Implications for health system resilience: Quantifying the impact of the COVID-19-related stay at home orders on cancer screenings and diagnoses in southeastern North Carolina, USA.

COVID-19 impacted hospital systems across the globe. Focus shifted to responding to increased healthcare demand while mitigating COVID-19 spread on their campuses. Mitigation efforts limited medical professional-patient interactions, including patient access to preventive cancer screenings. Data were gleaned from a health information exchange containing records on over 2 million patients in southeastern North Carolina, USA. This study tested five hypotheses: H1: Weekly cancer screenings significantly decreased during North Carolina's (NC) Stay-At-Home (SAH) orders; H2: Weekly cancer diagnoses significantly decreased during NC's SAH orders; H3: Weekly cancer screenings significantly increased after the end of NC's SAH orders; H4: Weekly cancer diagnoses significantly increased after the end of NC's SAH orders; and H5: Weekly advanced cancer diagnoses significantly increased after the end of NC's SAH orders. Time series regression analysis was employed to quantify trends. Results suggested strong support of H1 and H3, moderate support of H4, mixed support of H5, and no support of H2. For example, compared to before the SAH orders, we estimated 662.3 fewer weekly breast cancer screenings during the SAH orders (H1). After the SAH orders (H3), we estimated 232.5 more breast cancer screenings and 10.6 more breast cancer diagnoses. This work quantifies the impact of COVID-19 associated SAH orders on cancer screenings and diagnoses and suggests the potential for delayed or missed cancer diagnoses. This evident disruption in providing routine medical care also highlights the importance of strengthening health systems (or organizations) and improving resilience to natural disasters and infectious disease outbreaks.

Examining Mental Health Disorders in Overweight and Obese Pediatric Patients.

INTRODUCTION: We investigated the frequency and variation in three mental health diagnoses among obese or overweight children and adolescents. METHOD: Logistic regression was used to examine the association between the outcome variables-anxiety, depression, and adjustment disorders-with the following covariates: overweight/obesity status, sex, age, and race. RESULTS: Findings show anxiety, depressive, and adjustment disorder diagnoses were significantly higher for overweight or obese youth in our sample. In addition, diagnosis rates for one or more of these disorders increase as children grow into adolescence. Furthermore, we found significantly higher rates of depression and significantly lower rates of anxiety among youth who live in places with higher rates of poverty. DISCUSSION: Findings indicate a target age for providers to focus on mental health screening among overweight/obese patients: (1) early adolescence (aged 11-14 years) for depressive and adjustment disorders and (2) early childhood (aged 2-4 years) for anxiety disorder.

Using spatial and temporal modeling to visualize the effects of U.S. state issued stay at home orders on COVID-19.

Coronavirus disease 2019 dominated and augmented many aspects of life beginning in early 2020. Related research and data generation developed alongside its spread. We developed a Bayesian spatio-temporal Poisson disease mapping model for estimating real-time characteristics of the coronavirus disease in the United States. We also created several dashboards for visualization of the statistical model for fellow researchers and simpler spatial and temporal representations of the disease for consumption by analysts and data scientists in the policymaking community in our region. Findings suggest that the risk of confirmed cases is higher for health regions under partial stay at home orders and lower in health regions under full stay at home orders, when compared to before stay at home orders were declared. These results confirm the benefit of state-issued stay at home orders as well as suggest compliance to the directives towards the older population for adhering to social distancing guidelines.

Community vulnerability and mobility: What matters most in spatio-temporal modeling of the COVID-19 pandemic?

Community vulnerability is widely viewed as an important aspect to consider when modeling disease. Although COVID-19 does disproportionately impact vulnerable populations, human behavior as measured by community mobility is equally influential in understanding disease spread. In this research, we seek to understand which of four composite measures perform best in explaining disease spread and mortality, and we explore the extent to which mobility account for variance in the outcomes of interest. We compare two community mobility measures, three composite measures of community vulnerability, and one composite measure that combines vulnerability and human behavior to assess their relative feasibility in modeling the US COVID-19 pandemic. Extensions - via temporally dependent fixed effect coefficients - of the commonly used Bayesian spatio-temporal Poisson disease mapping models are implemented and compared in terms of goodness of fit as well as estimate precision and viability. A comparison of goodness of fit measures nearly unanimously suggests the human behavior-based models are superior. The duration at residence mobility measure indicates two unique and seemingly inverse relationships between mobility and the COVID-19 pandemic: the findings indicate decreased COVID-19 presence with decreased mobility early in the pandemic and increased COVID-19 presence with decreased mobility later in the pandemic. The early indication is likely influenced by a large presence of state-issued stay at home orders and self-quarantine, while the later indication likely emerges as a consequence of holiday gatherings in a country under limited restrictions. This study implements innovative statistical methods and furnishes results that challenge the generally accepted notion that vulnerability and deprivation are key to understanding disparities in health outcomes. We show that human behavior is equally, if not more important to understanding disease spread. We encourage researchers to build upon the work we start here and continue to explore how other behaviors influence the spread of COVID-19.