After the publication of our December 2007 supplement, &#8220;Health-related Quality of Life in Rheumatoid Arthritis,&#8221; the Editors learned that several authors were not given the opportunity to review the final proofs of their manuscripts. As a result, the following errors were subsequently identified. First, the co-author&#8217;s name was inadvertently omitted from the Introduction. The correct byline for the Introduction should read: Daniel E. Furst, MD, and Dinesh Khanna, MD.

Second, in the article &#8220;Generic Quality-of-life Assessment in Rheumatoid Arthritis,&#8221; by Tugwell, et al (

. 2007;13[suppl 9]:S224-S236), some of the indicators for Health Dimension and Psychometric Properties were missing from Table 1; some data in Table 3 were incorrect; and the reference numbers in Figures 1a and 1b were misnumbered. The corrected tables and figures appear on the following pages (see pages 235-238).

Finally, due to the extent of the errors in the article by Strand et al, &#8220;Improved Health-related Quality of Life With Effective Disease-modifying Antirheumatic Drugs: Evidence From Randomized Controlled Trials,&#8221; we are reprinting a corrected version of this article in this issue (see pages 239-253).

Articles

CORRECTION STATEMENT

Furst DE, Khanna D. (Am J Manag Care. 2007;13[suppl 9]:S216-S217.

Tugwell P, Idzerda L, Wells GA. (Am J Manag Care. 2007; 13[suppl 9]:S224-S236.

Strand V, Singh JA. (Am J Manag Care. 2007;[suppl 9]:S237-S251.

REPORTS

Improved Health-related Quality of Life With Effective Disease-modifying Antirheumatic Drugs: Evidence From Randomized Controlled Trials

: To quantify and characterize biopharmaceutical agents and new indications in late-stage development in the United States as of May 2006.

: Review of drug development databases and other secondary sources.

: Biopharmaceutical was defined as â€œany biology-based therapeutic that structurally mimics compounds found within the body.â€ Unique biopharmaceuticals, including new molecular entities or new indications in phase 2 or higher development, were identified and characterized through reviews of the literature, 5 drug development databases, a clinical trial database, and telephone inquiries with manufacturers.

: As of May 2006, there were 111 unique biopharmaceuticals in late-stage development for 190 indications. Of 111 unique agents in the pipeline, 87 are new molecular entities, and 24 are already approved for other indications. Overall, 38 disease categories were targeted, and at least 33 physician specialties are likely to be affected. The greatest proportion of agents (43 biopharmaceuticals and 83 indications) target cancer. More than 70% of agents in the pipeline will require administration by a healthcare provider. More than 50% of the indications in the pipeline will require long-term (chronic) treatment (defined as >1 year and excludes cancer).

: The steady growth of the US biopharmaceutical pipeline and consequent anticipated near-term approvals will increasingly affect third-party portfolio decision making. Cost of therapy, identifying the right drug for the right patient, and outcomes-based value should drive that decision process.

The growth of the biopharmaceutical pipeline and consequent approvals has increased each year from the first approval of human insulin in 1982. The growth of biopharmaceutical approvals is predicted to grow at a rate of 16% to 30%, compared with about a 4% growth of traditional small-molecule biopharmaceuticals.

 was undertaken to quantify and characterize the biopharmaceutical pipeline as a means to prepare third-party payers for the unique challenges posed by availability of these agents, including appropriate use, per-patient and aggregate costs, utilization management, site of care issues, product delivery, and incorporation of these agents into existing benefit and information technology structures. The present study was undertaken to answer several questions. Like the development of traditional small-molecule pharmaceuticals, do we see a similar leveling off in the biopharmaceutical pipeline? If not, are there characteristics beyond those identified in 2003 that require the attention of third-party payers? Finally, is there a profile or a growing trend within the biopharmaceutical pipeline that we can identify early to proactively prepare for the landscape of the near-term future biopharmaceutical market?

In this study, biopharmaceutical is defined as &#8220;any biology-based therapeutic that structurally mimics compounds found within the body.&#8221;

 This includes recombinant proteins, monoclonal and polyclonal antibodies, peptides, antisense oligonucleotides, therapeutic genes, and certain therapeutic vaccines. The basis for the definition is previously described.

 Agents were defined as late-stage biopharmaceuticals if they had been tested in a completed phase 2 clinical trial, were in a phase 2/3 or 3 clinical trial, or had been submitted for regulatory approval by the US Food and Drug Administration (FDA) for an unapproved indication as of May 2006. The data variables for this study include generic and brand names, manufacturer, stage of development, class of agent, target indication and disease, probable method of administration (eg, subcutaneous, intravenous, etc), physician specialty likely to use the agent for the indication, expected setting of administration (eg, ambulatory care, hospital inpatient, etc), proposed frequency of administration (eg, acute, chronic, etc), and whether the agent was already FDA approved for 1 or more other indications. Because of the frequent incongruence of information in different sources used to collect these data, numerous sources were consulted. 

As of May 2006, there were 111 unique biopharmaceuticals in late-stage development in the United States targeting 190 late-stage indications in 38 disease categories. Of 111 unique agents, 87 are new molecular entities, 24 are already approved by the FDA for other indications, and 25 have completed Phase 3 trials.

Cancer (including primary therapy and supportive care applications) is the most common disease category target for biopharmaceuticals (

). Immune-mediated inflammatory disorders constitute the second largest disease target (by category); more than 20% of agents in the pipeline target a range of these inflammatory diseases. Examples include rheumatoid arthritis, Crohn&#8217;s disease, ulcerative colitis, psoriasis, type 1 diabetes mellitus, and multiple sclerosis. Like cancer, immune-mediated inflammatory disease represents a significant clinical and economic burden to the US healthcare system.

Although the number and percentage of already FDAapproved biopharmaceuticals in the pipeline have not changed significantly, the number of indications they represent and the percentage of pipeline indications represented by these agents increased from 37 indications in 2003 (24% of pipeline indications) to 70 indications in 2006 (37% of pipeline indications), which is an 89% increase in indications and a 54% increase in the proportion of the pipeline. In addition, there is a greater percentage of &#8220;follow-on&#8221; indications in the pipeline in 2006 compared with 2003.

There is an increase in the number of agents and indications targeting cancer. In 2003, there were 30 cancer-related biopharmaceuticals (29% of pipeline agents) in late-stage development for 62 indications (40% of pipeline indications), while in 2006 there were 43 biopharmaceuticals (39% of pipeline agents) for 83 indications (44% of pipeline indications). In addition, the gap between cancer and other disease targets in the pipeline is widening. The second most common disease targets were infection in 2003 and blood disorders in 2006. There were 21 more cancer-related agents (21% of pipeline agents) than infection-related agents in 2003 versus 31 more cancer-related agents (28% of pipeline agents) than blood disorder agents in 2006 (an increase in the gap of 48%).

. Market access for biopharmaceuticals: new challenges. 

. Defining and characterizing the latestage biopharmaceutical pipeline. 

Biotechnology: Industry Outlook: How to Survive a Biotech Bear Market

. New York, NY: Cowen & Co LLC; July 2006.

. Med Ad News 200 - Worldâ€™s best selling medicines. 

TRENDS FROM THE FIELD

Research on characteristics of the biopharmaceutical pipeline and on changes since 2003 includes a description of new features and recommendations for health policy decision makers.

Characteristics of and Trends in the Late-stage Biopharmaceutical Pipeline

After the publication of our December 2007 supplement, &#8220;Health-related Quality of Life in Rheumatoid Arthritis,&#8221; the Editors learned that several authors were not given the opportunity to review the final proofs of their manuscripts. As a result, errors were subsequently identified. The co-author&#8217;s name was inadvertently omitted from the Introduction. The correct byline for the Introduction should read: Daniel E. Furst, MD, and Dinesh Khanna, MD.

to view the corrected version of this article.

The American Journal of Managed Care

Furst, DE; Khanna, D. (Am J Manag Care. 2007;13[suppl 9]:S216-S217)

After the publication of our December 2007 supplement, &#8220;Health-related Quality of Life in Rheumatoid Arthritis,&#8221; the Editors learned that several authors were not given the opportunity to review the final proofs of their manuscripts. As a result, errors were subsequently identified. In the article &#8220;Generic Quality-of-life Assessment in Rheumatoid Arthritis,&#8221; by Tugwell, et al (Am J Manag Care. 2007;13[suppl 9]:S224-S236), some of the indicators for Health Dimension and Psychometric Properties were missing from Table 1; some data in Table 3 were incorrect; and the reference numbers in Figures 1a and 1b were misnumbered.

to see a corrected version of this article.

Tugwell P, Idzerda L, Wells GA. (Am J Manag Care. 2007;13[suppl 9]:S224-S236)

After the publication of our December 2007 supplement, &#8220;Health-related Quality of Life in Rheumatoid Arthritis,&#8221; the Editors learned that several authors were not given the opportunity to review the final proofs of their manuscripts. As a result, errors were subsequently identified. Due to the extent of the errors in the article by Strand et al, &#8220;Improved Health-related Quality of Life With Effective Disease-modifying Antirheumatic Drugs: Evidence From Randomized Controlled Trials,&#8221; we have printed the corrected article in our April issue.

to see a corrected version of this article. 

Strand V, Singh JA. (Am J Manag Care. 2007;13[suppl 9]:S237-S251)

: To identify an optimal cut-point score on the Mini-Asthma Quality of Life Questionnaire (mini-AQLQ) to predict subsequent asthma exacerbations, and to determine the additional risk conferred by a prior history of acute episodes.

: Cross-sectional survey linked to administrative records.

: A total of 1006 HMO patients with active asthma completed surveys that included the mini-AQLQ and prior-year history of acute episodes. Surveys were linked to administrative data that captured asthma emergency department and hospital care (emergency hospital care) for the year after the survey. Optimal mini-AQLQ cut-point scores were determined by stepwise logistic regression analyses using subsequentyear asthma emergency hospital care as the outcome and various mini-AQLQ cut-points as the predictors. Predictive properties of the 2 risk factors (mini-AQLQ cut-points and prior acute episodes) were determined.

: A mini-AQLQ cut-point of 4.7 was most significantly associated with subsequent exacerbations in patients without a history of prior acute episodes. The presence of either a mini-AQLQ score <4.7 or a history of prior acute episodes provided high sensitivity (90.4%) and identified a group nearly 6 times more likely to require emergency hospital care than patients with neither risk factor. The presence of both risk factors provided high specificity (79.2%) and resulted in a risk ratio of 9.5 compared with the absence of both risk factors.

: Asthma-specific quality of life and a history of acute episodes can be used together to identify patients with clinically meaningful higher and lower risks of subsequent acute exacerbations.

Asthma-specific quality of life and prior history of exacerbations can be used to define future risks of asthma exacerbation. 

Either a score of <4.7 on the Mini-Asthma Quality of Life Questionnaire or a history of prior acute episodes provided high sensitivity (90.4%) and identified a group nearly 6 times more likely to require emergency hospital care than patients with neither risk factor. 

The presence of both risk factors provided high specificity (79.2%) and resulted in a risk ratio of 9.5 compared with the absence of both risk factors. 

Such information could be useful for determining baseline risks in clinical trials as well as for individual and population asthma management.

Asthma is a common medical problem that causes substantial morbidity, including hospitalizations and unscheduled physician and emergency department visits.

 Moreover, chronic asthma is associated with very large direct and indirect costs, with a large portion of the direct costs attributable to emergency hospital care.

Several studies have shown that asthma-specific quality of life is associated with the subsequent risk of emergency hospital care.

 For this information to be clinically useful, though, a cut-point score on the quality-of-life measure needs to be defined, so that patients can be classified meaningfully according to higher and lower risk for subsequent exacerbations. None of the prior studies have attempted to establish the optimal quality-of-life cut-point score that maximizes both sensitivity and sensitivity in predicting subsequent emergency hospital care.

In addition, to use quality of life clinically to predict subsequent emergency hospital care, its relationship to other clinically defined risk factors should be clarified. Prior acute episodes have been shown to be the strongest predictor of subsequent acute episodes.

 Three studies have shown that asthma quality of life is independent of prior acute episodes in predicting subsequent emergency hospital care,

 but these studies have not provided a clinically applicable algorithm that incorporates both prior utilization and asthma-specific quality of life. Moreover, 2 of the prior studies defined prior utilization based on computerized data, which may not be available in all clinical settings.

The purpose of this study was to use quality-of-life survey data as well as other information obtained from the patient to develop a practical algorithm for identifying patients at increased risk of subsequent emergency hospital care. This involved identifying a quality-of-life cutpoint score that optimally predicts subsequent emergency hospital care in patients with and without a history of unscheduled asthma visits in the past year and determining the additional risk conferred by a prior history of acute episodes.

Patients in this study have been previously described.

 Surveys were sent by the Kaiser Permanente Care Management Institute in August 2000 to a random sample of Kaiser Permanente Medical Care Program adult members aged 18-56 years from the Northern California (n = 3072) and Northwest (n = 543) regions who were diagnosed as having persistent asthma in 1999 based on Healthcare Effectiveness Data and Information Set (HEDIS) criteria.

 The HEDIS criteria comprise 1 or more of the following administrative database observations in a 12-month period: (1) 4 or more asthma medication dispensings, (2) 1 or more emergency department visits or hospitalizations with a primary diagnosis of asthma, or (3) 4 or more asthma outpatient visits with 2 or more asthma medications dispensings.

Completed surveys were returned from 2219 members (61%), of whom 1998 (90%) confirmed a diagnosis of physician- diagnosed asthma. The current study was restricted to the 1100 respondents who met the HEDIS criteria for persistent asthma in 2000 (as well as 1999) and thus had electronic utilization information for 2001 available. Of these patients, 1006 subjects had complete quality-of-life data (see Survey Information section) and are the subjects of this report. The study was approved by the Northern California Region and the Nortwest Region Kaiser Permanente Institutional Review Boards. Survey Information The survey included information regarding age, sex, race or ethnicity, educational attainment, household income, and smoking history. The survey also included the mini-Asthma Quality of Life Questionnaire (mini-AQLQ),

 which includes 15 questions in 4 domains (symptoms, activity, emotions, and environment). Scores range from 1-7, with higher scores indicating better quality of life. 

Prior utilization was assessed by the following questions:

1. &#8220;In the past 12 months, how many times did you get treatment for an acute asthma attack at a doctor&#8217;s office, urgent care facility or emergency department (ED)?&#8221; Responses range from &#8220;None&#8221; to &#8220;7 or more.&#8221;

2. &#8220;When was your most recent overnight hospitalization for asthma?&#8221; Responses included &#8220;I have never been hospitalized overnight for asthma,&#8221; &#8220;Within the last week,&#8221; &#8220;Within the last month,&#8221; &#8220;Within the last 6 months,&#8221; &#8220;Within the past year,&#8221; and &#8220;More than one year ago.&#8221;

Prior acute episodes were defined as an answer of one or more on question 1 above, an overnight hospitalization in the past year on question 2, or both.

Demographic and utilization characteristics of the population were evaluated by means of descriptive analyses. The optimal cut-point of the mini-AQLQ for predicting year 2001 emergency hospital care was determined by means of stepwise logistic regression analyses with emergency hospital care as the outcome and various mini-AQLQ cut-point levels as the predictors. Mini-AQLQ cut-points between 3.0 and 6.0 at 0.1 increments were offered as potential predictors. The analyses were done separately in patients with and without a history of acute episodes in the prior year.

Hypothesis testing for univariate analyses was by means of Wilcoxon (continuous variables) or chi-square (categorical variables) tests. Multivariable analyses were performed using logistic regression methodology in which the outcome was year 2001 emergency hospital care and the predictors were (1) mini-AQLQ score (optimal cut-point) and (2) prior acute episodes (as defined above, in the Survey Information section). To determine the clinical significance of the findings, predictive properties were evaluated. Sensitivity was defined as the proportion of subjects with subsequent utilization who were in the high-risk group. Specificity was defined as the proportion of participants without subsequent utilization who were not in the high-risk group. Positive predictive value was defined as the proportion of patients in the high-risk group with subsequent utilization. Negative predictive value was defined as the proportion of participants not in the high-risk group who did not experience subsequent utilization. Finally, a risk ratio was calculated as the risk of the outcome in patients with the risk factor or factors divided by the risk in patients without the risk factor or factors.

Nominal 2-tailed statistical significance for all analyses was set at 

 <.05. All analyses were performed using SAS statistical software (SAS version 8.2 for Windows, SAS Institute Inc, Cary, North Carolina).

A mini-AQLQ cut-point score of 4.7 was chosen in the stepwise logistic regression model for patients without prior acute episodes (odds ratio [OR] = 3.1; 95% confidence interval [CI] = 1.4, 6.9). An mini-AQLQ cut-point score of 4.3 was chosen in the stepwise logistic regression model for patients with prior acute episodes (OR = 3.5; 95% CI = 2.1, 5.9). In the total cohort, a low mini-AQLQ score (either cut-point) or a history of acute episodes in the prior year was associated with an approximate 20% risk of subsequent emergency hospital care compared with a 5% to 6% risk in patients without the predictor (

Although a cut-point of 4.3 was more significantly related to subsequent exacerbations in the logistic regression model than a cut-point of 4.7 in patients with prior episodes, the difference would appear to be clinically unimportant because the 2 cut-points produced equal ORs for subsequent exacerbations in these patients (Table 1). Because a cut-point of 4.7 was associated with higher ORs in the total cohort and in patients without prior acute episodes, this mini-AQLQ cut-point was chosen to define higher risk in subsequent analyses.

Prior acute episodes (OR = 3.8; 95% CI = 2.4, 6.1) and a mini-AQLQ score &#8804;4.7 (OR = 3.4; 95% CI = 2.1, 5.4) were independently related to emergency hospital care the following year in the multivariable logistic regression analysis. The C statistic for the area under the receiver operating characteristic curve was 0.69 for prior acute episodes alone, 0.68 for a mini-AQLQ score &#8804;4.7 alone, and 0.75 when both were included in the model. 

Because prior acute episodes and low mini-AQLQ score appeared to be independent predictors of subsequent emergency hospital utilization, the predictive properties of the presence of either or both of these risk factors were evaluated (

). The presence of either risk factor provided high sensitivity (90.4%) and identified a group nearly 6 times more likely to require emergency hospital care than patients with neither of these risk factors. The presence of both risk factors provided high specificity (79.2%), and patients with both risk factors had a risk ratio of 9.5 compared with patients who had neither risk factor (Table 2).

Emergency hospital care exacts substantial human and economic costs. Identification of patients at increased risk of requiring such care could facilitate targeted intervention to prevent such occurrences. The current study shows that information easily obtainable from the patient (asthma-specific quality of life and a history of episodes requiring unscheduled visits in the past year) can be used to identify patients at clinically meaningful increased risk of future acute episodes. Low quality of life (based on data-derived optimal cut-point scores) and prior acute episodes each independently increase the risk of requiring such care approximately 4-fold. Ninety percent of patients who subsequently required emergency hospital care had 1 of these risk factors. More than 25% of patients with both risk factors required subsequent asthma hospitalizations or ED visits, a nearly 10-fold increased risk compared with that of patients who had neither of these high-risk markers.

Prior studies identified low quality of life as a risk factor for subsequent asthma ED visits and hospitalizations,

 even after adjusting for prior emergency hospital utilization. 

 However, no prior studies attempted to determine the quality-of-life cut-point score that provides the best predictive information regarding subsequent utilization. In our prior study with the mini-AQLQ,7 we used a cut-point of 3.9, based on the cut-point for the lower quartile of patient scores. Tierney and colleagues,

 who administered the 32-item version of the AQLQ, used <4, 4-5, and >5 as their cut-points, but no rationale for those cut-points was provided. Although the relationship between asthma-specific quality of life and subsequent emergency hospital care is almost certainly linear, optimal cut-points are necessary for clinical purposes.

The current study has shown that the optimal cut-point score for predicting subsequent exacerbations varies depending on whether or not the patient has a history of recent exacerbations. The optimal cut-point score in patients with a history of prior exacerbations (4.3) was lower than the optimal cut-point score in patients without acute episodes requiring unscheduled care in the prior year (4.7). This makes sense because patients with prior exacerbations could be expected to have lower baseline quality-of-life scores than patients without such episodes. However, the current study also suggests that either cut-point defined the same degree of relative risk in patients with prior episodes and that a cut-point of 4.7 defined a somewhat higher degree of relative risk in patients without prior acute episodes (Table 1). Thus, because it would be more clinically useful to define 1 cut-point score, a cutpoint of 4.7 was chosen to use for both groups.

The information in this study could be used practically in both population and individual patient management. For population management, information about quality of life and prior acute episodes could be obtained by mailed survey or electronically. Use of both high-risk markers compared with only 1 marker increases potential sensitivity by 18% (from 76%-90%) and potential specificity by 30% (from 61%-79%) (Table 2). Depending on the goals and interventions involved in a specific population management initiative, a high-sensitivity approach (either high-risk marker) or a high-specificity approach (both high-risk markers) could be utilized. For individual patients, the survey could be completed before seeing the clinician. Patients with either high-risk marker have a substantial (20%) risk of subsequent acute episodes and thus would be candidates for aggressive management of triggers and comorbidities, consideration of a step-up in long-term control therapy, and provision of an explicit action plan for increased symptoms.14 Such an intervention would be even more important in patients with both high-risk markers, who have a 27% risk of subsequent acute episodes during the next year based on the current data (Table 2).

The information in this study also could potentially be used in clinical trials that test the effects of interventions on asthma exacerbations. There is recent interest in risk-adjusted efficacy in clinical trials&#8212;the assessment of efficacy outcomes in groups stratified by baseline risks.

 Prior acute episodes and low baseline mini-AQLQ scores defined by the cut-point in this study could be used to stratify baseline risk for risk-adjusted efficacy assessments of the effect of an intervention on subsequent asthma exacerbations.

An important issue is the generalizability of the current findings. It seems likely that the independent associations between quality of life and prior acute episodes in predicting subsequent exacerbations and the lower optimal quality-of-life cut-point score in patients with prior exacerbations would persist in other populations and with other asthma-specific quality-of-life tools. However, the quantitative relationships found in this study could be affected by characteristics of the healthcare system (an HMO with good access to chronic and acute care), the patients (nonpoor and white), and their therapy (a high proportion received inhaled corticosteroids), although it is not certain whether variations in these characteristics would strengthen or weaken the observed relationships. A particular issue regarding generalizability is the high educational status of the current sample. Because the mini- AQLQ depends on literacy, the current results may not apply to populations with lower literacy levels. It also is not clear whether the specific optimal cut-points defined in this study would be duplicated in other populations using the mini- AQLQ. Pending additional data, we recommend a high-risk mini-AQLQ cut-point score of 4.7.

This study does have some potential limitations. Not all patients completed questionnaires, although a 61% response rate is reasonable for this type of mail-in questionnaire study. The 2001 outcomes for the patients in this study compared with the general population who had persistent asthma in the 2 regions (G. Tom, unpublished data, 2003) for dispensing of >14 &#946;-agonist canisters (15% vs 13%) and for emergency hospital care (11% vs 9%) suggest that the responders had somewhat more severe or uncontrolled asthma than the nonresponders. Further selection bias could have been introduced by the unavailability of utilization data for all subjects who completed the survey. Because patients without utilization data would not have been considered to have persistent asthma in 2000, that also biases the study sample in favor of those with more severe asthma.

Another potential limitation of this study is that it did not include pulmonary function data. This means that the diagnosis of asthma was not necessarily confirmed by pulmonary function tests, although the current study definition of asthma (physician diagnosis) has been found to be valid for use in epidemiologic studies. 16 Lower pulmonary function, especially forced expiratory volume in 1 second (FEV

), has been shown to predict subsequent exacerbations.

It would be of interest to know whether FEV

 and the mini-AQLQ score would function independently as risk factors for subsequent exacerbations, but no study in which both have been measured has been published to our knowledge. One study found that a peak flow less than 50% of predicted values was a significant univariate predictor of exacerbations within 12 months, but peak flow was no longer a significant predictor when controlling for AQLQ scores.

Other questionnaires, such as the Asthma Therapy Assessment Questionnaire

 also have been shown to predict subsequent emergency hospital care. In one study, patients with Asthma Therapy Assessment Questionnaire scores of 3 or 4 (poorest control) were 2.5 times more likely to experience subsequent acute exacerbations than patients with Asthma Therapy Assessment Questionnaire scores of 0 (no control problems).

 In another study, patients with poorly controlled asthma based on Asthma Control Test scores &#8804;15 were 2.4 times more likely to require emergency hospital care in the following year than patients with controlled asthma based on an Asthma Control Test score &#8805;20.

 It would be of interest to compare the accuracy and ease of use of these questionnaires with the accuracy and ease of use of the mini-AQLQ in predicting subsequent exacerbations. However, such a study would require concurrent use of all of these tools and assessment of ease of use, which were not parts of the current study.

In summary, this study has shown that asthma-specific quality of life and a history of recent acute episodes can be used together to identify patients with substantially higher and lower risks of subsequent acute exacerbations. Optimal cut-points in additional populations and the relationship of these risk factors to lower FEV

 in predicting subsequent exacerbations require further study, as does comparison of the mini-AQLQ with other questionnaires for this purpose. In addition, it is hoped that future studies could demonstrate improved outcomes after targeted interventions in patients with these risk factors.

: From the Department of Allergy, Kaiser-Permanente Medical Center, San Diego, CA (MS, RSZ) and the Center for Health Research, Kaiser-Permanente Medical Care Program, Portland, OR (DM, WMV).

: The authors (RSZ, DM, WMV) report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article. Dr. Schatz is a consultant for GlaxoSmithKline, has a grant pending from Schering Plough, has received grants from GlaxoSmithKline, Sanofi-Aventis, and Merck, and has received lecture fees from GlaxoSmithKline and Genetech.

: This study was supported by the Kaiser-Permanente Care Management Institute, Oakland, CA.

: Concept and design (MS, RSZ, DM); acquisition of data (WMV); analysis and interpretation of data (MS, DM, WMV); drafting of the manuscript (MS, RSZ, DM); critical revision of the manuscript for important intellectual content (MS, RSZ, DM, WMV); statistical analysis (MS); obtaining funding (WMV); and administrative, technical, or logistic support (RSZ).

: Michael Schatz, MD, Department of  Allergy, Kaiser-Permanente Medical Center, 7060 Clairemont Mesa Blvd, San Diego,

2. Centers for Disease Control and Prevention (CDC).

 Asthma prevalence and control characteristics by race/ethnicityâ€”United States, 2002. 

. Health-related quality of life and future health care utilization for asthma. 

6. Tierney WM, Roesner JF, Seshadri R, Lykens MG, Murray MD, Weinberger M

. Assessing symptoms and peak expiratory flow rate as predictors of asthma exacerbations.

tools to subsequent medical utilization for asthma. 

9. Lieu TA, Quesenberry CP, Sorel ME, Mendoza GR, Leong AB

. Computer-based models to identify high-risk children with asthma. 

11. Schatz M, Cook EF, Joshua A, Petitti D

. Risk factors for asthma hospitalization

12. National Committee for Quality Assurance

. Washington, DC: National Committee for Quality Assurance; 2005.

14. National Heart, Lung, and Blood Institute, National Asthma Education and  revention Program

Expert Panel Report 3: Guidelines for the Diagnosis and Management of Asthma

http://www.nhlbi.nih.gov/guidelines/asthma/asthgdln.pdf

. Asthma and asthma-like symptoms in adults assessed by questionnaires: a literature review. 

18. Rasmussen F,Taylor DR, Flannery EM, et al

. Risk factors for hospital admission for asthma from childhood to young adulthood: a longitudinal population study.

20.Vollmer WM, Markson LE, Oâ€™Connor E, et al

. Association of asthma control with health care utilization: a prospective evaluation. 

. Reliability and predictive validity of the Asthma Control Test administered by telephone calls using speech recognition technology. 

METHODS

Asthma-specific quality of life and a history of acute episodes can be used together to identify risks of subsequent acute exacerbations.

Asthma-specific Quality of Life and Subsequent Asthma Emergency Hospital Care

Objective: To determine the effectiveness of shared care interventions designed to improve the management of chronic disease across the primary—specialty care interface.

Study Design and Methods: Systematic review using the Cochrane Collaboration method.

Results: Twenty studies were identified, 19 of which were randomized controlled trials. The majority of studies examined complex interventions and were of short duration. Results were mixed, with no consistent improvements in physical or mental health outcomes, psychosocial outcomes, hospital admissions, default or participation rates, recording of risk factors, and satisfaction with treatment. However, there were improvements in prescribing in the studies that considered this outcome. The methodologic quality of studies varied, with only a minority of studies of high-quality design. Cost data were limited and difficult to interpret across studies.

Conclusions: At present, there is insufficient evidence to support the introduction of shared care services into clinical practice. However, methodologic shortcomings, particularly inadequate length of follow-up, may account for this lack of evidence. Further research is needed to test models of collaboration across the primary—specialty care divide both in terms of effectiveness and sustainability over longer periods of time.

Clinicians and planners may presume that shared or integrated care will improve outcomes for chronic disease; however, further research is needed to test models of collaboration.

This review found insufficient evidence to support the introduction of shared care services into clinical practice.

As the majority of the 20 reviewed studies were of suboptimal quality and none lasted longer than 2 years, questions remain regarding the effectiveness of interventions to improve care across the primary—specialty care divide.

Shared care has been defined as the joint participation of primary and specialty care practitioners in “the planned delivery of care for patients with a chronic condition, informed by an enhanced information exchange, over and above routine discharge and referral notices.”

 It has been promoted for the management of many chronic conditions,

 with the assumption that it will deliver better care than either primary or specialty care alone. At times, there also has been an assumption that shared care will allow more efficient use of limited specialist resources. Shared care, also referred to as integrated care, frequently includes an interface worker, prespecified clinical protocols, referral guidelines, continuing education of participating clinicians, specifically designed information exchange systems, and ongoing audit and evaluation of services delivered. Theoretically, shared care presents an opportunity to provide patients with the benefits of specialist intervention combined with continuity of care and management of comorbidity from generalists who are responsible for all aspects of the patient’s healthcare beyond the specified chronic disease. Starfield described a “strong imperative” for a shared model of relationship between primary care and specialty care physicians in the management of common chronic conditions.

 However, little is known about the nature of the primary care—specialty care interface,

 and there is a need to identify evidence that will guide healthcare planning and provide a framework for improved chronic disease management.

We carried out a systematic review to determine the effectiveness of shared care interventions for the management of chronic disease across the primary—specialty care interface.

The protocol was peer-reviewed and published in the Cochrane library.

 Eligible studies included randomized controlled trials (RCTs), controlled clinical trials, controlled before-and-after studies, and interrupted time-series analyses. Participants were people or populations who had a specified chronic disease and who had been enrolled in a defined shared care service provided by primary and specialty care practitioners. Primary care was defined as “integrated, easy to access, healthcare services by clinicians who are accountable for addressing a large majority of personal health care needs, developing a sustained and continuous relationship with patients, and practicing in the context of family and community.”

 Specialty care was defined as care delivered in hospital settings such as outpatient clinics and emergency departments or in community settings where care was delivered based on a certain physiologic system or clinical condition, or based principally on the age of patients.

Shared care interventions were defined as any type of structured system that involved continuing collaborative clinical care between primary and specialty care practitioners in the management of patients with chronic diseases. These interventions were classified as “simple” if they used a single intervention and “multifaceted” if they incorporated more than 1 feature. We excluded interventions with no specified chronic disease management component (eg, interventions to improve care of elderly patients based solely on age). Outcomes included any objective measure of physical and mental health, well-being and functional impairment, hospital admissions, medication prescribing and adherence, treatment satisfaction, service utilization, risk factor recording, provider behavior, and measures of efficiency and cost. Attitude and knowledge outcomes were excluded.

The search strategy was based on the Cochrane Effective Practice and Organisation of Care Group (EPOC) search strategy (

) and used a combination of methodologic and subject terms relating to shared care, chronic disease, primary care, structured care, secondary care, and collaboration, combined with the methodology terms. The Appendix (available at 

) provides full search details. There were no language or age restrictions.

The titles and/or abstracts of potentially relevant studies were screened. Full-text copies of articles identified as potentially relevant were retrieved and independently assessed for inclusion. Disagreements about eligibility were resolved by consensus between reviewers. If necessary, authors were contacted to clarify the nature of the intervention.  Data Extraction and Analysis Two reviewers undertook data extraction independently using a modified version of the EPOC data collection checklist. The quality of all included studies was assessed by 2 independent reviewers using standard EPOC quality criteria (

Primary analyses were based on the principal outcome measure as defined by the study authors. We carried out metaanalysis where we considered it to be appropriate in relation to study characteristics and available data.

The search strategy identified 4968 titles/abstracts, of which 20 met the inclusion criteria for the review (Figure 1).

The broad search strategy resulted in a majority of the studies being ineligible based on objectives, study design, or content. Where there was a possibility that the study might be eligible (n = 80), 2 reviewers independently checked the full paper to determine eligibility and the authors were approached if clarification was needed (we e-mailed 7 authors, and 6 replied with further details of their intervention). Although the search strategy was designed to identify a range of study types, 19 of the 20 included studies were RCTs, and 1 was a controlled before-and-after study.

 The studies varied from 3 months to 2 years in duration, with the majority lasting 1 year. They included nearly 9000 participants with 8 groups of chronic conditions—including depression (6 studies); diabetes mellitus (4 studies); asthma/chronic obstructive pulmonary disease (COPD) (2 studies); chronic mental illness (2 studies); congestive cardiac failure; hypertension; cancer; and opiate misuse&mdash;as well as a group of patients with a variety of chronic conditions requiring long-term oral anticoagulation therapy (1 study each). The studies were carried out in a variety of healthcare settings in the United Kingdom, the United States, Australia, New Zealand, Denmark, Ireland, and Sweden. The intervention descriptions and the outcomes studies are presented in the Table.

The intervention groups generally were compared with a group of control patients receiving “usual care.” This usual care was provided in primary care  settings, 

 examined complex interventions involving combinations of prior agreement to care roles within each sector, clinical and referral guidelines, defined patient reviews in each sector, education and training for patients and professionals (principally for primary care professionals and workers at the primary—specialty care interface), and synchronized patient records and recall systems. The shared care interventions were driven by the specialist sector in 9 of the included studies.

These studies had relatively limited analysis of activity in primary care. The remaining 11 studies involved a clearer collaboration between both sectors, with more complete analysis of activity in both sectors.

Six of the studies had a clearly identified professional (usually a nurse specialist) outside of the study team whose role included the coordination of shared care.

Other studies reported that the service was coordinated by members of the specialist team or study team.

Three studies reported shared care interventions that were largely computer based.

Methodological Quality of Included Studies

Of the 19 RCTs, only 3 met all the quality criteria.

Six more studies met all quality criteria except protection against contamination, as they failed to take account of possible cluster effects.

 The method of randomization was unclear in 2 studies, with the Diabetes Integrated Care Evaluation (DICE) study describing a pragmatic randomization approach

 and the Llewellyn-Jones et al study using a controversial sequential randomization with baseline data collection not done concurrently.

 Only 4 studies included data on follow-up of providers.

In 2 of the studies, primary care providers were unaware that they were participating in an intervention study.

Nine of the included RCTs had a cluster design.

 Only 3 of those studies clearly incorporated clustering effects in both their power calculation and analysis.

An additional 2 studies had explicitly incorporated clustering in their analysis but not in their power calculations.

 The 1 controlled before-and-after study included in the review met EPOC quality criteria, although failed to account for a potential clustering effect at the general practice level and only reported preliminary data on process outcomes.

Seven studies (all RCTs) presented data comparing physical outcomes of the intervention group with those of the control group at study completion.

 These studies included patients with diabetes, hypertension, asthma, and COPD. Shared care was not associated with any statistically significant benefits in physical health outcomes, except in 1 study that found a significant improvement in 1 of the physical health measures (forced expiratory volume in 1 second) in patients with moderate to severe COPD.

Eight studies presented data on mental health outcomes. Six of these studies examined shared care for various forms of depression.

 Results were mixed, although the majority found improvements in the proportions who recovered or maintained remission from depression. However, meta-analysis of the proportions recovered from depression revealed no significant benefit for shared care (Figure 2). Results also were mixed in the 6 studies examining changes in mean depression scores, with 3 of the 6 studies indicating significant benefit for shared care. Meta-analysis of mean depression scores was not possible because of missing data. Two studies that targeted chronic mental illness found no significant benefit for shared care.

Of 5 studies that reported measures relating to quality of life and  well-being,

3 reported significant benefit for shared care. Four studies presented measures relating to functional impairment and disability, and 2 found a significant benefit for shared care in relation to functional impairment.

Byng et al also reported psychosocial measures analyzing the patients’ perceptions of met and unmet need, but found no significant difference between the groups for these measures.

Seven studies examined the effect of shared care on hospital admissions, 6 RCTs 

 They found mixed results consistent with shared care being associated with a reduction in hospital admissions in older patients and in those with higher levels of baseline morbidity. Reporting on the controlled before-and-after trial, Wood and Anderson found a significant reduction in the proportion of intervention-group patients with chronic mental illness who were readmitted in the 2 years after the introduction of shared care and a significantly lower median number of inpatient days.

Eight studies reported outcomes relating to appropriate prescribing. Five of these studies looked at proportions of patients receiving appropriate medication or appropriate doses of medication for their condition and reported mixed results.

Meta-analysis was carried out for the 4 studies with available data and indicated benefit for shared care (Figure 3). Holm et al reported a statistically significant benefit for patients on oral anticoagulation therapy who received shared care; these patients spent a higher percentage of time within the therapeutic interval for the international normalized ratio, a measure of  anticoagulation control.

 Five studies considered various measures of medication adherence and use.

 Meta-analysis of these studies indicated benefit for shared care (Figure 4).

Six studies reported measures relating to participation in or defaulting from services,

and 4 of these indicated significantly improved participation rates for patients receiving shared care. Other outcomes relating to treatment satisfaction, service utilization, recording of risk factors, and provider outcomes were mixed and difficult to interpret. Studies used different measures of patient satisfaction (proportion satisfied vs treatment satisfaction scores) that were difficult to compare, and even within categories, results were mixed. For example, patients in the shared care group in the DICE study were recruited from the specialist sector; as a result of being randomized to receive shared care, they had less contact with their specialists, which they were unhappy about.

 In the other studies with patient satisfaction as an outcome, satisfaction was increased in patients participating in shared care. Data relating to service utilization were difficult to interpret, as it was sometimes unclear what would constitute an improvement. In some cases, provider visits were expected to change in different directions depending on whether the aim was to reduce the number of healthcare contacts within either sector in an effort to shift care between sectors, or whether the intervention was intended to reduce overall contact by using planned disease-related visits more effectively.

Eleven studies reported cost data, although only 3 of these reported economic analyses linking costs to outcomes.

 Results were mixed, and comparison between studies was difficult as costs were reported in different currencies at different time points, with the majority not stating the year of pricing. There also was a variation in costs allocated to each sector depending on how health systems are organized in each country.

This review identified 20 studies examining shared care across the primary—specialty care interface in chronic disease management. The majority examined complex, multifaceted interventions for a variety of common chronic diseases, with diabetes and depression predominating. Shared care was primarily introduced to improve patient care through a variety of mechanisms. Hoskins et al state that a purpose of their intervention (a shared care service for diabetes) was to relieve pressure on specialist services and contain costs.

 However, it is possible that increasing activity in primary care will create further demands for specialist services; as quality of care improves, more cases and complications might be picked up. Only 3 studies were largely computer supported, which is surprising given the investment in and development of information technology in healthcare systems in industrialized countries. Only 1 study involved a parallel qualitative exploration of patients’ experience of the introduction of the new service.

 In general, there was minimal consideration of provider outcomes or satisfaction with services and very limited consumer involvement in designing or introducing shared care services. Further limitations of the review relate to the specific definition of shared care that was adopted, which focused on collaboration between primary and specialty care physicians. Collaborative care involving other disciplines was excluded, but may represent an alternate and potentially more effective way to improve outcomes for chronic disease.

This review indicates that consistent evidence for the effectiveness of shared care is lacking for the majority of outcomes studied. A few studies suggested that shared care may be more effective in certain patient groups, such as those with depression and other serious chronic mental health illness, those with high levels of morbidity at baseline such as the elderly, and those with moderate to severe congestive cardiac failure or COPD. However, these results were not consistent across all studies. The clearest evidence of benefit was for improved prescribing. Greater activity in relation to medication prescribing should have an important effect on outcomes in most chronic diseases. However, improvements in prescribing may take some time to improve physical health outcomes; and given the relatively short study durations, this potential effect of shared care was not detected.

Shared care had mixed effects on patient satisfaction with treatment, which may reflect the fact that the measurement of quality of health services is complex and should not be approached primarily through the “reductionist filter of user satisfaction.”

 The qualitative evaluation by Smith et al indicated that patients value shared care, identifying it particularly with the liaison nurse and practice nurses rather than the doctors involved.

We were unable to identify a simple reason for the mixed results between studies, although we considered the effect of computerized support and the effect of a specified liaison worker at the primary—specialty care interface. The interventions we examined were complex, and it often was difficult to determine the exact contribution of each component and the “active ingredient” within the range of interventions comprising the full shared care service.

 In the 6 studies that did consider the complex nature of their interventions, 3 stated that they were unable to define which of the elements of the intervention were effective.

Swindle et al considered the fact that the clinical nurse specialists seemed to have undertreated individuals with depression, and further exploration revealed that nurse specialists did not agree with many of the depression diagnoses that patients had received, based on a depression screening questionnaire on recruitment into the study.

Fundamentally, shared care should involve a genuine collaboration between primary and specialty care. It was usually difficult to determine whether this collaboration had happened in the research environment and to know how much collaboration occurred in clinical practice. Byng et al thought that the earlier detection of relapse rates in patients with chronic mental illness in the shared care group could be attributed to improvements in collaboration between primary and specialty care, but they did not attempt to measure whether this collaboration had occurred.

 Smith et al considered that their diabetes shared care intervention may have lacked effectiveness because of lack of access to a community dietician or funding for protected time for general practitioners.

One could argue that if shared care is not clearly effective in research settings, it is unlikely to work in everyday clinical practice. On the other hand, the majority of included studies involved interventions that were supported by research budgets. These interventions might have been more effective had they been better resourced. Many clinicians and health planners intuitively believe that shared care should improve outcomes. That may partly explain the relatively small number of included studies, as shared care services often are introduced in service delivery contexts without being piloted or subjected to the rigors of research evaluation.

Evidence to support the introduction of shared care services into clinical practice is limited, although methodologic shortcomings may account for this lack of evidence.

April 2008

Furst DE, Khanna D. (Am J Manag Care. 2007;13[suppl 9]:S216-S217.

Tugwell P, Idzerda L, Wells GA. (Am J Manag Care. 2007; 13[suppl 9]:S224-S236.

Strand V, Singh JA. (Am J Manag Care. 2007;[suppl 9]:S237-S251.

Improved Health-related Quality of Life With Effective Disease-modifying Antirheumatic Drugs: Evidence From Randomized Controlled Trials

Characteristics of and Trends in the Late-stage Biopharmaceutical Pipeline

Furst, DE; Khanna, D. (Am J Manag Care. 2007;13[suppl 9]:S216-S217)

Tugwell P, Idzerda L, Wells GA. (Am J Manag Care. 2007;13[suppl 9]:S224-S236)

Strand V, Singh JA. (Am J Manag Care. 2007;13[suppl 9]:S237-S251)

Asthma-specific Quality of Life and Subsequent Asthma Emergency Hospital Care

Does Sharing Care Across the Primary–Specialty Interface Improve Outcomes in Chronic Disease? A Systematic Review

Association of Insurance Coverage With Chlamydia Screening

A Strategic Plan for Integrating Cost-effectiveness Analysis Into the US Healthcare System

Costs of Treating Lower Respiratory Tract Infections