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Work 51 (2015) 327–336 DOI 10.3233/WOR-141949 IOS Press

The association between physical medicine and rehabilitation service utilization and disability duration following work-related fracture Amanda Younga,∗, Stasia Muhlnerb , Alicia Kurowskic and Manuel Cifuentesc a

Liberty Mutual Research Institute for Safety, Hopkinton, MA, USA Occupational Health Department, Kaiser Permanente, San Francisco, CA, USA c Work Environment, University of Massachusetts, Lowell, MA, USA b

Received 7 May 2013 Accepted 5 February 2014

Abstract. BACKGROUND: Rural residents with work-related fractures utilize healthcare differently and return to work (RTW) sooner than their similarly-injured urban peers. OBJECTIVE: To elucidate the relationship between physical medicine and rehabilitation (PM&R) service usage and workdisability duration following work-related injury. DESIGN: Retrospective cohort study, employing a two-phase sequential analysis. The project involved a longitudinal analysis of PM&R utilization and work-disability duration of 2,216 people across the U.S. who fractured a bone, received PM&R services, and had at least seven days off work. In the first phase of the analysis each individual was assigned a PM&R utilization score based on how similar his or her usage was to that typical of rural residents. The second phase tested the relationship between assigned PM&R utilization scores and work-disability duration. RESULTS: Differences in urban versus rural PM&R utilization included less total PM&R services and fewer passive services in the first 8 weeks for rural claimants. Among those off work for more than a month, rural residents used more active services just prior to RTW, with a gradual decreasing of services leading up to RTW. Controlling for covariates, aggregate PM&R utilization scores were found to relate to time to first RTW (Hazard Ratio = 1.66, p < 0.005). CONCLUSIONS: Findings suggest that using services in a way that is more consistent with rural patterns is associated with decreased work-disability durations. Consistent with previous studies, results suggest that passive services, prolonged episodes of care, and failure to focus on transitioning to self-management are related to longer work-disability durations. Keywords: Occupational-injury, rehabilitation, return-to-work, rural-urban differences, workers’ compensation

1. Introduction

∗ Corresponding author: Amanda Young, Liberty Mutual Research Institute for Safety, 71 Frankland Road, Hopkinton, MA 01748, USA. Tel.: +1 508 497 0221; E-mail: AmandaE.Young@Liberty Mutual.com.

The goal of post-injury care in Workers’ Compensation is to restore health and functioning, with the intention of preparing the injured worker for a timely, safe and sustainable return to the workplace. In pursuit of this aim, it is common for a person with a work-related injury to undertake a physical rehabilita-

c 2015 – IOS Press and the authors. All rights reserved 1051-9815/15/$35.00 

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tion program that can include physical, occupational, and chiropractic therapy services. However, the value of these programs is controversial because there is limited evidence in support of their effectiveness, and an absence of clear treatment guidelines. With increased awareness of the need to control health care costs, questions regarding the value of post-injury therapy regimes have been raised [1]. This is especially important in work-related fractures, as these are relatively common events, result in considerable work disability, and often involve physical therapy services, despite little scientific evidence supporting these practices. In our earlier work on urban versus rural differences in work disability duration following a work-related fracture, it was found that on average rural residents used fewer services following their injury and had shorter work-disability durations [2]. While this study provided some insight, it did not evaluate whether or not the observed differences were related to the characteristics of the services received. As outlined below, prior research suggests that, beyond quantity, other variables such as service timing, type and the intensity of delivery might also be influential. With regards to the timing of services following injury, previous studies have shown that early mobilization after a fracture results in faster return to work (RTW) [3,4] and faster restoration of functional range of motion [5]. Such findings suggest that the timing of physical therapy treatment may also influence return-to-work outcomes in our study population. With regards to service intensity, research suggests that higher intensity may improve outcomes such as adductor muscle strength and length of hospital stay in elderly patients after hip fracture [6,7]. Regarding type of service, adherence to clinical guidelines for more active than passive services for acute low back pain, has been found to be associated with better clinical outcomes and reduced costs [8]. Further, it has been found that passive physical therapy services provided postmeniscectomy may be counterproductive to work resumption [9]. Little information is available on optimal post-acute treatment of occupational fractures. To further elucidate the relationship between PM&R usage and work-disability duration following workrelated injury, the current study focused on determining if ‘rural style’ PM&R utilization was related to work-disability duration regardless of residential address. More specifically, PM&R utilization data was examined with the aim of determining urban vs. rural differences in: 1) the intensity of service usage, 2) the types of services used, and 3) the sequencing in which services were received. Observed differences were then related to work-disability duration.

2. Methods 2.1. Procedure This was a retrospective cohort study, employing a two-phase sequential analysis. Since rural residents with work-related fractures use healthcare services differently and return to work (RTW) sooner than similarly-injured urban peers [2], the first phase of data analysis involved generating PM&R utilization scores that captured the distinctive way in which rural residents utilized PM&R services following their injury. The second phase involved using survival analysis to determine whether the selected differences in PM&R usage were related to time to first RTW. We used this two-stage approach due to our incomplete understanding of what pattern of PM&R usage would likely facilitate a faster RTW. The alternative strategy of choosing a pattern, or patterns, a priori and testing these for their relationship to RTW would have allowed for only a few “stabs in the dark” and likely resulted in a combination of variables lacking in detail and an inflated experiment-wise Type I error rate. Adopting the chosen approach afforded the ability to identify a detailed and clinically relevant pattern of care more prevalent in a setting known to be associated with decreased disability duration following occupational injury. Once identified, this pattern could then be tested against disability duration without inflating the experiment-wise Type I error rate. Two years of post-injury claims data were analyzed. Data were extracted from the administrative records of a large insurance company, which accounts for 8–10% of the United States workers’ compensation (WC) coverage and has a wide distribution of coverage by state, industry type, and company size [10]. All workers from California, Florida, Illinois, Indiana, Michigan, New York, Oregon, Pennsylvania, and Texas who filed a new and accepted WC claim for a bone fracture between 1/1/2000 and 12/31/2001 were assessed for inclusion. States were chosen based on high market share (i.e. where the insurer had a substantial proportion of the WC market), diversity in geographic locale and urban-rural composition. The number of states chosen was limited so that jurisdictional differences could be controlled. Bone fractures were selected since these are injuries for which place of residence is unlikely to affect initial care-seeking behavior. Further, unlike many other injuries with less definitive diagnoses such as low back pain, fractures have well-defined treatments [11] that are likely to be similar regardless of where care

A. Young et al. / The association between healthcare utilization and work-disability duration

is sought. Cases were selected based on nature of injury codes. These codes are assigned by trained insurance industry coders and are selected based on injury description as reported on the ‘First Report of Injury’ form. Our final sample included WC claimants who had more than seven days off work, who received physical medicine services, and who had no missing data (N = 2,216). Procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2000. Data detailing participants’ PM&R utilization were downloaded from the insurer’s administrative database. These procedure codes included: 1) Centers for Medicare and Medicaid Services’ (CMS) Healthcare Common Procedure Coding System (HCPCS) level I codes, also known as Current Procedural Terminology (CPT) codes, and 2) HCPCS level II codes, which were used to identify services not included in the CPT codes. 2.2. Variables 2.2.1. Residential location As detailed in earlier works [2,12], a method was chosen that employed the US Census Bureau’s urban and rural taxonomy, which defines the urbanization of an area by its population density [13]. Claimants’ residential zip codes at the time of injury were assigned a ‘rurality’ percentage based on year 2000 population figures [14]. This score was derived by dividing the number of people defined as ‘rural’ living in the zip code by the total population in the zip, then multiplying by 100. As described previously [12], the relationship between rurality and work disability was tested using various groupings and, although the relationship was not found to be linear, a consistent relationship was observed. Due to the absence of important differences between the more rural groups and the cell-size requirements associated with prior analyses, rurality was dichotomized into “0–9% = urban” and “10–100% = rural”. 2.2.2. PM&R service utilization According to the American Board of Physical Medicine and Rehabilitation’s definition [15], PM&R services are those concerned with the diagnosis, evaluation, and management of persons with conditions requiring rehabilitation. Consistent with the specialties’ comprehensive, multidisciplinary approach, for the purpose of this study PM&R services were de-

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fined as encompassing: evaluations and re-evaluations, the applications of modalities (e.g. thermal, acoustic, light, mechanical, or electrical energy), the administration of therapeutic services, wound management, tests and measurements, and orthotic and prosthetic management [16]. PM&R providers may include physiatrist, physical, occupational, speech, and recreational therapists, as well as neuropsychologists, chiropractors, acupuncturists, athletic trainers, social workers and rehabilitation nurses. While this is true, in practical terms, it is likely that the majority of PM&R treatments are provided by physical therapists. Consistent with the American Medical Association’s Physicians’ CPT coding structure, codes ranging from 97000 to 97799 were viewed as being for PM&R services [17]. Based on the works of previous researchers [8,9,18], PM&R services were categorized into three groups: 1) active, 2) passive and 3) education, assessment and not otherwise classified/specified. Active therapies were considered those done by the patient and included exercises, training on exercises, physical conditioning, and work hardening, conditioning and job simulation. Passive therapies were considered those done to patients, required no exertion on their behalf, with examples including such services as hot or cold packs, electrical stimulation, traction, diathermy, infrared, ultraviolet light, ultrasound, hydrotherapy, massage, bandaging and taping, and manual therapies. The assessment, education and not otherwise classified/specified group included services that did not fall into the active or passive categories, or for which there was insufficient detail to make a confident allocation. In addition to service type, we also created variables capturing the timing of service receipt. These were numerous and included time from date of injury to first PM&R service, and if the first service occurred in the first 7 days of disability. To capture magnitude, we calculated the mean number of PM&R services per week until RTW. To capture intensity, we calculated the mean weekly services in weeks one up to eight of disability. Further, we created a number of additional variables with the aim of capturing a combination of service type, timing, magnitude and intensity (see Table 3). The creation of these variables was done by a member of the research team with recent clinical experience in the treatment of fractures (SM) and based on clinical judgment. Combinations of PM&R usage variables were tested for their ability to capture the difference between the usage typical of urban and rural residents. After a final model was chosen, we created an aggregate value (PM&R score) reflecting the individual’s usage in comparison to that of the typical rural resident (see data analysis section for further details).

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2.2.3. Covariates Adjustments for individual and injury characteristics (i.e. age, gender, state, body part injured, industry and occupation) were included in evaluating the relationship between patterns of care and outcomes. Part of body was dichotomized to digits (i.e. fingers and toes) vs. the remainder of the body. Industry and occupation were coded from free-text fields using the Standard Industry Classification (SIC) [19] and the International Standard Classification of Occupation (ISCO88) taxonomies [20] and were collapsed so that occupation was dichotomized as white-collar verses bluecollar and industry was collapsed into four categories: 1) manufacturing, 2) wholesale and retail trades, service, finance, and public administration, 3) transport, and 4) agriculture, mining and construction. Measures of severity included multiple vs. single body-part fractures, and open vs. closed fractures. 2.2.4. Time to First RTW Established methods for calculating time to first RTW were used [21,22]. This involved extracting the number of full lost-work days for which claimants received payment (i.e. temporary-total indemnity) before the first stop in payments and then approximating work-disability duration through taking into consideration state legislation regarding waiting (i.e. number of lost-work days before indemnity payments begin) and retroactive periods (i.e. number of lost-work days before claimants are paid for waiting period days). 2.3. Data analysis Phase 1 Data analysis began by testing PM&R usage variables for their relationship to place of residence (urban vs. rural) using t-tests and chi-squared analyses. In total, 64 PM&R utilization variables were tested. Once significant associations had been identified, the task of calculating an aggregate PM&R utilization score began. Using logistic regression, a week-by-week analysis to incorporate the timing of each participant’s RTW was conducted. As such, each week’s model incorporated only those individuals still receiving disability compensation payments. Variables were added to the model starting with each one’s first week of relevance. In cases where the individual returned to work prior to the data being available, as would be the case for people who returned to work prior to data relating to a defined period of interest (for example, usage in weeks 5 up to 8) the individual’s PM&R score was calcu-

lated with the information available up to the time of RTW. The variables addressing the usage of services in a given week represent what happened in the week of reference and are not a summation of what occurred over the defined period. For example, in the case of the variable capturing the use of two or more passive services in week 1 up to week 8, this was calculated on a weekly basis. If the individual was off work for three weeks and used four passive services each week, they would be coded as a “yes” for this variable. If an individual was off work for eight weeks, but did not use any passive services, they would be coded as a “no”. In total 165 combinations of variables were tested for their relationship to residential location (i.e. urban vs. rural). With the aims of parsimony and including variables that captured service provision proximal to the time of injury, a series of logistic regression analyses to determine the model of best fit was run. Our final PM&R utilization model was chosen based on goodness of fit indicator (model χ2 ), the distribution of predicted probability scores (aiming for symmetry, dispersion and like normal shape) percentage of correct residency type predictions (aiming for approximately equal for both the urban and rural groups), interpretability, and clinical importance (most proximal to the time of injury). Because our final model was chosen based on the way the variables performed in combination, this allowed for the inclusion of variables that were not significant at the bivariate level. Once our final model was chosen, we used logistic regression to create an aggregate PM&R score. This was achieved by assigning each claimant a probability of being a rural resident based on his or her PM&R services usage. These probabilities, also referred to as propensity scores, had the potential range of zero to one, higher scores signifying a higher probability of being a rural resident. Phase 2 For the second phase of our analysis, the PM&R Scores calculated in Phase 1 of the research, together with the previously employed covariates [2,12], were modeled against time to RTW using a Cox proportional hazards regression model.

3. Results In total, the sample received 256,977 PM&R services. Details of CPT codes accounting for more than 1% of the total PM&R services used are detailed in

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Table 1 Most commonly observed Current Procedural Terminology (CPT) codes appearing in sample’s Physical Medicine and Rehabilitation (PM&R) related billing data PM&R type Active

f

%∗

74,265 20,435 7,079

28.9 8.0 2.8

97545 97546

Therapeutic exercises to develop strength and endurance, range of motion and flexibility Use of dynamic activities to improve functional performance Neuromuscular reeducation of movement, balance, coordination, kinesthetic sense, posture, and/or proprioception for sitting and/or standing activities Work hardening/conditioning (initial) Work hardening/conditioning (additional hours)

4,308 3,574

1.7 1.4

97014 97140 97250† 97010 97035 97022 97145† 97124† 97018 97032 97265†

Application of a modality – electrical stimulation Manual therapy techniques (e.g., manual traction, mobilization/manipulation) Myofascial release Application of hot or cold packs Application of ultrasound Application of a modality – whirlpool Physical medicine treatment (additional time) Massage (stroking, compression, percussion) Application of a modality – paraffin bath Application of a modality – electrical stimulation Manual therapy – joint mobilization

23,492 16,211 15,405 13,700 8,383 6,789 6,523 6,393 5,696 3,650 3,299

9.1 6.3 5.9 5.3 3.3 2.6 2.5 2.5 2.2 1.4 1.9

CPT code

CPT description

97110 97530 97112

Passive

∗ Totals

to 85.42%. Codes accounting for less than 1% of all PM&R services are not presented; † Discontinued codes, but still used at the time of

billing.

Table 1. Of these 45.6% were categorized as active therapies (those done by the patient). The most commonly observed CPT codes for active therapy were 97110, 97530, 97112, 97545, 97546, and 97116 (ordered by frequency of observation, highest to lowest). Passive therapies (those done to patients) accounted for 50.8% of all PM&R services. The most frequently observed CPT codes for passive therapies were 97014, 97140, 97250, 97010, 97035, 97022, 97145, 97124, and 97018 (ordered by frequency of observation, highest to lowest). The assessment, education and not otherwise classified/specified grouping accounted for 3.6% of services. Ordered by frequency of observation (highest to lowest) the most common CPT codes in this grouping were 97001, 97750, 97002, and 97003. Details of time to RTW cross-tabulated by the demographic and injury characteristics are contained within Table 2. The mean time to RTW was 172 days (SD = 175.88, median = 107, 25th percentile = 66, 75th percentile = 196). Only 6.5% of the sample was off work for four weeks or less, and 79% were off work for more than eight weeks. Within the current sub-sample, a difference in time to RTW related to residential status (F = 0.002) was not observed. 3.1. Phase 1: Differences in Rural vs. Urban PM&R utilization Bivariate analysis of variables representing the type, timing, and intensity of PM&R utilization and residen-

tial location are presented in Table 3. Results revealed numerous urban-rural differences in the type, timing, and intensity of PM&R usage. The results of the logistic regression modeling indicated that the combination of four patterns of care best differentiated urban from rural PM&R utilization were: 1) the mean number of PM&R services received per week prior to RTW (rural residents averaged fewer services); 2) whether or not the claimant received two or more passive PM&R services per week in weeks 1 up to week 8 of disability (less common for rural residents); 3) the mean weekly number of active PM&R services received in weeks 5 up to week 8 (higher mean for rural residents); and 4) whether the claimant received at least three weekly passive PM&R services in week 1 up to week 4, followed by one or fewer passive PM&R services per week in week 5 up to week 8 (more common for rural residents). The model had a Likelihood Ratio Chi-Square of 80.50 (p < 0.001), and classified 47.3% of rural residents as having a rural pattern of use of PM&R and 64.2% of urban residents as having an urban pattern of PM&R. This finding is consistent with the idea that there will be diversity of healthcare usage within residential settings, and that there will be rural residents that behave more like urban residents and vice versa. The mean number of PM&R services received per week up to RTW, and the mean number of active PM&R services received per week in week 5 up to week 8 significantly predicted rural-

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A. Young et al. / The association between healthcare utilization and work-disability duration Table 2 Sample’s injury and demographic characteristics Injury and demographic characteristic Place of residence Urban Rural Age < 25 25–34  35 Gender Male Female State California Florida Illinois Indiana Michigan New York Oregon Pennsylvania Texas Industry Manufacturing Wholesale and retail, trades, services, public administration, and finances Transport Agriculture, mining and construction Occupation White collar Blue collar Body Part Digit Non-digit Fracture type Single Multiple Open Closed

n

Time to RTW (days)

1557 659

172.48 172.87

209 545 1553

139.14 154.35 172.60

1560 656

174.87 167.18

507 218 379 132 119 180 155 175 351

181.32 157.82 146.57 131.98 191.06 158.66 137.68 240.67 194.91

425 536 961 301

153.67 169.28 169.77 212.81

677 1539

171.19 173.20

313 1903

98.27 184.82

1874 342 186 2030

158.92 247.54 170.24 172.81

RTW = return to work.

ity (p < 0.001). Table 4 shows regression coefficients, Wald Chi-Square statistics, odds ratios and 95% confidence intervals for the four variables used to calculate the PM&R utilization scores. 3.2. Phase 2: The association between PM&R utilization and time to return to work The results of the Cox regression survival analysis (see Table 5) indicated that when the aggregated PM&R utilization scores (based on the four PM&R patterns of care variables outlined above), along with the covariates, were modeled to examine their association with RTW, the hazard ratio for the PM&R utilization score was found to be 1.66 (p < 0.005). The sample’s aggregated PM&R utilization scores ranged from 0 to 0.65. From this it can be suggested that a change from the most urban to the most rural style of

PM&R utilization would lead to a 43% higher chance of being at work at any moment in time. To obtain a full change in PM&R score is likely to be unrealistic. However, movement within the range appears possible. In practical terms, the effect of moving from the 25th to the 75th (i.e. from a PM&R score of 0.23 to 0.38) could result in an average 10% higher chance of being at work at any moment in time.

4. Discussion This study presents a novel method of identifying key indicators of care within complex, longitudinal data, and further supports findings from controlled studies through a large community-based sample. Significant associations were found between PM&R usage and work-disability duration following work-related

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Table 3 Bivariate analysis of the relationship between rurality and physical medicine and rehabilitation (PM&R) usage PM&R Utilization Variables Type Claimant had passive PM&R services prior to RTW Claimant had active PM&R services prior to RTW Timing Time from date of injury to first PM&R service First PM&R service occurred in first 7 days of disability Magnitude Mean PM&R services per week until RTW† Intensity Mean weekly PM&R services in weeks 1 up to 8 of disability Type x Timing Time from date of injury to first passive PM&R service First active PM&R service occurred within 30 days of date of injury Passive PM&R service received in week 1 of disability Type x Magnitude Mean weekly passive PM&R services until RTW Type x Intensity  2 weekly passive PM&R services in weeks 1 up to 8 of disability† Mean weekly active PM&R services in weeks 5 up to 8 of disability† Mean weekly active PM&R services in week 1 of disability Type x Timing x Magnitude x Intensity  3 weekly passive PM&R services (weeks 1 up to 4) then  1 weekly passive services (weeks 5 up to 8)†  2 active PM&R services weekly (weeks 1 up to 2) then  1 2 weekly passive services (weeks 3 up to 8)

OR

CI

0.626∗∗ 0.861

0.506–0.776 0.612–1.211

1.222∗ 0.897

1.016–1.469 0.697–1.152

0.812∗∗

0.768–0.859

0.902∗∗

0.855–0.952

1.003∗ 0.908 0.889

1.001–1.005 0.755–1.093 0.778–1.016

0.721∗∗

0.658–0.79

0.470∗∗ 0.973 0.924

0.339–0.651 0.906–1.046 0.749–1.14

1.480 0.336

0.483–4.542 0.076–1.48

∗ p < 0.05, ∗∗ p < 0.001, † Used to calculate the aggregate PM&R Utilization Score. RTW = return to work; OR = odds ratios; CI = 95% confidence intervals.

Table 4 Results of logistic regression used to calculate physical medicine and rehabilitation (PM&R) utilization scores (N = 2216)

Variables Mean PM&R services weekly until RTW  2 weekly passive PM&R services in weeks 1 up to 8 of disability (Yes = 1, No = 0) Mean weekly active PM&R services in weeks 5 up to 8 of disability  3 weekly passive PM&R services (weeks 1 up to 4) then  1 weekly passive services (weeks 5 up to 8) (Yes = 1, No = 0) ∗p

B −0.25 −0.37 0.18 0.92

Wald test Chi Square 46.96 3.47 16.45 2.29

Odds Ratio 0.779∗ 0.693 1.195∗ 2.522

95% CI Odds Ratio Lower Upper 0.73 0.84 0.47 1.02 1.10 1.30 0.76 8.36

< 0.001. Note for interpreting B: Rural = 1 and Urban = 0. RTW = return to work; CI = confidence intervals.

fracture. Regardless of actual residential location, claimants whose PM&R usage was more consistent with typical rural usage returned to work faster than those who used PM&R services in a way that was more urban in style. Usage that was found to be associated with a faster RTW involved the receipt of fewer weekly PM&R services up until the time of RTW, and fewer than three passive treatments in weeks 1 up to 8. In addition, claimants off work for more than four weeks, who returned to work sooner, typically received more active services in weeks 5 up to 8. Those who had been heavier users of passive care in weeks 1 to 4 (i.e. 3 or more services a week), returned to work fastest if they reduced their usage of passive services to 1 service or less in weeks 5 up to 8. When used in combination, results indicated that changing the way services are provided so that they are more in line with the way they

are used by the typical rural resident has the potential to significantly reduce work-disability duration following occupational injury. Although a novel method was employed, there is consistency between the current findings and prior research. A recent example was a study of health maintenance care for work-related low back pain provided by physical therapists or physicians found that the receipt of maintenance care was associated with higher rates of disability recurrence [23]. Similarly, others have found enhanced physical therapist involvement following occupational back pain and work-related upper extremity disorders to be associated with a delayed time to claim closure [24]. And a randomized controlled study into the effectiveness of supervised physical therapy following partial meniscectomy found no difference between those who received supervised care

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A. Young et al. / The association between healthcare utilization and work-disability duration Table 5 Relationship between physical medicine and rehabilitation (PM&R) utilization score and time to first return to work (n = 2214)

Variables PM&R score Age Gender Industry† Industry 1 vs. 4 Industry 2 vs. 4 Industry 3 vs. 4 State State 1 vs. 9 (CA vs. TX) State 2 vs. 9 (FL vs. TX) State 3 vs. 9 (IL vs. TX) State 4 vs. 9 (IN vs. TX) State 5 vs. 9 (MI vs. TX) State 6 vs. 9 (NY vs. TX) State 7 vs. 9 (OR vs. TX) State 8 vs. 9 (PA vs. TX) Body part (nondigit = 1) Occupation (white collar = 1) Multiple fractures Open fracture

B 0.508 −0.137 0.03 0.31 0.29 0.292 0.102 0.33 0.341 0.407 0.088 0.207 0.408 −0.215 −0.666 −0.045 −0.427 −0.158

df 1 1 1 3 1 1 1 8 1 1 1 1 1 1 1 1 1 1 1 1

Hazard Ratio 1.662∗∗ 0.872∗∗ 1.030

95% CI for Hazard Ratio Lower Upper 1.18 2.35 0.82 0.93 0.93 1.15

1.363∗∗ 1.332∗∗ 1.339∗∗

1.17 1.16 1.16

1.59 1.54 1.55

1.108 1.391∗∗ 1.407∗∗ 1.503∗∗ 1.092 1.230∗ 1.503∗∗ 0.807∗ 0.514∗∗ 0.956 0.652∗∗ 0.854∗

0.97 1.17 1.22 1.22 0.88 1.03 1.23 0.67 0.45 0.86 0.58 0.73

1.27 1.65 1.63 1.84 1.34 1.47 1.82 0.97 0.58 1.07 0.73 1.00

∗p

< 0.05, ∗∗ p < 0.001, † 1 = manufacturing, 2 = wholesale and retail, trades, services, public administration and finances, 3 = transport, 4 = agriculture, mining, and construction. CI = confidence intervals.

with a home program and those who only received a home program [25]. Similarly, a study of partial menisectomy in patients receiving workers’ compensation found that independent exercise was as effective as traditional outpatient services [26]. With respect to active versus passive services within a workers’ compensation population, it has been found that the receipt of any passive services following knee surgery was associated with longer disability duration [27]. And improved outcomes have been observed following workrelated musculoskeletal injuries where the early therapy used active rather than passive techniques [28]. When attempting to interpret the meaning of the contribution of the “weaning” variable (i.e. three passive or more PM&R services in week one, and up to week four, followed by one or fewer passive PM&R service per week from week five up to week eight) to the explained variance in time to RTW, it may be suggested that this variable adds to the explanatory power through an association with clinical recovery. That is, the withdrawal of passive treatment is associated with an improvement in the patient’s condition. Therefore, one would expect that if the patient’s condition is improving, he or she would be getting closer to being ready to RTW. While this sounds plausible, it should be noted that a number of weaning variables were tested for their relationship to residential location and contribution to the model. Findings were not consis-

tent and little relationship between the various weaning variables and RTW was observed when follow-up, post-hoc analyses were performed (results not shown). However, this is not to say that other patterns of service reduction are not related to RTW. Further research is needed to determine if the relationship between RTW and service reduction is causal and, if so, what treatment regime is related to a timely, safe and sustained RTW. The strengths of this study are as follows. 1) The focus is on fractures, significant and painful conditions where place of residence is unlikely to influence whether a person seeks medical care. 2) Early medical care is relatively standardized (regardless of whether delivered in a doctor’s office or emergency room). 3) The dataset is large which allows for investigation of quite subtle differences in PM&R utilization. 4) It is likely that the results are generalizable in that the sample represents a typical community practice, unlike other studies that have addressed patterns of care using small, controlled experiments. 5) The current study affords a real-life perspective unavailable elsewhere. 6) The focus on relatively early care gives insight as to the potential for altering long-term outcomes. 7) Finally, the usage pattern identified has the potential to improve return-to-work outcomes for claimants with work-related fractures based solely on changing their receipt of PM&R services.

A. Young et al. / The association between healthcare utilization and work-disability duration

However, the study also has its limitations. One of these was that there were many combinations of variables that could be used to form this final PM&R score and, thus, many different possible models could have a similar association with length of disability. In addition, many other types of medical services could be used to describe rural versus urban differences in healthcare utilization. It is possible and likely that other urban-rural service provision patterns differences exist and these also may be associated with improved returnto-work outcomes. When interpreting results it is important to remember that, although prior studies have shown improved health outcomes in urban people, the population we were studying is a workers’ compensation population and the outcome investigated was disability duration. As such, our findings apply to workers’ compensation claimants, who may have different motivating factors affecting their RTW than persons who are not receiving compensation [29]. The extent to which findings reflect a non-workers’ compensation population is unknown. This outcome of interest, time to RTW, was not a clinical measure, rather it was functional; therefore, conclusions should not necessarily imply improved health. A further consideration relates to not differentiating who provided the service (Physical Therapist, Occupational Therapist, Medical Doctor, etc.), or where it was performed (hospital, clinic, etc.). Not differentiating between service providers or locations was chosen consistent with the specialties’ comprehensive, multidisciplinary approach; however, the significance of provider type could be a topic for future research. While the study provides some insight into how PM&R usage may impact on disability duration, it raises questions pertaining to the reasons behind why delivery is different in urban and rural locations. Access to services has the potential to be a limiting factor; however, it should be noted that only a small proportion of the sample resided in very rural locations. As previous studies revealed, disability duration was not found to be significantly different when rural subgroupings were tested [12]. The finding that adults with multiple sclerosis living in suburban or urban areas were just as likely as their small-town and ruraldwelling counterparts to report unmet needs for physical therapy [30], further supports the idea that access is unlikely to explain the variation observed.

form the service provision plans of those working with injured workers to improve RTW following fracture. Subsequent evaluation of guidelines’ implementation for PM&R care, based on these and other similar results will be helpful to determine how these results can lead to improved outcomes for injured workers.

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