Serious opioid-related adverse outcomes associated with opioids prescribed by dentists.

ABSTRACT: Although nonsteroidal anti inflammatory drugs are superior to opioids in dental pain management, opioids are still prescribed for dental pain in the United States. Little is known about the serious adverse outcomes of short-acting opioids within the context of dental prescribing. The objective of this study was to evaluate adverse outcomes and persistent opioid use (POU) after opioid prescriptions by dentists, based on whether opioids were overprescribed or within recommendations. A cross-sectional analysis of adults with a dental visit and corresponding opioid prescription (index) from 2011 to 2018 within a nationwide commercial claims database was conducted. Opioid overprescribing was defined as >120 morphine milligram equivalents per Centers for Disease Control and Prevention guidelines. Generalized estimating equation models were used to assess adverse outcomes (emergency department visits, hospitalizations, newly diagnosed substance use disorder, naloxone administration, or death within 30 days from index) and POU (≥1 prescription 4-90 days postindex). Predicted probabilities are reported. Of 633,387 visits, 2.6% experienced an adverse outcome and 16.6% had POU. Adverse outcome risk was not different whether opioids were overprescribed or within recommendations (predicted probability 9.0%, confidence interval [CI]: 8.0%-10.2% vs 9.1%, CI: 8.1-10.3), but POU was higher when opioids were overprescribed (predicted probability 27.4%, CI: 26.1%-28.8% vs 25.2%, CI: 24.0%-26.5%). Visits associated with mild pain and those with substance use disorders had the highest risk of both outcomes. Findings from this study demonstrate that dental prescribing of opioids was associated with adverse outcomes and POU, even when prescriptions were concordant with guidelines. Additional efforts are required to improve analgesic prescribing in dentistry, especially in groups at high risk of opioid-related adverse outcomes.

Global consumption of antimicrobials: impact of the WHO Global Action Plan on Antimicrobial Resistance and 2019 coronavirus pandemic (COVID-19).

BACKGROUND: Little is known about the effect of the COVID-19 pandemic on antimicrobial consumption worldwide. OBJECTIVES: To describe the impact of the WHO Global Action Plan on Antimicrobial Resistance (GAP-AMR) on antimicrobial consumption pre-pandemic and to evaluate the impact of the COVID-19 pandemic on antimicrobial consumption worldwide. METHODS: A cross-sectional time-series analysis using a dataset of monthly purchases of antimicrobials (antibiotics, antivirals and antifungals) from August 2014 to August 2020. Antimicrobial consumption per 1000 population was assessed pre-pandemic by economic development status using linear regression models. Interventional autoregressive integrated moving average (ARIMA) models tested for significant changes with pandemic declaration (March 2020) and during its first stage from April to August 2020, worldwide and by country development status. RESULTS: Prior to the pandemic, antimicrobial consumption decreased worldwide, with a greater apparent decrease in developed versus developing countries (-8.4%, P = 0.020 versus -1.2%, P = 0.660). Relative to 2019, antimicrobial consumption increased by 11.2%, P < 0.001 in March 2020. The greatest increase was for antivirals in both developed and developing countries (48.2%, P < 0.001; 110.0%, P < 0.001) followed by antibiotics (6.9%, P < 0.001; 5.9%, P = 0.003). From April to August 2020, antimicrobial consumption decreased worldwide by 18.7% (P < 0.001) compared with the previous year. Specifically, antibiotic consumption significantly decreased in both developed and developing countries (-28.0%, P < 0.001; -16.8%, P < 0.001). CONCLUSIONS: The global decrease in antimicrobial consumption pre-pandemic suggests a positive impact of the WHO GAP-AMR. During the pandemic, an initial increase in antimicrobial consumption was followed by a decrease worldwide. AMR plans should specify measures to ensure full implementation of AMR efforts during health crises such as the COVID-19 pandemic.

Mapping global trends in vaccine sales before and during the first wave of the COVID-19 pandemic: a cross-sectional time-series analysis.

BACKGROUND: While the COVID-19 pandemic may have substantially hindered the provision of routine immunisation services worldwide, we have little data on the impact of the pandemic on vaccine supply chains. METHODS: We used time-series analysis to examine global trends in vaccine sales for a total of 34 vaccines and combination vaccines using data from the IQVIA MIDAS Database between August 2014 and August 2020 across 84 countries. We grouped countries into three income-level categories, and we modelled the changes in vaccine sales from April to August 2020 versus April to August 2019 using autoregressive integrated moving average models. RESULTS: In March 2020, global sales of vaccines dropped from 1211.1 per 100 000 to 806.2 per 100 000 population in April 2020, an overall decrease of 33.4%; however, the vaccine sales interruptions recovered disproportionately across economies. Between April 2020 and August 2020, we found a significant decrease of 20.6% (p<0.001) in vaccine sales across high-income countries (HICs), in contrast with a significant increase of 10.7% (p<0.001) across lower middle-income countries (LMICs), relative to the same period in 2019. From August 2014 through August 2020, monthly per capita vaccine sales across HICs remained, on average, at least four times higher than in LMICs and nearly three times higher than in upper middle-income countries. CONCLUSION: Our study revealed the heterogeneous impact of COVID-19 on vaccine sales across economies while underlining the substantial consistent disparities in per capita vaccine sales before and during the first wave of the COVID-19 pandemic. Action to ensure equitable distribution of vaccines is needed.

Modeling Patient No-Show History and Predicting Future Appointment Behavior at the Veterans Administration's Outpatient Mental Health Clinics: NIRMO-2.

INTRODUCTION: No-shows are detrimental to both patients' health and health care systems. Literature documents no-show rates ranging from 10% in primary care clinics to over 60% in mental health clinics. Our model predicts the probability that a mental health clinic outpatient appointment will not be completed and identifies actionable variables associated with lowering the probability of no-show. MATERIALS AND METHODS: We were granted access to de-identified administrative data from the Veterans Administration Corporate Data Warehouse related to appointments at 13 Veterans Administration Medical Centers. Our modeling data set included 1,206,271 unique appointment records scheduled to occur between January 1, 2013 and February 28, 2017. The training set included 846,668 appointment records scheduled between January 1, 2013 and December 31, 2015. The testing set included 359,603 appointment records scheduled between January 1, 2016 and February 28, 2017. The dependent binary variable was whether the appointment was completed or not. Independent variables were categorized into seven clusters: patient's demographics, appointment characteristics, patient's attendance history, alcohol use screening score, medications and medication possession ratios, prior diagnoses, and past utilization of Veterans Health Administration services. We used a forward stepwise selection, based on the likelihood ratio, to choose the variables in the model. The predictive model was built using the SAS HPLOGISTIC procedure. RESULTS: The best indicator of whether someone will miss an appointment is their historical attendance behavior. The top three variables associated with higher probabilities of a no-show were: the no-show rate over the previous 2 years before the current appointment, the no-show probability derived from the Markov model, and the age of the appointment. The top three variables that decrease the chance of no-showing were: the appointment was a new consult, the appointment was an overbook, and the patient had multiple appointments on the same day. The average of the areas under the receiver operating characteristic curves was 0.7577 for the training dataset, and 0.7513 for the test set. CONCLUSIONS: The National Initiative to Reduce Missed Opportunities-2 confirmed findings that previous patient attendance is one of the key predictors of a future attendance and provides an additional layer of complexity for analyzing the effect of a patient's past behavior on future attendance. The National Initiative to Reduce Missed Opportunities-2 establishes that appointment attendance is related to medication adherence, particularly for medications used for treatment of mood disorders or to block the effects of opioids. However, there is no way to confirm whether a patient is actually taking medications as prescribed. Thus, a low medication possession ratio is an informative, albeit not a perfect, measure. It is our intention to further explore how diagnosis and medications can be better captured and used in predictive modeling of no-shows. Our findings on the effects of different factors on no-show rates can be used to predict individual no-show probabilities, and to identify patients who are high risk for missing appointments. The ability to predict a patient's risk of missing an appointment would allow for both advanced interventions to decrease no-shows and for more efficient scheduling.