Margaret A. Mendes, PharmD

The number of opioid-related overdose deaths in the United States is estimated to have increased 6-fold over the past 2 decades.¹ In 2017, more than two-thirds of drug overdose deaths involved opioids, yielding a mortality rate of 14.9 per 100,000.² Not only does the opioid epidemic currently pose a significant public health crisis characterized by high morbidity and mortality, but it is also projected to worsen in coming years. According to Chen and colleagues, opioid overdose deaths are estimated to increase by 147% from 2015 to 2025.³ That projects almost 82,000 US deaths annually and > 700,000 deaths in this period—even before accounting for surges in opioid overdoses and opioid-related mortality coinciding with the COVID-19 pandemic.^3,4

As health systems and communities globally struggle with unprecedented losses and stressors introduced by the pandemic, emerging data warrants escalating concerns with regard to increased vulnerability to relapse and overdose among those with mental health and substance use disorders (SUDs). In a recent report, the American Medical Association estimates that opioid-related deaths have increased in more than 40 states with the COVID-19 pandemic.⁴

Veterans are twice as likely to experience a fatal opioid overdose compared with their civilian counterparts.⁵ While several risk mitigation strategies have been employed in recent years to improve opioid prescribing and safety within the US Department of Veterans Affairs (VA), veterans continue to overdose on opioids, both prescribed and obtained illicitly.⁶ Variables shown to be strongly associated with opioid overdose risk include presence of mental health disorders, SUDs, medical conditions involving impaired drug metabolism or excretion, respiratory disorders, higher doses of opioids, concomitant use of sedative medications, and history of overdose.^6-8 Many veterans struggle with chronic pain and those prescribed high doses of opioids were more likely to have comorbid pain diagnoses, mental health disorders, and SUDs.⁹ Dashboards and predictive models, such as the Stratification Tool for Opioid Risk Mitigation (STORM) and the Risk Index for Overdose or Serious Opioid-induced Respiratory Depression (RIOSORD), incorporate such factors to stratify overdose risk among veterans, in an effort to prioritize high-risk individuals for review and provision of care.^6,10,11 Despite recent recognition that overdose prevention likely requires a holistic approach that addresses the biopsychosocial factors contributing to opioid-related morbidity and mortality, it is unclear whether veterans are receiving adequate and appropriate treatment for contributing conditions.

There are currently no existing studies that describe health service utilization (HSU), medication interventions, and rates of opioid-related adverse events (ORAEs) among veterans after survival of a nonfatal opioid overdose (NFO). Clinical characteristics of veterans treated for opioid overdose at a VA emergency department (ED) have previously been described by Clement and Stock.¹² Despite improvements that have been made in VA opioid prescribing and safety, knowledge gaps remain with regard to best practices for opioid overdose prevention. The aim of this study was to characterize HSU and medication interventions in veterans following NFO, as well as the frequency of ORAEs after overdose. The findings of this study may aid in the identification of areas for targeted improvement in the prevention and reduction of opioid overdoses and adverse opioid-related sequelae.

Methods

This retrospective descriptive study was conducted at VA San Diego Healthcare System (VASDHCS) in California. Subjects included were veterans administered naloxone in the ED for suspected opioid overdose between July 1, 2013 and April 1, 2017. The study population was identified through data retrieved from automated drug dispensing systems, which was then confirmed through manual chart review of notes associated with the index ED visit. Inclusion criteria included documented increased respiration or responsiveness following naloxone administration. Subjects were excluded if they demonstrated lack of response to naloxone, overdosed secondary to inpatient administration of opioids, received palliative or hospice care during the study period, or were lost to follow-up.

Data were collected via retrospective chart review and included date of index ED visit, demographics, active prescriptions, urine drug screen (UDS) results, benzodiazepine (BZD) use corroborated by positive UDS or mention of BZD in index visit chart notes, whether overdose was determined to be a suicide attempt, and naloxone kit dispensing. Patient data was collected for 2 years following overdose, including: ORAEs; ED visits; hospitalizations; repeat overdoses; fatal overdose; whether subjects were still alive; follow-up visits for pain management, mental health, and addiction treatment services; and visits to the psychiatric emergency clinic. Clinical characteristics, such as mental health disorder diagnoses, SUDs, and relevant medical conditions also were collected. Statistical analysis was performed using Microsoft Excel and included only descriptive statistics.

 

Results

Ninety-three patients received naloxone in the VASDHCS ED. Thirty-five met inclusion criteria and were included in the primary analysis. All subjects received IV naloxone with a mean 0.8 mg IV boluses (range, 0.1-4.4 mg).

Most patients were male with a mean age of 59.8 years (Table 1). Almost all overdoses were nonintentional except for 3 suicide attempts that were reviewed by the Suicide Prevention Committee. Three patients had previously been treated for opioid overdose at the VA with a documented positive clinical response to naloxone administration.

At the time of overdose, 29 patients (82.9%) had an active opioid prescription. Of these, the majority were issued through the VA with a mean 117 mg morphine equivalent daily dose (MEDD). Interestingly, only 24 of the 28 patients with a UDS collected at time of overdose tested positive for opioids, which may be attributable to the use of synthetic opioids, which are not reliably detected by traditional UDS. Concomitant BZD use was involved in 13 of the 35 index overdoses (37.1%), although only 6 patients (17.1%) had an active BZD prescription at time of overdose. Seven patients (20.0%) were prescribed medication-assisted treatment (MAT) for opioid use disorder (OUD), with all 7 using methadone. According to VA records, only 1 patient had previously been dispensed a naloxone kit at any point prior to overdosing. Mental health and SUD diagnoses frequently co-occurred, with 20 patients (57.1%) having at least 1 mental health condition and at least 1 SUD.

Rates of follow-up varied by clinician type in the 6 months after NFO (Figure). Of those with mental health disorders, 15 patients (45.5%) received mental health services before and after overdose, while 8 (40.0%) and 10 (50.0%) of those with SUDs received addiction treatment services before and after overdose, respectively. Seven patients presented to the psychiatric emergency clinic within 6 months prior to overdose and 5 patients within the 6 months following overdose.

Of patients with VA opioid prescriptions, within 2 years of NFO, 9 (42.9%) had their opioids discontinued, and 18 (85.7%) had MEDD reductions ranging from 10 mg to 150 mg (12.5-71.4% reduction) with a mean of 63 mg. Two of the 4 patients with active BZD prescriptions at the time of the overdose event had their prescriptions continued. Seven patients (20.0%) were dispensed naloxone kits following overdose (Table 2).

Rates of ORAEs ranged from 0% to 17% with no documented overdose fatalities. Examples of AEs observed in this study included ED visits or hospitalizations involving opioid withdrawal, opioid-related personality changes, and opioid overdose. Five patients died during the study period, yielding an all-cause mortality rate of 14.3% with a mean time to death of 10.8 months. The causes of death were largely unknown except for 1 patient, whose death was reportedly investigated as an accidental medication overdose without additional information.

Repeat overdose verified by hospital records occurred in 4 patients (11.4%) within 2 years. Patients who experienced a subsequent overdose were prescribed higher doses of opioids with a mean MEDD among VA prescriptions of 130 mg vs 114 mg for those without repeat overdose. In this group, 3 patients (75.0%) also had concomitant BZD use, which was proportionally higher than the 10 patients (32.3%) without a subsequent overdose. Of note, 2 of the 4 patients with a repeat overdose had their opioid doses increased above the MEDD prescribed at the time of index overdose. None of the 4 subjects who experienced a repeat overdose were initiated on MAT within 2 years according to VA records.

Discussions

This retrospective study is representative of many veterans receiving VA care, despite the small sample size. Clinical characteristics observed in the study population were generally consistent with those published by Clement and Stock, including high rates of medical and psychiatric comorbidities.¹² Subjects in both studies were prescribed comparable dosages of opioids; among those prescribed opioids but not BZDs through the VA, the mean MEDD was 117 mg in our study compared with 126 mg in the Clement and Stock study. Since implementation of the Opioid Safety Initiative (OSI) in 2013, opioid prescribing practices have improved nationwide across VA facilities, including successful reduction in the numbers of patients prescribed high-dose opioids and concurrent BZDs.¹³

Despite the tools and resources available to clinicians, discontinuing opioid therapy remains a difficult process. Concerns related to mental health and/or substance-use related decompensations often exist in the setting of rapid dose reductions or abrupt discontinuation of opioids.⁶ Although less than half of patients in the present study with an active opioid prescription at time of index overdose had their opioids discontinued within 2 years, it is reassuring to note the much higher rate of those with subsequent decreases in their prescribed doses, as well as the 50% reduction in BZD coprescribing. Ultimately, these findings remain consistent with the VA goals of mitigating harm, improving opioid prescribing, and ensuring the safe use of opioid medications when clinically appropriate.

Moreover, recent evidence suggests that interventions focused solely on opioid prescribing practices are becoming increasingly limited in their impact on reducing opioid-related deaths and will likely be insufficient for addressing the opioid epidemic as it continues to evolve. According to Chen and colleagues, opioid overdose deaths are projected to increase over the next several years, while further reduction in the incidence of prescription opioid misuse is estimated to decrease overdose deaths by only 3% to 5.3%. In the context of recent surges in synthetic opioid use, it is projected that 80% of overdose deaths between 2016 and 2025 will be attributable to illicit opioids.³ Such predictions underscore the urgent need to adopt alternative approaches to risk-reducing measures and policy change.

The increased risk of mortality associated with opioid misuse and overdose is well established in the current literature. However, less is known regarding the rate of ORAEs after survival of an NFO. Olfson and colleagues sought to address this knowledge gap by characterizing mortality risks in 76,325 US adults within 1 year following NFO.¹⁴ Among their studied population, all-cause mortality occurred at a rate of 778.3 per 10,000 person-years, which was 24 times greater than that of the general population. This emphasizes the need for the optimization of mental health services, addiction treatment, and medical care for these individuals at higher risk.

 

Limitations

Certain factors and limitations should be considered when interpreting the results of this study. Given that the study included only veterans, factors such as the demographic and clinical characteristics more commonly observed among these patients should be taken into account and may in turn limit the generalizability of these findings to nonveteran populations. Another major limitation is the small sample size; the study period and by extension, the number of patients able to be included in the present study were restricted by the availability of retrievable data from automated drug dispensing systems. Patients without documented response to naloxone were excluded from the study due to low clinical suspicion for opioid overdose, although the possibility that the dose administered was too low to produce a robust clinical response cannot be definitively ruled out. The lack of reliable methods to capture events and overdoses treated outside of the VA may have resulted in underestimations of the true occurrence of ORAEs following NFO. Information regarding naloxone administration outside VA facilities, such as in transport to the hospital, self-reported, or bystander administration, was similarly limited by lack of reliable methods for retrieving such data and absence of documentation in VA records. Although all interventions and outcomes reported in the present study occurred within 2 years following NFO, further conclusions pertaining to the relative timing of specific interventions and ORAEs cannot be made. Lastly, this study did not investigate the direct impact of opioid risk mitigation initiatives implemented by the VA in the years coinciding with the study period.

Future Directions

Despite these limitations, an important strength of this study is its ability to identify potential areas for targeted improvement and to guide further efforts relating to the prevention of opioid overdose and opioid-related mortality among veterans. Identification of individuals at high risk for opioid overdose and misuse is an imperative first step that allows for the implementation of downstream risk-mitigating interventions. Within the VA, several tools have been developed in recent years to provide clinicians with additional resources and support in this regard.^6,15

No more than half of those diagnosed with mental health disorders and SUDs in the present study received outpatient follow-up care for these conditions within 6 months following NFO, which may suggest high rates of inadequate treatment. Given the strong association between mental health disorders, SUDs, and increased risk of overdose, increasing engagement with mental health and addiction treatment services may be paramount to preventing subsequent ORAEs, including repeat overdose.^6-9,11

Naloxone kit dispensing represents another area for targeted improvement. Interventions may include clinician education and systematic changes, such as implementing protocols that boost the likelihood of high-risk individuals being provided with naloxone at the earliest opportunity. Bystander-administered naloxone programs can also be considered for increasing naloxone access and reducing opioid-related mortality.¹⁶

Finally, despite evidence supporting the benefit of MAT in OUD treatment and reducing all-cause and opioid-related mortality after NFO, the low rates of MAT observed in this study are consistent with previous reports that these medications remain underutilized.¹⁷ Screening for OUD, in conjunction with increasing access to and utilization of OUD treatment modalities, is an established and integral component of overdose prevention efforts. For VA clinicians, the Psychotropic Drug Safety Initiative (PDSI) dashboard can be used to identify patients diagnosed with OUD who are not yet on MAT.¹⁸ Initiatives to expand MAT access through the ED have the potential to provide life-saving interventions and bridge care in the interim until patients are able to become established with a long-term health care practitioner.¹⁹

Conclusions

This is the first study to describe HSU, medication interventions, and ORAEs among veterans who survive NFO. Studies have shown that veterans with a history of NFO are at increased risk of subsequent AEs and premature death.^6,7,10,14 As such, NFOs represent crucial opportunities to identify high-risk individuals and ensure provision of adequate care. Recent data supports the development of a holistic, multimodal approach focused on adequate treatment of conditions that contribute to opioid-related risks, including mental health disorders, SUDs, pain diagnoses, and medical comorbidities.^3,14 Interventions designed to improve access, engagement, and retention in such care therefore play a pivotal role in overdose prevention and reducing mortality.

Although existing risk mitigation initiatives have improved opioid prescribing and safety within the VA, the findings of this study suggest that there remains room for improvement, and the need for well-coordinated efforts to reduce risks associated with both prescribed and illicit opioid use cannot be overstated. Rates of overdose deaths not only remain high but are projected to continue increasing in coming years, despite advances in clinical practice aimed at reducing harms associated with opioid use. The present findings aim to help identify processes with the potential to reduce rates of overdose, death, and adverse sequelae in high-risk populations. However, future studies are warranted to expand on these findings and contribute to ongoing efforts in reducing opioid-related harms and overdose deaths. This study may provide critical insight to inform further investigations to guide such interventions and highlight tools that health care facilities even outside the VA can consider implementing.

Acknowledgments

The authors would like to thank Jonathan Lacro, PharmD, BCPP, for his guidance with this important clinical topic and navigating IRB submissions.

The number of opioid-related overdose deaths in the United States is estimated to have increased 6-fold over the past 2 decades.¹ In 2017, more than two-thirds of drug overdose deaths involved opioids, yielding a mortality rate of 14.9 per 100,000.² Not only does the opioid epidemic currently pose a significant public health crisis characterized by high morbidity and mortality, but it is also projected to worsen in coming years. According to Chen and colleagues, opioid overdose deaths are estimated to increase by 147% from 2015 to 2025.³ That projects almost 82,000 US deaths annually and > 700,000 deaths in this period—even before accounting for surges in opioid overdoses and opioid-related mortality coinciding with the COVID-19 pandemic.^3,4

As health systems and communities globally struggle with unprecedented losses and stressors introduced by the pandemic, emerging data warrants escalating concerns with regard to increased vulnerability to relapse and overdose among those with mental health and substance use disorders (SUDs). In a recent report, the American Medical Association estimates that opioid-related deaths have increased in more than 40 states with the COVID-19 pandemic.⁴

Veterans are twice as likely to experience a fatal opioid overdose compared with their civilian counterparts.⁵ While several risk mitigation strategies have been employed in recent years to improve opioid prescribing and safety within the US Department of Veterans Affairs (VA), veterans continue to overdose on opioids, both prescribed and obtained illicitly.⁶ Variables shown to be strongly associated with opioid overdose risk include presence of mental health disorders, SUDs, medical conditions involving impaired drug metabolism or excretion, respiratory disorders, higher doses of opioids, concomitant use of sedative medications, and history of overdose.^6-8 Many veterans struggle with chronic pain and those prescribed high doses of opioids were more likely to have comorbid pain diagnoses, mental health disorders, and SUDs.⁹ Dashboards and predictive models, such as the Stratification Tool for Opioid Risk Mitigation (STORM) and the Risk Index for Overdose or Serious Opioid-induced Respiratory Depression (RIOSORD), incorporate such factors to stratify overdose risk among veterans, in an effort to prioritize high-risk individuals for review and provision of care.^6,10,11 Despite recent recognition that overdose prevention likely requires a holistic approach that addresses the biopsychosocial factors contributing to opioid-related morbidity and mortality, it is unclear whether veterans are receiving adequate and appropriate treatment for contributing conditions.

There are currently no existing studies that describe health service utilization (HSU), medication interventions, and rates of opioid-related adverse events (ORAEs) among veterans after survival of a nonfatal opioid overdose (NFO). Clinical characteristics of veterans treated for opioid overdose at a VA emergency department (ED) have previously been described by Clement and Stock.¹² Despite improvements that have been made in VA opioid prescribing and safety, knowledge gaps remain with regard to best practices for opioid overdose prevention. The aim of this study was to characterize HSU and medication interventions in veterans following NFO, as well as the frequency of ORAEs after overdose. The findings of this study may aid in the identification of areas for targeted improvement in the prevention and reduction of opioid overdoses and adverse opioid-related sequelae.

Methods

This retrospective descriptive study was conducted at VA San Diego Healthcare System (VASDHCS) in California. Subjects included were veterans administered naloxone in the ED for suspected opioid overdose between July 1, 2013 and April 1, 2017. The study population was identified through data retrieved from automated drug dispensing systems, which was then confirmed through manual chart review of notes associated with the index ED visit. Inclusion criteria included documented increased respiration or responsiveness following naloxone administration. Subjects were excluded if they demonstrated lack of response to naloxone, overdosed secondary to inpatient administration of opioids, received palliative or hospice care during the study period, or were lost to follow-up.

Data were collected via retrospective chart review and included date of index ED visit, demographics, active prescriptions, urine drug screen (UDS) results, benzodiazepine (BZD) use corroborated by positive UDS or mention of BZD in index visit chart notes, whether overdose was determined to be a suicide attempt, and naloxone kit dispensing. Patient data was collected for 2 years following overdose, including: ORAEs; ED visits; hospitalizations; repeat overdoses; fatal overdose; whether subjects were still alive; follow-up visits for pain management, mental health, and addiction treatment services; and visits to the psychiatric emergency clinic. Clinical characteristics, such as mental health disorder diagnoses, SUDs, and relevant medical conditions also were collected. Statistical analysis was performed using Microsoft Excel and included only descriptive statistics.

 

Results

Ninety-three patients received naloxone in the VASDHCS ED. Thirty-five met inclusion criteria and were included in the primary analysis. All subjects received IV naloxone with a mean 0.8 mg IV boluses (range, 0.1-4.4 mg).

Most patients were male with a mean age of 59.8 years (Table 1). Almost all overdoses were nonintentional except for 3 suicide attempts that were reviewed by the Suicide Prevention Committee. Three patients had previously been treated for opioid overdose at the VA with a documented positive clinical response to naloxone administration.

At the time of overdose, 29 patients (82.9%) had an active opioid prescription. Of these, the majority were issued through the VA with a mean 117 mg morphine equivalent daily dose (MEDD). Interestingly, only 24 of the 28 patients with a UDS collected at time of overdose tested positive for opioids, which may be attributable to the use of synthetic opioids, which are not reliably detected by traditional UDS. Concomitant BZD use was involved in 13 of the 35 index overdoses (37.1%), although only 6 patients (17.1%) had an active BZD prescription at time of overdose. Seven patients (20.0%) were prescribed medication-assisted treatment (MAT) for opioid use disorder (OUD), with all 7 using methadone. According to VA records, only 1 patient had previously been dispensed a naloxone kit at any point prior to overdosing. Mental health and SUD diagnoses frequently co-occurred, with 20 patients (57.1%) having at least 1 mental health condition and at least 1 SUD.

Rates of follow-up varied by clinician type in the 6 months after NFO (Figure). Of those with mental health disorders, 15 patients (45.5%) received mental health services before and after overdose, while 8 (40.0%) and 10 (50.0%) of those with SUDs received addiction treatment services before and after overdose, respectively. Seven patients presented to the psychiatric emergency clinic within 6 months prior to overdose and 5 patients within the 6 months following overdose.

Of patients with VA opioid prescriptions, within 2 years of NFO, 9 (42.9%) had their opioids discontinued, and 18 (85.7%) had MEDD reductions ranging from 10 mg to 150 mg (12.5-71.4% reduction) with a mean of 63 mg. Two of the 4 patients with active BZD prescriptions at the time of the overdose event had their prescriptions continued. Seven patients (20.0%) were dispensed naloxone kits following overdose (Table 2).

Rates of ORAEs ranged from 0% to 17% with no documented overdose fatalities. Examples of AEs observed in this study included ED visits or hospitalizations involving opioid withdrawal, opioid-related personality changes, and opioid overdose. Five patients died during the study period, yielding an all-cause mortality rate of 14.3% with a mean time to death of 10.8 months. The causes of death were largely unknown except for 1 patient, whose death was reportedly investigated as an accidental medication overdose without additional information.

Repeat overdose verified by hospital records occurred in 4 patients (11.4%) within 2 years. Patients who experienced a subsequent overdose were prescribed higher doses of opioids with a mean MEDD among VA prescriptions of 130 mg vs 114 mg for those without repeat overdose. In this group, 3 patients (75.0%) also had concomitant BZD use, which was proportionally higher than the 10 patients (32.3%) without a subsequent overdose. Of note, 2 of the 4 patients with a repeat overdose had their opioid doses increased above the MEDD prescribed at the time of index overdose. None of the 4 subjects who experienced a repeat overdose were initiated on MAT within 2 years according to VA records.

Discussions

This retrospective study is representative of many veterans receiving VA care, despite the small sample size. Clinical characteristics observed in the study population were generally consistent with those published by Clement and Stock, including high rates of medical and psychiatric comorbidities.¹² Subjects in both studies were prescribed comparable dosages of opioids; among those prescribed opioids but not BZDs through the VA, the mean MEDD was 117 mg in our study compared with 126 mg in the Clement and Stock study. Since implementation of the Opioid Safety Initiative (OSI) in 2013, opioid prescribing practices have improved nationwide across VA facilities, including successful reduction in the numbers of patients prescribed high-dose opioids and concurrent BZDs.¹³

Despite the tools and resources available to clinicians, discontinuing opioid therapy remains a difficult process. Concerns related to mental health and/or substance-use related decompensations often exist in the setting of rapid dose reductions or abrupt discontinuation of opioids.⁶ Although less than half of patients in the present study with an active opioid prescription at time of index overdose had their opioids discontinued within 2 years, it is reassuring to note the much higher rate of those with subsequent decreases in their prescribed doses, as well as the 50% reduction in BZD coprescribing. Ultimately, these findings remain consistent with the VA goals of mitigating harm, improving opioid prescribing, and ensuring the safe use of opioid medications when clinically appropriate.

Moreover, recent evidence suggests that interventions focused solely on opioid prescribing practices are becoming increasingly limited in their impact on reducing opioid-related deaths and will likely be insufficient for addressing the opioid epidemic as it continues to evolve. According to Chen and colleagues, opioid overdose deaths are projected to increase over the next several years, while further reduction in the incidence of prescription opioid misuse is estimated to decrease overdose deaths by only 3% to 5.3%. In the context of recent surges in synthetic opioid use, it is projected that 80% of overdose deaths between 2016 and 2025 will be attributable to illicit opioids.³ Such predictions underscore the urgent need to adopt alternative approaches to risk-reducing measures and policy change.

The increased risk of mortality associated with opioid misuse and overdose is well established in the current literature. However, less is known regarding the rate of ORAEs after survival of an NFO. Olfson and colleagues sought to address this knowledge gap by characterizing mortality risks in 76,325 US adults within 1 year following NFO.¹⁴ Among their studied population, all-cause mortality occurred at a rate of 778.3 per 10,000 person-years, which was 24 times greater than that of the general population. This emphasizes the need for the optimization of mental health services, addiction treatment, and medical care for these individuals at higher risk.

 

Limitations

Certain factors and limitations should be considered when interpreting the results of this study. Given that the study included only veterans, factors such as the demographic and clinical characteristics more commonly observed among these patients should be taken into account and may in turn limit the generalizability of these findings to nonveteran populations. Another major limitation is the small sample size; the study period and by extension, the number of patients able to be included in the present study were restricted by the availability of retrievable data from automated drug dispensing systems. Patients without documented response to naloxone were excluded from the study due to low clinical suspicion for opioid overdose, although the possibility that the dose administered was too low to produce a robust clinical response cannot be definitively ruled out. The lack of reliable methods to capture events and overdoses treated outside of the VA may have resulted in underestimations of the true occurrence of ORAEs following NFO. Information regarding naloxone administration outside VA facilities, such as in transport to the hospital, self-reported, or bystander administration, was similarly limited by lack of reliable methods for retrieving such data and absence of documentation in VA records. Although all interventions and outcomes reported in the present study occurred within 2 years following NFO, further conclusions pertaining to the relative timing of specific interventions and ORAEs cannot be made. Lastly, this study did not investigate the direct impact of opioid risk mitigation initiatives implemented by the VA in the years coinciding with the study period.

Future Directions

Despite these limitations, an important strength of this study is its ability to identify potential areas for targeted improvement and to guide further efforts relating to the prevention of opioid overdose and opioid-related mortality among veterans. Identification of individuals at high risk for opioid overdose and misuse is an imperative first step that allows for the implementation of downstream risk-mitigating interventions. Within the VA, several tools have been developed in recent years to provide clinicians with additional resources and support in this regard.^6,15

No more than half of those diagnosed with mental health disorders and SUDs in the present study received outpatient follow-up care for these conditions within 6 months following NFO, which may suggest high rates of inadequate treatment. Given the strong association between mental health disorders, SUDs, and increased risk of overdose, increasing engagement with mental health and addiction treatment services may be paramount to preventing subsequent ORAEs, including repeat overdose.^6-9,11

Naloxone kit dispensing represents another area for targeted improvement. Interventions may include clinician education and systematic changes, such as implementing protocols that boost the likelihood of high-risk individuals being provided with naloxone at the earliest opportunity. Bystander-administered naloxone programs can also be considered for increasing naloxone access and reducing opioid-related mortality.¹⁶

Finally, despite evidence supporting the benefit of MAT in OUD treatment and reducing all-cause and opioid-related mortality after NFO, the low rates of MAT observed in this study are consistent with previous reports that these medications remain underutilized.¹⁷ Screening for OUD, in conjunction with increasing access to and utilization of OUD treatment modalities, is an established and integral component of overdose prevention efforts. For VA clinicians, the Psychotropic Drug Safety Initiative (PDSI) dashboard can be used to identify patients diagnosed with OUD who are not yet on MAT.¹⁸ Initiatives to expand MAT access through the ED have the potential to provide life-saving interventions and bridge care in the interim until patients are able to become established with a long-term health care practitioner.¹⁹

Conclusions

This is the first study to describe HSU, medication interventions, and ORAEs among veterans who survive NFO. Studies have shown that veterans with a history of NFO are at increased risk of subsequent AEs and premature death.^6,7,10,14 As such, NFOs represent crucial opportunities to identify high-risk individuals and ensure provision of adequate care. Recent data supports the development of a holistic, multimodal approach focused on adequate treatment of conditions that contribute to opioid-related risks, including mental health disorders, SUDs, pain diagnoses, and medical comorbidities.^3,14 Interventions designed to improve access, engagement, and retention in such care therefore play a pivotal role in overdose prevention and reducing mortality.

Although existing risk mitigation initiatives have improved opioid prescribing and safety within the VA, the findings of this study suggest that there remains room for improvement, and the need for well-coordinated efforts to reduce risks associated with both prescribed and illicit opioid use cannot be overstated. Rates of overdose deaths not only remain high but are projected to continue increasing in coming years, despite advances in clinical practice aimed at reducing harms associated with opioid use. The present findings aim to help identify processes with the potential to reduce rates of overdose, death, and adverse sequelae in high-risk populations. However, future studies are warranted to expand on these findings and contribute to ongoing efforts in reducing opioid-related harms and overdose deaths. This study may provide critical insight to inform further investigations to guide such interventions and highlight tools that health care facilities even outside the VA can consider implementing.

Acknowledgments

The authors would like to thank Jonathan Lacro, PharmD, BCPP, for his guidance with this important clinical topic and navigating IRB submissions.

The number of opioid-related overdose deaths in the United States is estimated to have increased 6-fold over the past 2 decades.¹ In 2017, more than two-thirds of drug overdose deaths involved opioids, yielding a mortality rate of 14.9 per 100,000.² Not only does the opioid epidemic currently pose a significant public health crisis characterized by high morbidity and mortality, but it is also projected to worsen in coming years. According to Chen and colleagues, opioid overdose deaths are estimated to increase by 147% from 2015 to 2025.³ That projects almost 82,000 US deaths annually and > 700,000 deaths in this period—even before accounting for surges in opioid overdoses and opioid-related mortality coinciding with the COVID-19 pandemic.^3,4

As health systems and communities globally struggle with unprecedented losses and stressors introduced by the pandemic, emerging data warrants escalating concerns with regard to increased vulnerability to relapse and overdose among those with mental health and substance use disorders (SUDs). In a recent report, the American Medical Association estimates that opioid-related deaths have increased in more than 40 states with the COVID-19 pandemic.⁴

Veterans are twice as likely to experience a fatal opioid overdose compared with their civilian counterparts.⁵ While several risk mitigation strategies have been employed in recent years to improve opioid prescribing and safety within the US Department of Veterans Affairs (VA), veterans continue to overdose on opioids, both prescribed and obtained illicitly.⁶ Variables shown to be strongly associated with opioid overdose risk include presence of mental health disorders, SUDs, medical conditions involving impaired drug metabolism or excretion, respiratory disorders, higher doses of opioids, concomitant use of sedative medications, and history of overdose.^6-8 Many veterans struggle with chronic pain and those prescribed high doses of opioids were more likely to have comorbid pain diagnoses, mental health disorders, and SUDs.⁹ Dashboards and predictive models, such as the Stratification Tool for Opioid Risk Mitigation (STORM) and the Risk Index for Overdose or Serious Opioid-induced Respiratory Depression (RIOSORD), incorporate such factors to stratify overdose risk among veterans, in an effort to prioritize high-risk individuals for review and provision of care.^6,10,11 Despite recent recognition that overdose prevention likely requires a holistic approach that addresses the biopsychosocial factors contributing to opioid-related morbidity and mortality, it is unclear whether veterans are receiving adequate and appropriate treatment for contributing conditions.

There are currently no existing studies that describe health service utilization (HSU), medication interventions, and rates of opioid-related adverse events (ORAEs) among veterans after survival of a nonfatal opioid overdose (NFO). Clinical characteristics of veterans treated for opioid overdose at a VA emergency department (ED) have previously been described by Clement and Stock.¹² Despite improvements that have been made in VA opioid prescribing and safety, knowledge gaps remain with regard to best practices for opioid overdose prevention. The aim of this study was to characterize HSU and medication interventions in veterans following NFO, as well as the frequency of ORAEs after overdose. The findings of this study may aid in the identification of areas for targeted improvement in the prevention and reduction of opioid overdoses and adverse opioid-related sequelae.

Methods

This retrospective descriptive study was conducted at VA San Diego Healthcare System (VASDHCS) in California. Subjects included were veterans administered naloxone in the ED for suspected opioid overdose between July 1, 2013 and April 1, 2017. The study population was identified through data retrieved from automated drug dispensing systems, which was then confirmed through manual chart review of notes associated with the index ED visit. Inclusion criteria included documented increased respiration or responsiveness following naloxone administration. Subjects were excluded if they demonstrated lack of response to naloxone, overdosed secondary to inpatient administration of opioids, received palliative or hospice care during the study period, or were lost to follow-up.

Data were collected via retrospective chart review and included date of index ED visit, demographics, active prescriptions, urine drug screen (UDS) results, benzodiazepine (BZD) use corroborated by positive UDS or mention of BZD in index visit chart notes, whether overdose was determined to be a suicide attempt, and naloxone kit dispensing. Patient data was collected for 2 years following overdose, including: ORAEs; ED visits; hospitalizations; repeat overdoses; fatal overdose; whether subjects were still alive; follow-up visits for pain management, mental health, and addiction treatment services; and visits to the psychiatric emergency clinic. Clinical characteristics, such as mental health disorder diagnoses, SUDs, and relevant medical conditions also were collected. Statistical analysis was performed using Microsoft Excel and included only descriptive statistics.

 

Results

Ninety-three patients received naloxone in the VASDHCS ED. Thirty-five met inclusion criteria and were included in the primary analysis. All subjects received IV naloxone with a mean 0.8 mg IV boluses (range, 0.1-4.4 mg).

Most patients were male with a mean age of 59.8 years (Table 1). Almost all overdoses were nonintentional except for 3 suicide attempts that were reviewed by the Suicide Prevention Committee. Three patients had previously been treated for opioid overdose at the VA with a documented positive clinical response to naloxone administration.

At the time of overdose, 29 patients (82.9%) had an active opioid prescription. Of these, the majority were issued through the VA with a mean 117 mg morphine equivalent daily dose (MEDD). Interestingly, only 24 of the 28 patients with a UDS collected at time of overdose tested positive for opioids, which may be attributable to the use of synthetic opioids, which are not reliably detected by traditional UDS. Concomitant BZD use was involved in 13 of the 35 index overdoses (37.1%), although only 6 patients (17.1%) had an active BZD prescription at time of overdose. Seven patients (20.0%) were prescribed medication-assisted treatment (MAT) for opioid use disorder (OUD), with all 7 using methadone. According to VA records, only 1 patient had previously been dispensed a naloxone kit at any point prior to overdosing. Mental health and SUD diagnoses frequently co-occurred, with 20 patients (57.1%) having at least 1 mental health condition and at least 1 SUD.

Rates of follow-up varied by clinician type in the 6 months after NFO (Figure). Of those with mental health disorders, 15 patients (45.5%) received mental health services before and after overdose, while 8 (40.0%) and 10 (50.0%) of those with SUDs received addiction treatment services before and after overdose, respectively. Seven patients presented to the psychiatric emergency clinic within 6 months prior to overdose and 5 patients within the 6 months following overdose.

Of patients with VA opioid prescriptions, within 2 years of NFO, 9 (42.9%) had their opioids discontinued, and 18 (85.7%) had MEDD reductions ranging from 10 mg to 150 mg (12.5-71.4% reduction) with a mean of 63 mg. Two of the 4 patients with active BZD prescriptions at the time of the overdose event had their prescriptions continued. Seven patients (20.0%) were dispensed naloxone kits following overdose (Table 2).

Rates of ORAEs ranged from 0% to 17% with no documented overdose fatalities. Examples of AEs observed in this study included ED visits or hospitalizations involving opioid withdrawal, opioid-related personality changes, and opioid overdose. Five patients died during the study period, yielding an all-cause mortality rate of 14.3% with a mean time to death of 10.8 months. The causes of death were largely unknown except for 1 patient, whose death was reportedly investigated as an accidental medication overdose without additional information.

Repeat overdose verified by hospital records occurred in 4 patients (11.4%) within 2 years. Patients who experienced a subsequent overdose were prescribed higher doses of opioids with a mean MEDD among VA prescriptions of 130 mg vs 114 mg for those without repeat overdose. In this group, 3 patients (75.0%) also had concomitant BZD use, which was proportionally higher than the 10 patients (32.3%) without a subsequent overdose. Of note, 2 of the 4 patients with a repeat overdose had their opioid doses increased above the MEDD prescribed at the time of index overdose. None of the 4 subjects who experienced a repeat overdose were initiated on MAT within 2 years according to VA records.

Discussions

This retrospective study is representative of many veterans receiving VA care, despite the small sample size. Clinical characteristics observed in the study population were generally consistent with those published by Clement and Stock, including high rates of medical and psychiatric comorbidities.¹² Subjects in both studies were prescribed comparable dosages of opioids; among those prescribed opioids but not BZDs through the VA, the mean MEDD was 117 mg in our study compared with 126 mg in the Clement and Stock study. Since implementation of the Opioid Safety Initiative (OSI) in 2013, opioid prescribing practices have improved nationwide across VA facilities, including successful reduction in the numbers of patients prescribed high-dose opioids and concurrent BZDs.¹³

Despite the tools and resources available to clinicians, discontinuing opioid therapy remains a difficult process. Concerns related to mental health and/or substance-use related decompensations often exist in the setting of rapid dose reductions or abrupt discontinuation of opioids.⁶ Although less than half of patients in the present study with an active opioid prescription at time of index overdose had their opioids discontinued within 2 years, it is reassuring to note the much higher rate of those with subsequent decreases in their prescribed doses, as well as the 50% reduction in BZD coprescribing. Ultimately, these findings remain consistent with the VA goals of mitigating harm, improving opioid prescribing, and ensuring the safe use of opioid medications when clinically appropriate.

Moreover, recent evidence suggests that interventions focused solely on opioid prescribing practices are becoming increasingly limited in their impact on reducing opioid-related deaths and will likely be insufficient for addressing the opioid epidemic as it continues to evolve. According to Chen and colleagues, opioid overdose deaths are projected to increase over the next several years, while further reduction in the incidence of prescription opioid misuse is estimated to decrease overdose deaths by only 3% to 5.3%. In the context of recent surges in synthetic opioid use, it is projected that 80% of overdose deaths between 2016 and 2025 will be attributable to illicit opioids.³ Such predictions underscore the urgent need to adopt alternative approaches to risk-reducing measures and policy change.

The increased risk of mortality associated with opioid misuse and overdose is well established in the current literature. However, less is known regarding the rate of ORAEs after survival of an NFO. Olfson and colleagues sought to address this knowledge gap by characterizing mortality risks in 76,325 US adults within 1 year following NFO.¹⁴ Among their studied population, all-cause mortality occurred at a rate of 778.3 per 10,000 person-years, which was 24 times greater than that of the general population. This emphasizes the need for the optimization of mental health services, addiction treatment, and medical care for these individuals at higher risk.

 

Limitations

Certain factors and limitations should be considered when interpreting the results of this study. Given that the study included only veterans, factors such as the demographic and clinical characteristics more commonly observed among these patients should be taken into account and may in turn limit the generalizability of these findings to nonveteran populations. Another major limitation is the small sample size; the study period and by extension, the number of patients able to be included in the present study were restricted by the availability of retrievable data from automated drug dispensing systems. Patients without documented response to naloxone were excluded from the study due to low clinical suspicion for opioid overdose, although the possibility that the dose administered was too low to produce a robust clinical response cannot be definitively ruled out. The lack of reliable methods to capture events and overdoses treated outside of the VA may have resulted in underestimations of the true occurrence of ORAEs following NFO. Information regarding naloxone administration outside VA facilities, such as in transport to the hospital, self-reported, or bystander administration, was similarly limited by lack of reliable methods for retrieving such data and absence of documentation in VA records. Although all interventions and outcomes reported in the present study occurred within 2 years following NFO, further conclusions pertaining to the relative timing of specific interventions and ORAEs cannot be made. Lastly, this study did not investigate the direct impact of opioid risk mitigation initiatives implemented by the VA in the years coinciding with the study period.

Future Directions

Despite these limitations, an important strength of this study is its ability to identify potential areas for targeted improvement and to guide further efforts relating to the prevention of opioid overdose and opioid-related mortality among veterans. Identification of individuals at high risk for opioid overdose and misuse is an imperative first step that allows for the implementation of downstream risk-mitigating interventions. Within the VA, several tools have been developed in recent years to provide clinicians with additional resources and support in this regard.^6,15

No more than half of those diagnosed with mental health disorders and SUDs in the present study received outpatient follow-up care for these conditions within 6 months following NFO, which may suggest high rates of inadequate treatment. Given the strong association between mental health disorders, SUDs, and increased risk of overdose, increasing engagement with mental health and addiction treatment services may be paramount to preventing subsequent ORAEs, including repeat overdose.^6-9,11

Naloxone kit dispensing represents another area for targeted improvement. Interventions may include clinician education and systematic changes, such as implementing protocols that boost the likelihood of high-risk individuals being provided with naloxone at the earliest opportunity. Bystander-administered naloxone programs can also be considered for increasing naloxone access and reducing opioid-related mortality.¹⁶

Finally, despite evidence supporting the benefit of MAT in OUD treatment and reducing all-cause and opioid-related mortality after NFO, the low rates of MAT observed in this study are consistent with previous reports that these medications remain underutilized.¹⁷ Screening for OUD, in conjunction with increasing access to and utilization of OUD treatment modalities, is an established and integral component of overdose prevention efforts. For VA clinicians, the Psychotropic Drug Safety Initiative (PDSI) dashboard can be used to identify patients diagnosed with OUD who are not yet on MAT.¹⁸ Initiatives to expand MAT access through the ED have the potential to provide life-saving interventions and bridge care in the interim until patients are able to become established with a long-term health care practitioner.¹⁹

Conclusions

This is the first study to describe HSU, medication interventions, and ORAEs among veterans who survive NFO. Studies have shown that veterans with a history of NFO are at increased risk of subsequent AEs and premature death.^6,7,10,14 As such, NFOs represent crucial opportunities to identify high-risk individuals and ensure provision of adequate care. Recent data supports the development of a holistic, multimodal approach focused on adequate treatment of conditions that contribute to opioid-related risks, including mental health disorders, SUDs, pain diagnoses, and medical comorbidities.^3,14 Interventions designed to improve access, engagement, and retention in such care therefore play a pivotal role in overdose prevention and reducing mortality.

Although existing risk mitigation initiatives have improved opioid prescribing and safety within the VA, the findings of this study suggest that there remains room for improvement, and the need for well-coordinated efforts to reduce risks associated with both prescribed and illicit opioid use cannot be overstated. Rates of overdose deaths not only remain high but are projected to continue increasing in coming years, despite advances in clinical practice aimed at reducing harms associated with opioid use. The present findings aim to help identify processes with the potential to reduce rates of overdose, death, and adverse sequelae in high-risk populations. However, future studies are warranted to expand on these findings and contribute to ongoing efforts in reducing opioid-related harms and overdose deaths. This study may provide critical insight to inform further investigations to guide such interventions and highlight tools that health care facilities even outside the VA can consider implementing.

Acknowledgments

The authors would like to thank Jonathan Lacro, PharmD, BCPP, for his guidance with this important clinical topic and navigating IRB submissions.

Benzodiazepines (BZDs) are among the most commonly prescribed medications. A recent study found that in 2008, more than 5% of Americans used a BZD, and the percentage was almost 9% among Americans aged ≥ 65 years.^1,2 Among veterans, BZD use is even higher, in part because of the high prevalence of posttraumatic stress disorder (PTSD). One study found that more than 30% of veterans with PTSD received at least 1 BZD prescription.³ The risks associated with BZD treatment for PTSD are compounded by concurrent use of other sedatives and opioids prescribed for co-occurring chronic pain and insomnia.³

Older adults metabolize long-acting BZDs more slowly and generally have an increased sensitivity to the adverse effects (AEs) of all BZDs.⁴ In older adults, BZD use has been associated with cognitive decline, dementia, falls and consequent fractures, and adverse respiratory outcomes.^5-12 The risk of most but not all of these AEs was increased with higher BZD dose or long-term BZD use, which this quality improvement project (QIP) defines as having at least a 60-day supply of BZD prescriptions dispensed within the past year.

Long-term BZD use increases with age. One study found that, among patients receiving a BZD, the rate of long-term BZD use was more than double in older adults (31.4%) than it was in adults aged between 18 and 35 years (14.7%).² For these reasons, the 2012 Beers criteria of the American Geriatrics Society recommend avoiding all types of BZDs in the treatment of insomnia, agitation, or delirium in patients aged > 65 years.¹³ Despite this recommendation, the prevalence of BZD use in older adults remains high.¹⁴

Some innovative approaches have been developed to address the inappropriate use, including overuse and misuse, of BZDs in older adults.¹⁵ In one approach, direct-to-consumer (DTC) information is used to empower patients to collaborate with their physician to manage their health. Results from several studies suggest that providing older patients with information on BZD risks and benefits increases patient–physician interaction and thereby decreases inappropriate BZD use and improves health outcomes.^4,16,17 One study found that perceptions of BZD risks increased 1 week after exposure to a DTC educational brochure (EB), with intention to discuss BZD discontinuation with their physician higher for patients who received the EB than it was for those who did not (83.1% vs 44.3%; P < .0001).¹⁶ The EMPOWER (Eliminating Medications Through Patient Ownership of End Results) cluster randomized controlled trial assessed the effectiveness of a DTC EB focused on BZD risks in older adults.¹⁷ In that seminal study, patients who received a DTC EB were more likely than were comparison patients to discontinue BZD within 6 months (27% vs 5%; risk difference, 23%; 95% CI, 14%-32%).

The Veterans Integrated Systems Network (VISN) 22 Academic Detailing Program is a pharmacy educational outreach program that uses unbiased clinical guidelines to promote physicians’ safety initiatives and align prescribing behavior with best practices.^18-20 With BZD use among older veterans remaining high, the VISN 22 program initiated a clinical QIP modeled on the EMPOWER trial. Veterans in VISN 22 received the DTC EB, which included information on BZD risks and encouraged them to discuss their BZD treatment with their health care provider. VISN 22 was the first VISN in the VHA to implement the EMPOWER protocol.

As this was a QIP, all eligible veterans in VISN 22 were mailed the DTC EB, thus making it difficult to estimate the impact of the EB on BZD discontinuation in this VISN. Therefore, DTC EB efficacy was estimated by comparing BZD discontinuation between VISN 22 and VISN 21, an adjacent VISN that did not mail the DTC EB. To reduce selection bias associated with different controls in the 2 VISNs, the authors performed propensity score matching (PSM) to balance the covariates and provide an unbiased estimate of the mean treatment effect of the DTC EB in VISN 22 veterans who were included in the initial descriptive QIP and received the EB; these veterans were compared with VISN 21 veterans who did not receive the EB.

 

Methods

Two QIPs were undertaken to determine the impact of DTC EB on BZD use in older veterans in the VHA.

Quality Improvement Project 1

Design. A retrospective cohort analysis was performed. The VISN 22 catchment area, which encompasses VA facilities and clinics in southern California and southern Nevada, serves about 500,000 veterans, a substantial proportion of whom are aged ≥ 65 years. Among these older veterans are active long-term BZD users, who were defined as having ≥ 60-day supply of BZD prescriptions dispensed within the past year. Each active long-term user with a BZD prescription released within 200 days before the index date (the date the user was to meet with the prescribing physician) was mailed an EB 2 to 8 weeks in advance of the visit. Excluded from analysis were veterans with a schizophrenia, spinal cord injury, or seizure disorder diagnosis recorded in both their inpatient and outpatient medical records; veterans seen by Palliative Care within the past year; and veterans who died before analysis was completed.

Education Brochure. The EB for VISN 22 (Figure 1, see

Document

eappendix_1.pdf

Media Folder

FedPrac

)  was almost identical to the EB used in the EMPOWER trial.¹⁷ The language of the EMPOWER brochure was retained, but veteran-related images were added, and the BZD taper schedule was removed. Tannenbaum and colleagues incorporated constructivist learning into the Test Your Knowledge section of the EB.

Users interact with this section, acquire new knowledge, and reflect on what they already know. Also incorporated is cognitive dissonance, which motivates users to change by confronting inconsistencies in what they know about BZD safety and efficacy. The EB mailed to veterans included a peer champion’s story of successful discontinuation of BZDs. Reading this story is thought to lead to self-identification with the champion’s success, self-efficacy, and confidence in discontinuing BZDs.

Patients. The sample consisted of all veterans identified as meeting the inclusion criteria and being enrolled in VISN 22. The EB was mailed once to veterans on a rolling basis from December 2014 to February 2016. Change in BZD use was analyzed only after 9 to 24 months had passed since the index appointment with the prescribing physician. This period included 12 weeks for BZD taper and then 6 months after taper.

Analysis. For each veteran, monthly mean lorazepam equivalent (LE) was calculated using as many as 12 fills before the index date. Average daily dose of LE was calculated by dividing the sum of LE from all included prescriptions by total number of days between the first fill and the index date. The BZD prescription fills were evaluated after the index date. Veterans who received at least 1 prescription after the index date but then had no BZD prescription activity in VA clinics for 3 consecutive months during the 9-month observation period were recorded as having tapered and then discontinued BZD. Veterans who had no BZD prescription activity in VA clinics after the index date and during the 9-month observation period were recorded as having discontinued BZD without tapering. For veterans who had BZD prescription activity in VA clinics after the index date and during the 9-month observation period, mean LE was calculated by dividing the total LE for BZD prescriptions after the index date by number of days from the first fill after the index date to the date of analysis.

 

Quality Improvement Project 2

Design. A retrospective cohort analysis using PSM was performed on a subgroup of the QIP-1 sample to evaluate the impact of EB on BZD prescribing in the VA during 2 periods: 6 to 9 months and 6 to 12 months after the index date. A secondary outcome was discontinuation 1 to 12 months after the index date. Veterans in the analysis were active long-term BZD users, had at least 1 BZD prescription released within 200 days before the index date, were aged ≥ 65 years, and had an appointment scheduled with their BZD prescriber within 2 to 8 weeks (Figure 2). 

Excluded from analysis were veterans with a schizophrenia, spinal cord injury, or seizure disorder diagnosis recorded in both their inpatient and outpatient diagnosis medical records and veterans seen by palliative care within the past year. The authors performed an initial descriptive naïve analysis and then a naïve logistic regression analysis.

Patients. VISN 22 implemented QIP-2, a real-world application of a modified EMPOWER program, by identifying eligible veterans on a rolling basis from December 2014 to August 2015. All veterans who were identified and sent an EB during this period were included in the case group. The index date was defined as the first of the month the EB was mailed. Veterans with a pending appointment were chosen because the lead time would allow them to receive the EB and prepare to discuss it with the physician during the visit.

A comparator group was drawn from the adjacent VISN 21 catchment area, which encompasses VA facilities and clinics in Hawaii, northern California, and northern Nevada. During the observation period, VISN 21 did not mail any EBs specifically addressing BZD risks. Veterans in the comparator group had an appointment scheduled with their BZD prescribing physician within 4 weeks, were aged ≥ 65 years on the index date (first of the month before the next appointment, coinciding with the date EBs were sent to VISN 22 veterans), were active long-term BZD users, and had at least 1 BZD prescription released within 200 days before the index date. All patients were followed for up to 12 months after the index date, with BZD discontinuation recorded 9 and 12 months after the index date.

Propensity Score Matching

Propensity score (PS) was estimated with logistic regression analysis with treatment as the dependent variable and baseline characteristics as the independent variables.^21,22 One-to-one matching on the PS was performed using the nearest neighbor approach without replacements. Independent variables related to outcome but unrelated to EB exposure were selected for PS development.²² These variables included year of birth; male sex; Hispanic ethnicity; annual income; service connection status; region; body mass index; Charlson Comorbidity Index category; total baseline BZD dose; and diagnosis of AIDS, nonmetastatic cancer, metastatic cancer, chronic obstructive pulmonary disease (COPD), congestive heart failure (CHF), dementia, diabetes mellitus (DM), DM with complications, gastroesophageal reflux disease (GERD), general anxiety disorder (GAD), hemiparaplegia, liver disease (mild), liver disease (moderate to severe), myocardial infarction (MI), Parkinson disease, peptic ulcer disease (PUD), psychosis, renal disease, rheumatoid arthritis (RA), or substance use disorder (SUD).

 

The EMPOWER cluster randomized controlled trial (RCT) demonstrated the effectiveness of EB exposure in a Canadian population of elderly patients who were long-term BZD users.¹⁷ Randomized controlled trials are the gold standard for clinical trials because they can establish causal inference.^23-25 Given ethical and practical concerns, however, RCTs cannot be applied to all clinical scenarios. Although EMPOWER is reported to be an effective tool in reducing BZD use in older adults, its application in a real-world, large, integrated health care system remains untested. Observational studies are often conducted as an alternative to RCTs but are subject to selection bias because of their lack of randomization.²⁶ Therefore, robust research methods are needed to generate unbiased estimates of the impact of an intervention on an outcome. Propensity score matching simulates an RCT by balancing the covariates across treatment groups.^21,22,27 Observed patient characteristics are used to estimate PS, the probability that treatment will be received. Logistic or probit regression is used to balance the potential confounding covariates between the treatment groups.Once PSs are known, mean treatment effect can be estimated without the mean model.²⁸ In other words, PSM methods can be used to generate an unbiased estimate of the treatment.

Propensity Score Analysis

Baseline characteristics were compared using Student t test (continuous variables) and χ² test (discrete variables). Results are presented as means and standard deviations (continuous variables) and frequency and percentage (discrete variables).

The main outcome was BZD discontinuation 9 and 12 months after the index date. A postindex lag of 6 months was used to capture any tapering (Figure 2). Discontinuation, defined as 3 consecutive months of no BZD prescription on hand, was measured for 2 periods: 6 to 9 months and 6 to 12 months after the index date. A secondary outcome was discontinuation 1 to 12 months after the index date. An estimate was made of the difference in the proportions of BZD discontinuers who received the EB and BZD discontinuers who did not receive the EB, where mean treatment (risk difference) was presented as the absolute risk difference with a 95% CI. Standard errors and 95% CIs for the risk differences were generated with biased-corrected CIs from 1,000 bootstrap samples.

Sensitivity Analyses

Naïve multivariate logistic regression analysis was performed to evaluate the association between EB exposure and BZD discontinuation while controlling for potential confounders. Results are presented as odds ratios (ORs) and 95% CIs. Confounders identified were the same covariates used to generate the PSs.

Several analyses were performed to test the sensitivity of the methods applied using PSM by changing caliber size while maintaining the nearest neighbor approach without replacement. Linear regression analysis was performed with robust standard errors to estimate the risk difference of BZD discontinuation between EB-exposed and EB-unexposed veterans.

Statistical significance was set at P < .05. All statistical analyses were performed with Stata/SE Version 13 (College Station, TX).

Results

Quality Improvement Project 1

On a rolling basis from December 2014 to February 2016, the EB was mailed once to 3,896 VISN 22 veterans 2 to 8 weeks before a clinic appointment with their BZD prescribing physician. 

Of these veterans, 1,847 (47.4%) decreased their BZD dose; 458 (11.7%) tapered and then discontinued BZD (at least 1 prescription after index date, then no refill for at least 3 consecutive months); 455 (11.7%) immediately discontinued BZD (no refill for at least 3 consecutive months after index date); 607 (15.6%) increased their dose; and 529 (13.6%) did not change their dose. 

For the 1,847 veterans who decreased their dose, average daily dose (ADD) before index date was 3.17 mg LE, ADD reduction was 1.12 mg LE, and final ADD was 2.04 mg LE; of these veterans, 596 (32.3%) reduced their ADD more than 50% (ADD before index date, 2.68 mg LE; final ADD, 0.86 mg LE). The data are summarized in Table 1 and Figure 3.

 

Quality Improvement Project 2

Of all the VISN 22 and VISN 21 veterans, 24,420 met the inclusion and exclusion criteria. Of these 24,420 veterans, 2,020 (8.3%) were in VISN 22 and received the EB between December 2014 and August 2015 (QIP-1), and 22,400 (91.7%) were in VISN 21 and did not receive the EB.

Naïve Results Before PS Matching. In the naïve analyses, a larger proportion of EB-exposed vs unexposed veterans discontinued BZD; in addition, reductions were 6.6%, 7.4%, and 9.5% larger for 6 to 9 months, 6 to 12 months, and 1 to 12 months after the index date, respectively (P < .0001 for all comparisons; Table 2).

After controlling for potential confounders, the naïve logistic regression analyses found EB exposure was significantly associated with 44%, 32%, and 42% increases in the odds of BZD discontinuation for 6 to 9 months, 6 to 12 months, and 1 to 12 months after the index date, respectively (Table 3).

Propensity Score Matching. Before matching, there were significant differences in baseline characteristics of veterans who met the inclusion and exclusion criteria, with few exceptions (eAppendices 2 and 3, ).

   After PSM, mean bias was reduced from 6.5% to 1.8%. A total of 2,632 veterans (1,316 in each group) matched according to PSM criteria.

  After matching, there were no significant differences in baseline characteristics of EB-exposed and EB-unexposed veterans (eAppendix 4). 

Propensity Score Matching Results. Inspection of PSs revealed good coverage across treatment groups on a histogram plot and a kernel density plot (eAppendices 5 and 6).

  Table 4 lists the results of the PSM approaches. Risk differences in discontinuing BZD ranged from 6.6% to 6.9% for 6 to 9 months and from 6.5% to 7.1% for 6 to 12 months, in both cases benefiting EB-exposed veterans. 

Regarding the secondary outcome, a higher proportion of EB-exposed versus -unexposed veterans (7.35%-8.92%) discontinued BZD between 1 and 12 months. All risk differences in the sensitivity analyses were significant at α = 0.05 (2-tailed).

Discussion

This QIP was the first to evaluate the impact of an EMPOWER-modeled DTC EB in a large, integrated health care system in the U.S. It was also the first to demonstrate potential benefits of a DTC EB designed for older veterans who are long-term BZD users. In this QIP, which mailed the EB to 3,896 veterans, 1,847 (47.4%) decreased their BZD dose, 458 (11.7%) tapered and then discontinued BZD, and 455 (11.7%) immediately discontinued BZD. The total percentage of veterans who discontinued BZD (23.4%; 913/3,896) was similar to the 27% reported in the EMPOWER trial.¹⁷ However, the risk difference between the 1,316 EB-exposed VISN 22 veterans (QIP-1) and the 1,316 EB-unexposed VISN 21 veterans in this QIP was significantly lower than the 23% risk difference in EMPOWER (though it still demonstrated a significantly larger reduction for EB-exposed veterans).¹⁷

Given this inclusion of all qualifying veterans from the catchment area studied in this QIP, and given the ethical and practical concerns, an RCT was not possible. Therefore, PSM methods were used to balance the covariates across treatment groups and thereby simulate an RCT.^21,22,27 With use of the PSM approach, findings from the descriptive analysis were confirmed and potential selection bias reduced.

 

Study Limitations

The less robust risk difference found in this QIP has several possible explanations. The authors’ use of a DTC EB coincided with a national VA effort to reduce older veterans’ use of BZDs and other inappropriate medications. For instance, during the study period, academic detailing was being implemented to reduce use of BZDs, particularly in combination with opioids, across VHA facilities and clinics. (Academic detailing is a pharmacy educational outreach program that uses unbiased clinical guidelines to promote physicians’ safety initiatives and align prescribing behavior with best practices.^18-20) However, QIP-2 results and PS analysis of a subgroup of the original sample suggest that EB-exposed veterans were significantly more likely than were their unexposed counterparts were to discontinue BZD. To an extent, this analysis controlled for these other efforts to reduce BZD use in VHA clinics and can be considered a study strength.

Another limitation is the study design, which lacked a control group and did not consider the possibility that some facility or clinic physicians might influence others. Although the region variable was controlled for in PSM, the authors did not capture facility characteristics, including frequency of prescribing BZD and use of a protocol for enforcing the Beers criteria. Such confounders might have influenced outcomes. Unlike the EMPOWER trial,¹⁷ this QIP did not assess or exclude cognitively impaired veterans. It is reasonable to assume that these veterans might not understand some EB messages and consequently might fail to engage their physicians. Failure to initiate discussion with a physician would attenuate the impact of the EB.

Study Strengths

A strength of this QIP was its use of a DTC EB in a large, regional sample of older veterans in a real-world clinical setting. In addition, the study group (EB-exposed veterans) and the comparator group (EB-unexposed veterans) were from similar geographic areas (primarily California and Nevada).

Conclusion

Results of this study suggest that a DTC EB, designed to reduce BZD use among older veterans, was effective in helping patients lower their BZD dose and discontinue BZD. The likelihood of discontinuing BZD 9 and 12 months after the index date was significantly higher for veterans who received an EB modeled on the EMPOWER educational brochure than for a comparator group of veterans who did not receive the EB and were receiving care during the same observation period. In the future, it would be beneficial to use a design that controls for physician exposure to academic detailing focused on BZD reduction and that accounts for the cluster effects of facility practice. Despite these limitations, this QIP is the first real-world empirical example of using an EMPOWER-modeled DTC EB to decrease BZD use among older veterans. Furthermore, these results suggest that a DTC EB can be used to target other high-risk prescription drugs, such as opioids, particularly if alternative treatment options can be provided.

Acknowledgments
Dr. Hauser thanks Cathy, Anika, Katia, and Max Hauser, and Alba and Kevin Quinlan, for their support. In memory of Jirina Hauser, who died on Mother’s Day, May 14, 2017, at the age of 100.

Benzodiazepines (BZDs) are among the most commonly prescribed medications. A recent study found that in 2008, more than 5% of Americans used a BZD, and the percentage was almost 9% among Americans aged ≥ 65 years.^1,2 Among veterans, BZD use is even higher, in part because of the high prevalence of posttraumatic stress disorder (PTSD). One study found that more than 30% of veterans with PTSD received at least 1 BZD prescription.³ The risks associated with BZD treatment for PTSD are compounded by concurrent use of other sedatives and opioids prescribed for co-occurring chronic pain and insomnia.³

Older adults metabolize long-acting BZDs more slowly and generally have an increased sensitivity to the adverse effects (AEs) of all BZDs.⁴ In older adults, BZD use has been associated with cognitive decline, dementia, falls and consequent fractures, and adverse respiratory outcomes.^5-12 The risk of most but not all of these AEs was increased with higher BZD dose or long-term BZD use, which this quality improvement project (QIP) defines as having at least a 60-day supply of BZD prescriptions dispensed within the past year.

Long-term BZD use increases with age. One study found that, among patients receiving a BZD, the rate of long-term BZD use was more than double in older adults (31.4%) than it was in adults aged between 18 and 35 years (14.7%).² For these reasons, the 2012 Beers criteria of the American Geriatrics Society recommend avoiding all types of BZDs in the treatment of insomnia, agitation, or delirium in patients aged > 65 years.¹³ Despite this recommendation, the prevalence of BZD use in older adults remains high.¹⁴

Some innovative approaches have been developed to address the inappropriate use, including overuse and misuse, of BZDs in older adults.¹⁵ In one approach, direct-to-consumer (DTC) information is used to empower patients to collaborate with their physician to manage their health. Results from several studies suggest that providing older patients with information on BZD risks and benefits increases patient–physician interaction and thereby decreases inappropriate BZD use and improves health outcomes.^4,16,17 One study found that perceptions of BZD risks increased 1 week after exposure to a DTC educational brochure (EB), with intention to discuss BZD discontinuation with their physician higher for patients who received the EB than it was for those who did not (83.1% vs 44.3%; P < .0001).¹⁶ The EMPOWER (Eliminating Medications Through Patient Ownership of End Results) cluster randomized controlled trial assessed the effectiveness of a DTC EB focused on BZD risks in older adults.¹⁷ In that seminal study, patients who received a DTC EB were more likely than were comparison patients to discontinue BZD within 6 months (27% vs 5%; risk difference, 23%; 95% CI, 14%-32%).

The Veterans Integrated Systems Network (VISN) 22 Academic Detailing Program is a pharmacy educational outreach program that uses unbiased clinical guidelines to promote physicians’ safety initiatives and align prescribing behavior with best practices.^18-20 With BZD use among older veterans remaining high, the VISN 22 program initiated a clinical QIP modeled on the EMPOWER trial. Veterans in VISN 22 received the DTC EB, which included information on BZD risks and encouraged them to discuss their BZD treatment with their health care provider. VISN 22 was the first VISN in the VHA to implement the EMPOWER protocol.

As this was a QIP, all eligible veterans in VISN 22 were mailed the DTC EB, thus making it difficult to estimate the impact of the EB on BZD discontinuation in this VISN. Therefore, DTC EB efficacy was estimated by comparing BZD discontinuation between VISN 22 and VISN 21, an adjacent VISN that did not mail the DTC EB. To reduce selection bias associated with different controls in the 2 VISNs, the authors performed propensity score matching (PSM) to balance the covariates and provide an unbiased estimate of the mean treatment effect of the DTC EB in VISN 22 veterans who were included in the initial descriptive QIP and received the EB; these veterans were compared with VISN 21 veterans who did not receive the EB.

 

Methods

Two QIPs were undertaken to determine the impact of DTC EB on BZD use in older veterans in the VHA.

Quality Improvement Project 1

Design. A retrospective cohort analysis was performed. The VISN 22 catchment area, which encompasses VA facilities and clinics in southern California and southern Nevada, serves about 500,000 veterans, a substantial proportion of whom are aged ≥ 65 years. Among these older veterans are active long-term BZD users, who were defined as having ≥ 60-day supply of BZD prescriptions dispensed within the past year. Each active long-term user with a BZD prescription released within 200 days before the index date (the date the user was to meet with the prescribing physician) was mailed an EB 2 to 8 weeks in advance of the visit. Excluded from analysis were veterans with a schizophrenia, spinal cord injury, or seizure disorder diagnosis recorded in both their inpatient and outpatient medical records; veterans seen by Palliative Care within the past year; and veterans who died before analysis was completed.

Education Brochure. The EB for VISN 22 (Figure 1, see

Document

eappendix_1.pdf

Media Folder

FedPrac

)  was almost identical to the EB used in the EMPOWER trial.¹⁷ The language of the EMPOWER brochure was retained, but veteran-related images were added, and the BZD taper schedule was removed. Tannenbaum and colleagues incorporated constructivist learning into the Test Your Knowledge section of the EB.

Users interact with this section, acquire new knowledge, and reflect on what they already know. Also incorporated is cognitive dissonance, which motivates users to change by confronting inconsistencies in what they know about BZD safety and efficacy. The EB mailed to veterans included a peer champion’s story of successful discontinuation of BZDs. Reading this story is thought to lead to self-identification with the champion’s success, self-efficacy, and confidence in discontinuing BZDs.

Patients. The sample consisted of all veterans identified as meeting the inclusion criteria and being enrolled in VISN 22. The EB was mailed once to veterans on a rolling basis from December 2014 to February 2016. Change in BZD use was analyzed only after 9 to 24 months had passed since the index appointment with the prescribing physician. This period included 12 weeks for BZD taper and then 6 months after taper.

Analysis. For each veteran, monthly mean lorazepam equivalent (LE) was calculated using as many as 12 fills before the index date. Average daily dose of LE was calculated by dividing the sum of LE from all included prescriptions by total number of days between the first fill and the index date. The BZD prescription fills were evaluated after the index date. Veterans who received at least 1 prescription after the index date but then had no BZD prescription activity in VA clinics for 3 consecutive months during the 9-month observation period were recorded as having tapered and then discontinued BZD. Veterans who had no BZD prescription activity in VA clinics after the index date and during the 9-month observation period were recorded as having discontinued BZD without tapering. For veterans who had BZD prescription activity in VA clinics after the index date and during the 9-month observation period, mean LE was calculated by dividing the total LE for BZD prescriptions after the index date by number of days from the first fill after the index date to the date of analysis.

 

Quality Improvement Project 2

Design. A retrospective cohort analysis using PSM was performed on a subgroup of the QIP-1 sample to evaluate the impact of EB on BZD prescribing in the VA during 2 periods: 6 to 9 months and 6 to 12 months after the index date. A secondary outcome was discontinuation 1 to 12 months after the index date. Veterans in the analysis were active long-term BZD users, had at least 1 BZD prescription released within 200 days before the index date, were aged ≥ 65 years, and had an appointment scheduled with their BZD prescriber within 2 to 8 weeks (Figure 2). 

Excluded from analysis were veterans with a schizophrenia, spinal cord injury, or seizure disorder diagnosis recorded in both their inpatient and outpatient diagnosis medical records and veterans seen by palliative care within the past year. The authors performed an initial descriptive naïve analysis and then a naïve logistic regression analysis.

Patients. VISN 22 implemented QIP-2, a real-world application of a modified EMPOWER program, by identifying eligible veterans on a rolling basis from December 2014 to August 2015. All veterans who were identified and sent an EB during this period were included in the case group. The index date was defined as the first of the month the EB was mailed. Veterans with a pending appointment were chosen because the lead time would allow them to receive the EB and prepare to discuss it with the physician during the visit.

A comparator group was drawn from the adjacent VISN 21 catchment area, which encompasses VA facilities and clinics in Hawaii, northern California, and northern Nevada. During the observation period, VISN 21 did not mail any EBs specifically addressing BZD risks. Veterans in the comparator group had an appointment scheduled with their BZD prescribing physician within 4 weeks, were aged ≥ 65 years on the index date (first of the month before the next appointment, coinciding with the date EBs were sent to VISN 22 veterans), were active long-term BZD users, and had at least 1 BZD prescription released within 200 days before the index date. All patients were followed for up to 12 months after the index date, with BZD discontinuation recorded 9 and 12 months after the index date.

Propensity Score Matching

Propensity score (PS) was estimated with logistic regression analysis with treatment as the dependent variable and baseline characteristics as the independent variables.^21,22 One-to-one matching on the PS was performed using the nearest neighbor approach without replacements. Independent variables related to outcome but unrelated to EB exposure were selected for PS development.²² These variables included year of birth; male sex; Hispanic ethnicity; annual income; service connection status; region; body mass index; Charlson Comorbidity Index category; total baseline BZD dose; and diagnosis of AIDS, nonmetastatic cancer, metastatic cancer, chronic obstructive pulmonary disease (COPD), congestive heart failure (CHF), dementia, diabetes mellitus (DM), DM with complications, gastroesophageal reflux disease (GERD), general anxiety disorder (GAD), hemiparaplegia, liver disease (mild), liver disease (moderate to severe), myocardial infarction (MI), Parkinson disease, peptic ulcer disease (PUD), psychosis, renal disease, rheumatoid arthritis (RA), or substance use disorder (SUD).

 

The EMPOWER cluster randomized controlled trial (RCT) demonstrated the effectiveness of EB exposure in a Canadian population of elderly patients who were long-term BZD users.¹⁷ Randomized controlled trials are the gold standard for clinical trials because they can establish causal inference.^23-25 Given ethical and practical concerns, however, RCTs cannot be applied to all clinical scenarios. Although EMPOWER is reported to be an effective tool in reducing BZD use in older adults, its application in a real-world, large, integrated health care system remains untested. Observational studies are often conducted as an alternative to RCTs but are subject to selection bias because of their lack of randomization.²⁶ Therefore, robust research methods are needed to generate unbiased estimates of the impact of an intervention on an outcome. Propensity score matching simulates an RCT by balancing the covariates across treatment groups.^21,22,27 Observed patient characteristics are used to estimate PS, the probability that treatment will be received. Logistic or probit regression is used to balance the potential confounding covariates between the treatment groups.Once PSs are known, mean treatment effect can be estimated without the mean model.²⁸ In other words, PSM methods can be used to generate an unbiased estimate of the treatment.

Propensity Score Analysis

Baseline characteristics were compared using Student t test (continuous variables) and χ² test (discrete variables). Results are presented as means and standard deviations (continuous variables) and frequency and percentage (discrete variables).

The main outcome was BZD discontinuation 9 and 12 months after the index date. A postindex lag of 6 months was used to capture any tapering (Figure 2). Discontinuation, defined as 3 consecutive months of no BZD prescription on hand, was measured for 2 periods: 6 to 9 months and 6 to 12 months after the index date. A secondary outcome was discontinuation 1 to 12 months after the index date. An estimate was made of the difference in the proportions of BZD discontinuers who received the EB and BZD discontinuers who did not receive the EB, where mean treatment (risk difference) was presented as the absolute risk difference with a 95% CI. Standard errors and 95% CIs for the risk differences were generated with biased-corrected CIs from 1,000 bootstrap samples.

Sensitivity Analyses

Naïve multivariate logistic regression analysis was performed to evaluate the association between EB exposure and BZD discontinuation while controlling for potential confounders. Results are presented as odds ratios (ORs) and 95% CIs. Confounders identified were the same covariates used to generate the PSs.

Several analyses were performed to test the sensitivity of the methods applied using PSM by changing caliber size while maintaining the nearest neighbor approach without replacement. Linear regression analysis was performed with robust standard errors to estimate the risk difference of BZD discontinuation between EB-exposed and EB-unexposed veterans.

Statistical significance was set at P < .05. All statistical analyses were performed with Stata/SE Version 13 (College Station, TX).

Results

Quality Improvement Project 1

On a rolling basis from December 2014 to February 2016, the EB was mailed once to 3,896 VISN 22 veterans 2 to 8 weeks before a clinic appointment with their BZD prescribing physician. 

Of these veterans, 1,847 (47.4%) decreased their BZD dose; 458 (11.7%) tapered and then discontinued BZD (at least 1 prescription after index date, then no refill for at least 3 consecutive months); 455 (11.7%) immediately discontinued BZD (no refill for at least 3 consecutive months after index date); 607 (15.6%) increased their dose; and 529 (13.6%) did not change their dose. 

For the 1,847 veterans who decreased their dose, average daily dose (ADD) before index date was 3.17 mg LE, ADD reduction was 1.12 mg LE, and final ADD was 2.04 mg LE; of these veterans, 596 (32.3%) reduced their ADD more than 50% (ADD before index date, 2.68 mg LE; final ADD, 0.86 mg LE). The data are summarized in Table 1 and Figure 3.

 

Quality Improvement Project 2

Of all the VISN 22 and VISN 21 veterans, 24,420 met the inclusion and exclusion criteria. Of these 24,420 veterans, 2,020 (8.3%) were in VISN 22 and received the EB between December 2014 and August 2015 (QIP-1), and 22,400 (91.7%) were in VISN 21 and did not receive the EB.

Naïve Results Before PS Matching. In the naïve analyses, a larger proportion of EB-exposed vs unexposed veterans discontinued BZD; in addition, reductions were 6.6%, 7.4%, and 9.5% larger for 6 to 9 months, 6 to 12 months, and 1 to 12 months after the index date, respectively (P < .0001 for all comparisons; Table 2).

After controlling for potential confounders, the naïve logistic regression analyses found EB exposure was significantly associated with 44%, 32%, and 42% increases in the odds of BZD discontinuation for 6 to 9 months, 6 to 12 months, and 1 to 12 months after the index date, respectively (Table 3).

Propensity Score Matching. Before matching, there were significant differences in baseline characteristics of veterans who met the inclusion and exclusion criteria, with few exceptions (eAppendices 2 and 3, ).

   After PSM, mean bias was reduced from 6.5% to 1.8%. A total of 2,632 veterans (1,316 in each group) matched according to PSM criteria.

  After matching, there were no significant differences in baseline characteristics of EB-exposed and EB-unexposed veterans (eAppendix 4). 

Propensity Score Matching Results. Inspection of PSs revealed good coverage across treatment groups on a histogram plot and a kernel density plot (eAppendices 5 and 6).

  Table 4 lists the results of the PSM approaches. Risk differences in discontinuing BZD ranged from 6.6% to 6.9% for 6 to 9 months and from 6.5% to 7.1% for 6 to 12 months, in both cases benefiting EB-exposed veterans. 

Regarding the secondary outcome, a higher proportion of EB-exposed versus -unexposed veterans (7.35%-8.92%) discontinued BZD between 1 and 12 months. All risk differences in the sensitivity analyses were significant at α = 0.05 (2-tailed).

Discussion

This QIP was the first to evaluate the impact of an EMPOWER-modeled DTC EB in a large, integrated health care system in the U.S. It was also the first to demonstrate potential benefits of a DTC EB designed for older veterans who are long-term BZD users. In this QIP, which mailed the EB to 3,896 veterans, 1,847 (47.4%) decreased their BZD dose, 458 (11.7%) tapered and then discontinued BZD, and 455 (11.7%) immediately discontinued BZD. The total percentage of veterans who discontinued BZD (23.4%; 913/3,896) was similar to the 27% reported in the EMPOWER trial.¹⁷ However, the risk difference between the 1,316 EB-exposed VISN 22 veterans (QIP-1) and the 1,316 EB-unexposed VISN 21 veterans in this QIP was significantly lower than the 23% risk difference in EMPOWER (though it still demonstrated a significantly larger reduction for EB-exposed veterans).¹⁷

Given this inclusion of all qualifying veterans from the catchment area studied in this QIP, and given the ethical and practical concerns, an RCT was not possible. Therefore, PSM methods were used to balance the covariates across treatment groups and thereby simulate an RCT.^21,22,27 With use of the PSM approach, findings from the descriptive analysis were confirmed and potential selection bias reduced.

 

Study Limitations

The less robust risk difference found in this QIP has several possible explanations. The authors’ use of a DTC EB coincided with a national VA effort to reduce older veterans’ use of BZDs and other inappropriate medications. For instance, during the study period, academic detailing was being implemented to reduce use of BZDs, particularly in combination with opioids, across VHA facilities and clinics. (Academic detailing is a pharmacy educational outreach program that uses unbiased clinical guidelines to promote physicians’ safety initiatives and align prescribing behavior with best practices.^18-20) However, QIP-2 results and PS analysis of a subgroup of the original sample suggest that EB-exposed veterans were significantly more likely than were their unexposed counterparts were to discontinue BZD. To an extent, this analysis controlled for these other efforts to reduce BZD use in VHA clinics and can be considered a study strength.

Another limitation is the study design, which lacked a control group and did not consider the possibility that some facility or clinic physicians might influence others. Although the region variable was controlled for in PSM, the authors did not capture facility characteristics, including frequency of prescribing BZD and use of a protocol for enforcing the Beers criteria. Such confounders might have influenced outcomes. Unlike the EMPOWER trial,¹⁷ this QIP did not assess or exclude cognitively impaired veterans. It is reasonable to assume that these veterans might not understand some EB messages and consequently might fail to engage their physicians. Failure to initiate discussion with a physician would attenuate the impact of the EB.

Study Strengths

A strength of this QIP was its use of a DTC EB in a large, regional sample of older veterans in a real-world clinical setting. In addition, the study group (EB-exposed veterans) and the comparator group (EB-unexposed veterans) were from similar geographic areas (primarily California and Nevada).

Conclusion

Results of this study suggest that a DTC EB, designed to reduce BZD use among older veterans, was effective in helping patients lower their BZD dose and discontinue BZD. The likelihood of discontinuing BZD 9 and 12 months after the index date was significantly higher for veterans who received an EB modeled on the EMPOWER educational brochure than for a comparator group of veterans who did not receive the EB and were receiving care during the same observation period. In the future, it would be beneficial to use a design that controls for physician exposure to academic detailing focused on BZD reduction and that accounts for the cluster effects of facility practice. Despite these limitations, this QIP is the first real-world empirical example of using an EMPOWER-modeled DTC EB to decrease BZD use among older veterans. Furthermore, these results suggest that a DTC EB can be used to target other high-risk prescription drugs, such as opioids, particularly if alternative treatment options can be provided.

Acknowledgments
Dr. Hauser thanks Cathy, Anika, Katia, and Max Hauser, and Alba and Kevin Quinlan, for their support. In memory of Jirina Hauser, who died on Mother’s Day, May 14, 2017, at the age of 100.

Benzodiazepines (BZDs) are among the most commonly prescribed medications. A recent study found that in 2008, more than 5% of Americans used a BZD, and the percentage was almost 9% among Americans aged ≥ 65 years.^1,2 Among veterans, BZD use is even higher, in part because of the high prevalence of posttraumatic stress disorder (PTSD). One study found that more than 30% of veterans with PTSD received at least 1 BZD prescription.³ The risks associated with BZD treatment for PTSD are compounded by concurrent use of other sedatives and opioids prescribed for co-occurring chronic pain and insomnia.³

Older adults metabolize long-acting BZDs more slowly and generally have an increased sensitivity to the adverse effects (AEs) of all BZDs.⁴ In older adults, BZD use has been associated with cognitive decline, dementia, falls and consequent fractures, and adverse respiratory outcomes.^5-12 The risk of most but not all of these AEs was increased with higher BZD dose or long-term BZD use, which this quality improvement project (QIP) defines as having at least a 60-day supply of BZD prescriptions dispensed within the past year.

Long-term BZD use increases with age. One study found that, among patients receiving a BZD, the rate of long-term BZD use was more than double in older adults (31.4%) than it was in adults aged between 18 and 35 years (14.7%).² For these reasons, the 2012 Beers criteria of the American Geriatrics Society recommend avoiding all types of BZDs in the treatment of insomnia, agitation, or delirium in patients aged > 65 years.¹³ Despite this recommendation, the prevalence of BZD use in older adults remains high.¹⁴

Some innovative approaches have been developed to address the inappropriate use, including overuse and misuse, of BZDs in older adults.¹⁵ In one approach, direct-to-consumer (DTC) information is used to empower patients to collaborate with their physician to manage their health. Results from several studies suggest that providing older patients with information on BZD risks and benefits increases patient–physician interaction and thereby decreases inappropriate BZD use and improves health outcomes.^4,16,17 One study found that perceptions of BZD risks increased 1 week after exposure to a DTC educational brochure (EB), with intention to discuss BZD discontinuation with their physician higher for patients who received the EB than it was for those who did not (83.1% vs 44.3%; P < .0001).¹⁶ The EMPOWER (Eliminating Medications Through Patient Ownership of End Results) cluster randomized controlled trial assessed the effectiveness of a DTC EB focused on BZD risks in older adults.¹⁷ In that seminal study, patients who received a DTC EB were more likely than were comparison patients to discontinue BZD within 6 months (27% vs 5%; risk difference, 23%; 95% CI, 14%-32%).

The Veterans Integrated Systems Network (VISN) 22 Academic Detailing Program is a pharmacy educational outreach program that uses unbiased clinical guidelines to promote physicians’ safety initiatives and align prescribing behavior with best practices.^18-20 With BZD use among older veterans remaining high, the VISN 22 program initiated a clinical QIP modeled on the EMPOWER trial. Veterans in VISN 22 received the DTC EB, which included information on BZD risks and encouraged them to discuss their BZD treatment with their health care provider. VISN 22 was the first VISN in the VHA to implement the EMPOWER protocol.

As this was a QIP, all eligible veterans in VISN 22 were mailed the DTC EB, thus making it difficult to estimate the impact of the EB on BZD discontinuation in this VISN. Therefore, DTC EB efficacy was estimated by comparing BZD discontinuation between VISN 22 and VISN 21, an adjacent VISN that did not mail the DTC EB. To reduce selection bias associated with different controls in the 2 VISNs, the authors performed propensity score matching (PSM) to balance the covariates and provide an unbiased estimate of the mean treatment effect of the DTC EB in VISN 22 veterans who were included in the initial descriptive QIP and received the EB; these veterans were compared with VISN 21 veterans who did not receive the EB.

 

Methods

Two QIPs were undertaken to determine the impact of DTC EB on BZD use in older veterans in the VHA.

Quality Improvement Project 1

Design. A retrospective cohort analysis was performed. The VISN 22 catchment area, which encompasses VA facilities and clinics in southern California and southern Nevada, serves about 500,000 veterans, a substantial proportion of whom are aged ≥ 65 years. Among these older veterans are active long-term BZD users, who were defined as having ≥ 60-day supply of BZD prescriptions dispensed within the past year. Each active long-term user with a BZD prescription released within 200 days before the index date (the date the user was to meet with the prescribing physician) was mailed an EB 2 to 8 weeks in advance of the visit. Excluded from analysis were veterans with a schizophrenia, spinal cord injury, or seizure disorder diagnosis recorded in both their inpatient and outpatient medical records; veterans seen by Palliative Care within the past year; and veterans who died before analysis was completed.

Education Brochure. The EB for VISN 22 (Figure 1, see

Document

eappendix_1.pdf

Media Folder

FedPrac

)  was almost identical to the EB used in the EMPOWER trial.¹⁷ The language of the EMPOWER brochure was retained, but veteran-related images were added, and the BZD taper schedule was removed. Tannenbaum and colleagues incorporated constructivist learning into the Test Your Knowledge section of the EB.

Users interact with this section, acquire new knowledge, and reflect on what they already know. Also incorporated is cognitive dissonance, which motivates users to change by confronting inconsistencies in what they know about BZD safety and efficacy. The EB mailed to veterans included a peer champion’s story of successful discontinuation of BZDs. Reading this story is thought to lead to self-identification with the champion’s success, self-efficacy, and confidence in discontinuing BZDs.

Patients. The sample consisted of all veterans identified as meeting the inclusion criteria and being enrolled in VISN 22. The EB was mailed once to veterans on a rolling basis from December 2014 to February 2016. Change in BZD use was analyzed only after 9 to 24 months had passed since the index appointment with the prescribing physician. This period included 12 weeks for BZD taper and then 6 months after taper.

Analysis. For each veteran, monthly mean lorazepam equivalent (LE) was calculated using as many as 12 fills before the index date. Average daily dose of LE was calculated by dividing the sum of LE from all included prescriptions by total number of days between the first fill and the index date. The BZD prescription fills were evaluated after the index date. Veterans who received at least 1 prescription after the index date but then had no BZD prescription activity in VA clinics for 3 consecutive months during the 9-month observation period were recorded as having tapered and then discontinued BZD. Veterans who had no BZD prescription activity in VA clinics after the index date and during the 9-month observation period were recorded as having discontinued BZD without tapering. For veterans who had BZD prescription activity in VA clinics after the index date and during the 9-month observation period, mean LE was calculated by dividing the total LE for BZD prescriptions after the index date by number of days from the first fill after the index date to the date of analysis.

 

Quality Improvement Project 2

Design. A retrospective cohort analysis using PSM was performed on a subgroup of the QIP-1 sample to evaluate the impact of EB on BZD prescribing in the VA during 2 periods: 6 to 9 months and 6 to 12 months after the index date. A secondary outcome was discontinuation 1 to 12 months after the index date. Veterans in the analysis were active long-term BZD users, had at least 1 BZD prescription released within 200 days before the index date, were aged ≥ 65 years, and had an appointment scheduled with their BZD prescriber within 2 to 8 weeks (Figure 2). 

Excluded from analysis were veterans with a schizophrenia, spinal cord injury, or seizure disorder diagnosis recorded in both their inpatient and outpatient diagnosis medical records and veterans seen by palliative care within the past year. The authors performed an initial descriptive naïve analysis and then a naïve logistic regression analysis.

Patients. VISN 22 implemented QIP-2, a real-world application of a modified EMPOWER program, by identifying eligible veterans on a rolling basis from December 2014 to August 2015. All veterans who were identified and sent an EB during this period were included in the case group. The index date was defined as the first of the month the EB was mailed. Veterans with a pending appointment were chosen because the lead time would allow them to receive the EB and prepare to discuss it with the physician during the visit.

A comparator group was drawn from the adjacent VISN 21 catchment area, which encompasses VA facilities and clinics in Hawaii, northern California, and northern Nevada. During the observation period, VISN 21 did not mail any EBs specifically addressing BZD risks. Veterans in the comparator group had an appointment scheduled with their BZD prescribing physician within 4 weeks, were aged ≥ 65 years on the index date (first of the month before the next appointment, coinciding with the date EBs were sent to VISN 22 veterans), were active long-term BZD users, and had at least 1 BZD prescription released within 200 days before the index date. All patients were followed for up to 12 months after the index date, with BZD discontinuation recorded 9 and 12 months after the index date.

Propensity Score Matching

Propensity score (PS) was estimated with logistic regression analysis with treatment as the dependent variable and baseline characteristics as the independent variables.^21,22 One-to-one matching on the PS was performed using the nearest neighbor approach without replacements. Independent variables related to outcome but unrelated to EB exposure were selected for PS development.²² These variables included year of birth; male sex; Hispanic ethnicity; annual income; service connection status; region; body mass index; Charlson Comorbidity Index category; total baseline BZD dose; and diagnosis of AIDS, nonmetastatic cancer, metastatic cancer, chronic obstructive pulmonary disease (COPD), congestive heart failure (CHF), dementia, diabetes mellitus (DM), DM with complications, gastroesophageal reflux disease (GERD), general anxiety disorder (GAD), hemiparaplegia, liver disease (mild), liver disease (moderate to severe), myocardial infarction (MI), Parkinson disease, peptic ulcer disease (PUD), psychosis, renal disease, rheumatoid arthritis (RA), or substance use disorder (SUD).

 

The EMPOWER cluster randomized controlled trial (RCT) demonstrated the effectiveness of EB exposure in a Canadian population of elderly patients who were long-term BZD users.¹⁷ Randomized controlled trials are the gold standard for clinical trials because they can establish causal inference.^23-25 Given ethical and practical concerns, however, RCTs cannot be applied to all clinical scenarios. Although EMPOWER is reported to be an effective tool in reducing BZD use in older adults, its application in a real-world, large, integrated health care system remains untested. Observational studies are often conducted as an alternative to RCTs but are subject to selection bias because of their lack of randomization.²⁶ Therefore, robust research methods are needed to generate unbiased estimates of the impact of an intervention on an outcome. Propensity score matching simulates an RCT by balancing the covariates across treatment groups.^21,22,27 Observed patient characteristics are used to estimate PS, the probability that treatment will be received. Logistic or probit regression is used to balance the potential confounding covariates between the treatment groups.Once PSs are known, mean treatment effect can be estimated without the mean model.²⁸ In other words, PSM methods can be used to generate an unbiased estimate of the treatment.

Propensity Score Analysis

Baseline characteristics were compared using Student t test (continuous variables) and χ² test (discrete variables). Results are presented as means and standard deviations (continuous variables) and frequency and percentage (discrete variables).

The main outcome was BZD discontinuation 9 and 12 months after the index date. A postindex lag of 6 months was used to capture any tapering (Figure 2). Discontinuation, defined as 3 consecutive months of no BZD prescription on hand, was measured for 2 periods: 6 to 9 months and 6 to 12 months after the index date. A secondary outcome was discontinuation 1 to 12 months after the index date. An estimate was made of the difference in the proportions of BZD discontinuers who received the EB and BZD discontinuers who did not receive the EB, where mean treatment (risk difference) was presented as the absolute risk difference with a 95% CI. Standard errors and 95% CIs for the risk differences were generated with biased-corrected CIs from 1,000 bootstrap samples.

Sensitivity Analyses

Naïve multivariate logistic regression analysis was performed to evaluate the association between EB exposure and BZD discontinuation while controlling for potential confounders. Results are presented as odds ratios (ORs) and 95% CIs. Confounders identified were the same covariates used to generate the PSs.

Several analyses were performed to test the sensitivity of the methods applied using PSM by changing caliber size while maintaining the nearest neighbor approach without replacement. Linear regression analysis was performed with robust standard errors to estimate the risk difference of BZD discontinuation between EB-exposed and EB-unexposed veterans.

Statistical significance was set at P < .05. All statistical analyses were performed with Stata/SE Version 13 (College Station, TX).

Results

Quality Improvement Project 1

On a rolling basis from December 2014 to February 2016, the EB was mailed once to 3,896 VISN 22 veterans 2 to 8 weeks before a clinic appointment with their BZD prescribing physician. 

Of these veterans, 1,847 (47.4%) decreased their BZD dose; 458 (11.7%) tapered and then discontinued BZD (at least 1 prescription after index date, then no refill for at least 3 consecutive months); 455 (11.7%) immediately discontinued BZD (no refill for at least 3 consecutive months after index date); 607 (15.6%) increased their dose; and 529 (13.6%) did not change their dose. 

For the 1,847 veterans who decreased their dose, average daily dose (ADD) before index date was 3.17 mg LE, ADD reduction was 1.12 mg LE, and final ADD was 2.04 mg LE; of these veterans, 596 (32.3%) reduced their ADD more than 50% (ADD before index date, 2.68 mg LE; final ADD, 0.86 mg LE). The data are summarized in Table 1 and Figure 3.

 

Quality Improvement Project 2

Of all the VISN 22 and VISN 21 veterans, 24,420 met the inclusion and exclusion criteria. Of these 24,420 veterans, 2,020 (8.3%) were in VISN 22 and received the EB between December 2014 and August 2015 (QIP-1), and 22,400 (91.7%) were in VISN 21 and did not receive the EB.

Naïve Results Before PS Matching. In the naïve analyses, a larger proportion of EB-exposed vs unexposed veterans discontinued BZD; in addition, reductions were 6.6%, 7.4%, and 9.5% larger for 6 to 9 months, 6 to 12 months, and 1 to 12 months after the index date, respectively (P < .0001 for all comparisons; Table 2).

After controlling for potential confounders, the naïve logistic regression analyses found EB exposure was significantly associated with 44%, 32%, and 42% increases in the odds of BZD discontinuation for 6 to 9 months, 6 to 12 months, and 1 to 12 months after the index date, respectively (Table 3).

Propensity Score Matching. Before matching, there were significant differences in baseline characteristics of veterans who met the inclusion and exclusion criteria, with few exceptions (eAppendices 2 and 3, ).

   After PSM, mean bias was reduced from 6.5% to 1.8%. A total of 2,632 veterans (1,316 in each group) matched according to PSM criteria.

  After matching, there were no significant differences in baseline characteristics of EB-exposed and EB-unexposed veterans (eAppendix 4). 

Propensity Score Matching Results. Inspection of PSs revealed good coverage across treatment groups on a histogram plot and a kernel density plot (eAppendices 5 and 6).

  Table 4 lists the results of the PSM approaches. Risk differences in discontinuing BZD ranged from 6.6% to 6.9% for 6 to 9 months and from 6.5% to 7.1% for 6 to 12 months, in both cases benefiting EB-exposed veterans. 

Regarding the secondary outcome, a higher proportion of EB-exposed versus -unexposed veterans (7.35%-8.92%) discontinued BZD between 1 and 12 months. All risk differences in the sensitivity analyses were significant at α = 0.05 (2-tailed).

Discussion

This QIP was the first to evaluate the impact of an EMPOWER-modeled DTC EB in a large, integrated health care system in the U.S. It was also the first to demonstrate potential benefits of a DTC EB designed for older veterans who are long-term BZD users. In this QIP, which mailed the EB to 3,896 veterans, 1,847 (47.4%) decreased their BZD dose, 458 (11.7%) tapered and then discontinued BZD, and 455 (11.7%) immediately discontinued BZD. The total percentage of veterans who discontinued BZD (23.4%; 913/3,896) was similar to the 27% reported in the EMPOWER trial.¹⁷ However, the risk difference between the 1,316 EB-exposed VISN 22 veterans (QIP-1) and the 1,316 EB-unexposed VISN 21 veterans in this QIP was significantly lower than the 23% risk difference in EMPOWER (though it still demonstrated a significantly larger reduction for EB-exposed veterans).¹⁷

Given this inclusion of all qualifying veterans from the catchment area studied in this QIP, and given the ethical and practical concerns, an RCT was not possible. Therefore, PSM methods were used to balance the covariates across treatment groups and thereby simulate an RCT.^21,22,27 With use of the PSM approach, findings from the descriptive analysis were confirmed and potential selection bias reduced.

 

Study Limitations

The less robust risk difference found in this QIP has several possible explanations. The authors’ use of a DTC EB coincided with a national VA effort to reduce older veterans’ use of BZDs and other inappropriate medications. For instance, during the study period, academic detailing was being implemented to reduce use of BZDs, particularly in combination with opioids, across VHA facilities and clinics. (Academic detailing is a pharmacy educational outreach program that uses unbiased clinical guidelines to promote physicians’ safety initiatives and align prescribing behavior with best practices.^18-20) However, QIP-2 results and PS analysis of a subgroup of the original sample suggest that EB-exposed veterans were significantly more likely than were their unexposed counterparts were to discontinue BZD. To an extent, this analysis controlled for these other efforts to reduce BZD use in VHA clinics and can be considered a study strength.

Another limitation is the study design, which lacked a control group and did not consider the possibility that some facility or clinic physicians might influence others. Although the region variable was controlled for in PSM, the authors did not capture facility characteristics, including frequency of prescribing BZD and use of a protocol for enforcing the Beers criteria. Such confounders might have influenced outcomes. Unlike the EMPOWER trial,¹⁷ this QIP did not assess or exclude cognitively impaired veterans. It is reasonable to assume that these veterans might not understand some EB messages and consequently might fail to engage their physicians. Failure to initiate discussion with a physician would attenuate the impact of the EB.

Study Strengths

A strength of this QIP was its use of a DTC EB in a large, regional sample of older veterans in a real-world clinical setting. In addition, the study group (EB-exposed veterans) and the comparator group (EB-unexposed veterans) were from similar geographic areas (primarily California and Nevada).

Conclusion

Results of this study suggest that a DTC EB, designed to reduce BZD use among older veterans, was effective in helping patients lower their BZD dose and discontinue BZD. The likelihood of discontinuing BZD 9 and 12 months after the index date was significantly higher for veterans who received an EB modeled on the EMPOWER educational brochure than for a comparator group of veterans who did not receive the EB and were receiving care during the same observation period. In the future, it would be beneficial to use a design that controls for physician exposure to academic detailing focused on BZD reduction and that accounts for the cluster effects of facility practice. Despite these limitations, this QIP is the first real-world empirical example of using an EMPOWER-modeled DTC EB to decrease BZD use among older veterans. Furthermore, these results suggest that a DTC EB can be used to target other high-risk prescription drugs, such as opioids, particularly if alternative treatment options can be provided.

Acknowledgments
Dr. Hauser thanks Cathy, Anika, Katia, and Max Hauser, and Alba and Kevin Quinlan, for their support. In memory of Jirina Hauser, who died on Mother’s Day, May 14, 2017, at the age of 100.