Related Experiment Video

Updated: May 16, 2025

Setup of Consumer Wearable Devices for Exposure and Health Monitoring in Population Studies

Setup of Consumer Wearable Devices for Exposure and Health Monitoring in Population Studies

Published on: February 3, 2023

The Dynamic Impact of Community Social Vulnerability on COVID-19 Outcomes: A Census Tract-level Analysis of Fulton

Kamini Doraivelu¹, Steve Williams², Mallory Hazell²

¹Hubert Department of Global Health, Emory University, Atlanta, Georgia, USA.

Clinical Infectious Diseases : an Official Publication of the Infectious Diseases Society of America

|April 3, 2025

Abstract

Background:

Coronavirus disease 2019 (COVID-19) case and death rates have been variable across geography and time; an understanding of community social vulnerability and variation in COVID-19 outcomes is needed to better respond to acute public health needs and prioritize post-pandemic recovery.

Methods:

We analyzed records of confirmed COVID-19 cases (N = 106 037) and deaths (N = 2107) in Fulton County, Georgia, from 4 March 2020 to 11 November 2021. We developed the social vulnerability index-plus (SVI+) as a composite measure of 19 census tract-level social and health indicators summarizing community vulnerability. Ecological analyses included linear regression estimates of differences in weekly COVID-19 case and death rates per 100 000 population by SVI+ across 201 census tracts. Multilevel analyses included log-binomial regression estimates of relative risk of individual-level death among confirmed cases according to the SVI+. Calendar periods included Pre-Vaccine (March 2020 to 15 December 2020), Post-Vaccine/Pre-Delta variant surge (16 December 2020 to 30 June 2021), and post-vaccine/Delta variant surge (1 July 2021 to 11 November 2021).

Results:

We observed a positive association between SVI+ and both COVID-19 census tract-level case and death rates. Analyses by calendar period indicated that the strongest association between SVI+ and both case rates, and death rates was in the post-vaccine/Delta variant surge period. Analyses of individual-level death among cases showed a positive association between SVI+ and COVID-19 death after accounting for age, race, and sex. SVI+ and individual-level death were positively associated in all calendar periods.

Conclusions:

Even after vaccine availability, communities with higher social vulnerability experienced worse COVID-19 outcomes. The findings reiterate the importance of addressing social determinants by increasing public health efforts in vulnerable communities to mitigate health disparities in future pandemics.

Keywords:

COVID-19 community-level determinants of health health disparities social vulnerability

More Related Videos

A Modified Trier Social Stress Test for Vulnerable Mexican American Adolescents

A Modified Trier Social Stress Test for Vulnerable Mexican American Adolescents

Published on: July 10, 2017

Author Spotlight: Advancements in Multiplex Detection of Respiratory Viruses

Author Spotlight: Advancements in Multiplex Detection of Respiratory Viruses

Published on: November 10, 2023

Related Concept Videos

Factors Affecting the Risk of Infection

Factors Affecting the Risk of Infection

The hosts' susceptibility to infection depends on several factors. The integrity of the skin and mucous membranes helps protect the body against microbial attacks. When the skin is altered, the chance of infection, limb loss, and even death increases.
The integrity and count of the white blood cells help the body resist pathogens and fight infection. When impaired, it reduces the body's resistance to pathogens. The acidic pH levels of the gastrointestinal, genitourinary tracts, and skin...

Factors Affecting Illness

Factors Affecting Illness

When a person's physical, emotional, intellectual, social development or spiritual functioning is compromised, this deviation from a healthy normal state is called illness. Illness creates stress that in turn harms individuals. Irritation, anger, denial, hopelessness, and fear are behavioral and emotional changes an individual experiences in the phases of illness. A variety of factors influence a person's health and well-being.
For instance, risk factors are connected to illness,...

Bias in Epidemiological Studies

Bias in Epidemiological Studies

Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:

Principles of Disease Surveillance

Principles of Disease Surveillance

Disease surveillance is the systematic collection, analysis, and interpretation of health data essential to the planning, implementation, and evaluation of public health practice. This process integrates data dissemination to entities responsible for preventing and controlling disease, injury, and disability. Surveillance systems provide crucial information for action, helping public health authorities make informed decisions to manage and prevent outbreaks, ensure public safety, optimize...

Community Based Intervention

Community Based Intervention

Community-based interventions in mental health represent a paradigm shift from institution-centered care to treatments embedded within the fabric of local communities. By prioritizing inclusion and leveraging existing societal structures, this approach fosters a supportive environment conducive to addressing mental health challenges while promoting individual dignity and agency.
Foundations of Community Mental Health Programs
Central to the success of community-based interventions is the...

Statistical Methods for Analyzing Epidemiological Data

Statistical Methods for Analyzing Epidemiological Data

Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:

Articles linked to this work by shared authors, journal, and citation graph.

Programmatic effectiveness of tuberculosis preventive treatment for people with HIV in six high-tuberculosis-burden countries (PROTECT): a prospective cohort study and meta-analysis.

The lancet. HIV·2026

Diagnostic Tools to Establish the Presence of Peripheral Arterial Disease in People With Diabetes (DMPAD): A Multicenter Comparative Diagnostic Accuracy Study.

Diabetes care·2026

Respiratory Syncytial Virus Seasonality and Disease Severity Among Children Under 5 in Atlanta, Georgia, United States, 2018-2023.

Open forum infectious diseases·2026

Evaluating mental health integration in PrEP programs to reduce HIV incidence among US gay and bisexual men: a network modeling study.

BMC health services research·2026

CardioMorph Atlas: a statistical approach to evaluate association between pulmonary tuberculosis and cardiac morphology from chest X-rays.

EBioMedicine·2026

Self-administered digital vaccination decision-aids in primary care: A pilot study of COVID-19 vaccination in Atlanta, GA.

Digital health·2026

See all related articles