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BACKGROUND: Globally, prostate cancer is the second leading cause of cancer deaths among males. It is the most commonly diagnosed cancer in Australia. The quality of life of prostate cancer patients is poorer when compared to the general population due to the disease itself and its related complications. However, there is limited research on the geographic pattern of quality of life and its risk factors in Victoria. Therefore, an examination of the spatio-temporal pattern and risk factors of poor quality of life, along with the impact of spatial weight matrices on estimates and model performance, was conducted. METHOD: A retrospective study was undertaken based on the Prostate Cancer Outcome Registry-Victoria data. Patient data (n = 5238) were extracted from the Prostate Cancer Outcome Registry, a population-based clinical quality outcome assessment from 2015 to 2021. A Bayesian spatio-temporal multilevel model was fitted to identify risk factors for poor quality of life. This study also evaluated the impact of distance- and adjacency-based spatial weight matrices. Model convergence was assessed using Gelman-Rubin statistical plots, and model comparison was based on the Watanabe-Akaike Information Criterion. RESULTS: A total of 1906 (36.38%) prostate cancer patients who had undergone surgery experienced poor quality of life in our study. Belonging to the age group between 76 and 85 years (adjusted odds ratio (AOR) = 2.90, 95% credible interval (CrI): 1.39, 2.08), having a prostate-specific antigen level between 10.1 and 20.0 (AOR = 1.33, 95% CrI: 1.12, 1.58), and being treated in a public hospital (AOR = 1.35, 95% CrI: 1.17, 1.53) were significantly associated with higher odds of poor quality of life. Conversely, residing in highly accessible areas (AOR = 0.60, 95% CrI: 0.38, 0.94) was significantly associated with lower odds of poor prostate-specific antigen levels. Variations in estimates and model performance were observed depending on the choice of spatial weight matrices. CONCLUSION: Belonging to an older age group, having a high prostate-specific antigen level, receiving treatment in public hospitals, and remoteness were statistically significant factors linked to poor quality of life. Substantial spatio-temporal variations in poor quality of life were observed in Victoria across local government areas. The distance-based weight matrix performed better than the adjacency-based matrix. This research finding highlights the need to reduce geographical disparities in quality of life. The statistical methods developed in this study may also be useful to apply to other population-based clinical registry settings.
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Little is known about the methods and outcomes of patient-reported outcome measure (PROM) use among high-risk medical device registries. The objective of this scoping review was to assess the utility and predictive ability of PROMs in high-risk medical device registries. We searched Ovid Medline, Embase, APA PsychINFO, Cochrane Library, and Scopus databases for published literature. After searching, 4323 titles and abstracts were screened, and 262 full texts were assessed for their eligibility. Seventy-six papers from across orthopedic (n = 64), cardiac (n = 10), penile (n = 1), and hernia mesh (n = 1) device registries were identified. Studies predominantly used PROMs as an outcome measure when comparing cohorts or surgical approaches (n = 45) or to compare time points (n = 13) including pre- and postintervention. Fifteen papers considered the predictive ability of PROMs. Of these, 8 treated PROMs as an outcome, 5 treated PROMs as a risk factor through regression analysis, and 2 papers treated PROMs as both a risk factor and as an outcome. One paper described PROMs to study implant survival. To advance methods of PROM integration into clinical decision-making for medical devices, an understanding of their use in high-risk device registries is needed. This scoping review found that there is a paucity of studies using PROMs to predict long-term patient and clinical outcomes in high-risk medical device registries. Determination as to why PROMs are rarely used for predictive purposes in long-term data collection is needed if PROM data are to be considered suitable as real-world evidence for high-risk device regulatory purposes, as well as to support clinical decision-making.
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Background: Socioeconomic status (SES) is a well-established determinant of cardiovascular health. However, the relationship between SES and clinical outcomes in long-term out-of-hospital cardiac arrest (OHCA) is less well-understood. The Singapore Housing Index (SHI) is a validated building-level SES indicator. We investigated whether SES as measured by SHI is associated with long-term OHCA survival in Singapore. Methods: We conducted an open cohort study with linked data from the Singapore Pan-Asian Resuscitation Outcomes Study (PAROS), and the Singapore Registry of Births and Deaths (SRBD) from 2010 to 2020. We fitted generalized structural equation models, calculating hazard ratios (HRs) using a Weibull model. We constructed Kaplan-Meier survival curves and calculated the predicted marginal probability for each SHI category. Results: We included 659 cases. In both univariable and multivariable analyses, SHI did not have a significant association with survival. Indirect pathways of SHI mediated through covariates such as Emergency Medical Services (EMS) response time (HR of low-medium, high-medium and high SHI when compared to low SHI: 0.98 (0.88-1.10), 1.01 (0.93-1.11), 1.02 (0.93-1.12) respectively), and age of arrest (HR of low-medium, high-medium and high SHI when compared to low SHI: 1.02 (0.75-1.38), 1.08 (0.84-1.38), 1.18 (0.91-1.54) respectively) had no significant association with OHCA survival. There was no clear trend in the predicted marginal probability of survival among the different SHI categories. Conclusions: We did not find a significant association between SES and OHCA survival outcomes in residential areas in Singapore. Among other reasons, this could be due to affordable healthcare across different socioeconomic classes.