Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2025 Feb 1.
Published in final edited form as: J Subst Use Addict Treat. 2023 Dec 28;157:209285. doi: 10.1016/j.josat.2023.209285

Primary Care Nurse Practitioner Work Environments and Emergency Department Utilization Among Older Adults with Substance Use Disorders in Rural Areas

Eleanor Turi a, Amy L McMenamin b, Grant Martsolf c, Deborah Hasin d,e, Benjamin H Han f, Jianfang Liu b, Lusine Poghosyan b,e
PMCID: PMC10922346  NIHMSID: NIHMS1959158  PMID: 38159910

Abstract

Introduction:

The prevalence of substance use disorders (SUDs) is growing among older adults, and older adults in rural areas face disparities in access to SUD care. Rural older adults with SUDs commonly have comorbid chronic conditions that puts them at risk for frequent acute healthcare utilization. In rural areas, primary care for patients with SUDs are increasingly provided by nurse practitioners (NPs), and quality primary care services may decrease ED visits in this population. Yet, NP-delivered primary care for rural older adults with SUDs may be limited by work environment barriers, which include lack of support, autonomy, and visibility. This study assessed the relationship between the NP work environment and ED utilization among rural older adults with SUDs.

Methods:

This was a secondary analysis of cross-sectional data from a large survey of NPs in six U.S. states merged with Medicare claims. The study measured the NP work environment by the four subscales of the Nurse Practitioner Primary Care Organizational Climate Questionnaire (NP-PCOCQ), which measure 1) independent practice and support, 2) NP-physician relations, 3) NP-administration, and 4) professional visibility. Multilevel logistic regression models, adjusted for practice and patient covariates, assess the relationship between the NP work environment and all-cause ED use.

Results:

The sample included 1,152 older adults with SUDs who received care at 126 rural NP primary care practices. NP independent practice and support at the practice was associated with 49% lower odds of all-cause ED visits among older adults with SUDs. There were no relationships between the other NP-PCOCQ subscales and all-cause ED visits.

Conclusions:

Organizational support for NP independent practice is associated with lower odds of all-cause ED utilization among rural older adults with SUDs. Practice administrators should ensure that NPs have access to support and resources to enhance their ability to care for rural older adults with SUDs. Ultimately, these practice changes could reduce ED utilization and health disparities in this population.

1 -. Introduction

Substance use disorders (SUDs), specifically those involving illicit drugs but excluding alcohol use disorder, are growing among older adults ages 65 and older (Substance Abuse and Mental Health Services Administration, 2021a). A recent study among veterans found that from 2016 to 2019, the prevalence of these SUDs increased annually by 20.2% among women ages 65 and older and 12.7% among men ages 65 and older (Hoggatt et al., 2023). In 2021, almost 2 million older adults had a SUD (Substance Abuse and Mental Health Services Administration, 2021a). Older adults with SUDs are at increased risk for geriatric conditions (i.e., falls; mobility, hearing, and visual impairment; urinary incontinence; medical morbidity; chronic pain; insomnia) compared to older adults without SUDs, indicating the need for patient-centered geriatric care for older adults with SUDs (Han et al., 2022).

While the prevalence of SUDs is relatively similar across rural and urban older adults (Substance Abuse and Mental Health Services Administration, 2021a), rural older adults have reduced access to specialty SUD care, which can lead to delays in receiving necessary treatment, costly acute care utilization, and increased mortality (Kuo et al., 2021; Romo et al., 2018). Providers and programs for SUD treatment are sparse in rural areas. Specifically, in 2019, 1,149 rural counties did not have a provider who could prescribe buprenorphine—a medication for opioid use disorder—compared to only 57 urban counties (Barnett et al., 2019). Also, only 20% of syringe service programs—which reduce harms related to drug use—are in rural areas (Des Jarlais et al., 2015; Sawangjit et al., 2017). Also, 65.5% of all primary care shortage areas in the United States (U.S.) are rural (Health Resources and Services Administration, 2023), indicating a broader lack of access to care (specialty SUD settings or primary care) in rural areas. When older adults in rural areas can connect with care, their options for independent transportation to the appointment are restricted. Public transportation is rarely available in rural areas and many older adults no longer drive (National Aging and Disability Transportation Center, 2023). In these cases, care could be accessed remotely, yet telehealth treatment is often inaccessible for rural older adults with SUDs because of limited broadband access and reduced ability to navigate complicated technology (Roberts & Mehrotra, 2020).

When rural older adults do not receive treatment for their SUDs in the community, they are at increased risk for drug overdoses, exacerbation of existing chronic diseases, and, in turn, acute emergency departments (ED) utilization (Duggirala et al., 2022; Yeboah-Sampong et al., 2021). Reactively treating SUDs in the ED instead of comprehensively managing SUDs in the community contributes to healthcare inefficiencies and crowding of the healthcare system (Agency for Healthcare Research and Quality, 2018; Hou et al., 2022). Treatment for SUDs in primary care settings is associated with reduced drug use, improved chronic disease outcomes, and decreased healthcare costs (McLellan et al., 2014; Patnode et al., 2020). The small number of primary care providers available in rural areas often deliver SUD services due to even sparser access to specialty SUD providers in rural areas (Rural Health Information Hub, 2022).

Primary care practices in rural areas increasingly rely on the nurse practitioner (NP) workforce to care for older adults with SUDs (Andrilla, Patterson, et al., 2020; Nelson et al., 2019). NPs are more likely than physicians to practice in rural areas (Barnes et al., 2018; Buerhaus, 2018; Doescher et al., 2014). NPs are trained to deliver person-centered care (i.e., holistic, individualized focus on clients’ needs, preferences, and goals, shared-decision making, and therapeutic alliances), which is associated with reduced substance use, engagement in SUD care, and positive patient reported SUD outcomes (American Association of Nurse Practitioners, 2022; Marchand et al., 2019). Moreover, NPs are the fastest-growing rural SUD treatment provider (Andrilla et al., 2018; Andrilla, Jones, et al., 2020; Barnett et al., 2019). Specifically, half of new rural providers of buprenorphine are NPs, and 70.5% of rural NPs report accepting new patients with opioid use disorder for buprenorphine treatment, compared to 56.2% of rural physicians (Andrilla et al., 2018; Andrilla, Jones, et al., 2020; Barnett et al., 2019). Other studies have found that as the number of NPs in a county increase, ED visits for SUDs decrease (Norful et al., 2021). Therefore, NP-delivered SUD primary care services may be key to reducing ED use in this population.

While NPs are increasingly vital to the care of rural older adults with SUDs, barriers exist that prevent them from delivering high-quality patient care. This includes characteristics of the NP work environment such as communication and teamwork between NPs, physicians, and administrators; support for NP practice including access to clinic resources and staff, as well as organizational policies that support autonomous NP practice; and visibility of the NP role, skills, and knowledge among clinic leaders and staff (Poghosyan et al., 2013a). A favorable NP work environment (e.g., collegiality between physicians and NPs, care management resources for NPs) is associated with reduced ED utilization among older adults with a range of chronic diseases (Poghosyan, Liu, et al., 2022). Another characteristic of a favorable NP work environment is NP autonomous practice, which includes the ability to deliver comprehensive, continuous care to an independent panel of patients over time (Poghosyan, Liu, et al., 2017). Studies have shown that being cared for by the same provider over time is associated with 50% lower odds of multiple ED visits among patients with SUDs (Kendall et al., 2017). Thus, NP autonomy, which is supported by favorable work environments, may reduce ED use among patients with SUDs. Finally, other aspects of the NP work environment may also be particularly important for SUD care, including clinical support (LaBelle et al., 2016) and collegiality (Auty et al., 2020), which are associated with increased prescribing of buprenorphine.

Overall, a favorable NP work environment has been shown to reduce ED visits for a wide range of conditions and may be particularly important for reducing ED use among patients with SUDs, yet none have looked at ED utilization among older adults with SUDs in rural areas. Therefore, this study aimed to assess the impact of the NP work environment in primary care practices on ED utilization among older adults with SUDs in rural areas.

2 -. Methods

2.1. Data Source and Study Design

This study was a secondary data analysis of an existing cross-sectional dataset. The parent study team merged survey data on NP work environment in rural primary care practices in 2018-2019 with 2018 Medicare Fee For Service claims (excluding Medicare Advantage), including the following files: Chronic Conditions Warehouse (A, B, and carrier), Beneficiary Summary File (base and 27 chronic conditions) and Long Term Care minimum; Harrison et al., 2021). From the Medicare files, this study pulled information on SUDs, comorbidities, ED utilization, and patient characteristics. Below is a brief overview of the parent study that generated the dataset. This study, as well as the parent study, adhered to appropriate human subject protections and received Columbia University Institutional Review Board approval.

2.1.1. NP Surveys

The parent survey team recruited primary care NPs for survey participation from November 2018 to October 2019 using IQVIA OneKey, a healthcare industry database with up-to-date data on almost all office-based clinicians in the U.S. (DesRoches et al., 2015; Harrison et al., 2021). IQVIA provides information on provider names, practice names and locations, contact information, network affiliation, and national provider identifiers (DesRoches et al., 2015; Harrison et al., 2021). The parent study team sampled NPs from 6 states that had varied scope of practice (SOP) policies that regulate NP ability to autonomously diagnose, treat, and prescribe to patients (AANP, 2021). At the time of the parent survey, Arizona and Washington had full SOP regulations (i.e., NPs had full practice authority), New Jersey and Pennsylvania had reduced SOP regulations (i.e., required collaboration with physicians), and California and Texas had restricted SOP regulations (i.e., required supervision by physicians; AANP, 2021; Harrison et al., 2021).

The parent study team employed a Dillman approach for mixed-mode surveys to collect data from NPs and maximize the response rate (Dillman et al., 2014; Harrison et al., 2021). All NPs received a mail survey, an online link for completing the survey, and a unique individual identifier (Harrison et al., 2021). The parent survey achieved a 21.9% response rate, with a total respondent sample of 1,244 NPs across 988 primary care practices (Harrison et al., 2021).

The parent survey team defined practices as rural or urban using Rural-Urban Commuting Area (RUCA) codes, which use standard census measures of population density, levels of urbanization, and journey-to-work commuting to characterize all U.S. census tracts as rural or urban (Health Resources and Services Administration, 2016; Rural Health Research Center). RUCA codes, which range from 1-10, are often aggregated into smaller categories (Health Resources and Services Administration, 2016; Rural Health Research Center). To determine if practices were in rural or urban areas, the parent study team aggregated RUCA codes according to Categorization D, which classifies urban areas as those where 30% or more of workers go to a Census Bureau defined Urbanized Area (Health Resources and Services Administration, 2016; Rural Health Research Center).

2.1.2. Medicare Claims

Medicare Claims provided data on SUDs, ED utilization, and patient characteristics. ICD-10-CM inpatient and outpatient diagnostic codes and the 27 chronic conditions warehouse provided data on SUDs and patient comorbidities. Primary billing codes from Medicare FFS Part A (inpatient-acute care) and B (outpatient-clinician services) identified ED utilization. The base Medicare Beneficiary Summary File provided patient characteristics. The Long Term Care Minimum Dataset identified community dwelling older adults.

2.2. Study Sample

For this study, we extracted data on patients who were 1) age 65 and older, 2) continuously enrolled in Medicare Part A and B during 2018, 3) had at least one Medicare claim filed by at least one of the providers (i.e., NPs, physicians) from the surveyed rural practices, 4) community dwelling (i.e., <100 nursing home days in 2018; Amjad et al., 2016; Hovsepian et al., 2023) and 5) had at least one service ICD-10-CM claim for a SUD in 2018 (related to use of opioids, cannabis, sedatives, hypnotics, anxiolytics, stimulants, caffeine, hallucinogens, inhalants, other psychoactive substances, and polysubstance use; (American Psychiatric Association, 2022). We utilized the Elixhauser grouping of ICD-10-CM codes for “drug abuse”, which bundles all SUD ICD-10-CM codes together regardless of severity (e.g., moderate), remission status (i.e., early or sustained), or setting (e.g., controlled; American Psychiatric Association, 2022; Quan et al., 2005). The Elixhauser index is frequently used in health services research to study population health trends (Austin et al., 2015), therefore is appropriate in our practice-level analysis.

The parent study team used a common attribution approach to attribute patients to practices where they received care (Mehrotra et al., 2010). First, they attributed patients to the clinician that had at least 30% of the primary care evaluation and management paid amounts for that beneficiary in 2019 (Harrison et al., 2021; Mehrotra et al., 2010). In the rare cases (<1%) of ties, the parent study team selected one primary care clinician (Harrison et al., 2021; Mehrotra et al., 2010). They attributed clinicians to the practice by matching the national provider identifiers of providers in the claims and IQVIA data (Harrison et al., 2021; Poghosyan, Liu, et al., 2022).

2.3. Measures

2.3.1. Work Environment

To capture the work environment of rural NP practices, NPs completed the Nurse Practitioner Primary Care Organizational Climate Questionnaire (NP-PCOCQ), which includes 29 items grouped in 4 subscales (Poghosyan, Chaplin, et al., 2017; Poghosyan et al., 2019; Poghosyan et al., 2013b). The 4 subscales are: NP-physician relations (NP-PR; 7 items) which assesses the relationship, communication, and teamwork between NPs and physicians (e.g., “In my organization, physicians and NPs practice as a team”); independent practice and support (IPS; 9 items) which evaluates NPs’ perceptions on whether they receive adequate support from their organization for independent practice (e.g., “Physicians and NPs have similar support for care management”); professional visibility (PV; 4 items) which examines how well NP role is understood within their organizations (e.g., “In my practice setting, staff members have a good understanding about NP roles in the organization”); and NP-administration relations (NP-AR; 9 items), which assesses the relationship between NPs and administrators, including communication and teamwork (e.g., “Administration is open to NP ideas to improve patient care”; Poghosyan, Chaplin, et al., 2017; Poghosyan et al., 2019; Poghosyan et al., 2013b).

The NP-PCOCQ has high internal consistency with Cronbach’s alphas ranging from 0.87 to 0.95 (Poghosyan et al., 2013b), as well as construct, discriminant, and predictive validity (Poghosyan, Chaplin, et al., 2017). Scores range from 1-4, and higher scores on the subscales indicate a more favorable work environment (Poghosyan, Chaplin, et al., 2017; Poghosyan et al., 2019; Poghosyan et al., 2013b). We measured the NP work environment at the provider level for each subscale. We then averaged the subscale scores across NPs from the same practice because the work environment is a characteristic of the organization, not the individual (Lake, 2007).

2.3.2. Emergency Department Utilization

We measured ED utilization via any all-cause ED visit in 2018. Our outcome variable includes all-cause visits instead of SUD-specific visits to fully capture the association between the NP work environment and outcomes among this population with high needs and rates of multimorbidity. Older adults with SUDs often visit the ED for reasons other than SUDs, such as falls or chronic disease exacerbations, which could be related to substance use (Han et al., 2023). Also, there were only about 50 SUD-specific visits in our sample, which was not enough to have the power to conduct an analysis. We categorized all-cause ED visits as a binary measure on the beneficiary level (one or more ED visits = 1; no ED visits = 0).

2.3.3. Covariates

We controlled for patient-level covariates, including sex (binary [biological], male or female), age (continuous), race/ethnicity (categorical, non-Hispanic White, Black, Hispanic, or other), and number of comorbidities, measured by the count of chronic conditions from a list of 15 conditions considered to be clinically important, not overly inclusive, and key diseases to include in health services research as determined by a workgroup at the Department of Health and Human Services (Goodman et al., 2013). We included the following conditions: Alzheimer’s/dementia, myocardial infarction, asthma, atrial fibrillation, cancer, congestive heart failure, chronic kidney disease, chronic obstructive pulmonary disease, depression, diabetes, hyperlipidemia, ischemic heart disease, osteoporosis, rheumatoid arthritis, and stroke (Goodman et al., 2013).

We also controlled for practice-level covariates, including structural capabilities, practice type, SOP, and practice size. Structural capabilities are attributes of practices that can enhance access and quality of chronic care delivery, including after-hours care, care coordination, and chronic disease registries (Friedberg, Coltin, et al., 2009; Friedberg, Safran, et al., 2009; Martsolf et al., 2018). To measure this, we utilized the Structural Capabilities Index (SCI), which measures structural capabilities associated with delivery of high-quality primary care services (Friedberg, Coltin, et al., 2009; Friedberg, Safran, et al., 2009; Martsolf et al., 2018). Scores on the SCI range from 0-1 and higher scores indicate that a greater number of structural capabilities are available in a practice (Germack et al., 2022). NPs self-reported practice type, which we categorized as: federally qualified health centers, hospital-based clinics, rural health centers, physician practices, or other/unsure. We categorized practice-level NP SOP as full, reduced, or restricted state-level regulations on NP autonomous practice. Finally, we measured practice size as the number of NPs in the clinic.

2.4. Data Analysis

First, we produced descriptive statistics of NP and patient demographics and characteristics of rural NP practices that serve older adults with SUDs. We assessed our data for multicollinearity using the variance inflation factor (VIF). All VIF values were less than 5, indicating no concerns of multicollinearity (James et al., 2021). We determined that missing data was less than 5% in the NP survey (Hosmer & Lemeshow, 1989). The parent study team conducted a nonresponse analysis (Harrison et al., 2021), and the potential of this bias impacting our results is outlined in our limitations section.

We utilized multilevel logistic regression to assess the effect of the work environment (i.e., 4 NP-PCOCQ subscales) on ED visits. Multilevel models account for the clustering effect of patients nested within NP practices and communities (Maas & Hox, 2005). Our sample had 2-levels: patients and practices. To assess within-cluster correlations, we assessed the intraclass correlation (ICC) of the unconditional model using the random intercept logistic model method (Wu et al., 2012). The ICC was 0.11, which is between 0.05-0.20, the recommended ICC for multilevel models (Snijders & Bosker, 1999). We ran bivariate models assessing the impact of the work environment subscales on all-cause ED use. Covariates included the other NP-PCOCQ subscales, patient demographics and count of comorbidities, practice structural capabilities, practice type, NP SOP, and practice size. To assess the strength and direction of the associations, we reported adjusted odds ratios, 95% confidence intervals, and p-values, using an alpha level of 0.05. We conducted all analyses in SAS 9.4.

3 -. Results

3.1. Demographic Characteristics of Older Adults With SUDs in Rural Areas

Our final sample included 1,152 older adults with SUDs who received care at surveyed rural NP practices who were confirmed eligible. The average age was 72.5 (standard deviation [SD] = 6.2). Most patients were female sex (55.7%) and identified as non-Hispanic White (88.0%). Patients had an average of 4.2 chronic conditions (SD = 2.3) and 569 (49.4%) had an all-cause ED visit. Table 1 presents the demographic characteristics of older adults with SUDs in rural areas.

Table 1.

Demographic Characteristics of Older Adults with SUDs in Rural Areas (n = 1,152)

Characteristics Value
Age – mean (SD) 72.5 (6.2)
Female sex – n (%) 642 (55.7)
Race/Ethnicity – n (%)
  Non-Hispanic White 1,014 (88.0)
  Black 53 (4.6)
  Hispanic 49 (4.3)
  Other 36 (3.1)
Number of chronic conditions – mean (SD) 4.21 (2.3)
Patients with an all-cause ED visit– n (%) 569 (49.4)
Open in a new tab

Note. n = number; SD = standard deviation

3.2. Characteristics of Rural NP Practices

There were 126 rural NP primary care practices that served at least one older adult with SUDs. On average, these practices cared for 9.1 older adults with SUDs (SD = 10.9), ranging from 1 to 80 patients. Of the 126 practices, many were physician practices (34.1%) and located in restricted SOP states (42.7%). On average, there were 2.7 (SD = 2.24) NPs in the practices. On a scale from 1-4, with 4 being a more favorable work environment, NPs rated their support for independent practice highest (3.5, SD = 0.5) and their relations with administrators lowest (2.8, SD = 0.7). They rated the professional visibility as 3.2 (SD = 0.7) and relations with physicians as 3.3 (SD = 0.5). Table 2 provides details regarding the characteristics of the rural NP practices.

Table 2.

Characteristics of Rural NP Practices Serving Older Adults with SUDs (n = 126)

Characteristic Value
Type of Clinic – n (%)
  Federally qualified health center 33 (26.2)
  Hospital-based clinic 8 (6.4)
  Physician practice 43 (34.1)
  Rural health center 23 (18.3)
  Other/unsure 19 (15.1)
Number of NPs in the practice – mean (SD) 2.67 (2.2)
State-level NP scope of practice regulations – n (%)
  Full 35 (27.8)
  Reduced 36 (28.6)
  Restricted 55 (42.7)
Number of Patients Served by Practice – mean (SD) 662.79 (652.9)
Number of Patients with SUDs – mean (SD) 9.14 (10.9)
Range of Patients with SUDs – low – high 1 – 80
Structural Capabilities – mean (SD) 0.57 (0.2)
Work Environment
  NP Independent Practice & Support – mean (SD) 3.45 (0.5)
  Professional Visibility – mean (SD) 3.15 (0.7)
  NP-Physician Relations – mean (SD) 3.29 (0.5)
  NP-Administration Relations – mean (SD) 2.83 (0.7)
Open in a new tab

Note. SUDs = substance use disorders; NP = nurse practitioner; SD = standard deviation

3.3. The NP Work Environment and ED Utilization

Among older adults with SUDs in rural areas, a one-unit increase in the independent practice and support score was associated with 49% lower odds of an all-cause ED visit (adjusted odds ratio (AOR) = 0.51, 95% confidence interval [CI] = 0.27 – 0.997, p-value = 0.049). There was no relationship between professional visibility (AOR = 1.20, 95% CI = 0.72 – 1.99, p-value = 0.49), NP-administrative relations (AOR = 1.36, 95% CI = 0.87 – 2.14, p-value = 0.18), or NP-physician relations (AOR = 1.05, 95% CI = 0.62 – 1.79, p-value = 0.85) and all-cause ED utilization. Table 3 provides details regarding our inferential analysis.

Table 3.

Relationship Between NP Primary Care Work Environment and All-Cause ED Utilization Among Older Adults with SUDs in Rural Areas (n = 1,152)

Variable Unadjusted Adjusted
Odds Ratio 95% Confidence Interval (Lower bound - Upper bound) P-value Adjusted Odds Ratio 95% Confidence Interval (Lower Bound - Upper Bound) P-value
Independent variable
Independent practice & support 0.75 0.50 – 1.11 0.15 0.51 0.27 – 0.997 0.049*
Professional visibility 0.96 0.72 - 1.27 0.76 1.20 0.72 – 1.99 0.49
NP-Administration Relations 1.06 0.80 - 1.39 0.70 1.36 0.87 – 2.14 0.18
NP-Physician Relations 0.97 0.69 - 1.38 0.88 1.05 0.62 – 1.79 0.85
Covariates
Age 0.99 0.97 – 1.01 0.39
Race/Ethnicity (referent = White)
  Black 1.00 0.53 – 1.91 0.99
  Hispanic 0.94 0.49 – 1.79 0.85
  Other 0.55 0.26 – 1.19 0.13
Gender (referent = male) 1.08 0.84 – 1.40 0.55
Average number of chronic conditions (continuous) 1.22 1.15 – 1.29 <0.0001*
Clinic type (referent = physician practice)
  Federally Qualified
Health Center 1.76 1.03 – 2.99 0.04*
  Hospital-based clinic 1.98 0.80 – 4.91 0.14
  Rural health clinic 1.39 0.78 – 2.45 0.26
  Other/unsure 1.08 0.60 – 1.95 0.79
NP Scope of practice regulations (referent = full)
  Reduced 1.67 0.95 – 2.92 0.07*
  Restricted 1.65 1.06 – 2.58 0.03*
Structural capabilities 0.68 0.28 – 1.65 0.39
Number of NPs in the practice 1.03 0.95 – 1.12 0.45
Open in a new tab

Note. NP – nurse practitioner; ED = emergency department; SUD = substance use disorder;

*

= p-value is less than 0.10

4 -. Discussion

We assessed the relationship between the NP work environment and ED utilization among older adults with SUDs in rural areas. We found that organizational support for NP independent practice was associated with reduced all-cause ED utilization. No relationships were found between NP-administrative relations, professional visibility of NPs, NP-physician relations, and ED utilization. Prior research shows that NPs consistently care for underserved populations, such as older adults with SUDs in rural areas (National Academy of Medicine, 2021; Poghosyan & Carthon, 2017). Our findings suggest that if NPs are supported by their organizations and health systems, they are well-positioned to help reduce rural SUD disparities.

Modifying organizational policies surrounding support for NP independent practice may help address the growing drug overdose epidemic in the U.S. (Ahmad et al., 2022). Organizational policies can go above and beyond state regulations to restrict NP practice (Pittman et al., 2020; Poghosyan, Pulcini, et al., 2022). For example, within the same state, institutions vary in their requirements for the number of physician supervision sessions (Chapman et al., 2019). We found that organizational policies that support NP independent care are associated with reduced all-cause ED utilization among older adults with SUDs in rural areas. This finding is consistent with prior research demonstrating that support for NP independent practice is associated with improved care delivery, quality of care, and reduced health services utilization among patients with chronic conditions (Carthon et al., 2022; Liu et al., 2014; Poghosyan, Liu, et al., 2022; Poghosyan et al., 2018). This evidence can guide organizations as they build work environments that support NP care delivery (Poghosyan, Pulcini, et al., 2022). Practice administrators should remove requirements for NPs to discuss all patient care with physicians and restrictions on application of NP knowledge and skills (Poghosyan et al., 2013a). Also, administrators should also ensure that NP access to care management and ancillary staff is equal to that of physicians in the practice (Poghosyan et al., 2013a). Our study suggests that such organizational innovations may reduce ED utilization among older adults with SUDs in rural areas and contribute to reducing rural-urban health disparities.

Our findings suggest that state-level SOP restrictions, which inhibit NP contributions to health care delivery (National Academy of Medicine, 2021), may impede NP care delivery for older adults with SUDs in rural areas. Specifically, compared to full SOP regulations, we found that state-level reduced and restricted NP SOP regulations were associated with higher odds of all-cause ED utilization among older adults with SUDs in rural areas. This is consistent with prior research demonstrating that compared to reduced or restricted SOP regulations, full SOP regulations are associated with improved access to primary care services and buprenorphine in rural areas (Nguyen et al., 2021; Xue et al., 2016; Yang et al., 2021). However, SOP was a covariate in our final model; thus, future research should directly assess the relationship between state-level SOP regulations and health services utilization in this population.

We were unable to control for the availability of SUD-specific treatments in primary care practices in our analysis, which could have influenced the relationship between the NP work environment and ED utilization. For example, we were unable to control for the availability of buprenorphine, which is an efficacious medication for opioid use disorders (a subset of SUDs) that reduces opioid overdoses and relapse (Substance Abuse and Mental Health Services Administration, 2021b). Increased buprenorphine availability in primary care practices may decrease ED utilization for SUDs because fewer patients may experience overdoses or relapse (Lee et al., 2018; Robbins et al., 2021). Further, the work environment could influence buprenorphine availability because structured clinical support for primary care providers is associated with increased buprenorphine prescribing in primary care practices (LaBelle et al., 2016). Also, NPs may be more likely to utilize buprenorphine when they have a positive and supportive relationship with their physician colleagues (Auty et al., 2020; Speight et al., 2023). Therefore, a practice with a favorable NP work environment may have more buprenorphine available. This may, in turn, improve patient outcomes and reduce ED visits for overdoses or chronic disease exacerbations. Future research should directly assess the relationships between the availability of buprenorphine or other SUD treatments, the NP work environment, and ED utilization.

We did not find a relationship between NP-physician relations, NP-administration relations, or professional visibility and ED utilization. These work environment domains capture concepts such as relationships, communication, and understanding of NP roles and skills that may not directly impact outcomes among older adults with SUDs in rural areas (Poghosyan. Chaplin, et al., 2017; Poghosyan et al., 2019; Poghosyan et al., 2013b). Future research should replicate our study with a different sample to determine if these domains impact patient outcomes. Other studies have found that NP-physician and administrator relationships, teamwork, and visibility are associated with primary care NP job outcomes such as burnout, turnover, and intention to leave (Abraham et al., 2021; Kueakomoldej et al., 2022). We did not capture NP job outcomes in this study. Therefore, NP-physician relations, NP-administration relations, and professional visibility may have impacted NP job outcomes in our sample, while other aspects of the work environment—such as NP independent practice and support—impacted patient outcomes. Future research should include measures of NP job outcomes such as burnout, turnover, and intention to leave. These factors may directly impact patient outcomes such as ED use among older adults with SUDs in rural areas.

This study has limitations. Our data was cross-sectional; therefore, causality cannot be determined. We conducted our study in six U.S. states; thus, our findings may not be generalizable nationally. We also had a small sample size of 1,152 older adults with SUDs in rural areas. Future research should aim to replicate our results with a larger sample size and in other geographical areas. NPs self-reported their perceptions of the work environment. Although the study team assured NPs their survey responses would be kept confidential, their responses may have been biased by social desirability (Grimm, 2010). The researchers of the parent study made multiple efforts to maximize the low response rate of 21.9%, including multiple mail and email surveys, postcard reminders, and phone call follow-ups (Harrison et al., 2021). Survey respondents were somewhat more likely to practice in rural settings and be the only NP working in their practice, while non-respondents who could not be reached by phone were more likely to work in a state with full SOP regulations (Harrison et al., 2021). These patterned characteristics of NPs who responded to the survey may introduce bias and could have influenced our results. However, this response rate is typical of large-scale provider surveys (Brooks-Carthon et al., 2020), and the demographics (age, gender, race, ethnicity, years in practice, full/part time) of our sample are very similar to the national sample of NPs in the 2020 National Nurse Practitioner Sample Survey (AANP, 2020). Other practice and community level factors not measured in this study could have influenced outcomes in this population. Future research should aim to capture some of these factors, such as the availability of social services for SUDs (e.g., hotlines or websites for crisis intervention) and funding for public health initiatives. Overall, we believe our findings are novel and an important contribution to the nascent literature base on NP care for older adults with SUDs in rural areas.

5 -. Conclusions

We found that organizational support for NP independent practice—through adequate access to resources and policies that allow NPs to practice to the full scope of their licensure—is associated with reduced all-cause ED utilization among older adults with SUDs in rural areas. Practice administrators should modify organizational policies and structures to ensure that NPs can fully contribute to the care for this underserved population. Future research should study the associations between SUD treatment availability in primary care practices, the NP work environment, and ED utilization. Ultimately, improving the NP work environment may address the unique needs of rural older adults with SUDs.

Highlights.

  • Rural older adults with substance use disorders frequently use the emergency room.

  • Relations, teamwork, autonomy, and support may impact nurse practitioner (NP) care.

  • Support for NP independent practice associated with less emergency room use.

  • Organizational administrators should ensure NP autonomy and access to support.

Funding:

E.T. is funded by T32MH109433 and the National Clinician Scholars Program. A.M. is supported by R36HS029435; This study was funded by R01MD011514, PI: Poghosyan and K23DA043651, PI Han.

Footnotes

Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Conflicts of Interest: The authors have no conflicts of interest to disclose.

CRediT author statement:

Eleanor Turi: Conceptualization, Software, Formal Analysis, Writing – Original Draft, Writing – Review & Editing Amy L. McMenamin: Writing – Original Draft, Writing – Review & Editing Grant Martsolf: Writing – Original Draft, Writing – Review & Editing Deborah Hasin: Writing – Original Draft, Writing – Review & Editing Benjamin Han: Writing – Original Draft, Writing – Review & Editing Jianfang Liu: Software, Formal Analysis, Writing – Original Draft, Writing – Review & Editing Lusine Poghosyan: Conceptualization, Supervision, Writing – Original Draft, Writing – Review & Editing

References

  1. AANP. (2020). The state of the nurse practitioner profession 2020 (Results from the National Nurse Practitioner Sample Survey, Issue. https://www.aanp.org/practice/practice-related-research/research-reports
  2. AANP. (2021). State practice environment. Retrieved February 4th, 2022 from https://www.aanp.org/advocacy/state/state-practice-environment
  3. Abraham CM, Zheng K, Norful AA, Ghaffari A, Liu J, & Poghosyan L (2021, Feb). Primary care practice environment and burnout among nurse practitioners. Journal for Nurse Practitioners, 17(2), 157–162. 10.1016/j.nurpra.2020.11.009 [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Agency for Healthcare Research and Quality. (2018). Preventable emergency department visits. Retrieved February 10th, 2022 from https://www.ahrq.gov/research/findings/nhqrdr/chartbooks/carecoordination/measure2.html
  5. Ahmad F, Rossen L, & Sutton P (2022). Provisional drug overdose death counts. Retrieved February 12th, 2022 from https://www.cdc.gov/nchs/nvss/vsrr/drug-overdose-data.htm
  6. American Association of Nurse Practitioners. (2022). Standards of practice for nurse practitioners. Retrieved February 13, 2023 from https://www.aanp.org/advocacy/advocacy-resource/position-statements/standards-of-practice-for-nurse-practitioners
  7. American Psychiatric Association. (2022). Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, Text Revision. [Google Scholar]
  8. Amjad H, Carmichael D, Austin AM, Chang CH, & Bynum JP (2016, Sep 1). Continuity of care and health care utilization in older adults with dementia in fee-for-service medicare. JAMA Internal Medicine, 176(9), 1371–1378. 10.1001/jamainternmed.2016.3553 [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Andrilla CHA, Coulthard C, & Patterson DG (2018). Prescribing practices of rural physicians waivered to prescribe buprenorphine. American Journal of Preventive Medicine, 54(6 Suppl 3), S208–s214. 10.1016/j.amepre.2018.02.006 [DOI] [PubMed] [Google Scholar]
  10. Andrilla CHA, Jones KC, & Patterson DG (2020). Prescribing practices of nurse practitioners and physician assistants waivered to prescribe buprenorphine and the barriers they experience prescribing buprenorphine. Journal of Rural Health, 36(2), 187–195. 10.1111/jrh.12404 [DOI] [PubMed] [Google Scholar]
  11. Andrilla CHA, Patterson DG, Moore TE, Coulthard C, & Larson EH (2020). Projected contributions of nurse practitioners and physicians assistant to buprenorphine treatment services for opioid use disorder in rural areas. Medical Care Research & Review, 77(2), 208–216. 10.1177/1077558718793070 [DOI] [PubMed] [Google Scholar]
  12. Austin SR, Wong Y-N, Uzzo RG, Beck JR, & Egleston BL (2015). Why summary comorbidity measures such as the charlson comorbidity index and elixhauser score work. Medical Care, 53(9), e65–e72. https://www.jstor.org/stable/26418053 [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Auty SG, Stein MD, Walley AY, & Drainoni ML (2020). Buprenorphine waiver uptake among nurse practitioners and physician assistants: the role of existing waivered prescriber supply. Journal of Substance Abuse Treatment, 115, 108032. 10.1016/jjsat.2020.108032 [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Barnes H, Richards MR, McHugh MD, & Martsolf G (2018). Rural and nonrural primary care physician practices increasingly rely on nurse practitioners. Health Affairs (Millwood), 37(6), 908–914. 10.1377/hlthaff.2017.1158 [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Barnett ML, Lee D, & Frank RG (2019). In rural areas, buprenorphine waiver adoption since 2017 driven by nurse practitioners and physician assistants. Health Affairs (Millwood), 38(12), 2048–2056. 10.1377/hlthaff.2019.00859 [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Brooks-Carthon JM, Brom H, Poghosyan L, Daus M, Todd B, & Aiken L (2020). Supportive clinical practice environments associated with patient-centered care. The Journal for Nurse Practitioners, 16(4), 294–298. 10.1016/j.nurpra.2020.01.019 [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Buerhaus P. (2018). Nurse practitioners: a solution to america’s primary care crisis. https://www.aei.org/wp-content/uploads/2018/09/Nurse-practitioners.pdf?x91208
  18. Carthon MB, Brom H, Nikpour J, Todd B, Aiken L, & Poghosyan L (2022). Supportive practice environments are associated with higher quality ratings among nurse practitioners working in underserved areas. Journal of Nursing Regulation, 13(1), 5–12. 10.1016/s2155-8256(22)00028-x [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Chapman SA, Toretsky C, & Phoenix BJ (2019). Enhancing psychiatric mental health nurse practitioner practice: Impact of state scope of practice regulations. Journal of Nursing Regulation, 10(1), 35–43. 10.1016/S2155-8256(19)30081-X [DOI] [Google Scholar]
  20. Des Jarlais DC, Nugent A, Solberg A, Feelemyer J, Mermin J, & Holtzman D (2015). Syringe service programs for persons who inject drugs in urban, suburban, and rural areas - United States, 2013. The Morbidity and Mortality Weekly Report, 64(48), 1337–1341. 10.15585/mmwr.mm6448a3 [DOI] [PubMed] [Google Scholar]
  21. DesRoches CM, Barrett KA, Harvey BE, Kogan R, Reschovsky JD, Landon BE, … & Rich EC (2015). The results are only as good as the sample: Assessing three national physician sampling frames. Journal of General Internal Medicine, 30 Suppl 3(Suppl 3), S595–601. 10.1007/sn606-015-3380-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Dillman D, Smyth J, & Christian L (2014). Internet, phone, mail, and mixed-mode surveys: The tailored design method. In (Vol. 4). John Wiley & Sons, Inc. [Google Scholar]
  23. Doescher MP, Andrilla CH, Skillman SM, Morgan P, & Kaplan L (2014). The contribution of physicians, physician assistants, and nurse practitioners toward rural primary care: findings from a 13-state survey. Medical Care, 52(6), 549–556. 10.1097/MLR.0000000000000135 [DOI] [PubMed] [Google Scholar]
  24. Duggirala R, Khushalani S, Palmer T, Brandt N, & Desai A (2022). Screening for and management of opioid use disorder in older adults in primary care. Clinics in Geriatric Medicine, 38(1), 23–38. 10.1016/j.cger.2021.07.001 [DOI] [PubMed] [Google Scholar]
  25. Friedberg MW, Coltin KL, Safran DG, Dresser M, Zaslavsky AM, & Schneider EC (2009, Oct 6). Associations between structural capabilities of primary care practices and performance on selected quality measures. Annals of Internal Medicine, 151(7), 456–463. 10.7326/0003-4819-151-7-200910060-00006 [DOI] [PubMed] [Google Scholar]
  26. Friedberg MW, Safran DG, Coltin KL, Dresser M, & Schneider EC (2009). Readiness for the patient-centered medical home: Structural capabilities of Massachusetts primary care practices. Journal of General Internal Medicine, 24(2), 162–169. 10.1007/s11606-008-0856-x [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Germack HD, Harrison J, Poghosyan L, & Martsolf GR (2022). Practice patterns, work environments, and job outcomes of rural and urban primary care nurse practitioners. Medical Care Research and Review, 79(1), 161–170. 10.1177/1077558720974537 [DOI] [PubMed] [Google Scholar]
  28. Goodman RA, Posner SF, Huang ES, Parekh AK, & Koh HK (2013). Defining and measuring chronic conditions: imperatives for research, policy, program, and practice. Preventing Chronic Disease, 10, E66. 10.5888/pcdl0.120239 [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Grimm P. (2010). Social desirability bias. In Wiley International Encyclopedia of Marketing. 10.1002/9781444316568.wiem02057 [DOI] [Google Scholar]
  30. Han BH, Brennan JJ, Orozco MA, Moore AA, & Castillo EM (2023). Trends in emergency department visits associated with cannabis use among older adults in California, 2005-2019. Journal of the American Geriatric Society. 10.llll/jgs.18180 [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Han BH, Cotton BP, Polydorou S, Sherman SE, Ferris R, Arcila-Mesa M, … & McNeely J (2022, Jan-Feb 01). Geriatric conditions among middle-aged and older adults on methadone maintenance treatment: A pilot study. Journal of Addiction Medicine, 16(1), 110–113. 10.1097/adm.0000000000000808 [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Harrison JM, Germack HD, Poghosyan L, D’Aunno T, & Martsolf GR (2021). Methodology for a six-state survey of primary care nurse practitioners. Nurings Outlook, 69(4), 609–616. 10.1016/j.outlook.2021.01.010 [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Health Resources and Services Administration. (2016). List of rural counties and designated eligible census tracts in metropolitan counties. Retrieved March 11th, 2022 from https://www.hrsa.gov/sites/default/files/ruralhealth/resources/forhpeligibleareas.pdf
  34. Health Resources and Services Administration. (2023). Health Workforce Shortage Areas. Retrieved October 13th, 2023 from https://data.hrsa.gov/topics/health-workforce/shortage-areas
  35. Hoggatt KJ, Chawla N, Washington DL, & Yano EM (2023). Trends in substance use disorder diagnoses among Veterans, 2009-2019. American Journal of Addiction. 10.Ill1/ajad.13413 [DOI] [PubMed] [Google Scholar]
  36. Hosmer D, & Lemeshow S (1989). Applied Logistic Regression (1 ed.). John Wiley & Sons, Inc. [Google Scholar]
  37. Hou X, Liu L, & Cain J (2022, Dec). Can higher spending on primary healthcare mitigate the impact of ageing and non-communicable diseases on health expenditure? BMJ Global Health, 7(12). 10.1136/bmjgh-2022-010513 [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Hovsepian VE, Sadak T, Schlak AE, Liu J, & Poghosyan L (2023, Jul). The association between primary care practices’ structural capabilities and hospitalizations among persons living with dementia. Journal of Applied Gerontology, 42(7), 1414–1423. 10.1177/07334648231155444 [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. James G, Witten D, Hastie T, & Tibshirani R (2021). An introduction to statistical learning (2nd ed.). Springer. [Google Scholar]
  40. Kendall CE, Boucher LM, Mark AE, Martin A, Marshall Z, Boyd R, … & Bayoumi AM (2017, May 12). A cohort study examining emergency department visits and hospital admissions among people who use drugs in Ottawa, Canada. Harm Reduction Journal, 14(1), 16. 10.1186/sl2954-017-0143-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Kueakomoldej S, Liu J, Pittman P, Turi E, & Poghosyan L (2022). Practice environment and workforce outcomes of nurse practitioners in community health centers. Journal of Ambulatory Care Management, 45(A), 289–298. 10.1097/jac.0000000000000427 [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Kuo Y-F, Baillargeon J, & Raji MA (2021). Overdose deaths from nonprescribed prescription opioids, heroin, and other synthetic opioids in Medicare beneficiaries. Journal of Substance Abuse Treatment, 124. 10.1016/jjsat.2021.108282 [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. LaBelle CT, Han SC, Bergeron A, & Samet JH (2016, Jan). Office-based opioid treatment with buprenorphine (OBOT-B): Statewide implementation of the Massachusetts Collaborative Care Model in community health centers. Journal of Substance Abuse Treatment, 60, 6–13. 10.1016/jjsat.2015.06.010 [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Lake ET (2007, Apr). The nursing practice environment: measurement and evidence. Medical Care Research and Review, 64(2 Suppl), 104s-122s. 10.1177/1077558707299253 [DOI] [PubMed] [Google Scholar]
  45. Lee JD, Nunes EV Jr., Novo P, Bachrach K, Bailey GL, Bhatt S, …& Rotrosen J (2018). Comparative effectiveness of extended-release naltrexone versus buprenorphine-naloxone for opioid relapse prevention (X:BOT): a multicentre, open-label, randomised controlled trial. Lancet, 391(10118), 309–318. 10.1016/S0140-6736(17)32812-X [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Liu N, Finkelstein SR, & Poghosyan L (2014). A new model for nurse practitioner utilization in primary care: Increased efficiency and implications. Health Care Management Reiewv, 39(1), 10–20. 10.1097/HMR.0b013e318276fadf [DOI] [PubMed] [Google Scholar]
  47. Maas CJM, & Hox JJ (2005). Sufficient sample sizes for multilevel modeling. Methodology: European Journal of Research Methods for the Behavioral and Social Sciences, 1(3), 86–92. 10.1027/1614-2241.L3.86 [DOI] [Google Scholar]
  48. Marchand K, Beaumont S, Westfall J, MacDonald S, Harrison S, Marsh DC, … & Oviedo-Joekes E (2019). Conceptualizing patient-centered care for substance use disorder treatment: findings from a systematic scoping review. Substance Abuse Treatment, Prevention, and Policy, 14(1), 37. 10.1186/sl3011-019-0227-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Martsolf GR, Ashwood S, Friedberg MW, & Rodriguez HP (2018). Linking structural capabilities and workplace climate in community health centers. Inquiry, 55, 46958018794542. 10.1177/0046958018794542 [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. McLellan AT, Starrels JL, Tai B, Gordon AJ, Brown R, Ghitza U, … & McNeely J (2014). Can substance use disorders be managed using the chronic care model? Review and recommendations from a NIDA consensus group. Public Health Review, 35(2). 10.1007/BF03391707 [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. Mehrotra A, Adams JL, Thomas JW, & McGlynn EA (2010, May 18). The effect of different attribution rules on individual physician cost profiles. Annals of Internal Medicine, 152(10), 649–654. 10.7326/0003-4819-152-10-201005180-00005 [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. National Academy of Medicine. (2021). The future of nursing 2020-2030: charting a path to achieve health equity. The National Academies Press. 10.17226/25982 [DOI] [PubMed] [Google Scholar]
  53. National Aging and Disability Transportation Center. (2023). Older adults & transportation. Retrieved March 6, 2023 from https://www.nadtc.org/about/transportation-aging-disability/unique-issues-related-to-older-adults-and-transportation/
  54. Nelson LE, McMahon JM, Leblanc NM, Braksmajer A, Crean HF, Smith K, & Xue Y (2019). Advancing the case for nurse practitioner-based models to accelerate scale-up of HIV pre-exposure prophylaxis. Journal of Clinical Nursing, 28(1-2), 351–361. 10.Ill1/jocn.14675 [DOI] [PubMed] [Google Scholar]
  55. Nguyen T, Muench U, Andraka-Christou B, Simon K, Bradford WD, & Spetz J (2021). The association between scope of practice regulations and nurse practitioner prescribing of buprenorphine after the 2016 opioid bill. Medical Care Research and Review, 10775587211004311. 10.l177/10775587211004311 [DOI] [PMC free article] [PubMed] [Google Scholar]
  56. Norful AA, de Jacq K, Liu J, Ye S, Khadka S, & Poghosyan L (2021). The impact of nurse practitioner and physician assistant workforce supply on Medicaid-related emergency department visits and hospitalizations. Journal of the American Association of Nurse Practitioners, 33(12), 1190–1197. 10.1097/JXX.0000000000000542 [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. Patnode CD, Perdue LA, Rushkin M, Dana T, Blazina I, Bougatsos C, … & Chou R (2020). Screening for unhealthy drug use: updated evidence report and systematic review for the US Preventive Services Task Force. JAMA, 323(22), 2310–2328. 10.1001/jama.2019.21381 [DOI] [PubMed] [Google Scholar]
  58. Pittman P, Leach B, Everett C, Han X, & McElroy D (2020). NP and PA privileging in acute care settings: Do scope of practice laws matter? Medical Care Research & Review, 77(2), 112–120. 10.1177/1077558718760333 [DOI] [PubMed] [Google Scholar]
  59. Poghosyan L, & Carthon JMB (2017). The untapped potential of the nurse practitioner workforce in reducing health disparities. Policy, Politics, & Nursing Practice, 18(2), 84–94. 10.1177/1527154417721189 [DOI] [PubMed] [Google Scholar]
  60. Poghosyan L, Chaplin W, & Shaffer J (2017). Validation of Nurse Practitioner Primary Care Organizational Climate Questionnaire: a new tool to study nurse practitioner practice settings. Journal of Nursing Measurement, 25(1), 142–155. 10.1891/1061-3749.25.1.142 [DOI] [PubMed] [Google Scholar]
  61. Poghosyan L, Ghaffari A, & Shaffer J (2019). Nurse practitioner primary care organizational climate questionnaire: item response theory and differential item functioning. Journal of Clinical Nursing, 28(15-16), 2934–2945. 10.llll/jocn.14895 [DOI] [PubMed] [Google Scholar]
  62. Poghosyan L, Liu J, & Norful AA (2017). Nurse practitioners as primary care providers with their own patient panels and organizational structures: A cross-sectional study. International Journal of Nursing Studies, 74, 1–7. 10.1016/j.ijnurstu.2017.05.004 [DOI] [PMC free article] [PubMed] [Google Scholar]
  63. Poghosyan L, Liu J, Perloff J, D’Aunno T, Cato KD, Friedberg MW, & Martsolf G (2022). Primary care nurse practitioner work environments and hospitalizations and ED use among chronically ill Medicare beneficiaries. Medical Care, 60(7), 496–503. 10.1097/MLR.0000000000001731 [DOI] [PMC free article] [PubMed] [Google Scholar]
  64. Poghosyan L, Nannini A, & Clarke S (2013a). Organizational climate in primary care settings: implications for nurse practitioner practice. Journal of the American Association of Nurse Practitioners, 25(3), 134–140. 10.llll/j.1745-7599.2012.00765.x [DOI] [PubMed] [Google Scholar]
  65. Poghosyan L, Nannini A, Finkelstein S, Mason E, & Shaffer J (2013b). Development and psychometric testing of the Nurse Practitioner Primary Care Organizational Climate Questionnaire. Nursing Research, 62(5), 325–334. 10.1097/NNR.0b013e3182al31d2 [DOI] [PubMed] [Google Scholar]
  66. Poghosyan L, Norful A, Liu J, & Friedberg M (2018). Nurse practitioner practice environments in primary care and quality of care for chronic diseases. Medical Care, 56(9), 791–797. 10.1097/MLR.0000000000000961 [DOI] [PMC free article] [PubMed] [Google Scholar]
  67. Poghosyan L, Pulcini J, Chan GK, Dunphy L, Martsolf GR, Greco K, … & Solari-Twadell PA (2022). State responses to COVID-19: Potential benefits of continuing full practice authority for primary care nurse practitioners. Nursing Outlook, 70(1), 28–35. 10.1016/j.outlook.2021.07.012 [DOI] [PMC free article] [PubMed] [Google Scholar]
  68. Quan H, Sundararajan V, Halfon P, Fong A, Burnand B, Luthi JC, Saunders LD, Beck CA, Feasby TE, & Ghali WA (2005). Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data. Medical Care, 43(11), 1130–1139. 10.1097/01.mlr.0000182534.19832.83 [DOI] [PubMed] [Google Scholar]
  69. Robbins M, Haroz R, Mazzarelli A, Clements D, Jones CW, & Salzman M (2021, 2021/November/01/). Buprenorphine use and disparities in access among emergency department patients with opioid use disorder: A cross-sectional study. Journal of Substance Abuse Treatment, 130, 108405. 10.1016/j.jsat.2021.108405 [DOI] [PubMed] [Google Scholar]
  70. Roberts ET, & Mehrotra A (2020). Assessment of disparities in digital access among Medicare beneficiaries and implications for telemedicine. JAMA Internal Medicine, 180(10), 1386–1389. 10.1001/jamainternmed.2020.2666 [DOI] [PMC free article] [PubMed] [Google Scholar]
  71. Romo E, Ulbricht CM, Clark RE, & Lapane KL (2018). Correlates of specialty substance use treatment among adults with opioid use disorders. Addictive Behaviors, 86, 96–103. https://doi.org/https://doi.org/10.1016/j.addbeh.2018.03.012 [DOI] [PubMed] [Google Scholar]
  72. Rural Health Information Hub. (2022). Healthcare access in rural communities. Retrieved April 5, 2023 from https://www.ruralhealthinfo.org/topics/healthcare-access
  73. Rural Health Research Center. Rural-urban commuting area codes (RUCAs). Retrieved March 11th, 2022 from http://depts.washington.edu/uwruca/.
  74. Sawangjit R, Khan TM, & Chaiyakunapruk N (2017). Effectiveness of pharmacy-based needle/syringe exchange programme for people who inject drugs: a systematic review and meta-analysis. Addiction, 112(2), 236–247. 10.1111/add.13593 [DOI] [PubMed] [Google Scholar]
  75. Snijders T, & Bosker R (1999). Multilevel analysis. Thousand Oaks, CA: Sage. [Google Scholar]
  76. Speight C, Caiola C, Tyndall DE, & Scott ES (2023, Feb 1). Barriers and facilitators to nurse practitioner buprenorphine prescribing for opioid use disorder in primary care settings. Journal of the American Association of Nurse Practitioners, 35(2), 112–121. 10.1097/jxx.0000000000000811 [DOI] [PubMed] [Google Scholar]
  77. Substance Abuse and Mental Health Services Administration. (2021a). Results from the National Survey on Drug Use and Health: 2021 detailed tables. Retreived October 20th, 2021 from https://www.samhsa.gov/data/report/2021-nsduh-detailed-tables
  78. Substance Abuse and Mental Health Services Administration. (2021b). What is buprenorphine? Retrieved October 22nd, 2021 from https://www.samhsa.gov/medication-assisted-treatment/medications-counseling-related-conditions/buprenorphine
  79. Wu S, Crespi CM, & Wong WK (2012). Comparison of methods for estimating the intraclass correlation coefficient for binary responses in cancer prevention cluster randomized trials. Contemporary Clinical Trials, 33(5), 869–880. 10.1016/j.cct.2012.05.004 [DOI] [PMC free article] [PubMed] [Google Scholar]
  80. Xue Y, Ye Z, Brewer C, & Spetz J (2016). Impact of state nurse practitioner scope-of-practice regulation on health care delivery: systematic review. Nursing Outlook, 64(1), 71–85. 10.1016/j.outlook.2015.08.005 [DOI] [PubMed] [Google Scholar]
  81. Yang BK, Johantgen ME, Trinkoff AM, Idzik SR, Wince J, & Tomlinson C (2021). State nurse practitioner practice regulations and U.S. health care delivery outcomes: A systematic review. Medical Care Research and Review, 78(3), 183–196. 10.1177/1077558719901216 [DOI] [PubMed] [Google Scholar]
  82. Yeboah-Sampong S, Weber E, & Friedman S (2021). Emergency: hospitals are violating federal law by denying required care for substance use disorders in emergency departments. https://americanhealth.jhu.edu/themes/custom/bahi/assets/pdfs/LAC_Report.pdf

RESOURCES