Impact of COVID-19 in nutritional and functional status of survivors admitted in intensive care units during the first outbreak. Preliminary results of the NUTRICOVID study.

BACKGROUND & AIMS: COVID-19 patients present a high hospitalization rate with a high mortality risk for those requiring intensive care. When these patients have other comorbid conditions and older age, the risk for severe disease and poor outcomes after ICU admission are increased. The present work aims to describe the preliminary results of the ongoing NUTRICOVID study about the nutritional and functional status and the quality of life of adult COVID-19 survivors after ICU discharge, emphasizing the in-hospital and discharge situation of this population. METHODS: A multicenter, ambispective, observational cohort study was conducted in 16 public hospitals of the Community of Madrid with COVID-19 survivors who were admitted to the ICU during the first outbreak. Preliminary results of this study include data retrospectively collected. Malnutrition and sarcopenia were screened at discharge using MUST and SARC-F; the use of healthcare resources was measured as the length of hospital stay and requirement of respiratory support and tracheostomy during hospitalization; other study variables were the need for medical nutrition therapy (MNT); and patients' functional status (Barthel index) and health-related quality of life (EQ-5D-5L). RESULTS: A total of 176 patients were included in this preliminary analysis. Most patients were male and older than 60 years, who suffered an average (SD) weight loss of 16.6% (8.3%) during the hospital stay, with a median length of stay of 53 (27-89.5) days and a median ICU stay of 24.5 (11-43.5) days. At discharge, 83.5% and 86.9% of the patients were at risk of malnutrition and sarcopenia, respectively, but only 38% were prescribed MNT. In addition, more than 70% of patients had significant impairment of their mobility and to conduct their usual activities at hospital discharge. CONCLUSIONS: This preliminary analysis evidences the high nutritional and functional impairment of COVID-19 survivors at hospital discharge and highlights the need for guidelines and systematic protocols, together with appropriate rehabilitation programs, to optimize the nutritional management of these patients after discharge.

Saving lives through road safety risk factor interventions: global and national estimates.

Global road mortality is a leading cause of death in many low-income and middle-income countries. Data to support priority setting under current resource constraints are urgently needed to achieve Sustainable Development Goal (SDG) 3.6. This Series paper estimates the potential number of lives saved if each country implemented interventions to address risk factors for road injuries. We did a systematic review of all available evidence-based, preventive interventions for mortality reduction that targeted the four main risk factors for road injuries (ie, speeding, drink driving, helmet use, and use of seatbelt or child restraint). We used literature review variables and considered three key country-level variables (gross domestic product per capita, population density, and government effectiveness) to generate country-specific estimates on the potential annual attributable number of lives that would be saved by interventions focusing on these four risk factors in 185 countries. Our results suggest that the implementation of evidence-based road safety interventions that target the four main road safety risk factors could prevent between 25% and 40% of all fatal road injuries worldwide. Interventions addressing speed could save about 347 258 lives globally per year, and at least 16 304 lives would be saved through drink driving interventions. The implementation of seatbelt interventions could save about 121 083 lives, and 51 698 lives could be saved by helmet interventions. We identify country-specific estimates of the potential number of lives saved that would be attributable to these interventions. Our results show the potential effectiveness of the implementation and scaling of these interventions. This paper presents key evidence for priority setting on road safety interventions and shows a path for reaching SDG 3.6.

Tiled Sparse Coding in Eigenspaces for Image Classification.

The automation in the diagnosis of medical images is currently a challenging task. The use of Computer Aided Diagnosis (CAD) systems can be a powerful tool for clinicians, especially in situations when hospitals are overflowed. These tools are usually based on artificial intelligence (AI), a field that has been recently revolutionized by deep learning approaches. These alternatives usually obtain a large performance based on complex solutions, leading to a high computational cost and the need of having large databases. In this work, we propose a classification framework based on sparse coding. Images are first partitioned into different tiles, and a dictionary is built after applying PCA to these tiles. The original signals are then transformed as a linear combination of the elements of the dictionary. Then, they are reconstructed by iteratively deactivating the elements associated with each component. Classification is finally performed employing as features the subsequent reconstruction errors. Performance is evaluated in a real context where distinguishing between four different pathologies: control versus bacterial pneumonia versus viral pneumonia versus COVID-19. Our system differentiates between pneumonia patients and controls with an accuracy of 97.74%, whereas in the 4-class context the accuracy is 86.73%. The excellent results and the pioneering use of sparse coding in this scenario evidence that our proposal can assist clinicians when their workload is high.

Cost-Effectiveness of the COVID-19 Test, Trace and Isolate Program in Colombia.

BACKGROUND: During the COVID-19 pandemic, Test-Trace-Isolate (TTI) programs have been recommended as a risk mitigation strategy. However, many governments have hesitated to implement them due to their costs. This study aims to estimate the cost-effectiveness of implementing a national TTI program to reduce the number of severe and fatal cases of COVID-19 in Colombia. METHODS: We developed a Markov simulation model of COVID-19 infection combined with a Susceptible-Infected-Recovered structure. We estimated the incremental cost-effectiveness of a comprehensive TTI strategy compared to no intervention over a one-year horizon, from both the health system and the societal perspective. Hospitalization and mortality rates were retrieved from Colombian surveillance data. We included program costs of TTI intervention, health services utilization, PCR diagnosis test, productivity loss, and government social program costs. We used the number of deaths and quality-adjusted life years (QALYs) as health outcomes. Sensitivity analyses were performed. FINDINGS: Compared with no intervention, the TTI strategy reduces COVID-19 mortality by 67%. In addition, the program saves an average of $1,045 and $850 per case when observed from the social and the health system perspective, respectively. These savings are equivalent to two times the current health expenditures in Colombia per year. INTERPRETATION: The TTI program is a highly cost-effective public health intervention to reduce the burden of COVID-19 in Colombia. TTI programs depend on their successful and speedy implementation. FUNDING: This study was supported by the Colombian Ministry of Health through award number PUJ-04519-20 received by EPQ AVO and SDS declined to receive any funding support for this study. The contents are the responsibility of all the individual authors.

Examining the cost-effectiveness of personal protective equipment for formal healthcare workers in Kenya during the COVID-19 pandemic.

BACKGROUND: Healthcare workers are at a higher risk of COVID-19 infection during care encounters compared to the general population. Personal Protective Equipment (PPE) have been shown to protect COVID-19 among healthcare workers, however, Kenya has faced PPE shortages that can adequately protect all healthcare workers. We, therefore, examined the health and economic consequences of investing in PPE for healthcare workers in Kenya. METHODS: We conducted a cost-effectiveness and return on investment (ROI) analysis using a decision-analytic model following the Consolidated Health Economic Evaluation Reporting Standards (CHEERS) guidelines. We examined two outcomes: 1) the incremental cost per healthcare worker death averted, and 2) the incremental cost per healthcare worker COVID-19 case averted. We performed a multivariate sensitivity analysis using 10,000 Monte Carlo simulations. RESULTS: Kenya would need to invest $3.12 million (95% CI: 2.65-3.59) to adequately protect healthcare workers against COVID-19. This investment would avert 416 (IQR: 330-517) and 30,041 (IQR: 7243 - 102,480) healthcare worker deaths and COVID-19 cases respectively. Additionally, such an investment would result in a healthcare system ROI of $170.64 million (IQR: 138-209) - equivalent to an 11.04 times return. CONCLUSION: Despite other nationwide COVID-19 prevention measures such as social distancing, over 70% of healthcare workers will still be infected if the availability of PPE remains scarce. As part of the COVID-19 response strategy, the government should consider adequate investment in PPE for all healthcare workers in the country as it provides a large return on investment and it is value for money.

Applications of Artificial Intelligence, Machine Learning, Big Data and the Internet of Things to the COVID-19 Pandemic: A Scientometric Review Using Text Mining.

The COVID-19 pandemic has wreaked havoc in every country in the world, with serious health-related, economic, and social consequences. Since its outbreak in March 2020, many researchers from different fields have joined forces to provide a wide range of solutions, and the support for this work from artificial intelligence (AI) and other emerging concepts linked to intelligent data analysis has been decisive. The enormous amount of research and the high number of publications during this period makes it difficult to obtain an overall view of the different applications of AI to the management of COVID-19 and an understanding of how research in this field has been evolving. Therefore, in this paper, we carry out a scientometric analysis of this area supported by text mining, including a review of 18,955 publications related to AI and COVID-19 from the Scopus database from March 2020 to June 2021 inclusive. For this purpose, we used VOSviewer software, which was developed by researchers at Leiden University in the Netherlands. This allowed us to examine the exponential growth in research on this issue and its distribution by country, and to highlight the clear hegemony of the United States (USA) and China in this respect. We used an automatic process to extract topics of research interest and observed that the most important current lines of research focused on patient-based solutions. We also identified the most relevant journals in terms of the COVID-19 pandemic, demonstrated the growing value of open-access publication, and highlighted the most influential authors by means of an analysis of citations and co-citations. This study provides an overview of the current status of research on the application of AI to the pandemic.

Characterizing two outbreak waves of COVID-19 in Spain using phenomenological epidemic modelling.

Since the first case reported of SARS-CoV-2 the end of December 2019 in China, the number of cases quickly climbed following an exponential growth trend, demonstrating that a global pandemic is possible. As of December 3, 2020, the total number of cases reported are around 65,527,000 contagions worldwide, and 1,524,000 deaths affecting 218 countries and territories. In this scenario, Spain is one of the countries that has suffered in a hard way, the ongoing epidemic caused by the novel coronavirus SARS-CoV-2, namely COVID-19 disease. In this paper, we present the utilization of phenomenological epidemic models to characterize the two first outbreak waves of COVID-19 in Spain. The study is driven using a two-step phenomenological epidemic approach. First, we use a simple generalized growth model to fit the main parameters at the early epidemic phase; later, we apply our previous finding over a logistic growth model to that characterize both waves completely. The results show that even in the absence of accurate data series, it is possible to characterize the curves of case incidence, and construct a short-term forecast of 60 days in the near time horizon, in relation to the expected total duration of the pandemic.

Impact of contact tracing on COVID-19 mortality: An impact evaluation using surveillance data from Colombia.

BACKGROUND: Contact tracing is a crucial part of the public health surveillance toolkit. However, it is labor-intensive and costly to carry it out. Some countries have faced challenges implementing contact tracing, and no impact evaluations using empirical data have assessed its impact on COVID-19 mortality. This study assesses the impact of contact tracing in a middle-income country, providing data to support the expansion and optimization of contact tracing strategies to improve infection control. METHODS: We obtained publicly available data on all confirmed COVID-19 cases in Colombia between March 2 and June 16, 2020. (N = 54,931 cases over 135 days of observation). As suggested by WHO guidelines, we proxied contact tracing performance as the proportion of cases identified through contact tracing out of all cases identified. We calculated the daily proportion of cases identified through contact tracing across 37 geographical units (32 departments and five districts). Further, we used a sequential log-log fixed-effects model to estimate the 21-days, 28-days, 42-days, and 56-days lagged impact of the proportion of cases identified through contact tracing on daily COVID-19 mortality. Both the proportion of cases identified through contact tracing and the daily number of COVID-19 deaths are smoothed using 7-day moving averages. Models control for the prevalence of active cases, second-degree polynomials, and mobility indices. Robustness checks to include supply-side variables were performed. RESULTS: We found that a 10 percent increase in the proportion of cases identified through contact tracing is related to COVID-19 mortality reductions between 0.8% and 3.4%. Our models explain between 47%-70% of the variance in mortality. Results are robust to changes of specification and inclusion of supply-side variables. CONCLUSION: Contact tracing is instrumental in containing infectious diseases. Its prioritization as a surveillance strategy will substantially impact reducing deaths while minimizing the impact on the fragile economic systems of lower and middle-income countries. This study provides lessons for other LMIC.

Inferencia Variacional para la estimación de mortalidad COVID-19 en Portoviejo

Abstract: The severe acute respiratory syndrome pandemic by SARS-CoV-2 is caused, millions of confirmed cases and a high number of deaths are reported, the population dynamics are altered, a strong socio-economic impact, the collapse in the health system, the collapse in the education system, unemployment, are caused, among others. In this work, a logistic regression model is proposed, the dynamics of deaths in the period of the pandemic is modeled, two computational algorithms IVDA and NUTS are implemented, the posterior distribution in high dimensions is generated. To measure the relative success of the algorithms, three goodness-of-fit measures are estimated. Al realizar la inferencia bayesiana deseamos aproximar la distribución posterior de las variables latentes dados algunos datos (observaciones) jj(z;z) el problema es que la integral que involucra la distribución de interés es a menudo intratable y se debe usar métodos numéricos;la caracterización de la distribución posterior se realiza generalmente utilizando los métodos de Markov Chain Monte Carlo (MCMC) mediante muestreo repetido de la distribución.

Cost-effectiveness and return on investment of protecting health workers in low- and middle-income countries during the COVID-19 pandemic.

BACKGROUND: In this paper, we predict the health and economic consequences of immediate investment in personal protective equipment (PPE) for health care workers (HCWs) in low- and middle-income countries (LMICs). METHODS: To account for health consequences, we estimated mortality for HCWs and present a cost-effectiveness and return on investment (ROI) analysis using a decision-analytic model with Bayesian multivariate sensitivity analysis and Monte Carlo simulation. Data sources included inputs from the World Health Organization Essential Supplies Forecasting Tool and the Imperial College of London epidemiologic model. RESULTS: An investment of $9.6 billion USD would adequately protect HCWs in all LMICs. This intervention would save 2,299,543 lives across LMICs, costing $59 USD per HCW case averted and $4,309 USD per HCW life saved. The societal ROI would be $755.3 billion USD, the equivalent of a 7,932% return. Regional and national estimates are also presented. DISCUSSION: In scenarios where PPE remains scarce, 70-100% of HCWs will get infected, irrespective of nationwide social distancing policies. Maintaining HCW infection rates below 10% and mortality below 1% requires inclusion of a PPE scale-up strategy as part of the pandemic response. In conclusion, wide-scale procurement and distribution of PPE for LMICs is an essential strategy to prevent widespread HCW morbidity and mortality. It is cost-effective and yields a large downstream return on investment.