PEDIATRICS Vol. 110 No. 4 October 2002, pp. 797-804
Agreement Among Measures of Asthma Status: A Prospective Study of Low-Income Children With Moderate to Severe Asthma
* Division of General Pediatrics
Division of Allergy and Immunology, Department of Pediatrics
Center for Research in Disease Prevention, Department of Medicine, Stanford University School of Medicine, Palo Alto, California
¶ Cecil G. Sheps Center for Health Services Research, University of North Carolina at Chapel Hill, North Carolina
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONMETHODSRESULTSDISCUSSIONCONCLUSIONREFERENCES |
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Background. Because no validated "gold standard" for measuring asthma outcomes exists, asthma interventions are often evaluated using a large number of disease status measures. Some of these measures may be redundant, whereas others may be complementary. Use of multiple outcomes may lead to ambiguous results, increased type I error rates, and be an inefficient use of resources including caregiver and patient/participant time and effort. Understanding the relationship between these measures may facilitate more parsimonious and valid evaluation strategies without loss of information.
Objective. To assess the relationships between multiple measures of asthma disease status over time.
Design/Methods.We used data from a randomized, controlled trial of a comprehensive disease management program involving 119 disadvantaged inner-city children aged 5 to 12 years with moderate to severe asthma. Spearman correlations were calculated between the following asthma disease status measures: parent-reported disease symptoms, parent-reported health care utilization, functional health status using the American Academy of Pediatrics’ validated Child Health Survey for Asthma (CHSA), diary data (symptom scores, night wakings, and bronchodilator use), and pulmonary function tests at baseline, 32 weeks, 52 weeks, and changes from baseline to 52 weeks.
Results. Ninety-four (79%) of randomized patients participated at baseline and 52 weeks. Completion rates for outcome measures ranged from 79% (CHSA, spirometry data) to 64% (diary data). At baseline, asthma symptoms, health care utilization, and individual domains from the CHSA were significantly correlated (r = 0.21–0.53). These correlations were stable over the 52-week follow-up. Forced expiratory volume in 1 second and diary data did not correlate to any other measures at baseline, and these measures correlated only inconsistently with other measures at 32 weeks and 52 weeks. Baseline to 52-week changes in asthma symptoms, utilization, and the CHSA domains were significantly correlated (0.22–0.56), as were baseline to 52-week changes in symptom days, night wakings, and the CHSA domains (r = 0.24–0.64). Baseline to 52-week changes in forced expiratory volume in 1 second and diary data did not correlate with other measures.
Conclusions. These results suggest that asthma status and change in asthma status over time after introduction of a disease management intervention are best characterized by parent-reported symptoms, parent-reported utilization, and functional health status measures. Asthma diaries and pulmonary function tests did not seem to provide additional benefit, although they may play an important role in individual patient management. Our findings suggest a parsimonious evaluation strategy would include collection of key data elements regarding symptoms, utilization, and functional health status only, without loss of vital response information.
Key Words: asthma • measures • outcomes • correlation • functional health status • children
Abbreviations: FEV1, forced expiratory volume in 1 second • CHSA, Child Health Survey for Asthma
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONCONCLUSIONREFERENCES |
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Asthma is the most common chronic illness in children1 and an increasingly important public health problem worldwide. In the United States, asthma affects an estimated 5% to 10% of children under 18 years of age and is the most common reason for childhood hospitalizations.2 Data from the National Health Interview Survey show that between 1980 and 1994 the national prevalence of asthma increased 75%, and the asthma-specific death rate increased 147% for 5- to 14-year-olds.2 Asthma is associated with substantial health care expenditures3 and lower quality of life as evidenced by an additional 12.9 million more contacts with medical doctors, 10.1 million more missed school days, 200 000 more hospitalizations, increased risks for behavioral problems, school dysfunction, and a variety of other psychosocial problems when compared with nonasthmatic populations.4–7
In response to increases in asthma prevalence, morbidity, and mortality, clinicians and researchers have increased efforts to develop effective asthma interventions. The lack of a universally accepted "gold standard" of asthma disease status hampers clinicians’ and researchers’ efforts to evaluate and compare such interventions. In recent years, asthma outcome measures have increased in number and scope and standard outcome measures now include measures of physiologic status, clinical status, health care utilization, functional health status, patient education, and satisfaction.8,9 The presumed values of this expanded selection of outcomes are a more comprehensive understanding of the disease process at any given time, and more accurate assessments of the effects of interventions. The validity, reliability, and responsiveness of these measures, however, are often unknown and may vary across and within these 5 categories.8,10 Ironically, this uncertainty of outcome measure performance may have contributed to the disappointing or contradictory results witnessed in recent asthma intervention trials.11 Examples of uncertain measure performance include, the unclear validity of diary data (they correlate with physician diagnosed asthma severity in some studies12–14 but not in others15), the unclear validity of symptoms (they underestimate pulmonary function test-defined severity15 but provide a valid measure of disease severity when combined with forced expiratory volume in 1 second [FEV1]16), and the large variability and poor standardization of peak flow data across brands.17,18 In addition, the AirWatch peak flow meter (ENACT Health Management Systems, Mountain View, CA), which was used in this study, has been shown to be accurate and precise in the clinic setting19 but has unproven reliability in children and adults outside of the clinic.20 Even spirometry, shown to be reliable, reproducible, and standardized in adults8,21 may not be reliable in children resulting from age-related difficulty in performance.16
Adding to the uncertainty of the value of individual measures of asthma status is the fact that the existing literature has not demonstrated the extent to which these measures correlate. Hence, the level of redundancy in these measures is not known. It is important to identify a small number of measures that can be used efficiently and effectively to characterize asthma severity in symptomatic children. Use of multiple measures may lead to ambiguous results and to increased type I errors, busy clinicians have limited time to devote to asthma symptom assessment and counseling, and patients and/or study participants may be fatigued by long assessments and procedures potentially undermining motivation and data quality. Although conceptually many of these measures may evaluate different dimensions of health, the above factors provide strong incentives to be more parsimonious in choosing measures for clinical practice and clinical research.
In this study, we use data from a randomized, controlled trial of an asthma disease management program to examine the levels of agreement between multiple measures of asthma disease status and changes in disease status over 1 year. The results of such an analysis can be used to help choose the most appropriate measures for use in both clinical management and clinical research. In the absence of a true "gold standard" measure of asthma severity,9 correlations between conceptually related measures and sensitivity to changes over time can provide evidence of validity and help to identify the most promising measures for use in practice and research.
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METHODS |
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Study Population
Subjects were recruited from low income, urban areas in and around San Francisco, California, and San Jose, California, through direct referrals from pediatricians, school nurses, and the use of advertisements in primary care settings, community clinics, hospital emergency departments, university medical centers, and schools. Additional recruitment was achieved with the assistance of the American Lung Association and San Francisco Asthma Task Force, a community-based group dedicated to reducing asthma prevalence in an underserved area of San Francisco. Potential participants were screened by telephone for asthma severity, health care utilization, symptom frequency, and demographic characteristics. Children were eligible if they were between ages 5 to 12 years, diagnosed with asthma at least 6 months before enrollment, had parent reports of significant symptoms (defined as daily cough or wheeze, 2 asthma attacks per week, cough at rest or sleep, or daily bronchodilator therapy usage), or substantial health care utilization (1 hospitalization, 2 acute care visits, or 2 emergency department visits over the past year), and were of low socioeconomic status (defined as receiving MediCal or eligible for MediCal). Eligible children were scheduled for an enrollment visit.
At the enrollment visit, after completing baseline assessments, children were randomized into the control or the intervention groups within gender and age strata. Children in the intervention group received a disease management intervention (described below). At each study visit, trained study personnel administered all measures in English or Spanish (see below). All outcome measures were repeated at 8, 32, and 52 weeks, and the protocol was approved by the panels on Human Subjects in Medical Research at Stanford University, the University of California at San Francisco, and Santa Clara Valley Medical Center. Informed consent was obtained from all participating parents and assent was obtained from all children age 7 and above.
Asthma Status Measures
Spirometry
Pulmonary function tests were conducted using a BioMedic DX (BioMedic, San Clemente, CA) portable spirometer. Subjects were asked to perform a forced expiratory maneuver after maximal inhalation to measure FEV1, functional vital capacity, forced expiratory flow 25–75, and peak expiratory flow rate using standard protocols.22 A total of 3 maneuvers with <5% variability were recorded according to American Thoracic Society standards.22 A postbronchodilator assessment was done 20 minutes after inhalation of 2 puffs of albuterol (Schering-Plough, Kenilworth, NJ) by metered dose inhaler. For analysis, we used percent-predicted of normal based on height, sex, and ethnic variations.23
Child Health Survey for Asthma
The Child Health Survey for Asthma (CHSA),24,25 a quality of life instrument for children with asthma, was completed by 1 parent or guardian of each subject at weeks 0, 32, and 52. This instrument is divided into 5 domains including physical health, social activity of the child, social activity of the family, emotional health of the child, and emotional health of the family. The test-retest reliability, internal consistency, and construct validity of this instrument have been reported previously.24 Because of an error in our Spanish language translation of the CHSA, we omitted the emotional health of the family domain from our analysis.
Utilization Data
The CHSA also provided data to evaluate parent-reported emergency department use ("During the past 2 months, how many times has this child been to an emergency department for his/her asthma?"), parent-reported unscheduled or sick visits ("During the past 2 months, how many times has this child had an unscheduled or sick visit to a doctor for his/her asthma?"), and parent-reported hospital use ("During the past 2 months, how many times has this child stayed overnight in a hospital because of his/her asthma?"). Additional data derived from the CHSA included parent-reported symptoms ("During the past 2 months, how many times has this child wheezed?"), and parent-reported asthma attacks ("During the past 2 months, how many times has this child had an asthma attack?"). For the analysis, data on emergency department and unscheduled or sick visits to the doctor were summed to provide an estimate of the total number of urgent care visits.
Peak Flow Monitoring
The AirWatch (ENACT Health Management Systems, Mountain View, CA) electronic peak flow meter was used to measure and record peak expiratory flow rates. Both intervention and control subjects and their parents were given instructions on the correct use of the peak flow meter and the case manager demonstrated proper technique at each visit.9 Children were instructed to blow 3 individual, consecutive forceful exhalations with correct mouth placement in the morning and evening, before any asthma medications were used. Children were asked to demonstrate their technique at study visits and were corrected when needed. They were instructed to record the highest of the 3 readings on their symptom diary.
Asthma Symptom Diaries
Subjects and their parents were instructed to complete 14 days of data for five 2-week intervals (weeks 1–2, 9–10, 21–22, 31–32, and 51–52). Participants were asked to record morning and evening peak flows, night waking because of asthma, school days missed because of asthma, and number of daily puffs of a short-acting bronchodilator. Participants were also asked to score their asthma symptoms daily. Asthma symptoms were defined as wheezing, cough, chest tightness, and shortness of breath. A score of 0 was defined as no asthma symptoms. A score of 1 was defined as mild symptoms not interfering with favorite daily activities. A score of 2 was defined as moderate asthma symptoms that interfered with the child’s activities. A score of 3 was defined as serious asthma symptoms that prevented the child from participating in activities.
To improve estimates, we included data from a given 2-week diary period only for subjects who completed 7 or more days within that period. We calculated average values for the morning and evening peak flows for the recorded days. For the symptom variables, we accounted for missing data by using the percent of days recorded with the symptom of interest (eg, night wakings). Because morning and evening peak flows were highly correlated (
= 0.953), we included only average morning peak flow rates, along with percentage of nights with night waking, percentage of days with bronchodilator use, and percentage of days with a symptom score of 2 or greater in the correlational analysis. Because of inconsistent interpretation by subjects of missed school days because of asthma, this outcome was removed from the analysis.
Intervention
Subjects and their caretakers in the intervention group received instruction by an asthma case manager using a specific behaviorally-based curriculum designed to address the goals established by the National Heart, Lung, Blood Institutes.9 Intervention group subjects also received a Nintendo unit (Nintendo of America Inc, Redmond, WA) and an asthma-specific video game (Bronkie’s Asthma Adventure, Click Health Inc, Mountain View, CA) that taught asthma disease management skills. A case manager was assigned to each intervention subject to coordinate appointments, follow-up frequently with phone calls, determine needs, and reinforce education. Subjects in the intervention group also were provided toll-free phone access to an 18-hour per day asthma hotline staffed by bilingual (English and Spanish) nurses that provided patient specific asthma information. Finally, intervention group participants saw a board-certified Pediatric Allergist-Immunologist at the 2- and 4-week visits for a full history, physical examination, allergy skin testing, and creation of an acute and chronic asthma management plan. Summaries of asthma care plans were provided to the subjects’ families and primary care providers. Control group subjects were provided a peak flow meter with instructions for use, a Nintendo unit with a nonviolent video game unrelated to asthma, and allergy skin testing with results provided to the primary care provider when the patient completed the study. Intervention and control group subjects continued to receive asthma care from their primary care providers throughout the study period. Because ranks are not influenced by membership in the treatment or control group and asthma status, and because measures should be tested in children receiving different levels of care, the 2 study groups were pooled for this analysis.
Statistical Analysis
We performed 4 correlational analyses. The first analysis examined baseline correlations between each of the selected asthma outcome measures. To examine the stability of the relationships between various measures of asthma severity over time, and to eliminate the identification of random patterns, we repeated correlational analyses on data from weeks 32 and 52. If these various measures capture disease severity, we expect their correlations to be stable over time. If, however, some measures capture severity while others capture other constructs, such as coping mechanisms, their relationships may not be stable over time. We were also interested in the extent to which various asthma status measures captured changes in disease status over time. To assess the level of comparability across these measures in terms of their responsiveness to change in disease status we calculated difference scores for each variable using the baseline and week 52 values (ie, week 52 value minus baseline value). We then performed correlations between difference scores for each variable. The 8-week values were not evaluated because the health status survey was erroneously omitted at the 8-week visit at 1 site.
The correlational analysis included all available data. Because data were not normally distributed we used nonparametric Spearman rank correlation coefficients to avoid giving excess weight to extreme values. A 2-tailed 
of 0.05 defined statistical significance. Analyses were performed in Stata 6.0 (Stata Corp, College Station, TX).
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RESULTS |
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A total of 119 patients were enrolled, 60 assigned to the treatment group and 59 to the control group. Fifty-one (43%) of the 119 randomized completed the Spanish surveys. Retention was 79% at 52 weeks. Completion rates for asthma status measures ranged from 79% (CHSA, spirometry data) to 64% (diary data). The effective response rates for the 4 diary measures varied over time, from 85% to 88% at baseline to 64% to 69% at week 52. Significant correlations ranged from 0.22 to 0.53. Demographic characteristics for the analysis samples are described in Table 1. Control and intervention groups were not significantly different with regard to any variable evaluated (data available on request). Participants taking antiinflammatory medications were less likely to drop out of the study by 8 weeks (P = .04), 32 weeks (P = .03), and 52 weeks (P = .02). Females were less likely to be lost to follow-up by 32 weeks (P = .01), and 52 weeks (P = .02). Retained and lost subjects did not differ significantly with regard to their race, Medicaid status, age, home language, parental education, or asthma burden (number of wheezing episodes in past 2 months, number of asthma attacks in the past 2 months, number of hospitalizations in the past 2 months, and number of emergency visits in the past 2 months) at baseline.
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At baseline (Table 2), we found significant correlations between parent-reported wheezing episodes, asthma attacks, and the 4 included domains from the CHSA (physical health, social activity of the child, social activity of the family, and emotional health of the child). The parent-reported number of asthma attacks correlated significantly with the number of urgent care visits, whereas the number of parent-reported wheezing episodes had a negative correlation with the number of inpatient stays during the previous 2 months. The 4 included domains of the CHSA also were significantly correlated with one another.
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Data from the baseline asthma diaries showed that 3 of the 4 diary measures, symptom days, night wakings, and bronchodilator use, correlated significantly with one another. Diary measures were not correlated with measures of recent utilization, wheezing episodes, asthma attacks, or functional health status measures. The morning peak flow measure recorded in diaries did not correlate with any of the other variables collected with the exception of the laboratory measures of peak flow.
Laboratory-assessed pulmonary function measures at baseline generally did not correlate with other measures of asthma status assessed in the study including measures of recent symptoms, functional health status, and utilization. FEV1, however, was significantly negatively correlated with the number of inpatient stays. As expected, FEV1 was also associated with the change in FEV1 in response to albuterol.
The same correlational analyses were repeated at week 32 (Table 3) and week 52 (Table 4). We found that correlations between measures tended to be greater in magnitude at weeks 32 and 52 than at baseline. First, correlations that were significant at all 3 points tended to have higher correlation coefficients at weeks 32 and 52. Second, at 32 weeks and 52 weeks the number of recent urgent care visits correlated significantly with the 4 domains of the CHSA as well as symptom days, night wakings, and bronchodilator use reported in the diaries. Third, measures taken from patient diaries, which were not significantly correlated with other nondiary measure at baseline, were more likely to be correlated with parent-reported wheezing episodes and asthma attacks, and with the functional health status measures from the CHSA at weeks 32 and 52. The exception tended to be the peak flow measures reported in the diaries. Diary peak flows were generally poorly correlated with other measures of asthma symptoms, utilization, and functional status at all 3 time points. FEV1 was inconsistently correlated with other measures at the 3 time points.
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Because it is clinically important to be able to assess change in symptoms over time, we also examined whether 52-week changes (defined as 52-week value minus the baseline value) in each of these measures were correlated (Table 5). Changes in parent-reported wheezing episodes and asthma attacks were correlated with the changes in the number of urgent care visits and the four functional status domains of the CHSA. Changes in diary reports of symptom days and night wakings over one year were correlated with concurrent reported changes in the number of urgent care visits and the four functional status domains of the CHSA, and change in night wakings was correlated with change in peak flow measures reported in the diaries. As with the cross-sectional analyses, changes in pulmonary function tests were not consistently correlated with changes in other measures of asthma status, except changes in FEV1 were moderately correlated with changes in reported asthma attacks. Of note, although diary and laboratory peak flows were correlated cross-sectionally, changes in laboratory peak flow measures were not significantly correlated with changes in diary peak flows measures.
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DISCUSSION |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONCONCLUSIONREFERENCES |
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This study produced several chief findings. First, parent reported symptoms, health care utilization and functional health status all correlated at baseline, 32 weeks, and 52 weeks. Second, although asthma diaries may be useful in some settings, they did not consistently correlate with other conceptually related measures at baseline, 32 weeks, 52 weeks, or their changes over time. Third, laboratory measures of pulmonary function were not consistently correlated at any time point with the other clinically relevant measures of reported symptoms, health care utilization, functional health status, or changes in other measures over time. Finally, parent reported asthma disease status measures apparently improve with practice and familiarity. Each of these findings is discussed in depth below.
First, parent reported symptoms, health care utilization (especially urgent care visits) and functional health status all correlated at baseline, 32 weeks, and 52 weeks. These findings are consistent with a previous study of reported symptoms and CHSA scores.24 In our study, these three measures proved to have the most stable correlations over time. As a result, choosing 1 or more of these aspects of asthma status appears to give the best picture of clinically relevant asthma status and change in status over time. These findings are also consistent with previous studies that showed patient reported symptoms26–30 and functional health status measures31,32 can accurately define asthma status in children and adults.
Second, although asthma diaries may be useful in some settings (especially when patients are highly motivated to self-monitor their disease), they did not consistently correlate with other conceptually related measures at baseline, 32 weeks, 52 weeks, or their changes over time. The additional weakness of diaries displayed in this study was poor compliance. This poor compliance occurred despite a motivated study population with 18-hour per day access to personalized information (case manager or hot-line nurse), frequent telephone reminders, and 5 to 7 scheduled clinic visits. These results are consistent with previous reports of noncompliance with, and inaccuracies in, patient diary data.33,34 Given the challenges of collecting diary data and the poor stability in these measures, our results suggest that diary-derived data may not be optimal for evaluating asthma severity, tracking asthma status over time, or evaluating experimental asthma management interventions.
Third, laboratory measures of pulmonary function (spirometry) were not consistently correlated at any time point with the other clinically relevant measures of reported symptoms, health care utilization, functional health status, or changes in other measures over time. We recognize that spirometry remains the gold standard measure of pulmonary function, and it may offer valuable information for individual patient management. Spirometry should therefore continue to be a tool to assess the outcomes of asthma care. However, our findings suggest that either spirometry may not be effective at identifying improvement in asthmatic children exposed to an intervention, or more likely that spirometry measures a different aspect of asthma status than utilization, symptom scores, or quality of life does. If the latter, one could postulate that specific outcome measures might be more suitable for specific interventions than others (for example spirometry may be more suitable for measuring the short term effects of a new medication while a quality of life tool might be more suitable to monitor changes over the course of a disease management intervention). If true, the implication is that well-designed asthma trials should match their interventions with an ideal outcome measure to best determine effectiveness. This mapping of an appropriate outcome measure with a given intervention suggests that specific outcomes are not superior to others but rather ideal measures to monitor specific interventions exist. In our opinion this should be the subject of future research studies. Similar and contradictory findings regarding the correlations of PFTs and other asthma outcomes have been described in both the pediatric35–37 and adult literature.15,16,21,31,38–40
Finally, parent-reported asthma disease status measures apparently improve with practice and familiarity. This is demonstrated by increased correlations between conceptually-related measures over time. Intercorrelations and consistency between similar measures increased at weeks 32 and 52 compared with baseline. This observation may reflect a testing effect, in which study participants improve their ability to complete the survey and diaries more accurately over time. This also has implications for future studies and clinical practice. Repeated practice with measures over time may produce more reliable assessments of asthma symptoms.
A brief discussion regarding the change in time analysis is appropriate. Conceptually, we chose to compare differences between 52 weeks and baseline across measures to determine if changes occurred in a similar manner. This assumes that each measure is sensitive to changes in the disease process. If this assumption is not true for a specific measure, then differences would not correlate over time for that measure. Hence, measures insensitive to change would not correlate to those that are, and theoretically would be identified as not being very useful to monitor. In this study, PFTs were insensitive to changes in disease status over time. Therefore, we concluded that PFTs, in the context of a disease management intervention, added little value to the understanding of asthma status. Because subjects were randomly assigned at baseline to either the control or treatment group, no confounding related to treatment assignment should exist.
There are several limitations to this study. First, the study population was of similar socioeconomic status and all lived in the immediate San Francisco, California area. Although the subjects were ethnically diverse, the economic and geographic homogeneity of this population may have implications on the generalizability of the findings. Second, the study included a total of 119 patients, a number too small to allow subgroup analysis. Third, there was a 21% dropout rate, which may have affected our results. No differences existed with standard demographic parameters between dropouts and those who completed the study, but potential differences in unmeasured confounders between those who completed the study and those that dropped out may still exist. Finally, several of the measures being analyzed for correlation have not been shown to be valid, reliable measures. This makes interpretation of the findings more difficult as a lack of correlation could be attributed to poor measurement rather than a true lack of relationship between measures.
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CONCLUSION |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONCONCLUSIONREFERENCES |
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Our data from a diverse, low-income sample of children with symptomatic asthma suggest parent-reported symptoms, health care utilization, and functional health status evaluated using the CHSA are all significantly and consistently correlated over time. Selection of these measures may allow for a more efficient and reliable assessment strategy for both clinical management and clinical research. Selection of 1 or 2 of these measures most relevant to individual patients or to a specific study hypothesis may characterize both asthma symptom burden and changes over time. The collection of key data elements around symptoms, health care utilization, and functional health status after introduction of a disease management intervention may be sufficient, without loss of vital response information.
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ACKNOWLEDGMENTS |
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We gratefully acknowledge support of this project from the David and Lucile Packard Foundation.
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FOOTNOTES |
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Received for publication Dec 20, 2001; Accepted Jun 18, 2002.
Reprint requests to (P.J.S.) Lucile Packard Children’s Hospital at Stanford, 725 Welch Rd, Palo Alto, CA 94304. E-mail: psharek{at}leland.stanford.edu
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