Discontinuation of Buprenorphine Treatment for Opioid Use Disorder During the Coronavirus Disease-2019 Pandemic: A Multilevel Framework.

BACKGROUND: The coronavirus disease-2019 pandemic has been associated with large increases in opioid-related mortality, yet it is unclear whether specific subpopulations were especially likely to discontinue buprenorphine treatment for opioid use disorder as the pandemic ensued. OBJECTIVE: The aim was to assess predictors of buprenorphine discontinuation in the early months of the coronavirus disease-2019 pandemic (April-July 2020) compared with a prepandemic period (April-July 2019). DESIGN: In each time period, we estimated a multilevel regression models to assess risk of discontinuation in April-July for people who started buprenorphine in January-February. Models included person-level, prescriber-level, and area-level covariates. SUBJECTS: Individuals age 18 years or older in the all-payer IQVIA Longitudinal Prescription Claims. MEASURES: The primary outcome was buprenorphine discontinuation (ie, no filled prescriptions during the follow-up periods). RESULTS: Overall, 13.98% of patients discontinued buprenorphine in April-July 2020, less than the 15.71% in 2019 (P<0.001). In 2020, patient-level factors associated with discontinuation included younger age, male sex, shorter baseline possession ratio, and payment by cash. Compared with patients with a primary care physician prescriber, specialties most associated with discontinuation were pain medicine and physician assistant/nurse practitioner. Compared with the South Atlantic region, discontinuation risk was lowest in New England and highest in the West South Central States. The association between patient, prescriber, and geographic variables to risk of discontinuation was very similar in 2019 and 2020. CONCLUSIONS: While clinical and policy interventions may have mitigated opioid use disorder treatment discontinuation following the pandemic, such discontinuation is nevertheless common and varies by identifiable patient, provider and geographic factors.

Treatment of Opioid Use Disorder With Buprenorphine Among US Adolescents and Young Adults During the Early COVID-19 Pandemic.

PURPOSE: The COVID-19 pandemic's impact on buprenorphine treatment for opioid use disorder among adolescents and young adults (AYAs) is unknown. METHODS: We used IQVIA Longitudinal Prescription Claims, including US AYAs aged 12-29 with at least 1 buprenorphine fill between January 2018 and August 2020, stratifying by age group and insurance. We compared buprenorphine prescriptions in March-August 2019 to March-August 2020. RESULTS: The monthly buprenorphine prescription rate increased 8.3% among AYAs aged 12-17 but decreased 7.5% among 18- to 24-year-olds and decreased 5.1% among 25- to 29-year-olds. In these age groups, Medicaid prescriptions did not significantly change, whereas commercial insurance prescriptions decreased 12.9% among 18- to 24-year-olds and 11.8% in 25- to 29-year-olds, and cash/other prescriptions decreased 18.7% among 18- to 24-year-olds and 19.9% in 25- to 29-year-olds (p < .001 for all). DISCUSSION: Buprenorphine prescriptions paid with commercial insurance or cash among young adults significantly decreased early in the pandemic, suggesting a possible unmet treatment need among this group.

Assessing the association between area deprivation index on COVID-19 prevalence: a contrast between rural and urban U.S. jurisdictions.

BACKGROUND: The COVID-19 pandemic has impacted communities differentially, with poorer and minority populations being more adversely affected. Prior rural health research suggests such disparities may be exacerbated during the pandemic and in remote parts of the U.S. OBJECTIVES: To understand the spread and impact of COVID-19 across the U.S., county level data for confirmed cases of COVID-19 were examined by Area Deprivation Index (ADI) and Metropolitan vs. Nonmetropolitan designations from the National Center for Health Statistics (NCHS). These designations were the basis for making comparisons between Urban and Rural jurisdictions. METHOD: Kendall's Tau-B was used to compare effect sizes between jurisdictions on select ADI composites and well researched social determinants of health (SDH). Spearman coefficients and stratified Poisson modeling was used to explore the association between ADI and COVID-19 prevalence in the context of county designation. RESULTS: Results show that the relationship between area deprivation and COVID-19 prevalence was positive and higher for rural counties, when compared to urban ones. Family income, property value and educational attainment were among the ADI component measures most correlated with prevalence, but this too differed between county type. CONCLUSIONS: Though most Americans live in Metropolitan Areas, rural communities were found to be associated with a stronger relationship between deprivation and COVID-19 prevalence. Models predicting COVID-19 prevalence by ADI and county type reinforced this observation and may inform health policy decisions.

Differential impact of mitigation policies and socioeconomic status on COVID-19 prevalence and social distancing in the United States.

BACKGROUND: The spread of COVID-19 has highlighted the long-standing health inequalities across the U.S. as neighborhoods with fewer resources were associated with higher rates of COVID-19 transmission. Although the stay-at-home order was one of the most effective methods to contain its spread, residents in lower-income neighborhoods faced barriers to practicing social distancing. We aimed to quantify the differential impact of stay-at-home policy on COVID-19 transmission and residents' mobility across neighborhoods of different levels of socioeconomic disadvantage. METHODS: This was a comparative interrupted time-series analysis at the county level. We included 2087 counties from 38 states which both implemented and lifted the state-wide stay-at-home order. Every county was assigned to one of four equally-sized groups based on its levels of disadvantage, represented by the Area Deprivation Index. Prevalence of COVID-19 was calculated by dividing the daily number of cumulative confirmed COVID-19 cases by the number of residents from the 2010 Census. We used the Social Distancing Index (SDI), derived from the COVID-19 Impact Analysis Platform, to measure the mobility. For the evaluation of implementation, the observation started from Mar 1st 2020 to 1 day before lifting; and, for lifting, it ranged from 1 day after implementation to Jul 5th 2020. We calculated a comparative change of daily trends in COVID-19 prevalence and Social Distancing Index between counties with three highest disadvantage levels and those with the least level before and after the implementation and lifting of the stay-at-home order, separately. RESULTS: On both stay-at-home implementation and lifting dates, COVID-19 prevalence was much higher among counties with the highest or lowest disadvantage level, while mobility decreased as the disadvantage level increased. Mobility of the most disadvantaged counties was least impacted by stay-at-home implementation and relaxation compared to counties with the most resources; however, disadvantaged counties experienced the largest relative increase in COVID-19 infection after both stay-at-home implementation and relaxation. CONCLUSIONS: Neighborhoods with varying levels of socioeconomic disadvantage reacted differently to the implementation and relaxation of COVID-19 mitigation policies. Policymakers should consider investing more resources in disadvantaged counties as the pandemic may not stop until most neighborhoods have it under control.

Early relaxation of community mitigation policies and risk of COVID-19 resurgence in the United States.

This study aimed to assess the impact of coronavirus disease (COVID-19) prevalence in the United States in the week leading to the relaxation of the stay-at-home orders (SAH) on future prevalence across states that implemented different SAH policies. We used data on the number of confirmed COVID-19 cases as of August 21, 2020 on county level. We classified states into four groups based on the 7-day change in prevalence and the state's approach to SAH policy. The groups included: (1) High Change (19 states; 7-day prevalence change ≥50th percentile), (2) Low Change (19 states; 7-day prevalence change <50th percentile), (3) No SAH (11 states: did not adopt SAH order), and (4) No SAH End (2 states: did not relax SAH order). We performed regression modeling assessing the association between change in prevalence at the time of SAH order relaxation and COVID-19 prevalence days after the relaxation of SAH order for four selected groups. After adjusting for other factors, compared to the High Change group, counties in the Low Change group had 33.8 (per 100,000 population) fewer cases (standard error (SE): 19.8, p < 0.001) 7 days after the relaxation of SAH order and the difference was larger by time passing. On August 21, 2020, the No SAH End group had 383.1 fewer cases (per 100,000 population) than the High Change group (SE: 143.6, p < 0.01). A measured, evidence-based approach is required to safely relax the community mitigation strategies and practice phased-reopening of the country.

Assessing the Impact of Neighborhood Socioeconomic Characteristics on COVID-19 Prevalence Across Seven States in the United States.

Introduction: The spread of Coronavirus Disease 2019 (COVID-19) across the United States has highlighted the long-standing nationwide health inequalities with socioeconomically challenged communities experiencing a higher burden of the disease. We assessed the impact of neighborhood socioeconomic characteristics on the COVID-19 prevalence across seven selected states (i.e., Arizona, Florida, Illinois, Maryland, North Carolina, South Carolina, and Virginia). Methods: We obtained cumulative COVID-19 cases reported at the neighborhood aggregation level by Departments of Health in selected states on two dates (May 3rd, 2020, and May 30th, 2020) and assessed the correlation between the COVID-19 prevalence and neighborhood characteristics. We developed Area Deprivation Index (ADI), a composite measure to rank neighborhoods by their socioeconomic characteristics, using the 2018 US Census American Community Survey. The higher ADI rank represented more disadvantaged neighborhoods. Results: After controlling for age, gender, and the square mileage of each community we identified Zip-codes with higher ADI (more disadvantaged neighborhoods) in Illinois and Maryland had higher COVID-19 prevalence comparing to zip-codes across the country and in the same state with lower ADI (less disadvantaged neighborhoods) using data on May 3rd. We detected the same pattern across all states except for Florida and Virginia using data on May 30th, 2020. Conclusion: Our study provides evidence that not all Americans are at equal risk for COVID-19. Socioeconomic characteristics of communities appear to be associated with their COVID-19 susceptibility, at least among those study states with high rates of disease.