A Markov random field model with cumulative logistic functions for spatially dependent ordinal data.

This paper presents a class of regression models with cumulative logistic functions that are chiefly designed to analyse spatially dependent ordinal data. In contrast to previous works, the proposed model requires neither the sites to be regularly spaced nor the assumption of an underlying continuous variable. It belongs to a more general class of Markov random field models, and can be considered an extension of the ordinal regression model with the proportional odds link function. Our proposed model allows practitioners to interpret the model parameters using odds ratios. Apart from the theoretical developments, this work also highlights the practical aspects of model fitting, including parameterisation, selection of neighbourhood, and calculation of standard errors. Simulation studies with regularly and irregularly spaced sites were conducted. Modelling strategies including pseudo-likelihood methods were found to be useful in both settings. The proposed model and the non-spatial counterpart were applied to the daily air quality index measured in the United Kingdom. The results indicate the presence of spatial effects and the incorporation of spatial effects led to better model performance in terms of various goodness-of-fit measures.

Oral health knowledge is associated with oral health-related quality of life: a survey of first-year undergraduate students enrolled in an American university.

BACKGROUND: Oral health knowledge forms part of oral health literacy that enables individuals to inform appropriate oral health decisions and actions. Oral health-related quality of life (OHRQoL) characterizes self-perception of well-being influenced by oral health. This study aimed to examine the relationship between oral health knowledge and OHRQoL. METHODS: A random sample of 19-to-24-year-old first-year undergraduate students (n = 372) in Minnesota, United States of America was used. Each student was assessed with an online survey using the Comprehensive Measure of Oral Health Knowledge (CMOHK) and the OHRQoL items of the World Health Organization (WHO) Oral Health Questionnaire for Adults. Relationships between OHRQoL parameters and CMOHK together with other covariates were assessed using ordinal regression models. Associations between OHRQoL parameters were examined with the Kendall's tau-b method. RESULTS: Dry mouth (45%) was the most reported OHRQoL issue. The respondents showing good oral health knowledge were less likely to experience speech or pronunciation difficulty (ß=-1.12, p = 0.0006), interrupted sleep (ß=-1.43, p = 0.0040), taking days off (ß=-1.71, p = 0.0054), difficulty doing usual activities (ß=-2.37, p = 0.0002), or reduced participation in social activities due to dental or oral issues (ß=-1.65, p = 0.0078). CONCLUSIONS: This study suggested a protective effect of better oral health knowledge on specific OHRQoL issues. In addition to provision of affordable dental services, university-wide oral health education can be implemented to improve OHRQoL in undergraduate students.

Modelling COVID-19 pandemic control strategies in metropolitan and rural health districts in New South Wales, Australia.

COVID-19 remains a significant public health problem in New South Wales, Australia. Although the NSW government is employing various control policies, more specific and compelling interventions are needed to control the spread of COVID-19. This paper presents a modified SEIR-X model based on a nonlinear ordinary differential equations system that considers the transmission routes from asymptomatic (Exposed) and symptomatic (Mild and Critical) individuals. The model is fitted to the corresponding cumulative number of cases in metropolitan and rural health districts of NSW reported by the Health Department and parameterised using the least-squares method. The basic reproduction number [Formula: see text], which measures the possible spread of COVID-19 in a population, is computed using the next generation operator method. Sensitivity analysis of the model parameters reveals that the transmission rate had an enormous influence on [Formula: see text], which may be an option for controlling this disease. Two time-dependent control strategies, namely preventive (it refers to effort at inhibiting the virus transmission and prevention of case development from Exposed, Mild, Critical, Non-hospitalised and Hospitalised population) and management (it refers to enhance the management of Non-hospitalised and Hospitalised individuals who are infected by COVID-19) measures, are considered to mitigate this disease's dynamics using Pontryagin's maximum principle. The most sensible control strategy is determined through the cost-effectiveness analysis for the metropolitan and rural health districts of NSW. Our findings suggest that of the single intervention strategies, enhanced preventive strategy is more cost-effective than management control strategy, as it promptly reduces COVID-19 cases in NSW. In addition, combining preventive and management interventions simultaneously is found to be the most cost-effective. Alternative policies can be implemented to control COVID-19 depending on the policymakers' decisions. Numerical simulations of the overall system are performed to demonstrate the theoretical outcomes.

Visual grading experiments and optimization in CBCT dental implantology imaging: preliminary application of integrated visual grading regression.

This study uses a general formulation of integrated visual grading regression (IVGR) and applies it to cone beam computed tomography (CBCT) scan data related to anatomical landmarks for dental implantology. The aim was to assess and predict a minimum acceptable dose for diagnostic imaging and reporting. A skull phantom was imaged with a CBCT unit at various diagnostic exposures. Key anatomical landmarks within the images were independently reviewed by three trained observers. Each provided an overall image quality score. Statistical analysis was carried out to examine the acceptability of the images taken, using an IVGR analysis that was formulized as a three-stage protocol including defining an integrated score, development of an ordinal regression, and investigation of the possibility for dose reduction through estimated parameters. For a unit increase in the logarithm of radiation dose, the odds ratio that the integrated score for an image assessed by observers being rated in a higher category was 3.940 (95% confidence interval: 1.016-15.280). When assessed by the observers, the minimum dose required to achieve a 75% probability for an image to be classified as at least acceptable was 1346.91 mGy·cm2 dose area product (DAP), a 31% reduction compared to the 1962 mGy·cm2 DAP default dosage of the CBCT unit. The kappa values of the intra and inter-observer reliability indicated moderate agreements, while a discrepancy among observers was also identified because each, as expected, perceived visibility differently. The results of this work demonstrate the IVGR's predictive value of dose saving in the effort to reduce dose to patients while maintaining reportable diagnostic image quality.

Modelling the effects of cold temperature during the reproductive stage on the yield of chickpea (Cicer arietinum L.).

During the reproductive stage, chilling temperatures and frost reduce the yield of chickpea and limit its adaptation. The adverse effects of chilling temperature and frost in terms of the threshold temperatures, impact of cold duration, and genotype-by-environment-by-management interactions are not well quantified. Crop growth models that predict flowering time and yield under diverse climates can identify combinations of cultivars and sowing time to reduce frost risk in target environments. The Agricultural Production Systems Simulator (APSIM-chickpea) model uses daily temperatures to model basic crop growth but does not include penalties for either frost damage or cold temperatures during flowering and podding stages. Regression analysis overcame this limitation of the model for chickpea crops grown at 95 locations in Australia using 70 years of historic data incorporating three cultivars and three sowing times (early, mid, and late). We modified model parameters to include the effect of soil water on thermal time calculations, which significantly improved the prediction of flowering time. Simulated data, and data from field experiments grown in Australia (2013 to 2019), showed robust predictions for flowering time (n = 29; R2 = 0.97), and grain yield (n = 22; R2 = 0.63-0.70). In addition, we identified threshold cold temperatures that significantly affected predicted yield, and combinations of locations, variety, and sowing time where the overlap between peak cold temperatures and peak flowering was minimal. Our results showed that frost and/or cold temperature-induced yield losses are a major limitation in some unexpected Australian locations, e.g., inland, subtropical latitudes in Queensland. Intermediate sowing maximise yield, as it avoids cold temperature, late heat, and drought stresses potentially limiting yield in early and late sowing respectively.

A Review of COVID-19 Modelling Strategies in Three Countries to Develop a Research Framework for Regional Areas.

At the end of December 2019, an outbreak of COVID-19 occurred in Wuhan city, China. Modelling plays a crucial role in developing a strategy to prevent a disease outbreak from spreading around the globe. Models have contributed to the perspicacity of epidemiological variations between and within nations and the planning of desired control strategies. In this paper, a literature review was conducted to summarise knowledge about COVID-19 disease modelling in three countries-China, the UK and Australia-to develop a robust research framework for the regional areas that are urban and rural health districts of New South Wales, Australia. In different aspects of modelling, summarising disease and intervention strategies can help policymakers control the outbreak of COVID-19 and may motivate modelling disease-related research at a finer level of regional geospatial scales in the future.

Evaluation of COVID-19 Mitigation Policies in Australia Using Generalised Space-Time Autoregressive Intervention Models.

In handling the COVID-19 pandemic, various mitigation policies aiming at slowing the spread and protecting all individuals, especially the vulnerable ones, were implemented. A careful evaluation of the effectiveness of these policies is necessary so that policy-makers can implement informed decisions if another wave of COVID-19 or another pandemic happens in the future. This paper reports an assessment of some policies introduced by the Australian governments using a generalised space-time autoregressive model which incorporates multiple exogenous variables and delay effects. Our results show that the number of daily new cases from the states and territories are influenced by both temporal and spatial aspects. Business and border restrictions are found helpful in reducing the number of new cases a few days after implementation while gathering restrictions may not be effective.

Increased demand for amphetamine treatment in rural Australia.

BACKGROUND: A substantial increase in substance treatment episodes for methamphetamine problems suggests characteristics of the treatment population could have changed and that targeted treatment programs are required. To determine who methamphetamine treatment should be designed for this study has two aims. First, to empirically describe changes in amphetamine treatment presentations to a rural NSW drug and alcohol treatment agency over time. Second, to examine how these characteristics may affect the likelihood of being treated for amphetamines compared to other drugs. METHOD: The Australian Alcohol and Other Drug Treatment Services National Minimum Data Set (AODTS-NMDS) containing closed treatment episodes from a single agency from three time periods was used. Characteristics of people receiving amphetamine treatments in these three periods were compared and the effects of these characteristics on the odds of being treated for amphetamine were estimated using a logistic regression model. The characteristics utilised in the analysis include age, sex, Indigenous status, usual accommodation, living arrangement, source of referral and source of income. RESULTS: The proportion of amphetamine treatment episodes doubled from 2006/2007 to 2015/2016 and overtook alcohol as the most commonly treated principal drug of concern. The estimated proportion of amphetamine treatments showed an increment across all ages and for men and women. It was found that younger people, women, people in temporary accommodation or homeless, people who were self-referred and people whose main source of income was not through employment are more likely to be treated for amphetamine use. CONCLUSION: Significant changes over time in the age, sex and Indigenous status of people receiving treatment for amphetamine as the principal drug of concern requires service delivery to match demand from younger people, particularly women; and Indigenous people. The needs and preferences for treatment of younger women who use amphetamine will be important factors in treatment planning service providers who are more used to providing treatment for young men who use cannabis and older men who use alcohol. Further research on women's experiences in treatment and outcomes would be useful for informing treatment practices.

Seemingly unrelated intervention time series models for effectiveness evaluation of large scale environmental remediation.

Large scale environmental remediation projects applied to sea water always involve large amount of capital investments. Rigorous effectiveness evaluations of such projects are, therefore, necessary and essential for policy review and future planning. This study aims at investigating effectiveness of environmental remediation using three different Seemingly Unrelated Regression (SUR) time series models with intervention effects, including Model (1) assuming no correlation within and across variables, Model (2) assuming no correlation across variable but allowing correlations within variable across different sites, and Model (3) allowing all possible correlations among variables (i.e., an unrestricted model). The results suggested that the unrestricted SUR model is the most reliable one, consistently having smallest variations of the estimated model parameters. We discussed our results with reference to marine water quality management in Hong Kong while bringing managerial issues into consideration.