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.

Flexible Bayesian P-splines for smoothing age-specific spatio-temporal mortality patterns.

In this paper age-space-time models based on one and two-dimensional P-splines with B-spline bases are proposed for smoothing mortality rates, where both fixed relative scale and scale invariant two-dimensional penalties are examined. Model fitting and inference are carried out using integrated nested Laplace approximations, a recent Bayesian technique that speeds up computations compared to McMC methods. The models will be illustrated with Spanish breast cancer mortality data during the period 1985-2010, where a general decline in breast cancer mortality has been observed in Spanish provinces in the last decades. The results reveal that mortality rates for the oldest age groups do not decrease in all provinces.

Age-space-time CAR models in Bayesian disease mapping.

Mortality counts are usually aggregated over age groups assuming similar effects of both time and region, yet the spatio-temporal evolution of cancer mortality rates may depend on changing age structures. In this paper, mortality rates are analyzed by region, time period and age group, and models including space-time, space-age, and age-time interactions are considered. The integrated nested Laplace approximation method, known as INLA, is adopted for model fitting and inference in order to reduce computing time in comparison with Markov chain Monte Carlo (McMC) methods. The methodology provides full posterior distributions of the quantities of interest while avoiding complex simulation techniques. The proposed models are used to analyze prostate cancer mortality data in 50 Spanish provinces over the period 1986-2010. The results reveal a decline in mortality since the late 1990s, particularly in the age group [65,70), probably because of the inclusion of the PSA (prostate-specific antigen) test and better treatment of early-stage disease. The decline is not clearly observed in the oldest age groups. Copyright © 2016 John Wiley & Sons, Ltd.

A statistical algorithm showing coenzyme Q10 and citrate synthase as biomarkers for mitochondrial respiratory chain enzyme activities.

Laboratory data interpretation for the assessment of complex biological systems remains a great challenge, as occurs in mitochondrial function research studies. The classical biochemical data interpretation of patients versus reference values may be insufficient, and in fact the current classifications of mitochondrial patients are still done on basis of probability criteria. We have developed and applied a mathematic agglomerative algorithm to search for correlations among the different biochemical variables of the mitochondrial respiratory chain in order to identify populations displaying correlation coefficients >0.95. We demonstrated that coenzyme Q10 may be a better biomarker of mitochondrial respiratory chain enzyme activities than the citrate synthase activity. Furthermore, the application of this algorithm may be useful to re-classify mitochondrial patients or to explore associations among other biochemical variables from different biological systems.

Analyzing the evolution of young people's brain cancer mortality in Spanish provinces.

OBJECTIVES: To analyze the spatio-temporal evolution of brain cancer relative mortality risks in young population (under 20 years of age) in Spanish provinces during the period 1986-2010. METHODS: A new and flexible conditional autoregressive spatio-temporal model with two levels of spatial aggregation was used. RESULTS: Brain cancer relative mortality risks in young population in Spanish provinces decreased during the last years, although a clear increase was observed during the 1990s. The global geographical pattern emphasized a high relative mortality risk in Navarre and a low relative mortality risk in Madrid. Although there is a specific Autonomous Region-time interaction effect on the relative mortality risks this effect is weak in the final estimates when compared to the global spatial and temporal effects. CONCLUSIONS: Differences in mortality between regions and over time may be caused by the increase in survival rates, the differences in treatment or the availability of diagnostic tools. The increase in relative risks observed in the 1990s was probably due to improved diagnostics with computerized axial tomography and magnetic resonance imaging techniques.

Projections of cancer mortality risks using spatio-temporal P-spline models.

Cancer mortality risk estimates are essential for planning resource allocation and designing and evaluating cancer prevention and management strategies. However, mortality figures generally become available after a few years, making necessary to develop reliable procedures to provide current and near future mortality risks. In this work, a spatio-temporal P-spline model is used to provide predictions of mortality/incidence counts. The model is appropriate to capture smooth temporal trends and to predict cancer mortality/incidence counts in different regions for future years. The prediction mean squared error of the forecast values as well as an appropriate estimator are derived. Spanish prostate cancer mortality data in the period 1975-2008 will be used to illustrate results with a focus on cancer mortality forecasting in 2009-2011.

[Incidence rate trends for colorectal cancer in Navarre (North of Spain) in the 1990-2005 period]. / Tendencias en las tasas de incidencia de cáncer colorrectal en Navarra en el periodo 1990-2005.

BACKGROUND: In Spain, an increase in the incidence of colorectal cancer (CRC) has been observed in both sexes in recent years, probably due to an improved diagnostic, the westernization of dietary habits, and worse obesity levels, among others factors. In this work, the CRC incidence rate trends in different health areas in Navarre (northern Spain) are studied during the 1990-2005 period. METHODS: An estimated incidence trend curve for each health area and the corresponding confidence bands were obtained for each gender using P-spline models. RESULTS: These results show an increasing trend of CRC in most of the areas in both sexes, being less pronounced in women than in men. In the central area of Pamplona (the capital) a decreasing trend has been observed for men during the period studied. CONCLUSIONS: Primary prevention is the best strategy to change the increasing trend observed in most areas of the province of Navarre. However, a healthy lifestyle has long-term results, so it is important to have an early detection program that would serve as a short-term prevention strategy.

Gender-specific spatio-temporal patterns of colorectal cancer incidence in Navarre, Spain (1990-2005).

INTRODUCTION: In the last ten to twenty years, a stabilization or decline in colorectal cancer (CRC) incidence has been observed in some countries across the world but not in Spain. Our objective here is to assess the gender-specific CRC spatio-temporal pattern in the health areas of Navarre, a Spanish province, during the period 1990-2005. METHODS: For each gender, a model with spatio-temporal CAR (Conditional Autoregressive) distributions is used for smoothing the incidence risks. Smoothing is carried out in two dimensions: space and time, allowing for a different time evolution in each health area. An estimated incidence trend curve for each health area and the corresponding confidence bands are obtained. To analyze the evolution of the geographical patterns of CRC incidence risks, maps are also provided. RESULTS: In both genders, CRC shows an increasing trend in most of the areas. In the second half of the period 1998-2005 most of the areas have risks above one although not all statistically significant. In general females present equal or lower risks than males in all areas during the studied period. CONCLUSIONS: Colorectal cancer incidence risk is still increasing in the health areas of Navarre. Promoting healthful lifestyles for primary prevention and early detection programs could help to reverse the trend in the province.

Modelling risks in disease mapping.

In this article, we propose a strategy of analysis of mortality data with the aim of providing a guideline for epidemiologists and public health researchers to choose a reasonable model for estimating mortality (or incidence) risks. Maps displaying the crude mortality rates or ratios are usually misleading because of the instability of the estimators in low populated areas. As an alternative, many smoothing methods have been presented in the literature based on Poisson inference. They account for the extra-Poisson variation (overdispersion), frequently present in the homogeneous Poisson model, by incorporating random effects. Here, we recommend to test for the potential sources of extra-Poisson variation because, depending on them, the models which fit better the data may be different. Overdispersion can be mainly due to spatial autocorrelation, unstructured heterogeneity or to a combination of these two, and also, when studying very rare diseases, it can be due to an excess of zeros in the data. In this article, different situations the analyst may encounter are detailed and appropriate procedures for each case are presented. The alternative models are illustrated using mortality data provided by the Statistical Institute of Navarra, Spain.

The use of mixture models for identifying high risks in disease mapping.

Conventional approaches for estimating risks in disease mapping or mortality studies are based on Poisson inference. Frequently, overdispersion is present and this extra variability is modelled by introducing random effects. In this paper we compare two computationally simple approaches for incorporating random effects: one based on a non-parametric mixture model assuming that the population arises from a discrete mixture of Poisson distributions, and the second using a Poisson-normal mixture model which allows for spatial autocorrelation. The comparison is focused on how well each of these methods identify the regions which have high risks. Such identification is important because policy makers may wish to target regions associated with such extreme risks for financial assistance while epidemiologists may wish to target such regions for further study. The Poisson-normal mixture model is presented from both a frequentist, or empirical Bayes, and a fully Bayesian point of view. We compare results obtained with the parametric and non-parametric models specifically in terms of detecting extreme mortality risks, using infant mortality data of British Columbia, Canada, for the period 1981-1985, breast cancer data from Sardinia, for the period 1983-1987, and Scottish lip cancer data for 1975-1980. However, we also investigate the performance of these models in a simulation study. The key finding is that discrete mixture models seem to be able to locate regions which experience high risks; normal mixture models also work well in this regard, and perform substantially better when spatial autocorrelation is present.

Detecting interaction between random region and fixed age effects in disease mapping.

The purpose of this article is to draw attention to the possible need for inclusion of interaction effects between regions and age groups in mapping studies. We propose a simple model for including such an interaction in order to develop a test for its significance. The assumption of an absence of such interaction effects is a helpful simplifying one. The measure of relative risk related to a particular region becomes easily and neatly summarized. Indeed, such a test seems warranted because it is anticipated that the simple model, which ignores such interaction, as is in common use, may at times be adequate. The test proposed is a score test and hence only requires fitting the simpler model. We illustrate our approaches using mortality data from British Columbia, Canada, over the 5-year period 1985-1989. For this data, the interaction effect between age groups and regions is quite large and significant.