Farzana Jahan Phd, FHEA

Statistical disease mapping is a valuable public health tool, as it identifies spatial patterns of disease occurrence. However, the Modifiable Areal Unit Problem (MAUP) poses challenges to disease mapping, as the aggregation of geographic units can impact statistical inferences. The effect of the MAUP depends on contextual factors, for example the geographic structure, aggregation level, choice of model, and the underlying data-generating process. We conducted a comprehensive simulation study to understand the role of these factors on the MAUP in the context of Australian disease mapping. We aggregated and rezoned disease count data at a fine geographic scale before fitting spatial and non-spatial regression models to assess the impact of the MAUP on coefficients. To aid the exploration of simulation results, we developed an interactive Shiny application that enables detailed and interactive exploration of the simulation results. This study highlights the need for disease mapping researchers to analyse sensitivity with rezoning and aggregation tools.

Background
Alzheimer's disease (AD), the most common form of dementia, is marked by significant reductions in glucose metabolism. Such hypometabolism reflects underlying synaptic dysfunction, correlating with cognitive decline. Our study aimed to explore the impact of dietary patterns—specifically, the Western Diet and Prudent Diet—on change in glucose metabolism in brain regions associated with AD risk, [18F]Fluorodeoxyglucose positron emission tomography (FDG‐PET) imaging as a biomarker.

Method
Longitudinal data from 133 cognitively unimpaired older adults were analysed from the Western Australian Memory Study. Participants underwent dietary assessment using the Cancer Council of Victoria Food Frequency Questionnaire and completed FDG‐PET imaging up to three times over 43 months. Dietary patterns were identified through principal component analysis, yielding two patterns—named Western Diet and Prudent Diet. Pattern scores were computed by summing food group intakes weighted by their respective factor loadings. Linear mixed‐effect models evaluated the association between dietary adherence and brain glucose metabolism, including potential confounders. The cohort was stratified by apolipoprotein E (APOE) ε4 carrier status, a genetic risk factor for AD, to investigate potential differing effects.

Result
Adherence to a Western Diet, characterised by high sugars and saturated fats, was associated with a faster decline in glucose metabolism in the right fusiform gyrus among APOE ε4 carriers (β = ‐0.00012; SE = 0.00004; false discover rate adjusted p = .032), with no significant associations in APOE ε4 non‐carriers. Similarly, no significant associations were observed between the Prudent Diet, characterised by high intake of fruits, vegetables, and whole grains, and glucose metabolism, in both APOE ε4 carriers and non‐carriers.

Conclusion
Our study highlights the potential detrimental impact of a Western Diet on brain glucose metabolism, particularly for individuals at genetic risk for AD. The decline in glucose metabolism in the fusiform gyrus, a region essential for cognitive functions like facial recognition, emphasises the role of diet in brain health. Future research should examine the mechanisms linking diet to neurodegeneration and explore dietary interventions as preventive strategies against cognitive decline and dementia.

Background
Spatial data are often aggregated by area to protect the confidentiality of individuals and aid the calculation of pertinent risks and rates. However, the analysis of spatially aggregated data is susceptible to the modifiable areal unit problem (MAUP), which arises when inference varies with boundary or aggregation changes. While the impact of the MAUP has been examined previously, typically these studies have focused on well-populated areas. Understanding how the MAUP behaves when data are sparse is particularly important for countries with less populated areas, such as Australia. This study aims to assess different geographical regions’ vulnerability to the MAUP when data are relatively sparse to inform researchers’ choice of aggregation level for fitting spatial models.

Methods
To understand the impact of the MAUP in Queensland, Australia, the present study investigates inference from simulated lung cancer incidence data using the five levels of spatial aggregation defined by the Australian Statistical Geography Standard. To this end, Bayesian spatial BYM models with and without covariates were fitted.

Results and conclusion
The MAUP impacted inference in the analysis of cancer counts for data aggregated to coarsest areal structures. However, area structures with moderate resolution were not greatly impacted by the MAUP, and offer advantages in terms of data sparsity, computational intensity and availability of data sets.
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Cerebral amyloid angiopathy (CAA) is a type of cerebrovascular disorder characterised by the accumulation of amyloid within the leptomeninges and small/medium-sized cerebral blood vessels. Typically, cerebral haemorrhages are one of the first clinical manifestations of CAA, posing a considerable challenge to the timely diagnosis of CAA as the bleedings only occur during the later disease stages. Fluid biomarkers may change prior to imaging biomarkers, and therefore, they could be the future of CAA diagnosis. Additionally, they can be used as primary outcome markers in prospective clinical trials. Among fluid biomarkers, blood-based biomarkers offer a distinct advantage over cerebrospinal fluid biomarkers as they do not require a procedure as invasive as a lumbar puncture. This article aimed to provide an overview of the present clinical data concerning fluid biomarkers associated with CAA and point out the direction of future studies. Among all the biomarkers discussed, amyloid β, neurofilament light chain, matrix metalloproteinases, complement 3, uric acid, and lactadherin demonstrated the most promising evidence. However, the field of fluid biomarkers for CAA is an under-researched area, and in most cases, there are only one or two studies on each of the biomarkers mentioned in this review. Additionally, a small sample size is a common limitation of the discussed studies. Hence, it is hard to reach a solid conclusion on the clinical significance of each biomarker at different stages of the disease or in various subpopulations of CAA. In order to overcome this issue, larger longitudinal and multicentered studies are needed.

Early childhood experiences and environments impact children’s health, development and wellbeing throughout their lifetime. Children may experience disadvantage depending on the conditions in which they live, learn and grow. This can lead to immediate and long-term impacts at both the individual and societal levels. The COVID-19 pandemic likely has increased existing disadvantage for these children, contributing to worsened outcomes and increased inequity. Robust measurement of disadvantage during early childhood is essential to identifying effective strategies to address these inequities to optimise children’s health, development, and wellbeing. The Multi-Agency Data Integration Project (MADIP) First Five Years (FFY) project is an Australian Government administrative dataset that includes the Australian Early Development Census (AEDC). The AEDC is a valuable tool for monitoring childhood developmental inequities, it assesses aspects of children’s development across five key domains at the time of commencement of the first year of school. Together, this data can help to understand the impacts of multidimensional early childhood factors on children’s health, development and wellbeing, and to identify children at higher risk of developmental vulnerability. In our Phase One work, we used the MADIP-FFY-AEDC data, in collaboration with the Australian Government Department of Education, to conduct a rapid desktop review and data evaluation that demonstrated a range of factors that drove inequitable developmental outcomes in children. Our current Phase Two work expands on this work to further understand associations between key child disadvantage and priority population indicators and childhood developmental vulnerability. The key child disadvantage indicators in this project are guided by the Changing Children’s Chances (CCC) social determinants framework. Phase Two findings will provide further valuable insights into the subset of disadvantage and priority population indicators that best predict children’s developmental vulnerability that could be leveraged for policy purposes.

Background
The Mediterranean-Dietary Approaches to Stop Hypertension (MIND) diet is rich in leafy green vegetables, legumes, berries, wholegrains, fish, nuts, and olive oil and low in red and processed meats and discretionary foods (high in saturated fat, added sugar and added salt). Following a MIND diet is associated with a reduction in risk of cognitive decline and dementia. The lifestyle intervention known as the AUstralian multidomain Approach to Reduce dementia Risk by prOtecting brain health With lifestyle intervention study (AU-ARROW) is undergoing recruitment of participants. Here we report on the dietary quality of participants to date, based on their adherence to the MIND diet.

Method
A self-administered MIND diet questionnaire was completed by participants in the online REDCap platform as part of the screening surveys to determine participant eligibility into the AU-ARROW trial. Eligible participants were required to have a ‘poor diet’, which was a MIND diet score of ≤9 out of a maximum MIND diet score of 14. Comparative statistical analyses were conducted to determine the differences in dietary quality between eligible and ineligible participants.

Result
From the 39 participants who were screened to date, 16 participants were ineligible, and 23 were eligible. Ineligible participants had a higher total MIND diet score (mean = 10.5(SD = 0.6)) compared with eligible participants (mean = 7.5(SD = 1.8)). For ineligible participants, there was greater proportion adhering to MIND diet recommendations for fish, nuts, and poultry. No eligible participants adhered to MIND diet recommendations for leafy green vegetables, berries, and wholegrains. There was general compliance to recommendations for red meat and butter intake among both groups, but a smaller proportion of eligible participants adhered to the MIND diet recommendations for fried foods, sweets, and cheese, compared to ineligible participants.

Conclusion
Overall, improvements to the diet quality of both eligible and ineligible participants are required. Particularly for participants eligible to the AU-ARROW trial, a focus on improving adherence to core food group recommendations are required not only for cognitive health but also for general health and prevention of other chronic diseases. Education and counselling from dietitians are critical for supporting participants in dietary behaviour change.

Bayesian empirical likelihood (BEL) models are becoming increasingly popular as an attractive alternative to fully parametric models. However, they have only recently been applied to spatial data analysis for small area estimation. This study considers the development of spatial BEL models using two popular conditional autoregressive (CAR) priors, namely BYM and Leroux priors. The performance of the proposed models is compared with their parametric counterparts and with existing spatial BEL models using independent Gaussian priors and generalised Moran basis priors. The models are applied to two benchmark spatial datasets, simulation study and COVID-19 data. The results indicate promising opportunities for these models to capture new insights into spatial data. Specifically, the spatial BEL models outperform the parametric spatial models when the underlying distributional assumptions of data appear to be violated.

Background
Cancer atlases often provide estimates of cancer incidence, mortality or survival across small areas of a region or country. A recent example of a cancer atlas is the Australian cancer atlas (ACA), that provides interactive maps to visualise spatially smoothed estimates of cancer incidence and survival for 20 different cancer types over 2148 small areas across Australia.

Methods
The present study proposes a multivariate Bayesian meta-analysis model, which can model multiple cancers jointly using summary measures without requiring access to the unit record data. This new approach is illustrated by modelling the publicly available spatially smoothed standardised incidence ratios for multiple cancers in the ACA divided into three groups: common, rare/less common and smoking-related. The multivariate Bayesian meta-analysis models are fitted to each group in order to explore any possible association between the cancers in three remoteness regions: major cities, regional and remote areas across Australia. The correlation between the pairs of cancers included in each multivariate model for a group was examined by computing the posterior correlation matrix for each cancer group in each region. The posterior correlation matrices in different remoteness regions were compared using Jennrich’s test of equality of correlation matrices (Jennrich in J Am Stat Assoc. 1970;65(330):904–12. https://doi.org/10.1080/01621459.1970.10481133).

Results
Substantive correlation was observed among some cancer types. There was evidence that the magnitude of this correlation varied according to remoteness of a region. For example, there has been significant negative correlation between prostate and lung cancer in major cities, but zero correlation found in regional and remote areas for the same pair of cancer types. High risk areas for specific combinations of cancer types were identified and visualised from the proposed model.

Conclusions
Publicly available spatially smoothed disease estimates can be used to explore additional research questions by modelling multiple cancer types jointly. These proposed multivariate meta-analysis models could be useful when unit record data are unavailable because of privacy and confidentiality requirements.

The modern era is characterised as an era of information or Big Data. This has motivated a huge literature on new methods for extracting information and insights from these data. A natural question is how these approaches differ from those that were available prior to the advent of Big Data. We present a survey of published studies that present Bayesian statistical approaches specifically for Big Data and discuss the reported and perceived benefits of these approaches. We conclude by addressing the question of whether focusing only on improving computational algorithms and infrastructure will be enough to face the challenges of Big Data.

Analysis of spatial patterns of disease is a significant field of research. However, access to unit-level disease data can be difficult for privacy and other reasons. As a consequence, estimates of interest are often published at the small area level as disease maps. This motivates the development of methods for analysis of these ecological estimates directly. Such analyses can widen the scope of research by drawing more insights from published disease maps or atlases. The present study proposes a hierarchical Bayesian meta-analysis model that analyses the point and interval estimates from an online atlas. The proposed model is illustrated by modelling the published cancer incidence estimates available as part of the online Australian Cancer Atlas (ACA). The proposed model aims to reveal patterns of cancer incidence for the 20 cancers included in ACA in major cities, regional and remote areas. The model results are validated using the observed areal data created from unit-level data on cancer incidence in each of 2148 small areas. It is found that the meta-analysis models can generate similar patterns of cancer incidence based on urban/rural status of small areas compared with those already known or revealed by the analysis of observed data. The proposed approach can be generalized to other online disease maps and atlases.