Supply and quality of colonoscopy according to the characteristics of gastroenterologists in the French population-based colorectal-cancer screening program.

BACKGROUND: Since its complete roll-out in 2009, the French colorectal cancer screening program (CRCSP) experienced 3 major constraints [use of a less efficient Guaiac-test (gFOBT), stopping the supply of Fecal-Immunochemical-Test kits (FIT), and suspension of the program due to the coronavirus disease 2019 (COVID-19)] affecting its effectiveness. AIM: To describe the impact of the constraints in terms of changes in the quality of screening-colonoscopy (Quali-Colo). METHODS: This retrospective cohort study included screening-colonoscopies performed by gastroenterologists between Jan-2010 and Dec-2020 in people aged 50-74 living in Ile-de-France (France). The changes in Quali-colo (Proportion of colonoscopies performed beyond 7 mo (Colo_7 mo), Frequency of serious adverse events (SAE) and Colonoscopy detection rate) were described in a cohort of Gastroenterologists who performed at least one colonoscopy over each of the four periods defined according to the chronology of the constraints [gFOBT: Normal progress of the CRCSP using gFOBT (2010-2014); FIT: Normal progress of the CRCSP using FIT (2015-2018); STOP-FIT: Year (2019) during which the CRCSP experienced the cessation of the supply of test kits; COVID: Program suspension due to the COVID-19 health crisis (2020)]. The link between each dependent variable (Colo_7 mo; SAE occurrence, neoplasm detection rate) and the predictive factors was analyzed in a two-level multivariate hierarchical model. RESULTS: The 533 gastroenterologists (cohort) achieved 21509 screening colonoscopies over gFOBT period, 38352 over FIT, 7342 over STOP-FIT and 7995 over COVID period. The frequency of SAE did not change between periods (gFOBT: 0.3%; FIT: 0.3%; STOP-FIT: 0.3%; and COVID: 0.2%; P = 0.10). The risk of Colo_7 mo doubled between FIT [adjusted odds ratio (aOR): 1.2 (1.1; 1.2)] and STOP-FIT [aOR: 2.4 (2.1; 2.6)]; then, decreased by 40% between STOP-FIT and COVID [aOR: 2.0 (1.8; 2.2)]. Regardless of the period, this Colo_7 mo's risk was twice as high for screening colonoscopy performed in a public hospital [aOR: 2.1 (1.3; 3.6)] compared to screening-colonoscopy performed in a private clinic. The neoplasm detection, which increased by 60% between gFOBT and FIT [aOR: 1.6 (1.5; 1.7)], decreased by 40% between FIT and COVID [aOR: 1.1 (1.0; 1.3)]. CONCLUSION: The constraints likely affected the time-to-colonoscopy as well as the colonoscopy detection rate without impacting the SAE's occurrence, highlighting the need for a respectable reference time-to-colonoscopy in CRCSP.

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.

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.

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.