Written by the Nested Knowledge Team
It is well-known that the risk of morbidity and mortality from COVID-19 increases with age, with the highest risk for the most elderly members of the community. It is estimated that 13.4% of COVID-19 patients 80 and older die from the disease, compared to 8.6% of patients in their 70s, and 4% of patients in their 60s. In Minnesota alone, 80 percent of these deaths occur in long-term care facilities such as nursing homes and centers for assisted living. Therefore, it is especially important to identify patterns of contact with the elderly to limit their exposure. While scientists are studying the immune system and co-morbidities that make COVID19 more lethal for the older population, our team at Nested Knowledge explored patterns of COVID19 infection in the elderly who live in intergenerational households, with members across a range of age groups.
Our analysts used this shared datasheet of statistics on COVID-19 cases and mortality, which summarizes the raw data made available based on a range of sources here, including cases and deaths by country and population. The data on household size and intergenerational homes is sourced from a United Nations database.
We performed correlation analysis between the percentage confirmed/death rate with the following factors (i) average household size, (ii) percentage of a household with at least one member aged 60 or over, (iii) percentage of a household with at least one member aged 65 or over, (iv) percentage people aged 65 or over. We found that both confirmed cases and mortality correlated with high rates of intergenerational cohabitation (by country). Therefore, partial least squares was performed to fit the data, to quantitatively determine how much each variable influenced the outcome.
Some key results:
- The greater the number of elderly people, the higher number of confirmed cases of COVID-19 and their associated death rate; this finding confirms prior research at a country-by-country level. Our analysis shows that percentage of confirmed cases AND death rate have a relatively strong positive correlation with a higher number of older people (Fig. 1)
Figure 1. Correlation between the percentage of confirmed/death rate of COVID-19 and percentage people aged 65 or over.
- Most of the confirmed infection rate and death rate from COVID-19 relates to the sheer number of old people, not their living situation. Our analysis shows that the rate of intergenerational cohabitation is highly correlated with the number of old people in a country (Spearman correlation: 0.79, Pearson correlation: 0.78). After performing partial least squares, we have X1: old people rate, X2: intergenerational home rate
Confirmed rate = 0.0051 X1 + 0.0028 X2 – 0.0655
Death rate = 0.0004 X1 + 0.0002 X2 – 0.0055
This means having intergenerational home contributes less than half as much as just having an old population in general.
- Older people living with their family have a higher chance of COVID-19 infection and death. The percentage of confirmed cases AND the death rate have a moderate positive correlation with intergenerational home rate, i.e. the more intergenerational family, the higher rates of confirmed COVID-19 AND death (Fig. 2).
Figure 2. Correlation between the percentage confirmed/death rate and average household size, percentage of a household with at least one member aged 60 or over, and percentage of a household with at least one member aged 65 or over.
- On top of examining intergenerational homes, we also examined COVID19 confirmed cases and mortality based on average household size by country. Interestingly, the percentage of confirmed cases of COVID-19 AND associated death rate have a strong negative correlation with average household size, i.e. the larger household size, the lower number of confirmed cases AND the associated death rate. (Fig. 2). This was an interesting result showing that somehow a bigger household size could help. We speculate that this may represent young households with many children.
Some disclaimers: Pandemic infection rates are complex issues with a lot of variable factors. Every country has different population densities, timelines for the pandemic, varying diagnostic capabilities, differing standards for tracking patients and quality of healthcare in addition to how much social distancing is recognized and followed. Due to those considerations, our statisticians do not perform inferential statistics due to the potential impact of confounding variables, However, one thing that we do know is human contact spreads the virus faster, and so a culture of disregarding laws or rules surrounding social distancing *could* potentially lead to an increased occurrence of COVID-19 infection.
As the scientific community rallies around to fight the pandemic, we encourage data analytics experts and interested scientists can use our data sheet to build their data models; you can “mirror” it in your own Google Sheet using the ImportRange function. We hope that this can be a start for researchers to pool data and construct their own analysis on factors related to population, socioeconomics, culture, and other datasets.