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A growing number of individuals who are considered at high risk of cancer are now routinely undergoing population screening. However, noted harms such as radiation exposure, overdiagnosis, and overtreatment underscore the need for better temporal models that predict who should be screened and at what frequency. The mean sojourn time (MST), an average duration period when a tumor can be detected by imaging but with no observable clinical symptoms, is a critical variable for formulating screening policy. Estimation of MST has been long studied using continuous Markov model (CMM) with Maximum likelihood estimation (MLE). However, a lot of traditional methods assume no observation error of the imaging data, which is unlikely and can bias the estimation of the MST. In addition, the MLE may not be stably estimated when data is sparse. Addressing these shortcomings, we present a probabilistic modeling approach for periodic cancer screening data. We first model the cancer state transition using a three state CMM model, while simultaneously considering observation error. We then jointly estimate the MST and observation error within a Bayesian framework. We also consider the inclusion of covariates to estimate individualized rates of disease progression. Our approach is demonstrated on participants who underwent chest x-ray screening in the National Lung Screening Trial (NLST) and validated using posterior predictive p-values and Pearson's chi-square test. Our model demonstrates more accurate and sensible estimates of MST in comparison to MLE.

A growing number of individuals who are considered at high risk of cancer are now routinely undergoing population screening. However, noted harms such as radiation exposure, overdiagnosis, and overtreatment underscore the need for better temporal models that predict who should be screened and at what frequency. The mean sojourn time (MST), an average duration period when a tumor can be detected by imaging but with no observable clinical symptoms, is a critical variable for formulating screening policy. Estimation of MST has been long studied using continuous Markov model (CMM) with Maximum likelihood estimation (MLE). However, a lot of traditional methods assume no observation error of the imaging data, which is unlikely and can bias the estimation of the MST. In addition, the MLE may not be stably estimated when data is sparse. Addressing these shortcomings, we present a probabilistic modeling approach for periodic cancer screening data. We first model the cancer state transition using a three state CMM model, while simultaneously considering observation error. We then jointly estimate the MST and observation error within a Bayesian framework. We also consider the inclusion of covariates to estimate individualized rates of disease progression. Our approach is demonstrated on participants who underwent chest x-ray screening in the National Lung Screening Trial (NLST) and validated using posterior predictive p-values and Pearson’s chi-square test. Our model demonstrates more accurate and sensible estimates of MST in comparison to MLE.

AbstractLand-based transportation is a significant contributor to global greenhouse gas (GHG) emissions, with internal combustion engine vehicles being the predominant mode of transport in this sector. As the world population grows and urbanization accelerates, transport demand in metropolitan areas is projected to double by 2050, underscoring the critical need for sustainable transportation strategies, forecasting, and planning within an urban context. One challenge in this endeavor is the insufficient attention given to the distribution of vehicle grades within the overall car population during GHG impact assessment, despite the recognized importance of car grade by researchers and policymakers. In this study, we incorporate both engine type and vehicle grade distribution, derived from data on over 30,000 passenger cars registered with the Land Transport Authority of Singapore between 2018 and 2022, into our life cycle assessment analysis. This approach ensures that our scenario-based studies on prediction and planning are more comprehensive than those that consider only the most popular models. Furthermore, while Singapore benefits from a relatively clean electricity supply, the current limitations in charging infrastructure negatively affect the GHG impact of electric vehicles. The insights from this research will inform the development of transportation policies for metropolises like Singapore, contributing to the advancement of a more sustainable transportation system. [ABSTRACT FROM AUTHOR]

AbstractIntroductionMethodsResultsConclusionsWe sought to examine how COVID-19 affected opioid use disorder (OUD)-specific health care utilization for insured individuals with preexisting OUD during the first year of the pandemic.In this retrospective cohort study, using data from the Merative™ MarketScan® Commercial and Medicaid data, we examined pre- and post-pandemic changes receipt of therapy, medication for opioid use disorder (MOUD), and emergency department (ED) and inpatient admissions.The pandemic-exposed cohort was more likely to receive therapy; however, the commercial sample did not evidence change in the number of sessions received while Medicaid beneficiaries evidenced a significant decrease of 3.87 sessions per year, p < 0.001. We observed increases in the likelihood of receiving any MOUD among Medicaid beneficiaries, but significant decreases in months of MOUD for both commercial (2.16 months, p < 0.001) and Medicaid pandemic cohorts (1.94 months, p < 0.001) compared with their respective pre-pandemic cohorts. There were significant decreases in the likelihood of any and the count of emergency department visits across samples during the pandemic, all p < 0.001. For the Medicaid sample, there was a significant decrease in the likelihood of having an inpatient admission, p < 0.001, and there was a greater decrease in the number of inpatient days per year among commercially insured, p = 0.011 during the pandemic.Adults diagnosed with OUD before the pandemic experienced significant disruptions to care, particularly continuity of care and access to emergency services, despite efforts by policymakers to counter pandemic-related access barriers. [ABSTRACT FROM AUTHOR]

Background: The COVID-19 pandemic had dramatic adverse impacts on people with opioid use disorder (OUD), as evidenced by significant disruptions to care and unprecedented increases in drug overdoses. In this study, we evaluated the impacts of COVID-19 on utilization of emergency and inpatient care, and fatal and non-fatal overdoses among veterans with OUD., Methods: We used Veterans Health Administration (VHA) electronic medical record and mortality data to compare emergency department (ED) visits, inpatient hospitalizations, and fatal and non-fatal overdoses between a pandemic-exposed cohort of veterans with OUD observed both pre- and post-pandemic onset ( n = 53,803; observed January 2019-March 2021) to a matched pre-pandemic control group ( n = 53,803; observed October 2017-December 2019)., Results: Compared to pre-pandemic trends, there were significant decreases in the odds of ED and inpatient admissions and total number of ED and inpatient admissions during COVID-19. There was a significant decrease in the odds of having a recorded non-fatal overdose. The odds of overdose death increased during the pandemic compared to pre-pandemic trends., Conclusion: We observed significant decreases in the utilization of ED and inpatient care services, and fewer non-fatal overdoses, post-pandemic onset. Healthcare disruptions limiting access to emergency and inpatient care could account for the lower number of recorded non-fatal overdoses, potentially reflecting an underestimate of risk. In contrast, fatal overdoses increased during the pandemic compared to pre-pandemic trends. Lower utilization of emergency and inpatient care, and higher rates of fatal overdoses during the pandemic, suggest an exacerbation of unmet treatment need post-pandemic onset.