 To evaluate the prevalence of previously unrecognized osteoporosis in men admitted for long-term rehabilitation nursing home care.

A total of 1179 consecutive admissions to a VA rehabilitation center were reviewed. Men who were already diagnosed with osteoporosis, had confounding medical illness, were unable to complete the study, or who declined to participate were excluded. A total of 153 patients were enrolled and 106 were evaluated. Measurements included dual-energy X-ray absorptiometry of the hip and lumbar spine, biochemical and hormonal studies, and functional evaluation.

A total of 33 patients (31.1%) had osteoporosis (T-score at any site lumbar spine, total hip, or femoral neck <−2.5). Patients with osteoporosis were significantly older than those without: 68.4 ± 13.2 years versus 62.7 ± 12.1 years (P <.05), respectively. Body mass index (BMI) and weight were lower in men with osteoporosis: 23.4 ± 3.9 kg/m2 versus 28.7 ± 7.08 kg/m2 and 72.6 ± 14.4 kg versus 90.3 ± 23.8 kg, respectively (both, P <.001). There were no differences in use of medications thought to affect bone metabolism or functional status, or in hormonal and metabolic measurements. Hip and spine bone density were correlated (r = 0.3, P <.05). Multivariate analysis showed that hip bone density was independently associated with BMI.

Hip osteoporosis is common in this unscreened population, suggesting that screening should be more widely performed in veterans admitted to rehabilitation units. These data suggest that nutritional status could impact osteoporosis risk.

The prevalence of previously unrecognized osteoporosis in men admitted for long-term rehabilitation nursing home care was evaluated in a cross-sectional study.

Of the men admitted to a VA rehabilitation unit, 31.1% had osteoporosis. 

Men with osteoporosis were older and thinner than those without osteoporosis, but both groups had similar hormonal measurements, estimates of nutritional status, and functional status.

Because of the substantial morbidity and costs associated with osteoporosis in men, as well as our failure to identify simple predictors, it is important to emphasize screening programs to identify men at risk and institute appropriate interventions.

Osteoporosis is a significant health problem in the United States, and its impact is growing as the population ages. The annual financial burden from osteoporosis has been estimated at $13.9 billion (in 1995), primarily reflecting the cost of hip fracture, surgical repair, nursing home stays, and rehabilitation.

 A similar impact has been reported in other industrialized countries such as Belgium,

 and this impact will be felt throughout the world.

 To date, most of the studies on osteoporosis have focused on women, but it is increasingly apparent that men are affected as well. Estimates of osteoporosis in men have varied, but available data suggest that up to 30% of elderly (age >65 years) men will have an osteoporotic fracture in their lifetime.

 Available data on osteoporosis in nursing home patients are sparse, especially in men, but published estimates of prevalence of subnormal bone density range between 40% and 60%.

 Given that many high-risk patients (eg, patients on chronic glucocorticoids

) are not being screened in a systematic manner, available screening and treatment data may underestimate the total prevalence of osteoporosis in our population.

 Nursing home patients are at significant risk from osteoporosis and its sequelae, due in part to immobility, concurrent medical problems, poor nutrition, and especially the risk of falls.

 The potential benefits of exercise and hip pads to reduce the risk of hip fracture have been studied.

 We evaluated the prevalence of osteoporosis in a high-risk population: the largely male, elderly veteran population at the Center for Rehabilitation and Extended Care (CREC) of the VA Northern California Health Care System. In addition, we estimated factors that may contribute to metabolic bone disease.

 The CREC currently houses up to 120 patients on 3 wards. These patients are mostly male and elderly. Since January 1999, we have been using an on-site dual-energy x-ray absorptiometry (DEXA) machine to evaluate both trabecular and cortical bone density in the lumbar spine and hip.

 Consecutive male patients admitted to the CREC were recruited for the study. We excluded “short-stay” individuals (eg, those recovering from procedures and admitted for <23 hours). We also excluded patients admitted for respite care, those with an inability to cooperate because of dementia or other conditions, those with cancer metastatic to bone or known metabolic bone disease, and patients previously evaluated, diagnosed, or treated for osteoporosis. We obtained approval to approach the patients from providers caring for them; this step also gave us an opportunity to confirm, for example, exclusion for dementia. Patients also were approached in person; if they were off the ward for a home visit, physical therapy, and so forth, they might have been excluded. Written informed consent was obtained from each patient or his legal guardian. The study was approved by the Human Studies Subcommittee of the VA Northern California Health Care System.

 Screening was performed by DEXA, using an on-site Lunar XRC-1 (GE-Lunar, Madison, WI), which results in modest radiation exposure and allows serial measurements and evaluation of therapy.

 This instrument has a precision of 2% to 3% on repeated measurements of bone density. Osteoporosis was defined by World Health Organization criteria using T-scores of -2.5 or greater at either the lumbar spine, total hip, or the femoral neck. Bone density data were stratified by race-specific criteria in the Lunar database, in keeping with station policy.

 included overall nutritional health estimated by complete blood count (providing data on white blood cell count and hematocrit) and routine chemistries, including albumin and 25-hydroxy vitamin D. To estimate calcium absorption, 24-hour urinary calcium was used. The prostate-specific antigen and serum protein electrophoresis tests were used to estimate the possibility of occult malignancy contributing to poor bone density. Bone turnover was estimated by total alkaline phosphatase. Bone-active hormones were estimated by measurement of testosterone, parathyroid hormone (PTH), thyroid-stimulating hormone (TSH), and free thyroxine (to assess the thyroid axis40).

 Because of the variable and occasionally prolonged in-facility residence, laboratory data closest to the DEXA scan were selected if an analyte was measured more than once. Assayed but undetectable prostate-specific antigen was entered as “0” in the analysis. Parathyroid hormone that was assayed but not detected was entered as one-half the lower limit of detection in the analysis.

 Overall health was estimated in part by documenting active medical problems. We also calculated functional status both through case mix index (CMI) and activities of daily living (ADL) scores through the minimum data set package.

 Data are expressed as mean ± SD. The t test was used to compare continuous characteristics of the populations diagnosed with and without osteoporosis. The χ2 test was used to compare discontinuous variables such as ethnicity and use of bone-active medications such as phenytoin, barbiturates, and systemic glucocorticoids. Multivariate analysis using multiple linear regression (SigmaStat version 3.0; Systat Software, Inc, Point Richmond, CA) was used to identify predictors of bone density from variables including age, comorbidity (number of active diagnoses, functional status), nutrition (albumin, hematocrit, white blood cell count, vitamin D, and urinary calcium), and bone-active hormones (testosterone, PTH, TSH, and thyroxine). These variables were adapted from widely accepted causes of secondary osteoporosis in adults.

 The sponsor had no role in the collection of data, its analysis or interpretation, or approval of the manuscript.

 Between July 2000 and April 2006, 1179 consecutive admissions of men were reviewed. A total of 116 patients (9.84%) had already been screened or were receiving therapy for osteoporosis. Of the remaining 1063, 113 men (10.6%) were considered to be too demented on initial chart review to participate, 93 (8.7%) had cancer metastatic to bone or were receiving end-of-life care, and 17 (1.6%) died or were discharged prior to the study. The providers for all other patients were contacted to ascertain whether they were capable of participating. A total of 687 men were approached and either declined to participate, participation was discouraged by their provider, or participation was impractical for logistical reasons during the patient’s inpatient stay. Of the remainder, 153 patients met study criteria and gave written consent; 106 completed the studies. A total of 33 men (31.1%) were diagnosed with osteoporosis in any site; 26 had hip only, 4 both hip and spine, and 2 had spine only. One man had osteoporosis at Ward’s triangle only, but he was not included in the analysis. A total of 42 men had osteopenia (T-scores of −1 to −2.5 at lumbar spine or hip sites), 32 men had no evidence of osteoporosis or osteopenia, and 46 men enrolled in but did not complete the study, usually because of being discharged from the facility prior to completion. Of the 106 men who completed the study, 66 (62.3%) were self-described as white, 33 (31.1%) as African American, 6 (5.7%) as Hispanic, and 1 (0.9%) as Asian. This proportion is similar to the veteran population in our catchment area.

 The study was suspended in May 2002 due to a suspension of the facility Federalwide Assurance (FWA); with the restoration of the FWA, the study recommenced in spring 2003. This hiatus allowed us to estimate changes in practice patterns in osteoporosis screening. Prehiatus, 42 of 565 admitted patients (7.43%) had been screened or were treated; posthiatus, 74 of 614 patients (12%) had been screened or were treated. This difference in rates was statistically significant by the χ2 test.

 Results comparing men with and without osteoporosis are shown in the 

. The men with osteoporosis were slightly, but significantly, older than those without osteoporosis. Other clinical markers, including 24-hour urinary calcium, PTH, and alkaline phosphatase, were similar in men with and without osteoporosis. Both groups had low-normal 25-hydroxy vitamin D and low urinary calcium. Thyroidstimulating hormone was slightly but not significantly lower in men with osteoporosis compared with men without osteoporosis, although generally in the normal range. Weight and body mass index (BMI) were both significantly lower in men with osteoporosis than in those without.

 Comorbidity—as manifested by length of stay, number of active diagnoses on the problem list, ADL scores, and CMI scores&mdash;was comparable between the 2 groups. The number of men receiving medications thought to have bone activity, specifically barbiturate, phenytoin, and systemic glucocorticoids, was too small to calculate significant differences between the groups of men with and without osteoporosis. (Among men identified with osteoporosis, 1 man was on phenobarbital and 1 man was on ongoing glucocorticoids; none were on Dilantin. Among men without osteoporosis, none were taking phenobarbital or Dilantin, and 4 were taking glucocorticoids.) The men who enrolled in but did not complete the study were not significantly different from those with osteoporosis (data not shown) in terms of age and indices of comorbidity. The data were too incomplete otherwise to permit a more thorough comparison.

 Because only 153 of 840 men who were eligible and approached for the study agreed to participate, we were concerned that this sample be representative of the CREC population over the time frame of the study. Thus, we reviewed the roster of men deemed eligible for the study and identified a sample: All those whose Social Security number ended in “7” had their data reviewed (number chosen by a random number generator). This “unstudied” group (n = 63) had a self-described ethnic distribution similar to that of the study population. The “unstudied” group was slightly, but significantly, older (70.5 ± 11.7 years vs 64.5 ± 12.7 years; P <.01), but otherwise was similar in terms of active diagnoses, functional status, weight, and BMI.

 There was no difference in ethnic composition between men with and without osteoporosis by the χ2 test. Multiple linear regression analysis showed that the significant predictors of hip bone density were lumbar spine bone density (P <.05) and BMI (P <.001). Although lumbar spine bone density predicted hip density, very few men had overt osteoporosis of the spine. Because degenerative changes in the spine can contribute to spuriously high bone density readings, we evaluated our osteoporosis patients for spine degeneration. Lumbar DEXA readings were reviewed, and any patient who was described as having scoliosis, compression, spurring, degeneration, arthritis, or deformity was labeled. When these “degenerative” patients (n = 23) were compared with osteoporotic patients without degenerative changes (n = 10), there was a significant difference in lumbar T-score (−2.19 ± 0.7 compared with −0.51 ± 1.8, respectively; P <.001). Age, biochemical measurements, ADL scores, and CMI scores did not contribute significantly. There was no seasonal variation in 25-hydroxy vitamin D (not shown).

illustrates the correlation of hip bone density and spine bone density, as measured by T-score.

 In this study, we found that approximately 30% of a sample of men admitted to a VA rehabilitation unit had osteoporosis, and despite being older and thinner than a control group, they had no demonstrable metabolic, functional, or ethnic differences. In a very broad sense, our data are consistent with those of other recent studies. A study of men from the Miami VA nursing home demonstrated that slightly more than 62% had low bone mass (~36% with osteopenia, ~27% with osteoporosis), obesity seemed to correlate with osteoporosis (unlike our study), and ambulatory status did not predict bone mass.

 Although the overall prevalence of osteoporosis in the study by Paniagua et al was comparable to that reported here, their study was unlike ours in other ways

: they used heel densitometry, exclusion criteria were not defined, and demented patients were included and indeed made up a majority of the population. In another study that used heel densitometry as a screening technique, Gloth and Simonson evaluated more than 30,000 skilled nursing facility residents in 26 states; ~42% had T-scores below −2.5. Bedbound patients, those being treated for osteoporosis, and those with a “short life expectancy” were excluded.

 Although these reported prevalence statistics for osteoporosis were comparable to those in our study, our project reported vitamin D, testosterone, TSH, and PTH data, which these earlier reports did not. In a UK study, Aspray et al reported that nursing home residents have an osteoporosis prevalence of 69%; however, the majority of their patients were women.

 A study from New York suggested that elderly men and women admitted to an urban tertiary care hospital with osteoporotic fractures were a heterogeneous group, without clearly consistent predictors.

 In recognition of the enormous morbidity, mortality, and financial impact of osteoporosis, Tucci emphasized the need to screen and treat aggressively to prevent future fractures,

 particularly since osteoporotic fractures in men carry greater morbidity and mortality than those in women.

 Our data provide indirect evidence in support of the need to screen veterans admitted to rehabilitation facilities aggressively, because we were so limited in our ability to identify key factors that might predispose to osteoporosis in men. A randomized controlled trial comparing screening and not screening with respect to fracture outcome might be of value. Similarly, a study demonstrating that treatment prevents fracture in this population would be of interest. It also may be that enough data have accrued in a similar population to allow a modeling study.

 Inadequate calcium absorption and vitamin D may contribute to osteoporosis in a nursing home population. Although there was no clear difference in these parameters for patients with and without osteoporosis, both calcium absorption (as measured by 24-hour urine calcium) and circulating vitamin D were at the lower end of normal. Recent studies

 have suggested that the optimal circulating vitamin D concentration is 30 ng/mL or above; both our study populations were well below that level. This may be related to an age-associated decline in skin synthesis

 and appears to transcend age and race distinctions.50 Our assay did not distinguish between 25-hydroxy vitamin D3 and 25-hydroxy vitamin D2. The importance of nutrition may be indicated by our observation that hip bone mass density (T-score) was significantly related to BMI and body weight. Although bone mass is a component of lean body mass, BMI may reflect global nutrition as well as ongoing mechanical bone loading. These findings are distinct from those of Paniagua et al, who reported an increase in osteoporosis in heavier patients,

 suggesting that factors other than BMI alone (eg, activity level) play an important role.

Articles

CLINICAL

Men in a VA rehabilitation unit who had osteoporosis were older and thinner, but otherwise similar (metabolic and functional status) to control subjects.

Prevalence of Osteoporosis in Men in a VA Rehabilitation Center

 To compare changes in medication use and costs over time for management of painful diabetic peripheral neuropathy (pDPN) or postherpetic neuralgia (PHN) among patients in commercial health plans requiring prior authorization (PA) for pregabalin versus patients in plans without pregabalin PA policies.

Retrospective claims data were obtained for 2005 to 2007 from 6 health plans with pregabalin PA and 6 health plans without pregabalin PA. Differences in resource utilization and costs were compared between baseline and 1-year follow-up periods using a pre—post parallel-group design.

Adults diagnosed as having pDPN or PHN with at least 1 claim for pDPN- or PHN-specific pain medication were selected. Pharmacologic therapy, healthcare utilization, and expenditures were analyzed using bivariate statistics and generalized linear models via a difference-in-differences approach comparing cohorts year over year.

The 2 cohorts included 2084 patients in PA plans and 1320 patients in non-PA plans. Compared with non-PA plans, plans requiring PA experienced a 5.0—percentage point lower increase in patients using pregabalin year over year (P <.001). Utilization in PA plans of other anticonvulsants was 3.7 percentage points higher (P = .03), while nonopioid analgesic use was 5.2 percentage points lower (P = .01). There were no significant differences in opioid, antidepressant, or other pDPN or PHN medication use or pDPNor PHN-related total healthcare costs.

A PA policy for pregabalin was associated with lower pregabalin utilization, but there was no statistically significant effect on pDPN- or PHN-specific medication or healthcare expenditures.

This study compared healthcare utilization and expenditures between health plans with versus without a prior authorization (PA) policy for pregabalin.

Plans with PA had significantly lower proportions of patients with any pregabalin use. 

Plans with PA experienced higher use of other anticonvulsants; lower use of nonopioid analgesics; no differences in the use of opioids, antidepressants, or other painful diabetic peripheral neuropathy (pDPN)- or postherpetic neuralgia (PHN)-specific drugs; and no net cost effect. 

Although the PA policy accomplished the objective of controlling access to pregabalin, there were no statistically significant differences in pDPN- or PHN-specific medication or healthcare expenditures.

	Health plans and government-sponsored healthcare programs have been under constant and increasing pressure to balance costs of care with improved health outcomes and high-quality care.

 This is particularly true for managing prescription drug expenditures, one of the most visible sectors of healthcare spending.

 Various measures intended to strike that balance, including generic substitution, tiered copayment or cost sharing, step therapy, and prior authorizations (PAs),

 have been widely adopted by health plans and pharmacy benefit managers. Prior authorization, a requirement to obtain prior approval from the health plan or pharmacy benefit manager for prescription medication reimbursement, is recognized for its potential as an effective tool for encouraging appropriate and cost-effective drug utilization, reducing unnecessary prescription drug use, and controlling expenditure growth.

 have shown mixed evidence on the overall effect of PA policies, with some documenting potential for savings and others showing negative clinical and economic effects. Given the administrative costs of running the PA programs and the burden they put on healthcare providers (physicians, nurse practitioners, and pharmacists) and patients, managed care organizations are prudent to evaluate whether their PA programs are meeting the intended objectives.

 investigated the effect of PA policies on Medicaid patients diagnosed with postherpetic neuralgia (PHN) or having neuropathic pain associated with diabetic peripheral neuropathy (pDPN), comparing states with versus without such PA policies. Neuropathic pain associated with diabetic peripheral neuropathy, a complication of diabetes mellitus,

 and PHN, a complication of herpes zoster infection,

 are 2 neuropathic pain conditions that share common pharmacologic treatments, including opioids, antidepressants, and anticonvulsants.

 Although pregabalin (Lyrica [schedule V]; Pfizer Inc, New York, NY) is one of the medications indicated for and recommended as first-line treatment in pDPN and PHN,

 many health plans have restricted access to it by requiring PA for reimbursement. The study by Margolis et al

 found that, although state Medicaid programs’ pregabalin PA requirements accomplished the objective of lower pregabalin utilization, they were associated with the unintended effects of increased opioid use and disease-related healthcare costs. Because that study was undertaken in a Medicaid population among whom patient characteristics and policies are likely to differ from those in commercial health plans, the present study was undertaken to investigate the effect of a pregabalin PA policy among commercially insured patients with pDPN and PHN.

	The primary objective of this study was to investigate the overall effect of PA policies restricting access to pregabalin on utilization of pregabalin and other pharmacologic therapies. We also evaluated the difference in overall healthcare utilization and associated direct costs for the management of pDPN or PHN between patients in commercial health plans with versus without PA policies for pregabalin.

	The intervention evaluated was the introduction of a restriction on the use of pregabalin for the treatment of pDPN and PHN in the form of PA compared with unrestricted access to pregabalin for these indications. The effect of PA was assessed in commercial health plans using a pre—post parallel-group design. Calendar year 2005 was designated as the baseline (preindex) period. A 6-month PA initiation window followed the baseline period. The 1-year postindex follow-up period began on July 1, 2006, and ended on June 30, 2007. The study sample was limited to patients with a diagnosis of DPN or PHN at any point from 2005 to 2006. Evidence of treatment for symptomatic relief of DPN or PHN pain was required as a proxy for pDPN (all PHN is acknowledged as painful). Utilization and expenditure data were compared between the baseline and follow-up periods for the 2 cohorts of health plans with versus without PA for pregabalin. The study tested the following 3 hypotheses: (1) Access restrictions to pregabalin in the form of PAs are associated with lower utilization of pregabalin relative to a comparable population without such restrictions. (2) The use of PA for restricting pregabalin use in health plans is associated with higher utilization of other pharmacologic treatments for pDPN or PHN. (3) The use of PA for restricting pregabalin use in health plans is associated with higher overall and disease-related healthcare service utilization and expenditures for the management of pDPN or PHN.

	Years 2005-2007 of the Thomson Reuters MarketScan Commercial Claims and Encounters Database were used for this study. This database includes complete longitudinal records of inpatient services, outpatient services, long-term care, and prescription drug claims covered under various fee-for-service and capitated health plans, including exclusive provider organizations, preferred provider organizations, point-of-service plans, indemnity plans, and health maintenance organizations. Retrospective data were obtained from 12 commercial health plans, 6 that had implemented PA around the time pregabalin was launched and 6 with no known restriction on reimbursement for pregabalin. Plans with step therapy, quantity limits, or formulary restrictions other than PA were not included. Plans with tiered formularies were included. Health plan restrictions (PA, step therapy, etc) were not examined for pDPN- or PHN-related medications other than pregabalin. The health plans’ identities cannot be divulged because of contractual arrangements involved in acquiring their data.

	Patients were required to be a minimum of 18 years of age and continuously enrolled in the health plan with both medical and pharmacy benefits from January 1, 2005, through June 30, 2007. They were required to have at least 1 claim with an 

International Classification of Diseases, Ninth Revision, Clinical Modification

) diagnosis code for DPN (250.6x or 357.2x) or PHN (053.1x) at any point from 2005 to 2006 and at least 1 claim within 60 days after a DPN or PHN diagnosis from 2005 to 2006 for a medication used in treating pDPN or PHN (

). Patients with pDPN or PHN were analyzed as one group because of the small PHN sample size (in both cohorts, approximately 86% of patients had only pDPN).

	Patients with Medicare coverage were excluded to represent the perspective of a commercially insured employed population. Patients were also excluded if they had resided in a long-term care facility for 90 days or longer or if they had any claims with an ICD-9-CM diagnosis code for epilepsy (345.xx or 780.39), 1 inpatient or 2 outpatient nondiagnostic claims for cancer (140.xx-172.xx, 174.xx-208.xx, or 235.xx-239.xx, except basal cell and squamous cell skin cancers and benign neoplasms), or transplant surgery (codes available on request from the corresponding author) at any point during the study period. Table 3 summarizes the incremental attrition associated with each of these criteria and the resulting cohort sample sizes for PA and non-PA health plans.

	The study analysis period began on January 1, 2005, and ended on June 30, 2007. Calendar year 2005 was used as a baseline period. A 6-month PA initiation window from January to June 2006 followed the baseline period to minimize possible misclassification of data when the exact starting dates of the PA policies were not known and in recognition of the fact that the implementation date for PA would vary across plans. The 1-year postindex follow-up period ran from July 1, 2006, through June 30, 2007. The use of 1 year of follow-up data was chosen to provide equal lengths in the preindex and postindex periods. Pregabalin was approved for treatment of pDPN and PHN in the United States in September 2005; however, the volume of claims for the few patients receiving pregabalin in 2005 (3.7% of patients) was low (1.7 prescription claims per patient receiving pregabalin).

	Diagnoses and pharmacologic therapy were derived from medical and pharmacy claims received according to service dates in the study period. Drugs for treating pDPN or PHN were analyzed by drug class as summarized in Table 1 and for pregabalin individually. The pDPN or PHN diagnoses and comorbidities were determined from nondiagnostic medical claims.

	Healthcare utilization data included hospital admissions, length of stay, emergency department visits, physician office visits, other outpatient services, and numbers of outpatient prescriptions by class and for pregabalin individually. Expenditures were adjusted to 2007 constant US dollars, measured as the gross covered payments for all healthcare services or products (ie, the amount eligible for payment after applying pricing guidelines such as fee schedules and discounts and before applying deductibles, copayments, and coordination of benefits). Utilization and expenditures were measured for all medical and pharmacy claims. Medical claims were designated as DPN or PHN specific when a diagnosis for DPN or PHN appeared in any position in the claim.

	Bivariate descriptive statistics were used to characterize the study population in terms of all key dependent, independent, and control variables by year. All dependent and independent variables were compared in the 1-year postindex period (July 1, 2006, to June 30, 2007) with the 2005 baseline within cohort, and the year-over-year differences were contrasted between cohorts as follows: Difference in Differences = (Restricted Cohort 2006-2007 — Restricted Cohort 2005) – (Unrestricted Cohort 2006-2007 – Unrestricted Cohort 2005).

 χ2 Tests were used to evaluate differences for categorical variables, and t tests were used for continuous variables. Tests for statistically significant differences in outcome variations of PA plans versus non-PA plans before and after the intervention (difference in differences) were conducted. A significance level of .05 was used to determine statistical significance.

	Multivariable analyses were used to explore the association between PA restrictions for pregabalin and the outcomes of interest. A logit model was used to compare pregabalin use in the postindex period between the plans with versus without PA restriction. The logit model used a dichotomous outcome variable indicating the use of pregabalin in the postindex period and a stochastic error term to capture nonsystematic variation. The independent variable of interest was the indicator for enrollment in a health plan with a PA policy.

	Generalized estimating equation models were used to test the hypotheses of differential increments in outcomes between plans with versus without PA after controlling for the potential confounding effects of age, sex, geographic region, health plan type, other chronic pain conditions, and comorbidities. Cluster units were defined at the individual level because a given patient contributed data to the preperiod and postperiod. The following outcomes were included in the analysis: opioid use, nonopioid analgesic use, other pDPN or PHN medication utilization, and pDPN- or PHN-related healthcare costs.

	The key independent variables in the regressions included an indicator for enrollment in a health plan with a PA policy, a time indicator (for 2006-2007 vs 2005), age, sex, geographic region, health plan type (health maintenance organization, preferred provider organization, point of service, or other), and a set of diagnostic flags to control for the effect of comorbidities. In linear regression models, the difference-indifferences estimate was the coefficient associated with the interacted term for a health plan with a PA policy and the period indicator. Because this coefficient has no direct interpretation in nonlinear models, the marginal effects of the interaction terms were calculated to express these effects across the multivariable models using the same units of measure as the original dependent variable (eg, probability of use, number of claims, and US dollars).

	Among the generalized estimating equation models, logit link and binomial variance functions were used for modeling opioid use. Log link and negative binomial variance functions were used for counts outcomes (ie, utilization of nonopioid and other pDPN or PHN medications). Log link and gamma variance functions were used for pDPN- or PHN-related costs. The variance functions for the generalized estimating equation models were verified based on the results of the modified Park test on the raw scale residuals. The exchangeable correlation structure was implemented and verified by the quasi-likelihood under the independence model criterion as the best working correlation structure. Analyses were conducted using STATA release 9 (StataCorp LP, College Station, TX).

	A total of 3404 patients met all subject selection criteria (Table 3), with 2084 patients in PA plans and 1320 patients in non-PA plans. In both cohorts, approximately 86% of patients were diagnosed as having DPN and approximately 14% as having PHN (

). The PA cohort was slightly older (mean [SD] age, 52.3 [6.9] vs 51.6 [7.2] years, P = .004), with members residing primarily in the western and north central regions of the United States, compared with the non-PA cohort, whose members were located mostly in the southern region. Insurance coverage differed between cohorts, with mostly preferred provider organization and health maintenance organization insureds in PA plans, while non-PA members had predominantly preferred provider organization coverage and point of service to a lesser extent. Sex distribution and preindex comorbidity were not significantly different between the 2 cohorts. Analysis of preindex medication use (antidepressants not used in pDPN or PHN treatment, anxiolytics, hypnotics, migraine medications, and muscle relaxants) also showed no statistically significant differences between cohorts.

POLICY

Prior authorization for pregabalin in commercial insurance plans accomplished the objective of lower pregabalin utilization; however, there was no significant decrease in disease-related healthcare costs.

Healthcare Utilization and Cost Effects of Prior Authorization for Pregabalin in Commercial Health Plans

	The patient-centered medical home (PCMH) is viewed as an idea whose time has come. In theory, the PCMH enables better primary care centered on a more defined relationship between primary care physicians (PCPs) and patients, team-based primary care service delivery, enhanced care coordination and access to primary care, and improved quality through the use of disease registries and health information technology.

 Integral to the PCMH model is added payment for PCPs to help compensate them for enhanced care coordination, as well as to fund the personnel and technology-related investments needed in the practice to do medical home—type care.

	Proponents assert that the PCMH makes sense for a healthcare system that is fragmented, impersonal, and overly focused on high-cost specialty care. With demonstration projects under way nationally, they hope that implementation of the model can show enough quality improvement and cost savings to ensure its long-term survival. But something important is missing in the current medical home debate. Little serious attention is being paid to whether implementation of the medical home model can help solve several key problems already transforming our current primary care system, or if the model will succumb to these problems despite evidence that it works.

	One key problem involves the retention and growth of the primary care workforce. Primary care careers remain grossly unappealing to the vast majority of US medical students. For example, over the past 5 years, existing family medicine residency slots have been filled annually with US medical school graduates 45% of the time or less, despite declines in the total number of positions available.

 In addition, a recent survey showed that only 24 of 1177 fourth-year medical students intended to pursue careers in general internal medicine.

 These realities on the front end of PCP supply are bad enough. But we also see an increasingly large older cohort of PCPs who, even if they enthusiastically embrace medical home—type care as proponents suggest, will exit active practice in the next decade in numbers that cannot be matched by those coming into the field.

	If we are to believe common wisdom, insufficient compensation and undervaluing of primary care services in the healthcare marketplace are significant causes of the primary care recruitment problem. Yet these reasons oversimplify the reality. For example, it is hard to imagine how an extra $30,000 or $40,000 per year—optimistic estimates of what enhanced PCMH reimbursement can deliver to individual PCPs&mdash;will suddenly spur vast numbers of medical students to enter a generalist specialty such as internal medicine, family medicine, or pediatrics, which remain lower paying than almost all other medical specialties. Add to this the reality that primary care careers have a serious image problem among the medical student cohort, and it gets easier to suggest that the recruitment issue could undermine rather than be positively impacted by the medical home model.

	A second hypothesis to explain the recruitment problem is that profound value shifts have occurred in the medical student cohort. As much as money, they want the choice of a career that gives them flexibility and the ability to have a life outside of work.

 Many want a job with defined work boundaries. The cultures of many medical schools and residency programs do not promote primary care as a viable career choice for young medical students or residents. Within a training system geared to turning out specialists, a primary care career is increasingly perceived by many medical students as a “consolation prize” that involves a diffuse knowledge base, less intellectual rigor, and more everyday hassles than other careers like emergency and hospital medicine, or specialties like dermatology or radiology, all of which pay more than primary care and provide a “nine-to-five”—like workday.

	In fact, such perceptions may not be valid in the real world of primary care practice. However, the negative perceptions also are fueled by the fact that PCPs no longer have complex work like procedural medicine and hospital medicine within their occupational grasp and struggle with incorporating new types of complex work like behavioral healthcare into crowded workdays. The decreasing presence of PCPs in the hospital also lessens the exposure of medical students to everyday primary care, further promoting the negative stereotypes noted above.

	Proponents of the PCMH argue that the medical home ideal will give medical students more of what they desire in their careers, including sufficient compensation and greater worklife balance. But what if the opposite ends up to be true? What if effective medical home implementation in the average primary care practice exacerbates practice pressures and generates a negative image of primary care? It is possible, at least in the beginning, that some of the medical home principles put into practice could force PCPs to work longer and harder, without significant new compensation, particularly if they still have to pay the bills by continuing the existing business model of large numbers of face-to-face office visits. Within such a model, where assembly-line medicine is the norm, there will be fewer opportunities to build in the extended, patient-centric care offered by the PCMH. Innovations such as the use of integrated care teams that make maximum use of all the practice’s human resources may help alleviate the workload. But adopting a team-based clinical approach in a busy practice is not easy, does not always work in intended ways, and may require additional financial investment that practices operating under a razor-thin profit margin might be reluctant to make.

	Will young physicians with a different value set than their older cohorts see a more complicated primary care job (even if it involves roles for the PCP such as “team leader” and “care manager”) as something that (1) still doesn’t pay enough for the work required and (2) remains more life wrecking than other specialties that are “neat and clean” in their job and task structures? And can PCPs, especially younger ones, fulfill the varied demands of a clinical care consultant, clinical team leader, or clinical care manager without ongoing exposure to a diverse array of work such as hospital medicine, the absence of which could potentially undermine the level of mutual trust in existing patient relationships and self-confidence in clinical skills, and limit access to new clinical knowledge?

	A second problem involves shifting consumer preferences. The notion that patients want a traditional primary care delivery system with PCPs and their practices managing their care is a bedrock assumption on which support for the PCMH rests. But it could also be posited that many patients, particularly younger, healthier ones, do not necessarily think that a physician-centric primary care system is the only one that can work for them. Many of us have now come of age in a health system where fragmented, specialty care is the norm for many of our ailments. It also is a system where medical home—type primary care has not been seen for a long time, if at all, replaced by 15- to 20-minute office visits for discrete illness episodes that provide a fast-food experience for both doctor and patient.

	New, alternative forms of primary care delivery such as retail clinics are growing rapidly. These places are patronized disproportionately by younger adults.

 Some early evidence points to a general public with less apprehension than might be expected in receiving care in retail clinics that often are staffed by nonphysician care providers.

 The idea that such places might compete effectively with traditional primary care practices on the basis of more than a few low-level clinical diagnoses is anathema to many PCPs. But what if new delivery forms like retail clinics suit a wider range of consumer needs with respect to primary care, including greater convenience and speed, as well as lower cost?

	What if younger patients especially are not willing to learn about and commit themselves to the personal responsibilities required in being part of a PCMH, when many of their primary care service needs might be met cheaply and quickly while they also shop or pick up their prescriptions? What if competing forms of care like retail clinics end up poaching younger, healthier patients away from traditional primary care practices, leaving higher percentages of older, sicker patients for whom medical home—type care must be provided? Will this make implementation of the medical home model harder? Will it end up costing a lot more? If fewer younger, healthier patients actively participate in PCMHs, will that undermine some of what the model is supposed to do (ie, teach already-healthy individuals how to stay healthy) while at the same time leaving PCPs with a sicker cohort of clients in their practices who are less compliant and who require higher levels of basic acute care on a constant basis?

	I do not mean to disparage the medical home concept. In an ideal primary care practice environment, it might work well. But greater attention must be paid to building a solid foundation on which to implement it. Focusing more on the primary care workforce and consumer preferences would help attend to this necessity. For instance, although many PCPs have adapted to the current business model in ways that allow them to do their work efficiently, they likely have less familiarity with true care coordination, case management, and accessible care delivery because of fewer opportunities to engage in them over the past decade or more. Many practicing PCPs will require retraining to serve as case managers, care consultants, or clinical team leaders. Young students and residents interested in primary care will need better socialization to the realities of doing primary care work that is both medical home oriented and assembly line oriented, and must know ahead of time how much is expected of them in such a bifurcated practice model. And some consumers may need extended convincing that this more intrusive model of care is worth it for them, enough so that they are willing to bypass newer, cheaper, and more convenient forms of alternative primary care delivery and place their faith once again in the traditional primary care practice.

	If foundational elements like these are ignored, it strains credibility to think that the PCMH idea, which initially will complicate primary care delivery and impose stricter accountability on both doctors and patients, can provide a reliable, long-lasting hinge to swing a major component of the US health system in a more patient-centric direction.

 From the Department of Health Policy, Management, and Behavior, University at Albany, Rensselaer, NY.

Dr Hoff reports no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Concept and design; drafting of the manuscript; critical revision of the manuscript for important intellectual content; and administrative, technical, or logistic support.

Timothy Hoff, PhD, Department of Health Policy and Management, University at Albany, 1 University Pl, Rm 181, GEC Bldg, Rensselaer, NY 12144. E-mail: 

1. Patient-Centered Primary Care Collaborative. 

Joint principles of the patient centered medical home. http://pcpcc.net/content/jointprinciples- patient-centered-medical-home. Accessed October 16, 2009.

2. American Academy of Family Physicians. 

National Resident Matching Program. 2010 Match Summary and Analysis. http://www.aafp.org/ online/en/home/residents/match.html. Accessed May 25, 2010.

3. Hauer KE, Durning SJ, Kernan WN, et al. 

Factors associated with medical students’ career choices regarding internal medicine. 

Practice Under Pressure: Primary Care Physicians and Their Medicine in the Twenty-First Century. Piscataway, NJ: Rutgers University Press; 2009.

Influence of controllable lifestyle on recent trends in specialty choice by US medical students. 

6. Block SD, Clark-Charelli N, Peters AS, Singer JD. 

Academia’s chilly climate for primary care. 

7. Mehrotra A, Wang MC, Lave JR, Adams JL, McGlynn EA. 

Retail clinics, primary care physicians, and emergency departments: a comparison of patients’ visits. 

8. Deloitte Center for Health Solutions. 

Retail Clinics: Facts, Trends and Implications. http://www.deloitte.com/assets/Dcom-UnitedStates/ Local%20Assets/Documents/us_chs_RetailClinics_230708%281%29.pdf. Accessed October 18, 2009.

WEB EXCLUSIVE

For the medical home concept to work, greater attention must be focused on primary care workforce needs and consumer preferences.

The Shaky Foundation of the Patient-Centered Medical Home

With enactment of healthcare reform legislation now a fact, attention has quickly turned to its implementation and necessary preparations for the changes to come. Many of the provisions of the new law will not take effect for several years, but others have more aggressive time lines and no doubt are already receiving considerable attention within federal agencies that will need to prepare for those changes. One of these changes is the creation of a nonprofit comparative effectiveness research (CER) entity known as the Patient-Centered Outcomes Research Institute (PCORI). Although it will not be an agency within the federal government, PCORI will benefit from the significant CER effort currently under way as a result of the 2009 American Recovery and Reinvestment Act.

 The American Recovery and Reinvestment Act not only expanded the federal commitment to CER, but also required specific initiatives to define CER and to develop recommendations on priority areas for new research. These important activities will further inform the process and functions of the new PCORI.

Need for Credible Real-World CER Evidence

 The American Recovery and Reinvestment Act called for a study by the Institute of Medicine to define priorities for CER

 and also established the Federal Coordinating Council for Comparative Effectiveness Research, which was charged with coordination of all federal CER activities. Although this council was terminated under the new healthcare reform law, it did establish a definition for CER that is widely accepted and expected to be embodied in the work of PCORI. That definition includes the concept that CER should provide evidence on real-world effects of treatments, processes, and technologies—that is, the effects of medical care in the usual clinical setting.

 Although prescription drugs are subject to stringent requirements for regulatory approval and can be marketed only after demonstrating safety and efficacy in prospective, well-controlled trials, regulatory approval does not require demonstration of the effects in usual care settings. In addition, for some medical procedures, ethical and practical considerations may limit the opportunity for assessment of comparative effectiveness in formal clinical trials. Therefore, the challenge of providing information on the real-world effects of treatments and procedures is significant and should be addressed comprehensively with attention to rigorous scientific standards.

 Many consider the prospective randomized controlled trial (RCT) to be the gold standard for clinical evidence. However, challenges associated with the RCT as the principal method for CER include the cost and time required for conducting such studies and their potential for broad generalizability. These challenges make RCTs a problematic standard for the range and types of real-world evidence that are needed in a new era of CER. Increasingly, attention is turning to nonexperimental study designs using observational data sets that in some cases are derived from the records compiled from the daily interactions of patients and providers in the typical clinical office setting. In addition to data generated from electronic health records, which will increase in importance as a result of new government incentives for adoption of such systems, these data sets more commonly include the data from claims processing or disease registries. How best to use these data to inform the CER enterprise is a question of critical importance that is, as yet, unanswered.

 Research using observational data already occurs frequently, and has for a long time. Using such data for research is clearly consistent with the Institute of Medicine’s evolving concept of a “learning healthcare system” in which healthcare delivery continuously benefits as real-world evidence accumulates.

 Observational data are plentiful and readily available, and a growing number of payer organizations are recognizing the valuable resource available to them for understanding how treatments work in practice. But the concept of “good,” “sound,” or even “credible” observational research is not well understood or agreed upon, and this lack of agreement can result in uneven and unpredictable consideration of such research when making coverage and payment decisions. Lack of predictability wastes resources both for those reviewing and interpreting the results of observational research and for the suppliers of that research.

Reaching Stakeholder Consensus on Standards for Good Research

 Payers and researchers alike should strive for a consistent view of what constitutes good observational research, especially when it assesses the comparative benefits of medical treatment options. With the ever-increasing demand for answers to questions about the comparative benefits of medical treatments, the tasks of researchers who generate comparative evidence and payers who interpret it should be efficient and timely. Efforts in recent years have focused on standardizing the format in which evidence is presented for consideration by payers,

 and these efforts have enhanced the efficiency of reviewing and considering evidence. But broadly accepted criteria and standards for the quality of research evidence can improve both the efficiency of the process and the quality of patient care.

 The new healthcare reform legislation will establish a methodology committee that is likely to address many of these issues, but this committee will be created only after PCORI is established. The methodology committee then has 18 months to develop its recommendations for methodologic standards. The work of this committee can be aided if stakeholders come together to review the experience with observational research to date, share successes and failures with this approach, identify the methodologic gaps that limit the utility of observational comparative research, and agree on at least the outline of appropriate standards for CER using observational data. A cooperative effort toward this objective can contribute meaningfully to the work of the new methodology committee and in the near term can enhance the predictability of the quality of the research that is offered in support of medical treatments and therapies. Such an effort also can improve the predictability of the interpretation of this research by those who must make decisions about coverage and payment.

 An initial effort intended to improve the quality and usefulness of this type of research is described in this issue of 

 My organization, the National Pharmaceutical Council, provided the initial funding for the independent development of the principles for Good Research for Comparative Effectiveness (GRACE), which addresses many of the quality issues facing CER when observational data are used. These principles are not prescriptive and do not address or suggest solutions to unsolved methodologic problems, but they do focus on the issues that should be addressed or at least discussed when observational CER is reported or presented. They leave to the reviewer the task of judging the quality and thoroughness of the response to these issues in any given piece of research. By identifying the key issues to be addressed, the principles suggest a framework for addressing the quality of observational CER and may serve as the starting point for a broad-based, multistakeholder focus on this type of research.

 Demand continues for more and better evidence about the practical and real-world effects of medical treatments. Data sources that capture information about the healthcare experience continue to proliferate. As a result, there is an urgent need for consistency and harmonization of standards for evidence generation based on these data, as well as for interpretation and application of the findings that result from the research. Patients will be the ultimate beneficiaries of a successful effort to harmonize the generation, interpretation, and application of real-world evidence.

Gary Persinger and Les Paul, MD, MS, of the National Pharmaceutical Council contributed to this commentary.

From the National Pharmaceutical Council, Washington, DC.

The National Pharmaceutical Council provided funding to Outcome, Inc in support of the GRACE principles (see the article by Dreyer et al, in this issue [p 467]).

Mr Leonard reports serving as a board member for the Health Industry Forum, the National Health Council, and the Pharmacy & Therapeutics Society.

Concept and design; drafting of the manuscript; critical revision of the manuscript for important intellectual content; administrative, technical, or logistic support; and supervision.

Daniel T. Leonard, MA, President, National Pharmaceutical Council, 1501 M St, NW, Washington, DC 20005. E-mail: 

American Recovery and Reinvestment Act of 2009. February 2009. http://frwebgate.access.gpo. gov/cgi-bin/getdoc.cgi?dbname=111_cong_bills&docid=f:h1enr.pdf. Accessed April 5, 2010.

2. Committee on Comparative Effectiveness Research Prioritization, Institute of Medicine. 

Initial National Priorities for Comparative Effectiveness Research. June 2009. http://www.iom.edu/Reports/2009/ ComparativeEffectivenessResearchPriorities.aspx. Accessed April 5, 2010.

3. Federal Coordinating Council on Comparative Effectiveness Research. 

Report to the President and the Congress on comparative effectiveness research. http://www.hhs.gov/recovery/programs/cer/ execsummary.html. June 2009. Accessed April 5, 2010.

4. IOM Roundtable on Evidence-Based Medicine, Institute of Medicine. 

March 2007. http://www.iom.edu/Reports/2007/The-Learning- Healthcare-System-Workshop-Summary.aspx. Accessed April 5, 2010.

The AMCP Format for Formulary Submissions, version 3.0. October 2009. http://www.amcp.org/ format/pub.pdf. Accessed April 5, 2010.

6. Dreyer NA, Schneeweiss S, McNeil BJ, et al, for the GRACE Initiative. 

GRACE principles: recognizing high-quality observational studies of comparative effectiveness. 

COMMENTARY

Rigorous scientific standards are needed to address the challenge of providing information on the real-world effects of treatments and procedures.

Comparative Effectiveness and Real-World Evidence

 To evaluate the association between patterns of fragmented care and emergency department (ED) use among adult patients with diabetes and chronic kidney disease.

Observational study in an open healthcare system.

The study sample included patients with diabetes and chronic kidney disease (mean estimated glomerular filtration rate, 20-60 mL/min) and with an established primary care provider. Dispersion of care was defined by a fragmentation of care index (range, 0-1), with zero reflecting all care in 1 outpatient clinic and 1 reflecting each visit at a different clinic site. We used a negative binomial model to estimate the influence of fragmentation on ED use after adjusting for patient demographic characteristics, insurance, diabetes control, and number of comorbidities; results are reported as incidence rate ratios and associated 95% confidence intervals (CIs). The main outcome measure was the number of ED visits from 2002 to 2003.

Of 3873 patients with diabetes having an established primary care provider, 623 (16.1%) had chronic kidney disease and comprised the final study sample. On average, patients made 19.0 (95% CI, 18.5-20.4) outpatient visits and 1.2 (95% CI, 1.1-1.4) ED visits over the 2-year period. The median fragmentation of care index was 0.48; 14.3% of subjects had a fragmentation of care index of zero. In the adjusted model, a 0.1-U increase in the fragmentation of care index was associated with a 15% increase in the number of ED visits (incidence rate ratio, 1.15; 95% CI, 1.09-1.21).

The posited benefits of specialist referrals among patients with complex diabetes may be partially negated by care fragmentation. Better models for care coordination and stronger evidence of the marginal benefits of referrals are needed.

This investigation illustrates that seemingly appropriate referrals to subspecialty providers increased care fragmentation, which was associated with a higher rate of emergency department visits among patients with diabetes and chronic kidney disease.

These findings extend previous work that highlights the association of discontinuity of care with worse clinical outcomes, patient dissatisfaction, and provider confusion regarding their roles in care for patients with diabetes. 

The evidence base for the design of optimal systems of care should be strengthened, with attention to the risks and effectiveness of referrals for patients with diabetes.

Type 2 diabetes mellitus is associated with multiple comorbid illnesses, complications, and hospitalizations thought to be largely preventable by proper patient attention to self-care and by application of best practices of evidence-based care.

 Nonetheless, outcomes of care among patients with diabetes fall far short of expectations. Annual costs of diabetes in the United States are estimated to be more than $130 billion.

 Fewer than half of US patients with diabetes receive recommended health services,

 and a large body of research documents that patients who are less likely to receive needed services are likely to be poorer, less educated, members of racial/ethnic minorities, older, and uninsured.

 As with other ambulatory care—sensitive conditions, however, diabetes outcomes are thought to be improved by better continuity of care, usually defined by a preponderance of visits occurring with the same primary care provider. Emergency department (ED) use, which increased 26% between 1993 and 2003 to about 114 million visits annually, is thought to be a particularly sensitive measure to inadequate outpatient care.

 Providing coordinated continuity of care is challenging in managing the myriad complications that accompany diabetes. Attention to evidence-based guidelines for patients with diabetes even without comorbid conditions appropriately includes annual ophthalmology visits for dilated eye examinations and possibly visits for nutrition counseling and podiatric examinations.

 Patients with diabetes whose glycemia is difficult to control are recommended more aggressive treatment through consultations or comanagement with a diabetes specialist.

 Cardiac and neurologic consultations appropriately may be sought as cardiovascular, cerebrovascular, and neuropathic complications accrue. Professional nephrology organizations have called for earlier referral of patients having diabetes with evidence of renal impairment.

 Accompanying obesity often requires additional referrals for weight management, coexisting sleep apnea, gastroesophageal reflux disease, and arthritis. Approximately 1 in 4 patients with diabetes has coexisting major affective disorders that may require comanagement with a psychiatrist.

 In the context of evidence-based guidelines and customary standards of diabetes care, these frequently necessary referrals create the possibility of fragmentation (the dispersion of management of various aspects of a patient’s care to several providers, which may include the patient’s primary care provider or specialists); the effects of frequent referrals on the patient medical home,

 patient-centered quality, and healthcare utilization are largely unknown.

 In the present investigation, we examined the effects of care fragmentation on ED use in a particularly complex and vulnerable subgroup of patients having diabetes with kidney impairment. Our principal interest was to determine whether seemingly appropriate referrals to specialists for outpatient screening or consultation for coexisting conditions were offset by the potentially deleterious effects of care fragmentation. Emergency department use was selected as our principal outcome measure, as it is conventionally understood to be “preventable” by optimal ambulatory care of patients with diabetes.

 We selected adult patients having diabetes with chronic kidney disease to identify a homogeneous sample with regard to complexity and need for care. Patients eligible for inclusion in the study included all patients with diabetes older than 18 years and having 2 or more visits in the year before the study period to 1 of 10 primary care group practices within the MetroHealth System, a large public urban provider in Cuyahoga County, Ohio. We required 2 or more visits to the same site to increase the likelihood that these were patients and physicians who were in a continuous relationship, as well as to be consistent with American Diabetes Association recommendations that patients with diabetes have 1 primary care visit every 6 months.

 Data were obtained from electronic medical records over a 2-year study period (2002-2003). Inclusion criteria were as follows: a diabetes mellitus diagnosis in an encounter before the study period (

International Classification of Diseases, Ninth Revision, Clinical Modification 

] code 250.xx), age older than 18 years (determined by age at the beginning of the study period), and the presence of chronic kidney disease, defined as a 2-year mean glomerular filtration rate (GFR) between 20 and 60 mL/min. The GFR was calculated using the Mater Medical Research Institute equation, which relies on the variables of sex, race/ethnicity, and recorded creatinine levels.

 Patients were excluded from the study if they received their continuity care primarily from resident physicians or if their mean GFR was less than 20 mL/min.

 The primary outcome variable was the number of ED visits within the MetroHealth System made by patients over the 2-year study period. Any visit labeled as an ED visit in the encounter data contributed toward the count.

 We operationalized fragmentation of patient care using a fragmentation of care index (FCI). The FCI is based on a modified version of the previously validated continuity of care index.

 The continuity of care index, first described by Bice and Boxerman,

 is based on the number of different providers visited, the proportion of attended visits to each of those providers, and the total number of visits. Our FCI measure used clinics as the unit of measurement, rather than individual providers, and further modified the continuity of care index as follows to compute the FCI:

 where n indicates the total number of visits; 

, the number of clinics. The FCI can range from 0 (all visits to the same clinic) to 1 (each visit takes place at a different clinic). Visits  classified into 1 of the following categories, based on plausible referral specialties for diabetes-related comorbidities or monitoring, were included in the FCI: primary care, cardiology, neurology, endocrinology, ophthalmology, nephrology, urology, pulmonary medicine, podiatry, gastroenterology, and psychiatry.

 A practical demonstration of the behavior of the FCI may help in its interpretation. Assume a patient makes 12 visits to the primary care clinic, 1 visit to the ophthalmology clinic, and 2 visits to the podiatry clinic. Her FCI would be 0.36. If the patient’s glucose remains poorly controlled, the referring primary care physician might see her more frequently or might decide instead to refer her to the endocrinology clinic. If the patient makes an additional 4 visits to the primary care clinic, the FCI decreases 0.07, from 0.36 to 0.29. If the additional 4 visits instead are to the endocrinology clinic, the dispersion of visits to an additional provider would increase the FCI by 0.21, from 0.36 to 0.57.

 Potentially confounding demographic variables used in the analysis included race/ethnicity (black, white, Hispanic, or other), age at the beginning of the study period, sex, and insurance status (commercial, Medicaid, Medicare, or uninsured). Potential confounding clinical variables that were adjusted for included severity of diabetes (defined as the mean glycosylated hemoglobin level over the 2-year study period) and a count of comorbidities.

Fragmentation of care to specialists may be associated with higher rates of emergency department visits among patients with chronic illness.

June 2010

Prevalence of Osteoporosis in Men in a VA Rehabilitation Center

Healthcare Utilization and Cost Effects of Prior Authorization for Pregabalin in Commercial Health Plans

The Shaky Foundation of the Patient-Centered Medical Home

Comparative Effectiveness and Real-World Evidence

Care Fragmentation and Emergency Department Use Among Complex Patients With Diabetes

Outcomes and Lessons Learned From Evaluating TRICARE's Disease Management Programs

Is There a Relationship Between Early Statin Compliance and a Reduction in Healthcare Utilization?

Ambulatory Care Visits for Pap Tests, Abnormal Pap Test Results, and Cervical Cancer Procedures in the United States

GRACE Principles: Recognizing High-Quality Observational Studies of Comparative Effectiveness

Using Simulation to Identify and Resolve Threats to Patient Safety