Recent crises have underscored the importance that housing has in sustaining good health and, equally, its potential to harm health. Considering this and building on Howden-Chapman’s early glossary of housing and health and the WHO Housing and Health Guidelines, this paper introduces a range of housing and health-related terms, reflecting almost 20 years of development in the field. It defines key concepts currently used in research, policy and practice to describe housing in relation to health and health inequalities. Definitions are organised by three overarching aspects of housing: affordability (including housing affordability stress (HAS) and fuel poverty), suitability (including condition, accessibility and sustainable housing) and security (including precarious housing and homelessness). Each of these inter-related aspects of housing can be either protective of, or detrimental to, health. This glossary broadens our understanding of the relationship between housing and health to further promote interdisciplinarity and strengthen the nexus between these fields.

Extreme weather and climate-related disaster events are associated with a range of adverse health outcomes. People are not equally vulnerable to the adversity, experiencing varied patterns of long-term health trajectories in recovery depending on their vulnerabilities, capacities, and resiliencies. This study aims to identify latent mental and physical health trajectories and their associations with person- and place-based pre-disaster predictors. Using an Australian, population-based, longitudinal dataset spanning 2009-19, group-based multi-trajectory modelling was applied to identify the distinct mental, social, emotional, and physical health trajectories of people who had experienced damage to their home following a climate-related disaster event. Multinomial logistic regression was used to assess a series of social vulnerability predictors (demographic, socioeconomic, housing, health, neighbourhood, and geographical) of health patterns. We identified three distinct health trajectories. Most individuals experienced small or minimal health impacts at the time of the disaster year followed by a fast recovery. However, one-fifth of the exposed population were severely affected during and post disaster. This cohort had the worst mental and physical health prior to the disaster and experienced the largest decreases in mental and physical health and the lowest recoveries. Pre-existing mental and physical conditions were the most substantial risk factors, increasing the probability of experiencing high impact and slow recovery by 61% for mental health and 51% for physical health. In addition, vulnerability in the form of housing affordability stress, lower household income, and lack of community attachment, participation and safety were also significant independent risk factors for ongoing post-disaster health problems. Critically, people's mental and physical health recovery is dependent on pre-disaster vulnerabilities in health, resource access, and capacities. These findings could assist policymakers and health practitioners to more effectively target people most at risk and design prevention and response strategies to prevent the exacerbation of poor health and wellbeing.

Population ageing and urbanisation are global phenomena that call for an understanding of the impacts of features of the urban environment on older adults’ cognitive function. Because neighbourhood characteristics that can potentially have opposite effects on cognitive function are interdependent, they need to be considered in conjunction. Using data from an Australian national sample of 4141 adult urban dwellers, we examined the extent to which the associations of interrelated built and natural environment features and ambient air pollution with cognitive function are explained by cardiometabolic risk factors relevant to cognitive health. All examined environmental features were directly and/or indirectly related to cognitive function via other environmental features and/or cardiometabolic risk factors. Findings suggest that dense, interconnected urban environments with access to parks, blue spaces and low levels of air pollution may benefit cognitive health through cardiometabolic risk factors and other mechanisms not captured in this study. This study also highlights the need for a particularly fine-grained characterisation of the built environment in research on cognitive function, which would enable the differentiation of the positive effects of destination-rich neighbourhoods on cognition via participation in cognition-enhancing activities from the negative effects of air pollutants typically present in dense, destination-rich urban areas.

The environment we live in, and our lifestyle within this environment, can shape our cognitive health. We investigated whether sociodemographic, neighbourhood environment, and lifestyle variables can be used to predict cognitive health status in adults. Cross-sectional data from the AusDiab3 study, an Australian cohort study of adults (34–97 years) (n = 4141) was used. Cognitive function was measured using processing speed and memory tests, which were categorized into distinct classes using latent profile analysis. Sociodemographic variables, measures of the built and natural environment estimated using geographic information system data, and physical activity and sedentary behaviours were used as predictors. Machine learning was performed using gradient boosting machine, support vector machine, artificial neural network, and linear models. Sociodemographic variables predicted processing speed (r² = 0.43) and memory (r² = 0.20) with good accuracy. Lifestyle factors also accurately predicted processing speed (r² = 0.29) but weakly predicted memory (r² = 0.10). Neighbourhood and built environment factors were weak predictors of cognitive function. Sociodemographic (AUC = 0.84) and lifestyle (AUC = 0.78) factors also accurately classified cognitive classes. Sociodemographic and lifestyle variables can predict cognitive function in adults. Machine learning tools are useful for population-level assessment of cognitive health status via readily available and easy-to-collect data.