Spatial correlation in Bayesian logistic regression with misclassification

Spatial pattern and variations in the prevalence of congenital heart disease in children aged 4-18 years in the Qinghai-Tibetan Plateau

PURPOSE

This study aimed to investigate the spatial distribution pattern of the prevalence of congenital heart disease (CHD) in children in Qinghai-Tibetan Plateau (QTP), a high-altitude region in China.

METHODS

Epidemiological data from a survey on the prevalence of CHD in Qinghai Province including 288,066 children (4-18 years) were used in this study. The prevalence and distribution pattern of CHD was determined by sex, CHD subtype, and nationality and altitude. Spatial pattern analysis using Getis-Ord Gi^⁎ was used to identify the spatial distribution of CHD. Bayesian spatial binomial regression was performed to examine the relationship between the prevalence of CHD and environmental risk factors in the QTP.

RESULTS

The prevalence of CHD showed a significant spatial clustering pattern. The Tibetan autonomous prefecture of Yushu (average altitude > 4000 m) and the Mongolian autonomous county of Henan (average altitude > 3600 m) in Huangnan had the highest prevalence of CHD. Univariate analysis showed that with ascending altitude, the total prevalence of CHD, that in girls and boys with CHD, and that of the subtypes PDA and ASD increasing accordingly. Thus, environmental factors greatly contributed to the prevalence of CHD.

CONCLUSIONS

The prevalence of CHD shows significant spatial clustering pattern in the QTP. The CHD subtype prevalence clustering pattern has statistical regularity which would provide convenient clues of environmental risk factors. Our results may provide support to make strategies of CHD prevention, to reduce the incidence of CHD in high altitude regions of China.

Spatial measurement errors in the field of spatial epidemiology

Background

Spatial epidemiology has been aided by advances in geographic information systems, remote sensing, global positioning systems and the development of new statistical methodologies specifically designed for such data. Given the growing popularity of these studies, we sought to review and analyze the types of spatial measurement errors commonly encountered during spatial epidemiological analysis of spatial data.

Methods

Google Scholar, Medline, and Scopus databases were searched using a broad set of terms for papers indexed by a term indicating location (space or geography or location or position) and measurement error (measurement error or measurement inaccuracy or misclassification or uncertainty): we reviewed all papers appearing before December 20, 2014. These papers and their citations were reviewed to identify the relevance to our review.

Results

We were able to define and classify spatial measurement errors into four groups: (1) pure spatial location measurement errors, including both non-instrumental errors (multiple addresses, geocoding errors, outcome aggregations, and covariate aggregation) and instrumental errors; (2) location-based outcome measurement error (purely outcome measurement errors and missing outcome measurements); (3) location-based covariate measurement errors (address proxies); and (4) Covariate-Outcome spatial misaligned measurement errors. We propose how these four classes of errors can be unified within an integrated theoretical model and possible solutions were discussed.

Conclusion

Spatial measurement errors are ubiquitous threat to the validity of spatial epidemiological studies. We propose a systematic framework for understanding the various mechanisms which generate spatial measurement errors and present practical examples of such errors.

Spatial pattern in prevalence of paratuberculosis infection diagnosed with misclassification in Danish dairy herds in 2009 and 2013

Paratuberculosis is a chronic infection of economic importance to the dairy industry. The infection may be latent for years, which makes diagnostic misclassification a general challenge. The objective of this study was to identify the spatial pattern in infection prevalence, when results were adjusted for covariate information and diagnostic misclassification. Furthermore, we compared the estimated spatial pattern with the spatial pattern obtained without adjustment for misclassification. The study included 1242 herds in 2009 and 979 herds in 2013. The within-herd prevalence was modelled using a hierarchical logistic regression model and included a spatial component modelled by a continuous Gaussian field. The Stochastic Partial Differential Equation (SPDE) approach and Integrated Nested Laplace Approximation (INLA) were used for Bayesian inference. We found a significant spatial component, and our results suggested that the estimated range of influence and the overall location of areas with increased prevalence are not very sensitive to diagnostic misclassification.

Identification of biomarkers for the prognosis of pancreatic ductal adenocarcinoma with miRNA microarray data

BACKGROUND

The aim of this study was to explore the mechanism of chemotherapy resistance and to screen biomarkers of pancreatic ductal adenocarcinoma (PDAC).

METHODS

MicroRNA (miRNA) expression profile data for GSE38781 were downloaded from the Gene Expression Omnibus database. Differentially expressed miRNAs between short-overall survival (OS) and long-OS patients were screened with the limma package in R. The function and protein-protein interaction (PPI) network of the miRNA target genes were further investigated. Finally, multivariate statistical analysis was performed to verify the significant miRNAs obtained in our work.

RESULTS

In total, 66 miRNAs were identified to be differentially expressed. Gene ontology (GO) and pathway enrichment analysis showed that 163 miRNA target genes were mainly enriched in heart function, cancer development and angiogenesis. Ten nodes, including TGFBR1, TGFBR2, ACVR1 and SHC1, were found to be hub nodes in the PPI network. Multivariate statistical analysis showed 8 of the most significant miRNAs could completely distinguish the 2 groups of samples. Seven target genes (i.e., RET, ETS1, RHOA, NUMB, TIAM, ITGA5 and YY1) of the 8 significant miRNAs were found to be associated with control of cell fate decisions, T-cell lymphoma invasion and angiogenesis enhancement.

CONCLUSIONS

The heart function-related pathway, cell cycle, immune system and angiogenesis may be dysregulated in patients with poorer prognosis. The significant nodes (e.g., TGFBR1, TGFBR2, ACVR1 and SHC1) in the PPI network may be potential biomarkers for predicting outcomes for patients with pancreatic cancer. The significant miRNAs and gene targets may be potential biomarkers or therapeutic targets for PDAC.