Shanley Chong

Background: Diagnosis and management of cervical cancer require multiple investigations and specialist reviews, with delays leading to increased mortality and morbidity. Australia's Optimal Care Pathways (OCPs) recommends that patients with suspected diagnosis of cervical cancer should have their diagnostic processes completed within certain timeframes. Despite these guidelines, there is currently no benchmarking of cervical cancer timelines. Aims: This study aims to benchmark cervical cancer care and identify factors associated with delay. Materials and Methods: Patients who underwent radiotherapy for Stage I‐III cervical cancer between 2015 and 2024 at a major tertiary hospital in New South Wales (NSW) were included in the study. This hospital provides centralised gynae‐oncology care for all women in three NSW local health districts. Patients were stratified based on age, stage of disease, level of rurality and level of socioeconomic advantage/disadvantage. Each step of the diagnostic timeline was benchmarked against the OCPs. Univariate and multivariate logistic regression were performed to detect factors associated with delays. Results: The study cohort comprised 155 patients. Under 60% of women were managed in‐line with the OCPs recommended timelines. 86% ( n = 19/22) of women from the least disadvantaged socio‐economic tertile saw a gynae‐oncology specialist within the recommended timeframe compared to 47% (36/76) from the most disadvantaged socio‐economic tertile (OR: 6.24, CI: 1.60–24.27, p = 0.008). Conclusions: Recommended diagnostic timelines were not met in a significant proportion of women. Women from the greatest levels of socioeconomic disadvantage were more likely to face delays. Benchmarking processes need to be implemented in cancer care, to ensure equity in outcomes/processes for all patients.

Issue Addressed: Falls as a major cause of home injury and the pandemic‐fuelled trend of spending more time at home indicate a need for examining the current nature of serious fall injuries. We aimed to identify and describe clusters of fall injury in Sydney before and during the dramatic rise in the prevalence of working from/staying at home and describe individual and neighbourhood factors associated with such clusters.Methods: An analysis of 8115 fall‐related injuries requiring hospitalisation from 2018 to 2021 in a local health district in New South Wales, Australia, was conducted using SaTScan to identify statistically significant spatial clusters. The district was comprised of four local government areas (Areas A–D) which had varying socio‐demographic characteristics. For each year, logistic regression was used to examine associations with individual and neighbourhood factors.Results: The presence of significant geographic hotspots of fall injury hospitalisation consistently appeared in the west of Area B and along its borders with the west of Area C and the east of Area A before and since the rise in working from/staying at home. These clusters correlated closely with older age and socio‐economically disadvantaged areas.Conclusions: Spatial clusters of fall injuries occurring in the home environment were consistent before and during the rise in working from/staying at home. Taking into consideration individual and neighbourhood‐level factors associated with identified clusters, specifically older age and socio‐economic disadvantage, will enable more nuanced strategies to boost the effectiveness of intervention programmes.So What: Fall injury incidence was consistent in the study periods suggesting little if any effect of the rise in working from/staying at home on falls at home. Additionally, the need for geographically targeted fall interventions is warranted for identified high risk areas characterised by older age and disadvantaged populations.

Background: Childhood obesity is a major public health concern in Australia and the multicultural population of South-West Sydney. This study examined the influence of parents’ country of birth (COB) on the association between family socio-economic status (SES), assessed by area-level disadvantage and annual household income, and childhood obesity. Methods: The analysis of data from the cross-sectional Growing Healthy Kids in Southwest Sydney (GHK-SWS) baseline survey of 1,815 children aged 5-16 years living in South Western Sydney, Australia employed generalised linear multinomial mixed models, with results presented as odds ratio and 95% confidence intervals. Results: This study found a significant relationship between SES measures and the risk of overweight and obesity in the study population. The risks of childhood obesity are greatest in lower socioeconomic groups (across both SES metrics) regardless of their parents’ COB status. However, the overweight results are inconsistent across SES metrics and COB status. The risk of being overweight was lower for Australian-born parents living in disadvantaged areas but higher for those reporting lower income; and higher for Overseas-born parents living in disadvantaged areas but lower for those reporting low household income. Conclusion: Parents’ SES is a consistent predictor of childhood obesity, and this relationship is not modified by parents’ COB. However, the likelihood of a child being overweight varied between the parents’ SES measures and COB. Prioritisation of the target population for area-level preventive public health interventions to modify obesogenic factors is recommended for a) obese children living in most disadvantaged areas and moderate disadvantaged areas regardless of their parents’ COB; b) overweight and obese children of moderate disadvantaged areas regardless of their parents’ COB.

Background: Parents’ perception of children’s weight status has been recognised as one of the influencing factors when planning childhood overweight and obesity prevention programs. We aimed to examine whether children’s BMI modified the association between parents’ socio-demographic and economic characteristics and their perceptions of their children’s weight status in South Western Sydney Local Health District. Methods: We analysed 1,353 parents’ perceptions of their child’s weight status using the ‘Growing Healthy Kids-Population Health Survey’ baseline data. Generalised linear multinomial mixed models were used to determine the associations. Results are presented as odds ratio and associated 95% confidence intervals by BMI. Results: This study found that the weight status of about 23% of healthy-weight children, 65% of overweight children and 55% of obese children was incorrectly perceived by parents. Father as the primary parent and parents who completed ≤12 years of school education were less likely to perceive their overweight or obese children as overweight. Parents who lived in more disadvantaged areas were more likely to perceive their obese children as overweight. Parents who lived in moderate disadvantaged areas were less likely to perceive their overweight children as overweight. Parents who had reported an annual household income between $52,000-$103,999 were more likely to perceive their overweight children as of healthy weight and parents who reported an annual household income below $103,999 were twice as likely to perceive their obese children as having a healthy weight. Conclusion: The findings suggest that there may be benefit in designing interventions that focus on improving parents’ misperception of children’s weight status. The groups of parents who should be prioritised for preventive public health interventions to modify the parental misperceptions of their child’s weight status include a) fathers, b) parents who completed ≤12 years of school education and parents who had Trade, TAFE or Diploma as educational qualification, c) parents living in moderately disadvantaged areas and d) parents with low and high household-income.

The choice of a green space metric may affect what relationship is found with health outcomes. In this research, we investigated the relationship between percent green space area, a novel metric developed by us (based on the average contiguous green space area a spatial buffer has contact with), in three different types of buffers and type 2 diabetes (T2D). We obtained information about diagnosed T2D and relevant covariates at the individual level from the large and representative 45 and Up Study. Average contiguous green space and the percentage of green space within 500 m, 1 km, and 2 km of circular buffer, line-based road network (LBRN) buffers, and polygon-based road network (PBRN) buffers around participants’ residences were used as proxies for geographic access to green space. Generalized estimating equation regression models were used to determine associations between access to green space and T2D status of individuals. It was found that 30%–40% green space within 500 m LBRN or PBRN buffers, and 2 km PBRN buffers, but not within circular buffers, significantly reduced the risk of T2D. The novel average green space area metric did not appear to be particularly effective at measuring reductions in T2D. This study complements an existing research body on optimal buffers for green space measurement.

A growing literature has supported a relationship between greenspace and health. Various greenspace metrics exist; some are based on subjective measures while others are based on an objective assessment of the landscape. While subjective measures may better reflect individual feelings about surrounding greenspace and the resulting positive benefits thereof, they are expensive and difficult to collect. In contrast, objective measures can be derived with relative ease, in a timely fashion, and for large regions and populations. While there have been some attempts to compare objective and subjective measures of greenspace, what is lacking is a comprehensive assessment of a wide range of greenspace metrics against subjective measures of greenspace. We performed such an assessment using a set of three objective greenspace metrics and a survey of residents in Liverpool, New South Wales, Australia. Our study supported existing findings in that overall, there is very little agreement between perceived and objective greenspace metrics. We also found that tree canopy in 10 min walking buffers around residences was the objective greenspace measure in best agreement with perceived greenspace.