- A study has investigated whether considering localized estimates of health and socioeconomic factors could improve vaccine rollouts.
- The results suggest that health and socioeconomic factors together explain 43% of the variability in deaths across counties in the United States.
- The researchers say that localized estimates of disease could inform better geographical distribution of vaccines.
All data and statistics are based on publicly available data at the time of publication. Some information may be out of date. Visit our coronavirus hub for the most recent information on the COVID-19 pandemic.
As of mid-July 2021, the World Health Organization (WHO) has recorded more than 187 million cases of COVID-19, including more than 4 million deaths, worldwide.
The same data show that health authorities have administered over 3 billion COVID-19 vaccine doses so far. These vaccines have largely been allocated based on people’s susceptibility to contracting the virus and the risk of severe illness.
Several studies have shown that adverse outcomes from COVID-19 are linked to underlying health conditions, including cardiovascular disease,
Other studies have found that socioeconomic factors, such as
Vaccines for COVID-19 are predicted to be in short supply for the near future. Finding better ways to allocate them could reduce adverse outcomes from the virus more effectively.
Scientists from Columbia University, in New York, recently conducted a study to understand whether allocating COVID-19 vaccines according to health and socioeconomic factors could reduce the number of deaths from the disease.
They found that health and socioeconomic factors can explain different death rates from COVID-19 across counties in the U.S. and that allocating vaccines according to these factors could improve vaccine rollouts.
The findings were published in PLOS Medicine.
The scientists collected data from public sources. They gathered information about rates of health conditions across the U.S. from the Centers for Disease Control and Prevention (CDC). This included data about obesity, diabetes, chronic kidney disease, chronic heart disease, COPD, cholesterol levels, and high blood pressure.
They also gathered data about socioeconomic factors, including median per capita income, income inequality, the proportion of residents aged 65 and over, population density, and racial diversity, from County Health Rankings and the Social Vulnerability Index.
The team collected data about COVID-19 cases and deaths from The New York Times and USA FACTS, while information about residents living in nursing homes or facilities came from the 2010 U.S. Census.
The scientists conducted various statistical analyses to understand which factors had the most impact on deaths from COVID-19.
The researchers found that multiple health and socioeconomic factors can together explain 43% of the variability in deaths across U.S. counties.
Among these factors, chronic kidney disease and the proportion of the population living in nursing homes had the strongest individual effects. A 1% increase in either factor increased deaths by 43 and 39 per thousand people, respectively.
Other health factors also increased mortality rates from COVID-19. Chronic heart disease, diabetes, high cholesterol, and high blood pressure could explain between 24.6% and 27.5% of the variability in mortality rates between states.
Socioeconomic factors were generally less influential than health factors. Median per capita income had a slight effect, with every thousand dollar increase reducing mortality rates by 1.5 per thousand people.
Surprisingly, after accounting for multiple factors, the researchers found that obesity and income inequality were not significantly related to mortality rates from COVID-19.
This finding runs counter to previous studies, which suggest the opposite is true for both
The team also found that COPD had a negative association with adverse COVID-19 outcomes, meaning that people with the condition were less likely to die than those with other conditions after developing COVID-19. This is contrary to most other
Previous research has suggested that conditions including
However, the reasons why certain chronic conditions are associated with adverse COVID-19 outcomes are complex and varied, and likely range from existing organ damage to weakened immune responses.
The scientists conclude that data about national and subnational estimates of disease could inform more effective geographical distribution of vaccines.
They note that the case and mortality rates in their research do not completely account for age. Another limitation, they say, is that they could not account for population mixing between counties and mobility patterns, something that can spread SARS-CoV-2.
“The study was conducted in January 2021, in the initial stages of the COVID-19 vaccine rollout in the U.S. With significant uptake in the U.S. since, the influence of the study findings within [the] U.S. may be limited,” study author Sasikiran Kandula told Medical News Today.
“Globally, however, our findings suggest that it may be useful for transnational vaccine initiatives to look at national and subnational population profiles — chronic disease burdens [and] socioeconomic factors that impact access — in addition to raw population sizes while allocating vaccines.”
– Sasikiran Kandula
Derek M. Griffith — a professor of health systems administration and oncology at Georgetown University and the founder and co-director of the university’s Racial Justice Institute and Center for Men’s Health Equity — was not involved in the study but spoke to MNT about the findings.
He said, “The need to focus on more factors than those associated with age and occupation was something my team and I suggested previously.”
“While the authors critique the focus on individual characteristics like age,” he added, “the fact that the proportion of nursing home residents is one of the factors that accounts for some of the largest amounts of variance seems to suggest that focusing on nursing home residents would achieve the same outcome as the U.S. approach did.”
The study authors also note in their paper that they hypothesized two additional measures of socioeconomic disparities in a county: the “ratio of the 80th percentile income to 20th percentile as a measure of income inequality, and the proportion of non-white to white residents as a measure of racial diversity.”
However, Prof. Griffith noted that he finds these measures “less than ideal” because, “Segregation in the U.S. tends to be very different when we think about Asian American, African American, and Latinx groups.”
“Rarely do we even look at segregation in relation to Native Americans’ concentration in an area.”
“It would have been nice if [the authors] could have been more specific in their measure than white vs. non-white because the percentage of African Americans in an area has been found to be a more robust marker of disadvantage than the percentage of other non-white groups in the U.S.”
“Because of the racial and ethnic differences in COVID-19 mortality in the U.S., the authors may have looked at county-level factors in a more nuanced way.”
– Prof. Derek Griffith