The predictive model identifies the neighborhoods that have the greatest risk of COVID-19 infection

Reviewed by Emily Henderson, B.Sc.November 19, 2020

To help slow the spread of COVID-19 and save lives, UCLA public health and urban planning experts have developed a predictive model that pinpoints the populations in which Los Angeles County neighborhoods are most at risk of being infected.

The researchers hope that the new model, which can also be applied to other counties and jurisdictions, will assist decision makers, public health officials and scientists in the effective and equitable implementation of distribution, testing, closures and reopenings. vaccines and other virus mitigation measures.

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

Research data shows that neighborhoods characterized by a significant clustering of racial and ethnic minorities, low-income families, and unmet medical needs are the most vulnerable to COVID-19 infection, particularly areas in and around South Los Angeles and the eastern part of the San Fernando Valley.

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

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

Vickie Mays, St.udy Author, Fielding School of Public Health Professor of health policy and management and psychology, UCLA College

Mays, who also directs the National Institutes of Health-funded UCLA BRITE Center for Science, Research and Policy, worked with urban planner Paul Ong, director of the UCLA Center for Neighborhood Knowledge, to develop the indicator model, together to the co-authors of Chhandara Pech and Nataly Rios Gutierrez. The maps were created by Abigail Fitzgibbon.

Using data from the UCLA Center for Health Policy Research’s California Health Interview Survey from UCLA Fielding School, the US Census Bureau’s American Community Survey, and the California Department of Parks and Recreation, the researchers were able to determine how the four vulnerability indicators differentially predicted which race and which ethnic groups in Los Angeles County were most vulnerable to infection based on their geographic residence.

Racial and ethnic groups with the highest vulnerability

• Pre-existing conditions. The authors found that 73% of black residents live in neighborhoods with the highest rates of pre-existing health conditions such as diabetes, obesity and heart disease, as well as poor general health and food insecurity. This was followed by 70% of Latins and 60% of Cambodians, Hmong and Laotians or CHLs. In contrast, 60% of white residents live in areas with low or lower vulnerability.

• Barriers to accessing services. 40% of Latinos, 29% of Blacks, 22% of CHLs and 16% of “other Asians” reside in neighborhoods with the greatest barriers to health care, characterized by high percentages of non-US citizens, little knowledge of English language, lack of access to broadband computer service, lower health insurance rates, and poor access to vehicles for medical purposes. Only 7% of whites live in these neighborhoods.

• Built environment risk. 63% of CHLs, 55% of Latinos, 53% of Blacks and 32% of Whites live in areas considered to be at or most vulnerable due to the challenges of the built environment, which include high population density, housing crowded and a lack of parks and open spaces.

• Social vulnerability. According to the Centers for Disease Control, neighborhoods with high social vulnerability are characterized by a lower socioeconomic status and educational level, a higher prevalence of single-parent and multigenerational families, higher population density, poor knowledge of the English language and a lack of access to vehicles, among other factors. While only 8% of whites live in these neighborhoods, 42% of both blacks and Latinos, as well as 38% of CHLs.

How the model can help with COVID-19 mitigation efforts

“When the pandemic hit, we were slowed down by a lack of science and a lack of understanding of the ways in which health disparities in the lives of some of our most vulnerable populations made their risk of COVID-19 infection even greater.” , Mays said. “We thought the elderly and people in nursing homes were the most vulnerable, but we found that a lack of a number of social resources also contributes to a higher likelihood of contracting the infection.”

And while national statistics have shown that the virus has had a disproportionate effect on low-income communities and communities of color, knowing precisely which populations are the most vulnerable and where new infections are likely to occur is information. key to determining how scarce resources are allocated and when to open or close areas, Mays and NGOs said.

If, for example, English proficiency is an obstacle to accessing information and health services in a vulnerable neighborhood, health officials should develop campaigns in Spanish or another appropriate language highlighting the availability of tests, they point out. researchers.

If access to a car is an obstacle for families in a risk area, test sites should be made available. When crowded housing in a high-risk neighborhood is the predominant housing stock, testing resources need to be in place for entire families and hotel vouchers to be made available to help with 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 delivery sites and other needs.

And, importantly, identifying the areas and populations with the highest vulnerability will help decision makers prioritize vaccine distribution plans equally to include the most vulnerable early on.

In the long run, the researchers say, the model will also provide valuable information to planners so that they can target specific areas for developing less dense housing and more parks and open spaces, creating healthier neighborhoods that can better resist future pandemics by promoting in the at the same time equity in long-term health outcomes.