 2007--2023 to characterize growth, thematic concentration, and evolving methods. Annual publication counts rose sharply, with multiple change-points indicating phase-structured expansion; Negative Binomial models estimated roughly 10--11% year-over-year growth. Exposure--health co-occurrence departed strongly from independence, with canonical hazard--outcome dyads (e.g., extreme heat with heat-related impacts; floods/hurricanes with mental health) occurring far more often than expected even after accounting for marginal term popularity. A hierarchical logistic model for asthma-tagged records showed strong alignment with air-pollution-related exposures (including ozone and particulate matter) and relative under-representation of generic heat/temperature terms. Methodologically, modeling timescales shifted toward longer horizons over time, while at least one legacy method tag declined. Finally, we detected time- and geography-dependent annotation completeness, including decreased exposure-term coding in recent years, underscoring the need to model missingness when interpreting temporal trends.