Beyond standardized mortality ratios; some uses of smoothed age-specific mortality rates on small areas studies.

BACKGROUND: Most epidemiological risk indicators strongly depend on the age composition of populations, which makes the direct comparison of raw (unstandardized) indicators misleading because of the different age structures of the spatial units of study. Age-standardized rates (ASR) are a common solution for overcoming this confusing effect. The main drawback of ASRs is that they depend on age-specific rates which, when working with small areas, are often based on very few, or no, observed cases for most age groups. A similar effect occurs with life expectancy at birth and many more epidemiological indicators, which makes standardized mortality ratios (SMR) the omnipresent risk indicator for small areas epidemiologic studies. METHODS: To deal with this issue, a multivariate smoothing model, the M-model, is proposed in order to fit the age-specific probabilities of death (PoDs) for each spatial unit, which assumes dependence between closer age groups and spatial units. This age-space dependence structure enables information to be transferred between neighboring consecutive age groups and neighboring areas, at the same time, providing more reliable age-specific PoDs estimates. RESULTS: Three case studies are presented to illustrate the wide range of applications that smoothed age specific PoDs have in practice . The first case study shows the application of the model to a geographical study of lung cancer mortality in women. This study illustrates the convenience of considering age-space interactions in geographical studies and to explore the different spatial risk patterns shown by the different age groups. Second, the model is also applied to the study of ischaemic heart disease mortality in women in two cities at the census tract level. Smoothed age-standardized rates are derived and compared for the census tracts of both cities, illustrating some advantages of this mortality indicator over traditional SMRs. In the latest case study, the model is applied to estimate smoothed life expectancy (LE), which is the most widely used synthetic indicator for characterizing overall mortality differences when (not so small) spatial units are considered. CONCLUSION: Our age-space model is an appropriate and flexible proposal that provides more reliable estimates of the probabilities of death, which allow the calculation of enhanced epidemiological indicators (smoothed ASR, smoothed LE), thus providing alternatives to traditional SMR-based studies of small areas.

Crowdsourcing reproducible seizure forecasting in human and canine epilepsy.

SEE MORMANN AND ANDRZEJAK DOI101093/BRAIN/AWW091 FOR A SCIENTIFIC COMMENTARY ON THIS ARTICLE : Accurate forecasting of epileptic seizures has the potential to transform clinical epilepsy care. However, progress toward reliable seizure forecasting has been hampered by lack of open access to long duration recordings with an adequate number of seizures for investigators to rigorously compare algorithms and results. A seizure forecasting competition was conducted on kaggle.com using open access chronic ambulatory intracranial electroencephalography from five canines with naturally occurring epilepsy and two humans undergoing prolonged wide bandwidth intracranial electroencephalographic monitoring. Data were provided to participants as 10-min interictal and preictal clips, with approximately half of the 60 GB data bundle labelled (interictal/preictal) for algorithm training and half unlabelled for evaluation. The contestants developed custom algorithms and uploaded their classifications (interictal/preictal) for the unknown testing data, and a randomly selected 40% of data segments were scored and results broadcasted on a public leader board. The contest ran from August to November 2014, and 654 participants submitted 17 856 classifications of the unlabelled test data. The top performing entry scored 0.84 area under the classification curve. Following the contest, additional held-out unlabelled data clips were provided to the top 10 participants and they submitted classifications for the new unseen data. The resulting area under the classification curves were well above chance forecasting, but did show a mean 6.54 ± 2.45% (min, max: 0.30, 20.2) decline in performance. The kaggle.com model using open access data and algorithms generated reproducible research that advanced seizure forecasting. The overall performance from multiple contestants on unseen data was better than a random predictor, and demonstrates the feasibility of seizure forecasting in canine and human epilepsy.media-1vid110.1093/brain/aww045_video_abstractaww045_video_abstract.

Online learning algorithm for time series forecasting suitable for low cost wireless sensor networks nodes.

Time series forecasting is an important predictive methodology which can be applied to a wide range of problems. Particularly, forecasting the indoor temperature permits an improved utilization of the HVAC (Heating, Ventilating and Air Conditioning) systems in a home and thus a better energy efficiency. With such purpose the paper describes how to implement an Artificial Neural Network (ANN) algorithm in a low cost system-on-chip to develop an autonomous intelligent wireless sensor network. The present paper uses a Wireless Sensor Networks (WSN) to monitor and forecast the indoor temperature in a smart home, based on low resources and cost microcontroller technology as the 8051MCU. An on-line learning approach, based on Back-Propagation (BP) algorithm for ANNs, has been developed for real-time time series learning. It performs the model training with every new data that arrive to the system, without saving enormous quantities of data to create a historical database as usual, i.e., without previous knowledge. Consequently to validate the approach a simulation study through a Bayesian baseline model have been tested in order to compare with a database of a real application aiming to see the performance and accuracy. The core of the paper is a new algorithm, based on the BP one, which has been described in detail, and the challenge was how to implement a computational demanding algorithm in a simple architecture with very few hardware resources.

Mortality after emergency department discharge: an analysis of 453599 cases. / Mortalidad tras el alta desde el servicio de urgencias hospitalario: análisis de 453.599 episodios.

OBJECTIVES: To quantify and analyze mortality in patients who die within 30 days of discharge home from a hospital emergency department (ED). MATERIAL AND METHODS: All patients older than 14 years of age who were discharged home from the ED of a tertiary care hospital over a 5-year period were included. We collected age, sex, and other demographic variables, as well as the Charlson Comorbidity Index (CCI). The outcome variables of interest were 7-day and 30-day mortality and cause of death. Deaths were classified as expected and directly related to the emergency, expected but not directly related, unexpected and directly related, and unexpected and not directly related. A death was classified as an adverse event if it was directly related to a problem of diagnosis or management in the ED, underestimation of severity, or complications of a procedure. RESULTS: Of 519312 patients attended in the ED, 453 599 were discharged home. Of those discharged, 148 died at home within 7 days (32.63 deaths/100 000 discharges) and 355 died within 30 days (78.48 deaths/100 000 discharges). One hundred thirteen deaths (31.8%) were expected and related to the emergency 24.91/100 000), 169 (47.6%) were expected but unrelated 37.26/100 000), 4 (1.1%) were unexpected and related 1.10/100000), and 69 (19.4%) were unexpected and unrelated 15.21/100000). Deaths were considered adverse events related to ED care in 24.2% of the cases. Underestimation of severity was responsible for the highest proportion (10.7%) of such deaths. The median age of patients who died was 83 years, and the median Charlson comorbidity index (CCI) was 6. The most common cause of death was a malignant tumor (23.0%), followed by congestive heart failure (20.2%) and atherosclerotic cardiovascular disease (13.2%). Unexpected deaths related to ED care were significantly related to a higher proportion of adverse events related to diagnosis (P = .001), management (P = .004), and underestimation of severity (P .001). CONCLUSION: Early deaths after discharge home from a hospital ED occured in patients of advanced age with concomitant conditions. The main clinical settings were neoplastic and cardiovascular disease. Seven-day and 30-day mortality rates directly related to the emergency visit were low. Adverse events related to ED care played a role in about a quarter of the deaths after discharge.

OBJETIVO: Cuantificar y analizar la mortalidad de los pacientes dados de alta directamente desde un servicio de urgencias hospitalario (SUH) y que fallecen dentro de los primeros 30 días en el domicilio. METODO: Se incluyeron todos los pacientes mayores de 14 años dados de alta desde el SUH a domicilio durante 5 años en un hospital terciario. Se recogieron como variables demográficas, edad, sexo e índice de Charlson. Como variable evolutiva se investigó la mortalidad a 30 días, y si esta ocurrió en 7 o menos días o más de 7 días y la causa del fallecimiento. La mortalidad se clasificó como esperada y directamente relacionada, esperada y no directamente relacionada, no esperada y directamente relacionad, y no esperada y no directamente relacionada. Se determinó como evento adverso (EA) relacionada con la mortalidad si la muerte estaba relacionada con un problema diagnóstico o de manejo, de infraestimación de la gravedad o complicaciones del procedimiento. RESULTADOS: Fueron atendidos 519.312 episodios de los que 453.599 fueron dados de alta al domicilio. De estos, 148 fallecieron en domicilio a los 7 días (32,63/100.000 altas) y 355 fallecieron en los 30 días después del alta (78,48/100.000 altas): el 31,8% (n = 113) fueron fallecimientos esperados y relacionados (24,91/100.000 altas), el 47,6% (n = 169) esperados y no relacionados (37,26/100.000 altas), el 1,1% (n = 4) no esperados y relacionados (1,10/100.000 altas) y 19,4% (n = 69) no esperados y no relacionados (15,21/100.000 altas). En un 24,2% de los pacientes se detectaron EA relacionados con la asistencia en urgencias, el más frecuente EA fue la infraestimación de la gravedad (10,7%). La mediana de edad de los pacientes fallecidos era de 83 años y una mediana del índice de comorbilidad de Charlson (ICC) de 6 puntos. La principal etiología de fallecimiento fue la neoplasia maligna (23,0%), seguida de insuficiencia cardiaca congestiva (20,2%) y enfermedad cardiaca arteriosclerótica (13,2%). En los fallecimientos no esperados y relacionados, destaca una mayor proporción de EA por causa de problemas diagnósticos (p = 0,015), de manejo (p = 0,028) y de infraestimación de la gravedad (p = 0,004). CONCLUSIONES: Los pacientes que fallecen de forma precoz tras el alta de SUH en el domicilio son ancianos con comorbilidad y donde las principales causas de muerte son las enfermedades neoplásicas y las enfermedades cardiacas. Las muertes no esperadas y directamente relacionadas son poco frecuentes a los 7 y 30 días del alta. En una cuarta parte de los pacientes se detectaron EA relacionados con la asistencia en urgencias.

Effectiveness of nirsevimab introduction against respiratory syncytial virus in the Valencian Community: A preliminary assessment.

Respiratory syncytial virus (RSV) represents a high burden of disease in children and the primary cause of hospitalization, especially in children under 1 year old. In the Valencian Community (Spain), nirsevimab, a long-acting monoclonal antibody, was introduced for the RSV 2023-2024 season as a universal pre-exposure prophylaxis for high-risk children and those under 6 months old. This study examines its impact, coverage, and effectiveness. The campaign achieved 88.5 % coverage and 73.7 % of effectiveness. Analysis of over 27,000 susceptible children (over 24,000 immunized), showed that those immunized exhibited a threefold reduction in RSV incidence compared to non-immunized ones. To prevent one case, the number needed to immunize (NNI) was 63. Hospitalizations due to acute respiratory infections were almost two times lower in immunized children compared to non-immunized ones (0.9 % vs 1.6 %, respectively). These first results showcase the preliminary positive impact of this public health intervention.

Do socioeconomic inequalities in mortality vary between different Spanish cities? a pooled cross-sectional analysis.

BACKGROUND: The relationship between deprivation and mortality in urban settings is well established. This relationship has been found for several causes of death in Spanish cities in independent analyses (the MEDEA project). However, no joint analysis which pools the strength of this relationship across several cities has ever been undertaken. Such an analysis would determine, if appropriate, a joint relationship by linking the associations found. METHODS: A pooled cross-sectional analysis of the data from the MEDEA project has been carried out for each of the causes of death studied. Specifically, a meta-analysis has been carried out to pool the relative risks in eleven Spanish cities. Different deprivation-mortality relationships across the cities are considered in the analysis (fixed and random effects models). The size of the cities is also considered as a possible factor explaining differences between cities. RESULTS: Twenty studies have been carried out for different combinations of sex and causes of death. For nine of them (men: prostate cancer, diabetes, mental illnesses, Alzheimer's disease, cerebrovascular disease; women: diabetes, mental illnesses, respiratory diseases, cirrhosis) no differences were found between cities in the effect of deprivation on mortality; in four cases (men: respiratory diseases, all causes of mortality; women: breast cancer, Alzheimer's disease) differences not associated with the size of the city have been determined; in two cases (men: cirrhosis; women: lung cancer) differences strictly linked to the size of the city have been determined, and in five cases (men: lung cancer, ischaemic heart disease; women: ischaemic heart disease, cerebrovascular diseases, all causes of mortality) both kinds of differences have been found. Except for lung cancer in women, every significant relationship between deprivation and mortality goes in the same direction: deprivation increases mortality. Variability in the relative risks across cities was found for general mortality for both sexes. CONCLUSIONS: This study provides a general overview of the relationship between deprivation and mortality for a sample of large Spanish cities combined. This joint study allows the exploration of and, if appropriate, the quantification of the variability in that relationship for the set of cities considered.

Spatio-temporal small area surveillance of the COVID-19 pandemic.

The emergence of COVID-19 requires new effective tools for epidemiological surveillance. Spatio-temporal disease mapping models, which allow dealing with small units of analysis, are a priority in this context. These models provide geographically detailed and temporally updated overviews of the current state of the pandemic, making public health interventions more effective. These models also allow estimating epidemiological indicators highly demanded for COVID-19 surveillance, such as the instantaneous reproduction number R t , even for small areas. In this paper, we propose a new spatio-temporal spline model particularly suited for COVID-19 surveillance, which allows estimating and monitoring R t for small areas. We illustrate our proposal on the study of the disease pandemic in two Spanish regions. As a result, we show how tourism flows have shaped the spatial distribution of the disease in these regions. In these case studies, we also develop new epidemiological tools to be used by regional public health services for small area surveillance.

[Magnitude and factors associated with the solitary death phenomenon of the elderly in the Valencian Community.] / Magnitud y factores asociados al fenómeno de la muerte solitaria del anciano en la Comunidad Valenciana.

OBJECTIVE: In the Valencian Community 23% of the elderly people live alone, representing the solitary death among aged persons an unwanted effect of aging. Our aim was to determine the magnitude of this phenomenon and its risk factors in the population over 64 years of the CV during the period 2015-2017. METHODS: Cross-sectional study was carried out. Household deaths of residents over 64 years of the CV during the 2015-2017 period were analyzed, with records on medical and judicial death certificates. Adjusted incidence rates, sociodemographic characteristics and causes of death were described. For the analysis of risk factors, a multivariate logistic regression was performed, taking the adjusted Odds Ratio (OR) as an association measure. A significance level α=0.05 and 95% confidence intervals (CI) were used. RESULTS: 417 cases were found. The adjusted rates were: in 2015, 17.3 (95% CI: 14.7-20.2); in 2016, 14.5 (95% CI: 12.1-17.2); and in 2017, 13.2 (95% CI: 11,0-15.8). The most frequent causes were circulatory (52.5%) and external (19.2%). After adjustment, gender (OR M / H: 2.40; 95% CI: 1.87-3.06), age (OR ≥76 / <76: 4.56; 95% CI: 3.53 -5.90), disability (OR No / Yes: 0.51; 95% CI: 0.31-0.85), season (ref: spring; summer OR: 2.34; 95% CI: 1.63-3 , 37) and population nucleus (rural / urban OR: 2.20; 95% CI 1.58-3.08), remained associated whit the MSA. CONCLUSIONS: The solitary death among aged persons is a phenomenon scarcely studied in our environment from public health. The magnitude in the CV is relevant, with a greater risk in men and at younger ages, as well as in summer and urban areas. Presenting disability represents a certain protection.

OBJETIVO: En la Comunidad Valenciana un 23% de los ancianos viven solos, representando la muerte solitaria del anciano un efecto indeseado del envejecimiento. Nuestro objetivo fue determinar la magnitud de este fenómeno y sus factores de riesgo en la población mayor de 64 años de la CV durante el período 2015-2017. METODOS: Estudio observacional, transversal. Se analizaron las defunciones domiciliarias de residentes mayores de 64 años de la CV durante el período 2015-2017, con datos de los certificados médicos y judiciales de defunción. Se describieron las tasas de incidencia ajustadas, características sociodemográficas y causas de muerte. Para el análisis de factores de riesgo se realizó una regresión logística multivariante tomando como medida de asociación la Razón de Odds (OR) ajustada. Se usó un nivel de significación α=0,05 y unos IC del 95%. RESULTADOS: Se encontraron 417 casos. Las tasas ajustadas fueron: en 2015, 17,3 (IC95%: 14,7-20,2); en 2016, 14,5 (IC95%: 12,1-17,2); y en 2017, 13,2 (IC95%: 11,0-15,8). Las causas más frecuentes fueron circulatorias (52,5%) y externas (19,2%). Los factores asociados a la MSA fueron el sexo (OR M/H: 2,40; IC95%: 1,87-3,06), edad (OR ≥76/<76: 4,56; IC95%: 3,53-5,90), discapacidad (OR No/Sí: 0,51; IC95%: 0,31-0,85), estación (ref: primavera; OR verano: 2,34; IC95%: 1,63-3,37) y núcleo de población (OR rural/urbano: 2,20; IC95%1,58-3,08). CONCLUSIONES: La magnitud de la muerte en solitario en la Comunidad Valenciana es relevante, existiendo un mayor riesgo en hombres y a edades más tempranas, así como en verano y áreas urbanas. Presentar discapacidad representa una cierta protección.

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.

Spatio-temporal evolution of female lung cancer mortality in a region of Spain, is it worth taking migration into account?

BACKGROUND: The Comunitat Valenciana (CV) is a tourist region on the Mediterranean coast of Spain with a high rate of retirement migration. Lung cancer in women is the cancer mortality cause that has increased most in the CV during the period 1991 to 2000. Moreover, the geographical distribution of risk from this cause in the CV has been previously described and a non-homogenous pattern was determined. The present paper studies the spatio-temporal distribution of lung cancer mortality for women in the CV during the period 1987-2004, in order to gain some insight into the factors, such as migration, that have had an influence on these changes. METHODS: A novel methodology, consisting of a Bayesian hierarchical model, is used in this paper. Such a model allows the handling of data with a very high disaggregation, while at the same time taking advantage of its spatial and temporal structure. RESULTS: The spatio-temporal pattern which was found points to geographical differences in the time trends of risk. In fact, the southern coastal side of the CV has had a higher increase in risk, coinciding with the settlement of a large foreign community in that area, mainly comprised of elderly people from the European Union. CONCLUSION: Migration has frequently been ignored as a risk factor in the description of the geographical risk of lung cancer and it is suggested that this factor should be considered, especially in tourist regions. The temporal component in disease mapping provides a more accurate depiction of risk factors acting on the population.

Towards a Multidimensional Approach to Bayesian Disease Mapping.

Multivariate disease mapping enriches traditional disease mapping studies by analysing several diseases jointly. This yields improved estimates of the geographical distribution of risk from the diseases by enabling borrowing of information across diseases. Beyond multivariate smoothing for several diseases, several other variables, such as sex, age group, race, time period, and so on, could also be jointly considered to derive multivariate estimates. The resulting multivariate structures should induce an appropriate covariance model for the data. In this paper, we introduce a formal framework for the analysis of multivariate data arising from the combination of more than two variables (geographical units and at least two more variables), what we have called Multidimensional Disease Mapping. We develop a theoretical framework containing both separable and non-separable dependence structures and illustrate its performance on the study of real mortality data in Comunitat Valenciana (Spain).

Supervised filters for EEG signal in naturally occurring epilepsy forecasting.

Nearly 1% of the global population has Epilepsy. Forecasting epileptic seizures with an acceptable confidence level, could improve the disease treatment and thus the lifestyle of the people who suffer it. To do that the electroencephalogram (EEG) signal is usually studied through spectral power band filtering, but this paper proposes an alternative novel method of preprocessing the EEG signal based on supervised filters. Such filters have been employed in a machine learning algorithm, such as the K-Nearest Neighbor (KNN), to improve the prediction of seizures. The proposed solution extends with this novel approach an algorithm that was submitted to win the third prize of an international Data Science challenge promoted by Kaggle contest platform and the American Epilepsy Society, the Epilepsy Foundation, National Institutes of Health (NIH) and Mayo Clinic. A formal description of these preprocessing methods is presented and a detailed analysis in terms of Receiver Operating Characteristics (ROC) curve and Area Under ROC curve is performed. The obtained results show statistical significant improvements when compared with the spectral power band filtering (PBF) typical baseline. A trend between performance and the dataset size is observed, suggesting that the supervised filters bring better information, compared to the conventional PBF filters, as the dataset grows in terms of monitored variables (sensors) and time length. The paper demonstrates a better accuracy in forecasting when new filters are employed and its main contribution is in the field of machine learning algorithms to develop more accurate predictive systems.

Lessons from healthcare utilization in children with obstructive sleep apnoea syndrome. / Aprendiendo de la demanda asistencial en el síndrome de apnea del sueño infantil.

INTRODUCTION AND OBJECTIVES: Paediatric Obstructive Sleep Apnoea-Hypopnoea Syndrome (OSAS) is a multisystemic condition affecting child's health status that may be investigated analyzing demand for healthcare. OBJECTIVE: to quantify the frequency of medical consultations in children with OSAS over a 5-year period, compared to a healthy population. METHODS: A longitudinal, case-control, ambispective study was conducted at a hospital pertaining to the national public health system. 69 consecutive children referred for OSAS were recruited with no diseases other than OSAS so that healthcare demand was purely attributed to this condition. Matched healthy control children were selected to compare these data. Data regarding frequency of the medical consultations were obtained over 5 years: the year of the treatment ("Year0"), 1 and 2 years before ("Year -1" and "Year -2" respectively), and 1 and 2 years after treatment ("Year+1" and "Year+2") RESULTS: Frequentation Index (FI), as ratio between the use of health services by OSAS children and healthy controls was 1.89 during Year-2, and 2.15 during Year-1 (P<.05). Treatment diminishes utilization, with FI of 159 during year+1 and 1.72 during year+2 (P<.05). The main causes of attendance were otolaryngological and pneumological diseases, improving after treatment. CONCLUSIONS: Children suffering from OSAS demand more healthcare services, at least 2 years before treatment, implying that the disease could be present years before we manage it. Therapeutic actions improve healthcare services utilization, although remain higher than for controls, which suggests OSAS sequelae or residual disease.

A kernel-based spatio-temporal surveillance system for monitoring influenza-like illness incidence.

The threat of pandemics has made influenza surveillance systems a priority in epidemiology services around the world. The emergence of A-H1N1 influenza has required accurate surveillance systems in order to undertake specific actions only when and where they are necessary. In that sense, the main goal of this article is to describe a novel methodology for monitoring the geographical distribution of the incidence of influenza-like illness, as a proxy for influenza, based on information from sentinel networks. A Bayesian Poisson mixed linear model is proposed in order to describe the observed cases of influenza-like illness for every sentinel and week of surveillance. This model includes a spatio-temporal random effect that shares information in space by means of a kernel convolution process and in time by means of a first order autoregressive process. The extrapolation of this term to sites where information on incidence is not available will allow us to visualise the geographical distribution of the disease for every week of study. The following article shows the performance of this model in the Comunitat Valenciana's Sentinel Network (one of the 17 autonomous regions of Spain) as a real case study of this methodology.