UC Davis

Antimicrobial resistance (AMR) is a growing global concern that poses significant risks to human, animal, and environmental health. AMR describes organisms, including bacteria, viruses, and fungi, that have become resistant to the drugs used to kill or control them through evolutionary, environmental, or social pressures. The emergence and spread of AMR threaten decades of advancement in reducing morbidity and mortality from many infectious diseases and is one of the most pressing clinical and public health threats. In the United States alone, more than 2.8 million AMR infections occur annually, resulting in over 35,000 deaths and costing the healthcare system over $4.6 billion.

Community-acquired Methicillin-resistant Staphylococcus aureus (CA-MRSA) infections are a significant financial burden on healthcare systems and vary across populations. Several studies have documented infection disparities across racial-ethnic categories, economic indicators, and geography. Evidence suggests that differential infection rates are related to social determinants of health such as income, housing, and educational attainment. However, few studies connect infection disparities to the key mechanisms underlying the role of racial-ethnic category, income, or geography in shaping population health. The role of area-based determinants of infection has yet to be thoroughly examined, and addressing the challenges of AMR more broadly will require understanding the social, environmental, and economic processes that shape differential infection incidence.

This dissertation explores the systems and determinants associated with disparities in CA-MRSA infection presenting in California emergency departments (ED). The first paper examines the epidemiology of CA-MRSA skin and soft tissue infections (SSTIs) across California from 2016-2019, examining the spatial distribution of infections in a unique neighborhood boundary. CA-MRSA infections are historically associated with densely populated urban areas, but statewide ED data revealed that more rural and mountainous regions of California are experiencing a higher burden of CA-MRSA SSTIs. This highlights a potential change in the epidemiology of CA-MRSA SSTIs and warrants further investigation into identifying at-risk populations and possible infection pathways.

Electronic health records (EHR) are an increasingly utilized data source in public health, including throughout this dissertation. However, biases may exist when using EHR data for research, primarily due to the processes that drive whether someone is captured in the EHR. The second paper of this dissertation examines whether small-area inequalities in CA-MRSA infection that are observed in the EHR can be explained by selection bias. Using an agent-based model (ABM) to simulate the dynamics of healthcare-seeking behavior for CA-MRSA infection, we compared the spatial patterns of agents seeking care for their infection in the ED to empirical data. The ABM reproduced the observed infection prevalence for nine of the 21 geographies. The differences in prevalence across geographies did not reach the magnitude in the observed data, and the overall spatial patterns between the two had weak agreement. The results suggest that geographic disparities identified in the observed ED data are due to factors beyond selection bias and healthcare-seeking behaviors. Relative importance analyses point to environmental degradation, percent non-Hispanic white residents, and percent of residents living below the federal poverty level as potentially important determinants.

Finally, poverty is an often-cited driver of health disparities, and associations between poverty and CA-MRSA infection are well documented. However, the pathways through which poverty influences infection have yet to be thoroughly examined. The third paper aims to identify mediating variables explaining why area-level poverty is associated with CA-MRSA infection in Californians. Mediation analyses using Bayesian multilevel spatial regression revealed that the association between area-level poverty and CA-MRSA infection could be partially explained by spatial autocorrelation, living in a primary care shortage area, and environmental degradation in the neighborhood. Combined, the mediators explain approximately 6% of the odds of CA-MRSA infection for individuals living in neighborhoods with high poverty rates and 50% of the statistical association between area-level poverty and CA-MRSA infection. The statistical association between area-level poverty and infection was entirely explained by the mediators for individuals living in neighborhoods with low poverty rates.

Overall, this dissertation provides insight into the changing epidemiology of CA-MRSA infections in California, the accuracy of using EHR data in public health disparities research, and the mediating variables that explain the association between poverty and infection. Expanding our understanding of the determinants and mechanisms driving AMR and differential CA-MRSA infection incidence is essential for developing effective public health policies and interventions to reduce the burden of these infections. Addressing the challenges of AMR, including health disparities, will require multi-level approaches encompassing upstream facilitators, individual behavior, and social and environmental contexts.