Some contributions in disease mapping modeling

Disease mapping ha recibido un gran interés durante las tres últimas décadas. Esta área de investigación persigue el estudio de la distribución geográfica de eventos relacionados con la salud, tales como la mortalidad o la incidencia de enfermedades, agregados en unidades geográficas, con el fin de identificar principalmente aquellas localizaciones que presentan un mayor riesgo. La aplicación de métodos estadísticos avanzados para llevar a cabo las estimaciones de los riesgos resulta fundamental para obtener estimaciones precisas y profundizar en el entendimiento de la distribución geográfica de las enfermedades. En esta tesis nos centramos en la aplicación y evaluación de varias propuestas de modelización relevantes surgidas en la literatura del mapeo de enfermedades para la estimación de la distribución geográfica de la mortalidad considerando diferentes escenarios con datos reales. Específicamente, estudiamos la distribución de la mortalidad a nivel de sección censal en las principales ciudades de la Comunitat Valenciana y a nivel municipal en la Comunitat Valenciana y en el conjunto de toda España. La evaluación de dichas propuestas ha evidenciado algunos problemas estadísticos en su implementación por lo que nos planteamos como objetivo principal el desarrollo de nuevas propuestas metodológicas que permitan resolver los problemas encontrados en cada uno de los escenarios considerados. Así mismo, nos planteamos también como objetivo el desarrollo de un atlas nacional de mortalidad avanzado que permita visualizar de forma interactiva la distribución geográfica y la evolución temporal de la mortalidad por un gran número de causas y a lo largo de un gran periodo de estudio en el conjunto de toda España. La tesis que aquí presentamos es un compendio de tres artículos y se estructura de la siguiente forma. En el Capítulo 1 presentamos una introducción general, incluyendo una descripción de los objetivos, de los datos analizados en cada proyecto y del software utilizado. En el Capítulo 2 describimos el marco general de modelización en los estudios de mapeo de enfermedades así como las propuestas de modelización que han sido evaluadas en cada escenario. Resumimos también las limitaciones encontradas en dichas propuestas y las nuevas propuestas de modelización desarrolladas en esta tesis para resolverlas. El Capítulo 3 resume los principales resultados obtenidos en cada proyecto. Los Capítulos 4, 5 y 6 contienen los tres artículos de investigación publicados que componen este compendio. En el Capítulo 7 describimos la metodología utilizada en el desarrollo del atlas nacional de mortalidad de España y sus principales características y resultados. Por último, en el Capítulo 8 presentamos algunas conclusiones y posibles líneas de trabajo futuro.Disease mapping has received great interest for the past three decades. This research area pursues the study of the geographical distribution of health-related events, such as mortality or the incidence of diseases, aggregated in geographic units, in order to mainly identify those locations that show a higher risk. The application of advanced statistical methods to carry risk estimates out is essential to obtain accurate estimates and to improve the understanding of the geographical distribution of diseases. In this thesis, we focus on the application and evaluation of several relevant modeling proposals, emerged in the disease mapping literature, to estimate the mortality geographic distribution, considering different scenarios with real data. Specifically, we study the distribution of mortality at the census tract level in the main cities of the Valencian Region and at the municipal level in the Valencian Region and in the whole of Spain. The evaluation of these previously published proposals reveals some statistical problems in their implementations. Therefore, our main goal with this thesis is the development of new methodological proposals that allow solving the problems of these previously published proposals. Likewise, we also pursue the development of an advanced national mortality atlas that allows to interactively visualize the geographical distribution, and the temporal evolution, of mortality for a large number of causes and throughout a long period of study in the whole of Spain. This thesis is a compendium of three articles and an additional work, which are structured as follows. In Chapter 1, we present a general introduction, including a description of the objectives, the data analyzed in each work and the software used. In Chapter 2, we introduce the general problem of disease mapping, as well as the modeling proposals that have been improved in each of our works. We also summarize the limitations found in these proposals and the new modeling proposals developed in this thesis. Chapter 3 summarizes the main results obtained in each of the subsequent works. Chapters 4, 5 and 6 contain the three published research articles that make up this compendium. In Chapter 7, we describe the methodology used in the development of the Spanish National Atlas of Mortality and its main characteristics and results. Finally, in Chapter 8, we present some conclusions and possible lines of future work

On the use of adaptive spatial weight matrices from disease mapping multivariate analyses

Conditional autoregressive distributions are commonly used to model spatial dependence between nearby geographic units in disease mapping studies. These distributions induce spatial dependence by means of a spatial weights matrix that quantifies the strength of dependence between any two neighboring spatial units. The most common procedure for defining that spatial weights matrix is using an adjacency criterion. In that case, all pairs of spatial units with adjacent borders are given the same weight (typically 1) and the remaining non-adjacent units are assigned a weight of 0. However, assuming all spatial neighbors in a model to be equally influential could be possibly a too rigid or inappropriate assumption. In this paper, we propose several adaptive conditional autoregressive distributions in which the spatial weights for adjacent areas are random variables, and we discuss their use in spatial disease mapping models. We will introduce our proposal in a multivariate context so that the spatial dependence structure between spatial units is shared and estimated from a sufficiently large set of mortality causes. As we will see, this is a key aspect for making inference on the spatial dependence structure. We show that our adaptive modeling proposal provides more flexible and accurate mortality risk estimates than traditional proposals in which spatial weights for neighboring areas are fixed to a common value

On the convenience of heteroscedasticity in highly multivariate disease mapping

Highly multivariate disease mapping has recently been proposed as an enhancement of traditional multivariate studies, making it possible to perform the joint analysis of a large number of diseases. This line of research has an important potential since it integrates the information of many diseases into a single model yielding richer and more accurate risk maps. In this paper we show how some of the proposals already put forward in this area display some particular problems when applied to small regions of study. Specifically, the homoscedasticity of these proposals may produce evident misfits and distorted risk maps. In this paper we propose two new models to deal with the variance-adaptivity problem in multivariate disease mapping studies and give some theoretical insights on their interpretation

Some findings on zero-inflated and hurdle Poisson models for disease mapping

Zero excess in the study of geographically referenced mortality data sets has been the focus of considerable attention in the literature, with zero-inflation being the most common procedure to handle this lack of fit. Although hurdle models have also been used in disease mapping studies, their use is more rare. We show in this paper that models using particular treatments of zero excesses are often required for achieving appropriate fits in regular mortality studies since, otherwise, geographical units with low expected counts are oversmoothed. However, as also shown, an indiscriminate treatment of zero excess may be unnecessary and has a problematic implementation. In this regard, we find that naive zero-inflation and hurdle models, without an explicit modeling of the probabilities of zeroes do not fix zero excesses problems well enough and are clearly unsatisfactory. Results sharply suggest the need for an explicit modeling of the probabilities that should vary across areal units. Unfortunately, these more flexible modeling strategies can easily lead to improper posterior distributions as we prove in several theoretical results. Those procedures have been repeatedly used in the disease mapping literature and one should bear these issues in mind in order to propose valid models. We finally propose several valid modeling alternatives according to the results mentioned that are suitable for fitting zero excesses. We show that those proposals fix zero excesses problems and correct the mentioned oversmoothing of risks in low populated units depicting geographic patterns more suited to the data

Health Risk Assessment of Exposure to 15 Essential and Toxic Elements in Spanish Women of Reproductive Age: A Case Study

This case study investigates the exposure of 119 Spanish women of reproductive age to 5 essential (Co, Cu, Mn, V, Zn) and 10 toxic (Ba, Be, Cs, Ni, Pb, Pt, Sb, Th, Al, U) elements and assesses their risk. The essential elements (Co, Cu, Mn, V, and Zn) showed average concentrations (GM: geometric mean) of 0.8, 35, 0.5, 0.2, and 347 μg/L, respectively. Five of the toxic elements (Ba, Cs, Ni, Al, U) exhibited detection frequencies of 100%. The GM concentrations of the novel toxic elements were 12 μg/L (Al), 0.01 μg/L (Pt), 0.02 μg/L (U), 0.12 μg/L (Th), 0.009 μg/L (Be) and 4 μg/L (Cs). The urine analysis was combined with a survey to assess any variations between subgroups and potential predictors of exposure to elements in the female population. Significant differences were obtained between the rural and urban areas studied for the toxic element Cs, with higher levels found in mothers living in urban areas. In relation to diet, statistically significantly higher levels of essential (Cu) and toxic (Ba) elements were detected in women with a high consumption of fish, while mothers who consumed a large quantity of legumes presented higher levels of the toxic element Ni (p = 0.0134). In a risk-assessment context, hazard quotients (HQs) greater than 1 were only observed for the essential elements Zn and Cu in P95. No deficiency was found regarding the only essential element for which a biomonitoring equivalent for nutritional deficit is available (Zn). For the less-studied toxic elements (Al, Pt, U, Th, Be, and Cs), HQs were lower than 1, and thus, the health risk due to exposure to these elements is expected to be low for the female population under study

An Autoregressive Disease Mapping Model for Spatio-Temporal Forecasting

One of the more evident uses of spatio-temporal disease mapping is forecasting the spatial distribution of diseases for the next few years following the end of the period of study. Spatio-temporal models rely on very different modeling tools (polynomial fit, splines, time series, etc.), which could show very different forecasting properties. In this paper, we introduce an enhancement of a previous autoregressive spatio-temporal model with particularly interesting forecasting properties, given its reliance on time series modeling. We include a common spatial component in that model and show how that component improves the previous model in several ways, its predictive capabilities being one of them. In this paper, we introduce and explore the theoretical properties of this model and compare them with those of the original autoregressive model. Moreover, we illustrate the benefits of this new model with the aid of a comprehensive study on 46 different mortality data sets in the Valencian Region (Spain) where the benefits of the new proposed model become evident

Rare Diseases: Needs and Impact for Patients and Families: A Cross-Sectional Study in the Valencian Region, Spain

Families with rare diseases (RDs) have unmet needs that are often overlooked by health professionals. Describing these needs and the impact of the disease could improve their medical care. A total of 163 surveys were obtained from patients visiting primary care centres in the Valencian Region (Spain), during 2015–2017, with a confirmed or suspected diagnosis of RD. Of the 84.7% with a confirmed diagnosis, 50.4% had a diagnostic delay exceeding one year, and it was more prevalent among adults (62.2%). Families with paediatric patients were in a worse economic situation, with lower incomes and higher monthly disease-related expenses (€300 on average). These expenses were incurred by 66.5% of families and were mainly for medication (40.3%). Among them, 58.5% reported not being able to afford adjuvant therapies. The disease had an impact on 73.1% of families, especially on their routine and emotional state. Expenses, needs, and impacts were more frequent among families of patients with a history of hospitalisation or deterioration. Patients with delayed diagnosis had a higher consumption of drugs prior to diagnosis. People affected by RDs in the Valencian Region need therapies to improve their autonomy and emotional state. Health professionals should be aware of these needs

Geographical Variability in Mortality in Urban Areas: A Joint Analysis of 16 Causes of Death

The geographical distribution of mortality has frequently been studied. Nevertheless, those studies often consider isolated causes of death. In this work, we aim to study the geographical distribution of mortality in urban areas, in particular, in 26 Spanish cities. We perform an overall study of 16 causes of death, considering that their geographical patterns could be dependent and estimating the dependence between the causes of death. We study the deaths in these 26 cities during the period 1996–2015 at the census tract level. A multivariate disease mapping model is used in order to solve the potential small area estimation problems that these data could show. We find that most of the geographical patterns found show positive correlations. This suggests the existence of a transversal geographical pattern, common to most causes of deaths, which determines those patterns to a higher/lower extent depending on each disease. The causes of death that exhibit that underlying pattern in a more prominent manner are chronic obstructive pulmonary disease (COPD), lung cancer, and cirrhosis for men and cardiovascular diseases and dementias for women. Such findings are quite consistent for most of the cities in the study. The high positive correlation found between geographical patterns reflects the existence of both high and low-risk areas in urban settings, in general terms for nearly all the causes of death. Moreover, the high-risk areas found often coincide with neighborhoods known for their high deprivation. Our results suggest that dependence among causes of death is a key aspect to be taken into account when mapping mortality, at least in urban contexts.The authors acknowledge the support of the research grants PI16/00670, PI16/00755, PI16/01004, PI16/01187, PI16/01273, PI16/01281, and PI18/01313 of Instituto de Salud Carlos III, co-funded with FEDER grants

Biomonitoring of Phthalates, Bisphenols and Parabens in Children: Exposure, Predictors and Risk Assessment

Exposure to emerging contaminants, such as phthalates, bisphenols and parabens in children has been associated with possible neurodevelopment and endocrine alterations. In the present study, the biomonitoring of biomarkers in children (5–12 years old) from the Valencia Region (Spain) have been implemented using urines from the BIOVAL program. More than 75% of the children studied (n = 562) were internally exposed (>LOQ) to bisphenols and parabens, and the whole population assessed (n = 557) were exposed to at least one phthalate. The geometric means (GM) of the concentrations of bisphenol A, methyl paraben and propyl paraben were 0.9, 1.4 and 0.39 ng/mL, respectively. Regarding phthalates, monoethyl phthalate GM was 55.0 ng/mL and diethyl hexyl phthalate (as the sum of five metabolites) GM was 60.6 ng/mL. Despite the studied population being widely exposed, the detection frequencies and concentrations were in general lower than in previous studies involving children in Spain and in other countries in recent years. Furthermore, the risk assessment study concluded that the internal exposure to phthalates, bisphenols and parabens is lower than the guidance values established, and, therefore, a health risk derived from the exposure to these compounds in the studied population is not expected