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Resumen: Los resultados en salud materna se miden principalmente por un indicador negativo como lo es la razón de mortalidad materna (RMM). Muchos países presentan dificultades a la hora de medir este indicador, que requiere una buena cobertura en la identificación y registro de los nacidos vivos, así como una buena capacidad para identificar, reportar y clasificar a las muertes maternas. Si los casos identificados no responden a estos atributos, es posible que, los datos publicados no sean confiables por subregistro y/o por mala clasificación. Múltiples inconvenientes en la correcta vigilancia de la mortalidad materna han posicionado la aparición de un indicador positivo que pone su mirada no en las mujeres que murieron, sino en aquellas que en condiciones habituales debieron morir y sobrevivieron. Esto corresponde al llamado Near Miss Materno (NMM) de la literatura sajona cuya denominación en español es Morbilidad Materna Extremadamente Grave (MMEG). En el presente artículo se analizan varios aspectos de la MMEG, a saber: su definición, clasificación, ventajas de su vigilancia, como contribuye a reducir la mortalidad materna, así como indicadores para la comparabilidad de los datos. Abstract: Re***sults in maternal health are mainly measured by a negative indicator such as the maternal mortality ratio (MMR). Many countries present difficulties when measuring this indicator, which requires good coverage with regards to the identification and registration of live births, as well as a good capacity to identify, report and classify maternal deaths. If the identified cases do not respond to these attributes, it is possible that the published data will not be reliable due to underreporting and/or misclassification. Multiple inconveniences in the correct monitoring of maternal mortality have positioned the appearance of a positive indicator that focuses not on the women who died, but on those who in normal conditions should have died and, in turn, survived. This corresponds to what is known as Maternal Near Miss (MNM) in English language literature, and as Morbilidad Materna Extremadamente Grave (MMEG) in Spanish. In this article, various aspects of the MNM are analyzed, namely: its definition, classification, advantages of its surveillance, how it contributes to reducing maternal mortality, as well as indicators for data comparability.

The external purpose of Employer Branding is attraction, that is, the brand’s capability to be preferred to its competitors through attractiveness and reputation. This study aims to (1) propose the Italian adaptation of the Employer Attractiveness scale (EmpAt scale) in its “real” version; (2) propose a tool to evaluate employer reputation; and (3) measure the convergent and predictive validity of these two measures. An online survey was administered to a sample of convenience ( N = 407; 56% were women). The results of exploratory and confirmatory factor analyses are presented for both scales, including correlations and regression results to demonstrate reliability and validity. Organizations could use these two tools to perform preliminary analyses to improve their employer branding, being aware of their own image’s strengths and areas for improvement, and thus differentiating themselves from competitors.

Data are limited on whether to adjust high-dose chemotherapy before autologous hematopoietic cell transplant (autoHCT) in obese patients. This study explores the effects of dose adjustment on the outcomes of obese patients, defined as body mass index (BMI) ≥ 30 kg/m2. Dose adjustment was defined as a reduction in standard dosing ≥20%, based on ideal, reported dosing and actual weights. We included 2 groups of US patients who had received autoHCT between 2008 and 2014. Specifically, we included patients with multiple myeloma (MM, n = 1696) treated with high-dose melphalan and patients with Hodgkin or non-Hodgkin lymphomas (n = 781) who received carmustine, etoposide, cytarabine, and melphalan conditioning. Chemotherapy dose was adjusted in 1324 patients (78%) with MM and 608 patients (78%) with lymphoma. Age, sex, BMI, race, performance score, comorbidity index, and disease features (stage at diagnosis, disease status, and time to transplant) were similar between dose groups. In multivariate analyses for MM, adjusting for melphalan dose and for center effect had no impact on overall survival (P = .894) and treatment-related mortality (TRM) (P = .62), progression (P = .12), and progression-free survival (PFS; P = .178). In multivariate analyses for lymphoma, adjusting chemotherapy doses did not affect survival (P = .176), TRM (P = .802), relapse (P = .633), or PFS (P = .812). No center effect was observed in lymphoma. This study demonstrates that adjusting chemotherapy dose before autoHCT in obese patients with MM and lymphoma does not influence mortality. These results do not support adjusting chemotherapy dose in this population.

Patients who undergo hematopoietic stem cell transplantation (HSCT) often experience multiple bacterial infections during the early post-transplant period. In this article, we consider a semiparametric regression model that correlates patient- and transplant-related risk factors with inter-infection gap times. Existing regression methods for recurrent gap times are not directly applicable to study post-transplant infection because the initiating event (transplant) is different than the recurrent events of interest (post-transplant infections); as a result, the time from transplant to the first infection and the time elapsed between consecutive infections have distinct biological meanings and hence follow different distributions. Moreover, risk factors may have different effects on these two types of gap times. We propose a semiparametric estimation procedure to evaluate the covariate effects on time from transplant to thefirst infection and on gap times between consecutive infections simultaneously. The proposed estimator accounts for dependent censoring induced by within-subject correlation among recurrent gap times and length bias in the last censored gap time due to intercept sampling. We study the finite sample properties through simulations and present an application of the proposed method to the post-HSCT bacterial infection data collected at the University of Minnesota.

Leprosy, also known as Hansen's, is one of the listed neglected tropical diseases as a major health problem global. Treatment is one of the main alternatives, however, the scarcity of medication and its poor distribution are important factors that have driven the spread of the disease, leading to irreversible and multi-resistant complications. This paper uses a distribution methodology to optimize medication administration, taking into account the most relevant attributes for the epidemiological profile of patients and the deficit in treatment via Polychemotherapy. Multi-criteria Decision Methods were applied based on AHP-Electre model in a database with information from patients in the state of Para between 2015 and 2020. The results pointed out that 84% of individuals did not receive any treatment and, among these, the method obtained a gain in the distribution of 68% in patients with positive diagnosis for leprosy.