Phase I quality control framework for monitoring organ-at-risk dose.

Objective.The aim of this work was to develop a Phase I control chart framework for the recently proposed multivariate risk-adjusted Hotelling'sT2chart. Although this control chart alone can identify most patients receiving extreme organ-at-risk (OAR) dose, it is restricted by underlying distributional assumptions, making it sensitive to extreme observations in the sample, as is typically found in radiotherapy plan quality data such as dose-volume histogram (DVH) points. This can lead to slightly poor-quality plans that should have been identified as out-of-control (OC) to be signaled in-control (IC).Approach. We develop a robust iterative control chart framework to identify all OC patients with abnormally high OAR dose and improve them via re-optimization to achieve an IC sample prior to establishing the Phase I control chart, which can be used to monitor future treatment plans.Main Results. Eighty head-and-neck patients were used in this study. After the first iteration, P14, P67, and P68 were detected as OC for high brainstem dose, warranting re-optimization aimed to reduce brainstem dose without worsening other planning criteria. The DVH and control chart were updated after re-optimization. On the second iteration, P14, P67, and P68 were IC, but P40 was identified as OC. After re-optimizing P40's plan and updating the DVH and control chart, P40 was IC, but P14* (P14's re-optimized plan) and P62 were flagged as OC. P14* could not be re-optimized without worsening target coverage, so only P62 was re-optimized. Ultimately, a fully IC sample was achieved. Multiple iterations were needed to identify and improve all OC patients, and to establish a more robust control limit to monitor future treatment plans.Significance. The iterative procedure resulted in a fully IC sample of patients. With this sample, a more robust Phase I control chart that can monitor OAR doses of new plans was established.

Spatial clustering of heroin-related overdose incidents: a case study in Cincinnati, Ohio.

BACKGROUND: Drug overdose is one of the top leading causes of accidental death in the U.S., largely due to the opioid epidemic. Although the opioid epidemic is a nationwide issue, it has not affected the nation uniformly. METHODS: We combined multiple data sources, including emergency medical service response, American Community Survey data, and health facilities datasets to analyze distributions of heroin-related overdose incidents in Cincinnati, Ohio at the census block group level. The Ripley's K function and the local Moran's I statistics were performed to examine geographic variation patterns in heroin-related overdose incidents within the study area. Then, conditional cluster maps were plotted to examine a relationship between heroin-related incident rates and sociodemographic characteristics of areas as well as the resources for opioid use disorder treatment. RESULTS: The global spatial analysis indicated that there was a clustered pattern of heroin-related overdose incident rates at every distance across the study area. The univariate local spatial analysis identified 7 hot spot clusters, 27 cold spot clusters, and 1 outlier cluster. Conditional cluster maps showed characteristics of neighborhoods with high heroin overdose rates, such as a higher crime rate, a high percentage of the male, a high poverty level, a lower education level, and a lower income level. The hot spots in the Southwest areas of Cincinnati had longer distances to opioid treatment programs and buprenorphine prescribing physicians than the median, while the hot spots in the South-Central areas of the city had shorter distances to those health resources. CONCLUSIONS: Our study showed that the opioid epidemic disproportionately affected Cincinnati. Multi-phased spatial clustering models based on various data sources can be useful to identify areas that require more policy attention and targeted interventions to alleviate high heroin-related overdose rates.

COVID-19 economic impact payments and opioid overdose deaths.

BACKGROUND: Given the global economic recessions mediated by the COVID-19 pandemic and that many countries have implemented direct income support programs, we investigated the timing of the COVID-19 economic impact payments and opioid overdose deaths. METHODS: A longitudinal, observational study design that included data from the Ohio Department of Health was utilized. Statistical change point analyses were conducted to identify significant changes in weekly number of opioid overdose deaths from January 1 of 2018 to August 1 of 2020. Additional analyses including difference-in-difference, time series tests, interrupted time series regression analysis and Granger causality test were performed. RESULTS: A single change point was identified and occurred at week 16, 2020. For 2020, the median opioid overdose deaths numbers for weeks 1-16 and weeks 17-32 were 68.5 and 101, respectively. The opioid overdose deaths numbers from weeks 17-32 of 2020 were significantly higher than those in weeks 1-16 of 2020 and those in 2018 and 2019 (before and after week 16). The interrupted time series regression analysis indicated more than 203 deaths weekly for weeks 17-32 of 2020 compared to all other weeks. The result of the Granger causality test found that the identified change point (week 16 of 2020) directly influenced the increase in opioid overdose deaths in weeks 17-32 of 2020. CONCLUSION: The identified change point may refer to the timing of many factors, not only the economic payments and further research is warranted to investigate the potential relationship between the COVID-19 economic impact payments and overdose deaths.

During the COVID-19 Pandemic, Opioid Overdose Deaths Revert to Previous Record Levels in Ohio.

OBJECTIVE: During the COVID-19 pandemic, states have had to confront a drug overdose problem associated with the pandemic. The objective of this study was to identify the impact of the COVID-19 pandemic on the opioid epidemic in the state of Ohio by describing the changes in the quarterly opioid overdose deaths (OOD) over the last 10 years. METHODS: This longitudinal study included OOD data from death records obtained through the Ohio Department of Health. Temporal trend analysis and visualizations were performed on the OOD death rate per 100,000 quarterly from 2010 to 2020. Age, sex, and ethnicity were also analyzed. RESULTS: The OOD rate of 11.15 in Q2 of 2020 was statistically equivalent to the previous peak level of 10.87 in Q1 of 2017. There was a significant increase in the OOD rate from Q1 to Q2 of 2020 and a significant difference between the actual Q2 of 2020 OOD rate and the predicted OOD rate. The poisoning indicator fentanyl was present in 94% of OOD during Q2 of 2020. The total number of OOD remains highest in the White population. There was no significant difference between the actual and predicted OOD rates in the Black population of Q2 of 2020 based on the trend line. However, the OOD rate of 14.29 in Q2 of 2020 was significantly higher than the previous peak level of 8.34 in Q2 of 2017. The Q2 of 2020 OOD rates for 18 to 39 and 40+ age groups were significantly higher from what would be expected from the trend predictions. CONCLUSIONS: Based on these findings, Ohio has entered a COVID-19 pandemic mediated fourth wave in the opioid epidemic. These findings further suggest that as efforts are made to address the worldwide COVID-19 pandemic, states need to maintain their vigilance toward combating the local opioid epidemic.

A Risk-Adjusted Control Chart to Evaluate Intensity Modulated Radiation Therapy Plan Quality.

PURPOSE: This study aimed to develop a quality control framework for intensity modulated radiation therapy plan evaluations that can account for variations in patient- and treatment-specific risk factors. METHODS AND MATERIALS: Patient-specific risk factors, such as a patient's anatomy and tumor dose requirements, affect organs-at-risk (OARs) dose-volume histograms (DVHs), which in turn affects plan quality and can potentially cause adverse effects. Treatment-specific risk factors, such as the use of chemotherapy and surgery, are clinically relevant when evaluating radiation therapy planning criteria. A risk-adjusted control chart was developed to identify unusual plan quality after accounting for patient- and treatment-specific risk factors. In this proof of concept, 6 OAR DVH points and average monitor units serve as proxies for plan quality. Eighteen risk factors are considered for modeling quality: planning target volume (PTV) and OAR cross-sectional areas; volumes, spreads, and surface areas; minimum and centroid distances between OARs and the PTV; 6 PTV DVH points; use of chemotherapy; and surgery. A total of 69 head and neck cases were used to demonstrate the application of risk-adjusted control charts, and the results were compared with the application of conventional control charts. RESULTS: The risk-adjusted control chart remains robust to interpatient variations in the studied risk factors, unlike the conventional control chart. For the brainstem, the conventional chart signaled 4 patients with unusual (out-of-control) doses to 2% brainstem volume. However, the adjusted chart did not signal any plans after accounting for their risk factors. For the spinal cord doses to 2% brainstem volume, the conventional chart signaled 2 patients, and the adjusted chart signaled a separate patient after accounting for their risk factors. Similar adjustments were observed for the other DVH points when evaluating brainstem, spinal cord, ipsilateral parotid, and average monitor units. The adjustments can be directly attributed to the patient- and treatment-specific risk factors. CONCLUSIONS: A risk-adjusted control chart was developed to evaluate plan quality, which is robust to variations in patient- and treatment-specific parameters.