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One month ago, the UK released a plan on easing its lockdown policy. From May 17th, two households are allowed to mix in homes, while the rule of six could apply in places like pubs. As a result, people in the UK have finally embraced their “freedom day.” Laughter has again permeated pubs and outdoor areas. Nevertheless, the British government claims that this way toward freedom is not guaranteed to be irreversible.
According to the Prime minister, there will be “no credible route to a zero-Covid Britain nor indeed a zero-Covid world.” The idea is true for other countries. The spotlight on it is gradually fading, and vaccines are becoming available in more countries, yet facing this ongoing global crisis, lessons learned from the UK should be familiarized to the general public. This article will focus on the effect of mixed anti-epidemic policies on the old population because of the concern of the discriminative nature of the disease, that old people are more vulnerable to it.
A year ago, the excess mortality in the UK showed a nuance during the outbreak of COVID-19. Compared to Sweden, which did not apply any strict lockdown policy, Britain’s death rate is higher. This article discovers the contradiction between the relatively effective policies for the whole country and the significant death toll from COVID-19 in the elderly in the UK. Using regression models to analyze the data, testing the effectiveness of the lockdown policy, the level of enforcement of the UK government, and the causal relationship between policies and the death in care homes, the model analysis found that “Save the NHS (National Health Service)”. The lockdown policy is likely to contribute to the nuance of excess mortality in the UK. Under the condition that the UK itself has insufficient congenital anti-epidemic conditions, the anti-epidemic policy adopted by the UK government has caused multitudes of deaths among the elderly in care homes.
The first death from COVID-19 occurred in the UK on March 7. In the 15 days following the first death, the UK government did nothing to stop the spread of COVID-19 and even introduced a "herd immunity" policy during this period, leading to many infections. After adopting the policy of sealing off the cities on March 23, the level of epidemic prevention in the UK gradually returned to the average level in Europe. The process is well explained by the result, indicating that the lockdown policy should be adequate. However, the nuance exists.
Comparing a regression discontinuity chart for care homes and hospitals, the analysis found that there was a significant change in the number of care home deaths before and after April 13, while there were no discontinuities for hospital deaths. There are about 410,000 elderly people living in care homes in Britain. Care home deaths account for one-third of COVID-19 deaths in this outbreak.
On the big day for epidemic prevention policy development of the UK, 23rd of March, after adopting the model and data from Imperial College that predicted there would still be in the order of 510,000 deaths in the UK, British Prime Minister Boris Johnson announced a national lockdown rendering all Britain immobilized. Starting from the evening of March 23rd, measures will be strictly enforced: for the next three weeks, people in the UK will be banned from leaving their homes. On the other hand, the measure cannot be singled out from the NHS system. The level of protection from care homes may determine the excess mortality in the whole country during the COVID-19 pandemic.
By the 27th of March, about 15,000 beds had been freed up. The UK's general mobilization of the health care system, fearing being overwhelmed in the NHS, is trying to free up beds to cope with a potentially large number of patients. It was not until the 15th of April that the Government of the United Kingdom began the COVID-19 test for all discharged patients. As of July, 28,116 elderly patients had been transferred from hospitals to care homes in England. Putting a large number of suspected infected people back into the environment of a large number of susceptible people, the approach certainly puts pressure on care homes where hospitals should be. The COVID-19 outbreak spread rapidly through care homes.
After the NHS sent back numerous elderly people suspected of infection and other diseases to care homes to make room for beds, the British government started to close down the city. However, the city's lockdown is equivalent to placing vast amounts of susceptible people and infection sources in a closed environment, thus forming many infected people.
The strategies, saving the NHS and the lockdown announcement, have caused the vast death in care homes in the UK. Following the strategy, the authorities were worried about allocating the limited medical resources and thus emphasized saving the NHS, pushing the elderly back to the care homes. Many of the elderly infected cannot go to the hospital for treatment; similarly, some elderly people with other diseases have no access to complementary treatments and end up dying alone in care homes, contributing to Britain's high excess mortality rate.
Good intentions do not always match the right incentives. We have learned this lesson again but in a cruel way. In the case of health care regulation facing covid, the effects of mixing different policies should also be considered to make the incentives more predictable.
The Indian coronavirus variant is now expected to become the most dominant in the UK, and the number of people in the young population getting vaccinations is too low to prevent it from further infection. In the worst case, based on SPI-M-O’s prediction, the variant could put around 10,000 people in hospitals every day within months. Remembering that there is still a non zero chance to go back to lockdown, the truth of the cause of the nuance year ago should be known, especially when the government focuses on promoting vaccination rate for the young population.
The detailed explanation of the mathematical model in this paper can be found in the reference paper below.
Translated by Yunxi Liang and Haoxi Li