Organização da atenção primária à saúde de municípios de São Paulo, Brasil: modelo de atenção e coerência com as diretrizes do Sistema Único de Saúde / Organización de la atención primaria de salud de municipios de São Paulo, Brasil: modelo de atención y coherencia con las directrices del Sistema Único de Salud / Primary health care organization in municipalities of São Paulo, Brazil: a model of care aligned with the Brazilian Unified National Health System's guidelines

Resumo: Este trabalho tem como objetivo analisar os principais padrões de organização das redes municipais de serviços de atenção primária à saúde (APS) e avaliá-los segundo os indicadores de interface entre gestão e gerenciamento local. Trata-se de pesquisa avaliativa que analisou 461 municípios de São Paulo, Brasil, que participaram do Inquérito de Avaliação da Qualidade de Serviços de Atenção Básica (QualiAB) em 2017/2018, classificados segundo a composição dos arranjos organizacionais de 2.472 serviços de APS. Para avaliar os padrões identificados, foram selecionados oito indicadores de gestão e gerenciamento local. Os resultados apontam dois grupos de municípios: homogêneos, com serviços de um mesmo arranjo (43,6%); e heterogêneos, com diferentes arranjos (56,4%). Os grupos foram subdivididos em sete padrões que variaram entre homogêneo-tradicional, homogêneo-Estratégia Saúde da Família, homogêneo-misto e diferentes combinações no grupo heterogêneo. Todos os indicadores apontaram diferenças significativas entre os grupos (p < 0,001), com destaque para o grupo homogêneo-tradicional, com padrão organizacional distante do modelo desejado para uma APS abrangente e resolutiva, enquanto aqueles com unidades de saúde da família (USF), e com unidades básicas com agentes comunitários de saúde e/ou equipes de saúde da família (UBS/USF) demonstraram um padrão mais aproximado desse modelo - com ações de planejamento e avaliação comprometidos com a realidade local e com a qualificação do trabalho. Discute-se a importância das políticas implementadas pela gestão federal e estadual e seu poder de indução na definição do modelo de atenção à saúde na APS dos municípios.

Resumen: El trabajo tiene el objetivo de analizar los principales patrones de organización de las redes municipales de servicios de atención primaria de salud (APS) y evaluarlos conforme los indicadores de interfaz entre la dirección y gestión local. Se trata de una investigación evaluativa que analizó 461 municipios de São Paulo, Brasil, que participaron de la Encuesta de Evaluación de la Calidad de los Servicios de Atención Primaria (QualiAB) en 2017/2018, clasificados según la composición de los arreglos organizativos de 2.472 servicios de APS. Para evaluar los patrones identificados, se seleccionaron ocho indicadores de dirección y gestión local. Los resultados indican dos grupos de municipios: homogéneos, con servicios de un mismo arreglo (43,6%) e heterogéneos, con arreglos diferentes (56,4%). Los grupos se subdividieron en siete patrones que iban desde homogéneo-tradicional, homogéneo-Estrategia de Salud de la Familia, homogéneo-mixto y diferentes combinaciones en el grupo heterogéneo. Todos los indicadores señalaron diferencias significativas entre los grupos (p < 0,001), con destaque para el grupo homogéneo-tradicional, con patrón organizativo alejado del modelo deseado para una APS completa y resolutiva, mientras aquellos con unidades de salud de la familia (USF), y con unidades básicas con agentes comunitarios de salud y/o equipos de salud de la familia (UBS/USF) demostraron un patrón más cercano a este modelo -con acciones de planificación y evaluación comprometidas con la realidad local y con la calificación del trabajo. Se discute la importancia de las políticas implementadas por la gestión federal y la gestión estatal y su poder de inducción para definir el modelo de atención a la salud en la APS de los municipios.

Abstract: This study analyzes the main organization patterns used by primary health care (PHC) services in municipal networks and evaluates them according to indicators of local management-administration interface. Evaluative research analyzed 461 municipalities in São Paulo, Brazil, that participated in the Primary Care Services Quality Assessment Survey (QualiAB) in 2017/2018, classified according to the organizational arrangements composition of 2,472 PHC services. Eight indicators of local management and administration were selected to evaluate the identified patterns. Results indicate two groups of municipalities: homogeneous, with services presenting the same arrangement (43.6%); and heterogeneous, with different arrangements (56.4%). These were subdivided into seven patterns that ranged from homogeneous-traditional, homogeneous-Family Health Strategy, homogeneous-mixed, and different combinations in the heterogeneous group. All indicators showed significant differences between groups (p < 0.001), especially the homogeneous-traditional group, which presented an organizational pattern far from the desired model of a comprehensive and problem-solving PHC. Those integrated with family health units (FHU) and basic health units with community health workers and/or family health teams (BHU/FHU) showed a pattern closer to a comprehensive model - with planning and evaluation actions committed to the local reality and qualification of care. Implementation of federal and state policies are essential for defining the PHC health care model adopted by municipalities.

Itinerários terapêuticos no cuidado em saúde em comunidades quilombolas / Therapeutic itineraries in health care in Quilombola communities

Resumo Este artigo teve como objetivo mapear os itinerários terapêuticos no cuidado em saúde em comunidades quilombolas rurais no norte de Minas Gerais, Brasil. Trata-se de um recorte de uma pesquisa qualitativa realizada em seis comunidades visitadas. Os dados foram produzidos por meio de 18 entrevistas individuais, analisados pelo referencial teórico-metodológico dos itinerários terapêuticos e organizados em três temas empíricos. As narrativas permitiram a compreensão dos percursos trilhados no cuidado em saúde pela população quilombola, a identificação dos componentes do subsistema popular (recursos naturais, o uso de chás e remédios caseiros), do subsistema familiar (transmissão de conhecimentos e herança cultural de cuidados), e do subsistema profissional (nível hospitalar, cuidados médicos, atenção primária e especializada). As dificuldades de acesso não decorrem apenas das distâncias geográficas, envolvem aspectos mais amplos da determinação social, como o racismo institucional, a baixa oferta de serviços, a necessidade de pagamento para deslocamentos e procedimentos médicos. Nesse sentido, fazem-se necessárias uma abordagem e intervenções das políticas públicas frente às desigualdades étnico-raciais, econômicas e de acesso aos serviços de cuidado em saúde.

Abstract This article aimed to map therapeutic itineraries in health care within rural Quilombola communities in the north of Minas Gerais, Brazil. This is a section of a qualitative research conducted in six visited communities. The data was collected through 18 individual interviews, analyzed using the theoretical-methodological framework of Therapeutic Itineraries, and organized into three empirical themes. The narratives allowed for understanding the paths taken in health care by the Quilombola population, identifying the components of the popular subsystem (natural resources, the use of teas and home remedies), the family subsystem (transmission of knowledge and cultural heritage of care), and the professional subsystem (hospital level, medical care, primary and specialized attention). The difficulties of access are not only due to geographical distances, but also broader aspects of social determination, such as institutional racism, low availability of services, the need for payment for transportation and medical procedures. In this sense, it is necessary to have an approach and interventions from public policies to address ethnic-racial, economic, and access inequalities in health care services.

Occlusion development after premature loss of deciduous anterior teeth: preliminary results of a 24-month prospective cohort study

ABSTRACT Objective: This study aimed to evaluate occlusion development after premature loss or extraction of deciduous anterior teeth, by means of a prospective cohort study. Methods: Fifteen infants and children aged 1 to 5 years old were longitudinally assessed (with loss or extraction of deciduous anterior teeth [n = 9], and without tooth losses [n = 6]). Photographs and dental casts at the baseline and after 24 months of follow-up were performed. Dental casts were scanned, and linear measurements were made on the digitalized models (missing tooth space, arch perimeter, arch length, arch width, intercanine length and intercanine width). The t-test was used for groups comparisons (α = 0.05). Results: Individuals' mean age at baseline was 2.93 (± 1.18) years. No statistically significant differences were observed in the missing tooth space in the group with tooth loss during the 24 months of follow-up (p > 0.05). Arch perimeter, arch length, arch width, intercanine length and intercanine width did not show differences between the groups (p > 0.05). Qualitative photographic evaluation revealed other changes in the dental arches and occlusion, such as exfoliation and eruption of deciduous teeth, eruption of permanent teeth, self-correction or establishment of malocclusion, among others. Conclusion: The results suggest that the premature loss of deciduous anterior teeth does not affect the perimeter, length and width of the dental arches; however, other alterations that lead to malocclusion could be established.

RESUMO Objetivo: Este estudo teve como objetivo avaliar o desenvolvimento da oclusão após perda prematura ou extração de dentes decíduos anteriores, por meio de um estudo de coorte prospectivo. Métodos: Quinze bebês e crianças de 1 a 5 anos foram avaliados longitudinalmente (com perda ou extração de dentes anteriores decíduos [n = 9] e sem perdas dentárias [n = 6]). Foram realizadas fotografias e modelos dentais no início e após 24 meses de acompanhamento. Os modelos dentários foram escaneados e medidas lineares foram feitas nos modelos digitalizados (espaço dentário perdido, perímetro da arcada, comprimento da arcada, largura da arcada, comprimento intercaninos e largura intercaninos). O teste t foi utilizado para comparações entre grupos (α = 0,05). Resultados: A média de idade dos indivíduos no início do estudo foi de 2,93 (± 1,18) anos. Não foram observadas diferenças estatisticamente significativas no espaço dentário perdido no grupo com perda dentária durante os 24 meses de acompanhamento (p > 0,05). O perímetro da arcada, comprimento da arcada, largura da arcada, comprimento intercaninos e largura intercaninos não apresentaram diferenças entre os grupos (p> 0,05). A avaliação fotográfica qualitativa revelou alterações nas arcadas dentárias e na oclusão, como: esfoliação e erupção de dentes decíduos, erupção de dentes permanentes, autocorreção ou estabelecimento de má oclusão, entre outras. Conclusão: Os resultados sugerem que a perda prematura de dentes anteriores decíduos não afeta o perímetro, comprimento e largura das arcadas dentárias; entretanto, outras alterações que levam à má oclusão poderiam ser estabelecidas.

Predicting the outcome of death by CALL Score in COVID-19 patients

SUMMARY OBJECTIVE: The aim of this study was to assess the performance of the CALL Score tool in predicting the death outcome in COVID-19 patients. METHODS: A total of 897 patients were analyzed. Univariate and multivariate logistic regression analyses were conducted to determine the association between characteristics of the CALL Score and the occurrence of death. The relationship between CALL Score risk classification and the occurrence of death was also examined. Receiver operating characteristic curve analysis was performed to identify optimal cutoff points for the CALL Score and the outcome. RESULTS: The study revealed that age>60 years, DHL>500, and lymphocyte count ≤1000 emerged as independent predictors of death. Higher risk classifications of the CALL Score were associated with an increased likelihood of death. The optimal CALL Score cutoff point for predicting the death outcome was 9.5 (≥9.5), with a sensitivity of 70.4%, specificity of 80.3%, and accuracy of 80%. CONCLUSION: The CALL Score showed promising discriminatory ability for death outcomes in COVID-19 patients. Age, DHL level, and lymphocyte count were identified as independent predictors. Further validation and external evaluation are necessary to establish the robustness and generalizability of the CALL Score in diverse clinical settings.

The Effect of Aortic Angulation on Clinical Outcomes of Patients Undergoing Transcatheter Aortic Valve Replacement

ABSTRACT Introduction: The aim of this study was to assess the impact of aortic angulation (AA) on periprocedural and in-hospital complications as well as mortality of patients undergoing Evolut™ R valve implantation. Methods: A retrospective study was conducted on 264 patients who underwent transfemoral-approach transcatheter aortic valve replacement with self-expandable valve at our hospital between August 2015 and August 2022. These patients underwent multislice computer tomography scans to evaluate AA. Transcatheter aortic valve replacement endpoints, device success, and clinical events were assessed according to the definitions provided by the Valve Academic Research Consortium-3. Cumulative events included paravalvular leak, permanent pacemaker implantation, new-onset stroke, and in-hospital mortality. Patients were divided into two groups, AA ≤ 48° and AA > 48°, based on the mean AA measurement (48.3±8.8) on multislice computer tomography. Results: Multivariable logistic regression analysis was performed to identify predictors of cumulative events, utilizing variables with a P-value < 0.2 obtained from univariable logistic regression analysis, including AA, age, hypertension, chronic renal failure, and heart failure. AA (odds ratio [OR]: 1.73, 95% confidence interval [CI]: 0.89-3.38, P=0.104), age (OR: 1.04, 95% CI: 0.99-1.10, P=0.099), hypertension (OR: 1.66, 95% CI: 0.82-3.33, P=0.155), chronic renal failure (OR: 1.82, 95% CI: 0.92-3.61, P=0.084), and heart failure (OR: 0.57, 95% CI: 0.27-1.21, P=0.145) were not found to be significantly associated with cumulative events in the multivariable logistic regression analysis. Conclusion: This study demonstrated that increased AA does not have a significant impact on intraprocedural and periprocedural complications of patients with new generation self-expandable valves implanted.

An emergency room influx and trauma cases prediction in a Brazilian ophthalmological hospital by an ophthalmologist without code experience / Predição do influxo de atendimentos e casos de trauma em um serviço de urgência oftalmológico brasileiro realizado por um oftalmologista sem experiência em programação

ABSTRACT Purpose: The emergency medical service is a fundamental part of healthcare, albeit crowded emergency rooms lead to delayed and low-quality assistance in actual urgent cases. Machine-learning algorithms can provide a smart and effective estimation of emergency patients' volume, which was previously restricted to artificial intelligence (AI) experts in coding and computer science but is now feasible by anyone without any coding experience through auto machine learning. This study aimed to create a machine-learning model designed by an ophthalmologist without any coding experience using AutoML to predict the influx in the emergency department and trauma cases. Methods: A dataset of 356,611 visits at Hospital da Universidade Federal de São Paulo from January 01, 2014 to December 31, 2019 was included in the model training, which included visits/day and the international classification disease code. The training and prediction were made with the Amazon Forecast by 2 ophthalmologists with no prior coding experience. Results: The forecast period predicted a mean emergency patient volume of 216.27/day in p90, 180.75/day in p50, and 140.35/day in p10, and a mean of 7.42 trauma cases/ day in p90, 3.99/day in p50, and 0.56/day in p10. In January of 2020, there were a total of 6,604 patient visits and a mean of 206.37 patients/day, which is 13.5% less than the p50 prediction. This period involved a total of 199 trauma cases and a mean of 6.21 cases/day, which is 55.77% more traumas than that by the p50 prediction. Conclusions: The development of models was previously restricted to data scientists' experts in coding and computer science, but transfer learning autoML has enabled AI development by any person with no code experience mandatory. This study model showed a close value to the actual 2020 January visits, and the only factors that may have influenced the results between the two approaches are holidays and dataset size. This is the first study to apply AutoML in hospital visits forecast, showing a close prediction of the actual hospital influx.

RESUMO Objetivo: Esse estudo tem como objetivo criar um modelo de Machine Learning por um oftalmologista sem experiência em programação utilizando auto Machine Learning predizendo influxo de pacientes em serviço de emergência e casos de trauma. Métodos: Um dataset de 366,610 visitas em Hospital Universitário da Universidade Federal de São Paulo de 01 de janeiro de 2014 até 31 de dezembro de 2019 foi incluído no treinamento do modelo, incluindo visitas/dia e código internacional de doenças. O treinamento e predição foram realizados com o Amazon Forecast por dois oftalmologistas sem experiência com programação. Resultados: O período de previsão estimou um volume de 206,37 pacientes/dia em p90, 180,75 em p50, 140,35 em p10 e média de 7,42 casos de trauma/dia em p90, 3,99 em p50 e 0,56 em p10. Janeiro de 2020 teve um total de 6.604 pacientes e média de 206,37 pacientes/dia, 13,5% menos do que a predição em p50. O período teve um total de 199 casos de trauma e média de 6,21 casos/dia, 55,77% mais casos do que a predição em p50. Conclusão: O desenvolvimento de modelos era restrito a cientistas de dados com experiencia em programação, porém a transferência de ensino com a tecnologia de auto Machine Learning permite o desenvolvimento de algoritmos por qualquer pessoa sem experiencia em programação. Esse estudo mostra um modelo com valores preditos próximos ao que ocorreram em janeiro de 2020. Fatores que podem ter influenciados no resultado foram feriados e tamanho do banco de dados. Esse é o primeiro estudo que aplicada auto Machine Learning em predição de visitas hospitalares com resultados próximos aos que ocorreram.

Machine learning in predicting severe acute respiratory infection outbreaks / Aprendizado de máquina na previsão de surtos de síndrome respiratória aguda grave / Aprendizaje automático en la predicción de brotes de síndrome respiratorio agudo grave

Abstract: Severe acute respiratory infection (SARI) outbreaks occur annually, with seasonal peaks varying among geographic regions. Case notification is important to prepare healthcare networks for patient attendance and hospitalization. Thus, health managers need adequate resource planning tools for SARI seasons. This study aims to predict SARI outbreaks based on models generated with machine learning using SARI hospitalization notification data. In this study, data from the reporting of SARI hospitalization cases in Brazil from 2013 to 2020 were used, excluding SARI cases caused by COVID-19. These data were prepared to feed a neural network configured to generate predictive models for time series. The neural network was implemented with a pipeline tool. Models were generated for the five Brazilian regions and validated for different years of SARI outbreaks. By using neural networks, it was possible to generate predictive models for SARI peaks, volume of cases per season, and for the beginning of the pre-epidemic period, with good weekly incidence correlation (R2 = 0.97; 95%CI: 0.95-0.98, for the 2019 season in the Southeastern Brazil). The predictive models achieved a good prediction of the volume of reported cases of SARI; accordingly, 9,936 cases were observed in 2019 in Southern Brazil, and the prediction made by the models showed a median of 9,405 (95%CI: 9,105-9,738). The identification of the period of occurrence of a SARI outbreak is possible using predictive models generated with neural networks and algorithms that employ time series.

Resumo: Surtos de síndrome respiratória aguda grave (SRAG) ocorrem anualmente, com picos sazonais variando entre regiões geográficas. A notificação dos casos é importante para preparar as redes de atenção à saúde para o atendimento e internação dos pacientes. Portanto, os gestores de saúde precisam ter ferramentas adequadas de planejamento de recursos para as temporadas de SRAG. Este estudo tem como objetivo prever surtos de SRAG com base em modelos gerados com aprendizado de máquina usando dados de internação por SRAG. Foram incluídos dados sobre casos de hospitalização por SRAG no Brasil de 2013 a 2020, excluindo os casos causados pela COVID-19. Estes dados foram preparados para alimentar uma rede neural configurada para gerar modelos preditivos para séries temporais. A rede neural foi implementada com uma ferramenta de pipeline. Os modelos foram gerados para as cinco regiões brasileiras e validados para diferentes anos de surtos de SRAG. Com o uso de redes neurais, foi possível gerar modelos preditivos para picos de SRAG, volume de casos por temporada e para o início do período pré-epidêmico, com boa correlação de incidência semanal (R2 = 0,97; IC95%: 0,95-0,98, para a temporada de 2019 na Região Sudeste). Os modelos preditivos obtiveram uma boa previsão do volume de casos notificados de SRAG; dessa forma, foram observados 9.936 casos em 2019 na Região Sul, e a previsão feita pelos modelos mostrou uma mediana de 9.405 (IC95%: 9.105-9.738). A identificação do período de ocorrência de um surto de SRAG é possível por meio de modelos preditivos gerados com o uso de redes neurais e algoritmos que aplicam séries temporais.

Resumen: Brotes de síndrome respiratorio agudo grave (SRAG) ocurren todos los años, con picos estacionales que varían entre regiones geográficas. La notificación de los casos es importante para preparar las redes de atención a la salud para el cuidado y hospitalización de los pacientes. Por lo tanto, los gestores de salud deben tener herramientas adecuadas de planificación de recursos para las temporadas de SRAG. Este estudio tiene el objetivo de predecir brotes de SRAG con base en modelos generados con aprendizaje automático utilizando datos de hospitalización por SRAG. Se incluyeron datos sobre casos de hospitalización por SRAG en Brasil desde 2013 hasta 2020, salvo los casos causados por la COVID-19. Se prepararon estos datos para alimentar una red neural configurada para generar modelos predictivos para series temporales. Se implementó la red neural con una herramienta de canalización. Se generaron los modelos para las cinco regiones brasileñas y se validaron para diferentes años de brotes de SRAG. Con el uso de redes neurales, se pudo generar modelos predictivos para los picos de SRAG, el volumen de casos por temporada y para el inicio del periodo pre-epidémico, con una buena correlación de incidencia semanal (R2 = 0,97; IC95%: 0,95-0,98, para la temporada de 2019 en la Región Sudeste). Los modelos predictivos tuvieron una buena predicción del volumen de casos notificados de SRAG; así, se observaron 9.936 casos en 2019 en la Región Sur, y la predicción de los modelos mostró una mediana de 9.405 (IC95%: 9.105-9.738). La identificación del periodo de ocurrencia de un brote de SRAG es posible a través de modelos predictivos generados con el uso de redes neurales y algoritmos que aplican series temporales.

Photobiomodulation for Oral Mucositis - A Microcosting and Budget Impact Analysis from a Brazilian Perspective

ABSTRACT Objective: To measure the costs of preventive and therapeutic protocols of Photobiomodulation (PBM) for oral mucositis (OM) and their budgetary impact on Brazil's Ministry of Health (BMH). Material and Methods: A partial economic analysis was performed to estimate the costs using a bottom-up approach from a social perspective. Monetary values were assigned in Brazilian reais (BRL). The costs of the preventive protocol were calculated for five, 30, and 33 consecutive PBM sessions, depending on the antineoplastic treatment instituted. The costs of the therapeutic protocol were calculated for 5 or 10 sessions. The annual financial and budgetary impact was calculated considering the groups of oncologic patients with a higher risk of development of OM, such as those with head and neck and hematological cancer and pediatric patients. Results: The cost of a PBM session was estimated at BRL 23.75. The financial impact of providing one preventive protocol per year for all oncologic patients would be BRL 14,282,680.00, 0.030% of the estimated budget for hospital and outpatient care of the BMH in 2022. The financial and budgetary impacts of providing one treatment for OM for all patients in one year would be BRL 2,225,630.31 (0.005%, most optimistic scenario) and BRL 4,451,355.63 (0.009%, most pessimistic scenario). Conclusion: The budgetary impact of implementing PBM protocols in the Brazilian Healthcare System is small, even in a pessimistic scenario.

Forest yield prediction under different climate change scenarios using data intelligent models in Pakistan / Previsão de produção florestal em diferentes cenários de mudanças climáticas usando modelos inteligentes de dados no Paquistão

Abstract This study aimed to develop and evaluate data driven models for prediction of forest yield under different climate change scenarios in the Gallies forest division of district Abbottabad, Pakistan. The Random Forest (RF) and Kernel Ridge Regression (KRR) models were developed and evaluated using yield data of two species (Blue pine and Silver fir) as an objective variable and climate data (temperature, humidity, rainfall and wind speed) as predictive variables. Prediction accuracy of both the models were assessed by means of root mean squared error (RMSE), mean absolute error (MAE), correlation coefficient (r), relative root mean squared error (RRMSE), Legates-McCabe's (LM), Willmott's index (WI) and Nash-Sutcliffe (NSE) metrics. Overall, the RF model outperformed the KRR model due to its higher accuracy in forecasting of forest yield. The study strongly recommends that RF model should be applied in other regions of the country for prediction of forest growth and yield, which may help in the management and future planning of forest productivity in Pakistan.

Resumo Este estudo teve como objetivo desenvolver e avaliar modelos baseados em dados para previsão da produção florestal em diferentes cenários de mudanças climáticas na divisão florestal Gallies do distrito de Abbottabad, Paquistão. Os modelos Random Forest (RF) e Kernel Ridge Regression (KRR) foram desenvolvidos e avaliados usando dados de produção de duas espécies (pinheiro-azul e abeto-prateado) como uma variável objetiva e dados climáticos (temperatura, umidade, precipitação e velocidade do vento) como preditivos variáveis. A precisão da previsão de ambos os modelos foi avaliada por meio de erro quadrático médio (RMSE), erro absoluto médio (MAE), coeficiente de correlação (r), erro quadrático médio relativo (RRMSE), Legates-McCabe's (LM), índice de Willmott (WI) e métricas Nash-Sutcliffe (NSE). No geral, o modelo RF superou o modelo KRR devido à sua maior precisão na previsão do rendimento florestal. O estudo recomenda fortemente que o modelo RF seja aplicado em outras regiões do país para previsão do crescimento e produtividade florestal, o que pode ajudar no manejo e planejamento futuro da produtividade florestal no Paquistão.

Prediction of visceral leishmaniasis incidence using the Seasonal Autoregressive Integrated Moving Average model (SARIMA) in the state of Maranhão, Brazil / Previsão da incidência da leishmaniose visceral usando o modelo de média móvel integrado autorregressivo sazonal (SARIMA) no Maranhão, Brasil

Abstract Visceral leishmaniasis (VL) is an infectious disease predominant in countries located in the tropics. The prediction of occurrence of infectious diseases through epidemiologic modeling has revealed to be an important tool in the understanding of its occurrence dynamic. The objective of this study was to develop a forecasting model for the incidence of VL in Maranhão using the Seasonal Autoregressive Integrated Moving Average model (SARIMA). We collected monthly data regarding VL cases from the National Disease Notification System (SINAN) corresponding to the period between 2001 and 2018. The Box-Jenkins method was applied in order to adjust a SARIMA prediction model for VL general incidence and by sex (male or female) for the period between January 2019 and December 2013. For 216 months of this time series, 10,431 cases of VL were notified in Maranhão, with an average of 579 cases per year. With regard to age range, there was a higher incidence among the pediatric public (0 to 14 years of age). There was a predominance in male cases, 6437 (61.71%). The Box-Pierce test figures for overall, male and female genders supported by the results of the Ljung-Box test suggest that the autocorrelations of residual values act as white noise. Regarding monthly occurrences in general and by gender, the SARIMA models (2,0,0) (2,0,0), (0,1,1) (0,1,1) and (0,1,1) (2, 0, 0) were the ones that mostly adjusted to the data respectively. The model SARIMA has proven to be an adequate tool for predicting and analyzing the trends in VL incidence in Maranhão. The time variation determination and its prediction are decisive in providing guidance in health measure intervention.

Resumo A leishmaniose visceral (LV) é uma doença de natureza infecciosa, predominante em países de zonas tropicais. A predição de ocorrência de doenças infecciosas através da modelagem epidemiológica tem se revelado uma importante ferramenta no entendimento de sua dinâmica de ocorrência. O objetivo deste estudo foi desenvolver um modelo de previsão da incidência da LV no Maranhão usando o modelo de Média Móvel Integrada Autocorrelacionada Sazonal (SARIMA). Foram coletados os dados mensais de casos de LV através do Sistema de Informação de Agravos de Notificação (SINAN) correspondentes ao período de 2001 a 2018. O método de Box-Jenkins foi aplicado para ajustar um modelo de predição SARIMA para incidência geral e por sexo (masculino e feminino) de LV para o período de janeiro de 2019 a dezembro de 2023. Durante o período de 216 meses dessa série temporal, foram registrados 10.431 casos de LV no Maranhão, com uma média de 579 casos por ano. Em relação à faixa etária, houve maior registro no público pediátrico (0 a 14 anos). Houve predominância do sexo masculino, com 6437 casos (61,71%). Os valores do teste de Box-Pierce para incidência geral, sexo masculino e feminino reforçados pelos resultados do teste Ljung-Box sugerem que as autocorrelações de resíduos apresentam um comportamento de ruído branco. Para incidência mensal geral e por sexo masculino e feminino, os modelos SARIMA (2,0,0) (2,0,0), (0,1,1) (0,1,1) e (0,1,1) (2, 0, 0) foram os que mais se ajustaram aos dados, respectivamente. O modelo SARIMA se mostrou uma ferramenta adequada de previsão e análise da tendência de incidência da LV no Maranhão. A determinação da variação temporal e sua predição são determinantes no norteamento de medidas de intervenção em saúde.

Fully independent validation of eleven prognostic scores predicting progression to critically ill condition in hospitalized patients with COVID-19

Abstract Introduction COVID-19 remains an important threat to global health and maintains the challenge of COVID-19 hospital care. To assist decision making regarding COVID-19 hospital care many instruments to predict COVID-19 progression to critical condition were developed and validated. Objective To validate eleven COVID-19 progression prediction scores for critically ill hospitalized patients in a Brazilian population. Methodology Observational study with retrospective follow-up, including 301 adults confirmed for COVID-19 sequentially. Participants were admitted to non-critical units for treatment of the disease, between January and April 2021 and between September 2021 and February 2022. Eleven prognostic scores were applied using demographic, clinical, laboratory and imaging data collected in the first 48 of the hospital admission. The outcomes of greatest interest were as originally defined for each score. The analysis plan was to apply the instruments, estimate the outcome probability reproducing the original development/validation of each score, then to estimate performance measures (discrimination and calibration) and decision thresholds for risk classification. Results The overall outcome prevalence was 41.8 % on 301 participants. There was a greater risk of the occurrence of the outcomes in older and male patients, and a linear trend with increasing comorbidities. Most of the patients studied were not immunized against COVID-19. Presence of concomitant bacterial infection and consolidation on imaging increased the risk of outcomes. College of London COVID-19 severity score and the 4C Mortality Score were the only with reasonable discrimination (ROC AUC 0.647 and 0.798 respectively) and calibration. The risk groups (low, intermediate and high) for 4C score were updated with the following thresholds: 0.239 and 0.318 (https://pedrobrasil.shinyapps.io/INDWELL/). Conclusion The 4C score showed the best discrimination and calibration performance among the tested instruments. We suggest different limits for risk groups. 4C score use could improve decision making and early therapeutic management at hospital care.

Exploring the effect of the structural model of active aging on the self-assessment of quality of life among older people: A cross-sectional and analytical study

ABSTRACT BACKGROUND: Although studies have examined the relationship between variables associated with active aging and quality of life (QoL), no studies have been identified to have investigated the effect of a structural model of active aging on QoL in a representative sample of older people in the community. OBJECTIVE: To measure the domains and facets of QoL in older people and identify the effect of the structural model of active aging on the self-assessment of QoL. DESIGN AND SETTING: This cross-sectional analytical study included 957 older people living in urban areas. Data were collected from households using validated instruments between March and June 2018. Descriptive, confirmatory factor, and structural equation modeling analyses were performed. RESULTS: Most older people self-rated their QoL as good (58.7%), and the highest mean scores were for the social relationships domain (70.12 ± 15.4) and the death and dying facet (75.43 ± 26.7). In contrast, the lowest mean scores were for the physical domains (64.41 ± 17.1) and social participation (67.20 ± 16.2) facets. It was found that active aging explained 50% of the variation in self-assessed QoL and directly and positively affected this outcome (λ = 0.70; P < 0.001). CONCLUSION: Active aging had a direct and positive effect on the self-assessment of QoL, indicating that the more individuals actively aged, the better the self-assessment of QoL.

Isotemporal substitution analysis of time between sleep, sedentary behavior, and physical activity on depressive symptoms in older adults: a cross-sectional study

ABSTRACT BACKGROUND: Compared to young individuals, older adults participate more in sedentary behavior (SB) and less in physical activity (PA). These behaviors are associated with numerous adverse health factors. OBJECTIVE: The purpose of the study was to examine the hypothetical effects of substituting time spent sleeping, performing SB, and performing moderate-to-vigorous physical activity (MVPA) on depressive symptomatology in older adults. DESIGN AND SETTING: An analytical cross-sectional study employing exploratory survey methods was conducted in the city of Alcobaça in the state of Bahia, Brazil METHODS: The study included 473 older adults who answered a structured questionnaire during an interview. Exposure time to SB and PA level were assessed using the International Physical Activity Questionnaire, and depressive symptoms were analyzed using the short version of the Geriatric Depression Scale. An isotemporal replacement model was used to evaluate the effects of different SB sessions on depressive symptomatology. RESULTS: An increase in the risk of depressive symptoms was observed when MVPA and sleep time were substituted for the same SB time at all times tested, with maximum values of 40% and 20%, respectively. Opposite substitution of MVPA and sleep time increments reduced the risk of depressive symptomatology by 28% and 17%, respectively. CONCLUSIONS: The results of the present study indicate that replacing SB with the same amount of sleep or MVPA may reduce depressive symptoms. The longer the reallocation time, the greater are the benefits.

Modelos de agendamento e o acesso a imunização na atenção primária a saúde / Appointment models and access to immunization in primary health care / Modelos de cita y acceso a inmunización en atención primaria de salud

A Atenção Primária à Saúde é caracterizada como a porta de entrada preferencial do sistema de saúde com território adscrito. Desta forma, deve prover ações de promoção, prevenção e reabilitação da saúde. Sendo assim, a escassez de investigações sobre o acesso à saúde e a imunização levanta questões sobre como os modelos de agendamento impactam a disponibilidade e a acessibilidade das vacinas. Trata-se de um estudo reflexivo conduzido no período de junho a agosto de 2023. O estudo tem como base a análise discursiva das orientações do Ministério da Saúde, assim como a aceitabilidade, satisfação e, consequentemente, adesão à vacinação por parte dos usuários. A pesquisa originou-se a partir das provocações e debates realizados pelo Grupo de Pesquisa "Abordagens Tecnológicas no Cuidado à Saúde e Promoção da Saúde" da Escola de Enfermagem da Universidade de São Paulo (EEUSP). Está dividido em três seções que abordam os principais pontos de reflexão. O estudo considerou que é notável que a maneira como o usuário é recebido e tratado pelo sistema influencia diretamente a aceitabilidade, satisfação e, consequentemente, adesão à vacinação. Portanto, ao escolher e planejar o método de agendamento do serviço de saúde, as particularidades da área de vacinação devem ser consideradas, contemplando a demanda espontânea e a abordagem e controle do usuário.

Primary Health Care is characterized as the preferred gateway to the health system with an assigned territory. In this way, it must provide health promotion, prevention and rehabilitation actions. Therefore, the scarcity of research on access to healthcare and immunization raises questions about how scheduling models impact the availability and accessibility of vaccines. This is a reflective study conducted from June to August 2023. The study is based on the discursive analysis of the Ministry of Health's guidelines, as well as the acceptability, satisfaction and, consequently, adherence to vaccination by users. The research originated from the provocations and debates carried out by the Research Group "Technological Approaches in Health Care and Health Promotion" at the School of Nursing of the University of São Paulo (EEUSP). It is divided into three sections that address the main points of reflection. The study considered that it is notable that the way the user is received and treated by the system directly influences acceptability, satisfaction and, consequently, adherence to vaccination. Therefore, when choosing and planning the health service scheduling method, the particularities of the vaccination area must be considered, considering spontaneous demand and user approach and control.

La Atención Primaria de Salud se caracteriza por ser la puerta de entrada preferente al sistema de salud con un territorio asignado. De esta manera, debe brindar acciones de promoción, prevención y rehabilitación de la salud. Por lo tanto, la escasez de investigaciones sobre el acceso a la atención sanitaria y la inmunización plantea interrogantes sobre cómo los modelos de programación afectan la disponibilidad y accesibilidad de las vacunas. Se trata de un estudio reflexivo realizado de junio a agosto de 2023. El estudio se basa en el análisis discursivo de las directrices del Ministerio de Salud, así como de la aceptabilidad, satisfacción y, en consecuencia, adherencia a la vacunación por parte de los usuarios. La investigación surgió de las provocaciones y debates realizados por el Grupo de Investigación "Enfoques Tecnológicos en Atención y Promoción de la Salud" de la Escuela de Enfermería de la Universidad de São Paulo (EEUSP). Se divide en tres apartados que abordan los principales puntos de reflexión. El estudio consideró que se destaca que la forma en que el usuario es recibido y tratado por el sistema influye directamente en la aceptabilidad, satisfacción y, en consecuencia, en la adherencia a la vacunación. Por lo tanto, en la elección y planificación del método de programación de los servicios de salud se deben considerar las particularidades del área de vacunación, considerando la demanda espontánea y el abordaje y control de los usuarios.

Current research status of animal models for acute-on-chronic liver failure / 临床肝胆病杂志

Acute-on-chronic liver failure has complex conditions， rapid progression， and a high mortality rate， and further studies are still needed to clarify its pathogenesis and etiology. The establishment of animal models for acute-on-chronic liver failure can not only provide a good basis for exploring the pathogenesis of acute-on-chronic liver failure， but also provide an experimental basis for clinical treatment. Through a literature review， this article summarizes the methods commonly used to establish the animal models of acute-on-chronic liver failure， including carbon tetrachloride combined with LPS/GaIN， thioacetamide combined with LPS， serum albumin， and bile duct ligation. This article analyzes the characteristics of various animal models， so as to provide documentary and experimental bases for further exploration of more ideal animal models.

Establishment and application of animal models for portal vein thrombosis / 临床肝胆病杂志

Portal vein thrombosis （PVT） refers to thromboembolism that occurs in the extrahepatic main portal vein and/or intrahepatic portal vein branches. PVT is the result of the combined effect of multiple factors， but its pathogenesis remains unclear. Animal models are an important method for exploring the pathophysiological mechanism of PVT. Based on the different species of animals， this article reviews the existing animal models of PVT in terms of modeling methods， principles， advantages and disadvantages， and application.

Artificial intelligence ChatGPT in ophthalmology and scientific writing / 国际眼科杂志(Guoji Yanke Zazhi)

ChatGPT is a large language models(LLMs)that uses deep learning techniques to produce human-like responses to natural language inputs. It belongs to the family of generative pre-training transformer(GPT)models currently publicly available developed by OpenAI in November 2022. ChatGPT is capable of capturing the nuances and intricacies of human language, generating appropriate and contextually relevant responses. It can assist medical professionals in various tasks, such as research, diagnosis, patient monitoring, and medical education, from identifying research programs to assisting in clinical and laboratory diagnosis, to know new developments in their fields and scientific writing. ChatGPT has also attracted increasing attention and widely used in ophthalmology. However, the use of ChatGPT and other artificial intelligence tools in such tasks comes now with several limitations, ethical and legal concerns, such as credibility, plagiarism, copyright infringement, and biases. Future research can focus on developing new methods to mitigate these limitations while harnessing the benefits of ChatGPT in medicine and related aspects.

Forest yield prediction under different climate change scenarios using data intelligent models in Pakistan

Abstract This study aimed to develop and evaluate data driven models for prediction of forest yield under different climate change scenarios in the Gallies forest division of district Abbottabad, Pakistan. The Random Forest (RF) and Kernel Ridge Regression (KRR) models were developed and evaluated using yield data of two species (Blue pine and Silver fir) as an objective variable and climate data (temperature, humidity, rainfall and wind speed) as predictive variables. Prediction accuracy of both the models were assessed by means of root mean squared error (RMSE), mean absolute error (MAE), correlation coefficient (r), relative root mean squared error (RRMSE), Legates-McCabes (LM), Willmotts index (WI) and Nash-Sutcliffe (NSE) metrics. Overall, the RF model outperformed the KRR model due to its higher accuracy in forecasting of forest yield. The study strongly recommends that RF model should be applied in other regions of the country for prediction of forest growth and yield, which may help in the management and future planning of forest productivity in Pakistan.

Resumo Este estudo teve como objetivo desenvolver e avaliar modelos baseados em dados para previsão da produção florestal em diferentes cenários de mudanças climáticas na divisão florestal Gallies do distrito de Abbottabad, Paquistão. Os modelos Random Forest (RF) e Kernel Ridge Regression (KRR) foram desenvolvidos e avaliados usando dados de produção de duas espécies (pinheiro-azul e abeto-prateado) como uma variável objetiva e dados climáticos (temperatura, umidade, precipitação e velocidade do vento) como preditivos variáveis. A precisão da previsão de ambos os modelos foi avaliada por meio de erro quadrático médio (RMSE), erro absoluto médio (MAE), coeficiente de correlação (r), erro quadrático médio relativo (RRMSE), Legates-McCabes (LM), índice de Willmott (WI) e métricas Nash-Sutcliffe (NSE). No geral, o modelo RF superou o modelo KRR devido à sua maior precisão na previsão do rendimento florestal. O estudo recomenda fortemente que o modelo RF seja aplicado em outras regiões do país para previsão do crescimento e produtividade florestal, o que pode ajudar no manejo e planejamento futuro da produtividade florestal no Paquistão.

Prediction of visceral leishmaniasis incidence using the Seasonal Autoregressive Integrated Moving Average model (SARIMA) in the state of Maranhão, Brazil

Abstract Visceral leishmaniasis (VL) is an infectious disease predominant in countries located in the tropics. The prediction of occurrence of infectious diseases through epidemiologic modeling has revealed to be an important tool in the understanding of its occurrence dynamic. The objective of this study was to develop a forecasting model for the incidence of VL in Maranhão using the Seasonal Autoregressive Integrated Moving Average model (SARIMA). We collected monthly data regarding VL cases from the National Disease Notification System (SINAN) corresponding to the period between 2001 and 2018. The Box-Jenkins method was applied in order to adjust a SARIMA prediction model for VL general incidence and by sex (male or female) for the period between January 2019 and December 2013. For 216 months of this time series, 10,431 cases of VL were notified in Maranhão, with an average of 579 cases per year. With regard to age range, there was a higher incidence among the pediatric public (0 to 14 years of age). There was a predominance in male cases, 6437 (61.71%). The Box-Pierce test figures for overall, male and female genders supported by the results of the Ljung-Box test suggest that the autocorrelations of residual values act as white noise. Regarding monthly occurrences in general and by gender, the SARIMA models (2,0,0) (2,0,0), (0,1,1) (0,1,1) and (0,1,1) (2, 0, 0) were the ones that mostly adjusted to the data respectively. The model SARIMA has proven to be an adequate tool for predicting and analyzing the trends in VL incidence in Maranhão. The time variation determination and its prediction are decisive in providing guidance in health measure intervention.

Resumo A leishmaniose visceral (LV) é uma doença de natureza infecciosa, predominante em países de zonas tropicais. A predição de ocorrência de doenças infecciosas através da modelagem epidemiológica tem se revelado uma importante ferramenta no entendimento de sua dinâmica de ocorrência. O objetivo deste estudo foi desenvolver um modelo de previsão da incidência da LV no Maranhão usando o modelo de Média Móvel Integrada Autocorrelacionada Sazonal (SARIMA). Foram coletados os dados mensais de casos de LV através do Sistema de Informação de Agravos de Notificação (SINAN) correspondentes ao período de 2001 a 2018. O método de Box-Jenkins foi aplicado para ajustar um modelo de predição SARIMA para incidência geral e por sexo (masculino e feminino) de LV para o período de janeiro de 2019 a dezembro de 2023. Durante o período de 216 meses dessa série temporal, foram registrados 10.431 casos de LV no Maranhão, com uma média de 579 casos por ano. Em relação à faixa etária, houve maior registro no público pediátrico (0 a 14 anos). Houve predominância do sexo masculino, com 6437 casos (61,71%). Os valores do teste de Box-Pierce para incidência geral, sexo masculino e feminino reforçados pelos resultados do teste Ljung-Box sugerem que as autocorrelações de resíduos apresentam um comportamento de ruído branco. Para incidência mensal geral e por sexo masculino e feminino, os modelos SARIMA (2,0,0) (2,0,0), (0,1,1) (0,1,1) e (0,1,1) (2, 0, 0) foram os que mais se ajustaram aos dados, respectivamente. O modelo SARIMA se mostrou uma ferramenta adequada de previsão e análise da tendência de incidência da LV no Maranhão. A determinação da variação temporal e sua predição são determinantes no norteamento de medidas de intervenção em saúde.

Los determinantes de la estancia hospitalaria en un hospital universitario de alta complejidad

Objetivo: Establecer y cuantificar los determinantes de la estancia hospitalaria en un hospital universitario de Medellín de alta complejidad de Medellín, entre 2013 y 2018, valorar su importancia y modelar la estancia esperada. Metodología: Estudio observacional analítico retrospectivo de datos agregados. Siguiendo el método paso a paso, se corrieron siete modelos con estancia hospitalaria media como variable dependiente y las respectivas variables independientes: complejidad, oportunidad de apoyos diagnósticos, disponibilidad de insumos, casos de estancia prolongada y capacidad financiera. Se seleccionó el mejor modelo usando los criterios de ajuste Akaike e información Bayesiana, junto con las medidas de significancia global y significancia individual de los coeficientes. Se realizaron pruebas estadísticas de validez del modelo y se calcularon los coeficientes estandarizados. Resultados: Los valores medios de las variables más relevantes y su desviación estándar (de) fueron: estancia hospitalaria media, 8,09 días (de = 0,40); complejidad por consumo de recursos, 1,28 unidades (de = 0,07); apoyos diagnósticos, 90,74 mil estudios (de = 10,05); casos de estancia extrema, 4,36 % (de = 0,70), y complejidad por casuística, 1 (de = 0,03). Significancia global F = 55,2, p< 0,001. Significancia de los coeficientes: complejidad por consumo de recursos, p< 0,01; apoyos diagnósticos y casos de estancia extrema, p< 0,001; complejidad por casuística, p< 0,05. Coeficientes estandarizados: complejidad por consumo de recursos, 0,35; apoyos diagnósticos, 0,35; casos de estancia extrema, 0,26, y complejidad por casuística, 0,24. R2 ajustado 0,82. Conclusión: Los determinantes de la estancia hospitalaria en orden de importancia son: complejidad por consumo de recursos, apoyos diagnósticos, casos de estancia extrema, complejidad por casuística, inventario disponible y ganancias brutas.

Objective: To establish and quantify the determinants of hospital stay in a high complexity university hospital in Medellin between 2013 and 2018, assess their importance, and model the expected length of stay. Methodology: Retrospective analytical observational study of aggregate data. While following the method step by step, seven models were used, where mean hospital stay was the dependent variable and the respective independent variables were complexity, timeliness of diagnostic procedures, availability of supplies, cases of prolonged stay and financial capacity. The best model was selected using the Akaike and Bayesian information criterion, along with measures of both overall significance and individual significance of the coefficients. Statistical tests of model validity were performed and standardized coefficients were calculated. Results: The mean values of the most relevant variables and their standard deviation (SD) were: mean hospital stay, 8.09 days (SD = 0.40); complexity by resource consumption, 1.28 units (SD = 0.07); diagnostic procedures, 90.74 thousand studies (SD = 10.05); cases of extremely prolonged stay, 4.36% (SD = 0.70), and complexity by casuistry, 1 (SD = 0.03). Overall significance: F = 55.2, p < 0.001. Significance of coefficients: complexity by resource consumption, p < 0.01; diagnostic procedures and cases of extremely prolonged stay, p < 0.001; complexity by casuistry, p < 0.05. Standardized coefficients: complexity by resource consumption, 0.35; diagnostic procedures, 0.35; cases of extremely prolonged stay, 0.26; and complexity by casuistry, 0.24. Adjusted R2 0.82. Conclusion: In order of importance, the determinants of hospital stay are complexity by resource consumption, diagnostic procedures, extremely prolonged stay, complexity by casuistry, available inventory and gross profit.

Objetivo: Estabelecer e quantificar os determinantes da permanência hospitalar em um hospital universitário de alta complexidade de Medellín, entre 2013 e 2018, valorar sua importância e fazer a modelação da permanência esperada. Metodologia: Estudo observacional analítico retrospectivo de dados agregados. Seguindo o método passo a passo, foram aplicados sete modelos com permanência hospitalar média como variável dependente e as respectivas variáveis independentes: complexidade, oportunidade de apoios diagnósticos, disponibilidade de insumos, casos de permanência prolongada e capacidade financeira. Selecionou-se o melhor modelo usando os critérios de ajuste Akaike e informação Bayesiana, junto com as medidas de significância individual dos coeficientes. Realizaram-se provas estatísticas de validade do modelo e calcularam-se os coeficientes padronizados. Resultados: Os valores médios das variáveis mais relevantes e seu desvio-padrão (DP) foram: permanência hospitalar média, 8.09 dias (DP = 0,40); complexidade por consumo de recursos, 1,28 unidades (DP = 0,07); apoios diagnósticos, 90,74 mil estudos (DP = 10,05); casos de permanência extrema, 4,36 % (DP = 0,70), e complexidade por casuística, 1 (DP = 0,03). Significância global F = 55,2, p < 0,001. Significância dos coeficientes: complexidade por consumo de recursos, p < 0,01; apoios diagnósticos e casos de permanência extrema p < 0,001; complexidade por casuística, p < 0,05. Coeficientes padronizados: complexidade por consumo de recursos, 0,35; apoios diagnósticos, 0,35; casos de permanência extrema, 0,26 e complexidade por casuística, 0,24. R2 ajustado 0,82. Conclusão: Os determinantes da permanência hospitalar em ordem de importância são: complexidade por consumo de recursos, apoios diagnósticos, casos de permanência extrema, complexidade por casuística, inventário disponível e lucros brutos.