A spatial vulnerability index for environmental health

Extreme natural hazards are increasing in frequency and intensity. These natural changes in our environment, combined with man-made pollution, have substantial economic, social and health impacts globally. The impact of the environment on human health (environmental health) is becoming well understood in international research literature. However, there are significant barriers to understanding key characteristics of this impact, related to substantial data volumes, data access rights and the time required to compile and compare data over regions and time.

This study develops a replicable method for constructing vulnerability indices weighted by observed all-cause mortality and applies it to three environmental hazards: extreme heat, extreme cold, and air pollution. Indices were produced across multiple spatial (SA2, SA3, SA4, LGA) and temporal (weekly, monthly, yearly) resolutions, allowing fine-grained analysis of population vulnerability over time and space. We compare the mortality-weighted indices with equal-weight and principal component analysis (PCA) approaches, which, despite their widespread use, do not directly incorporate or align with observed health outcomes. An index derived using empirical weights based on all-cause mortality will be well-aligned with other positively correlated health outcomes and so increases the generality of the index while reducing the requirements for a comprehensive suite of outcome data which can be difficult to obtain in practice.

The mortality-weighted approach improves interpretability relative to methods that optimise statistical variance alone, such as PCA. The inclusion of time-series data and sub-yearly indices further reveal short-term fluctuations in vulnerability that are otherwise obscured in coarser temporal studies. Together, these results support a transparent and scalable framework for identifying population vulnerability and informing targeted public health and climate resilience strategies.