Acute Coronary Syndrome Treatment Costs from the Perspective of the Supplementary Health System

AbstractBackground:Acute coronary syndrome (ACS) is defined as a “group of clinical symptoms compatible with acute myocardial ischemia”, representing the leading cause of death worldwide, with a high clinical and financial impact. In this sense, the development of economic studies assessing the costs related to the treatment of ACS should be considered.Objective:To evaluate costs and length of hospital stay between groups of patients treated for ACS undergoing angioplasty with or without stent implantation (stent+ / stent-), coronary artery bypass surgery (CABG) and treated only clinically (Clinical) from the perspective of the Brazilian Supplementary Health System (SHS).Methods:A retrospective analysis of medical claims of beneficiaries of health plans was performed considering hospitalization costs and length of hospital stay for management of patients undergoing different types of treatment for ACS, between Jan/2010 and Jun/2012.Results:The average costs per patient were R$ 18,261.77, R$ 30,611.07, R$ 37,454.94 and R$ 40,883.37 in the following groups: Clinical, stent-, stent+ and CABG, respectively. The average costs per day of hospitalization were R$ 1,987.03, R$ 4,024.72, R$ 6,033.40 and R$ 2,663.82, respectively. The average results for length of stay were 9.19 days, 7.61 days, 6.19 days and 15.20 days in these same groups. The differences were significant between all groups except Clinical and stent- and between stent + and CABG groups for cost analysis.Conclusion:Hospitalization costs of SCA are high in the Brazilian SHS, being significantly higher when interventional procedures are required.

Gestão do conhecimento usando data mining: estudo de caso na Universidade Federal de Lavras

A gestão do conhecimento abrange toda a forma de gerar, armazenar, distribuir e utilizar o conhecimento, tornando necessária a utilização de tecnologias de informação para facilitar esse processo, devido ao grande aumento no volume de dados. A descoberta de conhecimento em banco de dados é uma metodologia que tenta solucionar esse problema e o data mining é uma técnica que faz parte dessa metodologia. Este artigo desenvolve, aplica e analisa uma ferramenta de data mining, para extrair conhecimento referente à produção científica das pessoas envolvidas com a pesquisa na Universidade Federal de Lavras. A metodologia utilizada envolveu a pesquisa bibliográfica, a pesquisa documental e o método do estudo de caso. As limitações encontradas na análise dos resultados indicam que ainda é preciso padronizar o modo do preenchimento dos currículos Lattes para refinar as análises e, com isso, estabelecer indicadores. A contribuição foi gerar um banco de dados estruturado, que faz parte de um processo maior de desenvolvimento de indicadores de ciência e tecnologia, para auxiliar na elaboração de novas políticas de gestão científica e tecnológica e aperfeiçoamento do sistema de ensino superior brasileiro.

Inferences about the global scenario of human T-cell lymphotropic virus type 1 infection using data mining of viral sequences

Human T-cell lymphotropic virus type 1 (HTLV-1) is mainly associated with two diseases: tropical spastic paraparesis/HTLV-1-associated myelopathy (TSP/HAM) and adult T-cell leukaemia/lymphoma. This retrovirus infects five-10 million individuals throughout the world. Previously, we developed a database that annotates sequence data from GenBank and the present study aimed to describe the clinical, molecular and epidemiological scenarios of HTLV-1 infection through the stored sequences in this database. A total of 2,545 registered complete and partial sequences of HTLV-1 were collected and 1,967 (77.3%) of those sequences represented unique isolates. Among these isolates, 93% contained geographic origin information and only 39% were related to any clinical status. A total of 1,091 sequences contained information about the geographic origin and viral subtype and 93% of these sequences were identified as subtype “a”. Ethnicity data are very scarce. Regarding clinical status data, 29% of the sequences were generated from TSP/HAM and 67.8% from healthy carrier individuals. Although the data mining enabled some inferences about specific aspects of HTLV-1 infection to be made, due to the relative scarcity of data of available sequences, it was not possible to delineate a global scenario of HTLV-1 infection.

A process for mining science & technology documents databases, illustrated for the case of "knowledge discovery and data mining"

This paper presents a process of mining research & development abstract databases to profile current status and to project potential developments for target technologies, The process is called "technology opportunities analysis." This article steps through the process using a sample data set of abstracts from the INSPEC database on the topic o "knowledge discovery and data mining." The paper offers a set of specific indicators suitable for mining such databases to understand innovation prospects. In illustrating the uses of such indicators, it offers some insights into the status of knowledge discovery research*.

Inteligência obtida pela aplicação de data mining em base de teses francesas sobre o Brasil

O assunto Brasil foi analisado na base de teses francesas DocThèses, compreendendo os anos de 1969 a 1999. Utilizou-se a técnica de Data Mining como ferramenta para obter inteligência e conhecimento. O software utilizado para a limpeza da base DocThèses foi o Infotrans, e, para a preparação dos dados, empregou-se o Dataview. Os resultados da análise foram ilustrados com a aplicação dos pressupostos da Lei de Zipf, classificando-se as informações em trivial, interessante e ruído, conforme a distribuição de freqüência. Conclui-se que a técnica do Data Mining associada a softwares especialistas é uma poderosa aliada no emprego de inteligência no processo decisório em todos os níveis, inclusive o nível macro, pois oferece subsídios para a consolidação, investimento e desenvolvimento de ações e políticas.

Use of data mining and spectral profiles to differentiate condition after harvest of coffee plants

This study aimed at identifying different conditions of coffee plants after harvesting period, using data mining and spectral behavior profiles from Hyperion/EO1 sensor. The Hyperion image, with spatial resolution of 30 m, was acquired in August 28th, 2008, at the end of the coffee harvest season in the studied area. For pre-processing imaging, atmospheric and signal/noise effect corrections were carried out using Flaash and MNF (Minimum Noise Fraction Transform) algorithms, respectively. Spectral behavior profiles (38) of different coffee varieties were generated from 150 Hyperion bands. The spectral behavior profiles were analyzed by Expectation-Maximization (EM) algorithm considering 2; 3; 4 and 5 clusters. T-test with 5% of significance was used to verify the similarity among the wavelength cluster means. The results demonstrated that it is possible to separate five different clusters, which were comprised by different coffee crop conditions making possible to improve future intervention actions.

Efficiency of distinct data mining algorithms for classifying stress level in piglets from their vocalization

Among the challenges of pig farming in today's competitive market, there is factor of the product traceability that ensures, among many points, animal welfare. Vocalization is a valuable tool to identify situations of stress in pigs, and it can be used in welfare records for traceability. The objective of this work was to identify stress in piglets using vocalization, calling this stress on three levels: no stress, moderate stress, and acute stress. An experiment was conducted on a commercial farm in the municipality of Holambra, São Paulo State , where vocalizations of twenty piglets were recorded during the castration procedure, and separated into two groups: without anesthesia and local anesthesia with lidocaine base. For the recording of acoustic signals, a unidirectional microphone was connected to a digital recorder, in which signals were digitized at a frequency of 44,100 Hz. For evaluation of sound signals, Praat® software was used, and different data mining algorithms were applied using Weka® software. The selection of attributes improved model accuracy, and the best attribute selection was used by applying Wrapper method, while the best classification algorithms were the k-NN and Naive Bayes. According to the results, it was possible to classify the level of stress in pigs through their vocalization.

Using data mining to identify factors that influence the degree of leg injuries in broilers

Locomotor problems prevent the bird to move freely, jeopardizing the welfare and productivity, besides generating injuries on the legs of chickens. The objective of this study was to evaluate the influence of age, use of vitamin D, the asymmetry of limbs and gait score, the degree of leg injuries in broilers, using data mining. The analysis was performed on a data set obtained from a field experiment in which it was used two groups of birds with 30 birds each, a control group and one treated with vitamin D. It was evaluated the gait score, the asymmetry between the right and left toes, and the degree of leg injuries. The Weka ® software was used in data mining. In particular, C4.5 algorithm (also known as J48 in Weka environment) was used for the generation of a decision tree. The results showed that age is the factor that most influences the degree of leg injuries and that the data from assessments of gait score were not reliable to estimate leg weakness in broilers.

Data mining techniques for identification of spectrally homogeneous areas using NDVI temporal profiles of soybean crop

The aim of this study was to group temporal profiles of 10-day composites NDVI product by similarity, which was obtained by the SPOT Vegetation sensor, for municipalities with high soybean production in the state of Paraná, Brazil, in the 2005/2006 cropping season. Data mining is a valuable tool that allows extracting knowledge from a database, identifying valid, new, potentially useful and understandable patterns. Therefore, it was used the methods for clusters generation by means of the algorithms K-Means, MAXVER and DBSCAN, implemented in the WEKA software package. Clusters were created based on the average temporal profiles of NDVI of the 277 municipalities with high soybean production in the state and the best results were found with the K-Means algorithm, grouping the municipalities into six clusters, considering the period from the beginning of October until the end of March, which is equivalent to the crop vegetative cycle. Half of the generated clusters presented spectro-temporal pattern, a characteristic of soybeans and were mostly under the soybean belt in the state of Paraná, which shows good results that were obtained with the proposed methodology as for identification of homogeneous areas. These results will be useful for the creation of regional soybean "masks" to estimate the planted area for this crop.

Vocalization data mining for estimating swine stress conditions

This study aimed to identify differences in swine vocalization pattern according to animal gender and different stress conditions. A total of 150 barrow males and 150 females (Dalland® genetic strain), aged 100 days, were used in the experiment. Pigs were exposed to different stressful situations: thirst (no access to water), hunger (no access to food), and thermal stress (THI exceeding 74). For the control treatment, animals were kept under a comfort situation (animals with full access to food and water, with environmental THI lower than 70). Acoustic signals were recorded every 30 minutes, totaling six samples for each stress situation. Afterwards, the audios were analyzed by Praat® 5.1.19 software, generating a sound spectrum. For determination of stress conditions, data were processed by WEKA® 3.5 software, using the decision tree algorithm C4.5, known as J48 in the software environment, considering cross-validation with samples of 10% (10-fold cross-validation). According to the Decision Tree, the acoustic most important attribute for the classification of stress conditions was sound Intensity (root node). It was not possible to identify, using the tested attributes, the animal gender by vocal register. A decision tree was generated for recognition of situations of swine hunger, thirst, and heat stress from records of sound intensity, Pitch frequency, and Formant 1.

Lifestyle factors, direct and indirect costs for a Brazilian airline company

OBJECTIVE To analyze lifestyle risk factors related to direct healthcare costs and the indirect costs due to sick leave among workers of an airline company in Brazil. METHODS In this longitudinal 12-month study of 2,201 employees of a Brazilian airline company, the costs of sick leave and healthcare were the primary outcomes of interest. Information on the independent variables, such as gender, age, educational level, type of work, stress, and lifestyle-related factors (body mass index, physical activity, and smoking), was collected using a questionnaire on enrolment in the study. Data on sick leave days were available from the company register, and data on healthcare costs were obtained from insurance records. Multivariate linear regression analysis was used to investigate the association between direct and indirect healthcare costs with sociodemographic, work, and lifestyle-related factors. RESULTS Over the 12-month study period, the average direct healthcare expenditure per worker was US$505.00 and the average indirect cost because of sick leave was US$249.00 per worker. Direct costs were more than twice the indirect costs and both were higher in women. Body mass index was a determinant of direct costs and smoking was a determinant of indirect costs. CONCLUSIONS Obesity and smoking among workers in a Brazilian airline company were associated with increased health costs. Therefore, promoting a healthy diet, physical activity, and anti-tobacco campaigns are important targets for health promotion in this study population.

Outpatient health service utilization and associated factors: a population-based study

OBJECTIVE: To identify factors that lead people to visit a doctor in Brazil and assess differences between socioeconomic groups. METHODS: A cross-sectional study comprising 1,260 subjects aged 15 or more was carried out in southern Brazil. Demographic, socioeconomic, health needs and regular source of care data were analyzed concerning visits to a doctor within two months from the interview. Adjusted prevalence ratios and 95% confidence intervals were calculated using Poisson regression. RESULTS: Adjusted PR showed that women having stressful life events, health insurance, and a regular doctor increased the outcome. A dose-related response was found with self-reported health, and the probability of visiting a doctor increased with health needs. Analysis in the chronic disease group revealed that uneducated lower income subjects had a 62% reduction in the chance of visiting a doctor compared to uneducated higher income ones. However, as it was seen a significant interaction between income and education, years of schooling increased utilization in this group. CONCLUSIONS: Results suggest the existence of health inequity in the poorest group that could be overcome with education. Specific measures reinforcing the importance of having a regular doctor may also improve access in the underserved group.

Impact of socioeconomic status on Brazilian elderly health

OBJECTIVE: To investigate the impact of socioeconomic status on elderly health. METHODS: The study was based on cross-sectional data from Survey on Health, Well-Being, and Aging in Latin America and the Caribbean. The sample comprised 2,143 non-institutionalized elderly aged 60 years and older living in the urban area of São Paulo, southeastern Brazil. Linear regression models estimated the effect of socioeconomic status indicators (years of schooling completed, occupation and purchasing power) on each one of the following health indicators: depression, self-rated health, morbidity and memory capacity. A 5% significance level was set. RESULTS: There was a significant effect of years of education and purchasing power on self-rated health and memory capacity when controlled for the variables number of diseases during childhood, bed rest for at least a month due to health problems during childhood, self-rated health during childhood, living arrangements, sex, age, marital status, category of health insurance, intake of medicines. Only purchasing power had an effect on depression. Despite the bivariate association between socioeconomic status indicators and number of diseases (morbidity), this effect was no longer seen after including the controls in the model. CONCLUSIONS: The study results confirm the association between socioeconomic status indicators and health among Brazilian elderly, but only for some dimensions of socioeconomic status and certain health outcomes.

Direct medical costs associated with schizophrenia relapses in health care services in the city of São Paulo

OBJECTIVE: To assess direct medical costs associated with schizophrenia relapses in mental health services. METHODS: The study was conducted in three health facilities in the city of São Paulo: a public state hospital; a Brazilian National Health System (SUS)-contracted hospital; and a community mental health center. Medical records of 90 patients with schizophrenia who received care in 2006 were reviewed. Information on inpatient expenditures was collected and used for cost estimates. RESULTS: Mean direct medical cost of schizophrenia relapses per patient was US$ 4,083.50 (R$ 8,167.58) in the public state hospital; US$ 2,302.76 (R$ 4,605.46) in the community mental health center; and US$ 1,198.50 (R$ 2,397.74) in the SUS-affiliated hospital. The main component was daily inpatient room rates (87% - 98%). Medication costs varied depending on the use of typical or atypical antipsychotic drugs. Atypical antipsychotic drugs were more often used in the community mental health center. CONCLUSIONS: Costs associated with schizophrenia relapses support investments in antipsychotic drugs and strategies to reduce disease relapse and the need for mental health inpatient services. Treating patients in a community mental health center was associated with medium costs and added the benefit of not depriving these patients from family life.

Determinants of the use of health care services: multilevel analysis in the Metropolitan Region of Sao Paulo

OBJECTIVE To evaluate the individual and contextual determinants of the use of health care services in the metropolitan region of Sao Paulo.METHODS Data from the Sao Paulo Megacity study – the Brazilian version of the World Mental Health Survey multicenter study – were used. A total of 3,588 adults living in 69 neighborhoods in the metropolitan region of Sao Paulo, SP, Southeastern Brazil, including 38 municipalities and 31 neighboring districts, were selected using multistratified sampling of the non-institutionalized population. Multilevel Bayesian logistic models were adjusted to identify the individual and contextual determinants of the use of health care services in the past 12 months and presence of a regular physician for routine care.RESULTS The contextual characteristics of the place of residence (income inequality, violence, and median income) showed no significant correlation (p > 0.05) with the use of health care services or with the presence of a regular physician for routine care. The only exception was the negative correlation between living in areas with high income inequality and presence of a regular physician (OR: 0.77; 95%CI 0.60;0.99) after controlling for individual characteristics. The study revealed a strong and consistent correlation between individual characteristics (mainly education and possession of health insurance), use of health care services, and presence of a regular physician. Presence of chronic and mental illnesses was strongly correlated with the use of health care services in the past year (regardless of the individual characteristics) but not with the presence of a regular physician.CONCLUSIONS Individual characteristics including higher education and possession of health insurance were important determinants of the use of health care services in the metropolitan area of Sao Paulo. A better understanding of these determinants is essential for the development of public policies that promote equitable use of health care services.