Improving geographic access to methadone clinics.

INTRODUCTION: Opioid misuse is a nationwide public health crisis. Methadone treatment is proven to be highly successful in preventing opioid use disorder, reducing the use of illicit drugs, and preventing overdoses. Clients acquire methadone daily from clinics, making geographic access crucial for the initiation of and adherence to treatment. METHODS: This work estimates unsatisfied methadone demand due to lack of geographic access at a census tract level and models the problem of identifying optimal locations to open new methadone clinics. The objective function of the model is a weighted combination of providing access to individuals with unmet methadone demand and improving the travel time of individuals currently attending a clinic. Data on existing methadone clinics and statewide methadone demand is acquired from Substance Abuse and Mental Health Services Administration (SAMHSA) surveys from 2019. Unsatisfied demand is estimated through a linear regression model after aggregating the population, heroin use, and satisfied methadone demand at the state level. RESULTS: Nationwide, we find 18.2 % of the United States population does not have geographic access to a methadone clinic and estimate 77,973 individuals in these areas would attend a clinic if geographic access barriers were removed (95 % CI: 67,413-88,532). In a case study of six Midwestern states, we find that geography significantly contributes to the value of opening additional clinics and we see large differences in expected gains between states sharing similar characteristics such as population and satisfied methadone demand. The number of additional clients served by opening one new clinic ranges from 180 to 804 across these six states, representing between 8.4 % and 16.2 % of state unmet demand. Between 1.2 % and 14.1 % of existing clients were reassigned with a single newly opened clinic, with a one-way average travel distance improvement between 6.3 and 11.9 miles / person / day for these clients. CONCLUSIONS: The results demonstrate the large unserved methadone demand in the United States, the significant improvement in methadone access for new and existing clients that can be achieved by opening new clinics, and the important role state-specific geography plays in these decisions.

Morphine Equivalent Total Dosage as Predictor of Adverse Outcomes in Opioid Prescribing.

OBJECTIVE: The objective of this work was to develop a risk prediction model for opioid overdose and opioid use disorder for patients at first opioid prescription and compare the predictive accuracy of morphine equivalent total dosage with the predictive accuracy of daily dosage . DESIGN: Records from patients 18-79 years of age with opioid prescriptions between January 1, 2016, and June 30, 2019, no prior history of adverse outcomes, and no malignant cancer diagnoses were collected from the electronic health record system of a medium-sized central Ohio health care system (n = 219,276). A Cox proportional-hazards model was developed to predict the adverse outcomes of opioid overdose and opioid use disorder from patient sociodemographic, pharmacological, and clinical diagnosis factors. RESULTS: During the study time frame, 573 patients experienced overdoses, and 2,571 patients were diagnosed with opioid use disorder. Morphine equivalent total dosage of opioid prescriptions was identified as a stronger predictor of adverse outcomes (C = 0.797) than morphine equivalent daily dosage (C = 0.792), with the best predictions coming from a model that includes both predictors (C = 0.803). In the model with both daily and total dosage predictors, patients receiving a high total / low daily dosage experienced a higher risk (hazard ratio [HR] = 2.17) than those receiving a low total / high daily dosage (HR = 2.02). Those receiving a high total / high daily dosage experienced the greatest risk of all (HR = 3.09). CONCLUSIONS: These findings demonstrate the value of including morphine equivalent total dosage as a predictor of adverse opioid outcomes and suggest that total dosage may be more strongly correlated with increased risk than daily dosage.

Antecedent blood pressure as a predictor of cardiovascular disease.

Elevated blood pressure (BP) is associated with greater risk of cardiovascular disease (CVD), and evidence suggests that prior BP levels may be at least as important as current BP in prediction models. We analyzed the determinants of CVD risk in Offspring Framingham Heart Study participants (n = 3344). The baseline Cox model included the traditional risk factors and current systolic BP to predict 20-year risk of CVD (643 events). Current systolic BP was significant, and the associated hazard ratio was 1.09 for 10 mm Hg (confidence interval [CI] 95%: 1.04-1.15). A second model used the traditional risk factors plus antecedent BP (hazard ratio [HR] = 1.19; CI 95%: 1.10-1.24). In a third model that included traditional risk factors and both current and antecedent BP, the antecedent BP was significant (HR = 1.18; CI 95%: 1.08-1.23), but the current BP was not statistically significant (HR = 1.01; CI 95%: 0.97-1.09). Antecedent BP showed a significantly stronger effect on risk of CVD than current BP.