A joint hierarchical model for the number of cases and deaths due to COVID-19 across the boroughs of Montreal

Montreal is the epicentre of the COVID-19 pandemic in Canada with highest number of deaths. The cumulative numbers of cases and deaths in the 33 areas of Montreal are modelled through bivariate hierarchical Bayesian models using Poisson distributions. The Poisson means are decomposed in the log scale as the sums of fixed effects and latent effects. The areal median age, the educational level, and the number of beds in long-term care homes are included in the fixed effects. To explore the correlation between cases and deaths inside and across areas, three bivariate models are considered for the latent effects, namely an independent one, a conditional autoregressive model, and one that allows for both spatially structured and unstructured sources of variability. As the inclusion of spatial effects change some of the fixed effects, we extend the Spatial+ approach to a Bayesian areal set up to investigate the presence of spatial confounding.

Identifying Extreme COVID-19 Mortality Risks in English Small Areas: A Disease Cluster Approach

The COVID-19 pandemic is having a huge impact worldwide and has highlighted the extent of health inequalities between countries but also in small areas within a country. Identifying areas with high mortality is important both of public health mitigation in COVID-19 outbreaks, and of longer term efforts to tackle social inequalities in health. In this paper we consider different statistical models and an extension of a recent method to analyze COVID-19 related mortality in English small areas during the first wave of the epidemic in the first half of 2020. We seek to identify hotspots, and where they are most geographically concentrated, taking account of observed area factors as well as spatial correlation and clustering in regression residuals, while also allowing for spatial discontinuities. Results show an excess of COVID-19 mortality cases in small areas surrounding London and in other small areas in North-East and and North-West of England. Models alleviating spatial confounding show ethnic isolation, air quality and area morbidity covariates having a significant and broadly similar impact on COVID-19 mortality, whereas nursing home location seems to be slightly less important.

Quantification of spatial subclonal interactions enhancing the invasive phenotype of paediatric glioma

Intra-tumour heterogeneity is an intrinsic property of all cancers. In some cases, such variation can be maintained by interactions between tumour subclones with distinct molecular and phenotypic characteristics. In paediatric gliomas, interactions can take the form of enhanced invasive phenotype, a hallmark of these malignancies. However, subclonal interactions are hard to quantify and difficult to distinguish from spatial confounding factors and experimental bias. Here we combine spatial computational modelling of cellular interactions and invasion, with co-evolution experiments of clonally disassembled primary glioma lines derived at autopsy. We design a Bayesian inference framework to quantify spatial subclonal interactions between molecular and phenotypically distinct lineages with different patterns of invasion. We show how this approach could discriminate genuine subclonal interactions where one clone enhanced the invasive phenotype of another, from apparent interactions that were only due to the complex dynamics of subclones growing in space. This study provides a new approach for the identification and quantification of spatial subclonal interactions in cancer.

Space-time covid-19 Bayesian SIR modeling in South Carolina

The Covid-19 pandemic has spread across the world since the beginning of 2020. Many regions have experienced its effects. The state of South Carolina in the USA has seen cases since early March 2020 and a primary peak in early April 2020. A lockdown was imposed on April 6th but lifting of restrictions started on April 24th. The daily case and death data as reported by NCHS (deaths) via the New York Times GitHUB repository have been analyzed and approaches to modeling of the data are presented. Prediction is also considered and the role of asymptomatic transmission is assessed as a latent unobserved effect. Two different time periods are examined and one step prediction is provided. The results suggest that both socio-economic disadvantage, asymptomatic transmission and spatial confounding are important ingredients in any model pertaining to county level case dynamics.

Potential Influence of Sewage Phosphorus and Wet and Dry Deposition Detected in Fish Collected in the Athabasca River North of Fort McMurray

The health of fish is a primary indicator of ecosystem response in the Oil Sands Region of northeastern Alberta. However, industrial activity is accompanied by other stressors, such as the discharge of sewage, municipal activity, forest fires, and natural weathering and erosion of bitumen. To combat the spatial confounding influences, we examined white sucker (Catostomus commersonii) captured in the Athabasca River at sites over time (2011–2019) and included covariates to account for the possible sources of influence. The analyses suggest spatially heterogeneous influences of natural factors on fish, such as discharge and air temperature, but also the influence of sewage phosphorus and precipitation. Among the stressors examined here, precipitation may be the most complex and may include a mixture of sources including inputs from tributaries, urban activity, industrial development, and forest fires. Although suggestive, the attribution of variance and detection of changes are affected by sample sizes in some years; these analyses may have missed effects or misspecified important relationships, especially in males. Despite these limitations, the analyses suggest potential differences may be associated with precipitation and highlight the need to integrate robust information on known and suspected stressors in future monitoring of aquatic ecosystems in the oil sands region and beyond.

Complementary and Integrative Health Approaches Are Underused Among Older Veterans With Musculoskeletal Pain

Complementary and integrative health (CIH) approaches are recommended in national policy guidelines as viable options for managing chronic pain, yet their use among Veterans has been suboptimal, especially for older Veterans. We identified 64,444 Veterans with a diagnosis of musculoskeletal disorders (MSD) who reported a moderate to severe pain intensity during primary care visits in 2013 from the Veterans Health Administration (VHA) electronic records. Using natural language processing (NLP), CIH use (acupuncture, chiropractic care and massage) was documented for 8169 (6.5%) of 125408 primary care visits in providers’ progress notes. Compared to their younger counterparts, older Veterans aged ≥ 65 years had 21% lower likelihood of using CIH during the year [Odds Ratio (OR): 0.79; 95% Confidence Intervals (CI): 0.73, 0.86] after accounting for demographic, clinical, temporal and spatial confounding using a generalized estimating equation logistic model. Non-white race/ethnicity, tobacco use, medical comorbidities and diagnosis of alcohol or substance use disorders were independently associated with less CIH use (ORs ranging 0.97-0.80, p<0.03-0.0001); whereas female gender, being married and number of MSD diagnoses were associated with greater CIH use (ORs ranging 1.13-1.30, p<0.0001). Redefining CIH use as chiropractic care alone [4.8% person-visits; OR: 0.78 (95% CI: 0.70, 0.86)] or incorporating structured data [9.0% person-visits; OR: 0.76 (95% CI: 0.70-0.82)] in the adjusted GEE model derived consistent results. Research to identify and address barriers to CIH use among older Veterans is encouraged.