A systematic review of Bayesian spatial-temporal models on cancer incidence and mortality

Application of Spatial Analysis for Electronic Health Records: Characterizing Patient Phenotypes and Emerging Trends

Background

Electronic health records (EHR) commonly contain patient addresses that provide valuable data for geocoding and spatial analysis, enabling more comprehensive descriptions of individual patients for clinical purposes. Despite the widespread use of EHR in clinical decision support and interventions, no systematic review has examined the extent to which spatial analysis is used to characterize patient phenotypes.

Objective

This study reviews advanced spatial analyses that employed individual-level health data from EHR within the US to characterize patient phenotypes.

Methods

We systematically evaluated English-language peer-reviewed articles from PubMed/MEDLINE, Scopus, Web of Science, and Google Scholar databases from inception to August 20, 2023, without imposing constraints on time, study design, or specific health domains.

Results

Only 49 articles met the eligibility criteria. These articles utilized diverse spatial methods, with a predominant focus on clustering techniques, while spatiotemporal analysis (frequentist and Bayesian) and modeling were relatively underexplored. A noteworthy surge (n = 42, 85.7%) in publications was observed post-2017. The publications investigated a variety of adult and pediatric clinical areas, including infectious disease, endocrinology, and cardiology, using phenotypes defined over a range of data domains, such as demographics, diagnoses, and visits. The primary health outcomes investigated were asthma, hypertension, and diabetes. Notably, patient phenotypes involving genomics, imaging, and notes were rarely utilized.

Conclusions

This review underscores the growing interest in spatial analysis of EHR-derived data and highlights knowledge gaps in clinical health, phenotype domains, and spatial methodologies. Additionally, this review proposes guidelines for harnessing the potential of spatial analysis to enhance the context of individual patients for future clinical decision support.

Comparison of Bayesian Spatiotemporal Models for Small-Area Life Expectancy: A Simulation Study

The purpose of this study was to assess the precision, uncertainty, and normality of small-area life expectancy estimates calculated using Bayesian spatiotemporal models. We hypothesized 6 scenarios in which all 247 districts of South Korea had the same year-specific female population of 500, 1,000, 2,000, 5,000, 10,000, and 25,000 individuals during the study period (2013-2017). We generated 1,000 hypothetical data sets for each scenario and calculated district-year life expectancies. The precision and uncertainty of life expectancy estimates were compared between 2 Bayesian spatiotemporal models and the traditional method and Bayesian spatial models. We examined the normality of the life expectancy distributions generated by each method and investigated an optimal cutoff value for the comparisons. The Bayesian spatiotemporal models produced precise life expectancy estimates. However, the 95% uncertainty interval contained the true value with a probability of less than 95%. The Bayesian spatiotemporal models violated the normality assumption in scenarios with small population sizes. Therefore, life expectancy comparisons should be conducted using a cutoff value that minimizes false-positive and false-negative rates. We propose 0.8 as a cutoff value for determining the statistical significance of the difference in life expectancy.

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

Advancements in Bayesian spatial and spatio-temporal modelling have been observed in recent years. Despite this, there are unresolved issues about the choice of appropriate spatial unit and adjacency matrix in disease mapping. There is limited systematic review evidence on this topic. This review aimed to address these problems. We searched seven databases to find published articles on this topic. A modified quality assessment tool was used to assess the quality of studies. A total of 52 studies were included, of which 26 (50.0%) were on infectious diseases, 10 (19.2%) on chronic diseases, 8 (15.5%) on maternal and child health, and 8 (15.5%) on other health-related outcomes. Only 6 studies reported the reasons for using the specified spatial unit, 8 (15.3%) studies conducted sensitivity analysis for prior selection, and 39 (75%) of the studies used Queen contiguity adjacency. This review highlights existing variation and limitations in the specification of Bayesian spatial and spatio-temporal models used in health research. We found that majority of the studies failed to report the rationale for the choice of spatial units, perform sensitivity analyses on the priors, or evaluate the choice of neighbourhood adjacency, all of which can potentially affect findings in their studies.

Spatial disparity and factors associated with dementia mortality: A cross-sectional study in Zhejiang Province, China

Objective

Evidence of spatial disparity in dementia mortality in China has been found to have higher dementia mortality in eastern and rural China. Regional factors of physical and social features may be influencing this spatial disparity. However, the extent of spatial difference in dementia mortality across small regional localities is unclear. This study aims to investigate the geographic variations in mortality and risk of all dementia subtypes and identify the effect of the associated environmental risk factors.

Methods

We used surveillance data on death reports from Alzheimer's disease and other forms of dementia in Zhejiang province from 2015 to 2019. We estimated the relative risk of dementia mortality using a Bayesian spatial model. We mapped predicted relative risk to visualize the risk of death from different types of dementia and to identify risk factors associated with dementia.

Results

Thirty thousand three hundred and ninety-eight deaths attributable to dementia as the underlying or related cause (multiple causes) were reported during 2015-2019. Counties and districts in the southeast and west of Zhejiang province had significantly higher standardized mortality ratios than others. Counties and districts with a smaller proportion of residents aged 60 years or older, poorer economic status, insufficient health resources, and worse pollution had a higher risk of deaths due to dementia.

Conclusion

Higher risks of dementia mortality were found in counties and districts with poorer economic status, insufficient health resources, and worse pollution in Zhejiang. Our study adds new evidence on the association between socioeconomic and environmental factors and the mortality risk due to dementia.

Bayesian spatio-temporal joint disease mapping of Covid-19 cases and deaths in local authorities of England

The overwhelming spatio-temporal nature of the spread of the ongoing Covid-19 pandemic demands urgent attention of data analysts and model developers. Modelling results obtained from analytical tool development are essential to understand the ongoing pandemic dynamics with a view to helping the public and policy makers. The pandemic has generated data on a huge number of interesting statistics such as the number of new cases, hospitalisations and deaths in many spatio-temporal resolutions for the analysts to investigate. The multivariate nature of these data sets, along with the inherent spatio-temporal dependencies, poses new challenges for modellers. This article proposes a two-stage hierarchical Bayesian model as a joint bivariate model for the number of cases and deaths observed weekly for the different local authority administrative regions in England. An adaptive model is proposed for the weekly Covid-19 death rates as part of the joint bivariate model. The adaptive model is able to detect possible step changes in death rates in neighbouring areas. The joint model is also used to evaluate the effects of several socio-economic and environmental covariates on the rates of cases and deaths. Inclusion of these covariates points to the presence of a north-south divide in both the case and death rates. Nitrogen dioxide, the only air pollution measure used in the model, is seen to be significantly positively associated with the number cases, even in the presence of the spatio-temporal random effects taking care of spatio-temporal dependencies present in the data. The proposed models provide excellent fits to the observed data and are seen to perform well for predicting the location specific number of deaths a week in advance. The structure of the models is very general and the same framework can be used for modelling other areally aggregated temporal statistics of the pandemics, e.g. the rate of hospitalisation.

Spatial Statistical Models: An Overview under the Bayesian Approach

Spatial documentation is exponentially increasing given the availability of Big Data in the Internet of Things, enabled by device miniaturization and data storage capacity. Bayesian spatial statistics is a useful statistical tool to determine the dependence structure and hidden patterns in space through prior knowledge and data likelihood. However, this class of modeling is not yet well explored when compared to adopting classification and regression in machine-learning models, in which the assumption of the spatiotemporal independence of the data is often made, that is an inexistent or very weak dependence. Thus, this systematic review aims to address the main models presented in the literature over the past 20 years, identifying the gaps and research opportunities. Elements such as random fields, spatial domains, prior specification, the covariance function, and numerical approximations are discussed. This work explores the two subclasses of spatial smoothing: global and local.

Efficacy and safety of acupuncture in patients with cancer-related fatigue: A protocol for systematic review and meta-analysis

BACKGROUND

Cancer-related fatigue (CRF) is considered a common complication of cancer or cancer treatment, which has a serious adverse effect on the life and of cancer patients, leading to a decline in their quality of life (QoL). The existing clinical trials revealed that acupuncture has a positive effect on CRF, and there are fewer adverse events confirmed in the corresponding systematic review. However, in recent years, new studies on using acupuncture to treat CRF were conducted, so in order to evaluate its efficacy, an updated systematic review. This protocol provides research methods for systematic review and meta-analysis of the safety and effectiveness of acupuncture for the treatment of CRF.

METHODS

We will searched the randomized controlled trial literature of acupuncture treatment for CRF in 4 English and 4 Chinese databases, including PubMed, MEDLINE, Web of Science, Cochrane Central Register of Controlled Trials (Central), China National Knowledge Infrastructure (CNKI), China Biomedical Literature Database(CBM), China Science Journal Database (VIP), and Wanfang Database. Simultaneously, other resources are manually retrieved which include reference lists of identified publications, conference articles, and grey literature. We also included the clinical randomized controlled trials of acupuncture treatment for CRF in the study. The search language is limited to Chinese and English. Two trained reviewers independently completed research screening, data extraction, and research quality assessment. RevMan (V.5.3) software was used to perform data statistical analysis and Grading of Recommendations Assessment, Development, and Evaluation (GRADE) was used to evaluate the quality of evidence.

RESULTS

This study is based on past and present clinical evidence to comprehensively evaluate the effectiveness and safety of acupuncture treatment for CRF.

CONCLUSION

Through this systematic review, we will provide the latest high-quality evidence of whether acupuncture treatment for CRF is effective and safe and also provide a theoretical basis for clinicians to choose acupuncture for the treatment of CRF.

SYSTEMATIC REVIEW REGISTRATION

INPLASY 202090049.