Socioeconomic Disparities Persist amongst Influenza Transmission Rates
Influenza (Flu) is a common viral respiratory illness that spreads seasonally in the United States each year. In 2020, a study was conducted by Casey M. Zipfel and Shweta Bansal to test weather health inequities were present in influenza transmission; it was found that inequities increase low socioeconomic status (SES) influenza transmission (Bansal and Zipfel, 2020). When comparing influenza transmission of low SES individuals to the transmission behavior of the rest of the population, the study considered how the contact patterns of these two groups differ. When analyzing these contact patterns, the following five differences were the drivers of disparities in influenza burden (Bansal and Zipfel, 2020):
- Higher assortativity amongst low SES individuals
- Low vaccine uptake
- Low Healthcare Utilization
- High Susceptibility from stressful environment factors
- Low absenteeism from work or school (low-income jobs do not typically give paid leave)
We can look at these disparities present amongst Low SES individuals from the perspective of the Branching Process model and the SIR model to demonstrate how these factors create disparities amongst the transmission rate of Influenza. Specifically, we will analyze how these factors increase the R0 value in Low SES communities.
To start, we will look at the higher assortativity that exists in Low SES communities as found in the study. Observing Figure 1 below, an assortative graph—with nodes representing people and edges representing contact between two nodes—is denser than its disassortative counterpart. As seen in the edX videos, people in a denser graph have a higher chance of becoming infected with a virus; with a denser graph comes more edges connected to each node. That being said, it is more likely that any given person (node) has more connections to infectious people than in a sparse graph, increasing the likelihood of that person contracting the virus. Therefore, since it was found that low SES individuals typically live in more assortative communities, a low SES individual is more likely to contact a virus in comparison to individuals of other populations, increasing the spread of the virus amongst low SES individuals.
For the next four factors listed in the study, we will analyze how they each affect the values of p and k, thus affecting the R0 value. To start, low healthcare utilization and high susceptibility from stressful environment factors both increase the susceptibility of a low SES individuals in comparison to the rest of the population, thus increasing p in low SES communities since any given person is more likely to contract Influenza.
Figure 1 compares the two networks of assortative and a disassortative communities. It is clear that assertive communities have denser networks than its counterpart.
Next, low vaccine uptake and low absenteeism from work or school increase the value k in Low SES communities. With lower vaccine uptake in low SES communities, any given induvial who is infected will come into contact with more people who are susceptible to the virus (since they have not yet received the vaccination). Furthermore, the type of employment for many low SES individuals does not offer paid leave, incentivizing these individuals who were potentially sick to still come into work—or to send sick children to school so that they could still go to work—instead of taking the time off to recover. That being said, low SES individuals once again tend to come into contact with a higher number of people—even once they are contagious. Therefore, the circumstances present in Low SES communities increases the value k.
It is clear that these socioeconomic disparities amongst Low SES communities increase the p and k values, thus increasing the R0 value in these communities. That being said, when any given induvial is infected with Influenza in a Low SES community, the expected number of people that they will spread the virus to is higher in comparison to other communities. Therefore, we have explained and demonstrated why this phenomenon—that socioeconomic disparities exist amongst Influenza transmission rates—exists through the use of networks and models, specifically the Branching Model and SIR Model.
Sources:
- https://www.medrxiv.org/content/medrxiv/early/2020/04/01/2020.03.30.20048017.full.pdf
- https://sph.umich.edu/pursuit/2020posts/how-scientists-quantify-outbreaks.html