Bayesian spatio-temporal modelling of depressive feelings among patients
School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia; 2Department of Epidemiology and Biostatistics, Institute of Public Health, College of Medicine and Healt, Australia
Contributions: (I) Conception and design: ZTProvision of study materials or patients: S Ahern, A Earnest; (IV) Collection and assembly of data: Zma, A Earnest; (VI) Manuscript writing: All authors; (VII) Final approval of manuscript: All authors.
Correspondence to: Arul Earnest, PhD. School of Public Health and Preventive Medicine, Monash University, Room 415, Level 4, 553 St Kilda Road, Melbourne, VIC 3004, Australia. Email: arul.earnest@monash.edu.
Background: In Australia, prostate cancer is the most commonly diagnosed cancer and the 2nd most common causes ofoblems including depression. One in six men with prostate cancer reported depression after prostate cancer surgery. Based on our literature review, there is limited evidence on area level factors associated with depre: (I) to identify area level determinants of depressive feelings among prostate cancer surgery patients; and (II) to evaluate the effect of different spatial scales on inference, called Modifiable Area Unit Problem (MAUP).
Methods: We a surgery. The smoothed area-specific relative risk of depressive feelings was mapped at the local government area (LGA) level. This areal level constitutes the third level of government, following the federal and state/territory levels. It provides a crucial overlay in overseeing local services and infrastructure, including community health services, local roads, parks, and recreational facilities. A Bayesian spatio-temporal conditiinformation criteria; the model with the lowest Deviance was selected as the best fit. For the final chosen model, relative risk (RR) along with the 95% credible intervals (CrI) were reported. Model convergence was assessed using the Brooks-Gelman-Rubin statistics plot. In addition, the effect of different spatial scales (MAUP) on inference was evaluated.
Results: The prevalence of reported depressive feelings was 11%. It was significantly associated with the index of relative s risk of depressive feelings by 14% (RR =0.86; 95% CrI: 0.67–0.97) compared to quartile one (most disadvantaged). At the postcode level, the strvel, it explained 64% of the variation. This indicates that the place where people live has a significant impact on the levels of depressive feelings.
Conclusions: The area-specific relative risk of depressive feelings was associated with IRSD. Spatio-temporal variations in t studies and reinforces the need to design effective interventions targeting high-risk areas to reduce depressive feelings. Based on thel registry settings
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