An epidemiological and Gis-based analysis of mortality in selected rare diseases

Las enfermedades raras (ER) son aquellas que no superan los 5 casos por cada 10000 habitantes en la Unión Europea. Los registros nacionales de fallecimientos de base poblacional, junto con las herramientas que ofrecen los Sistemas de Información Geográfica permiten analizar la mortalidad atribuida a las ER tanto en su dimensión espacial como temporal. Esta tesis doctoral tiene como objetivo general incrementar el conocimiento de la mortalidad debida a ER en España desde una perspectiva geográfica y epidemiológica, trabajando con las enfermedades de Huntington (EH), granulomatosis con poliangeitis (GPA) y enfermedades de las neuronas motoras (ENM). Los objetivos específicos fueron: (1) Identificar las problemáticas actuales en la elección de la unidad geográfica para trabajar con ER, proporcionando recomendaciones para escoger el nivel de agregación más adecuado; (2) Evaluar las tendencias temporales de la mortalidad debida a ER a lo largo de tres décadas; (3) Identificar patrones geográficos de mortalidad debida a ER para observar su variación entre las diferentes unidades geográficas y; (4) Describir las emisiones de metales pesados a ríos y explorar las asociaciones con la mortalidad atribuida a ENM. Se trabajó con la estadística de defunciones proporcionada por el INE, seleccionando aquellos fallecimientos atribuidos a las tres enfermedades estudiadas entre 1984 y 2016. Se analizó la idoneidad de cada uno de los tres niveles de agregación disponibles mediantes comparaciones estadísticas y cartográficas. Se calcularon indicadores de mortalidad como la tasa ajustada por edad, razón de mortalidad estandarizada (RME) y RME suavizada. También se comparó la mortalidad atribuida a ENM de zonas expuestas y no expuestas mediante una regresión de Poisson. El problema de la unidad de área modificable se presenta relevante trabajando con ER, siendo la comarca el nivel de agregación más óptimo para el cálculo epidemiológico y representaciones cartográficas de enfermedades de baja prevalencia. En la evolución temporal de la mortalidad se observó un incremento anual en EH del 3,44%. Para GPA se observó un incremento del 20,60% anual hasta 1992 y posteriormente un descenso del 1,91%. En el análisis geográfico de la mortalidad, las tres enfermedades mostraron variabilidad a lo largo del territorio español. En cuanto a la asociación de fallecimientos por ENM y la presencia de metales pesados, la mortalidad fue un 18,4% mayor en los municipios expuestos. Esta tesis doctoral ha enriquecido el conocimiento de la variabilidad temporal y espacial de la mortalidad atribuida a ER añadiendo un enfoque geográfico. Los resultados ofrecen una información muy valiosa para la planificación sanitaria y ofrecen pistas para futuras investigaciones que puedan ahondar en el descubrimiento de las causas que provocan las ER

Spatial epidemiology of a highly transmissible disease in urban neighbourhoods: Using COVID-19 outbreaks in Toronto as a case study

The emergence of infectious diseases in an urban area involves a complex interaction between the socioecological processes in the neighbourhood and urbanization. As a result, such an urban environment can be the incubator of new epidemics and spread diseases more rapidly in densely populated areas than elsewhere. Most recently, the Coronavirus-19 (COVID-19) pandemic has brought unprecedented challenges around the world. Toronto, the capital city of Ontario, Canada, has been severely impacted by COVID-19. Understanding the spatiotemporal patterns and the key drivers of such patterns is imperative for designing and implementing an effective public health program to control the spread of the pandemic. This dissertation was designed to contribute to the global research effort on the COVID-19 pandemic by conducting spatial epidemiological studies to enhance our understanding of the disease's epidemiology in a spatial context to guide enhancing the public health strategies in controlling the disease. Comprised of three original research manuscripts, this dissertation focuses on the spatial epidemiology of COVID-19 at a neighbourhood scale in Toronto. Each manuscript makes scientific contributions and enhances our knowledge of how interactions between different socioecological processes in the neighbourhood and urbanization can influence spatial spread and patterns of COVID-19 in Toronto with the application of novel and advanced methodological approaches. The findings of the outcomes of the analyses are intended to contribute to the public health policy that informs neighbourhood-based disease intervention initiatives by the public health authorities, local government, and policymakers. The first manuscript analyzes the globally and locally variable socioeconomic drivers of COVID-19 incidence and examines how these relationships vary across different neighbourhoods. In the global model, lower levels of education and the percentage of immigrants were found to have a positive association with increased risk for COVID-19. This study provides the methodological framework for identifying the local variations in the association between risk for COVID-19 and socioeconomic factors in an urban environment by applying a local multiscale geographically weighted regression (MGWR) modelling approach. The MGWR model is an improvement over the methods used in earlier studies of COVID-19 in identifying local variations of COVID-19 by incorporating a correction factor for the multiple testing problem in the geographically weighted regression models. The second manuscript quantifies the associations between COVID-19 cases and urban socioeconomic and land surface temperature (LST) at the neighbourhood scale in Toronto. Four spatiotemporal Bayesian hierarchical models with spatial, temporal, and varying space-time interaction terms are compared. The results of this study identified the seasonal trends of COVID-19 risk, where the spatiotemporal trends show increasing, decreasing, or stable patterns, and identified area-specific spatial risk for targeted interventions. Educational level and high land surface temperature are shown to have a positive association with the risk for COVID-19. In this study, high spatial and temporal resolution satellite images were used to extract LST, and atmospheric corrections methods were applied to these images by adopting a land surface emissivity (LSE) model, which provided a high estimation accuracy. The methodological approach of this work will help researchers understand how to acquire long time-series data of LST at a spatial scale from satellite images, develop methodological approaches for atmospheric correction and create the environmental data with a high estimation accuracy to fit into modelling disease. Applying to policy, the findings of this study can inform the design and implementation of urban planning strategies and programs to control disease risks. The third manuscript developed a novel approach for visualization of the spread of infectious disease outbreaks by incorporating neighbourhood networks and the time-series data of the disease at the neighbourhood level. The findings of the model provide an understanding of the direction and magnitude of spatial risk for the outbreak and guide for the importance of early intervention in order to stop the spread of the outbreak. The manuscript also identified hotspots using incidence rate and disease persistence, the findings of which may inform public health planners to develop priority-based intervention plans in a resource constraint situation

Ann Epidemiol

Purpose:Within the context of local increases in US heart disease death rates, we estimated when increasing heart disease death rates began by county among adults aged 35\u201364 years and characterized geographic variation.Methods:We applied Bayesian spatiotemporal models to vital statistics data to estimate the timing (i.e., the year) of increasing county-level heart disease death rates during 1999\u20132019 among adults aged 35\u201364 years. To examine geographic variation, we stratified results by US Census region and urban-rural classification.Results:The onset of increasing heart disease death rates among adults aged 35\u201364 years spanned the two-decade study period from 1999 to 2019. Overall, 43.5% (95% CI: 41.3, 45.6) of counties began increasing before 2011, with early increases more prevalent outside of the most urban counties and outside of the Northeast. Roughly one-in-five (18.4% [95% CI: 15.6, 20.7]) counties continued to decline throughout the study period.Conclusions:This variation suggests that factors associated with these geographic classifications may be critical in establishing the timing of changing trends in heart disease death rates. These results reinforce the importance of spatiotemporal surveillance in the early identification of adverse trends and in informing opportunities for tailored policies and programs.20222023-08-01T00:00:00ZCC999999/ImCDC/Intramural CDC HHSUnited States/35569702PMC92766381170

What Is the Impact of Early and Subsequent Epidemic Characteristics on the Pre-delta COVID-19 Epidemic Size in the United States?

It is still uncertain how the epidemic characteristics of COVID-19 in its early phase and subsequent waves contributed to the pre-delta epidemic size in the United States. We identified the early and subsequent characteristics of the COVID-19 epidemic and the correlation between these characteristics and the pre-delta epidemic size. Most (96.1% (49/51)) of the states entered a fast-growing phase before the accumulative number of cases reached (30). The days required for the number of confirmed cases to increase from 30 to 100 was 5.6 (5.1–6.1) days. As of 31 March 2021, all 51 states experienced at least 2 waves of COVID-19 outbreaks, 23.5% (12/51) experienced 3 waves, and 15.7% (8/51) experienced 4 waves, the epidemic size of COVID-19 was 19,275–3,669,048 cases across the states. The pre-delta epidemic size was significantly correlated with the duration from 30 to 100 cases (p = 0.003, r = −0.405), the growth rate of the fast-growing phase (p = 0.012, r = 0.351), and the peak cases in the subsequent waves (K1 (p < 0.001, r = 0.794), K2 (p < 0.001, r = 0.595), K3 (p < 0.001, r = 0.977), and K4 (p = 0.002, r = 0.905)). We observed that both early and subsequent epidemic characteristics contribute to the pre-delta epidemic size of COVID-19. This identification is important to the prediction of the emerging viral infectious diseases in the primary stage