Prevention, screening and treatment of colorectal cancer: a global and regional generalized cost effectiveness analysis

<p>Abstract</p> <p>Background</p> <p>Regional generalized cost-effectiveness estimates of prevention, screening and treatment interventions for colorectal cancer are presented.</p> <p>Methods</p> <p>Standardised WHO-CHOICE methodology was used. A colorectal cancer model was employed to provide estimates of screening and treatment effectiveness. Intervention effectiveness was determined via a population state-transition model (PopMod) that simulates the evolution of a sub-regional population accounting for births, deaths and disease epidemiology. Economic costs of procedures and treatment were estimated, including programme overhead and training costs.</p> <p>Results</p> <p>In regions characterised by high income, low mortality and high existing treatment coverage, the addition of screening to the current high treatment levels is very cost-effective, although no particular intervention stands out in cost-effectiveness terms relative to the others.</p> <p>In regions characterised by low income, low mortality with existing treatment coverage around 50%, expanding treatment with or without screening is cost-effective or very cost-effective. Abandoning treatment in favour of screening (no treatment scenario) would not be cost effective.</p> <p>In regions characterised by low income, high mortality and low treatment levels, the most cost-effective intervention is expanding treatment.</p> <p>Conclusions</p> <p>From a cost-effectiveness standpoint, screening programmes should be expanded in developed regions and treatment programmes should be established for colorectal cancer in regions with low treatment coverage.</p

Invasive adenoma and pituitary carcinoma: a SEER database analysis

Invasive pituitary adenomas and pituitary carcinomas are clinically indistinguishable until identification of metastases. Optimal management and survival outcomes for both are not clearly defined. The purpose of this study is to use the Surveillance, Epidemiology, and End Results (SEER) database to report patterns of care and compare survival outcomes in a large series of patients with invasive adenomas or pituitary carcinomas. One hundred seventeen patients diagnosed between 1973 and 2008 with pituitary adenomas/adenocarcinomas were included. Eighty-three invasive adenomas and seven pituitary carcinomas were analyzed for survival outcomes. Analyzed prognostic factors included age, sex, race, histology, tumor extent, and treatment. A significant decrease in survival was observed among carcinomas compared to invasive adenomas at 1, 2, and 5 years (p=0.047, 0.001, and 0.009). Only non-white race, male gender, and age ≥65 were significant negative prognostic factors for invasive adenomas (p=0.013, 0.033, and <0.001, respectively). There was no survival advantage to radiation therapy in treating adenomas at 5, 10, 20, or 30 years (p=0.778, 0.960, 0.236, and 0.971). In conclusion, pituitary carcinoma patients exhibit worse overall survival than invasive adenoma patients. This highlights the need for improved diagnostic methods for the sellar phase to allow for potentially more aggressive treatment approaches

An investigation of thresholds in air pollution-mortality effects

In this paper we introduce and implement new techniques to investigate threshold effects in air pollution-mortality relationships. Our key interest is in measuring the dose-response relationship above and below a given threshold level where we allow for a large number of potential explanatory variables to trigger the threshold effect. This is in contrast to existing approaches that usually focus on a single threshold trigger. We allow for a myriad of threshold effects within a Bayesian statistical framework that accounts for model uncertainty (i.e. uncertainty about which threshold trigger and explanatory variables are appropriate). We apply these techniques in an empirical exercise using daily data from Toronto for 1992-1997. We investigate the existence and nature of threshold effects in the relationship between mortality and ozone (O3), total particulate matter (PM) and an index of other conventionally occurring air pollutants. In general, we find the effects of the pollutants we consider on mortality to be statistically indistinguishable from zero with no evidence of thresholds. The one exception is ozone, for which results present an ambiguous picture. Ozone has no significant effect on mortality when we exclude threshold effects from the analysis. Allowing for thresholds we find a positive and significant effect for this pollutant when the threshold trigger is the average change in ozone two days ago. However, this significant effect is not observed after controlling for PM

Using machine learning to study the effect of medication adherence in Opioid Use Disorder

Background: Opioid Use Disorder (OUD) and opioid overdose (OD) impose huge social and economic burdens on society and health care systems. Research suggests that Medication for Opioid Use Disorder (MOUD) is effective in the treatment of OUD. We use machine learning to investigate the association between patient’s adherence to prescribed MOUD along with other risk factors in patients diagnosed with OUD and potential OD following the treatment. Methods: We used longitudinal Medicaid claims for two selected US states to subset a total of 26,685 patients with OUD diagnosis and appropriate Medicaid coverage between 2015 and 2018. We considered patient age, sex, region level socio-economic data, past comorbidities, MOUD prescription type and other selected prescribed medications along with the Proportion of Days Covered (PDC) as a proxy for adherence to MOUD as predictive variables for our model, and overdose events as the dependent variable. We applied four different machine learning classifiers and compared their performance, focusing on the importance and effect of PDC as a variable. We also calculated results based on risk stratification, where our models separate high risk individuals from low risk, to assess usefulness in clinical decision-making. Results: Among the selected classifiers, the XGBoost classifier has the highest AUC (0.77) closely followed by the Logistic Regression (LR). The LR has the best stratification result: patients in the top 10% of risk scores account for 35.37% of overdose events over the next 12 month observation period. PDC score calculated over the treatment window is one of the most important features, with better PDC lowering risk of OD, as expected. In terms of risk stratification results, of the 35.37% of overdose events that the predictive model could detect within the top 10% of risk scores, 72.3% of these cases were non-adherent in terms of their medication (PDC <0.8). Targeting the top 10% outcome of the predictive model could decrease the total number of OD events by 10.4%. Conclusions: The best performing models allow identification of, and focus on, those at high risk of opioid overdose. With MOUD being included for the first time as a factor of interest, and being identified as a significant factor, outreach activities related to MOUD can be targeted at those at highest risk