Socioeconomic Disparities and Antimicrobial Resistance

In 2019, the world was blissfully unaware of the upcoming public health threat that would change our lives in only a few fateful months. Meanwhile, hospitals and public health organizations were already engaged in an equally important and still ongoing fight against germs that have stopped responding to the medicines designed to treat them. This is known as antimicrobial resistance. 

With 1.27 million global deaths linked to antimicrobial resistance in 2019, the need to highlight the risks of these infections remains critical. As the COVID-19 pandemic exposed many health care inequities, my colleagues and I became interested in how neighborhood characteristics such as living conditions and financial status influence the prevalence of antimicrobial resistance.

We examined electronic health records from 2015 through 2020 for adults who visited two large hospital systems in the Dallas-Fort Worth (DFW), Texas area and tested positive for possible microbial infection. In particular, we analyzed data for patients whose lab results identified one of five common antimicrobial-resistant organisms, four of which the CDC has deemed to be urgent or serious threats. We first determined the locations of these patients. Then, we calculated the number of antimicrobial-resistant cases based on population size (2020 Census estimates) for each census tract and block group.  Census tracts are small community-sized geographic divisions while census block groups are even smaller, neighborhood-sized divisions where several block groups form a census tract.

Our findings are kickstarting a much-needed conversation. 

We used two measures, the Area Deprivation Index (ADI) and the Social Vulnerability Index (SVI), to describe each census tract and block group. The 2020 ADI measures deprivation in a census block group using variables like levels of poverty, education, housing, and employment. The 2020 SVI measures the vulnerability of a census tract to natural disasters and disease outbreaks. It includes variables such as economic status, minority status, and types of housing. For both indices, higher values indicate higher levels of deprivation or vulnerability.

Using location-based tests, we were able to pinpoint “hot spots” or localized pockets of antimicrobial resistance. Among those “hot spots,” many were also surrounded by areas with high ADI or SVI values (i.e., more deprivation or vulnerability) while pockets of low-resistance levels tended to be surrounded by low ADI or SVI values (i.e.,greater well-being and security), indicating an association between antimicrobial resistance and neighborhood conditions.

Our findings are kickstarting a much-needed conversation. Areas with greater deprivation have factors that could increase the spread of infections such as overcrowding, shared housing, poverty, and homelessness. By identifying areas with high ADI, we can tailor mitigation efforts, such as proper hygiene and hand washing techniques, to these disadvantaged neighborhoods to avoid future instances of antimicrobial resistance.