Trends in non-COVID-19 hospitalizations prior to and during the COVID-19 pandemic period, United States, 2017-2021.

COVID-19 pandemic-related shifts in healthcare utilization, in combination with trends in non-COVID-19 disease transmission and non-pharmaceutical intervention use, had clear impacts on rates of hospitalization for infectious and chronic diseases. Using a U.S. national healthcare billing database, we estimated the monthly incidence rate ratio of hospitalizations between March 2020 and June 2021 according to 19 ICD-10 diagnostic chapters and 189 subchapters. The majority of primary diagnoses for hospitalization showed an immediate decline in incidence during March 2020. Hospitalizations for reproductive neoplasms, hypertension, and diabetes returned to pre-pandemic levels during late 2020 and early 2021, while others, like those for infectious respiratory disease, did not return to pre-pandemic levels during this period. Our assessment of subchapter-level primary hospitalization codes offers insight into trends among less frequent causes of hospitalization during the COVID-19 pandemic in the U.S.

Modeling the Effectiveness of Healthcare Personnel Reactive Testing and Screening for the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Omicron Variant Within Nursing Homes.

The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Omicron variant has been hypothesized to exhibit faster clearance (time from peak viral concentration to clearance of acute infection), decreased sensitivity of antigen tests, and increased immune escape (the ability of the variant to evade immunity conferred by past infection or vaccination) compared to prior variants. These factors necessitate reevaluation of prevention and control strategies, particularly in high-risk, congregate settings like nursing homes that have been heavily impacted by other coronavirus disease 2019 (COVID-19) variants. We used a simple model representing individual-level viral shedding dynamics to estimate the optimal strategy for testing nursing home healthcare personnel and quantify potential reduction in transmission of COVID-19. This provides a framework for prospectively evaluating testing strategies in emerging variant scenarios when data are limited. We find that case-initiated testing prevents 38% of transmission within a facility if implemented within a day of an index case testing positive, and screening testing strategies could prevent 30% to 78% of transmission within a facility if implemented daily, depending on test sensitivity.

The interplay between human behavior and infectious disease dynamics

Human behavior and infectious disease are related in a dynamic feedback loop. This integral and ubiquitous relationship is often ignored in epidemiological modeling, leading to findings that overlook a crucial element in determining infectious disease transmission and thus have limited utility for public health purposes. Significant past work focuses on one side of the behavior-disease relationship: how contact behavior determines disease transmission. Far less attention has been paid to how disease changes social behavior and the dynamic effects of these behavioral changes on future disease spread. The work that does consider the entire behavior-disease feedback loop is largely theoretical and lacks support from empirical data. In this dissertation, I move this problem forward by characterizing the effects of disease on behavior through empirical data, exemplifying the downstream effects of the identified behavior changes on infectious disease dynamics through epidemiological models, and considering the impacts of population heterogeneity in these behaviors. In chapter 1, I consider how disease physiologically drives behavior change through sickness behaviors. In chapter 2, I characterize the ways that disease modifies behavior socio-politically, through public health policy and information-driven risk perception. In chapter 3, I demonstrate the effects of health inequities that drive population heterogeneity in infectious disease-related behaviors. I achieve this through integration of large-scale empirical data with complex epidemiological and statistical models to address case studies of respiratory-transmitted infectious diseases: influenza and COVID-19. I find that 1) sickness behaviors and absenteeism reduce transmission, and only a small portion of the population must engage in sickness behaviors in order to alter epidemic outcomes;2) subjective risk perception and state-level policies result in the largest reductions in social contact behavior during the COVID-19 pandemic, and typical behavior-disease models that account for objective risk will overestimate the impacts of behavior change on transmission;and 3) health inequities result in increased influenza among vulnerable populations that are often overlooked by epidemiological surveillance. The COVID-19 pandemic has exemplified that behavior is our first defense against transmission, thus understanding how behavior changes due to disease is a crucial step to understanding epidemiological dynamics and improving public health. (PsycInfo Database Record (c) 2021 APA, all rights reserved)

The missing season: The impacts of the COVID-19 pandemic on influenza.

Throughout the COVID-19 pandemic, many have worried that the additional burden of seasonal influenza would create a devastating scenario, resulting in overwhelmed healthcare capacities and further loss of life. However, many were pleasantly surprised: the 2020 Southern Hemisphere and 2020-2021 Northern Hemisphere influenza seasons were entirely suppressed. The potential causes and impacts of this drastic public health shift are highly uncertain, but provide lessons about future control of respiratory diseases, especially for the upcoming influenza season.

Health inequities in influenza transmission and surveillance.

The lower an individual's socioeconomic position, the higher their risk of poor health in low-, middle-, and high-income settings alike. As health inequities grow, it is imperative that we develop an empirically-driven mechanistic understanding of the determinants of health disparities, and capture disease burden in at-risk populations to prevent exacerbation of disparities. Past work has been limited in data or scope and has thus fallen short of generalizable insights. Here, we integrate empirical data from observational studies and large-scale healthcare data with models to characterize the dynamics and spatial heterogeneity of health disparities in an infectious disease case study: influenza. We find that variation in social and healthcare-based determinants exacerbates influenza epidemics, and that low socioeconomic status (SES) individuals disproportionately bear the burden of infection. We also identify geographical hotspots of influenza burden in low SES populations, much of which is overlooked in traditional influenza surveillance, and find that these differences are most predicted by variation in susceptibility and access to sickness absenteeism. Our results highlight that the effect of overlapping factors is synergistic and that reducing this intersectionality can significantly reduce inequities. Additionally, health disparities are expressed geographically, and targeting public health efforts spatially may be an efficient use of resources to abate inequities. The association between health and socioeconomic prosperity has a long history in the epidemiological literature; addressing health inequities in respiratory-transmitted infectious disease burden is an important step towards social justice in public health, and ignoring them promises to pose a serious threat.