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Issues Addressed: Tobacco product availability is higher in socioeconomically disadvantaged areas, which can further widen tobacco-related health and disease burden inequities. This study aimed to describe retail availability of tobacco products in South Australia and examine the association between tobacco vendor location, population's socioeconomic status (SES) and tobacco smoking prevalence., Methods: Cross-sectional 2022 tobacco vendor licence data and 2021-2022 state-wide population health survey data from the South Australian Department of Health were used. Tobacco vendors were enumerated by Statistical Area 2 (SA2) using geocoding software, with SA2s assigned health survey derived smoking prevalence, SES, remoteness category, area size, and population size., Results: As of 2022, there were 1723 tobacco vendors in South Australia and the overall tobacco smoking prevalence across the state was 11.8%. Regression analyses indicated that tobacco vendor density increased with socioeconomic disadvantage and geographic remoteness, and that smoking prevalence was higher in low SES areas. Vendor density was not related to smoking prevalence., Conclusions: Findings are consistent with existing research indicating greater tobacco availability in socially disadvantaged areas. This supports that tobacco vendor saturation may be directed to areas in a way that promotes tobacco availability for vulnerable populations. Our finding that smoking prevalence was unrelated to tobacco availability contrasts existing literature and should be carefully interpreted. SO WHAT?: This is the first study to map tobacco retailers across South Australia, contributing needed evidence on the intersection of tobacco vendor density, social disadvantage, and smoking prevalence., (© 2023 Australian Health Promotion Association.)

Purpose: There is increasing interest in the measurement of cognitive effort during listening tasks, for both research and clinical purposes. Quantification of task-evoked pupil responses (TEPRs) is a psychophysiological method that can be used to study cognitive effort. However, light level during cognitively demanding listening tasks may affect TEPRs, complicating interpretation of listening-related changes. The objective of this study was to examine the effects of light level on TEPRs during effortful listening across a range of signal-to-noise ratios (SNRs)., Method: Thirty-six adults without hearing loss were asked to repeat target sentences presented in background babble noise while their pupil diameter was recorded. Light level and SNRs were manipulated in a 4 × 4 repeated-measures design. Repeated-measures analyses of variance were used to measure the effects., Results: Peak and mean dilation were typically larger in more adverse SNR conditions (except for SNR -6 dB) and smaller in higher light levels. Differences in mean and peak dilation between SNR conditions were larger in dim light than in brighter light., Conclusions: Brighter light conditions make TEPRs less sensitive to variations in listening effort across levels of SNR. Therefore, light level must be considered and reported in detail to ensure sensitivity of TEPRs and for comparisons of findings across different studies. It is recommended that TEPR testing be conducted in relatively low light conditions, considering both background illumination and screen luminance., Supplemental Material: https://doi.org/10.23641/asha.25676538.

Soil carbon accounting and prediction play a key role in building decision support systems for land managers selling carbon credits, in the spirit of the Paris and Kyoto protocol agreements. Land managers typically rely on computationally complex models fit using sparse datasets to make these accounts and predictions. The model complexity and sparsity of the data can lead to over-fitting, leading to inaccurate results when making predictions with new data. Modellers address over-fitting by simplifying their models and reducing the number of parameters, and in the current context this could involve neglecting some soil organic carbon (SOC) components. In this study, we introduce two novel SOC models and a new RothC-like model and investigate how the SOC components and complexity of the SOC models affect the SOC prediction in the presence of small and sparse time series data. We develop model selection methods that can identify the soil carbon model with the best predictive performance, in light of the available data. Through this analysis we reveal that commonly used complex soil carbon models can over-fit in the presence of sparse time series data, and our simpler models can produce more accurate predictions., (© 2024. The Author(s).)