A comparison of content from across contemporary Australian population health surveys.

OBJECTIVE: Associations between place and population health are of interest to researchers and policymakers. The objective of this paper is to explore, summarise and compare content across contemporary Australian geo-referenced population health survey data sets. METHODS: A search for recent (2015 or later) population health surveys from within Australia containing geographic information from participants was conducted. Survey response frames were analysed and categorised based on demographic, risk factor and disease-related characteristics. Analysis using interactive Sankey diagrams shows the extent of content overlap and differences between population health surveys in Australia. RESULTS: Thirteen Australian geo-referenced population health survey data sets were identified. Information captured across surveys was inconsistent as was the spatial granularity of respondent information. Health and demographic features most frequently captured were symptoms, signs and clinical findings from the International Statistical Classification of Diseases and Related Health Problems version 11, employment, housing, income, self-rated health and risk factors, including alcohol consumption, diet, medical treatments, physical activity and weight-related questions. Sankey diagrams were deployed online for use by public health researchers. CONCLUSIONS: Identifying the relationship between place and health in Australia is made more difficult by inconsistencies in information collected across surveys deployed in different regions in Australia. IMPLICATIONS FOR PUBLIC HEALTH: Public health research investigating place and health involves a vast and inconsistent patchwork of information within and across states, which may impact broad-scale research questions. The tools developed here assist public health researchers to identify surveys suitable for their research queries related to place and health.

Australians' perceptions of the potential effects of increased access to alcohol via autonomous delivery services: A multi-method study.

INTRODUCTION: Rapid technological change will affect how people access harmful products. For example, automated delivery services are forecast to provide fast and affordable product access options for those purchasing alcohol. Information about the potential impacts of such innovations on alcohol purchase and consumption behaviors is lacking. The aim of this study was to explore how consumers may respond to future scenarios where alcohol is available via a range of autonomous alcohol delivery options. METHODS: In a two-stage process, qualitative individual interviews (n = 100) and a quantitative online survey (n = 1078) were conducted with Australians aged 18+ years. Quotas were used to achieve national representation on key demographic variables. Participants were exposed to scenarios outlining how autonomous vehicles are likely to be used for alcohol deliveries in the future and asked to discuss their intentions to use such services and their perceptions of any impacts on alcohol consumption at a societal level. RESULTS: Automated alcohol deliveries were generally considered to be highly convenient and therefore likely to be popular. Around one-third (37%) of survey respondents reported an intention to use such automated alcohol delivery services once they are available and almost half (47%) expected overall levels of alcohol consumption to increase across the population. CONCLUSION: Given potential levels of uptake, careful consideration needs to be given to the development and implementation of appropriate policy and regulatory frameworks to minimize the increased risk of alcohol-related harm associated with enhanced alcohol availability resulting from the emerging availability of autonomous delivery services.

A framework for considering the utility of models when facing tough decisions in public health: a guideline for policy-makers.

The COVID-19 pandemic has brought the combined disciplines of public health, infectious disease and policy modelling squarely into the spotlight. Never before have decisions regarding public health measures and their impacts been such a topic of international deliberation, from the level of individuals and communities through to global leaders. Nor have models-developed at rapid pace and often in the absence of complete information-ever been so central to the decision-making process. However, after nearly 3 years of experience with modelling, policy-makers need to be more confident about which models will be most helpful to support them when taking public health decisions, and modellers need to better understand the factors that will lead to successful model adoption and utilization. We present a three-stage framework for achieving these ends.

Dimensionality reduction for visualizing high-dimensional biological data.

High throughput technologies used in experimental biological sciences produce data with a vast number of variables at a rapid pace, making large volumes of high-dimensional data available. The exploratory analysis of such high-dimensional data can be aided by human interpretable low-dimensional visualizations. This work investigates how both discrete and continuous structures in biological data can be captured using the recently proposed dimensionality reduction method SONG, and compares the results with commonly used methods UMAP and PHATE. Using simulated and real-world datasets, we observe that SONG produces insightful visualizations by preserving various patterns, including discrete clusters, continuums, and branching structures in all considered datasets. More importantly, for datasets containing both discrete and continuous structures, SONG performs better at preserving both the structures compared to UMAP and PHATE. Furthermore, our quantitative evaluation of the three methods using downstream analysis validates the on par quality of the SONG's low-dimensional embeddings compared to the other methods.

IMPARO: inferring microbial interactions through parameter optimisation.

BACKGROUND: Microbial Interaction Networks (MINs) provide important information for understanding bacterial communities. MINs can be inferred by examining microbial abundance profiles. Abundance profiles are often interpreted with the Lotka Volterra model in research. However existing research fails to consider a biologically meaningful underlying mathematical model for MINs or to address the possibility of multiple solutions. RESULTS: In this paper we present IMPARO, a method for inferring microbial interactions through parameter optimisation. We use biologically meaningful models for both the abundance profile, as well as the MIN. We show how multiple MINs could be inferred with similar reconstructed abundance profile accuracy, and argue that a unique solution is not always satisfactory. Using our method, we successfully inferred clear interactions in the gut microbiome which have been previously observed in in-vitro experiments. CONCLUSIONS: IMPARO was used to successfully infer microbial interactions in human microbiome samples as well as in a varied set of simulated data. The work also highlights the importance of considering multiple solutions for MINs.