Forecasting the impact of coronavirus disease during delivery hospitalization: an aid for resource utilization

The impact of COVID-19 pandemic on obstetrics and gynecology hospitalization rate and on reasons for seeking emergency care: a systematic review and meta-analysis

BACKGROUND

During the lockdown due to COVID-19 pandemic, utilization of emergency care units has been reported to be reduced for obstetrical and gynaecological reasons. The aim of this systematic review is to assess if this phenomenon reduced the rate of hospitalizations for any reason and to evaluate the main reasons for seeking care in this subset of the population.

METHODS

The search was conducted using the main electronic databases from January 2020 to May 2021. The studies were identified with the use of a combination of: "emergency department" OR "A&E" OR "emergency service" OR "emergency unit" OR "maternity service" AND "COVID-19" OR "COVID-19 pandemic" OR "SARS-COV-2" and "admission" OR "hospitalization". All the studies that evaluated women going to obstetrics & gynecology emergency department (ED) during the COVID-19 pandemic for any reason were included.

RESULTS

The pooled proportion (PP) of hospitalizations increased from 22.7 to 30.6% during the lockdown periods, in particular from 48.0 to 53.9% for delivery. The PP of pregnant women suffering from hypertensive disorders increased (2.6 vs 1.2%), as well as women having contractions (52 vs 43%) and rupture of membranes (12.0 vs 9.1%). Oppositely, the PP of women having pelvic pain (12.4 vs 14.4%), suspected ectopic pregnancy (1.8 vs 2.0), reduced fetal movements (3.0 vs 3.3%), vaginal bleeding both for obstetrical (11.7 vs 12.8%) and gynecological issues (7.4 vs 9.2%) slightly reduced.

CONCLUSION

During the lockdown, an increase in the proportion of hospitalizations for obstetrical and gynecological reasons has been registered, especially for labor symptoms and hypertensive disorders.

A simulation model for predicting hospital occupancy for Covid-19 using archetype analysis

COVID-19 pandemic has sent millions of people to hospitals worldwide, exhausting on many occasions the capacity of healthcare systems to provide care patients required to survive. Although several epidemiological research works have contributed a variety of models and approaches to anticipate the pandemic spread, very few have tried to translate the output of these models into hospital service requirements, particularly in terms of bed occupancy, a key question for hospital managers. This paper proposes a tool for predicting the current and future occupancy associated with COVID-19 patients of a hospital to help managers make informed decisions to maximize the availability of hospitalization and intensive care unit (ICU) beds and ensure adequate access to services for confirmed COVID-19 patients. The proposed tool uses a discrete event simulation approach that uses archetypes (i.e., empirical models of trajectories) extracted from empirical analysis of actual patient trajectories. Archetypes can be fitted to trajectories observed in different regions or to the particularities of current and forthcoming variants using a rather small amount of data. Numerical experiments on realistic instances demonstrate the accuracy of the tool's predictions and illustrate how it can support managers in their daily decisions concerning the system's capacity and ensure patients the access the resources they require.

Assessing the impact of blue and green spaces on mental health of disabled children: A scoping review

During recent decades, there has been a growing consideration of the role of blue and green spaces on mental health of children, but there is insufficient attention in the literature to the mental health of children with disabilities. This paper presents an overview of the evidence on how blue and green spaces affect the mental health of children with various disabilities. A database search found twenty studies eligible for the review, after several consecutive screening stages. Most studies used a cross-sectional design and were carried out in Europe. The results consistently indicate that blue and green space can reduce emotional, behavioral, and social problems in disabled children. A protective association was found between the level of blue or greenness and depressive and anxiety symptoms. Moreover, in most of the studies there were no significant changes in the result after adjusting for socioeconomic confounders. Generally, there is an identified need for more short-term exposure studies in this area, focusing on the impact of landscape design elements on mental health of disabled children. The findings of this scoping review call on urban planners, health care workers and decision makers to consider appropriate measures and interventions providing more blue and green space exposure to disabled children.

Real-time estimation and forecasting of COVID-19 cases and hospitalizations in Wisconsin HERC regions for public health decision making processes

BACKGROUND

The spread of the COVID-19 (SARS-CoV-2) and the surging number of cases across the United States have resulted in full hospitals and exhausted health care workers. Limited availability and questionable reliability of the data make outbreak prediction and resource planning difficult. Any estimates or forecasts are subject to high uncertainty and low accuracy to measure such components. The aim of this study is to apply, automate, and assess a Bayesian time series model for the real-time estimation and forecasting of COVID-19 cases and number of hospitalizations in Wisconsin healthcare emergency readiness coalition (HERC) regions.

METHODS

This study makes use of the publicly available Wisconsin COVID-19 historical data by county. Cases and effective time-varying reproduction number [Formula: see text] by the HERC region over time are estimated using Bayesian latent variable models. Hospitalizations are estimated by the HERC region over time using a Bayesian regression model. Cases, effective Rt, and hospitalizations are forecasted over a 1-day, 3-day, and 7-day time horizon using the last 28 days of data, and the 20%, 50%, and 90% Bayesian credible intervals of the forecasts are calculated. The frequentist coverage probability is compared to the Bayesian credible level to evaluate performance.

RESULTS

For cases and effective [Formula: see text], all three time horizons outperform the three credible levels of the forecast. For hospitalizations, all three time horizons outperform the 20% and 50% credible intervals of the forecast. On the contrary, the 1-day and 3-day periods underperform the 90% credible intervals. Questions about uncertainty quantification should be re-calculated using the frequentist coverage probability of the Bayesian credible interval based on observed data for all three metrics.

CONCLUSIONS

We present an approach to automate the real-time estimation and forecasting of cases and hospitalizations and corresponding uncertainty using publicly available data. The models were able to infer short-term trends consistent with reported values at the HERC region level. Additionally, the models were able to accurately forecast and estimate the uncertainty of the measurements. This study can help identify the most affected regions and major outbreaks in the near future. The workflow can be adapted to other geographic regions, states, and even countries where decision-making processes are supported in real-time by the proposed modeling system.

Insight into Delta variant dominated second wave of COVID-19 in Nepal

OBJECTIVE

To study the spreading nature of Delta variant (B.1.617.2) dominated COVID-19 in Nepal to help the policymakers assess and manage health care facilities and vaccination programs.

METHODS

Deterministic mathematical models in the form of systems of ordinary differential equations were developed to describe the COVID-19 transmission in the high- and the low-risk regions of Nepal. The models were validated using the multiple data sets containing daily new cases in the whole country, the high-risk region, the low-risk region, and cases needing medical care, ICU, and ventilator.

RESULTS

We found the reproduction number of R_t=4.2 at the beginning of the second wave, larger than the first wave (∼1.8 estimated previously), indicating that the transmissibility of Delta variant is higher than the wild-type circulated during the first wave. Model predicts that ∼5% of the COVID-19 cases were reported in Nepal, estimating the seroprevalence of ∼63.9% as of July 2021, consistent with the survey conducted by the Government of Nepal. The seroprevalence was expected to reach 94.46% by April 2022, among which ∼46% would have both infection and vaccination. The expected cases from September 2021 to April 2022 is 111,300, among which 11,890 people might need medical care, 3590 need ICU, and 953 need ventilators. The COVID-19 cases and medical care needs could be significantly reduced with proper implementation of vaccination and social distancing.

CONCLUSIONS

The data-driven mathematical models are useful to assess control programs in resource-limited countries. The appropriate combination of vaccination and social distancing are necessary to keep the pandemic under-control and manage the medical care facilities in Nepal.

Maternal mortality during the COVID-19 pandemic in Mexico: a preliminary analysis during the first year

Background

In Mexico, the COVID-19 pandemic led to preventative measures such as confinement and social interaction limitations that paradoxically may have aggravated healthcare access disparities for pregnant women and accentuated health system weaknesses addressing high-risk patients’ pregnancies. Our objective is to estimate the maternal mortality ratio in 1 year and analyze the clinical course of pregnant women hospitalized due to acute respiratory distress syndrome and COVID-19.

Methods

A retrospective surveillance study of the national maternal mortality was performed from February 2020–February 2021 in Mexico related to COVID-19 cases in pregnant women, including their outcomes. Comparisons were made between patients who died and those who survived to identify prognostic factors and underlying health conditions distribution.

Results

Maternal Mortality Ratio increased by 56.8% in the studied period, confirmed COVID-19 was the cause of 22.93% of cases. Additionally, unconfirmed cases represented 4.5% of all maternal deaths. Among hospitalized pregnant women with Acute Respiratory Distress Syndrome consistent with COVID-19, smoking and cardiovascular diseases were more common among patients who faced a fatal outcome. They were also more common in the age group of < 19 or > 38. In addition, pneumonia was associated with asthma and immune impairment, while diabetes and increased BMI increased the odds for death (Odds Ratio 2.30 and 1.70, respectively).

Conclusions

Maternal Mortality Ratio in Mexico increased over 60% in 1 year during the pandemic; COVID-19 was linked to 25.4% of maternal deaths in the studied period. Lethality among pregnant women with a diagnosis of COVID-19 was 2.8%, and while asthma and immune impairment increased propensity for developing pneumonia, obesity and diabetes increased the odds for in-hospital death. Measures are needed to improve access to coordinated well-organized healthcare to reduce maternal deaths related to COVID-19 and pandemic collateral effects.

Systematic review of predictive mathematical models of COVID-19 epidemic

BACKGROUND

Various mathematical models were published to predict the epidemiological consequences of the COVID-19 pandemic. This systematic review has studied the initial epidemiological models.

METHODS

Articles published from January to June 2020 were extracted from databases using search strings and those peer-reviewed with full text in English were included in the study. They were analysed as to whether they made definite predictions in terms of time and numbers, or contained only mathematical assumptions and open-ended predictions. Factors such as early vs. late prediction models, long-term vs. curve-fitting models and comparisons based on modelling techniques were analysed in detail.

RESULTS

Among 56,922 hits in 05 databases, screening yielded 434 abstracts, of which 72 articles were included. Predictive models comprised over 70% (51/72) of the articles, with susceptible, exposed, infectious and recovered (SEIR) being the commonest type (mean duration of prediction being 3 months). Common predictions were regarding cumulative cases (44/72, 61.1%), time to reach total numbers (41/72, 56.9%), peak numbers (22/72, 30.5%), time to peak (24/72, 33.3%), hospital utilisation (7/72, 9.7%) and effect of lockdown and NPIs (50/72, 69.4%). The commonest countries for which models were predicted were China followed by USA, South Korea, Japan and India. Models were published by various professionals including Engineers (12.5%), Mathematicians (9.7%), Epidemiologists (11.1%) and Physicians (9.7%) with a third (32.9%) being the result of collaborative efforts between two or more professions.

CONCLUSION

There was a wide diversity in the type of models, duration of prediction and the variable that they predicted, with SEIR model being the commonest type.

Analysis and Prediction of COVID-19 Using SIR, SEIQR, and Machine Learning Models: Australia, Italy, and UK Cases

The novel coronavirus disease, also known as COVID-19, is a disease outbreak that was first identified in Wuhan, a Central Chinese city. In this report, a short analysis focusing on Australia, Italy, and UK is conducted. The analysis includes confirmed and recovered cases and deaths, the growth rate in Australia compared with that in Italy and UK, and the trend of the disease in different Australian regions. Mathematical approaches based on susceptible, infected, and recovered (SIR) cases and susceptible, exposed, infected, quarantined, and recovered (SEIQR) cases models are proposed to predict epidemiology in the above-mentioned countries. Since the performance of the classic forms of SIR and SEIQR depends on parameter settings, some optimization algorithms, namely Broyden–Fletcher–Goldfarb–Shanno (BFGS), conjugate gradients (CG), limited memory bound constrained BFGS (L-BFGS-B), and Nelder–Mead, are proposed to optimize the parameters and the predictive capabilities of the SIR and SEIQR models. The results of the optimized SIR and SEIQR models were compared with those of two well-known machine learning algorithms, i.e., the Prophet algorithm and logistic function. The results demonstrate the different behaviors of these algorithms in different countries as well as the better performance of the improved SIR and SEIQR models. Moreover, the Prophet algorithm was found to provide better prediction performance than the logistic function, as well as better prediction performance for Italy and UK cases than for Australian cases. Therefore, it seems that the Prophet algorithm is suitable for data with an increasing trend in the context of a pandemic. Optimization of SIR and SEIQR model parameters yielded a significant improvement in the prediction accuracy of the models. Despite the availability of several algorithms for trend predictions in this pandemic, there is no single algorithm that would be optimal for all cases.

Covid-19 pandemic and pregnancy

At the end of 2019, a new coronavirus disease, COVID-19, emerged and quickly spread around the world. Severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2), the causative virus of this disease, belongs to the β-coronavirus family, together with SARS and middle east respiratory syndrome, and has similar biological characteristics to these viruses. For obstetricians, the susceptibility and prognoses of pregnant women and the effects of the infection on the fetus have been the focus of attention; however, at present, the seriousness of the disease in pregnant women is not apparent, and COVID-19 does not increase the rate of miscarriage, stillbirth, preterm labor or teratogenicity. Even so, carriers might transmit SARS-CoV-2 to pregnant women. Thus, we must keep in mind that all medical personnel must understand and maintain standard precautions in their clinical and laboratory practices.

COVID-19: instruments for the allocation of mechanical ventilators-a narrative review

After the World Health Organization declared COVID-19 to be a pandemic, the elaboration of comprehensive and preventive public policies became important in order to stop the spread of the disease. However, insufficient or ineffective measures may have placed health professionals and services in the position of having to allocate mechanical ventilators. This study aimed to identify instruments, analyze their structures, and present the main criteria used in the screening protocols, in order to help the development of guidelines and policies for the allocation of mechanical ventilators in the COVID-19 pandemic. The instruments have a low level of scientific evidence, and, in general, are structured by various clinical, non-clinical, and tiebreaker criteria that contain ethical aspects. Few instruments included public participation in their construction or validation. We believe that the elaboration of these guidelines cannot be restricted to specialists as this question involves ethical considerations which make the participation of the population necessary. Finally, we propose seventeen elements that can support the construction of screening protocols in the COVID-19 pandemic.

Informed Consent for Emergency Obstetric Care During COVID-19 Pandemic

Informed consent process has become a challenging issue before surgery for any emergency obstetric care during this COVID pandemic. There is an increased risk of morbidity if there is a need of intensive care unit postoperatively and a risk of high mortality if patient has symptoms of COVID-19. Admission to intensive care unit adds on to the financial burden to the patient. Also, there is an increased risk of perinatal anxiety and depression during the COVID pandemic. When an asymptomatic carrier develops symptoms of COVID after delivery or caesarean section, the morbidity increases. So we have designed an informed consent form for patients undergoing emergency obstetric surgeries incorporating some points specific for COVID-19.