Department of Life Sciences, University of Coimbra, Centre for Functional Ecology, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal.
We ran the models for the two samples in panel data, and ran an OLS model to look for significance and signal of the variables, and found that the following factors were significant (see details in Annex 1).
3.1 Ecosystem Significant Indicators
- Air and water pollution index was a consistent and significant indicator in both samples, increasing mortality.
- Air pollution index was found significant, increasing mortality.
- Mean temperature in March was also a very significant indicator in both samples supporting existing literature, that the higher temperature, the lower mortality,
- UV radiation was also found significant, but not as much as the mean temperature as it is the yearly average and is not adjusted to the month. In any case, mean temperature in March also contains information about the level of UV radiation.
3.2 Health Significant Indicators
- Lung cancers mortality was the most significant indicator of this category, increasing mortality.
- Prevalence of hypertension was also found significant.
- Prevalence of diabete was found significant in both samples.
3.3 Socio-economic Significant Variables
- Percentage above 65 years old was a consistent significant variable across the models and samples, increasing mortality the higher the percentage of people within that age group.
- Mean age of the population was also found significant with the same direction (the older the median age, the more mortality).
- Air traffic per capita was found to be a significant variable in the various models that we ran with a positive effect on mortality.
- Population concentration was found to be a significant variable.
- Residents of nursing homes as a percentage of the total population was found to increase mortality.
3.4 Mobility Significant Factors
- Suspension of incoming flights was found to be an indicator to reduce mortality in the samples.
- Mandatory quarantine was equally found significant also in the two samples, reducing mortality.
- Mobility reduction in public space was found significant in both samples, increasing mortality.
The variables not found significant were: Latitude, health care index, expenses with health care per capita, GDP per capita.
Generally speaking, epidemiological knowledge was supported in the models here presented, and some recommendations can be derived.
In the models conducted, it was shown that, as expected, the elders indeed are one of the highest risk groups. Yet, the number of elders residents in nursing homes per thousand inhabitants significantly increases the mortality, and appears to be a more significant factor than the percentage of the population above 65 years old. It would be interesting to have data regarding the size of the nursing house (average number of residents per nursing house) to understand if larger ones lead to higher spreading in this risk group and therefore to even higher mortality. One of the conclusions is that nursing homes should be highly controlled during outbreaks. Another conclusion is that concentrating the elderly population in nursing homes, especially if they have many residents, may foster the spread of viruses. Therefore, this should be taken into consideration when planning the support to the elders, promoting smaller nursing homes, to be more resilient in these situations, or to spread them in smaller units in case of outbreaks as such.
Secondly, the immunological condition of the host is also a relevant factor. UV radiations and mean temperature indicators allow us to consider that in warmer countries or during the warmer seasons the populations are less at risk. Exposure to solar radiation during the Winter is beneficial to strengthen the immunological system, and therefore, even the population that should be more protected (the risk groups), should keep having some time of direct sun per day. Also for patients being treated, some solar exposure per day can be considered. At last, colder countries should have more intensive care facilities in proportion to the total populations for when these outbreaks occur.
Thirdly, the higher the prevalence of certain health conditions (i.e. respiratory problems, diabetes and hypertension) advise us to give more attention to people with them. They should have preference for being tested and should be more closely accompanied in comparison to those without these diseases. Respiratory problems may be addressed by improving air quality, as air pollution is one of the relevant variables. Diabetes and hypertension are often affected by food habits, lack of exercise or having a stressful life. At a structural level, these topics also should be further emphasized as they also contribute to increase the mortality level during the outbreaks of this kind of virus. Countries with high prevalence of these risk groups should also be more concerned regarding the dimension of their intensive care facilities.
Contagion factors such as proportion of flights to the whole population and concentration of population increase mortality, meaning that they help to foster transmission channels. This means that rural places or less connected regions in general should be less concerned in comparison to more urbanized and connected areas. The latter should be better prepared to provide a better response from their health care system, again by having more intensive care facilities in proportion to the population to respond to these outbreaks.
Mobility policies on suspending flights or enforcing quarantines to the passengers was also found to have a similar effect of reducing mortality. Yet, mandatory quarantines are less restrictive and therefore have lower overall socio-economic impacts. Anyway, during the beginning of the outbreak, in case a country fears the collapse of its health system, any of these policies can be temporarily considered.
The only counterintuitive result was regarding the reduction of community mobility in public spaces, which apparently is not being able to reduce mortality in the period between 3 and 7 weeks after the implementation of such measures. Therefore, we shall also not expect that it is reducing the influx of people in the health care facilities in the following weeks to their implementation as mortality is a good proxy of the number of hospitalizations.
This suggests that the social distancing benefits by reducing overall contacts between people, may have been counterbalanced by contradictory effects, and it would be interesting to research possibilities of opposite effects that neutralize the positive ones. Therefore, some research questions could be investigated about other impacts of limiting the contact between people in the public spaces. According to the regional director of WHO for Europe, Hans Kluge, strong mobility restriction measures reduced the personnel in nursing residencies (with high-risk groups for COVID-19 such as advanced age population and those with underlying mental and physical illnesses) and there was also not enough testing and lack of equipment for the professionals being provided. Other possible reasons that could be investigated is if mobility restriction policies are prolonging/augmenting Winter effects, namely by increasing the duration and frequency of contacts between the persons inside the household and this may increase the opportunities for the virus to spread as also it favours the atmospheric conditions for the virus with less solar exposure. Additionally, this lower exposition to UV radiation leads to lower immunology of the host. It may also happen, that when the first cases are diagnosed in a country, the actual spread of the virus is much higher than initially thought, so a large number of people already may have it and by being closed at home, will promote the right conditions to spread to the other household members.
Source: Ecosystem, Health, Socio-economic and Mobility Factors for COVID-19 Mortality | Journal of Advances in Medicine and Medical Research
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