Patient dissatisfaction associated with physician-patient linguistic discordance in California clinics: an analytical cross-sectional study.

BACKGROUND: Patient satisfaction is considered as a product of two psychological processes, a cognitive one, including expectations and perceptions, and an emotional one resulting from the congruence between expectation and subjective perception of the user. The objective was to identify the factors associated with the level of perceived satisfaction in patients treated in 36 nonprofit health clinics that offer comprehensive health care services in four counties in the state of California, United States. METHODS: Cross-sectional analytical study in 14 clinics in four California counties. It consisted of the application of a 30-item questionnaire to determine the degree of patient satisfaction with the clinic. The factorial composition of the quality of care and clinic quality components was analyzed and two factors with an Eigen value greater than 1 were obtained. RESULTS: A total of 846 responses were registered. Factor analysis identified two underlying dimensions: Physician Attitude and Empathy. It was found that the discordance in language between the physician and the patient generates a difference in the perception of satisfaction. Patients who prefer to speak English have better satisfaction than those who speak Spanish. Spanish speakers who do not have interpreter have lower satisfaction than those who do (p < 0,01). CONCLUSIONS: The most important sociodemographic cofactor was language. Satisfaction decreased in Spanish-speaking patients who were not proficient in the use of English since they expressed fewer comments and doubts.

A comparison of statistical methods for modeling count data with an application to hospital length of stay.

BACKGROUND: Hospital length of stay (LOS) is a key indicator of hospital care management efficiency, cost of care, and hospital planning. Hospital LOS is often used as a measure of a post-medical procedure outcome, as a guide to the benefit of a treatment of interest, or as an important risk factor for adverse events. Therefore, understanding hospital LOS variability is always an important healthcare focus. Hospital LOS data can be treated as count data, with discrete and non-negative values, typically right skewed, and often exhibiting excessive zeros. In this study, we compared the performance of the Poisson, negative binomial (NB), zero-inflated Poisson (ZIP), and zero-inflated negative binomial (ZINB) regression models using simulated and empirical data. METHODS: Data were generated under different simulation scenarios with varying sample sizes, proportions of zeros, and levels of overdispersion. Analysis of hospital LOS was conducted using empirical data from the Medical Information Mart for Intensive Care database. RESULTS: Results showed that Poisson and ZIP models performed poorly in overdispersed data. ZIP outperformed the rest of the regression models when the overdispersion is due to zero-inflation only. NB and ZINB regression models faced substantial convergence issues when incorrectly used to model equidispersed data. NB model provided the best fit in overdispersed data and outperformed the ZINB model in many simulation scenarios with combinations of zero-inflation and overdispersion, regardless of the sample size. In the empirical data analysis, we demonstrated that fitting incorrect models to overdispersed data leaded to incorrect regression coefficients estimates and overstated significance of some of the predictors. CONCLUSIONS: Based on this study, we recommend to the researchers that they consider the ZIP models for count data with zero-inflation only and NB models for overdispersed data or data with combinations of zero-inflation and overdispersion. If the researcher believes there are two different data generating mechanisms producing zeros, then the ZINB regression model may provide greater flexibility when modeling the zero-inflation and overdispersion.

A population structure-sensitive mathematical model assessing the effects of vaccination during the third surge of COVID-19 in Italy.

We provide a non-autonomous mathematical model to describe some of the most relevant parameters associated to the COVID-19 pandemic, such as daily and cumulative deaths, active cases, and cumulative incidence, among others. We will take into consideration the ways in which people from four different age ranges react to the virus. Using an appropriate transmission function, we estimate the impact of the third surge of COVID-19 in Italy. Also, we assess two different vaccination programmes. In one of them, a single shot is administered to all citizens over 16 years old before second shots are available. In the second model, first and second shots are administered to each citizen within, approximately, 20 days of time-gap.

A SIR-type model describing the successive waves of COVID-19.

It is well-known that the classical SIR model is unable to make accurate predictions on the course of illnesses such as COVID-19. In this paper, we show that the official data released by the authorities of several countries (Italy, Spain and The USA) regarding the expansion of COVID-19 are compatible with a non-autonomous SIR type model with vital dynamics and non-constant population, calibrated according to exponentially decaying infection and death rates. Using this calibration we construct a model whose outcomes for most relevant epidemiological paramenters, such as the number of active cases, cumulative deaths, daily new deaths and daily new cases (among others) fit available real data about the first and successive waves of COVID-19. In addition to this, we also provide predictions on the evolution of this pandemic in Italy and the USA in several plausible scenarios.

Panorama histórico de la enfermedad diarreica aguda en México y el futuro de su prevención / Historical overview of acute infectious diarrhea in Mexico and future preventive strategies

Resumen: Objetivo: Revisar los aspectos epidemiológicos de la enfermedad diarreica aguda (EDA) a través de la historia de México y analizar las estrategias que potencialmente podrán prevenir su aparición en la población mexicana. Material y métodos: Se realizó una búsqueda sistematizada utilizando los siguientes descriptores de las ciencias de la salud: diarrea, morbilidad, mortalidad, México y promoción de la salud de los últimos 20 años (1878-2018). Resultados: Se obtuvieron más de 8 600 artículos que fueron evaluados en función de los objetivos de la presente publicación. Conclusión: Como resultado de una revisión sistemática se observó que, gracias a las estrategias implementadas a lo largo del tiempo, se ha logrado graduar los matices de riesgo de la EDA; ello permite ahora plantear estrategias que guiarán a la prevención de ese padecimiento, de la mano de políticas que incluyan aspectos higiénico-dietéticos, innovaciones farmacéuticas y aplicaciones tecnológicas en medidas sanitarias.

Abstract: Objective: To analyze the epidemiological aspects of AID through Mexican history and the potential strategies to prevent AID in Mexican population. Materials and methods: A systematic review was performed exploring the key words, diarrhea, morbidity, mortality, Mexico, health promotion for the last 20 years (1978-2018). Results: Over 8 600 articles were obtained; all of them were evaluated to consider those follow the aim of the present work. Conclusion: The result of the performed systematic review denoted the influence of AID in Mexican public health policy the adopted actions diminished the AID's associated risks and allowed future strategies to prevent it; those actions must include hygienic and dietetic measures, pharmaceutical innovations and technological tools applied to health policies.

[Historical overview of acute infectious diarrhea in Mexico and future preventive strategies]. / Panorama histórico de la enfermedad diarreica aguda en México y el futuro de su prevención.

OBJECTIVE: To analyze the epidemiological aspects of AID through Mexican history and the potential strategies to pre- vent AID in Mexican population. MATERIALS AND METHODS: A systematic review was performed exploring the key words, diarrhea, morbidity, mortality, Mexico, health promotion for the last 20 years (1978-2018). RESULTS: Over 8 600 articles were obtained; all of them were evaluated to consider those follow the aim of the present work. CONCLUSIONS: The result of the performed systematic review denoted the influence of AID in Mexican public health policy; the adopted actions diminished the AID's associated risks and allowed future strategies to prevent it; those actions must include hygienic and dietetic measures, pharmaceutical innovations and technological tools applied to health policies.

OBJETIVO: Revisar los aspectos epidemiológicos de la enfermedad diarreica aguda (EDA) a través de la historia de México y analizar las estrategias que potencialmente podrán prevenir su aparición en la población mexicana. MATERIAL Y MÉTODOS: Se realizó una búsqueda sistematizada utilizando los siguientes descriptores de las ciencias de la salud: diarrea, morbilidad, mortalidad, México y promoción de la salud de los últimos 20 años (1878-2018). RESULTADOS: Se obtuvieron más de 8 600 artículos que fueron evaluados en función de los objetivos de la presente publicación. CONCLUSIONES: Como resultado de una revisión sistemática se observó que, gracias a las estrategias implementadas a lo largo del tiempo, se ha logrado graduar los matices de riesgo de la EDA; ello permite ahora plantear estrategias que guiarán a la prevención de ese padecimiento, de la mano de políticas que incluyan aspectos higiénico-dietéticos, innovaciones farmacéuticas y aplicaciones tecnológicas en medidas sanitarias.

Systematic identification and classification of adverse events in human research.

Widely accepted standards and safeguards for research participants now include systematic surveillance and recording of adverse events. In the absence of a uniform regulation or structure for such reporting, each institution must now establish suitable yet efficient procedures to accomplish this task. We report herein our single center experience with a customized data collection, storage and review system specifically designed to identify and react appropriately to adverse events. Adverse events are classified by each investigator using three criteria in specific order: seriousness, expectedness and relatedness to the investigational intervention. Once classified, events are entered into an online database that includes collation, retrieval and search capabilities. Events meeting specified criteria are reviewed and adjudicated on a weekly basis by The University of Connecticut Research Adverse Events Committee, which makes advisory recommendations to the hospital's two Institutional Research Boards ranging from modification of informed consent to study suspension. Three hundred and seventy-one serious adverse events from > 900 studies were reviewed in the previous academic year. Our system, which combines timely on-line reporting with regular surveillance, provides a potential model that meets the need for comprehensive yet practical adverse events assessment and reporting.