Glaciological Mass balance of Znosko Glacier, King George Island.

The present article objective is to determine the net mass balance of the glacier Znosko for periods 2018-2019 and 2019-2020. It is situated on King George Island which belongs to the groups Shetland of the South, Antarctic Peninsula region. For this objective, during February 2018 a net of 19 stakes (which were controlled once during February 2019 and 2020) were installed on the glacier ablation zone, drilling in the accumulation zone and about flights using unmanned aerial vehicles (UAV) to control the glacier zone and geomorphological changes. For the year 2020, it was determined a glacier area of 1.71 ± 0.02 km2, moreover, using five different methods of interpolation, it was obtained on average, as a result, a specific net balance of -590.7 ± 46.6 mm w.e (in water equivalent) for 2018-2019 and -686.7 ± 28.2 mm w.e for 2019-2020, being the ELA altitude 146.5 ± 18.2 m and 144.2 ± 8.3 m respectively. The two consecutive years represent negative net mass balances which are in accordance with other similar studies on this region, also glacier data were obtained on a zone that is characterized by its difficult access.

Systematic review: fluid biomarkers and machine learning methods to improve the diagnosis from mild cognitive impairment to Alzheimer's disease.

Mild cognitive impairment (MCI) is often considered an early stage of dementia, with estimated rates of progression to dementia up to 80-90% after approximately 6 years from the initial diagnosis. Diagnosis of cognitive impairment in dementia is typically based on clinical evaluation, neuropsychological assessments, cerebrospinal fluid (CSF) biomarkers, and neuroimaging. The main goal of diagnosing MCI is to determine its cause, particularly whether it is due to Alzheimer's disease (AD). However, only a limited percentage of the population has access to etiological confirmation, which has led to the emergence of peripheral fluid biomarkers as a diagnostic tool for dementias, including MCI due to AD. Recent advances in biofluid assays have enabled the use of sophisticated statistical models and multimodal machine learning (ML) algorithms for the diagnosis of MCI based on fluid biomarkers from CSF, peripheral blood, and saliva, among others. This approach has shown promise for identifying specific causes of MCI, including AD. After a PRISMA analysis, 29 articles revealed a trend towards using multimodal algorithms that incorporate additional biomarkers such as neuroimaging, neuropsychological tests, and genetic information. Particularly, neuroimaging is commonly used in conjunction with fluid biomarkers for both cross-sectional and longitudinal studies. Our systematic review suggests that cost-effective longitudinal multimodal monitoring data, representative of diverse cultural populations and utilizing white-box ML algorithms, could be a valuable contribution to the development of diagnostic models for AD due to MCI. Clinical assessment and biomarkers, together with ML techniques, could prove pivotal in improving diagnostic tools for MCI due to AD.

Classification of longitudinal profiles using semi-parametric nonlinear mixed models with P-Splines and the SAEM algorithm.

In this work, we propose an extension of a semiparametric nonlinear mixed-effects model for longitudinal data that incorporates more flexibility with penalized splines (P-splines) as smooth terms. The novelty of the proposed approach consists of the formulation of the model within the stochastic approximation version of the EM algorithm for maximum likelihood, the so-called SAEM algorithm. The proposed approach takes advantage of the formulation of a P-spline as a mixed-effects model and the use of the computational advantages of the existing software for the SAEM algorithm for the estimation of the random effects and the variance components. Additionally, we developed a supervised classification method for these non-linear mixed models using an adaptive importance sampling scheme. To illustrate our proposal, we consider two studies on pregnant women where two biomarkers are used as indicators of changes during pregnancy. In both studies, information about the women's pregnancy outcomes is known. Our proposal provides a unified framework for the classification of longitudinal profiles that may have important implications for the early detection and monitoring of pregnancy-related changes and contribute to improved maternal and fetal health outcomes. We show that the proposed models improve the analysis of this type of data compared to previous studies. These improvements are reflected both in the fit of the models and in the classification of the groups.

Spatial and temporal distribution of reported dengue cases and hot spot identification in Quezon City, Philippines, 2010-2017.

BACKGROUND: Dengue remains a major public health problem in the Philippines, particularly in urban areas of the National Capital Region. Thematic mapping using geographic information systems complemented by spatial analysis such as cluster analysis and hot spot detection can provide useful information to guide preventive measures and control strategies against dengue. Hence, this study was aimed to describe the spatiotemporal distribution of dengue incidence and identify dengue hot spots by barangay using reported cases from Quezon City, the Philippines from 2010 to 2017. METHODS: Reported dengue case data at barangay level from January 1, 2010 to December 31, 2017 were obtained from the Quezon City Epidemiology and Surveillance Unit. The annual incidence rate of dengue from 2010 to 2017, expressed as the total number of dengue cases per 10,000 population in each year, was calculated for each barangay. Thematic mapping, global cluster analysis, and hot spot analysis were performed using ArcGIS 10.3.1. RESULTS: The number of reported dengue cases and their spatial distribution varied highly between years. Local clusters were evident during the study period. Eighteen barangays were identified as hot spots. CONCLUSIONS: Considering the spatial heterogeneity and instability of hot spots in Quezon City across years, efforts towards the containment of dengue can be made more targeted, and efficient with the application of hot spot analysis in routine surveillance. This may be useful not only for the control of dengue but also for other diseases, and for public health planning, monitoring, and evaluation.

Abordaje transforaminal percutáneo para el diagnóstico y tratamiento de un caso de espondilodiscitis tuberculosa lumbar con absceso epidural / Percutaneous transforaminal approach for the diagnosis and treatment of a case of lumbar tuberculous spondylodiscitis with epidural abscess

RESUMEN La enfermedad de Pott o espondilodiscitis tuberculosa representa menos del 1% de los casos de tuberculosis. Sin embargo, el curso clínico indolente de esta enfermedad hace que su diagnóstico sea generalmente tardío, causando con ello la destrucción de las estructuras vertebrales. La deformidad resultante de la columna vertebral requiere tratamiento quirúrgico, el cual es complejo y requiere una estancia hospitalaria prolongada. Se describe el caso de un paciente varón de 52 años que fue diagnosticado de espondilodiscitis lumbar luego de la correspondiente evaluación clínica y estudios imagenológicos sin mostrar respuesta al tratamiento antibiótico empírico. Un abordaje transforaminal percutáneo, técnica mínimamente invasiva, permitió el diagnóstico de espondilodiscitis tuberculosa y la mejoría sintomática del paciente.

SUMMARY Pott's disease or tuberculous spondylodiscitis accounts for less than 1% of tuberculosis cases. However, the indolent clinical course of this condition leads to a generally delayed diagnosis and to the destruction of the vertebral structures. The resulting spinal deformity requires surgical treatment, which is complex and demands a prolonged hospital stay. In this report, we present the case of a 52-year-old male patient who was diagnosed with lumbar spondylodiscitis by clinical and imaging studies, without response to an empiric antibiotic treatment. Transforaminal percutaneous approach, a minimally invasive technique, allowed the diagnosis of tuberculous spondylodiscitis and a symptomatic improvement of the patient.

A Bayesian nonparametric model for classification of longitudinal profiles.

Across several medical fields, developing an approach for disease classification is an important challenge. The usual procedure is to fit a model for the longitudinal response in the healthy population, a different model for the longitudinal response in the diseased population, and then apply Bayes' theorem to obtain disease probabilities given the responses. Unfortunately, when substantial heterogeneity exists within each population, this type of Bayes classification may perform poorly. In this article, we develop a new approach by fitting a Bayesian nonparametric model for the joint outcome of disease status and longitudinal response, and then we perform classification through the clustering induced by the Dirichlet process. This approach is highly flexible and allows for multiple subpopulations of healthy, diseased, and possibly mixed membership. In addition, we introduce an Markov chain Monte Carlo sampling scheme that facilitates the assessment of the inference and prediction capabilities of our model. Finally, we demonstrate the method by predicting pregnancy outcomes using longitudinal profiles on the human chorionic gonadotropin beta subunit hormone levels in a sample of Chilean women being treated with assisted reproductive therapy.

A Bayesian Mixture Cure Rate Model for Estimating Short-Term and Long-Term Recidivism.

Mixture cure rate models have been developed to analyze failure time data where a proportion never fails. For such data, standard survival models are usually not appropriate because they do not account for the possibility of non-failure. In this context, mixture cure rate models assume that the studied population is a mixture of susceptible subjects who may experience the event of interest and non-susceptible subjects that will never experience it. More specifically, mixture cure rate models are a class of survival time models in which the probability of an eventual failure is less than one and both the probability of eventual failure and the timing of failure depend (separately) on certain individual characteristics. In this paper, we propose a Bayesian approach to estimate parametric mixture cure rate models with covariates. The probability of eventual failure is estimated using a binary regression model, and the timing of failure is determined using a Weibull distribution. Inference for these models is attained using Markov Chain Monte Carlo methods under the proposed Bayesian framework. Finally, we illustrate the method using data on the return-to-prison time for a sample of prison releases of men convicted of sexual crimes against women in England and Wales and we use mixture cure rate models to investigate the risk factors for long-term and short-term survival of recidivism.

"I am not promiscuous enough!": Exploring the low uptake of HIV testing by gay men and other men who have sex with men in Metro Manila, Philippines.

The Philippines faces a severe HIV epidemic among gay and other men who have sex with men (MSM). HIV testing uptake remains low. A case series of 12 men from Metro Manila were interviewed to explore barriers to uptake of HIV testing services. Most did not see the need to get tested for HIV despite significant risk, based on the misconception they were feeling well or showed no symptoms. Being of a higher socioeconomic class, feeling morally superior to other gay men, distance of the testing facility, fear of what will happen once infected, fear of HIV- and sexual stigma, fear of side effects of antiretroviral drugs and fear of high health care expenses after testing positive for HIV were key reasons why MSM kept postponing their test. Misconceptions about HIV risk, disease, and treatment and care need to be addressed in order to increase uptake of HIV services in this population.

Error-rate estimation in discriminant analysis of non-linear longitudinal data: A comparison of resampling methods.

Consider longitudinal observations across different subjects such that the underlying distribution is determined by a non-linear mixed-effects model. In this context, we look at the misclassification error rate for allocating future subjects using cross-validation, bootstrap algorithms (parametric bootstrap, leave-one-out, .632 and [Formula: see text]), and bootstrap cross-validation (which combines the first two approaches), and conduct a numerical study to compare the performance of the different methods. The simulation and comparisons in this study are motivated by real observations from a pregnancy study in which one of the main objectives is to predict normal versus abnormal pregnancy outcomes based on information gathered at early stages. Since in this type of studies it is not uncommon to have insufficient data to simultaneously solve the classification problem and estimate the misclassification error rate, we put special attention to situations when only a small sample size is available. We discuss how the misclassification error rate estimates may be affected by the sample size in terms of variability and bias, and examine conditions under which the misclassification error rate estimates perform reasonably well.

Predicting pregnancy outcomes using longitudinal information: a penalized splines mixed-effects model approach.

We propose a semiparametric nonlinear mixed-effects model (SNMM) using penalized splines to classify longitudinal data and improve the prediction of a binary outcome. The work is motivated by a study in which different hormone levels were measured during the early stages of pregnancy, and the challenge is using this information to predict normal versus abnormal pregnancy outcomes. The aim of this paper is to compare models and estimation strategies on the basis of alternative formulations of SNMMs depending on the characteristics of the data set under consideration. For our motivating example, we address the classification problem using a particular case of the SNMM in which the parameter space has a finite dimensional component (fixed effects and variance components) and an infinite dimensional component (unknown function) that need to be estimated. The nonparametric component of the model is estimated using penalized splines. For the parametric component, we compare the advantages of using random effects versus direct modeling of the correlation structure of the errors. Numerical studies show that our approach improves over other existing methods for the analysis of this type of data. Furthermore, the results obtained using our method support the idea that explicit modeling of the serial correlation of the error term improves the prediction accuracy with respect to a model with random effects, but independent errors. Copyright © 2017 John Wiley & Sons, Ltd.

Desigualdades en desarrollo infantil temprano entre prestadores públicos y privados de salud y factores asociados en la Región Metropolitana de Chile / Early child development inequalities and associated factors between public and private providers at metropolitan region in Chile

El desarrollo infantil temprano es un determinante de la salud física, mental y social de poblaciones. Conocer la situación de desarrollo de base, previo a la instalación de «Chile Crece Contigo¼, es clave para efectos de su evaluación. Objetivo: Comparar el desarrollo infantil temprano y factores asociados de preescolares del sector público y del sector privado de salud en la línea de base. Pacientes y método: Una muestra de 1.045 niños de la Región Metropolitana, de 30 a 58 meses, 52% hombres, 671 del sector público y 380 del privado de salud. Se evaluaron mediante el Inventario de Desarrollo Battelle-1 y una encuesta psicosocial en sus hogares aplicada al cuidador principal. Resultados: El 14,4% del sector privado y el 30,4% de la red pública tenían desarrollo alterado. No hubo diferencias en el área adaptativa entre ambos grupos (26,3% vs 29,2%). En las áreas cognitiva (8,8% vs 12,1%), personal-social (13,2% vs 32,5%), motriz (19,2% vs 35,3%) y comunicación (19,0% vs 36,8%) las diferencias fueron estadísticamente significativas. Mediante regresión logística se determinó que, independiente del nivel socioeconómico, son factores de riesgo: Apgar < 7 (OR: 5,4; IC 95%: 1,24-23,84), tener enfermedades crónicas de la infancia (OR: 1,3; IC 95%: 1,11-1,42); protector es: hogar con recursos para el aprendizaje y juego (OR: 0,8; IC 95%: 0,76-0,89). Conclusión: Estos resultados son un aporte al conocimiento de la situación del desarrollo infantil y para relevar su importancia para las políticas sociales en pediatría.

Early child development is a population determinant of physical, mental and social health. To know the base line situation prior to the implementation of "Chile grows with you" (Chile Crece Contigo) is key to its evaluation. Objective: To compare early child development and associated factors at baseline in pre-school children from public and private health sectors. Patients and method: The sample consisted of 1045 children aged 30-58 months, 52% male, and 671 from the public and 380 from the private sector of the metropolitan region in Chile were evaluated using Battelle Developmental Inventory-1 and a household interview of primary carer. Results: Abnormal child development was found in 14.4% of children in the private sector compared to 30.4% in the public sector. There were no differences in adaptive area (26.3% vs 29.2%), but there were statistically significant differents in cognitive (8.8% vs 12.1%), social-personal (13.2% vs 32.5%), motor (19.2% vs 35.3%), and communication (19% vs 36.8%) development. The logistic regression showed that, independent of socioeconomic level, the risk factors are: Apgar < 7 (OR: 5.4; 95% CI: 1.24-23.84); having childhood chronic diseases (OR: 1.3; 95% CI: 1.11-1.42). Protective factor is: home with resources to learn and play (OR: 0.8; 95% CI: 0.76-0.89).

Bayesian regression analysis of data with random effects covariates from nonlinear longitudinal measurements.

Joint models for a wide class of response variables and longitudinal measurements consist on a mixed-effects model to fit longitudinal trajectories whose random effects enter as covariates in a generalized linear model for the primary response. They provide a useful way to assess association between these two kinds of data, which in clinical studies are often collected jointly on a series of individuals and may help understanding, for instance, the mechanisms of recovery of a certain disease or the efficacy of a given therapy. When a nonlinear mixed-effects model is used to fit the longitudinal trajectories, the existing estimation strategies based on likelihood approximations have been shown to exhibit some computational efficiency problems (De la Cruz et al., 2011). In this article we consider a Bayesian estimation procedure for the joint model with a nonlinear mixed-effects model for the longitudinal data and a generalized linear model for the primary response. The proposed prior structure allows for the implementation of an MCMC sampler. Moreover, we consider that the errors in the longitudinal model may be correlated. We apply our method to the analysis of hormone levels measured at the early stages of pregnancy that can be used to predict normal versus abnormal pregnancy outcomes. We also conduct a simulation study to assess the importance of modelling correlated errors and quantify the consequences of model misspecification.

[Early child development inequalities and associated factors between public and private providers at metropolitan region in Chile]. / Desigualdades en desarrollo infantil temprano entre prestadores públicos y privados de salud y factores asociados en la Región Metropolitana de Chile.

Early child development is a population determinant of physical, mental and social health. To know the base line situation prior to the implementation of "Chile grows with you" (Chile Crece Contigo) is key to its evaluation. OBJECTIVE: To compare early child development and associated factors at baseline in pre-school children from public and private health sectors. PATIENTS AND METHOD: The sample consisted of 1045 children aged 30-58 months, 52% male, and 671 from the public and 380 from the private sector of the metropolitan region in Chile were evaluated using Battelle Developmental Inventory-1 and a household interview of primary carer. RESULTS: Abnormal child development was found in 14.4% of children in the private sector compared to 30.4% in the public sector. There were no differences in adaptive area (26.3% vs 29.2%), but there were statistically significant differents in cognitive (8.8% vs 12.1%), social-personal (13.2% vs 32.5%), motor (19.2% vs 35.3%), and communication (19% vs 36.8%) development. The logistic regression showed that, independent of socioeconomic level, the risk factors are: Apgar<7 (OR: 5.4; 95% CI: 1.24-23.84); having childhood chronic diseases (OR: 1.3; 95% CI: 1.11-1.42). Protective factor is: home with resources to learn and play (OR: 0.8; 95% CI: 0.76-0.89). CONCLUSION: These results are another input about early child development situation and its importance for paediatric social policy.

Study protocol for the Maule Cohort (MAUCO) of chronic diseases, Chile 2014-2024.

BACKGROUND: Maule Cohort (MAUCO), a Chilean cohort study, seeks to analyze the natural history of chronic diseases in the agricultural county of Molina (40,000 inhabitants) in the Maule Region, Chile. Molina´s population is of particular interest because in the last few decades it changed from being undernourished to suffering excess caloric intake, and it currently has the highest national rates of cardiovascular diseases, stomach cancer and gallbladder cancer. Between 2009 and 2011 Molina´s poverty rate dropped from 24.1 % to 13.5 % (national average 20.4 %); in this period the county went from insufficient to almost complete basic sanitation. Despite these advances, chemical pollutants in the food and air are increasing. Thus, in Molina risk factors typical of both under-developed and developed countries coexist, generating a unique profile associated with inflammation, oxidative stress and chronic diseases. METHODS/DESIGN: MAUCO is the core project of the recently established Advanced Center for Chronic Diseases (ACCDiS), Universidad de Chile & Pontificia Universidad Católica de Chile. In this study, we are enrolling and following 10,000 adults aged 38 to 74 years over 10 years. All eligible Molina residents will be enrolled. Participants were identified through a household census. Consenting individuals answer an epidemiological survey exploring risk factors (psycho-social, pesticides, diet, alcohol, and physical activity), medical history and physical and cognitive conditions; provide fasting blood, urine, and saliva samples; receive an electrocardiogram, abdominal ultrasound and bio-impedance test; and take a hand-grip strength test. These subjects will be re-interviewed after 2, 5 and 7 years. Active surveillance of health events is in place throughout the regional healthcare system. The MAUCO Bio-Bank will store 30 to 50 aliquots per subject using an NIH/NCI biorepository system for secure and anonymous linkage of samples with data. DISCUSSION: MAUCO´s results will help design public health interventions tailored to agricultural populations in Latin America.

Envejecimiento poblacional como reto de la ciencia, la técnica y la sociedad / Population aging as challenge of the science, the technique and society

Introducción: el incremento en el número de personas ancianas impone un impostergable reto para las políticas y estrategias en salud en aras de satisfacer sus necesidades y brindarles la posibilidad de vivir sus últimos años con excelente calidad de vida. Objetivo: reflexionar sobre la repercusión que en la ciencia, la técnica y la sociedad tiene el envejecimiento poblacional para el mundo y Cuba. Material y métodos: se realizó una revisión bibliográfica obtenida desde la biblioteca virtual de salud en INFOMED, a partir de 2006 a 2015, donde se ponen de relieve los problemas sociales de la ciencia y la técnica relacionados con el envejecimiento mundial y específicamente en Cuba. Desarrollo: los avances tecnológicos y científicos han permitido que hoy gran parte de la vida de una persona transcurra como adulto mayor. La crisis económica mundial que se está viviendo y que afectará a todos en forma proporcional los niveles de desarrollo que se haya alcanzado, deja al lado la salud de la población envejecida y la atención del Estado al sector salud y hasta donde se revisó. Cuba es uno de los países que tiene un programa estatal que se ocupa de los 7 aspectos fundamentales necesarios para conseguir una longevidad activa y satisfactoria. Conclusiones: los adelantos científico-técnicos alcanzados aún no satisfacen las necesidades de una población envejecida. La inversión que los gobiernos hagan en concepto de ciencia y tecnología para el bien social debe estar encaminada, no solo a prolongar años de vida sino a lograr una calidad de vida satisfactoria en una población mundial que cada día será más envejecida(AU)

Introduction: the increment in the number of old people imposes an inevitable challenge for the politicians and strategies in health for the sake of to satisfy its necessities and to offer them the possibility to live its last years with excellent quality of life. Objective: reflect about repercussion that on the science, the technique and the society has on population aging both to the World and Cuba. Material and Methods: it was carry out a logical historical documental analysis starting from a bibliographical review where are put in relief the social problems of the science and the technique relating to the world aging and specifically in Cuba. Development: the technological advances and scientists have allowed that today great part of the life of a person lapses like elder. The world economic crisis that is living and that it will affect all in proportional form at the developmental levels reached, it leaves at side the aged population's health and the attention from the State to the sector health and up to where we review, Cuba is the only country of the world that has a state program that is in charge of each one of the 7 fundamental aspects that are necessary to get an active and satisfactory longevity. Conclusions: the scientific-technicians advances have not yet reached satisfy the necessities of an aged population. The investment that the governments make in science concept and technology for social one should not be guided to prolong years of life alone also to achieve a quality of satisfactory life in a world population that every day will be more aged(AU)

Pyrosequencing-Based Assessment of the Microbial Community Structure of Pastoruri Glacier Area (Huascarán National Park, Perú), a Natural Extreme Acidic Environment.

The exposure of fresh sulfide-rich lithologies by the retracement of the Nevado Pastoruri glacier (Central Andes, Perú) is increasing the presence of heavy metals in the water as well as decreasing the pH, producing an acid rock drainage (ARD) process in the area. We describe the microbial communities of an extreme ARD site in Huascarán National Park as well as their correlation with the water physicochemistry. Microbial biodiversity was analyzed by FLX 454 sequencing of the 16S rRNA gene. The suggested geomicrobiological model of the area distinguishes three different zones. The proglacial zone is located in the upper part of the valley, where the ARD process is not evident yet. Most of the OTUs detected in this area were related to sequences associated with cold environments (i.e., psychrotolerant species of Cyanobacteria or Bacteroidetes). After the proglacial area, an ARD-influenced zone appeared, characterized by the presence of phylotypes related to acidophiles (Acidiphilium) as well as other species related to acidic and cold environments (i.e., acidophilic species of Chloroflexi, Clostridium and Verrumicrobia). Sulfur- and iron-oxidizing acidophilic bacteria (Acidithiobacillus) were also identified. The post-ARD area was characterized by the presence of OTUs related to microorganisms detected in soils, permafrost, high mountain environments, and deglaciation areas (Sphingomonadales, Caulobacter or Comamonadaceae).

Classification of longitudinal data through a semiparametric mixed-effects model based on lasso-type estimators.

We propose a classification method for longitudinal data. The Bayes classifier is classically used to determine a classification rule where the underlying density in each class needs to be well modeled and estimated. This work is motivated by a real dataset of hormone levels measured at the early stages of pregnancy that can be used to predict normal versus abnormal pregnancy outcomes. The proposed model, which is a semiparametric linear mixed-effects model (SLMM), is a particular case of the semiparametric nonlinear mixed-effects class of models (SNMM) in which finite dimensional (fixed effects and variance components) and infinite dimensional (an unknown function) parameters have to be estimated. In SNMM's maximum likelihood estimation is performed iteratively alternating parametric and nonparametric procedures. However, if one can make the assumption that the random effects and the unknown function interact in a linear way, more efficient estimation methods can be used. Our contribution is the proposal of a unified estimation procedure based on a penalized EM-type algorithm. The Expectation and Maximization steps are explicit. In this latter step, the unknown function is estimated in a nonparametric fashion using a lasso-type procedure. A simulation study and an application on real data are performed.

Memory clinic experience under a social security health system in Costa Rica / Experiência de uma clínica de memória do sistema social de saúde da Costa Rica

Dementia and mild cognitive impairment (MCI) are an increasingly prevalent clinical entity in our , showing an increasing incidence with age. Objective: The purpose of this study was to identify the main types of dementia and MCI treated in a memory disorders unit in Costa Rica. Methods: A consecutive and standardized register of patients diagnosedwith dementia and MCI at the memory disorders unit of the National Geriatrics and Gerontology Hospital (NGGH) was analyzed. Results: Dementia was diagnosed in 63.5% of the 3572 cases, whereas 10.6% met criteria for MCI. The most frequent type of dementia was Alzheimers disease (47.1%), followed by vascular pathology (28.9%), mixed forms (17.2%) and other types (6.8%). In MCI, 69.5% were of amnestic multiple domain type and 14.3% were non-amnestic multiple domain, while 41.3% were of vascular and 35.8% of neurodegenerative etiology. Mean age was 79.6±6.7 years and 64.7% were women in dementia cases whereas mean age was 76.4±6.9 years and 62.1% were women in MCI. Mean years of schooling was 4.95±4.09 years and 6.87±4.71, while mean time between onset of symptoms and clinical diagnosis was 3.2±2.6 years and 2.67±2.69 years, in dementia and MCI, respectively. Conclusion: The determination of the main types of dementia and MCI in Costa Rica and their main features has allowed the registration of comprehensive, hitherto unavailable information that will be useful for the management and strategic planning of public health care.

Demência e transtorno cognitivo leve (CCL) é uma entidade clínica cada vez mais prevalente, mostrando uma incidência crescente com a idade. Objetivo: O objetivo deste estudo foi o de identificar os principais tipos de demência e CCL tratados em uma unidade de transtornos da memória de Costa Rica. Métodos: Um registo consecutivo e padronizado de pacientes com diagnóstico de demência e CCL na unidade de transtorno de memória do Hospital Geral de Geriatria e Gerontologia. Resultados: Demência foi diagnosticada em 63,5% dos 3.572 casos, enquanto 10,6% tiveram diagnóstico de CCL. O tipo mais frequente de demência foi a doença de Alzheimer (47,1%), seguido de doença vascular (28,9%), formas mistas (17,2%) e outros tipos (6,8%). No CCL, 69,5% eram de tipo amnéstico de múltiplos domínios e 14,3% eram não amnésticos de múltiplos domínios, enquanto 41,3% eram de etiologia vascular e 35,8% de etiologia neurodegenerativa. A média de idade foi de 79,6±6,7 anos e 64,7% eram mulheres em casos de demência ao passo que a média de idade foi de 76,4±6,9 anos e 62,1% eram mulheres nos casos de CCL. A média de anos de estudo foi de 4,95±4,09 anos e 6,87±4,71, o tempo entre o início dos sintomas e o diagnóstico clínico médio foi de 3,2±2,6 anos e 2,67±2,69 anos em demência e CCL, respectivamente. Conclusão: A determinação dos principais tipos de demência e CCL na Costa Rica e as suas principais características permitiu o registro de informação abundante que era desconhecida e que será útil para a gestão e planejamento estratégico da saúde pública.