Recent research suggests that governments should consider socioeconomic data when allocating COVID-19 vaccinations. Getty Images: Bing Guan/Bloomberg
  • The study examined whether localized estimates of socioeconomic and health factors could be used to improve vaccine rollouts.
  • Results show that 43% of variation in deaths between counties in the United States can be attributed to health and socioeconomic factors.
  • Researchers believe that it is possible to better distribute vaccines geographically based on localized disease estimates.

The World Health Organization (WHO), which has more than 187 million cases worldwide of COVID-19 as of mid-July 2021 had recorded over 4 million deaths.

These same data also show that over 3 billion COVID-19 vaccinations have been administered by health authorities to date. These vaccines were largely allocated based upon the susceptibility of people to the virus and the risk for severe illness.

Numerous studies have shown that COVID-19 adverse effects can be linked to other health conditions such as diabetesTrustedSource, heart diseaseTrustedSource, obesityTrustedSource, chronic obstructive lung disease (COPD), and othersTrustedSource.

Another study found that socioeconomic factors such as race, ethnicity, and income can have an impact on COVID-19 cases.

The prediction that vaccines for COVID-19 will be in short supply in the near future is that they are . They could be distributed in a more efficient manner to reduce the negative effects of the virus.

Columbia University in New York recently completed a study to determine if COVID-19 vaccinations could be allocated according to socioeconomic and health factors. This may help to reduce the number deaths due to the disease.

The researchers found that different COVID-19 death rates can be explained by health and socioeconomic factors across the U.S., and that vaccines could be allocated based on these factors to improve vaccine rollouts.

These findings were published in PLOS Medicine.

Public Data Analysis

Scientists gathered data from public sources. The Centers for Disease Control and Prevention provided data about the prevalence of various health conditions in the United States. These included information about obesity, diabetes and chronic kidney disease.

They also collected data on socioeconomic factors such as median per capita income, income inequality and proportion of residents 65+, population density and racial diversity from County Health Rankings.

The New York Times and USA FACTS provided data on COVID-19 deaths and cases. The 2010 U.S. Census provided information on residents in nursing homes and facilities.

To understand the factors that had the greatest impact on COVID-19 deaths, scientists performed statistical analyses.

Researchers found that 43% of variation in deaths between U.S. states can be attributed to multiple socioeconomic and health factors.

The strongest individual effects were seen in chronic kidney disease and nursing home residents. An increase of either factor by 1% would result in a 43- and 39-per-million deaths, respectively.

COVID-19 also shows an increase in mortality rates due to other health factors. Variability in mortality rates among states could be explained by chronic heart disease, diabetes and high cholesterol.

Health factors had a greater influence on socioeconomic factors than they did. The median income per capita had a small effect on mortality rates, with each thousand dollars of increase decreasing the rate by 1.5 per 1,000 people.

Surprisingly though, researchers discovered that obesity and income inequality did not have a significant relationship with mortality rates for COVID-19.

This is contrary to previous studies that have shown the opposite for income inequalityTrustedSource and obesityTrustedSource.

Also, the team found that COPD was associated with less severe COVID-19 outcomes. This means that COPD patients were less likely than people with other conditions to die. This contrasts with most resultsTrusted Source regarding COVID-19 outcomes in people with COPD.

To a more effective geographic vaccine distribution

Research has shown that chronic kidney diseaseTrustedSource, diabetes and heart disease are associated with increased levels of pro-inflammatory cytokines. Research has shown that high blood pressure, heart disease, diabetesTrustedSource, and high levels of pro-inflammatory chemicals in the blood increase levels. These chemical messengers are chemical messengers. A rise in these cytokines can cause oxidative stress, which triggers an immune response.

The reasons for adverse COVID-19 outcomes in certain chronic conditions can be complex and vary. They could range from organ damage to weak immune responses.

AgeTrusted source is a risk factor for adverse outcomes in COVID-19 patients. This could explain the higher death rates in people who live in nursing homes.

Scientists conclude that information about subnational and national estimates of disease can help to improve the geographical distribution of vaccines.

The case and mortality rates used in their research don’t fully account for age, they say. They also noted that they were unable to account for the possibility of SARS-CoV-2 spreading through population mixing across different counties.

“The study was done in January 2021 during the initial stages in the COVID-19 vaccine rollout. However, there has been significant uptake in the U.S., so the impact of the study findings in [the] U.S. might be limited,” Sasikiran Kandula, study author, told Medical News Today.

Globally, however, our findings suggest it may be helpful for transnational vaccination initiatives to look at subnational and national population profiles — chronic diseases burdens [and] socioeconomic variables that impact access — while allocating vaccines.”

– Sasikiran Kadula

It is important to take a more complex approach

Derek M. Griffith, a Georgetown University professor of oncology and health systems administration who is also the codirector of the university’s Racial justice Institute and Center for Men’s Health Equity, was not part of the study but spoke with MNT to discuss the findings.

He stated, “The need for more factors than those related to age and occupation was something that my team and I suggested before.”

He said that while the authors criticize the emphasis on individual characteristics such as age, he also stated that “the fact that nursing home residents is one factor that accounts for the greatest amounts of variance seems suggest that focusing only on nursing home resident would produce the same result as the U.S. approach.”

In their paper, the study authors note that they also hypothesized two additional measures for socioeconomic disparities within a county: the ratio of the income of the 80th percentile to the 20th percentile to measure income inequality and the proportion non-white to black residents to measure racial diversity.

Professor Griffith said that these measures are “less than ideal”, because “Segregation in America tends to be very different if we think about Asian American and African American groups, as well as Latinx groups.”

“Rarely do you even consider segregation in terms of Native Americans’ concentrations in an area.”

“It would be nice if the authors could have been more specific than white vs non-white, because the percentage African Americans living in an area has been shown to be a stronger marker of disadvantage that the percentage of non-white groups in America.”

“Because there are racial differences in COVID-19 deaths in the U.S., it is possible that the authors could have looked at county-level factors more nuanced.”

– Professor Derek Griffith

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