Marc Tennant’s research while affiliated with University of the Western Cape and other places

Background Many rural residents face barriers to eye care access. Government workforce data show a maldistribution of eye care providers in Western Australia however, no detailed information is available on regional coverage within the state. This study aimed to identify and map all eye care services in rural and remote Western Australia and to calculate the regional workforce rates. Methods A survey was sent to all optometry and ophthalmology practices in rural and remote Western Australia with questions on practitioner and equipment availability. Information on visiting services was captured directly through organisations as well as the survey. Geographic information systems were used to map service locations, calculate coverage of services relative to the population, and calculate full-time equivalent (FTE) per 100,000 of the population for each profession. Results A total of 58 optometry practices, 8 ophthalmology practices, 113 visiting optometry services and 43 visiting ophthalmology services in rural and remote Western Australia were identified and mapped. Most of the population (97.2%) lived within 100 km of an eye care service. Overall FTE per 100,000 for optometry and ophthalmology were 12.1 and 2.4, respectively. Regional FTE per 100,000 ranged from 2.1 to 19.5 for optometry and 0 to 4.2 for ophthalmology. Conclusions The results show broad coverage of visiting services but highlights regional discrepancies in optometry and ophthalmology workforce rates, with several regions failing to meet FTE recommendations.

Objectives: Oro-facial trauma affects physical, psychological, and social well-being. This study assesses changes in oro-facial trauma hospitalisation rates among adults during the pre-lockdown period, ‘lockdown year’, and post-lockdown period in Australia and the United Kingdom (UK). The findings provide insights into healthcare delivery and resource allocation during public health emergencies to inform future preventive strategies. Methods: Hospitalisation data for adults (aged 20+) with oro-facial trauma, classified using ICD-10 codes, were collected. Age-standardised rates (ASRs) per 100,000 population were calculated for each period. Comparative analyses evaluated hospitalisation rates during the ‘lockdown year’ relative to three years before and after. The 11 oro-facial trauma ICD codes were grouped into major and minor trauma to evaluate differential impacts. Results: This study highlighted a statistically significant reduction in oro-facial trauma hospitalisation rates during the ‘lockdown year’ in both Australia (38.8%) and the UK (35.7%) compared to the pre-lockdown period. Although rates increased post-lockdown, they remained lower than pre-lockdown levels, with a 35.5% reduction in Australia and a 25.1% reduction in the UK. Additionally, while the ASR significantly increased for major trauma in the post-lockdown years compared to the lockdown year, the increase for minor trauma was not statistically significant in both countries. Conclusions: COVID-19 lockdowns led to a significant reduction in oro-facial trauma hospitalisations. Post-lockdown rates increased as activities resumed but did not return to pre-lockdown levels, suggesting lasting behavioural shifts. These findings highlight the role of external factors (e.g., mobility and social behaviour) in oro-facial trauma rates and can inform targeted preventive strategies for high-risk periods.

Objective: This study examined the distribution of residential aged care facilities in relation to the aged population and socioeconomic profile at the smallest geographical unit in Australia’s metropolitan areas, regional centers, and large rural towns. Methods: An observational study was designed using the most recent open-source data from the Australian government. The aged population data and RACF locations were aggregated by SA1, remoteness, and socioeconomic profile, which were defined using the modified Monash model (MMM) and the index of relative socioeconomic disadvantage (IRSD). Remoteness was categorized into metropolitan areas, regional centers, and large rural towns, while socioeconomic factors were analyzed based on IRSD 10 deciles. The study utilized a Quantum Geographic Information System (QGIS) to perform the analysis and mapping of RACF distribution and accessibility. All collected data files were overlaid and linked using the join function in QGIS for comprehensive analysis at the Statistical area level 1 (SA1). Centroids were calculated for each SA1 to represent the geographical center of the area. The buffer function was performed to determine the proportion of the aged population residing within the specified distance thresholds of 2.5 km, 5 km, and over a 5 km radius from these facilities. Results: Geographic disparities: The findings indicate shortages of RACF places’ availability, with only 5% of the older population living in metropolitan and large rural towns having access to such places. The number drops to 4% for those living in regional centers. The majority of older metropolitan residents reside within a 5 km radius of RACFs, compared to large rural areas (14%) and regional centers (23%), where the elders reside over the study distance threshold. The shortage of available places and the travel distances to these services may lead older adults who cannot live independently to seek alternative aged care services or remain at home. This might result in inadequate health services tailored to their specific needs which may contribute to poor health outcomes, increased in emergency room presentations, and unplanned hospital admissions. Socioeconomic Disparities: The distribution of RACFs varied significantly across different socioeconomic profiles in favor of people with a lower socioeconomic background across the study, measured using the IRSD. Area. Conclusion: Urgent policy interventions are needed to expand RACFs’ capacity and to establish new facilities in areas with the greatest need. The aged health system needs increased funding, improved workforce conditions, and equitable resource allocation to create a sustainable aged care system with a high-end quality of care.

Objective This study investigates the geographical distribution of private hospitals in Australian capital cities in relation to the Index of Relative Socioeconomic Disadvantage. Methods Using Geographic Information System analysis, the study examined how private hospitals are distributed across different socioeconomic quartiles, providing a comprehensive visualisation of health care accessibility. Results The results indicate an unequal distribution with a substantial concentration of private hospitals within the vicinity of communities classified in the highest socioeconomic classification. This raises significant concerns about health care equity, particularly in light of the increased strain on health care systems before, during and after the COVID‐19 pandemic. Conclusions This study underscores the need for targeted policy interventions to enhance the resilience and accessibility of the private health care sector, specifically targeting disadvantaged communities. It suggests that comprehensive, geographically‐informed data is crucial for policymakers to make informed decisions that promote health equity in the postpandemic landscape.

Objective The advent of the COVID‐19 pandemic has accentuated the critical importance of epidemic preparedness within national healthcare systems. This study presents a geospatial analysis aimed at optimising the placement of field hospitals in Australia to ensure adequate healthcare access during pandemics. Methods The latest census data from the Australian Bureau of Statistics were integrated with the spatial locations of current emergency departments within Australian capitals. Buffer zones with a 7.5 km radius were created around the public emergency departments (EDs). Buffer zones outside the 7.5 km radius that exhibited high population densities were categorised into high‐density (red), moderate‐density (yellow) and low‐density (green) zones to prioritise and tailor healthcare responses during a pandemic. The identification of high‐density population areas outside the ED radius enabled the stratification of potential sites for ED facilities. Results Ninety‐one potential field hospital sites were identified across Australia's capital cities. The findings indicate that the addition of these sites would place over 95% of the population within a 7.5 km radius of an ED facility. This network of proposed sites is designed to serve a spectrum of population densities, ensuring equitable healthcare access for both densely populated urban centres and less populated areas. Conclusions This study underscores the potential of field hospitals to strengthen Australia's public health system against emergencies. By advocating for the creation of 91 field hospitals within a 7.5 km reach for over 95% of urban dwellers across major cities, it demonstrates a strategic approach to ensure comprehensive ED coverage. Drawing on international examples, including China's Fangcang hospitals, the USA's post‐acute care (PAC) facilities and the United Kingdom's National Health Service (NHS) Nightingale Hospitals, it highlights the need for healthcare agility and scalability, especially during pandemic outbreaks. The research presents a blueprint for field hospital deployment, marking a significant advancement in public health logistics and protection across Australia's varied demographic and geographical landscapes.

D1ental caries remains the most common chronic disease in childhood, affecting almost half of all children globally. Dental care and examination of children living in remote and rural areas is an ongoing challenge that has been compounded by COVID. The development of a validated system with the capacity to screen large numbers of children with some degree of automation has the potential to facilitate remote dental screening at low costs. In this study, we aim to develop and validate a deep learning system for the assessment of dental caries using color dental photos. Three state-of-the-art deep learning networks namely VGG16, ResNet-50 and Inception-v3 were adopted in the context. A total of 1020 child dental photos were used to train and validate the system. We achieved an accuracy of 79% with precision and recall respectively 95% and 75% in classifying ‘caries’ versus ‘sound’ with inception-v3.