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Characteristics and Outcomes of Health Care Professionals with Substance Use Disorders

Info: 11624 words (46 pages) Dissertation
Published: 10th Dec 2019

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Tags: Health and Social Care

Characteristics and Outcomes of Health Care Professionals with Substance Use Disorders in the Unites States: A Retrospective Descriptive Study

 

Abstract

Substance use disorders (SUD) among healthcare professionals (HCPs) are significant and persistent problems. The risk factors for relapse during first year of enrollment in recovery monitoring programs are unknown. Moreover, it is unknown if the use of opioids is associated with a higher relapse rate compared with alcohol and non-opioids. The objective of this study is to examine the characteristics of HCPs with SUD that cause them to relapse during their first year of enrollment in recovery monitoring programs. Also, to gain a better understanding of the impaired HCPs by developing composite descriptions of: 1) relapsed versus non-relapsed; and 2) opioid users versus non-opioid users. A retrospective cohort study of 1755 HCPs enrolled in recovery monitoring programs in the United States, followed up between 2003 and 2016. This study aims to investigate initial differences in characteristics between subjects who relapsed and did not relapse as well as between subjects who are opioid users and non-opioids. Moreover, the study will examine if opioids are the primary drug of choice among people who relapse. The primary outcome measure of this study are factors associated with relapse or being an opioid user. Opioid users were (n=423, 26%) and only (n=48, 3%) who had first relapse. There was no significant association between first relapse and being an opioid user (p value > 0.05). In fact, there was a significant association between age and marital status with first relapse (p value < 0.05). Gender, age, marital status, and location has significant association with being opioid users (p value < 0.05). Divorced, Separated, and Widowed are 3.194 (95% CI: 1.535 – 6.644) times more likely to relapse than Single HCPs while Cohabitating and Married are 1.751 (95% CI: 0.885 – 3.464) times more likely to relapse than Single HCPs. Males were 1.344 (95% CI: 1.042 – 1.734) times more likely to be an opioid user than females. Subjects 40 years of age and older are 0.389 (95% CI: 0.306 – 0.495) times more likely to be an opioid user than those under the age of 40. In conclusion, the risk of relapse with substance use was increased in HCPs who are Divorced, Separated, and Widowed as well as Cohabitating and Married. The risk of being an opioid user was increased in male HCPs and those 40 years of age and older. These observations should be considered in monitoring the recovery of HCPs in order to inform healthcare policy makers or providers to improve the health and wellbeing of HCPs in the workplace.

Introduction

Substance use disorder (SUD) is a major health problem in the United States (U.S.) and, in simple terms, it means the misuse of drugs and/or alcohol. In 2013, more than 8% of the U.S. population aged 12 or older have SUD [1]. Health care professionals (HCPs) are at risk for this disorder due to several risk factors and SUD in HCPs has become a serious and complex issue for patient safety. General risk factors include social factors such as family history of the disorder, psychiatric factors such as depression, and biological factors such as genetic predisposition. Specific risk factors for HCPs include easy access to drugs, stressful work environments, and the belief that drugs assist with coping [2].

HCPs with SUD are referred to recovery monitoring programs such as Physicians Health Programs (PHPs) and Alternative-to-Disciplines programs (ATD) [3, 4]. The purpose of these recovery monitoring programs is to help addicted HCPs seek assistance and avoid punitive interventions. Therefore, HCPs can maintain their licenses while maintaining their sobriety. Moreover, patient safety will be improved because of deterring relapse time among HCPs.

A key feature of recovery monitoring programs for HCPs is the agreement contract. This contract includes several requirements that need to be done on regular basis such as urine tests, follow-up with outpatient treatment programs, and attendance at 12-step meetings. These requirements vary from program to program and are customized per profession therefore the outcomes also may vary.

Literature Review

Studies showed that rates of SUDs among HCPs are similar to the general population, but HCPs demonstrate significantly higher levels of opioid abuse [5, 6, 7]. However, another study showed that in cases where multiple drug use is common, as with HCPs, alcohol is the most used drug, followed by opioids [8]. Moreover, HCPs appear to be vulnerable to SUD because of work-related stress, easy access to drugs, and personal matters such as physical or psychological health needs, financial issues, and family relations [9]. Thus, patient safety may be placed at risk when HCPs practice with active, untreated SUD [10]. In addition, HCPs with SUD are more likely to prescribe drugs for themselves and gain access to drugs when they administer patient medication [9].

Recovery monitoring programs aim to protect the public by helping the HCPs remain sober [11].  Recovery monitoring programs typically require HCPs with SUD to sign a contractual agreement to monitor their compliance to the program’s requirements to ensure the HCPs are not impaired when practicing and thus enhance patient safety.  These requirements include random drug tests, attendance at outpatient treatment programs, attendance at 12-step meetings and other support groups. The length of programs and frequency of urine testing as well as other requirements varies with profession and state (See Appendix 1). Once the agreement is signed, the HCP will be supervised and monitored for which he/she will be referred to the regulator in case of relapse or failure to adhere to the agreement [14]. A relapse occurs when a HCP uses alcohol or other drugs non-medically, as well as fails to be compliant with the treatment session.

There are various treatment programs in the United States in which most of the published data on the outcomes of these programs are limited to physicians who are treated and monitored by PHPs [14, 15, 20]. However, fewer studies focused on other types of clinicians who are referred to ATD, such as nurses [7, 12, 13]. PHPs and ATDs provide coordination, monitoring, and expertise in the care of HCPs, which has led to positive outcomes when combined with treatment [14, 15]. The ultimate positive outcome is relapse avoidance, thus ensuring patient safety as well as helping HCPs to maintain their licensure.

There are several factors that contribute to successful recovery as well as positive outcomes among HCPs due to the monitoring program [16]. According to McLellan et al. (2008), physicians have high rates of recovery when involved in long-term continuing care and monitoring programs [15]. In addition, monitoring HCPs with random drug tests after treatment is an effective way in maintaining high abstinence rates [17]. Furthermore, longer monitoring programs are more likely to improve the long-term success rate in addicted HCPs [18].

A majority of HCPs with SUD have been found to use alcohol and those who used drugs primarily have used benzodiazepines and opiates [5, 8]. According to one study by Domino et al. (2005), use of major opioids is associated with a higher relapse rate compared with alcohol and non-opioids [20]. The objective of their study was to test the hypothesis that chemically dependent HCPs using a major opioid (e.g., fentanyl, sufentanil, morphine, meperidine) as their drug of choice are at higher risk of relapse. They found that twenty-five percent (74 of 292 individuals) had at least 1 relapse in which the use of a major opioid increased the risk of relapse significantly in the presence of a coexisting psychiatric disorder (HR, 5.79; 95% CI, 2.89-11.42). They concluded that the risk of relapse with substance use was increased in HCPs who used a major opioid or had a coexisting psychiatric illness or a family history of a SUD.

Regarding gender, one study found that female gender was associated with a higher risk for drug use than male among physicians. According to Lin et al. (2013), female physicians are more likely to suffer role strain, role deprivation, and gender role conflict [9]. The majority of chemically dependent nurses were female and caucasian, while their mean age was 40 years [19].

Regarding profession, physicians and nurses are considered high-risk for SUDs due to the enormous amounts of stress that they deal with because of their jobs [30, 31]. Among physicians, the disciplines typically associated with substance abuse are emergency medicine, anesthesiology, and psychiatry [22]. Similarly, among nurses, the disciplines typically associated with substance abuse are also emergency medicine, anesthesiology, and psychiatry [29].

Previous studies lack the risk factors that contribute to the likelihood of relapse after initial treatment for substance use. Virtually every study of chemical dependency among HCPs has had relatively short follow-ups, limitations in statistical methods or analyses, and variable intensity of monitoring. Moreover, previous studies focused on only physicians and their disciplines, or only nurses and their disciplines, while our study is looking at the HCPs in total. Thus, the purpose of this study is to give a broad sense of what characteristics of all these HCPs is associated with a relapse. Another purpose of this study is to develop a composite description of relapsed HCPs versus non-relapsed HCPs. Moreover, this study is to develop also a composite description of opioid users versus non-opioid users among HCPs. The composite will be useful to gain a better understanding of the impaired HCPs, and to evaluate the effectiveness of current programs for treatment and monitoring. In addition, by identifying the various potential determinants of relapse among HCPs could inform healthcare policy makers or providers to improve the health and wellbeing of HCPs in the workplace.

Using the data from a cohort of 1755 HCPs enrolled in recovery monitoring programs in the United States, this study aims to investigate initial differences in characteristics between subjects who relapsed and did not relapse as well as between subjects who are opioid users and non-opioid. Moreover, the study will examine if opioids are the primary drug of choice among people who relapse. The primary outcome measure of this study a determination of the factors associated with relapse or being an opioid user. The secondary outcome measure is to determine if opioids are associated with a higher rate relapse in HCPs since using of major opioids has been associated with a higher relapse rate compared with alcohol and non-opioids [20].

Methods

To address the aims of the proposed study, a secondary data analysis will be conducted for outcome data collected on a cohort of 1755 HCPs who enrolled in a recovery monitoring program between 2003 and 2016. This program monitored participant demographics, results, drug history, and compliance with contract using Recovery Management Services (RMS) by FirstLab.

FirstLab (now FirstSource Solutions) is a full service compliance management solutions company founded in 1989 as a subsidiary of FHC Health Systems (now RID Ventures, LLC), of Norfolk Virginia [23]. FirstLab has provided drug and alcohol testing services as well as managing substance abuse testing programs to meet the needs of its diverse client base, which includes Fortune 500 companies, law enforcement agencies, state and municipal governments, and treatment facilities. FirstLab created RMS for professional health monitoring programs to offer a customized menu of services, test panels and payment options that met the unique needs of different industry and state monitoring programs. In 2008, FirstLab acquired National Confederation of Professional Services, Inc. (NCPS), of Newport News, Virginia. This acquisition made FirstLab the largest administrator of professional health monitoring services in North America, with clients nationwide.

The RMS monitors HCPs with SUD using a web-based data collection and management system that provides regulatory bodies (e.g. state licensing boards) across the U.S. with secure, consistent, and reliable compliance monitoring tools. Information from the RMS database includes the following: participant demographics (e.g. age, gender, race/ethnicity, marital status, location), results (e.g. overall result of tested drugs), drug history (e.g. drug of choice), and compliance with contract (e.g. frequency of required testing) as well as length of time in the program.

The analytic sample of the proposed study will consist of 1755 HCPs who were monitored for at least one year between 2003 – 2016. The primary dependent variable in the proposed study is relapse rate in their first or second year. Since the data file does not have exact enrollment date, subjects who relapsed in the second year were included for two reasons: subjects might relapse in their first year assuming their start date was in the second half of the year; second is to maximize sample size of relapsed group therefore increasing validity. HCPs will be divided into two groups: HCPs with no relapse and HCPs with at least one relapse. A relapse occurs when HCPs had a positive drug test, confirmed positive, and reconfirmed positive within their first year in the program.

The independent variables are age, gender, marital status, location, and drug of choice. The age has been classified into two groups: “under 40” and “40 and older”, in order to examine if young adults (under 40) are at higher risk of being opioid users [25]. Marital Status has been stratified into three groups: “Single”, “Married and/or Cohabitating”, and “Divorced, Separated, or Widowed”. Also, location has been grouped into four major areas: “Northeast”, “Midwest”, “South”, and “West” according to the U.S. Census Bureau [24]. HCPs are considered opioid addicts when their primary drug of choice is an opioid. Drug of choice will be categorized into four groups: opioids, cocaine, alcohol and non-opioids (See Table 1).

Table 1: Drug of choice categories

Drug of Choice  
   
Category 1: OPIOIDS  
DEXTROMETHORPHAN/LEVORPHANOL

DIHYDROCODEINE

HYDROCODONE

PENTAZOCINE

NORPROPOXYPHENE

MORPHINE

OXYCODONE

OTHER OPIATES

Category 2: COCAINE  
Category 3: ALCOHOL  
Category 4: NON-OPIOIDS  
BUTALBITAL

NITRATE

MEPHOBARBITAL

AMPHETAMINES

MEPROBAMATE

STENBOLONE

TIBOLONE

CLORAZEPATE

TRAMADOL

ALPRAZOLAM

ALPHA-HYDROXYALPRAZOLAM

 

PHENCYCLIDINE

BENZODIAZEPINES

CARISOPRODOL

THC Metabolite

CHLORPHENIRAMINE

DIAZEPAM

FLURAZEPAM

NORDIAZEPAM

ETHANOL

GHB

N-DESMETHYLDIAZEPAM

The data will be analyzed using SPSS 20.0 and Microsoft Excel. Missing data of any participant’s variables will exclude that patient from the study. The characteristics of HCPs groups will be summarized using frequencies for categorical variables and means and standard deviations for continuous variables. Chi-square analysis and odds ratio will be used to examine data. A backward analysis logistic regression will be used to predict the binary dependent variable outcome (Relapse/No Relapse), as well as Drug of Choice (Opioid users/Non-opioid users).

Results

A total of 1608 out of 1755 participants were included in the study after removing 147 participants who had missing data of any of the variables.

 

Table 2: Distribution of cohort by their drug of choice between relapsed vs. non-relapsed

Drug of Choice No Relapse

N (%)

Relapse

N (%)

Total

N (%)

  1560 (100) 48 (100) 1608 (100)
Category 1: OPIOIDS 411 (26) 12 (25) 423 (26)
DEXTROMETHORPHAN/LEVORPHANOL 238 (15) 9 (19) 247 (15)
OTHER OPIATES 83 (5) 2 (4) 85 (5)
HYDROCODONE 63 (4) 1 (2) 64 (4)
PENTAZOCINE 19 (1) 0 19 (1)
NORPROPOXYPHENE 3 (0.2) 0 3 (0.2)
MORPHINE 2 (0.1) 0 2 (0.1)
OXYCODONE 2 (0.1) 0 2 (0.1)
DIHYDROCODEINE 1 (0.1) 0 1 (0.1)
Category 2: COCAINE 25 (2) 0 25 (2)
 
Category 3: ALCOHOL 8 (1) 2 (4) 10 (1)
 
Category 4: NON-OPIOIDS 1116 (72) 34 (71) 1150 (72)
BUTALBITAL 705 (45) 21 (44) 726 (45)
NITRATE 118 (8) 4 (8) 122 (8)
MEPHOBARBITAL 71 (5) 1 (2) 72 (4)
AMPHETAMINES 56 (4) 4 (8) 60 (4)
MEPROBAMATE 45 (3) 2 (4) 47 (3)
STENBOLONE 27 (2) 0 27 (2)
TIBOLONE 15 (1) 0 15 (1)
CLORAZEPATE 13 (1) 0 13 (1)
TRAMADOL 11 (1) 0 11 (1)
ALPRAZOLAM 9 (1) 1 (2) 10 (1)
ALPHA-HYDROXYALPRAZOLAM 7 (0.4) 1 (2) 8 (0.5)
PHENCYCLIDINE 8 (0.5) 0 8 (0.5)
BENZODIAZEPINES 7 (0.4) 0 7 (0.4)
CARISOPRODOL 5 (0.4) 0 5 (0.3)
THC Metabolite 6 (0.4) 0 6 (0.3)
CHLORPHENIRAMINE 4 (0.2) 0 4 (0.2)
DIAZEPAM 2 (0.1) 1 (1.9) 3 (0.2)
FLURAZEPAM 2 (0.1) 0 2 (0.1)
NORDIAZEPAM 2 (0.1) 0 2 (0.1)
ETHANOL 1 (0.1) 0 1 (0.1)
GHB 1 (0.1) 0 1 (0.1)
N-DESMETHYLDIAZEPAM 1 (0.1) 0 1 (0.1)

Distribution of cohort by drug of choice

Table 2 shows the most frequent drug of choice used among the cohort as well as relapsed group compared to non-relapsed group. The most frequent opioid drugs used were Dextromethorphan/Levorphanol, Hydrocodone, and Pentazocine. The most frequent non-opioid drugs used were Butalbital, Nitrate, and Mephobarbital. Since there were so few cocaine and alcohol users recorded in the result (< 2%), cocaine and alcohol were combined into non-opioids, thus leaving only two categories for the drug of choice: Opioids and Non-opioids.

Cohort Characteristics

Table 3 shows the cohort characteristics as well as the characteristics for the specific group between relapse and non-relapse. The participants were predominantly men (65%) in which those of age 40 years and above were slightly more in number versus those under 40 years of age (52% compared to 48% respectively). Half of participants were Single (52%), while one-third of participants were Cohabitating/Married (34%). Furthermore, almost half of the participants (47%) were from West region of the U.S. while one third (35%) were from South region. Only one-quarter of the participants were opioid users (n=423, 26%) compared to non-opioid users (n=1185, 74%).

Relapsed versus Non-Relapsed

As shown in Table 3, there were 48 (3%) participants who relapsed compared to 1560 (97%) who did not relapse. The percentage of men who had a relapse was more than women (54% compared to 46% respectively). Majority of participants in both groups were 40 years of age and above. Among relapsed group, almost twice as many of 40 years and older relapsed compared to those under 40 (67% compared to 33% respectively). Majority of Cohabitating/Married (38%) and Single (33%) participants relapsed. For those who relapsed, Non-opioid users (75%) were more than Opioid users (25%). More than half of participants who had first relapsed (63%) were from West region of the U.S. while almost one quarter (25%) were from South region.

Table 3: Cohort Characteristics (Relapsed vs. Non-Relapsed)

Characteristics Total No. (%) of Individuals (N=1608) No. (%) of Individuals who had first relapse  (N=48) No. (%) of Individuals who did not relapse (N=1560)
N 1608 (100) 48 (3) 1560 (97)
Gender
Women 562 (35) 22 (46) 540 (35)
Men 1046 (65) 26 (54) 1020 (65)
Age
< 40 777 (48) 16 (33) 761 (49)
> = 40 831 (52) 32 (67) 799 (51)
Marital Status
Single 830 (52) 16 (33) 814 (52)
Cohabitating/Married 541 (34) 18 (38) 523 (34)
Divorced/Separated/Widowed 237 (15) 14 (29) 223 (14)
Location
             Midwest 110 (7) 3 (6) 107 (7)
             Northeast 181 (11) 3 (6) 178 (11)
             South 564 (35) 12 (25) 552 (35)
             West 753 (47) 30 (63) 723 (46)
Drug of Choice
Opioids 423 (26) 12 (25) 411 (26)
Non-Opioids 1185 (74) 36 (75) 1149 (74)

 

 

Opioid users vs. Non-Opioid users

Table 4 shows the characteristics of participants whose drug of choice was Opioids compared to Non-opioids. As shown in Table 4, the majority of opioid users were men (73%). Participants under age of 40 used opioids more than the older group (67% compared to 33%). The majority of the Single participants were Opioid users compared to Cohabitating/Married (65% compared to 25%). The majority of participants who were opioid users were from South and West regions of the U.S. (46% and 36% respectively).

 

 

Table 4: Cohort Characteristics (Opioid users vs. Non-Opioid users)

 

Characteristics Total No. (%) of Individuals (N=1608) No. (%) of Opioid users (N=423) No. (%) of Non-opioid users (N=1185)
N 1608 (100) 423 (26) 1185 (74)
Gender
Women 562 (35) 115 (27) 447 (38)
Men 1046 (65) 308 (73) 738 (62)
Age
< 40 777 (48) 285 (67) 492 (42)
> = 40 831 (52) 138 (33) 693 (58)
Marital Status
Single 830 (52) 277 (65) 553 (47)
Cohabitating/Married 541 (34) 106 (25) 435 (37)
Divorced/Separated/Widowed 237 (15) 40 (9) 197 (17)
Location
Midwest 110 (7) 25 (6) 85 (7)
Northeast 181 (11) 50 (12) 131 (11)
South 564 (35) 195 (46) 369 (31)
West 753 (47) 153 (36) 600 (51)
Relapse
Relapse 48 (3) 12 (3) 36 (3)
Non-Relapse 1560 (97) 411 (97) 1149 (97)

 

Relapsed group Characteristics

Table 5 shows the characteristics of the relapsed group that represents only 3% of the cohort (N=48/1608). Almost one quarter of relapsed group are opioid users (25%) and majority of them were under the age of 40. Cohabitating/Married participants relapsed more (38%) compared to other marital status categories. Among them, the majority of Divorced/Separated/Widowed participants were Opioid users (42%). As far as the location, more than half came from the West region of the U.S. Also, 58% of Opioid users came from the West region.

 

Table 5: Characteristics of Relapsed Group (Opioid users vs. Non-Opioid users)

Characteristics Total No. (%) of Individuals (N=48) No. (%) of opioid users (N=12) No. (%) of non-opioid users (N=36)
N 48 (100) 12 (25) 36 (75)
Gender
Women 22 (46) 6 (50) 16 (44)
Men 26 (54) 6 (50) 20 (56)
Age
< 40 16 (33) 7 (58) 9 (25)
> = 40 32 (67) 5 (42) 27 (75)
Marital Status
Single 16 (33) 3 (25) 13 (36)
Cohabitating/Married 18 (38) 4 (33) 14 (39)
Divorced/Separated/Widowed 14 (29) 5 (42) 9 (25)
Location
Midwest 3 (6) 0 (0) 3 (8)
Northeast 3 (6) 0 (0) 3 (8)
South 12 (25) 5 (42) 7 (19)
West 33 (63) 7 (58) 25 (64)

 

 

Chi square and odds ratio results:

Table 6 shows the factors associated with first relapse among the cohort and their significance. Gender, location, and being an opioid user had no association with relapse since their p value > 0.05. However, there was significant association between age and marital status with first relapse (p value < 0.05).

Table 6: Univariate analysis showing factors associated with first relapse among cohort

               
    Χ2 P-Value OR (95% CI)      
Gender   2.578 .108 .626 (.351 – 1.114) No association was found between gender and first relapse (Χ2> = 2.578, p = 0.108).

Males have a 0.626 lower odds of relapse versus females.

Age   4.451 .035 1.905 (1.037 – 3.500) There was a significant association between age and first relapse (Χ2> = 4.451, p = .035).  HCPs

40 years and older have a 1.905 higher odds of relapse versus those under 40.

Marital Status   10.411 .005   There was a significant association between marital status and first relapse (Χ2> = 10.411, p = .005).
Location 5.154 .161   No association was found between location and first relapse (Χ2> = 5.154, p = 0.161).
Opioid User   .044 .835 .932 (.480 – 1.808) No association was found between opioid users and first relapse (Χ2> = .044, p = 0.835).

Opioid users have a 0.932 lower odds of relapse versus non-opioid users.

 

 

Table 7 shows the factors associated with being opioid users among the cohort and their significance. Gender, age, marital status, and location show a significant association with being opioid user since their p value < 0.05. However, there is no significant association between first relapse and being an opioid user (p value > 0.05).

 

 

 

 

Table 7: Univariate analysis showing factors associated with Opioids among cohort

               
    Χ2 P-Value OR (95% CI)      
Gender   15.217 .000 1.622 (1.271 – 2.071) There was a significant association between gender and using opioid as a primary drug of choice (Χ2> = 15.217, p < α = .05).

Males have a 1.622 higher odds of being opioid users versus females.

Age   83.459 .000 .344 (.272 – .434) There was a significant association between age and using opioids as the primary drug of choice. (Χ2> = 83.459, p < .001).

HCPs 40 and older have a 0.344 lower odds of being opioid users versus those under 40.

Marital Status   44.828 .000   There was a significant association between marital status and using opioids as primary drug of choice (Χ2> = 44.828, p < .001).
Location   34.703 .000   There was a significant association between location and using opioids as primary drug of choice (Χ2> = 34.703, p < .001).
Relapse   .044 .835 .932 (.480 – 1.808) No association was found between opioid users and relapse (Χ2> = .044, p = 0.835).

Relapsed group has a 0.932 lower odds of being opioid users.

Table 8 shows the factors associated with being opioid users among the relapsed group and their significance. Gender, marital status, and location had no significant association with being an opioid user (p value > 0.05). However, there was a significant association between age and being an opioid user (p value < 0.05).

 

Table 8: Univariate analysis showing factors associated with Opioids among relapse group

               
    Χ2 P-Value OR (95% CI)      
Gender   .112 .738 .800 (.216 – 2.961) No significant association between gender and using opioid as a primary drug of choice (Χ2> = .112, p = .738).

Males have a 0.800 lower odds of being opioid users versus females.

Age   4.500 .034 .238 (.060 – .940) There was a significant association between age and using opioids as the primary drug of choice. (Χ2> = 4.500, p = .034).

HCPs 40 and older have a 0.238 lower odds of being opioid users versus those under 40.

Marital Status   1.265 .531 No significant association between marital status and using opioids as primary drug of choice (Χ2> = 1.265, p = .531).
Location   3.822 .281 No significant association between location and using opioids as primary drug of choice (Χ2> = 3.822, p = .281).
     

 

Logistic regression results:

Table 9 shows the results of logistic regression analysis to predict relapse probability between age and marital status among the cohort since they showed significant relationship in the Univariate analysis. Marital status has a highly significant overall effect since the p value < 0.05 except for Cohabitating and Married (p value = 0.108). Divorced, Separated, and Widowed are 3.194 (95% CI: 1.535 – 6.644) times more likely to relapse than Single HCPs while Cohabitating and Married are 1.751 (95% CI: 0.885 – 3.464) times more likely to relapse than Single HCPs.

 

 

Table 9: Dependent variable: First Relapse

Variables in the Equation
B S.E. Wald df Sig. Exp(B) 95% C.I.for EXP(B)
Lower Upper
Step 1a @40_and_older .242 .396 .375 1 .540 1.274 .587 2.767
Single 5.456 2 .065
Cohab-Married .410 .424 .938 1 .333 1.507 .657 3.460
Div-Sep-Wid 1.007 .449 5.036 1 .025 2.737 1.136 6.596
Constant -3.986 .272 215.253 1 .000 .019
Step 2a Single 9.667 2 .008
Cohab-Married .560 .348 2.589 1 .108 1.751 .885 3.464
Div-Sep-Wid 1.161 .374 9.657 1 .002 3.194 1.535 6.644
Constant -3.929 .252 242.277 1 .000 .020
a. Variable(s) entered on step 1: @40_and_older, Marital_group.

 

Table 10 shows the results of logistic regression analysis to predict the probability of being an opioid user based on gender, age, marital status, and location among cohort. Except marital status, all variables have highly significant overall effect except for Northeast and Midwest (p value = 0.147 and 0.937). Males were 1.344 (95% CI: 1.042 – 1.734) times more likely to be an opioid user than females.  Subjects 40 years of age and older are 0.389 (95% CI: 0.306 – 0.495) times more likely to be an opioid user than those under the age of 40. Participants who live in the South are 1.587 (95% CI: 1.222 – 2.061) times more likely to be an opioid user than those who live in the West. Participants who live in the Northeast are 1.325 (95% CI: 0.905 – 1.939) times more to be an opioid user than those who live in the West. Participants who live in the Midwest are 1.020 (95% CI: 0.624 – 1.667) times more to be an opioid users than those who live in the West.

 

Table 10: Dependent variable: Opioid user

Variables in the Equation
B S.E. Wald df Sig. Exp(B) 95% C.I.for EXP(B)
Lower Upper
Step 1a Is_Male .281 .131 4.583 1 .032 1.324 1.024 1.712
@40_and_older -.881 .155 32.288 1 .000 .414 .306 .561
Marital_group .688 2 .709
Marital_group(1) -.070 .165 .183 1 .669 .932 .675 1.288
Marital_group(2) -.180 .218 .682 1 .409 .835 .544 1.281
West 12.577 3 .006
Midwest .009 .251 .001 1 .973 1.009 .617 1.650
Northeast .273 .195 1.973 1 .160 1.314 .898 1.925
South .456 .134 11.619 1 .001 1.577 1.214 2.049
Constant -.983 .146 45.503 1 .000 .374
Step 2a Is_Male .296 .130 5.201 1 .023 1.344 1.042 1.734
@40_and_older -.944 .123 59.028 1 .000 .389 .306 .495
West 12.927 3 .005
Midwest .020 .251 .006 1 .937 1.020 .624 1.667
Northeast .281 .194 2.099 1 .147 1.325 .905 1.939
South .462 .133 12.015 1 .001 1.587 1.222 2.061
Constant -1.014 .140 52.430 1 .000 .363
a. Variable(s) entered on step 1: Is_Male, @40_and_older, Marital_group, Region.

 

 

Table 11 shows the results of logistic regression analysis to predict the probability of being an opioid user based on age among relapsed group. Age has a significant overall effect (p value < 0.05). Subjects 40 years of age and older are 0.238 (95% CI: 0.060 – 0.940) times more likely to be an opioid user than those under the age of 40.

Table 11: Dependent variable: Opioid user among Relapsed group

Variables in the Equation
B S.E. Wald df Sig. Exp(B) 95% C.I.for EXP(B)
Lower Upper
Step 1a @40_and_older -1.435 .701 4.194 1 .041 .238 .060 .940
Constant -.251 .504 .249 1 .618 .778
  1. Variable(s) entered on step 1: @40_and_older.

Discussion

SUD is a major health problem in the U.S. and in simple terms, it means the misuse of drugs and/or alcohol. In 2013, more than 8% of the U.S. population aged 12 or older have SUD [1]. HCPs are at risk for this disorder due to several risk factors and SUD in HCPs has become a serious and complex issue for patient safety. This study focused on identifying the factors associated with first relapse as well as characterizing factors associated with opioid use among HCPs with SUD. These factors include age, gender, marital status, location, and drug of choice. The following discusses these factors and compares some of the differences in our findings with previous studies with the hope to provide guidance or improving policies and guidelines of recovery monitoring programs to prevent first relapse by focusing on high risk individuals.

A previous study by Domino et al. found that the risk of relapse with substance use was markedly increased in HCPs who used opioids [20], however our analysis found no association between opioid users and relapse (Χ2> = .044, p = 0.835). In fact, relapsed group has 0.932 (95% CI: 0.480 – 1.808) probability of being opioid users. Therefore, we cannot reject the null hypothesis that the true odds ratio equals 1. However, our analysis included 1608 participants’ nationwide compared to Domino et al. which included only 292 participants from only one location, the Washington Physician’s Health Program [20]. Also, all 292 participants were only prescribers (physicians, dentists and physicians assistants), while our study sample included other HCPs such as nurses and pharmacists. Thus, our findings are more generalizable than Domino et al. due to our larger sample size that was collected from all over the 50 states.

Domino et al. categorized drug of choice into major opioid (e.g., fentanyl, sufentanil, morphine, meperidine) in which our study sample had no individual who used any of these drugs except two participants who used morphine [20]. Moreover, our sample had only 1% who used alcohol as a drug of choice compared to 56% in Domino et al. [20]. Thus, we cannot assume whether or not alcohol contributed to first relapse. Also, our analysis had only 3% (48 of 1608 individuals) who had 1 relapse in their first two years, while Domino et al. study had 14% (43 of 292 individuals). Their study analysis included other confounders that might contribute to relapse such as family history of SUDs, smoking status, and coexisting psychiatric disorder in which our data analysis were missing these important variables.

Previous studies showed that alcohol is the most used drug, followed by opioids and those who used drugs primarily have used benzodiazepines [5, 8]. Our findings showed that only 26% (423 out of 1608 individuals) have used opioids, 1% have used alcohol and less than 1% have used benzodiazepines. Our analysis also shows that hydrocodone and dextromethorphan/levorphanol were primarily used by the cohort and relapse group. Although our findings showed that only 1% have used alcohol compared to 56% in Domino et al. (2005), this could be explained in two reasons [20]. First, our data includes a “Primary Drug of Choice” that was already classified by FirstLab for each individual. Thus, accuracy might be affected in entering the correct drug of choice since using more than one substance is common. Second, our data was collected between 2003 and 2016 while Domino et al. (2005) had their data collected between 1991 and 2001 [20]. For which our sample could be influenced by the opioid epidemic that started during the 2000s due to major impact on the practice of medicine. OxyContin’s impact on the practice of medicine was an epidemic as it got introduced in 1996 and the drug companies began marketing their own narcotic painkillers for routine injuries by 2005 [26]. By 2010, one out of every five doctor’s visits in the U.S. for pain resulted in a prescription for narcotic painkillers, that later on caused patients to abuse them and/or seek cheaper alternatives e.g. heroin [26, 27, 28]. The increase in prescribing led to an increase in drug users which in turn led to an increase in the number of people with opioids substance abuse.  This is different than in the time Domino et al. (2005) conducted the study where use of opioids was frowned upon and thus fewer people were exposed to opioids and fewer were addicted. Therefore, our data could have had a significant effect by this change in the drug abuse as compared to alcohol abuse in the past. Thus, possibly resulting in our data consisting only 1% alcohol users compared to opioid and non-opioid users.

Regarding gender, one study by Lin et al. (2013) found that the female physician was associated with a higher risk for SUDs than the male physician [9]. That is because female physicians are more likely to suffer role strain, role deprivation, and gender role conflict that might be specific to Taiwan where culture might influence this finding. However, their study did not examine the association of gender and being opioid users. Since our study looked at HCPs in total regardless of their specific professions, our findings showed that majority of HCPs with SUDs were males. Moreover, logistic regression analysis revealed that males are 1.345 times more likely to be an opioid user than females.

Beaman et al. (2010) found that the majority of nurses with SUDs were female and caucasian, while their mean age was 40 years [19]. As compared to our study, the study sample for Beaman et al. (2010) was limited to 552 nurses enrolled in one location which is the Indiana State Nurses Assistance Program. Opiates and alcohol were the most frequently abused drugs among nurses. Regardless of profession and ethnicity, our results showed that a majority of opioid users were male (65%) and also male were the majority (73%) among opioid users.

Those who were opioid user and under age of 40 were (67%) compared to the opioid users above 40. For which our analysis shows that under the age of 40 are 2.570 (1/0.389) times more likely to be an opioid users than 40 years and older. Relevant data from Martins et al., (2016) study show that, among the general population, adults who are under age of 40 are more likely to become opioid users than they were in years past [25]. According to Martins et al. (2016), a review of federal data between 2002 and 2014 found the odds of becoming opioid users increased 37% among age group (18 – 25) while among age group (26 – 34) the risk of being opioid users increased from 11% to 24% [25].

Limitations

The study used a retrospective cohort design, and since it is an observational study, risk factors represent associations not causation. One of the limitation in this study is the small sample size especially for the relapse group N=48/1608. That is because we included only participants who had only their first relapse in their first or second year unlike other studies that included participants who had any relapse at any time. Also, because our data is limited to one company that contracted with various states, it may not be nationally representative of all recovery monitoring programs across the U.S. Because each recovery monitoring program such as the PHP operates independently, their system of record keeping varies. Furthermore, our data has missing variables such as coexisting psychiatric illness, family history of SUD, and medical profession/specialty. For which these missing variables are major limitations in our study. In HCPs with SUD, the presence of a coexisting psychiatric illness or a family history of substance use disorder significantly increased the likelihood of relapse [20]. Furthermore, previous studies found associations between medical profession or specialty and drug of choice since specific medical professions have more access to opioids than others e.g. Anesthesiologists [21].

Conclusion

This study developed a composite description of relapsed HCPs versus non relapsed HCPs as well as opioid users versus non-opioid users enrolled in the recovery monitoring programs. The composite will be useful to inform healthcare policy makers or providers to improve the health and wellbeing of HCPs in the workplace. Further research is needed to examine the factors contributing to a higher prevalence of SUD among HCPs who are younger than 40 of age as well as those who are divorced, separated, and widowed. Given the significant patient care implications and the potential negative physical, psychological, and legal consequences of SUD, healthcare policy makers needs to raise awareness of this problem and the resources available for early intervention and treating affected HCPs.

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APPENDIX 1

Requirements of contract varies among states and professions. Below examples from Delaware (Table A), New Jersey (Table B), Delaware (Table C), and Maryland (Table D).

Table A: Requirements of contract in Delaware by profession.

Profession DELAWARE
  Minimum Urine Test Frequency Minimum number of times to attend support group Frequency of Reporting Progress Average length of program Minimum Cost
Physicians 36 year one

24 year two

18 year three and beyond

(unless relapse)

Individual based on the licensee’s

treatment recommendation.

Progress reported to DPHMP-
·Bi-wkly check in,
·mthly tx reports,
-quarterly MMF,
·mthly workplace reports.

Progress reported to DPR-
·Weekly Count
·Monthly Status
·A non-compliance
·When compliance summary requested

Min. 5 years Regular panels bundled costs range from $41.20 to $58.94 (includes collection, analysis, and MRO). One panel, less frequent, is $95.05. Average cost of test would be $50.

Year one average cost (toxicology)- $1,800
Year two average cost (toxicology)- $1,200
Year three average cost (toxicology)- $900

This does not include tx costs.

Physician Assistants Min. 5 years
Nurses Min. 3 years

longer for advanced

practice nurses

Pharmacists Min. 5 years
Dentists Min. 5 years
Physical Therapists Min. 3 years
Occupational Therapists Min. 3 years

Source: Delaware Professionals’ Health Monitoring Program. https://www.delawaremonitoring.com/

Table B: Requirements of contract in New Jersey by profession.

Profession NEW JERSEY
  Minimum Urine Test Frequency Minimum number of times to attend support group Frequency of Reporting Progress Average length of program Minimum Cost
Physicians

 

 

 

 

 

Physician Assistants

 

 

 

 

 

Nurses

 

 

 

 

 

Pharmacists

 

 

 

 

 

Dentists

 

 

 

 

Physical Therapists

 

 

 

 

Occupational Therapists

The PAP, after developing the treatment plan, will refer the program participant to the appropriate level of care and skilled therapist to begin the execution of the treatment plan.  Upon completion of the initial (primary) treatment, the PAP will follow the participant and keep in close contact with selected consultants that are involved in the subsequent phases of the participants care.  The PAP “monitors” the participant’s recovery by reviewing regularly at face-to-face monthly visits, each element of the treatment plan to assist the recovery process.  Whenever indicated, the PAP will make adjustments in the treatment plan when necessitated by new developments or a relapse in the progression of recovery.  In some cases the treatment plan will include random testing to verify recovery and the effectiveness of the prescribed treatment plan.  Enrollment into the treatment program is usually for a minimum of five years.  The frequency of the follow up visits and the random monitoring become less and less as the program participant progresses in his or her recovery. Monthly 5 years  Varies

Source: Professional Assistance Program of New Jersey, Inc. http://www.papnj.org/services.html

Table C: Requirements of contract in Pennsylvania by profession.

Profession PENNSYLVANIA
  Minimum Urine Test Frequency Minimum number of times to attend support group Frequency of Reporting Progress Average length of program Minimum Cost
Physicians Monthly Quarterly 5 years
Physician Assistants 3 years
Nurses Quarterly 3 years
Pharmacists 3 years
Dentists Quarterly 3 years
Physical Therapists 3 years
Occupational Therapists 3 years

Source 1: Pennsylvania Physicians’ Health Programs. https://www.pamedsoc.org

Source 2: Professional Health Monitoring Programs (PHMP). http://www.dos.pa.gov/ProfessionalLicensing/OtherServices/ProfessionaHealthMonitoringPrograms

Table D: Requirements of contract in Maryland by profession.

Profession MARYLAND
  Minimum Urine Test Frequency Minimum number of times to attend support group Frequency of Reporting Progress Average length of program Minimum Cost
Physicians Monitoring
A participant with a verified problem typically enters into a five-year contract with the MPHP that allows for active monitoring of the participant’s progress. The contract encompasses the participant’s treatment plan, incorporating recommendations made by the evaluator. The case manager will monitor the participants’ progress with treatment, vocational status, and if appropriate, the participant will undergo random toxicology screening.1
5 years  Varies
Physician Assistants 5 years
Nurses The conditions and terms of the agreement include requirements of continued rehabilitation and treatment with submission of progress reports, weekly participation in support groups, random toxicology screens, employer reports and self reports. Additionally, the agreement may limit where the participant can work, hours of work, and administration of controlled substances.2
Pharmacists The pharmacist is expected to refrain from employment in pharmacy for a designated period of time depending on the recommendations from the treatment provider. The client is required to provide random witnessed urine samples and any unauthorized positive results are reported to the employer. The client is responsible for maintaining weekly telephone contact with his or her assigned PEAC monitor. If the Board of Pharmacy executes a disciplinary order pertaining to the pharmacist’s license during the period of the treatment monitoring agreement PEAC will advocate and assist in the preparation and review of a dossier to be presented to the Board of Pharmacy for the purpose of appealing or modifying the conditions of the disciplinary order.3 Weekly telephone contact
Dentists
Physical Therapists
Occupational Therapists

Source 1: Maryland Physician Health Program. http://healthymaryland.org/physician-health/physician-health-program/

Source 2: Maryland Board of Nursing Rehabilitation Program. http://mbon.maryland.gov/Pages/rehab-program.aspx

Source 3: PEAC Maryland. http://www.peacmaryland.org/monitoring.html

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