Individual and county-level factors associated with use of multiple prescribers and multiple pharmacies to obtain opioid prescriptions in California

Analysis of Opioid Poisoning in Medically Underserved Rural Areas: An Evaluation of International Statistical Classification of Diseases Codes from the State of South Dakota

Background

Rural hospitals and patient population tend to be medically underserved. The states with more rural population dispensed the most opioids per person in the last 10 years. We aimed to explore if rurality contributed to the likelihood of higher opioid adversity and how it affected substance-use rehabilitation in federally designated Medically Underserved Areas (MUAs).

Methods

We analyzed data dispensed by the South Dakota Department of Health (DOH) on opioid-led poisoning International Classification of Disease (ICD) codes that were active within the state in the last decade. After locating MUA rural and partially rural counties, we cross profiled the counties to the state datasets. Assessments were conducted using the PROC SURVEY methods in SAS version 9.3 (SAS Institute) and checked for multicollinearity with the Belsley-Kuh-Welsch technique. Finally, we used the American Hospital Association (AHA) database for analyzing substance use rehabilitation availability on per hospital basis.

Results

The chi-square statistic for comparing opioid codes against non-opioid codes distributed among three categories, rural, non-rural, and partially rural was significant at the limit of p <0.05. 81.134% of opioid-led poisoning codes were activated in a rural county. Only four hospitals had substance-use rehabilitation, three of which were in a non-rural area. More people from the teenage and early-adulthood years (10-19) were prone to opioid usage.

Conclusions

Rural counties in South Dakota were more likely to dispense opioid care and not have access to rehabilitation. We also found that as the opioid dispensing rate at hospitals within a state decreased as the state had less rural counties. Introducing public programs to train more physicians and cutting down cost of non-opioid based care may lower opioid distribution and increase rehabilitation options in rural hospitals.

Association between county-level sociodemographic characteristics and county-level differences in opioid dispensing

Background

While overall opioid prescribing has been decreasing in the United States, the rates of prescribing at the county level have been variable. Previous studies show that social determinants of health (the social and economic conditions in which we live) may play a role in opioid prescribing; however, researchers have not examined this relationship across US counties. This cross-sectional study seeks to determine whether county-level sociodemographic characteristics (e.g., economic, housing, social environment, healthcare environment, and population characteristics) are associated with county level differences in opioid dispensing.

Methods

Data from 2,881 counties in the United States from 2017 to 2018 were used for this study. Opioid dispensing was measured using morphine milligram equivalents (MME) per capita. Spatial error models were used to measure the association between county-level sociodemographic characteristics and MME per capita while adjusting for spatial correlation between neighboring counties.

Results

In the adjusted model, counties with a higher percentage of people below the poverty line, with less than a 4-year college degree, and without health insurance were associated with higher MME dispensed per capita, as were counties with higher percentages of families headed by a single parent, persons separated or divorced, and those with disabilities. Conversely, minority race/ethnicity and rural population were associated with lower opioid dispensing.

Conclusions

County-level sociodemographics can differ in their association with opioid dispensing, hence examining which county-level factors help in improving opioid prescribing, and implementing overdose prevention strategies that tackle these factors is important.

Evaluating Opioid Dispensing Rates among Pediatrics and Young Adults based on CURES Data Reporting in California from 2015-2019

Background

Receipt of opioid prescriptions in pediatric and young adult patients may be a risk factor for future opioid misuse. Data from prescription drug monitoring programs provide insight on outpatient opioid use. In our study, we analyzed the opioid dispensing rates for pediatrics and young adults in California.

Methods

A secondary analysis was performed from 2015-2019 using Controlled Utilization Review and Evaluation System data. This database provides dispensing data of controlled substances in California. Patients younger than 25 years who were prescribed opiates were analyzed by county. We further divided them into two groups (children: ≤14 years; adolescents and young adult: 15-24 years). Descriptive statistics and heat maps were used to illustrate the trends in opioid usage among different age groups.

Results

The overall percentages for the number of opioids being dispensed to patients aged <25 years have decreased over the past four years. In 2015, 6 out of 58 counties in California were considered "high-rate" with >2.9% of opioids dispensed to patients younger than 25 years old; in 2019, this number reduced to zero. Patients 25 and older received a higher proportion of opioids compared to younger populations; in 2019, 35.91% of opioids were dispensed to patients 45-64, and 8.92% to patients younger than 25.

Conclusion

Pediatric opioid prescriptions have declined over the recent years. However, a high degree of variability of prescription rates between demographic counties was noted. More studies are warranted in order to understand this discrepancy in opioid prescribing among pediatric and young adult patients.

Income inequality and opioid prescribing rates: Exploring rural/urban differences in pathways via residential stability and social isolation

While opioid prescribing rates have drawn researchers' attention, little is known about the mechanisms through which income inequality affects opioid prescribing rates and even less focuses on whether there is a rural/urban difference in mediating pathways. Applying mediation analysis techniques to a unique ZIP code level dataset from several sources maintained by the Centers for Medicare and Medicaid Services, we explicitly examine two mechanisms through residential stability and social isolation by rural/urban status and find that (1) income inequality is not directly related to opioid prescribing rates, but it exerts its influence on opioid prescribing via poor residential stability and elevated social isolation; (2) social isolation accounts for two-thirds of the mediating effect of income inequality on opioid prescribing rates among urban ZIP codes, but the proportion halves among rural ZIP codes; (3) residential stability plays a larger role in understanding how income inequality matters in rural than in urban ZIP codes; and (4) beneficiary characteristics only matter in urban ZIP codes. These findings offer nuanced insight into how income inequality affects opioid prescribing rates and suggests that the determinants of opioid prescribing rates vary by rural/urban status. Future research may benefit from identifying place-specific factors for opioid prescribing rates.

Urban-rural variation in the socioeconomic determinants of opioid overdose

BACKGROUND

Prescription opioid overdose (POD) and heroin overdose (HOD) rates have quadrupled since 1999. Community-level socioeconomic characteristics are associated with opioid overdoses, but whether this varies by urbanicity is unknown.

METHODS

In this serial cross-sectional study of zip codes in 17 states, 2002-2014 (n = 145,241 space-time units), we used hierarchical Bayesian Poisson space-time models to analyze the association between zip code-level socioeconomic features (poverty, unemployment, educational attainment, and income) and counts of POD or HOD hospital discharges. We tested multiplicative interactions between each socioeconomic feature and zip code urbanicity measured with Rural-Urban Commuting Area codes.

RESULTS

Percent in poverty and of adults with ≤ high school education were associated with higher POD rates (Rate Ratio [RR], 5% poverty: 1.07 [95% credible interval: 1.06-1.07]; 5% low education: 1.02 [1.02-1.03]), while median household income was associated with lower rates (RR, $10,000: 0.88 [0.87-0.89]). Urbanicity modified the association between socioeconomic features and HOD. Poverty and unemployment were associated with increased HOD in metropolitan areas (RR, 5% poverty: 1.12 [1.11-1.13]; 5% unemployment: 1.04 [1.02-1.05]), and median household income was associated with decreased HOD (RR, $10,000: 0.88 [0.87-0.90]). In rural areas, low educational attainment alone was associated with HOD (RR, 5%: 1.09 [1.02-1.16]).

CONCLUSIONS

Regardless of urbanicity, elevated rates of POD were found in more economically disadvantaged zip codes. Economic disadvantage played a larger role in HOD in urban than rural areas, suggesting rural HOD rates may have alternative drivers. Identifying social determinants of opioid overdoses is particularly important for creating effective population-level interventions.

Past-year Prescription Drug Monitoring Program Opioid Prescriptions and Self-reported Opioid Use in an Emergency Department Population With Opioid Use Disorder

BACKGROUND

Despite increasing reliance on prescription drug monitoring programs (PDMPs) as a response to the opioid epidemic, the relationship between aberrant drug-related behaviors captured by the PDMP and opioid use disorder is incompletely understood. How PDMP data should guide emergency department (ED) assessment has not been studied.

OBJECTIVES

The objective was to evaluate a relationship between PDMP opioid prescription records and self-reported nonmedical opioid use of prescription opioids in a cohort of opioid-dependent ED patients enrolled in a treatment trial.

METHODS

PDMP opioid prescription records during 1 year prior to study enrollment on 329 adults meeting Diagnostic and Statistical Manual IV criteria for opioid dependence entering a randomized clinical trial in a large, urban ED were cross-tabulated with data on 30-day nonmedical prescription opioid use self-report. The association among these two types of data was assessed by the Goodman and Kruskal's gamma; a logistic regression was used to explore characteristics of participants who had PDMP record of opioid prescriptions.

RESULTS

During 1 year prior to study enrollment, 118 of 329 (36%) patients had at least one opioid prescription (range = 1-51) in our states' PDMP. Patients who reported ≥15 of 30 days of nonmedical prescription opioid use were more likely to have at least four PDMP opioid prescriptions (20/38; 53%) than patients reporting 1 to 14 days (14/38, 37%) or zero days of nonmedical prescription opioid use (4/38, 11%; p = 0.002). Female sex and having health insurance were significantly more represented in the PDMP (p < 0.05 for both).

CONCLUSION

PDMPs may be helpful in identifying patients with certain aberrant drug-related behavior, but are unable to detect many patients with opioid use disorder. The majority of ED patients with opioid use disorder were not captured by the PDMP, highlighting the importance of using additional methods such as screening and clinical history to identify opioid use disorders in ED patients and the limitations of PDMPs to detect opioid use disorders.

Prescription opioid poisoning across urban and rural areas: identifying vulnerable groups and geographic areas

AIMS

To determine (1) whether prescription opioid poisoning (PO) hospital discharges spread across space over time, (2) the locations of 'hot-spots' of PO-related hospital discharges, (3) how features of the local environment contribute to the growth in PO-related hospital discharges and (4) where each environmental feature makes the strongest contribution.

DESIGN

Hierarchical Bayesian Poisson space-time analysis to relate annual discharges from community hospitals to postal code characteristics over 10 years.

SETTING

California, USA.

PARTICIPANTS

Residents of 18 517 postal codes in California, 2001-11.

MEASUREMENTS

Annual postal code-level counts of hospital discharges due to PO poisoning were related to postal code pharmacy density, measures of medical need for POs (i.e. rates of cancer and arthritis-related hospital discharges), economic stressors (i.e. median household income, percentage of families in poverty and the unemployment rate) and concentration of manual labor industries.

FINDINGS

PO-related hospital discharges spread from rural and suburban/exurban 'hot-spots' to urban areas. They increased more in postal codes with greater pharmacy density [rate ratio (RR) = 1.03; 95% credible interval (CI) = 1.01, 1.05], more arthritis-related hospital discharges (RR = 1.08; 95% CI = 1.06, 1.11), lower income (RR = 0.85; 95% CI = 0.83, 0.87) and more manual labor industries (RR = 1.15; 95% CI = 1.10, 1.19 for construction; RR = 1.12; 95% CI = 1.04, 1.20 for manufacturing industries). Changes in pharmacy density primarily affected PO-related discharges in urban areas, while changes in income and manual labor industries especially affected PO-related discharges in suburban/exurban and rural areas.

CONCLUSIONS

Hospital discharge rates for prescription opioid (PO) poisoning spread from rural and suburban/exurban hot-spots to urban areas, suggesting spatial contagion. The distribution of age-related and work-place-related sources of medical need for POs in rural areas and, to a lesser extent, the availability of POs through pharmacies in urban areas, partly explain the growth of PO poisoning across California, USA.

Provider Patient-Sharing Networks and Multiple-Provider Prescribing of Benzodiazepines

BACKGROUND

Prescription benzodiazepine overdose continues to cause significant morbidity and mortality in the US. Multiple-provider prescribing, due to either fragmented care or "doctor-shopping," contributes to the problem.

OBJECTIVE

To elucidate the effect of provider professional relationships on multiple-provider prescribing of benzodiazepines, using social network analytics.

DESIGN

A retrospective analysis of commercial healthcare claims spanning the years 2008 through 2011. Provider patient-sharing networks were modelled using social network analytics. Care team cohesion was measured using care density, defined as the ratio between the total number of patients shared by provider pairs within a patient's care team and the total number of provider pairs in the care team. Relationships within provider pairs were further quantified using a range of network metrics, including the number and proportion of patients or collaborators shared.

MAIN MEASURES

The relationship between patient-sharing network metrics and the likelihood of multiple prescribing of benzodiazepines.

PARTICIPANTS

Patients between the ages of 18 and 64 years who received two or more benzodiazepine prescriptions from multiple providers, with overlapping coverage of more than 14 days.

RESULTS

A total of 5659 patients and 1448 provider pairs were included in our study. Among these, 1028 patients (18.2 %) received multiple prescriptions of benzodiazepines, involving 445 provider pairs (30.7 %). Patients whose providers rarely shared patients had a higher risk of being prescribed overlapping benzodiazepines; the median care density was 8.1 for patients who were prescribed overlapping benzodiazepines and 10.1 for those who were not (p < 0.0001). Provider pairs who shared a greater number of patients and collaborators were less likely to co-prescribe overlapping benzodiazepines.

CONCLUSIONS

Our findings demonstrate the importance of care team cohesion in addressing multiple-provider prescribing of controlled substances. Furthermore, we illustrate the potential of the provider network as a surveillance tool to detect and prevent adverse events that could arise due to fragmentation of care.

Prevalence, prescribed quantities, and trajectory of multiple prescriber episodes for benzodiazepines: A 2-year cohort study

BACKGROUND

Little is known about the use of multiple prescribers for benzodiazepines, which might reflect fragmented patient care and increases the risk of hospital admission for drug dependence or poisoning. Therefore, we aimed to identify the prevalence, prescribed quantities, and trajectory of multiple prescriber episodes for benzodiazepines.

METHODS

We conducted a 2-year cohort study of 1178,361 recipients aged 0-74 years using a large health insurance claims database in Japan. We quantified multiple prescriber episodes for benzodiazepines occurring in ambulatory care settings in a baseline and subsequent year by (1) counting the number of unique providers within a 12-month period, (2) calculating the maximum number of unique providers within a single month, and (3) identifying consecutive overlapping prescriptions of over 30 days duration.

RESULTS

Among 58,314 patients with a benzodiazepine prescription during the baseline year, 282 (0.5%) filled prescriptions from four or more providers within a 12-month period, 439 (0.8%) filled prescriptions from three or more providers within a single month, and 757 (1.3%) filled consecutive overlapping prescriptions. The odds for multiple prescriber episodes were significantly higher among patients with multiple chronic conditions. Consecutive overlapping prescriptions had the best accuracy to detect patients with potentially questionable prescribed quantities as well as to predict those with multiple prescriber episodes in the subsequent year.

CONCLUSIONS

These results highlight the need for pharmacists to increase their involvement in prescription oversight and for health insurance agencies to implement a prescription monitoring program to screen for patients with multiple prescriber episodes for benzodiazepines.

The POPPY Research Programme protocol: investigating opioid utilisation, costs and patterns of extramedical use in Australia

INTRODUCTION

Opioid prescribing is increasing in many countries. In Australia, there is limited research on patterns of prescribing and access, or the outcomes associated with this use. The aim of this research programme is to use national dispensing data to estimate opioid use and costs, including problematic or extramedical use in the Australian population.

METHODS AND ANALYSIS

In a cohort of persons dispensed at least one opioid in 2013, we will estimate monthly utilisation and costs of prescribed opioids, overall and according to individual opioid formulations and strengths. In a cohort of new opioid users, commencing therapy between 1 July 2009 and 31 December 2013, we will examine patterns of opioid use including initiation of therapy, duration of treatment and concomitant use of opioids and other prescribed medicines. We will also examine patterns of extramedical opioid use based on indicators including excess dosing, use of more than one opioid concomitantly, doctor/pharmacy shopping and accelerated time to prescription refill.

ETHICS AND DISSEMINATION

This protocol was approved by the NSW Population and Health Services Ethics Committee (March 2014) and data access approved by the Department of Human Services External Review Evaluation Committee (June 2014). This will be one of the first comprehensive Australian studies with the capability to investigate individual patterns of use and track extramedical use. In the first instance our analysis will be based on 5 years of dispensing data but will be expanded with ongoing annual data updates. This research has the capability to contribute significantly to pharmaceutical policy within Australia and globally. In particular, the trajectory of extramedical prescription-opioid use has been the subject of limited research to date. The results of this research will be published widely in general medical, pharmacoepidemiology, addiction and psychiatry journals.

Increasing trends in Schedule II opioid use and doctor shopping during 1999-2007 in California

PURPOSE

To examine the age and gender-specific trends of Schedule II opioid use among California residents, with special reference to multiple provider users (doctor shoppers).

METHODS

Utilizing data from the California Prescription Drug Monitoring Program, we examined age and gender-specific trends of Schedule II opioid use during calendar years 1999-2007. Specifically, we analyzed the following: (1) the prevalence of Schedule II opioid users among California's population and (2) the proportion of these opioid users who were doctor shoppers (defined as an individual who used more than five different prescribers for all Schedule II opioids he or she obtained in a calendar year).

RESULTS

Among all age and gender groups, the prevalence of Schedule II opioid users in California increased by 150%-280% and the prevalence of doctor shoppers among users increased by 111%-213% over 9 years. The prevalence of opioid users was lowest among 18-44 year old men (1.25%) and highest among 65-year and older women (5.31%) by 2007. The prevalence of doctor shoppers was approximately 1.4% among those up to age 64 years and 0.5% among those 65 years and older. The gender difference in doctor shoppers among all age groups was negligible. On average, the cumulative morphine-equivalent amount of Schedule II opioid per individual obtained per year was threefold to sixfold higher for doctor shoppers than for the general population across different age and gender groups.

CONCLUSIONS

Age and gender differences in opioid use were relatively small, whereas the trends for use of opioids and multiple providers grew at a disquieting rate.

Evaluation of controlled prescriptions with a diagnosis of surgical intervention

OBJECTIVE

Drugs subject to control (DSC) are commonly used in the preoperative, per-operative and postoperative periods. In this study, details of DSC use, that are required to be prescribed to green prescriptions (GP) or red prescriptions (RP), for a surgical diagnosis were evaluated.

MATERIAL AND METHODS

Controlled drug prescriptions (CDP) between January to December 2009 were investigated in the archives of the Istanbul Local Health Authority. 660 CDPs indicating a surgical diagnosis were retrospectively evaluated and those prescribed by surgical departments (SD)were compared to those written by internal medicine departments (ID).

RESULTS

SD physicians prescribed 72.7% of CDPs and 89.5% of prescriptions were for GP, and 10.5% were for RP. The physicians of SD were found to prescribe more RP than ID physicians. Most of the CDPs were written for women (58.8%) and mostly in private hospitals (40.9%). Among all the physicians, orthopedic surgeons prescribed these drugs the most (20.9%). Tramadol was the most common drug found in CDPs (55.5%).

CONCLUSION

DSCs, which have difficult prescription procedures, are used frequently in surgical interventions. The study shows that not only SD physicians, but also ID physicians prefer CDP. The study provides important clues to the usage patterns of DSCs. Recently evolving health care systems including prescriptions should use these findings obtained from the study.