868Harnessing new approaches and contemporary methods for better evidence on housing and health
Authors: Singh Ankur 
Published: September 2021

Organisation(s):

The Melbourne School of Population and Global Health, Healthy Cities at the University of Adelaide, The Australian Institute of Machine Learning.

Key contact person: Doctor Ankur Singh

Focus and outcomes for participants

The symposium will focus on four emerging methods which, because of the complexity and pervasiveness of the concepts housing and health, are highly relevant to progressing new knowledge in the field:

New methods will be presented in the context of past and current international research in the field. Presentations will be interactive; embedded within each presentation will be discussion points to engage participants and extend discussion.

Rationale for the symposium, including for its inclusion in the Congress

A good housing system has the potential to play a key role in preventing poor health, and maximising good health. Much of the epidemiological research on which evidence for action rests however, describes generalised associations and correlations rather than intervention-oriented causal pathways or context appropriate predictions. Why? Housing and health are both complex concepts to measure and understand and the stark differences in the composition of people in less stable and unaffordable types of housing compared to the people considered to be well-housed makes it difficult to measure, let alone compare, health outcomes.

Recent advances in methods and conceptual thinking have enabled us to do better at identifying underlying causal pathways generating a body of research that has utilised longitudinal data, fixed effects and hybrid regression analyses and marginal structural models to examine pathways between housing affordability and tenure (including social housing) and mental health.

There is more to do however, with developments in methods rapidly occurring alongside greater data availability increasing the scope for causally focussed or more accurately predictive research on housing and health.

The main theme of the congress is ‘Methodological Innovation in Epidemiology’ and a subtheme is ‘Translation from research to policy and practice’. Our symposium addresses both these themes. It will present the application of causal inference, machine learning, natural experiments and use of multistate simulation models to generate policy-relevant research for transforming housing policies.

Presentation program

Introduction and overview of the field, Peter Phibbs (Not confirmed)

Peter Phibbs is a geographer, planner and social economist who been undertaking housing research for more than 25 years. He is Head of Urban Planning and Policy at the University of Sydney and Director of the Henry Halloran Trust. His recent research has been on the development of the affordable housing sector in Australia, the role of planning in affordable housing delivery, tenancy issues in remote Indigenous communities as well as the use of shared ownership models to improve affordability outcomes

Natural experiments for housing and health, Rebecca Bentley

Professor Rebecca Bentley is a Principal Research Fellow in Social Epidemiology in the Centre for Health Equity, Melbourne School of Population and Global Health. Over the past ten years, Rebecca has developed a research program exploring the role of housing and residential location in shaping health and wellbeing in Australia.

Machine learning for prediction & precision, Emma Baker

Emma Baker is Professor of Housing Research and an ARC Future Fellow. Her work examines the impact of housing and location in urban and regional environments, producing academic, as well as policy-relevant research. Dr Baker's recent publications include analyses of the housing implications of economic, social, and spatial change in Australia, work quantifying health effects of housing tenure and affordability, research on the effects of precarious and vulnerable housing.

Opportunities and challenges in using multistate lifetable models for housing interventions, Ankur Singh

Ankur is a Lecturer (Epidemiology) and a Research Fellow in Social Epidemiology at the Melbourne School of Population and Global Health. In his current role, Ankur applies advanced quantitative as well as evidence synthesis methods such as multilevel modelling, causal mediation techniques, simulation modelling based on multistate lifetables, and systematic and scoping reviews. Within the Centre for Health Equity, Ankur works collaboratively with a team of researchers interested in quantitative research on Social and Spatial Epidemiology. Key focus areas of the research group include housing related health inequalities, intergenerational health inequalities and urban environments and health.

Maximising the research power of longitudinal data, Zoe Aitken (Not confirmed)

Zoe Aitken is a research fellow at the Gender and Women's Health Unit at the Melbourne School of Population and Global Health. She has been working at the University of Melbourne since 2011 to pursue her interest in social epidemiology and was awarded an NHMRC postdoctoral scholarship in April 2015. She has a particular interest in the analysis of longitudinal studies to answer causal questions about the complex interplay between socio-economic disadvantage and health.

Flexible modelling and effective visualisation, Koen Simons

Dr Koen Simons obtained a Masters degree in Physics form the University of Gent and a Masters degree in Statistics from the Katholieke Universiteit Leuven, focussing on sparsity and shrinkage estimators. He completed his PhD at the Vrije Universiteit Brussel, Belgium, performing simulation studies of ensemble methods with applications for short-term health effects of air pollution. He is currently providing biostatistical advise for both clinical trials and epidemiological studies at Western Health and RMIT, and applying causal inference models to problems in health equity.

Conclusions and final comments

Names of facilitator or chair

Rebecca Bentley