Tuesday , November 24 2020

The predictive model identifies settlements where there is the highest risk of COVID-19 infection

To help slow the spread of COVID-19 and save lives, UCLA public health and urban planning experts have developed a predictive model that determines which populations in which neighborhoods of Los Angeles County have the highest risk of infection.

The researchers hope that the new model, which can be applied to other counties and jurisdictions, will help decision-makers, public health officials and scientists in the efficient and equitable implementation of vaccine distribution, testing, closure and reopening and other virus mitigation measures.

The model maps Los Angeles County neighborhoods by neighborhood, based on four important indicators that are known to significantly increase a person’s medical vulnerability to COVID-19 infection; existing medical conditions, barriers to accessing health care, the characteristics of the built environment and socio-economic challenges.

Research shows that neighborhoods characterized by significant groups of racial and ethnic minorities, low-income households and unmet medical needs are most susceptible to COVID-19 infection, especially areas in and around South Los Angeles and the eastern San Fernando Valley.

Communities along the coast and in the northwestern part of the district, which are disproportionately white and with higher incomes, have been found to be least vulnerable.

The model we have includes specific resource vulnerabilities that can lead public health officials and local leaders across the country to use already available local data to determine which groups are most vulnerable in which neighborhoods and how to prevent new infections to save lives. ”

Vickie Mais, St.udi A.uthor, Fielding School of Public Health Professor of Health Policy and Management and Psychology, UCLA College

Mais, who also directs the National Institutes of Health UCLA BRITE Center for Science, Research and Policy, collaborated with urban planner Paul Ong, director of the UCLA Center for Neighborhood Knowledge, to develop a model of indicators, along with co-authors of the study. Chhandara Pech and Natalie Rios Gutierrez. The maps were created by Abigail Fitzgibbon.

Using data from the UCLA UCLA Center for Health Policy Research, the UCLA California Health Policy Research Survey, the U.S. Census Bureau Survey, and the California Department of Parks and Recreation, researchers were able to determine how four vulnerability indicators predicted differently which racial and ethnic groups in Los Angeles County were most susceptible to infections based on their geographic residence.

Racial and ethnic groups with the greatest vulnerability

  • Assumed conditions. The authors found that 73% of Montenegrins live in neighborhoods with the highest rates of pre-existing health conditions such as diabetes, obesity and heart disease, as well as poor overall health and food insecurity. It was followed by 70% of Latin Americans and 60% of Cambodians, Hmong and Laos or CHL. In contrast, 60% of white people live in areas with little or no vulnerability.
  • Barriers to accessing services. Forty percent of Latinos, 29% of blacks, 22% of CHLs, and 16% of “other Asians” live in the neighborhoods with the highest barriers to health care, characterized by a large proportion of non-Americans, poor English language skills, lack of access to computer broadband, lower health insurance rates, and poor access to vehicles for medical purposes. Only 7% of whites live in these settlements.
  • Risk of the built environment. Sixty-three percent of CHL, 55% of Hispanics, 53% of blacks, and 32% of whites live in areas considered high or most vulnerable to built-up environmental challenges, including high population density, housing congestion, and lack of parks and open spaces.
  • Social vulnerability. According to the Centers for Disease Control, neighborhoods with high social vulnerability are characterized by lower socioeconomic status and education, higher prevalence of single-parent and multigenerational households, higher housing density, poorer English language skills and lack of access to vehicles, among other factors. Although only 8% of whites live in these settlements, 42% of blacks and Latinos live, as does 38% of CHL.

How the model can help in efforts to mitigate the occurrence of COVID-19

“When it hit a pandemic, we were slowed by a lack of science and a lack of understanding of the ways in which health differences in the lives of some of our most vulnerable populations make the risk of COVID-19 infection even greater,” Mace said. “We thought that the elderly and people in nursing homes were the most vulnerable, but we found that the lack of a certain number of social resources contributes to a higher probability of becoming infected.”

While national statistics have shown that the virus has had a disproportionate effect on low-income communities and on colored communities, knowing exactly which populations are most at risk and where new infections are likely to occur is crucial in determining how scarce resources are distributed. and when to open or close areas, Mace and Ong said.

If, for example, English language proficiency is a barrier to accessing health information and services in a sensitive neighborhood, health professionals should develop campaigns in Spanish or another appropriate language, emphasizing the availability of testing, the researchers emphasize.

If access to the car is an obstacle for families in the risk area, places for walking tests should be provided. When overcrowded accommodation in a high-risk settlement is the predominant housing stock, testing resources should be established for entire households and hotel vouchers that will be available to assist in quarantine after a positive test.

The data can also provide critical knowledge and insights to social service providers, emergency agencies and volunteers on where to direct their time and resources, such as where to set up food distribution points and other supplies.

Most importantly, identifying the areas and populations with the greatest vulnerability will help decision-makers give fair vaccine distribution plans a fair priority in order to include the most vulnerable early.

Researchers say the long-term model will also provide valuable information to urban planners to target specific areas for less dense housing and more parks and open spaces, creating healthier neighborhoods that can better withstand future pandemics while promoting equity in long-term health outcomes.


UCLA Fielding School of Public Health

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