Lee-Carter method for forecasting mortality for Peruvian Population

In this article, we have modeled mortality rates of Peruvian female and male populations during the period of 1950-2017 using the Lee-Carter (LC) model. The stochastic mortality model was introduced by Lee and Carter (1992) and has been used by many authors for fitting and forecasting the human mortality rates. The Singular Value Decomposition (SVD) approach is used for estimation of the parameters of the LC model. Utilizing the best fitted auto regressive integrated moving average (ARIMA) model we forecast the values of the time dependent parameter of the LC model for the next thirty years. The forecasted values of life expectancy at different age group with $95\%$ confidence intervals are also reported for the next thirty years. In this research we use the data, obtained from the Peruvian National Institute of Statistics (INEI).Comment: 16 pages, 6 figure

Lee-Carter method for forecasting mortality for Peruvian Population

In this article, we have modeled mortality rates of Peruvian female and male populations during the period of 1950-2017 using the two-factor Lee-Carter (LC) model. The stochastic mortality model was introduced by Lee and Carter (1992) and has been used by many authors for fitting and forecasting the human mortality rates. The Singular Value Decomposition (SVD) approach is used for estimation of the parameters of the LC model. Utilizing the best fitted auto regressive integrated moving average (ARIMA) model we forecast the values of the time dependent parameter of the LC model for the next thirty years. The forecasted values of life expectancy at different age group with 95% confidence intervals are also reported for the next thirty  years. In this research we use the data, obtained from the Peruvian National Institute of Statistics (INEI).  In this article, we have modeled mortality rates of Peruvian female and male populations during the period of 1950-2017 using the two-factor Lee-Carter (LC) model. The stochastic mortality model was introduced by Lee and Carter (1992) and has been used by many authors for fitting and forecasting the human mortality rates. The Singular Value Decomposition (SVD) approach is used for estimation of the parameters of the LC model. Utilizing the best fitted auto regressive integrated moving average (ARIMA) model we forecast the values of the time dependent parameter of the LC model for the next thirty years. The forecasted values of life expectancy at different age group with 95% confidence intervals are also reported for the next thirty  years. In this research we use the data, obtained from the Peruvian National Institute of Statistics (INEI). &nbsp

Estimating the prevalence of anemia rates among children under five in Peruvian districts with a small sample size

In this paper we attempt to answer the following question: ``Is it possible to obtain reliable estimates for the prevalence of anemia rates in children under five years in the districts of Peru?'' Specifically, the interest of the present paper is to understand to which extent employing the basic and the spatial Fay-Herriot models can compensate for inadequate sample size in most of the sampled districts, and whether the way of choosing the spatial neighbors has an impact on the resulting inference. Furthermore, it is raised the question of how to choose an optimal way to define the neighbours. We present an illustrative analysis using the data from the Demographic and Family Health Survey of the year 2019, and the National Census carried out in 2017.Comment: 26 pages, 7 figure

DNA binding and meiotic chromosomal localization of the drosophila nod kinesin-like protein

AbstractThe Drosophila no distributive disjunction (nod) gene encodes a kinesin-like protein that has been proposed to push chromosomes toward the metaphase plate during female meiosis. We report that the nonmotor domain of the nod protein can mediate direct binding to DNA. Using an antiserum prepared against bacterially expressed nod protein, we show that during prometaphase nod protein is localized on oocyte chromosomes and is not restricted to either specific chromosomal regions or to the kinetochore. Thus, motorbased chromosome-microtubule interactions are not limited to the centromere, but extend along the chromosome arms, providing a molecular explanation for the polar ejection force

Identification of stromal ColXα1 and tumor-infiltrating lymphocytes as putative predictive markers of neoadjuvant therapy in estrogen receptor-positive/HER2-positive breast cancer

Patient characteristics. x indicates the case was included for the assay. (XLSX 44 kb

Impact of race, ethnicity, and BMI on achievement of pathologic complete response following neoadjuvant chemotherapy for breast cancer: a pooled analysis of four prospective Alliance clinical trials (A151426)

Previous studies demonstrated poor response to neoadjuvant systemic therapy (NST) for breast cancer among black women and women who are overweight or obese but this may be due to chemotherapy under dosing. We assessed associations of race, ethnicity and body mass index (BMI) with pathologic complete response (pCR) in clinical trial populations

¿Es posible obtener estimaciones confiables del porcentaje de anemia y retraso en el crecimiento en niños menores de cinco años en los distritos más pobres del Perú?

In this article, we describe and apply the Fay-Herriot model with spatially correlated random area effects (Pratesi & Salvati, 2006) in order to predict the prevalence of anemia and childhood stunting in Peruvian districts. This prediction is based on data from the Demographic and Family Health Survey of the year 2019, which collects information about anemia and childhood stunting in children under the age of 12 years, as well as the National Census carried out in 2017. Our main objective is to produce reliable predictions for districts with sample sizes too small to provide accurate direct estimates, as well as for districts not included in the sample. The basic Fay-Herriot model (Fay & Herriot, 1979) addresses this issue by incorporating auxiliary information, typically available from administrative or census records. The Fay-Herriot model with spatially correlated random area effects, in addition to auxiliary information, integrates geographical data about the areas, such as latitude and longitude. This allows for the modeling of spatial autocorrelations, which are not uncommon in socioeconomic and health surveys. To evaluate the mean square error of the aforementioned predictors, we employ the parametric bootstrap procedure developed in Molina et al. (2009)En este artículo, obtenemos predicciones confiables del porcentaje de niños con anemia y el porcentaje de niños con retraso del crecimiento por distrito en el Perú, utilizando los datos de la Endes del año 2019 y del censo nacional realizado el año 2017, en los distritos donde el tamaño de la muestra no es suficiente para implementar una estimación directa, y en los distritos no muestreados. Como el objetivo principal de las encuestas nacionales es describir el estado de la población (por ejemplo, la salud, el estado de empleo y desempleo, gastos familiares, educación, etc.), uno de los problemas más comunes de las encuestas nacionales es que estas son generalmente planeadas de tal forma que tengan una buena representación solamente a nivel nacional, nacional urbano, nacional rural o región natural. Por tal motivo, la inferencia a niveles más desagregados, como a nivel distrital o provincial, no es confiable por tener muestras pequeñas a estos niveles. Es decir, hay muchos distritos que no fueron incluidos en la muestra o no cuentan con suficientes observaciones para realizar estimaciones adecuadas al nivel provincial y distrital utilizando estimadores directos. En este trabajo, presentamos y aplicamos el modelo de Fay-Herriot espacial (Pratesi & Salvati, 2006) para obtener estimaciones robustas de la prevalencia de anemia y de retraso en el crecimiento en la niñez en los distritos del Perú donde no se tiene observaciones o estas son pocas para poder hacer inferencia. Este tipo de modelos usa la información global del censo y la combina con la información local y espacial de la Endes para obtener estimadores fiables de las variables de interés

Body Mass Index, PAM50 Subtype, and Outcomes in Node-Positive Breast Cancer: CALGB 9741 (Alliance)

BACKGROUND: Obesity at diagnosis is associated with poor prognosis in women with breast cancer, but few reports have been adjusted for treatment factors. METHODS: CALGB 9741 was a randomized trial of dose density and sequence of chemotherapy for node-positive breast cancer. All patients received doxorubicin, cyclophosphamide, and paclitaxel, dosed by actual body weight. Height and weight at diagnosis were abstracted from patient records, and the PAM50 assay was performed from archived specimens using the NanoString platform. Relationships between body mass index (BMI), PAM50, and recurrence-free and overall survival (RFS and OS) were evaluated using proportional hazards regression, adjusting for number of involved nodes, estrogen receptor (ER) status, tumor size, menopausal status, drug sequence, and dose density. All statistical tests were two-sided. RESULTS: Baseline height and weight were available for 1909 of 2005 enrolled patients; 1272 additionally had subtype determination by PAM50. Median baseline BMI was 27.4kg/m(2). After 11 years of median follow-up, there were 619 RFS events and 543 deaths. Baseline BMI was a statistically significant predictor of RFS (adjusted hazard ratio [HR] for each five-unit increase in BMI = 1.08, 95% confidence interval [CI] = 1.02 to 1.14, P = .01) and OS (adjusted HR = 1.08, 95% CI = 1.01 to 1.14, P = .02) BMI and molecular phenotypes were independent prognostic factors for RFS, with no statistically significant interactions detected. CONCLUSIONS: BMI at diagnosis was a statistically significant prognostic factor in a group of patients receiving optimally dosed chemotherapy. Additional research is needed to determine the impact of weight loss on breast cancer outcomes and to evaluate whether this impact is maintained across tumor subtypes