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The ongoing COVID‐19 pandemic has brought terrifying effects for labor markets all around the world. Just as we witness rapid changes in terms of the ways of working (working from home), we are also observing an increase in unemployment. The ways in which major corporations with international operations process their global talent management (GTM) already represents a challenge in relatively stable times and clearly, in a period of such great and sustained turbulence as current experienced, this task becomes still more difficult. Hence, our research aims to study the impact of GTM on the international performance of major companies during the COVID‐19 pandemic period. To this end, we surveyed a sample of 59 large companies that act in external markets. Through recourse to multiple linear regressions, we conclude that GTM practices return positive impacts on levels of international performance. Our research returns theoretical implications in terms of the application of integrated GTM models and with the results of significant relevance to corporations operating internationally, and thus enabling them to better understand which strategic human resource management policies will return the best GTM results. [ABSTRACT FROM AUTHOR]

Ageing has increased the use of health services, with a corresponding rise in avoidable hospitalizations. We aimed to assess and characterize the perceived risk of hospitalization in primary health care (PHC). 118 individuals aged ≥65 years, PHC patients, were assessed using the Community Risk Assessment Instrument by their General Practitioner, who identified their perceived risk of hospitalization, at one year. The instrument is composed of three domains (mental state, daily living activities (ADLs) state and medical state). Multivariate logistic regression was used to identify the best model to predict the risk of hospitalization. Four models were estimated, one for each domain and one with all the variables of the instrument. 58.5% were identified as being at risk of hospitalization. The best predictive models are those that include functionality assessment variables (ADL model and Community Assessment of Risk Instrument model). The model that includes all the variables of three domains presents the best predictive value. Mobility problems (Odds Ratio (OR) 16.18 [CI: 1.63–160.53]), meal preparation (OR 10.93 [CI: 1.59–75.13]), communication (OR 6.91 [CI: 1.37–34.80]) and palliative care (OR 4.84 [CI: 1.14–20.58]) are the best predictors of hospitalization risk. The use of multidimensional assessment tools can allow the timely identification of people at risk, contributing to a reduction in hospitalizations. [ABSTRACT FROM AUTHOR]

Background Vaccination programs are essential for the containment of the coronavirus disease 2019 pandemic, which has hit haemodialysis populations especially hard. Early reports suggest a reduced immunologic response to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccines in dialysis patients, in spite of a high degree of seroconversion. We aimed to identify risk factors for a reduced efficacy of an mRNA vaccine in a cohort of haemodialysis patients. Method In a multicentre study, including 294 Portuguese haemodialysis patients who had received two doses of BNT162b2 with a 3-week interval, immunoglobulin G-class antibodies against the SARS-CoV-2 spike protein were determined 3 weeks after the first dose (M1) and 6 weeks after the second dose (M2). The threshold for seroconversion was 10 UR/mL. Demographic and clinical data were retrieved from a quality registry. Adverse events were registered using a questionnaire. Results At M2, seroconversion was 93.1% with a median antibody level of 197.5 U/mL (1.2–3237.0) and a median increase of 180.0 U/mL (−82.9 to 2244.6) from M1. Age [beta −8.9; 95% confidence interval (95% CI) −12.88 to −4.91; P < 0.0001], ferritin >600 ng/mL (beta 183.93; 95% CI 74.75–293.10; P = 0.001) and physical activity (beta 265.79; 95% CI 30.7–500.88; P = 0.03) were independent predictors of SARS-CoV-2 antibody levels after two vaccine doses. Plasma albumin >3.5 g/dL independently predicted the increase of antibody levels between both doses (odds ratio 14.72; 95% CI 1.38 to 157.45; P = 0.03). Only mild adverse reactions were observed in 10.9% of patients. Conclusions The SARS-CoV-2 vaccine BNT162b2 is safe and effective in haemodialysis patients. Besides age, iron status and nutrition are possible modifiable modulators of the immunologic response to SARS-CoV-2 mRNA vaccines. These data suggest the need for an early identification of populations at higher risk for diminished antibody production and the potential advantage of the implementation of oriented strategies to maximize the immune response to vaccination in these patients. [ABSTRACT FROM AUTHOR]

Background Experiences from the first wave of the 2019 coronavirus disease (COVID-19) pandemic can aid in the development of future preventive strategies. To date, risk prediction models for COVID-19-related incidence and outcomes in hemodialysis (HD) patients are missing. Methods We developed risk prediction models for COVID-19 incidence and mortality among HD patients. We studied 38 256 HD patients from a multi-national dialysis cohort between 3 March and 3 July 2020. Risk prediction models were developed and validated, based on predictors readily available in outpatient HD units. We compared mortality among patients with and without COVID-19, matched for age, sex and diabetes. Results During the observational period, 1259 patients (3.3%) acquired COVID-19. Of these, 62% were hospitalized or died. Mortality was 22% among COVID-19 patients with odds ratios 219.8 [95% confidence interval (CI) 80.6–359] to 342.7 (95% CI 60.6–13 595.1), compared to matched patients without COVID-19. Since the first wave of the pandemic affected most European countries during the study, the risk prediction model for incidence of COVID-19 was developed and validated in European patients only [ N  = 22 826 area under the ROC curve(AUC)Dev 0.64, AUCVal 0.69]. The model for prediction of mortality was developed in all COVID-19 patients (AUCDev 0.71, AUCVal 0.78). Angiotensin receptor blockers were independently associated with a lower incidence of COVID-19 in European patients. Conclusions We identified modifiable risk factors for COVID-19 incidence and outcome in HD patients. Our risk prediction tools can be readily applied in clinical practice. This can aid in the development of preventive strategies for future waves of COVID-19. [ABSTRACT FROM AUTHOR]

Ageing has increased the use of health services, with a corresponding rise in avoidable hospitalizations. We aimed to assess and characterize the perceived risk of hospitalization in primary health care (PHC). 118 individuals aged ≥65 years, PHC patients, were assessed using the Community Risk Assessment Instrument by their General Practitioner, who identified their perceived risk of hospitalization, at one year. The instrument is composed of three domains (mental state, daily living activities (ADLs) state and medical state). Multivariate logistic regression was used to identify the best model to predict the risk of hospitalization. Four models were estimated, one for each domain and one with all the variables of the instrument. 58.5% were identified as being at risk of hospitalization. The best predictive models are those that include functionality assessment variables (ADL model and Community Assessment of Risk Instrument model). The model that includes all the variables of three domains presents the best predictive value. Mobility problems (Odds Ratio (OR) 16.18 [CI: 1.63–160.53]), meal preparation (OR 10.93 [CI: 1.59–75.13]), communication (OR 6.91 [CI: 1.37–34.80]) and palliative care (OR 4.84 [CI: 1.14–20.58]) are the best predictors of hospitalization risk. The use of multidimensional assessment tools can allow the timely identification of people at risk, contributing to a reduction in hospitalizations. [ABSTRACT FROM AUTHOR]

Health as faced, with the challenges of the last decade, an increase in costs. There is constant pressure to achieve maximum efficiency, with control of expenditure, ensuring the access of the population to health care, without neglecting the quality of these care. In Portugal, with the permanent implementation of health reforms to respond to the constant new challenges, it is essential to carry out more careful diagnoses, which include the clinical and economic aspects, considering the specificities of Hospital Units. This study aims to define a model of surgical activity prediction in the Public Hospital Units in the Portuguese National Health Service, based on the Surgical Case Mix Index, Surgical Average Length of Stay and Hospital Bed Capacity. A multiple linear regression was performed involving a sample of 41 Public Hospital Units. The results appoint to the existence of a correlation between the variables analyzed, and it is possible to find a statistically significant predictive model of surgical activity. The correlation of Surgical Activity is strong with Hospital Bed Capacity or Surgical Case Mix Index, and moderate with Surgical Average Length of Stay. [ABSTRACT FROM AUTHOR]