Forecasting hospital discharges for respiratory conditions in Costa Rica using climate and pollution data.

Respiratory diseases represent one of the most significant economic burdens on healthcare systems worldwide. The variation in the increasing number of cases depends greatly on climatic seasonal effects, socioeconomic factors, and pollution. Therefore, understanding these variations and obtaining precise forecasts allows health authorities to make correct decisions regarding the allocation of limited economic and human resources. We aimed to model and forecast weekly hospitalizations due to respiratory conditions in seven regional hospitals in Costa Rica using four statistical learning techniques (Random Forest, XGboost, Facebook's Prophet forecasting model, and an ensemble method combining the above methods), along with 22 climate change indices and aerosol optical depth as an indicator of pollution. Models were trained using data from 2000 to 2018 and were evaluated using data from 2019 as testing data. During the training period, we set up 2-year sliding windows and a 1-year assessment period, along with the grid search method to optimize hyperparameters for each model. The best model for each region was selected using testing data, based on predictive precision and to prevent overfitting. Prediction intervals were then computed using conformal inference. The relative importance of all climatic variables was computed for the best model, and similar patterns in some of the seven regions were observed based on the selected model. Finally, reliable predictions were obtained for each of the seven regional hospitals.

A New Mixture Model With Cure Rate Applied to Breast Cancer Data.

We introduce a new modelling for long-term survival models, assuming that the number of competing causes follows a mixture of Poisson and the Birnbaum-Saunders distribution. In this context, we present some statistical properties of our model and demonstrate that the promotion time model emerges as a limiting case. We delve into detailed discussions of specific models within this class. Notably, we examine the expected number of competing causes, which depends on covariates. This allows for direct modeling of the cure rate as a function of covariates. We present an Expectation-Maximization (EM) algorithm for parameter estimation, to discuss the estimation via maximum likelihood (ML) and provide insights into parameter inference for this model. Additionally, we outline sufficient conditions for ensuring the consistency and asymptotic normal distribution of ML estimators. To evaluate the performance of our estimation method, we conduct a Monte Carlo simulation to provide asymptotic properties and a power study of LR test by contrasting our methodology against the promotion time model. To demonstrate the practical applicability of our model, we apply it to a real medical dataset from a population-based study of incidence of breast cancer in São Paulo, Brazil. Our results illustrate that the proposed model can outperform traditional approaches in terms of model fitting, highlighting its potential utility in real-world scenarios.

Using the illness-death model to estimate age- and sex-standardized incidence rates of diabetes in Mexico from 2003 to 2015.

BACKGROUND: We aimed to estimate the age-specific and age-standardized incidence rate of diabetes for men and women in Mexico between 2003 and 2015, and to assess the relative change in incidence of diabetes between 2003 and 2015. METHODS: We use a partial differential equation describing the illness-death model to estimate the incidence rate (IR) of diabetes for the years 2003, 2009 and 2015 based on prevalence data from National Health Surveys conducted in Mexico, the mortality rate of the Mexican general population and plausible input values for age-specific mortality rate ratios associated with diabetes. RESULTS: The age-standardized IR of diabetes per 1000 person years (pryr) was similar among men (IRm) and women (IRw) in the year 2003 (IRm 6.1 vs. IRw 6.5 1000/pryr), 2009 (IRm: 7.0 vs. IRw: 8.4 1000/pryr), and in 2015 (IRm 8.0 vs. IRw 10.6 1000/pryr). The highest incident rates were observed among men and women in the 60-69 age group. CONCLUSIONS: Overall, the incidence rate of diabetes in Mexico between the years 2003 and 2015 remained stable. However, rates were markedly higher among women in the age group 40-49 and 50-59 in the year 2015 compared with rates in 2003.

Impact of the 2022 New Mexico, US wildfires on air quality and health.

The 2022 wildfires in New Mexico, United States, were unparalleled compared to past wildfires in the state in both their scale and intensity, resulting in poor air quality and a catastrophic loss of habitat and livelihood. Among all wildfires in New Mexico in 2022, six wildfires were selected for our study based on the size of the burn area and their proximity to populated areas. These fires accounted for approximately 90 % of the total burn area in New Mexico in 2022. We used a regional chemical transport model and data-fusion technique to quantify the contribution of these six wildfires (April 6 to August 22) on particulate matter (PM2.5: diameter ≤ 2.5 µm) and ozone (O3) concentrations, as well as the associated health impacts from short-term exposure. We estimated that these six wildfires emitted 152 thousand tons of PM2.5 and 287 thousand tons of volatile organic compounds to the atmosphere. We estimated that the average daily wildfire smoke PM2.5 across New Mexico was 0.3 µg/m3, though 1 h maximum exceeded 120 µg/m3 near Santa Fe. Average wildfire smoke maximum daily average 8-h O3 (MDA8-O3) contribution was 0.2 ppb during the study period over New Mexico. However, over the state 1 h maximum smoke O3 exceeded 60 ppb in some locations near Santa Fe. Estimated all-cause excess mortality attributable to short term exposure to wildfire PM2.5 and MDA8-O3 from these six wildfires were 18 (95 % Confidence Interval (CI), 15-21) and 4 (95 % CI: 3-6) deaths. Additionally, we estimate that wildfire PM2.5 was responsible for 171 (95 %: 124-217) excess cases of asthma emergency department visits. Our findings underscore the impact of wildfires on air quality and human health risks, which are anticipated to intensify with global warming, even as local anthropogenic emissions decline.

Temporally Dynamic, Cohort-Varying Value-Added Models.

We aim to estimate school value-added dynamically in time. Our principal motivation for doing so is to establish school effectiveness persistence while taking into account the temporal dependence that typically exists in school performance from one year to the next. We propose two methods of incorporating temporal dependence in value-added models. In the first we model the random school effects that are commonly present in value-added models with an auto-regressive process. In the second approach, we incorporate dependence in value-added estimators by modeling the performance of one cohort based on the previous cohort's performance. An identification analysis allows us to make explicit the meaning of the corresponding value-added indicators: based on these meanings, we show that each model is useful for monitoring specific aspects of school persistence. Furthermore, we carefully detail how value-added can be estimated over time. We show through simulations that ignoring temporal dependence when it exists results in diminished efficiency in value-added estimation while incorporating it results in improved estimation (even when temporal dependence is weak). Finally, we illustrate the methodology by considering two cohorts from Chile's national standardized test in mathematics.

Statistical modeling of maximum temperature in Guinea.

A statistical analysis of maximum temperature from twelve weather stations in parts of Guinea is provided. Using maximum likelihood estimation, maximum temperature data was fitted by the Generalized Extreme Value distribution. Data from all of the twelve stations were adequately fit by the Generalized Extreme Value distribution. Return level estimates are provided. Significant trends in maximum temperature were found for four of the stations. The four stations exhibited significant positive trends at the 5% significance level.

Estimativa de prevalência de doença de Chagas crônica nos municípios brasileiros / Estimation of prevalence of chronic Chagas disease in Brazilian municipalities / Estimación de la prevalencia de la enfermedad de Chagas crónica en los municipios brasileños

[RESUMO]. Objetivo. Este estudo teve como objetivo estimar a prevalência da doença de Chagas (DC) crônica (DCC) na população brasileira, em mulheres e em mulheres em idade fértil. Métodos. Foi realizada uma metanálise da literatura para extrair dados de prevalência de DCC na população brasileira, em mulheres e em mulheres em idade fértil, em municípios do Brasil, no período 2010–2022. Indi- cadores relacionados com a DCC disponíveis nos sistemas de informação em saúde foram selecionados em escala municipal. A modelagem estatística dos dados extraídos da metanálise em função daqueles obtidos dos sistemas de informação foi aplicada a modelos lineares, lineares generalizados e aditivos. Resultados. Foram selecionados os cinco modelos mais adequados de um total de 549 modelos testados para obtenção de um modelo de consenso (R2 ajustado = 54%). O preditor mais importante foi o cadastro autorreferido de DCC do sistema de informação da Atenção Primária à Saúde. Dos 5 570 munícipios brasi- leiros, a prevalência foi estimada como zero em 1 792 (32%); nos 3 778 municípios restantes, a prevalência média da doença foi estimada em 3,25% (± 2,9%). O número de portadores de DCC foi estimado na popu- lação brasileira (~3,7 milhões), mulheres (~2,1 milhões) e mulheres em idade fértil (~590 mil). A taxa de reprodução da doença foi calculada em 1,0336. Todas as estimativas se referem ao intervalo 2015–2016. Conclusões. As prevalências estimadas de DCC, especialmente entre mulheres em idade fértil, evidenciam o desafio da transmissão vertical em municípios brasileiros. Estas estimativas são comparadas aos padrões de projeções matemáticas, sugerindo sua incorporação ao Pacto Nacional para a Eliminação da Transmissão Vertical da DC.

[ABSTRACT]. Objective. The objective of this study is to estimate the prevalence of chronic Chagas disease (CCD) in Brazil: in the general population, in women, and in women of childbearing age. Methods. A meta-analysis of the literature was conducted to extract data on the prevalence of CCD in munici- palities in Brazil in the 2010–2022 period: in the general population, in women, and in women of childbearing age. Municipal-level CCD indicators available in health information systems were selected. Statistical mode- ling of the data extracted from the meta-analysis (based on data obtained from information systems) was applied to linear, generalized linear, and additive models. Results. The five most appropriate models were selected from a total of 549 models tested to obtain a con- sensus model (adjusted R2 = 54%). The most important predictor was self-reported CCD in the primary health care information system. Zero prevalence was estimated in 1 792 (32%) of Brazil’s 5 570 municipalities; in the remaining 3 778 municipalities, average prevalence of the disease was estimated at 3.25% (± 2.9%). The number of carriers of CCD was estimated for the Brazilian population (~3.7 million), for women (~2.1 million) and for women of childbearing age (~590 000). The disease reproduction rate was calculated at 1.0336. All estimates refer to the 2015–2016 period. Conclusions. The estimated prevalence of CCD, especially among women of childbearing age, highlights the challenge of vertical transmission in Brazilian municipalities. Mathematical projections suggest that these estimates should be included in the national program for the elimination of vertical transmission of Chagas disease.

[RESUMEN]. Objetivo. El objetivo de este estudio fue estimar la prevalencia de la enfermedad de Chagas crónica en la población brasileña en general, en las mujeres y en las mujeres en edad fértil. Métodos. Se realizó un metanálisis de la bibliografía para extraer datos sobre la prevalencia de la enfermedad de Chagas crónica en la población brasileña en general, en las mujeres y en las mujeres en edad fértil, en los municipios de Brasil durante el período 2010-2022. Se seleccionaron los indicadores relacionados con esa enfermedad disponibles en los sistemas municipales de información de salud. La modelización estadística de los datos extraídos del metanálisis, en función de los obtenidos de los sistemas de información, se aplicó a modelos lineales, lineales generalizados y aditivos. Resultados. Se seleccionaron los cinco modelos más apropiados de un total de 549 modelos evaluados, para obtener un modelo de consenso (R2 ajustado = 54%). El factor predictor más importante fue el registro de la enfermedad de Chagas crónica autodeclarada en el sistema de información de atención primaria de salud. De los 5570 municipios brasileños, en 1792 (32%) la prevalencia estimada fue nula y en los 3778 restantes la prevalencia media fue del 3,25% (± 2,9%). El número estimado de pacientes con enfermedad de Chagas crónica en la población brasileña en general, en las mujeres y en las mujeres en edad fértil fue de ~3,7 millo- nes, ~2,1 millones y ~590 000, respectivamente. La tasa calculada de reproducción de la enfermedad fue de 1,0336. Todas las estimaciones se refieren al período 2015-2016. Conclusiones. La prevalencia estimada de la enfermedad de Chagas crónica, especialmente en las mujeres en edad fértil, pone de manifiesto el desafío que representa la transmisión vertical en los municipios brasi- leños. Estas estimaciones están en línea con los patrones de las proyecciones matemáticas, y sugieren la necesidad de incorporarlas al Pacto Nacional para la Eliminación de la Transmisión Vertical de la Enfermedad de Chagas.

Non-stationary Bayesian spatial model for disease mapping based on sub-regions.

This paper aims to extend the Besag model, a widely used Bayesian spatial model in disease mapping, to a non-stationary spatial model for irregular lattice-type data. The goal is to improve the model's ability to capture complex spatial dependence patterns and increase interpretability. The proposed model uses multiple precision parameters, accounting for different intensities of spatial dependence in different sub-regions. We derive a joint penalized complexity prior to the flexible local precision parameters to prevent overfitting and ensure contraction to the stationary model at a user-defined rate. The proposed methodology can be used as a basis for the development of various other non-stationary effects over other domains such as time. An accompanying R package fbesag equips the reader with the necessary tools for immediate use and application. We illustrate the novelty of the proposal by modeling the risk of dengue in Brazil, where the stationary spatial assumption fails and interesting risk profiles are estimated when accounting for spatial non-stationary. Additionally, we model different causes of death in Brazil, where we use the new model to investigate the spatial stationarity of these causes.

A New Auto-Regressive Multi-Variable Modified Auto-Encoder for Multivariate Time-Series Prediction: A Case Study with Application to COVID-19 Pandemics.

The SARS-CoV-2 global pandemic prompted governments, institutions, and researchers to investigate its impact, developing strategies based on general indicators to make the most precise predictions possible. Approaches based on epidemiological models were used but the outcomes demonstrated forecasting with uncertainty due to insufficient or missing data. Besides the lack of data, machine-learning models including random forest, support vector regression, LSTM, Auto-encoders, and traditional time-series models such as Prophet and ARIMA were employed in the task, achieving remarkable results with limited effectiveness. Some of these methodologies have precision constraints in dealing with multi-variable inputs, which are important for problems like pandemics that require short and long-term forecasting. Given the under-supply in this scenario, we propose a novel approach for time-series prediction based on stacking auto-encoder structures using three variations of the same model for the training step and weight adjustment to evaluate its forecasting performance. We conducted comparison experiments with previously published data on COVID-19 cases, deaths, temperature, humidity, and air quality index (AQI) in São Paulo City, Brazil. Additionally, we used the percentage of COVID-19 cases from the top ten affected countries worldwide until May 4th, 2020. The results show 80.7% and 10.3% decrease in RMSE to entire and test data over the distribution of 50 trial-trained models, respectively, compared to the first experiment comparison. Also, model type#3 achieved 4th better overall ranking performance, overcoming the NBEATS, Prophet, and Glounts time-series models in the second experiment comparison. This model shows promising forecast capacity and versatility across different input dataset lengths, making it a prominent forecasting model for time-series tasks.

Informing policy via dynamic models: Cholera in Haiti.

Public health decisions must be made about when and how to implement interventions to control an infectious disease epidemic. These decisions should be informed by data on the epidemic as well as current understanding about the transmission dynamics. Such decisions can be posed as statistical questions about scientifically motivated dynamic models. Thus, we encounter the methodological task of building credible, data-informed decisions based on stochastic, partially observed, nonlinear dynamic models. This necessitates addressing the tradeoff between biological fidelity and model simplicity, and the reality of misspecification for models at all levels of complexity. We assess current methodological approaches to these issues via a case study of the 2010-2019 cholera epidemic in Haiti. We consider three dynamic models developed by expert teams to advise on vaccination policies. We evaluate previous methods used for fitting these models, and we demonstrate modified data analysis strategies leading to improved statistical fit. Specifically, we present approaches for diagnosing model misspecification and the consequent development of improved models. Additionally, we demonstrate the utility of recent advances in likelihood maximization for high-dimensional nonlinear dynamic models, enabling likelihood-based inference for spatiotemporal incidence data using this class of models. Our workflow is reproducible and extendable, facilitating future investigations of this disease system.

Safety performance of dedicated and preferential bus lanes using multivariate negative binomial models for Bogotá, Colombia.

Public transport priority systems such as Bus Rapid Transit (BRT) and Buses with High Level of Service (BHLS) are top-rated solutions to mobility in low-income and middle-income cities. There is scientific agreement that the safety performance level of these systems depends on their functional, operational, and infrastructure characteristics. However, there needs to be more evidence on how the different characteristics of bus corridors might influence safety. This paper aims to shed some light on this area by structuring a multivariate negative binomial model comparing crash risk on arterial roads, BRT, and BHLS corridors in Bogotá, Colombia. The analyzed infrastructure includes 712.1 km of arterial roads with standard bus service, 194.1 km of BRT network, and 135.6 km of BHLS network. The study considered crashes from 2015 to 2018 -fatalities, injuries, and property damage only- and 30 operational and infrastructure variables grouped into six classes -exposure, road design, infrastructure, public means of transport, and land use. A multicriteria process was applied for model selection, including the structure and predictive power based on [i] Akaike information criteria, [ii] K-fold cross-validation, and [iii] model parsimony. Relevant findings suggest that in terms of observed and expected accident rates and their relationship with the magnitude of exposure -logarithm of average annual traffic volumes at the peak hour (LOG_AAPHT) and the percentage of motorcycles, cars, buses, and trucks- the greatest risk of fatalities, injuries, and property damage occurs in the BHLS network. BRT network provides lower crash rates in less severe collisions while increasing injuries and fatalities. When comparing the BHLS network and the standard design of arterial roads, BHLS infrastructure, despite increasing mobility benefits, provides the lowest safety performance among the three analyzed networks. Individual factors of the study could also contribute to designing safer roads related to signalized intersection density and curvature. These findings support the unique characteristics and traffic dynamics present in the context of Bogotá that could inform and guide decisions of corresponding authorities in other highly dense urban areas from developing countries.

Single proxy control.

Negative control variables are sometimes used in nonexperimental studies to detect the presence of confounding by hidden factors. A negative control outcome (NCO) is an outcome that is influenced by unobserved confounders of the exposure effects on the outcome in view, but is not causally impacted by the exposure. Tchetgen Tchetgen (2013) introduced the Control Outcome Calibration Approach (COCA) as a formal NCO counterfactual method to detect and correct for residual confounding bias. For identification, COCA treats the NCO as an error-prone proxy of the treatment-free counterfactual outcome of interest, and involves regressing the NCO on the treatment-free counterfactual, together with a rank-preserving structural model, which assumes a constant individual-level causal effect. In this work, we establish nonparametric COCA identification for the average causal effect for the treated, without requiring rank-preservation, therefore accommodating unrestricted effect heterogeneity across units. This nonparametric identification result has important practical implications, as it provides single-proxy confounding control, in contrast to recently proposed proximal causal inference, which relies for identification on a pair of confounding proxies. For COCA estimation we propose 3 separate strategies: (i) an extended propensity score approach, (ii) an outcome bridge function approach, and (iii) a doubly-robust approach. Finally, we illustrate the proposed methods in an application evaluating the causal impact of a Zika virus outbreak on birth rate in Brazil.

Best practices for your confirmatory factor analysis: A JASP and lavaan tutorial.

Confirmatory factor analysis (CFA) is a fundamental method for evaluating the internal structural validity of measurement instruments. In most CFA applications, the measurement model serves as a means to an end rather than an end in itself. To select the appropriate model, prior validity evidence is crucial, and items are typically assessed on an ordinal scale, which has been used in the applied social sciences. However, textbooks on structural equation modeling (SEM) often overlook this specific case, focusing on applications estimable using maximum likelihood (ML) instead. Unfortunately, several popular commercial SEM software packages lack suitable solutions for handling this 'typical CFA', leading to confusion and suboptimal decision-making when conducting CFA in this context. This article conceptually contributes to this ongoing discussion by presenting a set of guidelines for conducting a typical CFA, drawing from recent empirical research. We provide a practical contribution by introducing and developing a tutorial example within the JASP and lavaan software platforms. Supplementary materials such as videos, files, and scripts are freely available.

Prediction models for physical function in COVID-19 survivors.

BACKGROUND: The burden of caring for patients who have survived COVID-19 will be enormous in the coming years, especially with respect to physical function. Physical function has been routinely assessed using the Post-COVID-19 Functional Status (PCFS) scale. AIM: This study built prediction models for the PCFS scale using sociodemographic data, clinical findings, lung function, and muscle strength. METHOD: Two hundred and one patients with post-COVID-19 syndrome (PCS) completed the PCFS scale to assess physical function. Their levels of general fatigue were also assessed using the Functional Assessment of Chronic Illness Therapy-Fatigue (FACIT-F) scale, handgrip strength (HGS), and spirometry. RESULTS: The number of participants who scored 0 (none), 1 (negligible), 2 (slight), 3 (moderate), and 4 (severe) on the PCFS scale was 25 (12%), 40 (20%), 39 (19%), 49 (24%), and 48 (24%), respectively. The PCFS scale was significantly correlated with the following variables: FACIT-F score (r = -0.424, P < 0.001), HGS (r = -0.339, P < 0.001), previous hospitalization (r = 0.226, P = 0.001), body mass index (r = 0.163, P = 0.021), and sex (r = -0.153, P = 0.030). The regression model with the highest coefficient of regression (R = 0.559) included the following variables: age, sex, body mass index, FACIT-F, HGS, and previous hospitalization. CONCLUSIONS: Worse general fatigue and HGS are associated with more severe physical function impairments in PCS patients. Furthermore, a history of prior hospitalization results in worse physical function. Thus, prediction models for the PCFS scale that incorporate objective measures enable a better assessment of the physical function of these patients, thus helping in the selection of candidates for a program of physical reconditioning.

Assessing the incorporation of latent variables in the estimation of the value of a statistical life.

For many years, the economic literature has recognized the role of attitudes, beliefs, and perceptions in estimating the value of a statistical life (VSL). However, few applications have attempted to include them. This article incorporates the perceived controllability and concern about traffic and cardiorespiratory risks to estimate VSL using a hybrid choice model (HCM). The HCM allows us to include unobserved heterogeneity and improve behavioral realism explicitly. Using data from a choice experiment conducted in Santiago, Chile, we estimate a VSL of US$3.78 million for traffic risks and US$2.06 million for cardiorespiratory risks. We found that higher controllability decreases the likelihood that the respondents would be willing to pay for risk reductions in both risks. On the other hand, concern about these risks decreases the willingness to pay for traffic risk reductions but increases it for cardiorespiratory risk reductions.

Statistical analysis of parameters and adsorption isotherm models.

The present work intends to discuss parameter estimation and statistical analysis in adsorption. The Langmuir and Tóth isotherm models are compared for a set of carbon dioxide adsorption data on 13X zeolite from literature at different temperatures: 303, 323, 373, and 423 K. Statistical analyses were performed under frequentist and Bayesian perspectives. Under the frequentist statistical view, parameters were estimated using Maximum Likelihood estimation (MLE). Statistical analyses of parameters were performed by confidence regions in terms of elliptical approximation and likelihood region, while the evaluation of models was performed by chi-square statistics. The results showed that, for these nonlinear models, the elliptical region offers a poor approximation of the parameter estimates' confidence region, especially for the most correlated parameter pairs. Additionally, the four-parameter Tóth's equation yields less correlated parameters than the three-parameter Langmuir model. From a Bayesian perspective, the Markov chain Monte Carlo (MCMC) technique facilitated the reconstruction of the probability density functions of parameters as well as enabled the propagation of parametric uncertainties in the model responses. Finally, the accurate assessment of experimental uncertainty significantly influences the evaluation of models and their respective parameters.

A Global Systematic Literature Review of Ecosystem Services in Reef Environments.

Ecosystem services (ES) embrace contributions of nature to human livelihood and well-being. Reef environments provide a range of ES with direct and indirect contributions to people. However, the health of reef environments is declining globally due to local and large-scale threats, affecting ES delivery in different ways. Mapping scientific knowledge and identifying research gaps on reefs' ES is critical to guide their management and conservation. We conducted a systematic assessment of peer-reviewed articles published between 2007 and 2022 to build an overview of ES research on reef environments. We analyzed the geographical distribution, reef types, approaches used to assess ES, and the potential drivers of change in ES delivery reported across these studies. Based on 115 articles, our results revealed that coral and oyster reefs are the most studied reef ecosystems. Cultural ES (e.g., subcategories recreation and tourism) was the most studied ES in high-income countries, while regulating and maintenance ES (e.g., subcategory life cycle maintenance) prevailed in low and middle-income countries. Research efforts on reef ES are biased toward the Global North, mainly North America and Oceania. Studies predominantly used observational approaches to assess ES, with a marked increase in the number of studies using statistical modeling during 2021 and 2022. The scale of studies was mostly local and regional, and the studies addressed mainly one or two subcategories of reefs' ES. Overexploitation, reef degradation, and pollution were the most commonly cited drivers affecting the delivery of provisioning, regulating and maintenance, and cultural ES. With increasing threats to reef environments, the growing demand for assessing the contributions to humans provided by reefs will benefit the projections on how these ES will be impacted by anthropogenic pressures. The incorporation of multiple and synergistic ecosystem mechanisms is paramount to providing a comprehensive ES assessment, and improving the understanding of functions, services, and benefits.

Development and internal validation of a clinical prediction model for spontaneous abortion risk in early pregnancy.

OBJECTIVE: This study aimed to develop and internally validate a prediction model for estimating the risk of spontaneous abortion in early pregnancy. METHODS: This prospective cohort study included 9,895 pregnant women who received prenatal care at a maternal health facility in China from January 2021 to December 2022. Data on demographics, medical history, lifestyle factors, and mental health were collected. A multivariable logistic regression analysis was performed to develop the prediction model with spontaneous abortion as the outcome. The model was internally validated using bootstrapping techniques, and its discrimination and calibration were assessed. RESULTS: The spontaneous abortion rate was 5.95% (589/9,895) 1. The final prediction model included nine variables: maternal age, history of embryonic arrest, thyroid dysfunction, polycystic ovary syndrome, assisted reproduction, exposure to pollution, recent home renovation, depression score, and stress score 1. The model showed good discrimination with a C-statistic of 0.88 (95% CI 0.87‒0.90) 1, and its calibration was adequate based on the Hosmer-Lemeshow test (p = 0.27). CONCLUSIONS: The prediction model demonstrated good performance in estimating spontaneous abortion risk in early pregnancy based on demographic, clinical, and psychosocial factors. Further external validation is recommended before clinical application.

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.