A proposed analytical approach to estimate excess daily mortality rates in Ecuador

Background

The COVID-19 pandemic has proved deadly all over the globe; however, one of the most lethal outbreaks occurred in Ecuador.

Aims

This study aims to highlight the pandemic's impact on the most affected countries worldwide in terms of excess deaths per capita and per day.

Methods

An ecological study of all-cause mortality recorded in Ecuador was performed. To calculate the excess deaths relative to the historical average for the same dates in 2017, 2018, and 2019, we developed a bootstrap method based on the central tendency measure of mean. A Poisson fitting analysis was used to identify trends on officially recorded all-cause deaths and COVID-19 deaths. A bootstrapping technique was used to emulate the sampling distribution of our expected deaths estimator μ ⌢ d e a t h s by simulating the data generation and model fitting processes daily since the first confirmed case.

Results

In Ecuador, during 2020, 115,070 deaths were reported and 42,453 were cataloged as excess mortality when compared to 2017-2019 period. Ecuador is the country with the highest recorded excess mortality in the world within the shortest timespan. In one single day, Ecuador recorded 1,120 deaths (6/100,000), which represents an additional 408% of the expected fatalities.

Conclusion

Adjusting for population size and time, the hardest-hit country due to the COVID-19 pandemic was Ecuador. The mortality excess rate shows that the SARS-CoV-2 virus spread rapidly in Ecuador, especially in the coastal region. Our results and the proposed new methodology could help to address the real situation of the number of deaths during the initial phase of pandemics.

The Effect of the COVID-19 Pandemic on Non-COVID-19 Deaths: Population-Wide Retrospective Cohort Study

BACKGROUND

Health care avoidance in the COVID-19 pandemic has been widely reported. Yet few studies have investigated the dynamics of hospital avoidance behavior during pandemic waves and inferred its impact on excess non-COVID-19 deaths.

OBJECTIVE

This study aimed to measure the impact of hospital avoidance on excess non-COVID-19 deaths in public hospitals in Hong Kong.

METHODS

This was a retrospective cohort study involving 11,966,786 patients examined between January 1, 2016, and December 31, 2021, in Hong Kong. All data were linked to service, treatment, and outcomes. To estimate excess mortality, the 2-stage least squares method was used with daily tallies of emergency department (ED) visits and 28-day mortality. Records for older people were categorized by long-term care (LTC) home status, and comorbidities were used to explain the demographic and clinical attributes of excess 28-day mortality. The primary outcome was actual excess death in 2020 and 2021. The 2-stage least squares method was used to estimate the daily excess 28-day mortality by daily reduced visits.

RESULTS

Compared with the prepandemic (2016-2019) average, there was a reduction in total ED visits in 2020 of 25.4% (548,116/2,142,609). During the same period, the 28-day mortality of non-COVID-19 ED deaths increased by 7.82% (2689/34,370) compared with 2016-2019. The actual excess deaths in 2020 and 2021 were 3143 and 4013, respectively. The estimated total excess non-COVID-19 28-day deaths among older people in 2020 to 2021 were 1958 (95% CI 1100-2820; no time lag). Deaths on arrival (DOAs) or deaths before arrival (DBAs) increased by 33.6% (1457/4336) in 2020, while non-DOA/DBAs increased only by a moderate 4.97% (1202/24,204). In both types of deaths, the increases were higher during wave periods than in nonwave periods. Moreover, non-LTC patients saw a greater reduction in ED visits than LTC patients across all waves, by more than 10% (non-LTC: 93,896/363,879, 25.8%; LTC: 7,956/67,090, 11.9%). Most of the comorbidity subsets demonstrated an annualized reduction in visits in 2020. Renal diseases and severe liver diseases saw notable increases in deaths.

CONCLUSIONS

We demonstrated a statistical method to estimate hospital avoidance behavior during a pandemic and quantified the consequent excess 28-day mortality with a focus on older people, who had high frequencies of ED visits and deaths. This study serves as an informed alert and possible investigational guideline for health care professionals for hospital avoidance behavior and its consequences.

A Parsimonious Description and Cross-Country Analysis of COVID-19 Epidemic Curves

In a given country, the cumulative death toll of the first wave of the COVID-19 epidemic follows a sigmoid curve as a function of time. In most cases, the curve is well described by the Gompertz function, which is characterized by two essential parameters, the initial growth rate and the decay rate as the first epidemic wave subsides. These parameters are determined by socioeconomic factors and the countermeasures to halt the epidemic. The Gompertz model implies that the total death toll depends exponentially, and hence very sensitively, on the ratio between these rates. The remarkably different epidemic curves for the first epidemic wave in Sweden and Norway and many other countries are classified and discussed in this framework, and their usefulness for the planning of mitigation strategies is discussed.

Death tolls of COVID-19: Where come the fallacies and ways to make them more accurate

The death toll of the coronavirus disease 2019 (COVID-19) sparked much controversy since its advent in December 2019. Underestimation because of under testing and deaths happening outside the hospitals were important causes. Bold revisions of the diagnostic criteria leading to dramatic changes in death tolls by different governments were observed in attempts to generate more accurate estimates. On the other hand, the influence, censorship and manipulation on case and death data from top political leaders of some countries could create important impacts on the death toll. Baseline mortality data of previous years may help make more accurate estimates of the actual death toll. The pitfalls and strategies during such processes could become valuable lessons to leaders and policymakers worldwide as more accurate statistics serve to navigate policies to combat this pandemic in the days and months to come.

Spring in London with Covid-19: a personal view

This is a personal view from London as the Covid-19 pandemic continues to spread here and the situation changes from day to day. As such it can only be a snapshot caught in time; it is not a diary of events. The Coronavirus Act 2020 gives Government enormous powers and was passed by Parliament in one day of debate immediately before it closed early for the Easter break. In March, the government imposed a "lockdown: the closure of all" but "essential" businesses and people other than essential workers must work from home but are allowed out for exercise and food shopping but must maintain 2 m apart, the "social distancing rule". The aim is to suppress the spread of the virus, reduce the death toll and "protect the National Health Service (NHS)" which needed time to empty wards and expand its intensive care unit (ICU) capability to deal with an expected influx of thousands of very sick patients. I discuss whether this strategy is working, how and why it has rapidly been altered to respond to criticism. Why was the Government so slow to seek the help of private laboratories to assist with testing? Why was the personal protective equipment (PPE) guidance altered only after criticism? I look at the impact of the lockdown on the UK economy, the changes to practice of medicine and speeding of scientific research. Cooperating with the lockdown has its price; is it harming the health and mental health of children, people living in households with potentially abusive partners or parents and those who are disabled or financially desperate? Is the cure worse than the disease? The Economy is being devastated by the lockdown and each day of lockdown it is worse. Is litigation being seeded even now by the pandemic? Notwithstanding unprecedented Government financial help many businesses are on the edge of collapse, people will lose their jobs and pensioners income. The winners include pharmacies, supermarkets, online food retailers, Amazon, online apps, providers of video games, services, streaming and scientific research laboratories, manufacturers of testing kits, ventilators, hand sanitisers, coffins, undertakers, etc. The British public is cooperating with lockdown but are we less productive at home? Parents with babies and children often child minders, school, grandparents or paid help which is not now available. Will current reliance on video-conferencing and video calls permanently change the way we work and will we need smaller city offices? Will we travel less? Will medical and legal practice and civil and criminal trials be generally carried out remotely? Will social distancing with self-isolation and job losses and business failures fuel depression? Is Covid-19 comparable to past epidemics like the Plague and Spanish flu?