A Systematic Review of Areal Units and Adjacency Used in Bayesian Spatial and Spatio-Temporal Conditional Autoregressive Models in Health Research

Tessema, Zemenu Tadesse; Tesema, Getayeneh Antehunegn; Ahern, Susannah; Earnest, Arul

doi:10.3390/ijerph20136277

Cited by 2 publications

(2 citation statements)

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“…Spatial epidemiology is increasingly used in medical research, such as assessing the availability of medical resources ( 10 ), exploring spatial patterns of disease, discovering spatial clustering of disease, and identifying influencing factors that can explain disease clustering and regional differences in disease ( 11 , 12 ). It provides basic data for health policy makers to determine priorities, allocate medical resources and decide on disease prevention and control interventions ( 13 ).…”

Section: Discussionmentioning

confidence: 99%

Spatial epidemiological analysis of the burden of liver cancer in China

Meng,

Yang,

et al. 2024

Transl Cancer Res

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Background Liver cancer is one of the most common cancers in the world, with unique regional variations in disability-adjusted life years (DALY) rate, nearly 50% of liver cancer cases occur in China. Therefore, understanding the epidemiological characteristics of liver cancer is of utmost importance. In this study, to analyze the spatial distribution characteristics and clustering of the DALY rate of liver cancer in 1990 and 2017 in China based on provincial administrative divisions, and to explore its possible influencing factor. Methods The DALY rate data of liver cancer at the provincial level in China were collected, the global autocorrelation of the DALY rate was analyzed by Moran’s I, the local autocorrelation of the DALY rate was analyzed by Getis-Ord-Gi*, and the influencing factors related to the DALY rate were analyzed by the least squares regression model. Results The DALY rate of liver cancer in China generally showed an increasing trend. The DALY rate increased in 22 provinces and decreased in nine provinces. In 2017, the distribution of DALY rate in all provinces showed heterogeneity, with the highest DALY rate in Guangxi (1,363.37/100,000) and the lowest in Beijing (315.78/100,000). In 2017, the low and low clustering were mainly concentrated in Inner Mongolia, Ningxia, Shanxi, Hebei, and Tianjin. The low and high clustering in Yunnan, Guizhou, and Guangdong, were surrounded by the high clustering in neighboring provinces, high and high concentration is mainly concentrated in Hunan and neighboring provinces. The results of the least square regression model showed that the per capita years of education, hepatitis B incidence and the proportion of population over 65 years old had an impact on the DALY rate of liver cancer (P<0.05). Conclusions The DALY rate of liver cancer in China showed an overall increasing trend. In 2017, the DALY rate of liver cancer in China had a spatial aggregation in the whole country, and the per capita years of education, the incidence of hepatitis B and the proportion of population over 65 years old had an impact on the DALY rate of liver cancer in space.

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Section: Discussionmentioning

confidence: 99%

Spatial epidemiological analysis of the burden of liver cancer in China

Meng,

Yang,

et al. 2024

Transl Cancer Res

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“…We found a considerable percentage change in estimate for variables like PSA and age when fitting models using the adjacency-based spatial weight matrix and the distance-based spatial weight matrix. Effect size becomes bigger for distance-based weights due to greater smoothing by distance-based weights and the nature of spatial variation for covariates [ 46 ].…”

Section: Discussionmentioning

confidence: 99%

Bayesian Spatio-Temporal Multilevel Modelling of Patient-Reported Quality of Life following Prostate Cancer Surgery

Tessema,

Tesema,

Wah

et al. 2024

Healthcare

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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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A Systematic Review of Areal Units and Adjacency Used in Bayesian Spatial and Spatio-Temporal Conditional Autoregressive Models in Health Research

Cited by 2 publications

References 100 publications

Spatial epidemiological analysis of the burden of liver cancer in China

Spatial epidemiological analysis of the burden of liver cancer in China

Bayesian Spatio-Temporal Multilevel Modelling of Patient-Reported Quality of Life following Prostate Cancer Surgery

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