The Washington and Ohio Worker (WOW) Study: Comparing State Payer Strategies to Prevent Unsafe Opioid Prescribing
Conditions: Opioid Use, Opioid-Related Disorders
Sponsor: University of Washington

Comparative Effectiveness of Two State Payer Strategies to Prevent Unsafe Opioid Prescribing  
(PCORI UOP-2017C2-8509) 
 
Study Protocol 
 
November 1, 2022 
 
 
 
 

Original: 4/16/2019 
 
2 
Revised: 3/4/2020, 12/15/2020, 11/1/2022 
1. Background  
 
Use and misuse of opioids is now recognized as a major U.S. public health problem, leading to significant 
morbidity and mortality. While most acute pain episodes (0-6 weeks) resolve, some go on to subacute 
pain (6-12 weeks), and a minority of individuals develop chronic pain (> 3 months). Musculoskeletal 
(MSK) conditions were among the top 5 conditions contributing to years lived with disability (YLD) in the 
U.S. between 1990 and 2010. Common MSK conditions are accounting for an increasingly large fraction 
of long-term work disability in the U.S. Importantly, these are also the patients who have received 
disproportionate amounts of chronic opioids, accounting for the largest relative costs to Medicare. 
Prescribing of both opioids and concurrent sedative-hypnotics for acute and chronic MSK pain increased 
dramatically from 2001 to 2010.  
 
The body of evidence to date suggests that, among individuals with non-cancer pain: (1) even short-
term opioid use early after injury is associated with greater risk of long-term opioid use and disability; 
(2) opioid use poses risks of serious harms, including overdose death, particularly when prescribed at 
high doses and/or concurrently with sedative-hypnotic medications; and (3) evidence is lacking 
concerning effectiveness of long-term opioid therapy for chronic pain. The clearest path to preventing 
transition to chronic opioid use and associated morbidity or addiction is to reduce unnecessary 
prescribing during the acute and subacute phases of pain.  
 
Both prior authorization (PA) and other insurer-based drug utilization programs are increasingly being 
used in an attempt to stem the tide of the opioid epidemic and to reduce unsafe opioid prescribing. 
Review programs have proven effective in decreasing use and overall costs of more expensive 
medications. This study focuses on two states, Washington (WA) and Ohio (OH), which have witnessed 
the devastating consequences of the growing opioid crisis. WA was an early leader in state efforts to 
combat the opioid crisis; OH’s efforts have come more recently. WA and OH offer a unique opportunity 
to study opioid prescribing interventions because both states have regulatory authority over health care 
delivery to all injured workers, and are the two largest states with a population-based exclusive State 
Fund. Further, both states have initiated distinct prescribing guideline-concordant opioid review 
programs (ORPs), allowing for a unique natural experiment to compare the effectiveness of two 
substantially different approaches to reducing unsafe opioid prescribing. 
 
2. Research Objectives 
 
Specific aim 1. Examine the comparative effectiveness of prospective prior authorization (PA) with hard 
stops in WA versus retrospective review (RR) with prescriber notification in OH in reducing unsafe opioid 
prescribing.  
Hypothesis 1. Prospective PA with hard stops (WA) will be associated with statistically significantly less 
unsafe opioid prescribing compared to RR and prescriber notification (OH). 
 
Specific aim 2. Examine the comparative effectiveness of WA’s PA program versus OH’s RR program 
with regard to patient reported outcomes (pain, function, quality of life, return to work), harms 
(presence of opioid use disorder, opioid poisoning events) and work disability outcomes at 6 weeks, 6 
months, and 12 months from date of injury.  
Hypothesis 2. Prospective PA with hard stops (WA) will be associated with statistically significantly 
improved patient outcomes compared to RR and prescriber notification (OH). 
 
Specific aim 2A. For injured workers in WA, examine unsafe opioid prescribing and patient-reported 

Original: 4/16/2019 
 
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Revised: 3/4/2020, 12/15/2020, 11/1/2022 
outcomes and work disability at 6 and 12 months for patients receiving an established, coordinated, 
stepped care management program during the first 12 weeks following injury plus prospective PA versus 
patients receiving usual care subject to PA only.  
Hypothesis 2A. Specialized care delivered through a stepped care management program plus 
prospective PA will be associated with statistically significantly lower rates of unsafe opioid prescribing 
and improved patient outcomes compared to patients receiving usual care and PA only.   
 
Specific aim 3. Using qualitative methods, identify key environmental, programmatic and policy factors 
that influenced the design, implementation and impact of the PA program in WA and the RR program in 
OH. This information will lead to the development of an Opioid Review Tool educational module for 
dissemination to both public and private payers.  
 
3. Study Design 
 
Study type: Observational cohort study, with prospective and retrospective components. Aims 1 and 2 
rely primarily on administrative data for a prospective cohort, with longitudinal surveys conducted 
among a subset of the cohort. Aim 1 also includes a retrospective time series analysis. Aim 3 relies on 
qualitative research, including individual in-depth interviews and key informant interviews. 
 
4. Study Population and Sample 
 
Study Population and Setting 
 
Workers’ Compensation (WC). WC provides no-fault industrial insurance coverage for most employers 
and workers in WA and OH. Benefits include medical treatment for workers who are injured or develop 
an occupational disease as a result of their work activities. Workers with an accepted WC claim may be 
eligible for partial wage replacement benefits (time loss) after 3 lost work days in WA and after 7 lost 
work days in OH. WA and OH are the largest 2 of only 4 states that have an exclusive State Fund—no 
private WC insurance companies operate in these states, which makes them particularly well-suited to 
population-based research. WC covers >97% of jobs covered by unemployment insurance in both states, 
and the share of all WC benefits paid by the two State Funds (vs. self-insurance) are very similar; 82% for 
OH and 78% for WA. The populations covered by the WA and OH WC programs are representative of 
gender, age, race, and ethnicity in the working populations in the two states.  
 
WA Department of Labor and Industries (DLI). The DLI State Fund provides WC insurance for over 2/3 
of workers in WA (the remainder work for large self-insured companies and will be excluded due to 
insufficient available data). Roughly 93,000 State Fund WC claims are accepted annually. Of these, 
almost 20% involve time loss benefits. Of all accepted WC claims, 32% are women and 68% are men. 
Overall, about 21% are ≤ 24 years old at injury, 27% are 25 to 34, 24% are 35 to 44, 19% are 45 to 54, 
and 9% are 55 or older. Race and ethnicity are not recorded; however, in our prior research, 
approximately 70% of workers were white, 16% were Latino, and 13% reported other racial or ethnic 
backgrounds. In WA in 2016, there were over 11,000 workers with opioid prescriptions in the first 6 
weeks after injury. 
 
OH Bureau of Workers’ Compensation (BWC). The BWC is the largest state-fund insurance system in 
the U.S. and one of the top 10 largest underwriters of WC insurance in the nation, insuring about 60% of 
OH’s workforce. The volume of injured workers in OH is quite similar to WA. The BWC State Fund has 
about 90,000 accepted claims per year, and about 12% involve time loss benefits. Overall, 36% are 

Original: 4/16/2019 
 
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Revised: 3/4/2020, 12/15/2020, 11/1/2022 
women and 64% are men. About 15% are ≤ 24 years old at time of injury, 23% are 25 to 34, 20% are 35 
to 44, 22% are 45 to 54 and 20% are 55 or older. The BWC does not collect data on race or ethnicity. In 
the BWC State Fund, there is an approximate annual prevalence of 20,000 opioid users.  
 
Inclusion Criteria 
 
Aims 1 & 2. Injured workers in OH and WA who (1) file a new accepted State Fund claim, and (2) fill at 
least one opioid prescription, paid for by WC, during the first 6 weeks after date of injury. The 
denominator for both time series described in the Analytical Methods section (Aim 1. Analysis Topic A) 
has the same inclusion criteria, except that criterion (2) does not apply. The same criteria—including 
criterion (2)—apply to the numerator for the first time series. The numerator for the second time series 
described is similar, except that criterion (2) is instead the presence of unsafe opioid prescribing during 
the subacute phase (6-12 weeks).   
 
Aim 3 key informants. Policy makers and stakeholders who were involved in the creation and 
implementation of the WA or OH ORP. 
 
Aim 3 provider in-depth interviews. Providers that had at least one patient whose opioid prescription 
paid for through WC had been reviewed in accordance with the state’s ORP. 
 
Aim 3 patient in-depth interviews. Patients who had an opioid prescription for at least 6 weeks paid for 
by WC or whose prescription payments have been reviewed in accordance with the state’s ORP.  
 
Exclusion Criteria 
 
Aims 1, 2, & 3 (patient in-depth interviews only). Injured workers who are under 18 years of age. 
 
Aims 1 & 2. Current cancer diagnosis and (for surveys only) non-English or -Spanish speakers, physically 
or mentally incapable of completing the survey, incarcerated, or deceased. 
 
Aim 3. Non-English speakers. 
 
Number of Study Groups/Cohorts 
 
The primary analysis involves 2 groups/cohorts of injured workers, 1 from WA and 1 from OH. 
Secondary analyses involve two subgroups of injured workers within WA, defined by whether the 
injured worker’s attending provider participates in Centers of Occupational Health and Education 
(COHE) care delivery. 
 
Enrollment/Sample Size 
 
Aims 1 & 2.  The time series for Aim 1 will be based on data for all injured workers in OH and WA who 
file a new State Fund claim from January 1, 2010 through December 31, 2020. For the prospective 
cohort analysis, we anticipate including 4,636 eligible workers in WA and 779 in OH for a one-year 
cohort. Using WC claims, pharmacy billing data, and potentially prescription drug monitoring program 
(PDMP) data from each state, we will track opioid prescribing for each injured worker for a full year after 
date of injury. The worker survey will largely be drawn from this cohort, depending on the length of time 
needed to conduct an adequate number of surveys. All workers will be interviewed initially at 4-6 weeks 

Original: 4/16/2019 
 
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Revised: 3/4/2020, 12/15/2020, 11/1/2022 
after injury (baseline). Workers who complete the baseline survey in the first year of recruitment will be 
asked to complete follow-up surveys at 6- and 12-months after injury. Workers who complete the 
baseline survey after the first year of recruitment will be asked to only do the 6-month follow-up survey 
due to time constraints. We anticipate that roughly 3,120 workers in WA and 525 workers in OH will 
complete baseline surveys. We anticipate roughly 1,700 completed 12-month outcome surveys from 
WA and 234 from OH.  
 
Aim 3 key informants. No more than 12 interviews in WA, supplemented with policy documents to add 
detail. In OH, we will start with policy documents and use key informant interviews with individuals 
outside of BWC to complement and supplement the historical account.  
 
Aim 3 individual in-depth interviews. We will conduct 20-30 patient interviews and approximately 10 
provider interviews in each state.  
  
Sampling Method 
 
Aims 1 & 2. Consecutive sampling. The entire eligible one-year cohort will be used for analyses based on 
administrative data, and consecutive sampling will be used to enroll survey participants from that cohort 
until target enrollment numbers are reached. 
 
Aim 3 key informants. Purposive and snowball sampling. Potential key informants will be identified and 
invited to participate based on specific characteristics. Snowball sampling from these initial interviews 
will allow us to expand the sample until the body of interview data achieves theoretical saturation in 
each state. 
 
Aim 3 individual in-depth interviews. Purposive and quota sampling. Potential interview participants 
will be identified and invited to participate based on specified characteristics until the body of interview 
data achieves theoretical saturation in each state. For provider interviews, we will also ask interviewed 
providers to recommend peers with experience prescribing opioids for WC patients (snowball sampling). 
 
5. Exposures 
 
The primary exposures of interest are the WC-based opioid review programs (ORPs) in each state: (1) 
prospective prior authorization (PA) with hard stops in WA, and (2) retrospective review (RR) with 
prescriber notification in OH. Secondarily, the COHE program is an exposure of interest, within WA only. 
 
WA opioid review. Under WA regulatory procedures, payment for any opioid prescription billed beyond 
six weeks after the injury is denied unless the prescriber has submitted a checklist attesting to 
completion of all best practices from the regulations/guidelines. For example, the provider must 
document clinically meaningful improvement in pain and function during acute use. Thus, the WA 
intervention is prospective and includes a “hard stop” subject to an insurer’s decision based on 
regulatory authority.  
 
OH opioid review. The OH regulations are similar to those in WA regarding best practices, but differ in 
approach. Rather than a “hard stop” on opioid prescription payments, an automated report identifies 
new opioid prescriptions beyond the acute phase. OH WC pharmacy and clinical staff review the medical 
record at 12 weeks to determine whether best practices were met. If not, the prescriber is notified via 
letter, and cases are reviewed again at 16 weeks. If best practices have still not been met, the prescriber 

Original: 4/16/2019 
 
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Revised: 3/4/2020, 12/15/2020, 11/1/2022 
and worker are notified via letter that opioid coverage will end in 4 weeks. The OH program relies on 
retrospective chart review and communication of best practices to prescribers. 
 
Centers of Occupational Health and Education (COHE) care delivery, in WA. The focus of the COHEs is 
to deliver occupational health best practices during the first 12 weeks after injury, during the transition 
from acute/subacute to chronic pain. The COHE delivery system has been developed over the past 
decade, and now approximately 50% of workers in WA receive care under these innovative 
arrangements. Critical elements of the COHE model include organizing care delivery on a population 
basis, high level executive health system support, ongoing care coordination by health service 
coordinators (HSCs), and stepped care management. Specific best practices include screening for 
psychosocial barriers to recovery using the validated Functional Recovery Questionnaire (FRQ); initiation 
of graded exercise; education regarding psychosocial barriers to recovery; and, more recently, a brief 
series of cognitive behavioral and reactivation techniques, i.e., activity coaching. COHE is a health care 
systems intervention, rather than a specific health care service or menu of services, that is primarily 
focused on improving the coordination of care at the health care community level, as well as with DLI. 
The COHE model consists of enhanced provider education regarding occupational health best practices 
and stepped-care features. Both usual care and COHE-based care in the WA workers' compensation 
(WC) system include the typical mix of health care services delivered for work-related injuries in 
Washington State, such as chiropractic care, physical therapy, evidence-based imaging services, and 
guideline-concordant surgery.  
 
Enhanced Care Program (ECP), in OH. Although OH has no program entirely comparable to COHE, the 
OH BWC did initiate the ECP as a pilot program in 2016, to assess the effect of improving care 
coordination and treatment planning for injured workers with knee sprains. The pilot was established in 
selected counties in northeast Ohio. Providers participate on a voluntary basis and agree to use a new 
treatment planning form for injured workers with knee sprains and to coordinate care for those needing 
referral for physical therapy or specialty care. Participating providers receive 15% added payment for 
evaluation and management visits. The pilot is being extended during the next two years throughout OH 
but will remain limited to injured workers with knee sprains. 
 
 
 

Original: 4/16/2019 
 
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Revised: 3/4/2020, 12/15/2020, 11/1/2022 
6. Outcomes 
 
The primary outcome measure, unsafe opioid prescribing, will be measured as shown in Table 1 using 
pharmacy billing data from OH and WA, and validated using state PDMP data. Table 2 presents 
secondary outcome measures, and Table 3 presents exploratory/other outcome measures. 
 
Table 1. Primary Outcome Measures 
Title 
Description 
Timeframe (after injury) 
Unsafe opioid prescribing (subacute phase):  
Days supply 
 >7 days supply 
6-12 weeks 
Unsafe opioid prescribing (chronic phase):  
Chronic 
≥60 days supply 
3-6, 6-9, and 9-12 months 
Unsafe opioid prescribing (chronic phase):  
Concurrent 
Concurrent 
sedatives/hypnotics 
3-6, 6-9, and 9-12 months 
Unsafe opioid prescribing (chronic phase):  
High dose 
≥50 MED/day 
3-6, 6-9, and 9-12 months 
Unsafe opioid prescribing (chronic phase):  
Composite 
Meeting ≥1 of the 3 
chronic-phase metrics 
3-6, 6-9, and 9-12 months 
MED, morphine equivalent dose. 
 
 

Original: 4/16/2019 
 
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Revised: 3/4/2020, 12/15/2020, 11/1/2022 
Table 2. Secondary Outcome Measures 
Title 
Data Source 
Description 
Timeframe (after 
injury) 
Pain intensity 
Survey 
PEG-3 Scale  
6 weeks, 6 months, 
12 months 
Physical function/pain 
interference 
Survey 
PEG-3 Scale  
6 weeks, 6 months, 
12 months 
Emotional functioning-
Anxiety 
Survey 
PHQ-4 (Patient Health 
Questionnaire for anxiety and 
depression) 
6 weeks, 6 months, 
12 months 
Emotional functioning-
Mood 
Survey 
PHQ-4 (Patient Health 
Questionnaire for anxiety and 
depression)  
6 weeks, 6 months, 
12 months 
Global improvement  
Survey 
Patient Global Impression of 
Change scale  
6 months, 12 months 
Disability 
Survey 
Does health now limit activities 
during a typical day 
6 months, 12 months 
Quality of life 
Survey 
EQ-5D-5L (EuroQoL-5 
dimensions)  
6 weeks, 6 months, 
12 months 
Patient experience 
with opioid review 
procedures 
Survey 
Patient experience with opioid 
review procedures 
6 months, 12 months 
Perceived outcomes of 
insurer decisions from 
patient’s perspective 
Survey 
Perceived outcomes of insurer 
decisions from patient’s 
perspective 
6 months, 12 months 
Overall patient 
satisfaction 
Survey 
Overall patient satisfaction 
6 weeks, 6 months, 
12 months 
Satisfaction with pain-
related treatment 
Survey 
Satisfaction with pain-related 
treatment 
6 weeks, 6 months, 
12 months 
Working full-time, part-
time or not working 
Survey 
Working full-time, part-time or 
not working 
6 weeks, 6 months, 
12 months 
Are wages more, less 
or about the same as 
before injury 
Survey 
Are wages more, less or about 
the same as before injury 
6 weeks, 6 months, 
12 months 
Work status 
WC claims data 
Proxy: compensated time loss 
6 weeks, 6 months, 
12 months 
Opioid poisoning 
events 
WC billing data 
ICD-10-CM codes 
12 months 
WC, workers’ compensation. 
 
Table 3. Exploratory/Other Outcome Measures 
Title 
Data  
Source 
Description 
Timeframe  
(after injury) 
Opioid use disorder  
Survey 
TAPS (The Tobacco, Alcohol, Prescription medications, 
and other Substance Tool) – opiate pain reliever 
subset 
12 months 

Original: 4/16/2019 
 
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Revised: 3/4/2020, 12/15/2020, 11/1/2022 
7. Analytical Methods:  Quantitative Analyses 
 
Note: Because this is an observational study, and because we must submit this study protocol before 
Ohio data are made available, much of this plan is tentative. Updates will be made once more is known 
regarding the availability, completeness, and cross-state comparability of various data fields, data 
definitions, data generating processes, and procedures. A summary of the various planned analyses is 
provided in Table 4, followed by further detail regarding each analysis. 
 
Admin, administrative data; COHE, Centers of Occupational Health and Education; GEE, generalized 
estimating equations; ITSA, interrupted time series analysis; OH, Ohio; PA, prior authorization; RR, 
retrospective review; WA, Washington. 
 
 
Aim 1 
 
Specific aim 1. Examine the comparative effectiveness of prospective prior authorization (PA) with hard 
stops in WA versus retrospective review (RR) with prescriber notification in OH in reducing unsafe opioid 
prescribing.  
Hypothesis 1. Prospective PA with hard stops (WA) will be associated with statistically significantly less 
unsafe opioid prescribing compared to RR and prescriber notification (OH). [See Analysis Topic B] 
 
Table 4.  Summary of Quantitative Data Analyses  
Aim  Analysis Topic 
Analytic Approach 
Outcome 
Sample  
1 
A. Compare long-term 
prescribing patterns 
across states and 
identify discontinuities  
Time series/ITSA 
Any opioid prescription  
(acute) 
Unsafe opioid prescribing  
(subacute) 
OH/WA 
2010-2020 
1 
B. PA vs RR 
GEE/propensity score  
& subgroup analysis 
Unsafe opioid prescribing  
OH/WA  
cohorts 
1 
C. PA vs RR 
GEE/propensity score 
Unsafe opioid prescribing  
OH/WA  
survey samples  
1 
D. Prescribing data 
validation 
Compare WC billing data  
with PDMP data 
Unsafe opioid prescribing  
OH/WA  
cohorts 
1 
E. Frequency/outcomes 
of opioid review 
decisions  
Descriptives (N/percent 
that are/are not 
authorized) 
Opioid review decisions  
OH/WA  
cohorts 
1 
F. Subgroup response 
to opioid review 
decisions  
Subgroup analysis using 
PDMP data  
 
Opioids obtained from 
different prescriber or 
different payer 
OH/WA  
cohorts 
1 
G. Describe usual care 
Descriptives (rates) 
Health care utilization 
OH/WA  
cohorts 
2 
H. PA vs RR 
GEE/propensity score  
& subgroup analysis 
Secondary outcomes 
OH/WA  
survey samples 
2A 
I. COHE vs  
non-COHE 
GEE/propensity score  
& subgroup analysis 
Unsafe opioid prescribing  
WA cohort & 
survey sample 
2A 
J. COHE vs  
non-COHE 
GEE/propensity score  
& subgroup analysis 
Secondary outcomes 
WA  
survey sample 

Original: 4/16/2019 
 
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Revised: 3/4/2020, 12/15/2020, 11/1/2022 
Aim 1. Analysis Topic A: Compare long-term prescribing patterns across states and identify 
discontinuities 
 
This analysis will be based on a time series approach. We will construct data points for each month from 
January 1, 2010 through December 31, 2020. For each state, we will construct two separate time series. 
The first is the proportion of WC claims with any opioid prescription in the acute phase (0-6 weeks). The 
second is the proportion of WC claims with unsafe opioid prescribing (defined earlier) during the 
subacute phase (6-12 weeks). For each, calendar month denominators for the time series will be 
assigned based on date of injury, and numerators will be calculated for each one-month set of claims 
using the pharmacy billing data for up to three months after date of injury. 
 
The resulting four time series will be inspected to identify secular trends and discontinuities/unexpected 
changes that might indicate a policy or regulatory action, or an external factor that might have affected 
opioid prescribing pattern. A timeline of opioid-related policy events affecting WA and/or OH (e.g., 
legislation, regulations, guidelines, large-scale interventions) will be constructed and overlaid on these 
time series to assess patterns and potential confounding related to the state-based environments. As 
part of Aim 3 (described below), qualitative methods will be used to understand the causes of 
unexplained fluctuations.  
 
Finally, we will test whether implementation of the WA PA program (implemented in July 2013) or the 
OH RR program (implemented in March 2019) was associated with a statistically significant reduction in 
the rate of unsafe opioid prescribing using standard interrupted time series analysis (ITSA) techniques.1,2 
 
 
Aim 1. Analysis Topic B: Compare PA to RR with respect to unsafe opioid prescribing 
 
This analysis is focused on the primary outcome for this study, unsafe opioid prescribing (defined 
earlier), using administrative data for the full WA and OH cohorts. We are planning for a one-year 
prospective timeframe, beginning July 2019 (based on dates of injury). Our cohort size estimates are 
4,636 eligible workers in WA and 779 in OH .  
 
Using WC claims and billing data from each state, we will track opioid prescribing for each injured 
worker for a full year after date of injury in order to test Hypothesis 1: Prospective PA with hard stops 
(WA) will be associated with statistically significantly less unsafe opioid prescribing compared to RR and 
prescriber notification (OH). 
 
The primary outcome is the composite unsafe prescribing measure (i.e., meeting one or more of the 3 
chronic-phase criteria in Table 1), modeled longitudinally over the three 3-month time windows, in 
order to assess the comparative effectiveness of PA/RR on the transition from early opioid therapy to 
longer-term unsafe opioid prescribing. Generalized estimating equations (GEE) panel models will be 
used to account for outcomes being measured during several time windows within individual patients.3 
 
1  Linden, A. (2015). Conducting interrupted time series analysis for single and multiple group comparisons. The 
Stata Journal, 15(2), 480-500.  
2  Penfold, R. B., & Zhang, F. (2013). Use of interrupted time series analysis in evaluating health care quality 
improvements. Acad Pediatr, 13(6 Suppl), S38-44.  
3  Aloisio, K. M., Swanson, S. A., Micali, N., Field, A., & Horton, N. J. (2014). Analysis of partially observed clustered 
data using generalized estimating equations and multiple imputation. Stata J, 14(4), 863-883. 

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The working correlation structure will be specified as exchangeable (this may be adjusted later in the 
Statistical Analysis Plan), and robust variance estimates will be used. (Though using the wrong 
correlation structure can be inefficient, GEE is not highly sensitive to using the wrong correlation 
structure, as long as there are at least 100 patient clusters and robust variance estimates are used—we 
expect to have over 3,000 patient clusters.) 
 
The primary predictor is binary (1 for WA cases, 0 for OH cases). The GEE logit link function will be used 
to test the Aim 1 hypothesis, with unsafe opioid prescribing specified as a binary dependent variable in 
each time window. These regression models will provide an estimate of the odds of unsafe opioid 
prescribing in WA compared to OH, controlling for baseline differences in the two groups. The subacute 
phase measure, and also each individual component of the composite measure (i.e., chronic use, 
concurrent sedatives, high dose), will also be tested as distinct outcomes, which will provide information 
on potential variation in mechanisms and timing of ORP effect across WA and OH. Time will be included 
in the models (assigning an indicator for quarter since the date of injury; 0, 1, 2, 3), and interacted with 
state in order to assess (1) change in unsafe opioid prescribing over time and (2) differences in such 
change by state.  
 
Covariates will be reassessed after more information is available regarding the availability, 
completeness, and cross-state comparability of various data fields in the OH data. Our initial plan is to 
include the following covariates for this analysis topic:  
• 
Age 
• 
Gender 
• 
Pre-injury wage 
• 
Marital status 
• 
Number of dependent children 
• 
Injury type 
• 
Occupation 
• 
Industry 
• 
Rural/urban residence 
• 
Provider type, specialty, and COHE or ECP affiliation  
• 
Functional Comorbidity Index (FCI) and cancer diagnosis, based on WC billing data 
• 
PDMP data, if available (see Analysis Topic D), will be used to control for opioid prescriptions in 
the 3 months prior to injury 
• 
As-yet undetermined utilization-based variables that may help adjust for injury severity and 
treatment intensity (e.g., hospitalizations, number of emergency department visits in the first 6 
weeks, number of provider visits, time until first opioid prescription, presence and timing of 
physical therapy, imaging, surgery, etc.) 
• 
COHE and/or ECP participation may be included as an additional covariate, depending on 
findings from exploratory stratified analyses by COHE/ECP participation. We expect small 
numbers of ECP claims relative to COHE, and have no advance information on relative effect 
size.  

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The limited number of independent variables facilitates their full inclusion in the causal model, and 
restricts the value of propensity score techniques for this analysis topic. However, a propensity score 
predicting state jurisdiction (WA versus OH) will be calculated for each patient, and propensity score 
techniques will be used to construct comparable WA and OH samples by restricting cases to the region 
of common support (i.e., propensity score ranges for WA and OH will be compared, and cases above or 
below the overlapping range will be excluded). This approach should improve comparability of the 
samples, i.e., patients with covariate patterns that are typical of only one of the states will be excluded.   
 
Subgroup analysis will be conducted in an exploratory manner. We will include interaction terms 
(various covariates*state) in sub-analyses to assess whether WA PA compared to OH RR has differential 
outcomes for particular patient subgroups (e.g., back sprain, other MSK injuries, other injury types). 
 
 
Aim 1. Analysis Topic C: Compare PA to RR, using survey sample to provide more covariates 
 
This analysis will proceed as described for Analysis Topic B, but include only the subset of the WA/OH 
cohorts that were included in the survey. Depending on survey eligibility and response rates, up to 100% 
of survey respondents may be drawn from the underlying WA/OH cohorts (however, it is possible that 
survey accrual may need to continue beyond cohort accrual). This will allow us to test the Aim 1 
hypothesis in a sensitivity analysis that provides additional control for confounding via the inclusion of 
baseline survey covariates and full use of propensity score techniques, as described for Analysis Topic H 
below. 
 
 
Aim 1. Analysis Topic D: Prescribing data validation 
 
We are in the process of confirming the feasibility of obtaining PDMP data (and specifically the payer 
variable) from each state for the full WA/OH cohorts and all patients included in the survey sample. 
Presuming we are able to obtain the necessary PDMP data, the following analyses are planned. 
 
We will construct the various unsafe opioid prescribing variables using PDMP data, using the same 
methods as used for WC billing data. Further, should the payer variable be available to us, we will 
construct two parallel sets of outcome measures using PDMP data: (1) using all PDMP prescriptions, 
regardless of payer; and (2) using only prescriptions with WC as the payer. (If the payer variable is 
unavailable, we will construct only the first measure.) Concordance of the outcome measures by data 
source (and potentially with and without payer restriction) will be assessed using Cohen's kappa.  
 
Sensitivity of findings to the data source used for the unsafe opioid prescribing outcome measures will 
be assessed by using the alternate measures in hypothesis tests, as described under Analysis Topics B, C, 
and I. 
 
 
Aim 1. Analysis Topic E: Frequency and outcomes of opioid review decisions 
 
To the extent feasible, we will document the frequency of opioid review decisions and their outcomes 
(patients were or were not authorized to continue using opioids beyond the respective review periods) 
for each state. We are investigating the existence of these data and feasibility of obtaining them from 

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each state. This is planned as a simple descriptive analysis (counts and percentages). 
 
 
Aim 1. Analysis Topic F: Subgroup response to opioid review decisions 
 
Using PDMP data, we will explore whether patient subgroups within the WA and/or OH cohorts respond 
differently to review decisions ending opioid coverage (e.g., whether opioids were prescribed by a 
different prescriber or obtained using a different payment source). Patient subgroups of interest for this 
analysis topic include—but may not be limited to—patients on higher daily opioid doses, those taking 
opioids longer-term, those with back sprain, those still off work at 6 weeks, gender, age, etc. DEA 
numbers in PDMP data can be used to determine whether opioids were prescribed by a different 
provider following an opioid review decision ending opioid coverage. If the PDMP payer variable is 
available, we can also assess whether opioids were obtained using a non-WC payer following review 
decisions ending opioid coverage. Subgroups will be exploratory and may also be defined within the 
survey subset for this topic, so that subgroups constructed using baseline survey variables (e.g., pain 
level) can also be assessed. 
 
 
Aim 1. Analysis Topic G: Describe usual care 
 
We will describe usual care by measuring health care utilization indicators (e.g., timing and rates for 
hospitalization, physical therapy, imaging, surgery, etc.), using WC billing data for the full cohort in both 
states. Further, we will describe differences in such indicators between COHE and non-COHE patients in 
WA, and ECP and non-ECP patients in Ohio. Descriptive information about usual care will be stratified by 
injury type. 
 
 
 
Aim 2 
 
Specific aim 2. Examine the comparative effectiveness of WA’s PA program versus OH’s RR program with 
regard to patient reported outcomes (pain, function, quality of life, return to work), harms (presence of 
opioid use disorder, opioid poisoning events) and work disability outcomes at 6 weeks, 6 months, and 12 
months from date of injury. 
Hypothesis 2. Prospective PA with hard stops (WA) will be associated with statistically significantly 
improved patient outcomes compared to RR and prescriber notification (OH). [See Analysis Topic H] 
 
 
Aim 2. Analysis Topic H: Compare PA to RR with respect to secondary outcomes 
 
This analysis is focused on testing Hypothesis 2 (stated just above). Most secondary and exploratory 
patient outcomes will be based on survey data, but several will be based on WC data (see Tables 2 and 
3).  
 
Surveys will be conducted at approximately 6 weeks (baseline), 6 months, and 12 months after injury. 
We anticipate that roughly 3,120 workers in WA and 525 workers in OH will complete baseline surveys. 
Based on estimated response rates, we anticipate roughly 1,700 completed 12-month outcome surveys 
from WA and 234 from OH. Depending on survey eligibility and response rates, up to 100% of survey 

Original: 4/16/2019 
 
14 
Revised: 3/4/2020, 12/15/2020, 11/1/2022 
respondents may be drawn from the underlying WA/OH cohorts (however, it is possible that survey 
accrual may need to continue beyond cohort accrual). 
 
The primary predictor is binary (1 for WA cases, 0 for OH cases). We will specify GEE panel models as 
described for Analysis Topic B to account for outcome measures at multiple time points for each patient 
(6 months, 12 months). Appropriate GEE link functions will be specified for each outcome variable, e.g., 
identity link for continuous variables, logit link for binary variables, and log link for counts (Tables 2 and 
3). Comparisons of clinically important improvements in patient pain and functioning will be based on 
the proportion of patients who achieve clinically meaningful improvement (relative to baseline) on our 
outcome measures at 6 and 12 months, as defined by 30% or greater improvement. We will include 
time/state interactions in order to assess (1) change in outcomes over time and (2) differences in such 
changes by state. 
 
Covariates described under Analysis Topic B will also be included for this analysis, in addition to baseline 
covariates collected during the survey. Additional survey-based covariates will include the following (this 
list may be adjusted as the survey is finalized with stakeholder input): 
• 
Education  
• 
Household Income 
• 
Race/ethnicity 
• 
Health status 
• 
Comorbidities (survey-based FCI, current cancer treatment) 
• 
Pain intensity 
• 
Physical function/pain interference  
• 
Emotional functioning-Anxiety 
• 
Emotional functioning-Mood 
• 
Quality of life 
• 
Work status and barriers to work 
• 
Treatment for pain 
• 
Satisfaction with treatment 
• 
Communication with health care provider 
• 
Alcohol and tobacco use 
 
Using these covariates, we will calculate a propensity score. In order to minimize the loss of survey data, 
we plan to include this propensity score in the causal models along with important covariates. However, 
if needed to achieve balance on baseline variables, we will restrict the analytic sample to the region of 
common support and/or implement propensity score matching.  
 
Subgroup analysis will be conducted in an exploratory manner. We will include interaction terms 
(various covariates*state) in sub-analyses to assess whether WA PA compared to OH RR has differential 
outcomes for particular patient subgroups (e.g., back sprain, other MSK injuries, other injury types). We 

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15 
Revised: 3/4/2020, 12/15/2020, 11/1/2022 
will conduct stratified analysis for strata defined by (a) number and strength of opioid prescriptions 
during the acute period (0-6 weeks), and (b) continued time loss at 6 weeks.  
 
 
 
Aim 2A 
 
Specific aim 2A. For injured workers in WA, examine unsafe opioid prescribing and patient-reported 
outcomes and work disability at 6 and 12 months for patients receiving an established, coordinated, 
stepped care management program during the first 12 weeks following injury plus prospective PA versus 
patients receiving usual care subject to PA only.  
Hypothesis 2A. Specialized care delivered through a stepped care management program plus 
prospective PA will be associated with statistically significantly lower rates of unsafe opioid prescribing 
and improved patient outcomes compared to patients receiving usual care and PA only. [See Analysis 
Topics I and J] 
 
Aim 2A. Analysis Topic I: Compare COHE to non-COHE within WA with respect to unsafe opioid 
prescribing 
 
This analysis is focused on the primary outcome for this study, unsafe opioid prescribing (defined 
earlier), using administrative and survey data for the full WA cohort. Our estimate is that we will have 
about 4,636 eligible workers in WA, and that approximately half will have an attending provider 
affiliated with COHE.  
 
This analysis will test Hypothesis 2A (stated just above) with respect to unsafe opioid prescribing, using 
parallel analyses to those described in Analysis Topic B, and including the sensitivity analysis described in 
Analysis Topic C. The only adjustments are that models will include only the WA cohort, and the binary 
primary predictor and propensity score calculations will be based on COHE status rather than on state 
jurisdiction (1 for COHE, 0 for non-COHE). 
 
 
Aim 2A. Analysis Topic J: Compare COHE to non-COHE within WA with respect to secondary outcomes 
 
This analysis is focused on the secondary and exploratory patient outcomes for this study, using 
administrative and survey data for the WA survey sample. Surveys will be conducted at approximately 6 
weeks (baseline), 6 months, and 12 months after injury. Our estimates are rough and will be updated as 
survey enrollment proceeds. However, we anticipate that roughly 3,120 workers in WA will complete 
baseline surveys, that roughly 1,700 will completed 12-month outcome surveys, and that approximately 
half of those interviewed will have an attending provider affiliated with COHE. 
 
This analysis will test Hypothesis 2A (stated above) with respect to patient outcomes, using parallel 
analyses to those described in Analysis Topic H. Most secondary and exploratory patient outcomes will 
be based on survey data, but several will be based on WC data (see Tables 2 and 3). The only 
adjustments to Analysis Topic H are that models will include only the WA survey sample, and the binary 
primary predictor and propensity score calculations will be based on COHE status rather than on state 
jurisdiction (1 for COHE, 0 for non-COHE). 
 
 

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Revised: 3/4/2020, 12/15/2020, 11/1/2022 
 
Additional Information Relevant to Quantitative Analyses for Aims 1, 2, and 2A  
 
Subgroups of Interest 
 
Planned subgroup analyses are listed under the relevant analysis topics above. Because all subgroup 
analysis will be exploratory, non-exhaustive examples of potential subgroups have been specified. New 
subgroups of interest may be identified via exploratory data analysis and/or proposed ad hoc by the 
principal investigator, co-investigators, or stakeholders. The total number of subgroup analyses—and all 
subgroups and outcomes analyzed—will be reported, along with subgroup definitions and procedures 
used. If indicated, multiple comparisons will be accounted for using p-value adjustment. 
 
 
Operational Definitions for Eligibility Criteria 
 
These definitions will need to be aligned as much as possible across the two states. The following 
operational definitions are based on our history of working with WA WC data. They may need to be 
modified after preliminary OH WC data are available for inspection. 
 
• 
Inclusion criteria: 
o New State Fund claim: Identified by each state WC agency 
o At least one opioid prescription during the first 6 weeks after date of injury: Based on WC 
pharmacy billing data 
o Ability to speak English or Spanish (for the survey only): This will be determined by 
interviewers during the contact/consent process 
• 
Exclusion criteria:  
o Under 18 years of age: Age at injury, calculated as the difference between birthdate and 
date of injury 
 
Operational Definitions for Key Covariates 
  
Operational definitions will need to be aligned as much as possible across the two states. Thus, key 
covariates and their operational definitions will need to be determined after (1) preliminary OH WC data 
are available for inspection and (2) the survey is finalized with stakeholder input. 
 
 
Clustering 
 
Study objectives and the primary and secondary outcomes for Aims 1 and 2/2A pertain to the individual 
worker level. Multiple outcome measures for each worker (both administrative and survey based) 
require accounting for within-person correlation, which motivates the use of GEE. However, provider-
level opioid prescribing patterns may induce intraclass correlation among injured workers treated by a 
particular provider. Each ORP decision is specific to an individual patient; however, providers either 
request PA in WA or are notified of RR in OH. These experiences may cause providers to adjust their 
practice for other patients in their care, impacting outcomes for provider-based patient clusters. The 
vast majority of providers prescribe opioids to very few WC patients. However, there may be some 
dependence of patient observations within the few higher-volume providers. Injured workers may have 
multiple prescribing providers over the course of a WC claim, which complicates cluster identification. 

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Revised: 3/4/2020, 12/15/2020, 11/1/2022 
The primary predictor for Aim 2A, COHE status, is assigned to injured workers based on provider-level 
COHE affiliation. COHE program features are determined at the WC system level but its effects may vary 
depending on provider adherence and individual worker circumstances. In order to obtain accurate 
standard errors and confidence intervals, we plan to specify models using robust standard error 
estimates and accounting for potential clustering by primary attending provider. This approach by 
necessity assumes person clusters are perfectly nested within attending provider clusters; however, 
attending provider can change over time. In practice such changes are uncommon in WA, and we are 
uncertain whether such changes can be tracked in OH. If feasible, we will assess clustering based on 
different approaches (e.g., first attending provider, attending provider in place for the longest period of 
time during the acute phase, or accounting for clustering only at the person level). We expect these 
approaches to yield similar results, but in the event they do not, we will proceed with the most 
conservative approach (highest standard errors). 
 
Missing Data  
 
Assumptions about the nature and prevalence of missing data made below will be assessed once 
preliminary WC and survey data sets are available. The administrative claims and pharmacy data are 
virtually 100% complete in WA; we will assess OH data when available, but expect similar completeness 
for all covariates associated with worker compensation or provider billing.  
 
We expect relatively high survey response rates, based on previous research in these injured worker 
populations. Barriers to recruitment include being unable to locate and contact injured workers, and 
injured workers choosing to not participate. The address and phone numbers of injured workers who 
are receiving WC benefits are largely complete and accurate because contact information is required in 
order to receive disability benefits. In order to increase the response rate, all eligible workers identified 
by WA and OH WC systems will be sent an introductory letter 1 week prior to the initiation of the phone 
surveys. Workers will be able to schedule a time for the survey or can opt out at that point. Workers will 
be paid $50 for the baseline survey, $30 for the 6 month survey, and $50 for the 12 month survey ($130 
total). The University of Washington (UW) Survey Research Division (SRD) will make 10 call attempts. 
The SRD research staff are extensively trained in standardized interviewing techniques and methods, 
effective calling strategies, building rapport and interest, and refusal prevention, which all aid in 
minimizing attrition and maximizing response rates. The survey interviewers are trained to minimize 
item non-response, while supporting autonomy not to answer individual questions. 
 
Based on these efforts, we anticipate limited amounts of missing data for individual survey items, under 
5%. We do expect larger and non-negligible amounts of missing 12-month outcome data relative to 6-
month outcome data on the surveys, based on projected attrition.  
 
We will use standard techniques to identify the nature (randomness) of missing data. Where missing 
data are minimal (<5%) and random (no clear associations with outcomes or predictors of interest), we 
will use casewise deletion; we expect this approach will be adequate for administrative data and 
baseline survey data. For the GEE panel models testing survey-based patient outcomes, we will use 
multiple imputation methods, based on an expected larger amount of missing data and the likely 
association of missingness with previous unsafe opioid prescribing.4 
 
 
4  Aloisio, K. M., Swanson, S. A., Micali, N., Field, A., & Horton, N. J. (2014). Analysis of partially observed clustered 
data using generalized estimating equations and multiple imputation. Stata J, 14(4), 863-883.  

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Revised: 3/4/2020, 12/15/2020, 11/1/2022 
We will examine predictors of survey non-response and loss-to-follow-up with descriptive statistics 
comparing the two groups (and potential differences by state), and potential differences will be noted 
when interpreting results from the analyses of survey data. Non-response adjustment using post-
stratification weighting techniques will be implemented, if warranted due to differential non-response. 
We will record and report reasons for dropout and missing data, run sensitivity analyses if indicated, and 
account for all patients in reports. 
 
 
8. Analytical Methods: Qualitative Analyses 
 
Specific aim 3. Using qualitative methods, identify key environmental, programmatic and policy factors 
that influenced the design, implementation and impact of the PA program in WA and the RR program in 
OH. This information will lead to the development of an Opioid Review Tool educational module for 
dissemination to both public and private payers. 
 
Aim 3 will provide important contextual information and insights to enable us to explain why the two 
states developed different strategies in designing and implementing their respective ORPs, and insight 
into ORP impact on providers and patients, beyond health outcomes.  
 
Key Informants 
 
During Year 2, we will conduct key informant interviews with policy makers and stakeholders who were 
involved in the creation and implementation of each state’s ORP, including ORP reviewing pharmacists 
and nurses. Initial key informants in each state have already been identified; these are individuals, as 
available, who were important in the ORP design and implementation process (however, we will be 
unable to interview current and former BWC employees due to an ongoing lawsuit in OH). The 
remainder of the key informants will be identified through (a) snowball sampling, by asking each 
interviewed informant to name others involved in the policy-making process in their state, and (b) 
noting the identity of key players in ORP design and implementation mentioned in the policy documents 
we collect and explore. Each interview is likely to last 60-90 minutes. Key informants will be asked to 
share a range of information about the policy making process, including the origins of the ORP 
regulations about which they have expertise; considerations & alternatives explored; inputs to the 
policy-making process; how the specifics were designed and implemented; how necessary resources 
were obtained; push-back, setbacks, and decision-making milestones. Interviews will be digitally 
recorded and professionally transcribed. Once an initial picture of the policy development process has 
been obtained through key informants, policy documents that could provide additional details will be 
collected and analyzed.  These documents may include reports, transcripts, testimony, position 
statements, policy drafts, and other materials produced by policy makers, issue stakeholders, executive 
agencies, and legislative hearings and committees.   
 
Key informant interviews and policy document collection will be designed initially around Aim 3, but 
expanded to generate the data necessary to address Aim 1 as well.  We will employ qualitative methods 
to understand the causes of unexplained fluctuations in the time series described in Analysis Topic A.  
Specifically, we will conduct targeted key informant interviews and analyze the specific policy 
documents most relevant to the time period immediately prior to any observed fluctuations. Interviews 
and document analyses necessary to complete Aim 1 will be timed to occur in tandem with the larger 
set of key informant interviews and policy documents necessary for Aim 3.   
 

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Revised: 3/4/2020, 12/15/2020, 11/1/2022 
All qualitative data will be analyzed using a grounded theory approach, aided by Excel spreadsheets and 
NVivo qualitative data management software. These methods will focus on identifying causal factors 
that shaped state choices about which ORPs to design, how to implement them, and how these 
programs should evolve over time. Analytic methods will focus on inductive coding first, in order to 
facilitate identification of the broadest possible range of causal themes.  Analysis will begin as soon as 
the first key informant interviews are complete; simultaneous data collection and analysis will allow 
later interviews to be informed by earlier ones, so that the fullest possible picture of policymaking can 
emerge. 
 
Individual In-Depth Interviews 
 
We will conduct 20 to 30 patient and around 10 provider interviews in each state. Interviewees will be 
identified from the lists provided to us by each state’s WC agencies (and also through snowball sampling 
in the case of provider interviews). Participants will be recruited from among: (1) patients who had an 
opioid prescription for at least 6 weeks paid for by WC or whose prescription payments have been 
reviewed in accordance with the state’s ORP, and (2) prescribing providers that had at least one patient 
whose opioid prescription paid for through WC had been reviewed in accordance with the state’s ORP. 
Patient interviews will explore the impact of the ORPs and related policies on pain management, 
function, disability, return to work, their relationship with their provider, and their feelings about WC.  
Provider interviews will explore changes in their use of opioid prescriptions for injured workers, why 
they made these changes, and the role of ORPs and related policies on their prescribing changes; impact 
on feelings about clinical autonomy; views of the legitimate role of opioids in pain management; and 
reflections on consequences and effectiveness of the ORP, whether it should be changed, and whether 
the ORP affected their attitudes toward the state WC agency or their willingness to treat WC patients.  
 
Interviews will be led by Dr. Padamsee and the qualitative research assistants in WA and OH. Each 
interview will last approximately 1-1.5-hours. Interviews will be audio-taped and professionally 
transcribed. Transcripts and notes will be analyzed using a grounded theory approach, aided by Excel 
spreadsheets and NVivo qualitative data management software.  
 
 
9. Data Management Plan (DMP) 
 
See Appendix A. 
 
 
10. Data Safety Monitoring Plan (DSMP) 
 
See Appendix B. 
 

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Revised: 3/4/2020, 12/15/2020, 11/1/2022 
Appendix A. Data Management Plan 
 
Data collection 
Data will be generated from the following sources: 
 
1. Washington and Ohio state workers’ compensation (WC) systems  
 
Includes: Contact information (for recruitment), claim descriptors, sociodemographic 
information, employment information at time of injury, injury information, medical and facility 
billing data, pharmacy billing data, provider information, time loss days and costs, prescription 
drug monitoring program (PDMP) data.  
 
2. Survey of participants in the Washington and Ohio workers’ compensation system administered 
by the Survey Research Division (SRD) of the Social Development Research Group at the 
University of Washington 
 
Includes: Sociodemographic information, comorbidities, pain intensity, physical function and 
pain interference, treatment for pain, quality of life, emotional functioning, questions specific to 
opioid use disorder, disability, work status, wages, patient experience with the opioid review 
procedures, perceived outcomes of the insurer decisions from the patient perspective, 
satisfaction with treatment, communication with health care provider, and alcohol and tobacco 
use. 
 
3. Individual in-depth interviews and key informant interviews administered by The Ohio State 
University research team  
 
Includes:  
(patient interviews) the impact of the ORPs and related policies on pain management, function, 
disability, return to work, their relationship with their provider, and their feelings about WC. 
 
(provider interviews) changes in their use of opioid prescriptions for injured workers, why they 
made these changes, and the role of ORPs and related policies on their prescribing changes; 
impact on feelings about clinical autonomy; views of the legitimate role of opioids in pain 
management; and reflections on consequences and effectiveness of the ORP, whether it should 
be changed, and whether the ORP affected their attitudes toward the state WC agency or their 
willingness to treat WC patients. 
 
(key informant interviews) origins of the opioid review program regulations, considerations and 
alternatives explored, inputs to the policy-making process, how the specifics were designed and 
implemented, how necessary resources were obtained, push-back, setbacks, and decision-
making milestones. 
 
Data organization  
Raw administrative data (i.e. WC claims data, pharmacy billing data, and PDMP data) will be received 
from the Washington and Ohio workers’ compensation systems. These files will be kept in separate 
folders for each state and designated as read-only so that they cannot be changed.  A log in Microsoft 
Excel in each folder will keep track of these files as they come in and will include date and time received, 
size, description, and other relevant meta-data.  

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Revised: 3/4/2020, 12/15/2020, 11/1/2022 
 
Similarly, survey data will be received from the Survey Research Division, stored as a read-only file in a 
separate folder, and tracked via a log in Microsoft Excel with relevant meta-data.   
 
Merged data files for analysis will be read-only and stored in a separate folder. This folder will also have 
a log in Microsoft Excel that contains the meta-data in order to keep track of the latest version. This 
folder will be accessible by team members doing the statistical analysis.  All analysis work will be kept in 
folders separate from the data folders.  
 
Data handling 
The Data Manager(s) will be responsible for managing the data, including de-identifying and merging 
datasets. In both states, each WC claim has a unique Claim ID and a unique Claimant ID, allowing for the 
identification of multiple claims by one patient. Data files for the WC claims data and pharmacy billing 
data will be linked using these IDs.  
 
After merging datasets, the Claim ID and Claimant ID will be replaced with Study IDs.  A separate linkage 
file with the Claim ID and the Study ID will be retained and will be stored in a separate folder only 
accessible by the Data Manager(s), Research Coordinator, and Project Director.   
 
Administrative workers’ compensation data will be stripped of most direct patient identifiable 
information before it is received by this project. For surveys and individual in-depth interviews, some 
identifiable information (e.g. name, address, phone number) will be necessary for recruitment. The Data 
Manager(s) will produce deduplicated lists of recruitment targets for the baseline study for the Survey 
Research Division. The recruitment lists will include the UW-created Study ID, as well as the information 
necessary for recruitment. Survey data received back from the Survey Research Division will contain the 
study ID (but not the identifiable information).  
 
As described above, version control for all incoming raw data (i.e. administrative and survey data), as 
well as the merged read-only analysis files, will be managed using Microsoft Excel logs of the relevant 
metadata. All files and folders are automatically backed up daily by the Department of Environmental 
and Occupational Health Sciences computing system. All data transfer and linkage procedures will meet 
strict Human Subjects (IRB) requirements. 
 
For the qualitative portions of the research (i.e. individual in-depth interviews and key informant 
interviews), the lead qualitative analysis investigator and qualitative research assistant will be 
responsible for data management. Data will be stored on a secure server with backup to a separate 
server. Data will be de-identified after collection, and only these two individuals will have access to the 
key linking direct identifiers to the Study ID. Other staff responsible for analysis will only have access to 
the de-identified data.  
 
Linkage of PDMP data 
The linkage approach for the PDMP data may vary across states. The process in Washington is as 
follows:  
 
1) Department of Labor and Industries (DLI) staff routinely send a list of claimant IDs and direct 
identifiers for WC claimants to the PDMP vendor (Appriss), on a monthly basis;  
2) the vendor links the personal identifiers provided by DLI to PDMP records using their internal 
algorithms/fuzzy logic;  

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Revised: 3/4/2020, 12/15/2020, 11/1/2022 
3) the vendor provides DLI with the full history of PDMP records linked to those claimants, with 
claimant first name, last name, and date of birth attached to each record;  
4) DLI staff link claimant first name, last name, and date of birth in the PDMP records to the same 
fields in the original file sent to Appriss to retrieve claimant ID; and  
5) using claimant ID, DLI staff are able to link PDMP records to WC claim IDs and claims data for 
each individual injured worker.  
 
Thus, in Washington, PDMP data would be delivered to the research team in the same way as other WC 
administrative data, with claim ID available for linkage to other data files. Ohio does not yet have a 
routine batch delivery system in place for PDMP data, but they do have statutory authorization to 
receive PDMP information, and we will be working with them to set this up. However, if a similar system 
cannot be set up for Ohio, we will obtain data using their current mechanism, whereby Ohio Bureau of 
Workers’ Compensation staff look up and download PDMP records for individual WC claimants, using 
first name, last name, and date of birth.  
 
Data description 
 
In addition to the logs contained in each data folder, a chronological log will be kept with any data 
decisions such as deleting a case or the coding of missing values.  All code will be held to a 
documentation standard that includes a header (name of file, author, date, purpose, input data, and 
output data/analysis/documents) and ample documentation of all steps in the code.  In addition, a data 
dictionary will be created that describes all of the variables used in the study including missing value 
coding and derived/created variables.  There will be separate data dictionaries for each aim of the 
project. 
 
Data storage and preservation 
All raw data and analysis data sets will be stored on a secure server. Data from this server is encrypted 
and stored on departmental storage daily, as well as backed up daily to a different location. Offsite 
backup of this location occurs quarterly, with storage of up to one year. Qualitative data is similarly 
stored on an offsite secure server, with a separate backup server. 
 
Data maintaining 
Once the study is complete, all raw data and analysis data sets will be stored on a secure server until 
after the end of the applicable records retention period, as required by the University of Washington 
Records Management Services.  
 
Data sharing 
The Principal Investigator, the Project Director, the Research Coordinator and the Data Manager(s) will 
work jointly to oversee and manage any data sharing. Possible sharing will be restricted to analyses of 
de-identified worker survey data and limited workers’ compensation and pharmacy data. At the end of 
the study, the study team will evaluate whether de-identified worker survey data will be made available. 
We will request permission to share limited data from the workers’ compensation systems (e.g. injury 
type) to link with the survey data. No data with direct identifiers will be shared and we will not keep any 
identifiers after the end of the study and relevant retention periods.  
 
The data may also be shared via the University of Washington Data Collaborative (UWDC) 
(https://datahub.csde.washington.edu/contact-us/) after the study is completed.  This system is a highly 
secure data environment with an infrastructure for screening collaborators.  Prior to becoming part of 

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the UWDC, the data will be de-identified and the appropriate permissions from the Washington and 
Ohio workers’ compensation systems will be obtained.  
 
 
 
 

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Appendix B. Data Safety and Monitoring Plan 
 
Study Description 
This study has three components (a) analysis of administrative data from the Washington state and Ohio 
state workers’ compensation systems, (b) an observational study (based on surveys) of workers in these 
workers’ compensation systems, and (c) patient and health care provider individual in-depth interviews 
and a key informant study of opioid policy.  
 
This study has been reviewed as "minimal risk" by the University of Washington IRB and all protocols 
and requirements set forth by the IRB will be followed. 
 
There are two main issues addressed by this document: (a) minimizing the risk to the study participants, 
and (b) data security. 
 
A. Individuals who will monitor the research study 
 
Principal Investigator (PI): Gary Franklin 
Project Director (PD): Deborah Fulton-Kehoe 
Research Coordinator: Andrea Elmore 
 
B.  Procedures for monitoring study safety 
Data and safety monitoring is ongoing throughout the study. Physical offices and file cabinets are locked 
and only accessible by authorized study personnel. Computer security includes not keeping identifiable 
data on local computers but only on secure off-site servers, only used by study team members, and 
strong passwords (see additional details below). Computer security breaches are monitored by system 
administrators who will immediately contact both the PI and PD with any concerns. All personnel are 
trained in human subject protection.    
 
C.  Participant risk 
The primary risks to research subjects in this study are breach of privacy and confidentiality, and 
possible discomfort with the data collection process. These risks are being minimized through 
comprehensive data security and confidentiality measures, in compliance with UW Human Subjects 
Division requirements, and by informed consent and respect for voluntary responses for worker surveys, 
individual in-depth interviews, and key informant interviews. This is an observational study to compare 
the effectiveness of pre-existing and ongoing interventions conducted by each state, which are 
governed by each state’s regulations. The interventions under study (i.e., prior authorization and 
retrospective review of opioid prescriptions) are mandated by state regulation, and are not subject to 
change based on study findings, except through the longer-term dissemination and translation process. 
Consequently, we do not plan to set up a Data Safety Monitoring Board.   
 
1. Expected risks 
 
a. Participation in Worker Surveys 
 
(1) a feeling of invasion of privacy because of the researchers having their contact 
information, (2) possible discomfort with the interview process such as fatigue and/or 
boredom while completing the telephone assessments, and (3) potential breach of 
confidentiality. Some participants may also experience mild anxiety, frustration, and/or 

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Revised: 3/4/2020, 12/15/2020, 11/1/2022 
stress while answering sensitive questions about depression, pain, and mood. As a result of 
answering questions about pain, some participants may focus more on their pain, which 
may lead to a temporary increase in pain intensity. 
 
b. Patient In-Depth Interviews 
 
The risk to participants would be discomfort with the interview conversation and potential 
breach of confidentiality. 
 
c. Provider In-Depth Interviews 
 
The risk to participants would be discomfort with the interview conversation and potential 
breach of confidentiality. 
 
d. Key Informant Interviews 
 
The only anticipated risk of participating in this research is that the interviewee may choose 
to share information that could jeopardize their employment if current or future employers 
learn of the interview content. 
 
Protection against risks 
 
a. Worker Surveys 
 
Subjects are recruited into the study via a letter describing the study, which will also provide a phone 
number for them to call to opt out of the study and avoid a phone call. The recruitment mailing and 
interviewer will state that the workers’ compensation agency provided the contact information to the 
researchers.  Research interviews will ask about health status, pain, pain interference with function, 
quality of life, use of opioid medications, benefits from and problems with use of opioid medications, 
depressive symptoms, and indicators of potential misuse of opioid analgesics. Subjects will be instructed 
that they can skip any questions they do not wish to answer. Any concerns related to the research may 
be addressed by telephoning the investigators, other research staff, or human subjects protection staff 
at the University of Washington. Also included will be an information sheet that will include phone 
numbers for the Survey Research Division, the research coordinator, the Principal Investigator, and the 
IRB. All participants will be informed of their right to withdraw from the study at any point without 
adversely impacting any of their medical care or relationship with workers’ compensation. 
 
b. Patient and Provider In-Depth Interviews 
 
Recruitment letters and phone calls will explain that the state workers' compensation agency provided 
the study team with the potential participant's contact information. Both will also (a) describe the topics 
that will be covered during the interview, and (b) emphasize that information provided by the 
participants will be treated as fully confidential, and that no potentially identifying information will be 
utilized in any reports or publications that emerge from the study. Both these points will be repeated at 
the start of the interview. In addition, the interviewer will emphasize that we hope the information 
revealed by the interview will help lead to better policies and better help for patients in the future. 
 

Original: 4/16/2019 
 
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Revised: 3/4/2020, 12/15/2020, 11/1/2022 
The interviews will be audio recorded, which may increase the risk of breach of confidentiality.  Media 
will be stored in locked rooms and drawers. The audio recordings will be professionally transcribed in 
Ohio under Dr. Tasleem Padamsee’s (the Lead Qualitative Investigator) direction. Electronic transfers of 
audio files and transcripts will be encrypted during transmission via a mechanism such as secure FTP, 
and approved by IT departments of the University of Washington and The Ohio State University. 
Transcripts will be checked for any potential identifying information that could have come up during the 
course of the interview; this information will be deleted before the data are analyzed. Only de-identified 
data will be provided to project staff for analysis. No individuals will be identified in any presentations, 
reports, or publications that result from this study. 
 
c. Key Informant Interviews 
 
Key informants will be informed that they are not obligated to share anything or answer any questions 
that might jeopardize current or future employment. Their confidentiality will be protected in the 
following ways. Included in the consent will be the option for the informant to be identified or choose 
anonymity. If the interviewee agrees to be recorded, their interview will be transcribed once it is 
completed. Both the recording and the transcript will be kept in the secure possession of the researcher. 
If anonymity is chosen, transcriptions of the interviews will be coded so the informant’s identity is 
protected. No identifying information will appear on the transcript of the interview, and both the 
transcript and recording will be kept physically separate from any information that could identify the 
participant. No individuals will be identified in any presentations, reports, or publications that result 
from this study. 
 
Monitoring Participant Safety 
 
Worker Surveys:  The Survey Research Division will report any concerns about research subjects to the 
Research Coordinator.  If any particular questions seem to cause repeated distress those questions will 
be re-evaluated and likely revised.  
 
During the patient and provider in-depth interviews, if something causes discomfort the interviewer will 
address this directly and provide community resources, if appropriate. 
 
Key informants will be professionals talking about their work-related activities. We do not therefore 
anticipate any safety risks. Should key informants become uncomfortable during the conversation, the 
interviewer will volunteer to change the topic or end the interview, allow time for a break, and offer 
community resources as appropriate. 
 
D.  Protecting the confidentiality of participant data 
We will take multiple steps to protect participants’ privacy. All data collected for data analysis will have 
direct identifiers (claim ID and name) removed as soon as possible, labeled with a code number that is 
unique to each participant in the study, and maintained separately from any identifying information 
excluding exceptions described in more detail below. All of the data collected from participants are for 
research purposes only.  
 
University of Washington  
Department of Environmental and Occupational Health Sciences (DEOHS) 
 
SERVERS/SERVICES 

Original: 4/16/2019 
 
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Revised: 3/4/2020, 12/15/2020, 11/1/2022 
The data will be stored on a secure internal network server. All data will be encrypted when transferred 
from workers’ compensation to the secure internal network server and when viewed on the server. The 
server is housed in a locked server room to which only our department’s IT staff have physical access. 
The backup system is at the UW Data Center. The room is a shared colocation but the backup system is 
in a secure cabinet to which only IT staff have access. The IT staff have so-called “root” access that 
would allow access to the system's data. Assurance of necessary confidentiality is part of the condition 
of this paid employment. Access to this server is logged and relies on having valid user credentials.  
Desktop computers used to access the system are likewise restricted to the Department of 
Environmental and Occupational Health Sciences account holders and measures have been taken to 
reduce unauthorized access including locked facility and offices and elements such as locking 
screensavers. 
 
Each member of the research team will have an individual username and password for the secure 
server; these usernames and passwords will not be shared. Passwords will be required to be of sufficient 
length and complexity to reasonably protect them from being guessed by humans or computers. 
Passwords will be changed periodically, and changed immediately if there is suspicion of compromise.  
 
Server operators will take reasonable actions on a regular basis to ensure that the server system is not 
vulnerable to attack. The server will be kept in a secure location and subject to regular inventory to 
ensure that loss or theft is identified. Server operators will promptly inform the research team of any 
suspected breaches. Any actual or suspected loss, theft or improper use of (or access to) the data will be 
reported immediately to the University of Washington IRB.  
 
The data will be processed completely internal to the server. If it becomes necessary to retain the 
original data on a disk either for direct use or backup, the data will be encrypted, and such disks stored 
in a locked drawer, cabinet or other container. Data transferred to the research team over the internet 
will be encrypted during transmission via a mechanism such as secure FTP. 
  
DEVICES 
No data with direct or indirect identifiers will be placed on any desktop computer or portable device 
including laptop computers, notebook/netbook computers, smart phones, USB or flash drives, portable 
hard disks, iPads, tablet computers, and DVDs. Local desktop computers will be configured for secure 
operations, to limit access to the specific person or persons authorized to use the device. Applications 
being used on these devices will also be configured to protect access and transfer of data. Operating 
systems and applications will be kept up to date by installing revisions, patches and upgrades. Secure 
devices will be stored in a way that minimizes the possibility of loss or theft. An established device 
management and monitoring process will ensure that the devices are appropriately configured, patches 
to operating systems are installed, devices have not been stolen or lost, and devices are used 
appropriately. 
 
DIRECT IDENTIFIERS 
Only data for the surveys will contain name and contact information when received from workers’ 
compensation. These are needed to recruit study participants. This information will be kept in a 
separate password protected folder by the Data Manager(s). The Data Manager(s) will receive this 
weekly data from workers’ compensation, remove duplicates, and screen the data for study eligibility. 
Once identifying the new potential participants, a unique identifier will be added, and this information 
will be put into a separate folder that is accessible by the Survey Research Division. The key that links 
direct identifiers to unique study IDs will be kept by the Data Manager(s) and isolated from any other 

Original: 4/16/2019 
 
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Revised: 3/4/2020, 12/15/2020, 11/1/2022 
data.  Participant survey data received back from the Survey Research Division to the Department of 
Environmental and Occupational Health Sciences will only contain the unique study ID and no direct 
identifiers.  
 
University of Washington  
Survey Research Division (SRD) 
Social Development Research Group (SDRG) 
 
SDRG’s workplace suite is secured with locked, individual password-access doors.  Computer networks 
are secured through a firewall and individual internal access to shared files is controlled through the 
domain server. Individual computers are password protected.   
 
All surveys will be administered following standard survey administration procedures developed by 
SDRG and approved by the University of Washington’s IRB. To ensure confidentiality, study identification 
codes will be used. Master lists that link study codes to identifiers are maintained separately and are 
stored off-line on a CD in locked files.  Interviews conducted over the phone by research staff will use 
computer-assisted interviewing technology that automatically transfers data from each completed 
interview to a secure server. The resulting electronic data files will be maintained in password protected 
directories within SDRG’s internal computer network.  
 
The Ohio State University 
 
SERVERS/SERVICES 
All data will be stored on a secure network server. The Ohio State University secure server has been 
vetted by the Office of the Chief Information Officer’s data risk assessment procedure, and determined 
to be compliant with HIPAA and to meet all security needs for data containing protected health 
information. Access to project folders on this server will be locked, and allowed only to IRB-approved 
project personnel who have completed appropriate Human Subjects and Responsible Conduct of 
Research certifications.  
 
The server on which data will be stored is housed at the State of Ohio Computing Center (SOCC), an 
offsite locked facility to which only a limited number of university IT staff have physical access. The 
backup server is located separately in the SOCC. Server operators take reasonable actions on a regular 
basis to ensure that the server system is not vulnerable to attack. Server operators will promptly inform 
the research team of any suspected breaches. Any actual or suspected loss, theft or improper use of (or 
access to) the data will be reported immediately to the University of Washington IRB.  
 
DEVICES 
No data with direct or indirect identifiers will be placed on any desktop computer or portable device 
including laptop computers, notebook/netbook computers, smart phones, USB or flash drives, portable 
hard disks, iPads, tablet computers, and DVDs. Only de-identified data will be analyzed on local desktop 
and laptop computers. These computers are all College of Public Health authorized machines, which are 
configured for secure operations and limit access to the individuals with strong passwords (as described 
above). Operating systems and applications will be kept up to date by installing revisions, patches and 
upgrades. Secure devices will be stored in a way that minimizes the possibility of loss or theft. An 
established device management and monitoring process will ensure that the devices are appropriately 
configured, patches to operating systems are installed, devices have not been stolen or lost, and devices 
are used appropriately. 

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Revised: 3/4/2020, 12/15/2020, 11/1/2022 
 
DIRECT IDENTIFIERS 
Identifiable original data from in-depth interviews and key informant interviews will be stored on secure 
server sites separately from de-identified data for analysis. Only the lead qualitative analysis investigator 
and qualitative research assistants will have access to the original data, and they will de-identify the 
data before other staff can access it for analysis purposes. The key that links direct identifiers from in-
depth interviews and key informant interviews to unique study ID numbers will be accessible only to 
those two individuals, and will be kept separately on the secure server from both the original 
identifiable data and the de-identified data used for analysis. Data transferred between members of the 
research team over the internet will be encrypted during transmission via a mechanism such as secure 
FTP, authorized by the IT staff of both The Ohio State University and the University of Washington. 
Individual in-depth and key informant data will be de-identified as soon as possible after data collection, 
and only de-identified data will be used for all analytic purposes. Identifying data will be destroyed once 
all analyses from the study are complete.  
  
E. Identifying, reviewing, and reporting adverse events and unanticipated problems to the applicable 
IRBs, FDA, and any other and any other applicable governmental agencies or other monitoring bodies, 
consistent with legal requirements and requirement so of the IRB. 
 
Possible adverse events: 
 
Participant discomfort that the survey staff, individual in-depth interviewers, or key informant 
interviewers become aware of will be reported to the Research Coordinator. If the discomfort cannot be 
managed by the identifying staff as described above, the Research Coordinator will report the adverse 
event to the Human Subjects Division.  The Research Coordinator will review these reports with the PI 
and study staff at regular staff meetings.  
 
Any data security breach will also be reported to the Research Coordinator, the PI, the IRB, and PCORI. 
 
F. For multi-site studies, the procedures for ensuring compliance with the DSMP and requirements for 
reporting across research study sites. 
 
Tom Wickizer, PI of The Ohio State University study site, will be responsible for ensuring compliance 
with the DSMP at The Ohio State University and for reporting to the University of Washington site any 
problems.