Collaborative Decision Model for Allocating Intensive Care Units Beds with Scarce Resources in Health Systems: A Portfolio Based Approach under Expected Utility Theory and Bayesian Decision Analysis

The COVID-19 pandemic has brought health systems to the brink of collapse in several regions around the world, as the demand for health care has outstripped the capacity of their services, especially regarding intensive care. In this context, health system managers have faced a difficult question: who should be admitted to an intensive care unit (ICU), and who should not? This paper addresses this decision problem using Expected Utility Theory and Bayesian decision analysis. In order to estimate the chances of survival for patients, a structured protocol has been proposed conjointly with physicians, based on the Sequential Organ Failure Assessment (SOFA) score. A portfolio selection approach is proposed to support tackling the ICU allocation problem. A simulation study shows that the proposed approach is more advantageous than other approaches already presented in the literature, with respect to the number of lives saved. The patients' probabilities of survival inside and outside the ICU are important parameters of the model. However, assessing such probabilities can be a difficult task for health professionals. In order to give due treatment to the imprecise information regarding these probabilities, a Monte Carlo simulation is used to estimate the probabilities of recommending a patient be admitted to the ICU is the most appropriate decision, given the conditions presented. The methodology was implemented in an Information and Decision System called SIDTriagem, which is available online for free. With regards to managerial implications, SIDTriagem has a great potential to help in the response to public health emergencies systems as it facilitates rational decision-making regarding allocating ICU beds when resources are scarce.

Protective measures feasibility for infants of low income SARS-CoV-2 positive breastfeeding mothers: a prospective multicenter cohort study.

AIM: The aim was to evaluate the feasibility of protective measures for infants of low-income SARS-CoV-2 positive breastfeeding mothers. BACKGROUND: Breastfeeding mothers with SARS-CoV-2 positive should avoid exposing the infant through protective measures (PM), but it could be challenging in a low-income population. METHODS: A prospective, multicenter study was conducted between July and October 2020 (BRACOVID). The participants were recruited at birth and interviewed through a structured questionnaire at seven and 14 days in the home environment. The feasibility of PM during breastfeeding at home was defined by guidelines recommendations (mask using, handwashing, and distancing from newborn when not breastfeeding). Three groups according to the feasibility of guidelines: complete guidelines feasibility (CG): all PM; partial guidelines feasibility (PG): at least one PM feasible; no guidelines (NG): infeasibility to all of PM. Flu-like neonatal symptoms, mothers' breastfeeding practices. We evaluated the association between PM feasibility and socioeconomic factors. RESULTS: 117 infected mothers from 17 Brazilian hospitals were enrolled. 47 (40%) mothers followed all recommendations, 14 (11.9%) could not practice at least one recommendation, and 50 (42.7%) did not execute any of them. The breastfeeding rate was 98%. Factors associated with infeasibility were monthly family income < 92.7 dollars/person, high housing density (>1 inhabitant/room), teenage mothers, responsive feeding, and poor schooling. Regarding infants' flu-like symptoms, 5% presented symptoms at fourteen days (NG group). CONCLUSION: The guidelines were not applied to infants of SARs-CoV-positive mothers in 54.6% of the dyads since the recommendations were unviable in their environments. During pandemics, we should look for feasible and effective guidelines to protect neonates from low-income populations. IMPLICATIONS FOR NURSING MANAGEMENT: Poor socioeconomic conditions lead to the unfeasibility of protective measures for infants of low-income SARS-CoV-2 positive breastfeeding mothers during the isolation period in the pandemics. The orientations and the support provided to dyad should consider the socioeconomic factors to guide feasible measures in the home environment and promote adequate protections; only an individual approach will allow a safe environment for low-income infants.

Association between government policy and delays in emergent and elective surgical care during the COVID-19 pandemic in Brazil: a modeling study.

BACKGROUND: The impact of public health policy to reduce the spread of COVID-19 on access to surgical care is poorly defined. We aim to quantify the surgical backlog during the COVID-19 pandemic in the Brazilian public health system and determine the relationship between state-level policy response and the degree of state-level delays in public surgical care. METHODS: Monthly estimates of surgical procedures performed per state from January 2016 to December 2020 were obtained from Brazil's Unified Health System Informatics Department. Forecasting models using historical surgical volume data before March 2020 (first reported COVID-19 case) were constructed to predict expected monthly operations from March through December 2020. Total, emergency, and elective surgical monthly backlogs were calculated by comparing reported volume to forecasted volume. Linear mixed effects models were used to model the relationship between public surgical delivery and two measures of health policy response: the COVID-19 Stringency Index (SI) and the Containment & Health Index (CHI) by state. FINDINGS: Between March and December 2020, the total surgical backlog included 1,119,433 (95% Confidence Interval 762,663-1,523,995) total operations, 161,321 (95%CI 37,468-395,478) emergent operations, and 928,758 (95%CI 675,202-1,208,769) elective operations. Increased SI and CHI scores were associated with reductions in emergent surgical delays but increases in elective surgical backlogs. The maximum government stringency (score = 100) reduced emergency delays to nearly zero but tripled the elective surgical backlog. INTERPRETATION: Strong health policy efforts to contain COVID-19 ensure minimal reductions in delivery of emergent surgery, but dramatically increase elective backlogs. Additional coordinated government efforts will be necessary to specifically address the increased elective backlogs that accompany stringent responses.

The impact of coronavirus outbreak on breastfeeding guidelines among Brazilian hospitals and maternity services: a cross-sectional study.

BACKGROUND: The World Health Organization recognizes exclusive breastfeeding a safe source of nutrition available for children in most humanitarian emergencies, as in the current pandemic caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). Despite the Brazilian national guideline protecting breastfeeding practices, there are many concerns about protecting infants from their infected mothers. This study aimed to analyze how the Brazilian hospitals and maternity services promote and support mothers suspected or diagnosed with coronavirus disease (COVID-19). METHODS: This is a descriptive cross-sectional and multicenter study which collected data from 24 Brazilian hospitals and maternity services between March and July 2020. Representatives of the institutions completed a questionnaire based on acts to promote and support breastfeeding, the Baby-Friendly Hospital Initiative, and Brazil's federal law recommendations. RESULTS: The results showed that in delivery rooms, 98.5% of the services prohibited immediate and uninterrupted skin-to-skin contact between mothers and their infants and did not support mothers to initiate breastfeeding in the first hour. On the postnatal ward, 98.5% of the services allowed breastfeeding while implementing respiratory hygiene practices to prevent transmission of COVID-19. Companions for mothers were forbidden in 83.3% of the hospitals. Hospital discharge was mostly between 24 and 28 h (79.1%); discharge guidelines were not individualized. Additionally, a lack of support was noticed from the home environment's health community network (83.3%). Hospital and home breast pumping were allowed (87.5%), but breast milk donation was not accepted (95.8%). There was a lack of guidance regarding the use of infant comforting strategies. Guidelines specific for vulnerable populations were not covered in the material evaluated. CONCLUSIONS: In Brazil, hospitals have not followed recommendations to protect, promote, and support breastfeeding during the COVID-19 outbreak. The disagreement between international guidelines has been a major issue. The absence of recommendations on breastfeeding support during the pandemic led to difficulties in developing standards among hospitals in different regions of Brazil and other countries worldwide. The scientific community needs to discuss how to improve maternal and infant care services to protect breastfeeding in the current pandemic.

Decision Model for Allocation of Intensive Care Unit Beds for Suspected COVID-19 Patients under Scarce Resources.

This paper puts forward a decision model for allocation of intensive care unit (ICU) beds under scarce resources in healthcare systems during the COVID-19 pandemic. The model is built upon a portfolio selection approach under the concepts of the Utility Theory. A binary integer optimization model is developed in order to find the best allocation for ICU beds, considering candidate patients with suspected/confirmed COVID-19. Experts' subjective knowledge and prior probabilities are considered to estimate the input data for the proposed model, considering the particular aspects of the decision problem. Since the chances of survival of patients in several scenarios may not be precisely defined due to the inherent subjectivity of such kinds of information, the proposed model works based on imprecise information provided by users. A Monte-Carlo simulation is performed to build a recommendation, and a robustness index is computed for each alternative according to its performance as evidenced by the results of the simulation.

Utility-Based Multicriteria Model for Screening Patients under the COVID-19 Pandemic.

In this paper, a utility-based multicriteria model is proposed to support the physicians to deal with an important medical decision-the screening decision problem-given the squeeze put on resources due to the COVID-19 pandemic. Since the COVID-19 emerged, the number of patients with an acute respiratory failure has increased in the health units. This chaotic situation has led to a deficiency in health resources. Thus, this study, using the concepts of the multiattribute utility theory (MAUT), puts forward a mathematical model to aid physicians in the screening decision problem. The model is used to generate which of the three alternatives is the best one for where patients with suspected COVID-19 should be treated, namely, an intensive care unit (ICU), a hospital ward, or at home in isolation. Also, a decision information system, called SIDTriagem, is constructed and illustrated to operate the mathematical model proposed.