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In order to address one of the most challenging problems in hospital management - patients' absenteeism without prior notice - this study analyses the risk factors associated with this event. To this end, through real data from a hospital located in the North of Portugal, a prediction model previously validated in the literature is used to infer absenteeism risk factors, and an explainable model is proposed, based on a modified CART algorithm. The latter intends to generate a human-interpretable explanation for patient absenteeism, and its implementation is described in detail. Furthermore, given the significant impact, the COVID-19 pandemic had on hospital management, a comparison between patients' profiles upon absenteeism before and during the COVID-19 pandemic situation is performed. Results obtained differ between hospital specialities and time periods meaning that patient profiles on absenteeism change during pandemic periods and within specialities.
ABSTRACT
Background and aim: The kinetics of antibody production in response to coronavirus disease 2019 (COVID-19) infection is not well-defined yet. This study aimed to evaluate the antibody responses to SARS-CoV-2 and its dynamics during 9-months in a cohort of patients infected during the first phase of the pandemic. As a secondary aim, it was intended to evaluate the factors associated with different concentrations of IgG antibodies. Methods: A prospective cohort study was conducted from June 2020 to January 2021. This study recruited a convenience sample of adult individuals who where recently diagnosed with COVID-19 and were living in mainland Portugal. A total of 1,695 blood samples were collected from 585 recovered COVID-19 patients up to 9 months after SARS-CoV-2 acute infection. A blood sample was collected at baseline and three, 6 and 9 months after SARS-CoV-2 acute infection to assess the concentration of IgG antibody against SARS-CoV-2. Results: The positivity rate of IgG reached 77.7% in the first 3 months after symptom onset. The IgG persists at all subsequent follow-up time-points, which was 87.7 and 89.2% in the 6th and 9th months after symptom onset, respectively. Three distinct kinetics of antibody response were found within the 9 months after infection. Kinetic 1 (K1) was characterized by a constant low IgG antibody concentration kinetic (group size: 65.2%); kinetic 2 (K2), composed by constant moderate IgG kinetic (group size: 27.5%) and kinetic 3 (K3) characterized by higher IgG kinetic (group size: 7.3%). People with ≥56 years old (OR: 3.33; CI 95%: [1.64; 6.67]; p-value: 0.001) and symptomatic COVID-19 (OR: 2.08; CI 95%: [1.08; 4.00]; p-value: 0.031) had higher odds of a "Moderate IgG kinetic." No significant association were found regarding the "Higher IgG kinetic." Conclusion: Our results demonstrate a lasting anti-spike (anti-S) IgG antibody response at least 9 months after infection in the majority of patients with COVID-19. Younger participants with asymptomatic disease have lower IgG antibody positivity and possibly more susceptible to reinfection. This information contributes to expanding knowledge of SARS-CoV-2 immune response and has direct implications in the adoption of preventive strategies and public health policies.
Subject(s)
COVID-19 , Immunoglobulin G , Adult , Humans , Middle Aged , Prospective Studies , SARS-CoV-2 , Asymptomatic DiseasesABSTRACT
Background and aim The kinetics of antibody production in response to coronavirus disease 2019 (COVID-19) infection is not well-defined yet. This study aimed to evaluate the antibody responses to SARS-CoV-2 and its dynamics during 9-months in a cohort of patients infected during the first phase of the pandemic. As a secondary aim, it was intended to evaluate the factors associated with different concentrations of IgG antibodies. Methods A prospective cohort study was conducted from June 2020 to January 2021. This study recruited a convenience sample of adult individuals who where recently diagnosed with COVID-19 and were living in mainland Portugal. A total of 1,695 blood samples were collected from 585 recovered COVID-19 patients up to 9 months after SARS-CoV-2 acute infection. A blood sample was collected at baseline and three, 6 and 9 months after SARS-CoV-2 acute infection to assess the concentration of IgG antibody against SARS-CoV-2. Results The positivity rate of IgG reached 77.7% in the first 3 months after symptom onset. The IgG persists at all subsequent follow-up time-points, which was 87.7 and 89.2% in the 6th and 9th months after symptom onset, respectively. Three distinct kinetics of antibody response were found within the 9 months after infection. Kinetic 1 (K1) was characterized by a constant low IgG antibody concentration kinetic (group size: 65.2%);kinetic 2 (K2), composed by constant moderate IgG kinetic (group size: 27.5%) and kinetic 3 (K3) characterized by higher IgG kinetic (group size: 7.3%). People with ≥56 years old (OR: 3.33;CI 95%: [1.64;6.67];p-value: 0.001) and symptomatic COVID-19 (OR: 2.08;CI 95%: [1.08;4.00];p-value: 0.031) had higher odds of a "Moderate IgG kinetic.” No significant association were found regarding the "Higher IgG kinetic.” Conclusion Our results demonstrate a lasting anti-spike (anti-S) IgG antibody response at least 9 months after infection in the majority of patients with COVID-19. Younger participants with asymptomatic disease have lower IgG antibody positivity and possibly more susceptible to reinfection. This information contributes to expanding knowledge of SARS-CoV-2 immune response and has direct implications in the adoption of preventive strategies and public health policies.
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The COVID-19 pandemic has caused major damage and disruption to social, economic, and health systems (among others). In addition, it has posed unprecedented challenges to public health and policy/decision-makers who have been responsible for designing and implementing measures to mitigate its strong negative impact. The Portuguese health authorities have used decision analysis techniques to assess the impact of the pandemic and implemented measures for counties, regions, or across the entire country. These decision tools have been subject to some criticism and many stakeholders requested novel approaches. In particular, those which considered the dynamic changes in the pandemic's behaviour due to new virus variants and vaccines. A multidisciplinary team formed by researchers from the COVID-19 Committee of Instituto Superior Técnico at the University of Lisbon (CCIST analyst team) and physicians from the Crisis Office of the Portuguese Medical Association (GCOM expert team) collaborated to create a new tool to help politicians and decision-makers to fight the pandemic. This paper presents the main steps that led to the building of a pandemic impact assessment composite indicator applied to the specific case of COVID-19 in Portugal. A multiple criteria approach based on an additive multi-attribute value theory aggregation model was used to build the pandemic assessment composite indicator. The parameters of the additive model were devised based on an interactive socio-technical and co-constructive process between the CCIST and GCOM team members. The deck of cards method was the adopted technical tool to assist in the assessment the value functions as well as in the assessment of the criteria weights. The final tool was presented at a press conference and had a powerful impact on the Portuguese media and on the main health decision-making stakeholders in the country. In this paper, a completed mathematical and graphical description of this tool is presented.
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INTRODUCTION: Assessment of SARS-CoV-2 seroprevalence may detect the real spread of the virus because antibody data can provide a long-lasting measure of infection. Existing serological studies in Portugal have tested new serology methods, albeit with small sample sizes and a lack the focus on geographical regions with a high rate of infection cases. The aim of this study was to estimate the serological prevalence of SARS-CoV-2 in Vila Nova de Gaia, the most populous municipality in the north of Portugal and one of those most affected during the first pandemic wave. MATERIAL AND METHODS: A cross-sectional observational study was conducted between June 23rd and July 17th, 2020. Included in the cohort were 18- to 74-year-old men and women living in the municipality of Vila Nova de Gaia, who were sampled through a nonprobabilistic quota-based approach. Cases with a previous RT-PCR diagnosis of COVID-19 were excluded. Sociodemographic and clinical information was collected using a self-administered, written questionnaire. Blood samples were collected for serological laboratory analysis to detect and quantify SARS-CoV-2 anti-IgG antibodies. RESULTS: We tested 2754 participants. Our results show a SARS-CoV-2 seroprevalence of 3.03% (95% confidence interval: 2.37% - 3.87%). Being a smoker (odds ratio: 0.382, 95% confidence interval: 0.147 - 0.99) and having symptoms of COVID-19 (odds ratio: 2.480, 95% confidence interval: 1.360 - 4.522) were consistently associated with lower and higher odds of SARS-CoV-2 antibody presence, respectively, regardless of the analytic design. Moreover, without adjusting for any variables, having had contact with an infected person within the household was associated with increased odds of a positive test (odds ratio: 9.684, 95% confidence interval: 4.06 - 23.101); after adjusting, having self-reported chronic diseases (odds ratio: 0.448, 95% confidence interval: 0.213 - 0.941) was associated with decreased odds. CONCLUSION: This was the first study to estimate the serological prevalence of SARS-CoV-2 in one of the most populous municipalities in Portugal, representing the first step in the development of an epidemiological surveillance system in Portugal, which can help to improve the diagnosis of COVID-19.