Body composition and resting energy expenditure in a group of children with achondroplasia.

BACKGROUND: Persons with achondroplasia develop early obesity, which is a comorbidity associated with other complications. Currently, there are no validated specific predictive equations to estimate resting energy expenditure in achondroplasia. METHODS: We analyzed the influence of body composition on this parameter and determined whether predictive models used for children with standard height are adjusted to achondroplasia. In this cross-sectional study, we measured anthropometric parameters in children with achondroplasia. Fat mass was obtained using the Slaughter skinfold-thickness equation and resting energy expenditure was determined with a Fitmate-Cosmed calorimeter and with predictive models validated for children with average height (Schofield, Institute of Medicine, and Tverskaya). RESULTS: All of the equations yielded a lower mean value than resting energy expenditure with indirect calorimetry (1256±200 kcal/day [mean±SD]) but the closest was the Tverskaya equation (1017 ± 64 kcal/day), although the difference remained statistically significant. We conclude that weight and height have the greatest influence on resting energy expenditure. CONCLUSION: We recommend studying the relationship between body composition and energy expenditure in achondroplasia in more depth. In the absence of valid predictive models suitable for clinical use to estimate body composition and resting energy expenditure in achondroplasia, it is recommended to use the gold standard methods by taking into account certain anthropometric parameters.

A scalable approach for short-term disease forecasting in high spatial resolution areal data.

Short-term disease forecasting at specific discrete spatial resolutions has become a high-impact decision-support tool in health planning. However, when the number of areas is very large obtaining predictions can be computationally intensive or even unfeasible using standard spatiotemporal models. The purpose of this paper is to provide a method for short-term predictions in high-dimensional areal data based on a newly proposed "divide-and-conquer" approach. We assess the predictive performance of this method and other classical spatiotemporal models in a validation study that uses cancer mortality data for the 7907 municipalities of continental Spain. The new proposal outperforms traditional models in terms of mean absolute error, root mean square error, and interval score when forecasting cancer mortality 1, 2, and 3 years ahead. Models are implemented in a fully Bayesian framework using the well-known integrated nested Laplace estimation technique.

Percepción de las fuentes de apoyo que facilitan la adherencia a los tratamientos / Perception of sources of support that facilitate adherence to the treatments

Introducción: La falta de adherencia a los tratamientos aumenta la probabilidad de fracaso terapéutico y complicaciones innecesarias. Objetivo: Explorar las fuentes de apoyo percibidas por los pacientes con enfermedades crónicas que les facilitan la adherencia a los tratamientos. Método: Estudio cualitativo descriptivo con enfoque fenomenológico, en el que se reclutaron ocho pacientes crónicos complejos, sin distinción de género, ni edad, residentes en Barcelona, y que tras evaluar la adherencia farmacológica con los test de Morisky-Green y el test de Batalla diera como resultado ser adherente al régimen terapéutico. Tras lo cual se efectuaron dos preguntas abiertas que fueron grabadas en un dispositivo de audio y transcritas textualmente. Para el análisis de los datos, se utilizó el método Colaizzi. Resultados: Surgieron tres grupos temáticos: 1) Tener el apoyo familiar; 2) Comprensión de la enfermedad y estrategias que facilitan la adhesión a los tratamientos; y 3) Relación con los profesionales de referencia. Conclusiones: La fuente más importante de apoyo percibida por los participantes, es la familia, coincidiendo con otros autores. El hallazgo de este estudio, es que se evidencia a medicina y enfermería como instigadores que fomentan su empoderamiento y como fuente de influencia reconocida que ha permitido su asimilación y capacitación para adoptar medidas que les ayudan a seguir las pautas farmacoterapéuticas. (AU)

Introduction: Non-adherence to treatments increases the probability of therapeutic failure and unnecessary complications. Objective: To explore the sources of support perceived by patients with chronic diseases that facilitate adherence to treatment. Method: Qualitative descriptive study with a phenomenological approach, in which eight complex chronic patients were recruited, without distinction of gender or age, residents of Barcelona, and who, after evaluating pharmacological adherence with the Morisky-Green test and the test of Batalla resulted in being adherent to the therapeutic regimen.After which, two open questions were asked that were recorded on an audio device and transcribed verbatim. For data analysis, the method Colaizzi was used. Results: Three thematic groups emerged: 1) Having family support; 2) Understanding of the disease and strategies that facilitate adherence to treatments; and 3) Relationship with reference professionals. Conclusions: The most important source of support perceived by the participants is the family, coinciding with other authors. The finding of this study is that shows medicine and nursing are evidenced as instigators that promote their empowerment and as a recognized source of influence that has allowed their assimilation and training to adopt measures that help them follow the pharmacotherapeutic guidelines. (AU)

Small area variations in non-affective first-episode psychosis: the role of socioeconomic and environmental factors.

BACKGROUND: There is strong evidence supporting the association between environmental factors and increased risk of non-affective psychotic disorders. However, the use of sound statistical methods to account for spatial variations associated with environmental risk factors, such as urbanicity, migration, or deprivation, is scarce in the literature. METHODS: We studied the geographical distribution of non-affective first-episode psychosis (NA-FEP) in a northern region of Spain (Navarra) during a 54-month period considering area-level socioeconomic indicators as putative explanatory variables. We used several Bayesian hierarchical Poisson models to smooth the standardized incidence ratios (SIR). We included neighborhood-level variables in the spatial models as covariates. RESULTS: We identified 430 NA-FEP cases over a 54-month period for a population at risk of 365,213 inhabitants per year. NA-FEP incidence risks showed spatial patterning and a significant ecological association with the migrant population, unemployment, and consumption of anxiolytics and antidepressants. The high-risk areas corresponded mostly to peripheral urban regions; very few basic health sectors of rural areas emerged as high-risk areas in the spatial models with covariates. DISCUSSION: Increased rates of unemployment, the migrant population, and consumption of anxiolytics and antidepressants showed significant associations linked to the spatial-geographic incidence of NA-FEP. These results may allow targeting geographical areas to provide preventive interventions that potentially address modifiable environmental risk factors for NA-FEP. Further investigation is needed to understand the mechanisms underlying the associations between environmental risk factors and the incidence of NA-FEP.

Big problems in spatio-temporal disease mapping: Methods and software.

BACKGROUND AND OBJECTIVE: Fitting spatio-temporal models for areal data is crucial in many fields such as cancer epidemiology. However, when data sets are very large, many issues arise. The main objective of this paper is to propose a general procedure to analyze high-dimensional spatio-temporal areal data, with special emphasis on mortality/incidence relative risk estimation. METHODS: We present a pragmatic and simple idea that permits hierarchical spatio-temporal models to be fitted when the number of small areas is very large. Model fitting is carried out using integrated nested Laplace approximations over a partition of the spatial domain. We also use parallel and distributed strategies to speed up computations in a setting where Bayesian model fitting is generally prohibitively time-consuming or even unfeasible. RESULTS: Using simulated and real data, we show that our method outperforms classical global models. We implement the methods and algorithms that we develop in the open-source R package bigDM where specific vignettes have been included to facilitate the use of the methodology for non-expert users. CONCLUSIONS: Our scalable methodology proposal provides reliable risk estimates when fitting Bayesian hierarchical spatio-temporal models for high-dimensional data.

Multivariate Bayesian spatio-temporal P-spline models to analyze crimes against women.

Univariate spatio-temporal models for areal count data have received great attention in recent years for estimating risks. However, models for studying multivariate responses are less commonly used mainly due to the computational burden. In this article, multivariate spatio-temporal P-spline models are proposed to study different forms of violence against women. Modeling distinct crimes jointly improves the precision of estimates over univariate models and allows to compute correlations among them. The correlation between the spatial and the temporal patterns may suggest connections among the different crimes that will certainly benefit a thorough comprehension of this problem that affects millions of women around the world. The models are fitted using integrated nested Laplace approximations and are used to analyze four distinct crimes against women at district level in the Indian state of Maharashtra during the period 2001-2013.

Using mortality to predict incidence for rare and lethal cancers in very small areas.

Incidence and mortality figures are needed to get a comprehensive overview of cancer burden. In many countries, cancer mortality figures are routinely recorded by statistical offices, whereas incidence depends on regional cancer registries. However, due to the complexity of updating cancer registries, incidence numbers become available 3 or 4 years later than mortality figures. It is, therefore, necessary to develop reliable procedures to predict cancer incidence at least until the period when mortality data are available. Most of the methods proposed in the literature are designed to predict total cancer (except nonmelanoma skin cancer) or major cancer sites. However, less frequent lethal cancers, such as brain cancer, are generally excluded from predictions because the scarce number of cases makes it difficult to use univariate models. Our proposal comes to fill this gap and consists of modeling jointly incidence and mortality data using spatio-temporal models with spatial and age shared components. This approach allows for predicting lethal cancers improving the performance of individual models when data are scarce by taking advantage of the high correlation between incidence and mortality. A fully Bayesian approach based on integrated nested Laplace approximations is considered for model fitting and inference. A validation process is also conducted to assess the performance of alternative models. We use the new proposals to predict brain cancer incidence rates by gender and age groups in the health units of Navarre and Basque Country (Spain) during the period 2005-2008.

A unique cardiac electrocardiographic 3D model. Toward interpretable AI diagnosis.

Mathematical models of cardiac electrical activity are one of the most important tools for elucidating information about heart diagnostics. In this paper, we present an efficient mathematical formulation for this modeling simple enough to be easily parameterized and rich enough to provide realistic signals. It relies on a five dipole representation of the cardiac electric source, each one associated with the well-known waves of the electrocardiogram signal. Beyond the physical basis of the model, the parameters are physiologically interpretable as they characterize the wave shape, similar to what a physician would look for in signals, thus making them very useful in diagnosis. The model accurately reproduces the electrocardiogram signals of any diseased or healthy heart. This new discovery represents a significant advance in electrocardiography research. It is especially useful for diagnosis, patient follow-up or decision-making on new therapies; is also a promising tool for well-performing, transparent and interpretable AI approaches.

[Perception of the educational environment in residents of medical specialties in Chilean universities]. / Percepción del ambiente educacional de postgrado en residentes de especialidades médicas de universidades chilenas.

BACKGROUND: The Learning Environment (LE) influences the performance of students, learning, social life, mental health, and the future of work. AIM: To assess the learning environment (LE) among medical residents of 64 specialties. MATERIAL AND METHODS: Two validated instruments "Postgraduate Hospital Education Environment Measure" (PHEEM) and "Ambulatory Care Learning Educational Environment" (ACLEEM), and open questions were answered online by 1259 residents from 15 universities. A descriptive and analytical statistical analysis and semantic deductive-inductive analyses of open questions were performed. RESULTS: LE was positive rather than negative (PHEEM of 100.5 points (79-116) and ACLEEM of 138.5 points (120-157)). An age over 32 years, male sex, studying in a private university, being in first year of residence and being in a non-surgical specialty were associated with a better PHEEM score (p < 0.05). For ACLEEM, the first year of specialty, a non-surgical specialty and studying in a private university were associated with better scores (p < 0.05). Two programs had excellent LE (Pathological Anatomy and Ophthalmology) and no specialty had a very poor performance or many problems. Aspects of teaching, clinical activities, and teachers were strengths reported by students. Aspects to improve were teaching, protected times and clinical activities. CONCLUSIONS: LE among medical specialties had more positive than negative features, but with areas that should be improved.

Percepción del ambiente educacional de postgrado en residentes de especialidades médicas de universidades chilenas / Perception of the educational environment in residents of medical specialties in Chilean universities

BACKGROUND: The Learning Environment (LE) influences the performance of students, learning, social life, mental health, and the future of work. Aim: To assess the learning environment (LE) among medical residents of 64 specialties. MATERIAL AND METHODS: Two validated instruments "Postgraduate Hospital Education Environment Measure" (PHEEM) and "Ambulatory Care Learning Educational Environment" (ACLEEM), and open questions were answered online by 1259 residents from 15 universities. A descriptive and analytical statistical analysis and semantic deductive-inductive analyses of open questions were performed. Results: LE was positive rather than negative (PHEEM of 100.5 points (79-116) and ACLEEM of 138.5 points (120-157)). An age over 32 years, male sex, studying in a private university, being in first year of residence and being in a non-surgical specialty were associated with a better PHEEM score (p < 0.05). For ACLEEM, the first year of specialty, a non-surgical specialty and studying in a private university were associated with better scores (p < 0.05). Two programs had excellent LE (Pathological Anatomy and Ophthalmology) and no specialty had a very poor performance or many problems. Aspects of teaching, clinical activities, and teachers were strengths reported by students. Aspects to improve were teaching, protected times and clinical activities. CONCLUSIONS: LE among medical specialties had more positive than negative features, but with areas that should be improved.

Space-time interactions in Bayesian disease mapping with recent tools: Making things easier for practitioners.

Spatio-temporal disease mapping studies the distribution of mortality or incidence risks in space and its evolution in time, and it usually relies on fitting hierarchical Poisson mixed models. These models are complex for practitioners as they generally require adding constraints to correctly identify and interpret the different model terms. However, including constraints may not be straightforward in some recent software packages. This paper focuses on NIMBLE, a library of algorithms that contains among others a configurable system for Markov chain Monte Carlo (MCMC) algorithms. In particular, we show how to fit different spatio-temporal disease mapping models with NIMBLE making emphasis on how to include sum-to-zero constraints to solve identifiability issues when including spatio-temporal interactions. Breast cancer mortality data in Spain during the period 1990-2010 is used for illustration purposes. A simulation study is also conducted to compare NIMBLE with R-INLA in terms of parameter estimates and relative risk estimation. The results are very similar but differences are observed in terms of computing time.

Estimating LOCP cancer mortality rates in small domains in Spain using its relationship with lung cancer.

The distribution of lip, oral cavity, and pharynx (LOCP) cancer mortality rates in small domains (defined as the combination of province, age group, and gender) remains unknown in Spain. As many of the LOCP risk factors are preventable, specific prevention programmes could be implemented but this requires a clear specification of the target population. This paper provides an in-depth description of LOCP mortality rates by province, age group and gender, giving a complete overview of the disease. This study also presents a methodological challenge. As the number of LOCP cancer cases in small domains (province, age groups and gender) is scarce, univariate spatial models do not provide reliable results or are even impossible to fit. In view of the close link between LOCP and lung cancer, we consider analyzing them jointly by using shared component models. These models allow information-borrowing among diseases, ultimately providing the analysis of cancer sites with few cases at a very disaggregated level. Results show that males have higher mortality rates than females and these rates increase with age. Regions located in the north of Spain show the highest LOCP cancer mortality rates.

Enfrentamiento del paciente con dolor abdominal / Approach to the patient with abdominal pain

El dolor abdominal es una causa frecuente de consulta ambulatoria, sus causas son múltiples e incluyen patologías de riesgo vital u otras de bajo riesgo que requieren principalmente tratamiento y no requieren mayores estudios. La historia clínica y el examen físico son las herramientas principales para poder sospechar la etiología de la enfermedad que está causando el dolor abdominal. Es por esto que realizar una historia completa, haciendo preguntas dirigidas a confirmar o descartar sospechas diagnósticas, asociado a un examen físico completo y sistemático, es la principal forma de orientar el diagnóstico y estudio posterior del paciente. Conocer el cuadro clínico de las enfermedades que dentro de sus síntomas pueden presentar dolor abdominal, especialmente aquellas que son de riesgo vital permite poder diagnosticarlas con la rapidez que se requiere. Así mismo, es necesario reconocer aquellas enfermedades que son más frecuentes y que no requieren mayores estudios sino tratamiento básico, habitualmente ambulatorio. Existen personas que, por sus condiciones de salud o edad, pueden tener presentaciones atípicas de cuadros habituales o mayores riesgos de tener enfermedades infrecuentes o de mayor riesgo vital, que es importante reconocer al momento de la evaluación.

Abdominal pain is a common complaint in ambulatory medicine. It has multiples causes,including lifethreatening pathologies and other benign in which the need in treatment and no other tests. The clinical history and physical examination are the key to suspect the etiology of the underlying disease. A complete history includes make direct questions to confirm or discard the diagnostic suspects and with a complete and systematic physical examination are the main way to get the diagnosis and treatment of the patient. The clinician must know the syndromes which includes abdominal pain, especially those life-threatening which requires urgent treatment or surgery. Also needs to know the frequent benign syndromes that can be safely treated symptomatically with no further investigation. Older adults or patients with comorbidities may present with unusual causes of abdominal pain or may have an atypical presentation of common disorders. Also, may present more frequent serious etiologies that may require urgent interventions.

Cytokinins are involved in drought tolerance of Pinus radiata plants originating from embryonal masses induced at high temperatures.

Vegetative propagation through somatic embryogenesis is an effective method to produce elite varieties and can be applied as a tool to study the response of plants to different stresses. Several studies show that environmental changes during embryogenesis could determine future plant development. Moreover, we previously reported that physical and chemical conditions during somatic embryogenesis can determine the protein, hormone and metabolite profiles, as well as the micromorphological and ultrastructural organization of embryonal masses and somatic embryos. In this sense, phytohormones are key players throughout the somatic embryogenesis process as well as during numerous stress-adaptation responses. In this work, we first applied different high-temperature regimes (30 °C, 4 weeks; 40 °C, 4 days; 50 °C, 5 min) during induction of Pinus radiata D. Don somatic embryogenesis, together with control temperature (23 °C). Then, the somatic plants regenerated from initiated embryogenic cell lines and cultivated in greenhouse conditions were subjected to drought stress and control treatments to evaluate survival, growth and several physiological traits (relative water content, water potential, photosynthesis, stomatal conductance and transpiration). Based on those preliminary results, even more extreme high-temperature regimes were applied during induction (40 °C, 4 h; 50 °C, 30 min; 60 °C, 5 min) and the corresponding cytokinin profiles of initiated embryonal masses from different lines were analysed. The results showed that the temperature regime during induction had delayed negative effects on drought resilience of somatic plants as indicated by survival, photosynthetic activity and water- use efficiency. However, high temperatures for extended periods of time enhanced subsequent plant growth in well-watered conditions. High-temperature regime treatments induced significant differences in the profile of total cytokinin bases, N6-isopentenyladenine, cis-zeatin riboside and trans-zeatin riboside. We concluded that phytohormones could be potential regulators of stress-response processes during initial steps of somatic embryogenesis and that they may have delayed implications in further developmental processes, determining the performance of the generated plants.

Induction of Radiata Pine Somatic Embryogenesis at High Temperatures Provokes a Long-Term Decrease in DNA Methylation/Hydroxymethylation and Differential Expression of Stress-Related Genes.

Based on the hypothesis that embryo development is a crucial stage for the formation of stable epigenetic marks that could modulate the behaviour of the resulting plants, in this study, radiata pine somatic embryogenesis was induced at high temperatures (23 °C, eight weeks, control; 40 °C, 4 h; 60 °C, 5 min) and the global methylation and hydroxymethylation levels of emerging embryonal masses and somatic plants were analysed using LC-ESI-MS/ MS-MRM. In this context, the expression pattern of six genes previously described as stress-mediators was studied throughout the embryogenic process until plant level to assess whether the observed epigenetic changes could have provoked a sustained alteration of the transcriptome. Results indicated that the highest temperatures led to hypomethylation of both embryonal masses and somatic plants. Moreover, we detected for the first time in a pine species the presence of 5-hydroxymethylcytosine, and revealed its tissue specificity and potential involvement in heat-stress responses. Additionally, a heat shock protein-coding gene showed a down-regulation tendency along the process, with a special emphasis given to embryonal masses at first subculture and ex vitro somatic plants. Likewise, the transcripts of several proteins related with translation, oxidative stress response, and drought resilience were differentially expressed.

Risk Factors for Adult Depression: Adverse Childhood Experiences and Personality Functioning.

Background: Depressive disorder is one of the main health problems worldwide. Many risk factors have been associated with this pathology. However, while the association between risks factors and adult depression is well established, the mechanisms behind its impact remains poorly understood. A possible, yet untested explanation is the mediating impact of levels of personality functioning, i.e., impairments with regard to self and interpersonal. Method: Around 162 patients were assessed at the beginning of their therapy, with regard to risk factors, such as sociodemographic, physical, hereditary (Information Form), and adverse childhood experiences (ACE; CTQ). Depressive symptoms (Beck Depression Inventory, BDI) and personality functioning (OPD-SQ) were also measured. Associations between the related variables as well as other possible covariates were examined by means of zero-order correlations and bootstrapping-based mediation analysis. Results: Of all the risk factors taken into account, level of education and physical illness were associated with depression. On the other hand, the most significant predictor of depressive symptomatology was ACE, and this relationship was mediated by personality functioning. This indicates that patients presenting adverse childhood experiences are more likely to develop deficiencies in personality functioning, which in turn increases their likelihood of developing depressive symptomatology. Conclusion: These results reaffirm the importance of incorporating risk and vulnerability factors such as personality functioning in understanding depression.

Space-time analysis of ovarian cancer mortality rates by age groups in spanish provinces (1989-2015).

BACKGROUND: Ovarian cancer is a silent and largely asymptomatic cancer, leading to late diagnosis and worse prognosis. The late-stage detection and low survival rates, makes the study of the space-time evolution of ovarian cancer particularly relevant. In addition, research of this cancer in small areas (like provinces or counties) is still scarce. METHODS: The study presented here covers all ovarian cancer deaths for women over 50 years of age in the provinces of Spain during the period 1989-2015. Spatio-temporal models have been fitted to smooth ovarian cancer mortality rates in age groups [50,60), [60,70), [70,80), and [80,+), borrowing information from spatial and temporal neighbours. Model fitting and inference has been carried out using the Integrated Nested Laplace Approximation (INLA) technique. RESULTS: Large differences in ovarian cancer mortality among the age groups have been found, with higher mortality rates in the older age groups. Striking differences are observed between northern and southern Spain. The global temporal trends (by age group) reveal that the evolution of ovarian cancer over the whole of Spain has remained nearly constant since the early 2000s. CONCLUSION: Differences in ovarian cancer mortality exist among the Spanish provinces, years, and age groups. As the exact causes of ovarian cancer remain unknown, spatio-temporal analyses by age groups are essential to discover inequalities in ovarian cancer mortality. Women over 60 years of age should be the focus of follow-up studies as the mortality rates remain constant since 2002. High-mortality provinces should also be monitored to look for specific risk factors.

Short-term results of early switch from Ranibizumab to Aflibercept in poor or non responder age related macular degeneration in clinical practice.

BACKGROUND: To evaluate the change in best corrected visual acuity (VA) and central macular thickness (CMT) following treatment with intravitreal aflibercept (AFL) in patients poorly responders or non responders to ranibizumab (RBZ). METHODS: Charts of patients injected with RBZ from January 2016 to December 2018 (548 cases) due to neovascular age-related macular degeneration (nAMD) were reviewed. Fifty-six cases met our criteria for poor responders to RBZ (CMT decreased between 5 and 15% over treatment) or for non responders to RBZ (CMT decreased less than 5% or increased over treatment). RESULTS: After the third AFL injection, CMT decreased from 384.38 ± 123.20 µm to 296.18 ± 70.52 µm in the non-responder group and from 320.00 ± 82.05 µm to 282.27 ± 56.86 µm in the poor responder group. Although decrease in macular thickness was overall achieved 3 months after switching to AFL, it was not translated in VA improvement. CONCLUSIONS: it was observed that nAMD patients classified as RBZ non-responders tend to respond better to AFL than RBZ poor-responders anatomically, without correspondent improvement in VA.

Mandatory notifications of type 1 diabetes incident cases in Chilean children, 2006 to 2014: A population-based study.

Type 1 diabetes mellitus (T1D) incidence in children varies across regions and countries, showing a continuous rise globally. Chile has mandatory T1D notification and guaranteed access to diagnosis and treatment since 2005, providing a strong model to evaluate T1D epidemiology. OBJECTIVE: To determine T1D incidence in Chilean population under 20 years between 2006 and 2014. METHODS: We reviewed mandatory notifications of T1D in Chile's public health system. RESULTS: A total of 4153 T1D cases in population under 20 years were notified from 2006 to 2014. Median age was 14 years and 51% were male. The average annual T1D incidence was 12 per 100 000 population, with an increase from 10.2 in 2006 to 13.8 in 2014 (ß 0.5 95% confidence interval [CI] 0.4-0.7, P < .001). A significantly increasing linear trend of T1D incidence was observed in groups of 0 to 4 years (ß 0.33, 95% CI 0.06-0.59, P = .02), 5 to 9 years (ß 0.68 95% CI 0.27-1.10, P = .006), and 10 to 14 (ß 0.94, 95% CI 0.67-1.20, P < .001), but increase was less pronounced in the oldest children aged between 15 and 19 years (ß 0.22, 95% CI -0.03 to 0.44, P = .052). The lowest regional T1D incidence was observed in the Araucanía region, which has the highest rate of indigenous population. CONCLUSION: Incidence rates of T1D in Chile, evaluated through a mandatory notification program, are rapidly increasing in children and adolescents. If increasing trends persist, Chile will reach T1D incidence rates of Western developed countries in the next decade.

Online relative risks/rates estimation in spatial and spatio-temporal disease mapping.

BACKGROUND AND OBJECTIVE: Spatial and spatio-temporal analyses of count data are crucial in epidemiology and other fields to unveil spatial and spatio-temporal patterns of incidence and/or mortality risks. However, fitting spatial and spatio-temporal models is not easy for non-expert users. The objective of this paper is to present an interactive and user-friendly web application (named SSTCDapp) for the analysis of spatial and spatio-temporal mortality or incidence data. Although SSTCDapp is simple to use, the underlying statistical theory is well founded and all key issues such as model identifiability, model selection, and several spatial priors and hyperpriors for sensitivity analyses are properly addressed. METHODS: The web application is designed to fit an extensive range of fairly complex spatio-temporal models to smooth the very often extremely variable standardized incidence/mortality risks or crude rates. The application is built with the R package shiny and relies on the well founded integrated nested Laplace approximation technique for model fitting and inference. RESULTS: The use of the web application is shown through the analysis of Spanish spatio-temporal breast cancer data. Different possibilities for the analysis regarding the type of model, model selection criteria, and a range of graphical as well as numerical outputs are provided. CONCLUSIONS: Unlike other software used in disease mapping, SSTCDapp facilitates the fit of complex statistical models to non-experts users without the need of installing any software in their own computers, since all the analyses and computations are made in a powerful remote server. In addition, a desktop version is also available to run the application locally in those cases in which data confidentiality is a serious issue.