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Original Research: QUALITY OF LIFE |

Development of a Parent-Proxy Quality-of-Life Chronic Cough-Specific Questionnaire*: Clinical Impact vs Psychometric Evaluations FREE TO VIEW

Peter A. Newcombe, PhD; Jeanie K. Sheffield, PhD; Elizabeth F. Juniper, MSc; Julie M. Marchant, MBBS; Ria A. Halsted; I. Brent Masters, MBBS; Anne B. Chang, PhD
Author and Funding Information

*From the School of Psychology (Drs. Newcombe and Sheffield), University of Queensland, Ipswich, QLD, Australia; QOL Tech (Ms. Juniper), West Sussex, UK; the Department of Respiratory Medicine (Drs. Marchant and Masters, and Ms. Halsted), Royal Children’s Hospital, Brisbane, QLD, Australia; and the Menzies School of Health Research (Dr. Chang), Darwin, NT, Australia.

Correspondence to: Peter A. Newcombe, PhD, School of Social Work and Applied Human Sciences, School of Psychology, The University of Queensland, 11 Salisbury Rd, Ipswich, QLD, Australia 4305; e-mail: newc@psy.uq.edu.au



Chest. 2008;133(2):386-395. doi:10.1378/chest.07-0888
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Published online

Background: Chronic cough affects at least 7% of children, and the impact of this on families is significant. Although adult cough-specific quality-of-life (QOL) instruments have been shown to be a useful cough outcome measure, no suitable cough-specific QOL for parents of children with chronic cough exists. This article compares two methods of item reduction (clinical impact and psychometric) and reports on the statistical properties of both QOL instruments.

Method: One hundred seventy children (97 boys and 73 girls; median age, 4 years; interquartile range, 3 to 7.25 years) and one of their parents participated. A preliminary 50-item parent cough-specific QOL (PC-QOL) questionnaire was developed from conversations with parents of children with chronic cough (ie, cough for > 3 weeks). Parents also completed generic QOL questionnaires (eg, Pediatric Quality of Life Inventory, version 4.0 [PedsQL4.0] and the 12-item Short Form Health Survey, version 2 [SF-12v2]).

Results: The clinical impact and psychometric method of item reduction resulted in 27-item and 26-item PC-QOL questionnaires, respectively, with approximately 50% of items overlapping. Internal consistency among the final items from both methods was excellent. Some evidence for concurrent and criterion validity of both methods was established as significant correlations were found between subscales of the PC-QOL questionnaire and the scales of the SF-12v2 and PedsQL4.0 scores. The PC-QOL questionnaire derived from both methods was sensitive to change following an intervention.

Conclusion: Chronic cough significantly impacts on the QOL of both parents and children. Although the PC-QOL questionnaires derived from a clinical impact method and from a psychometric method contained different items, both versions were shown to be internally consistent and valid. Further testing is required to compare both final versions to objective and subjective cough measures.

Figures in this Article

Despite the prevalence of cough,1the significant burden,23 and the negative impact on quality of life (QOL),45 there is relatively little specific research on cough in children in whom medications are often overused.67 Good clinical research requires patient-relevant outcomes, and QOL is the current “gold standard.”89 Disease-specific QOL scales have superior specificity and sensitivity over generic QOL scales.1011 However, there are no cough-specific QOL measures for pediatric subjects.

Health-related measurement scales fall into the following two general categories: (1) generic types that are focused on general health and psychological well-being (eg, 12-item Short Form Health Survey, version 2 [SF-12v2]12); and (2) disease-specific types such as those developed by Juniper and colleagues9,13that utilize the clinical impact method. The development of both types have common principles, but the generic types are usually validated and tested with large sample sizes using exploratory and confirmatory factor analysis (FA).14 In contrast, the development and validation of disease-specific types are often based on a clinical impact method that considers both the “importance” assigned to an item and its frequency of endorsement. Validation can include a comparison with biological objective measurement (eg, spirometry for asthma QOL scales,9,15) as well as a comparison with a generic QOL scale. The clinical impact method has been shown to be superior to the FA method for disease-specific QOL,16but even recently developed disease-specific QOL scales still utilize the psychometric method for validation.17 As cough is a symptom and not a disease, it is unclear which item reduction method might be more suitable.

Adult cough-specific QOL scales45 highlighted the need for cough-specific QOL and demonstrated the poor appreciation by physicians of their patients’ QOL. Factors such as urinary incontinence in these adult cough QOL questionnaires are clearly inappropriate for young children. Furthermore, the importance of pediatric-specific QOL instruments is increasingly recognized.18 Also, in pediatrics another level of complexity is added as young children are unable to verbally express themselves adequately. Hence, it is standard practice for parents to be proxy assessors of their young child’s medical condition. However, parents/caregivers are themselves affected by their child’s medical condition; therefore, the parents’ own QOL assessments are relevant. Thus, QOL assessments used in pediatrics concern the parents/caregivers themselves (such as in the SF-12v2 scales12) or are proxy QOL assessments (parental perception of their child’s illness [eg, Pediatric Quality of Life Inventory 4.0 (PedsQL4.0)]).

This study describes a comparison of psychometric (using FA) and clinical impact analyses for the development and validation of a parent cough-specific QOL (PC-QOL) questionnaire. The PC-QOL instrument is designed to assess the impact of a child’s cough on the parent/caregiver as well as the parental perception of their child’s QOL. Research by Juniper and colleagues9,1516 has elucidated the importance of this approach in the development of such an inventory as the different approaches to item reduction can lead to significantly different instruments and outcomes. It is also important to determine whether each is statistically sound while being sensitive to change.

Participants

One hundred seventy children < 18 years of age (97 boys and 73 girls; median age, 4.0 years; interquartile range [IQR], 3.00 to 7.25; 10 children were ≥ 13 years of age) and one of their parents were recruited. All children presented to the Royal Children’s Hospital, newly referred with chronic cough (cough for > 3 weeks19). The exclusion criteria were as follows: presence of previously diagnosed respiratory diseases (eg, cystic fibrosis); classic asthma (recurrent wheeze or dyspnea responsive to β2-agonist therapy); other underlying neurodevelopmental disorders; or congenital heart disease. Written consent was obtained, and the study was approved by the hospital’s human ethics committee.

Procedure

For each child presenting, one parent completed a validated cough score,20 and the following three questionnaires: a specifically developed PC-QOL questionnaire; the SF-12v212; and the PedsQL4.0.21 The PC-QOL questionnaire was administered by an interviewer, and the rest were self-completed.

A subset of parents (the last 54 participants) was contacted to complete a follow-up of the PC-QOL, PedsQL4.0, and SF-12v2. Children in this subset were involved in a medical intervention that consisted of a variety of investigations and treatments, depending on the child’s clinical history and examination findings.19,22 Cough was deemed to have resolved when parents answered “no” to the question “Is your child still coughing?” at follow-up.

Materials
Cough Score:

Parents rated their child’s cough on a 6-point scale (0, no cough; 5, severe cough and cannot perform activities), with increasing scores reflecting greater interference to activities. This rating (a cough-scoring diary) has been previously validated against an objective cough meter, and changes in this subjective cough rating have been shown to reflect changes in cough counts.20,23

PC-QOL:

The generation of items for the PC-QOL is described in the supplementary data. The PC-QOL questionnaire (Appendix) has 50 items rated with a 7-point Likert-type scale, as follows: 30 items relating to the frequency of particular feelings (items 1 to 30); and 20 items reflecting concerns or worries (items 31 to 50). Lower scores for the scales reflected greater frequency and greater concerns or worries. For analysis, item scales were reverse-scored so that higher numbers reflected greater frequency or worry.

SF-12v2:

The SF-12v2,12 which is a shortened version of the 36-item Short Form Health Survey, version 2, provides eight subscales and two summary scales (ie, the physical component summary and the mental component summary). Higher scores reflect better health states.

PedsQL4.0:

This 23-item, generic, multidimensional questionnaire21 is designed for parental reports of their child’s QOL. Each item has a 5-point Likert-type scale (0, never a problem; 4, almost always a problem). These were reverse-scored so that lower scores reflected more negative functioning. The items load on to four dimensions of “functioning” (ie, physical, emotional, social, and school functioning). The inventory caters to children in four age groups (2 to 4, 5 to 7, 8 to 12, and 13 to 18 years of age) and has been used to validate disease-specific and symptom-specific QOL instruments.,24

Statistical Analysis

Clinical impact, as outlined by Juniper and colleagues,9,1516 was derived from a product of the item mean (“importance”) and the proportion of respondents endorsing the item (“frequency”). To be included in the proportion endorsing, respondents either reported the item as “once in a while” or higher (frequency items) or as “a little worried/concerned” or higher (worry items). The items were then ranked according to the “impact” score.

For psychometric analyses, exploratory FA was used (1) to determine the underlying psychometric structure of the PC-QOL questionnaire and (2) to reduce the number of items to allow participants to complete it in a timely fashion. Principal components extraction was used along with oblique (direct oblimin) rotation that permitted the resulting factors to correlate. The retention of numbers of factors was based on the scree test and the strength of their eigenvalues. Criteria for the exclusion of items included the following: (1) the item did not specifically relate to the cough and its potential consequences; (2) the item did not focus on the child and family; (3) the wording of an item was not easily understood; and (4) the loading of an item on a factor was < 0.60 (36% of the variance explained) as well as clinical opinion and relevance. Items were also excluded if they were deemed to be “difficult” (ie, items of similar means within a factor25) or “redundant” (ie, overlapping content and high interitem correlations26).

To investigate the psychometric properties of the PC-QOL, reliability (internal consistency) and validity (concurrent) were examined. Further analyses were conducted to determine whether the instrument (and its domains) was sensitive to change over time following medical interventions. Nonparametric analyses were used throughout.

Clinical Impact

The 50 QOL items were ranked according to the strength of their impact (Table 1 ). Based on an arbitrary cutoff at a natural break in impact rating, the 27 highest impact scores were selected. The reduced QOL scale was internally consistent (α = 0.94) with a median impact score of 4.35 (IQR, 3.26 to 5.2). Interitem correlations ranged from Spearman correlation (rs) = 0.12 to 0.87 (median, 0.40).

Psychometric Method

Two separate factor analyses were conducted, one on the 30 frequency items and another on the 20 worry items. Three factors were extracted for the frequency domain (63.31% variance explained), and two factors for the worry domain (55.33% variance explained). Twenty-six items were retained in the final form with no items cross-loading across factors. Table 2 shows the results of the principal components analysis. In the frequency domain, factor 1 (emotions) accounted for 39.89% of the variance and was highlighted by items such as “feeling upset because of your child’s cough?” Factor 2 (interference; 10.87% of the variance) included items that highlighted the degree to which chronic cough might hinder a family’s daily routines. Factor 3 (annoyance; 10.55% of the variance) included items that appeared to indicate annoyance with the cough. Correlations among the three factors ranged from Spearman correlation (rs) = 0.34 (interference with annoyance), to rs = 0.41 (interference with emotions), to rs = 0.55 (annoyance with emotions; all p < 0.05).

For the worry domain, the first worry factor (fragility; 39.74% of the variance) highlighted concerns over the child’s fragility. The second worry factor (serious health) explained 15.58% of the variance. The two worry factors also significantly correlated (rs = 0.37; p < 0.05).

All subscale Cronbach α values (Table 2) and the full-scale Cronbach α value (α = 0.92) were high, reflecting moderate-to-strong internal consistency. These high internal consistencies were repeated at a second testing time (median time interval, 55 days; IQR, 31 to 110 days) with factor α values ranging from 0.85 to 0.96 (full-scale α = 0.97).

Interitem Spearman correlations for each of the five factors showed the following mean values: emotions, 0.57 (range, 0.46 to 0.66); interference, 0.43 (range, 0.35 to 0.57); annoyance, 0.45 (range, 0.32 to 0.64); fragility, 0.39 (range, 0.24 to 0.51); and serious health, 0.50 (range, 0.36 to 0.66). These demonstrate little item redundancy within the subscales of the proposed PC-QOL questionnaire.

Comparison of Methods

The final versions of both item-reduction methods were shown to be internally reliable. However, the degree of item overlap (ie, 13 of the items [48%] identified by the clinical impact method were also included in the psychometric method, while 13 of the psychometrically derived items [50%] were also identified by the clinical impact method) might lead to potentially different questionnaires, resulting in distinctive outcomes. Table 3 lists items from the clinical impact method that would not have been included if the psychometric method were to be adopted. Of note, four of the six highest impact items were not part of the FA, but all items from the emotions factor were included. Table 4 lists items from the psychometric method that would not have been included with the impact method. Most were from the frequency domain with no annoyance factor items being represented in the clinical impact method.

Validity Issues

Issues of concurrent and criterion validity for the PC-QOL questionnaire were examined through Spearman correlations with the subscales of the PedsQL4.0 and the SF-12v2 (Table 5 ). For the PedsQL4.0, a number of high absolute correlation values were not significant due to the small sample size (n = 20 for children 2 to 4 years old) and the corrected adjusted α levels employed as a result of the number of pairwise correlations. Despite this, there appears to be some evidence for criterion validity (especially for the psychometric factors of interference and fragility). Similarly for the SF-12v2, high absolute values were found for the QOL scores (for both the psychometric and clinical impact methods) with role physical, social functioning, and role emotional factors of the SF-12v2. As can be seen from Table 5, the pattern of correlations shows that the two-item reduction methods are distinctive to the extent that the psychometric method results in some stronger correlations together with greater sensitivity due to partialing (dividing) of the global total score into factors.

Sensitivity to Change

Both item-reduction methods were sensitive to change across time following intervention. Descriptive data (medians with IQRs) along with Wilcoxon tests of difference are presented in Table 6 . All subscales from the psychometric analysis showed significant improvement in parent-reported QOL following the intervention (all p < 0.001) [Fig 1 ]. Similarly, improvements in PC-QOL scores derived by clinical impact were also significant at p < 0.001. Further evidence for the sensitivity of the PC-QOL questionnaire to change was shown after the intervention at which time parents identified whether their child had ceased coughing. Descriptive data (medians with IQRs) and the Mann-Whitney tests of difference are presented in Table 7 . As can be seen from Table 7, those parents whose children had not ceased coughing reported significantly greater frequency of concern and worries than those whose children had ceased coughing. Again, these findings were supported by the QOL impact scores.

The present study sought to generate a reliable and valid pediatric QOL instrument for parents/caregivers of children with chronic cough. A PC-QOL instrument is a necessary addition to our understanding of the impact of children’s cough on caregivers’ mental health and subsequent burden. The original 50 items were parent-derived and thus reflected issues of relevance to parents. The two methods of identifying items for the QOL instrument resulted in some overlap with 13 common items. Both had excellent internal reliability, correlated to a generic QOL (thus valid), and were sensitive to change (significant difference demonstrated when the children’s cough resolved after intervention). The psychometric method also identified two domains of QOL with three frequency factors (emotions, interference, and annoyance) and two worry factors (fragility and serious illness). All factors were highly reliable, as evidenced by their strong Cronbach α values. Correlations among these factors were strong and significant, indicating some overlap but also sufficient distinctiveness to provide useful and discrete information.

The validity of both final versions of the PC-QOL instrument was confirmed, to some extent, by comparing the responses to previously validated measures of a generic adult QOL instrument (ie, SF-12v2) and a generic multidimensional pediatric QOL instrument (PedsQL4.0). Use of the psychometric method did, however, result in stronger absolute correlations with the PedsQL4.0 than did the clinical impact method, leading to more confidence in the validity of its construct. Importantly, the PC-QOL instrument derived from both methods was sensitive, with all scales and total scores showing significant change following medical interventions for the child’s cough. However, this requires further testing, specifically as it needs to be related to an objective cough measure as well as to a subjective cough measure.

An objective cough meter was not utilized in this study as this was not deemed feasible given the number of children tested. Furthermore, as most of the children were below the age required for reliable measurements for the pediatric cough sensitivity test,27 this measure (as utilized for the Leicester QOL instrument4) could not be used. However, the psychometric method-derived PC-QOL scale, and not the clinical method-derived scale, did show some relationship to the cough score, a measure that has been validated against cough counts measured objectively on a cough meter.20 We have previously shown that, in children, objective cough-severity indexes measure different aspects of cough and that the choice of cough outcome measures depends on the reason for performing the measurement.23

Subjective measures are likely to best reveal the acuteness of cough from the perspective of those who matter (ie, the child patient and their parents/caregivers). They can also provide key insights into the clinical management of cough problems in pediatrics. Hence, the importance of measuring the impact of symptoms on well-being should not be underestimated and has been reflected in the increasing use of QOL inventories as an outcome measure in intervention clinical trials.28 However, subjective measures are not expected to replace objective measures of cough but are a necessary complement as a patient-related outcome. The reliable and validated PC-QOL instrument reported in this study satisfies these criteria. With further testing, we believe that the PC-QOL instrument will become a valuable tool in the evaluation of future treatment and clinical research, and therefore will contribute to improving the QOL of children with chronic cough and their parents. Although we have shown that the PC-QOL instrument derived by both the clinical impact and psychometric methods are reliable, valid, and sensitive to change, more work needs to be completed to validate the discriminant utility of the measure. Whether the PC-QOL scale generated by the psychometric method is superior to that derived by the clinical impact method when assessed against an objective biological measure is yet to be determined.

Abbreviations: FA = factor analysis; IQR = interquartile range; PC-QOL = parent cough-specific quality of life; PedsQL4.0 = Pediatric Quality of Life Inventory 4.0; QOL = quality of life; rs = Spearman correlation; SF-12v2 = 12-item Short Form Health Survey, version 2

This study was supported by the Royal Children’s Hospital Foundation. Dr. Chang is supported by a National Health and Medical Research Council, Australia, Practitioner Fellowship.

The authors have reported to the ACCP that no significant conflicts of interest exist with any companies/organizations whose products or services may be discussed in this article.

Table Graphic Jump Location
Table 1. Impact Rank Order of the PC-QOL Items
Table Graphic Jump Location
Table 2. Factor Loadings, Eigenvalues, Percentages of Variance, and Internal Consistency Measures for the Final 26-Item PC-QOL Inventory Along With Interfactor Correlations and Correlations With Clinical Impact Total Scores*
* 

FF = frequency factor; WF = worry factor.

Table Graphic Jump Location
Table 3. Items From the Clinical Impact Method That Were Not Included Using the Psychometric Method
Table Graphic Jump Location
Table 4. Items From the Psychometric Method That Were Not Included in the Impact Method
Table Graphic Jump Location
Table 5. Spearman Correlations Between the Subscale and Total Scores of the PC-QOL (Psychometric and Clinical Impact Analyses) and the Dimensions of the PedsQL4.0 (2- to 4-Year-Old Age Group) and the SF-12v2*
* 

See Table 2 for abbreviations not used in the text.

 

p < 0.05.

 

p < 0.01.

§ 

p < 0.001.

Table Graphic Jump Location
Table 6. Preintervention and Postintervention Scores for QOL Factors and Clinical Impact Scores*
* 

Values are given as median (IQR), unless otherwise indicated. See Table 2 for abbreviations not used in the text.

 

p < 0.001 (Wilcoxon signed rank test).

Figure Jump LinkFigure 1. Preintervention and postintervention median subscale scores (psychometric method) for the PC-QOL as reported by parents.Grahic Jump Location
Table Graphic Jump Location
Table 7. Scores for QOL Factors and Clinical Impact Scores for Those Children Who Did and Did Not Cease Coughing at Postintervention*
* 

Values are given as the median (IQR), unless otherwise indicated.

 

Mann-Whitney U test.

 

p < 0.001.

§ 

p < 0.01.

Table Graphic Jump Location
Table 8. 
Table Graphic Jump Location
Table 8A. Appendix—Continued
Table Graphic Jump Location
Table 8B. Appendix—Continued

We thank Prof. Varni and the Mapi Research Institute for allowing us to utilize the PedsQL4.0 Inventory without cost.

Irwin, RS (2006) Introduction to the diagnosis and management of cough: ACCP Evidence-Based Clinical Practice Guidelines.Chest129(suppl),25S-S27
 
Cornford, CS Why patients consult when they cough: a comparison of consulting and non-consulting patients.Br J Gen Pract1998;48,1751-1754. [PubMed]
 
Faniran, AO, Peat, JK, Woolcock, AJ Persistent cough: is it asthma?Arch Dis Child1998;79,411-414. [PubMed] [CrossRef]
 
Birring, SS, Prudon, B, Carr, AJ, et al Development of a symptom specific health status measure for patients with chronic cough: Leicester Cough Questionnaire (LCQ).Thorax2003;58,339-343. [PubMed]
 
French, CT, Irwin, RS, Fletcher, KE, et al Evaluation of a cough-specific quality-of-life questionnaire.Chest2002;121,1123-1131. [PubMed]
 
Thomson, F, Masters, IB, Chang, AB Persistent cough in children: overuse of medications.J Paediatr Child Health2002;38,578-581. [PubMed]
 
Russell, G Very high dose inhaled corticosteroids: panacea or poison?Arch Dis Child2006;91,802-804. [PubMed]
 
Juniper, EF Interpreting quality of life data: should we listen to the patient or the clinician?Ann Allergy Asthma Immunol2003;91,115-116. [PubMed]
 
Juniper, EF, Guyatt, GH, Feeny, DH, et al Measuring quality of life in the parents of children with asthma.Qual Life Res1996;5,27-34. [PubMed]
 
Wiebe, S, Guyatt, G, Weaver, B, et al Comparative responsiveness of generic and specific quality-of-life instruments.J Clin Epidemiol2003;56,52-60. [PubMed]
 
Kalpaklioglu, AF, Kara, T, Kurtipek, E, et al Evaluation and impact of chronic cough: comparison of specific vs generic quality-of-life questionnaires.Ann Allergy Asthma Immunol2005;94,581-585. [PubMed]
 
Ware, J, Jr, Kosinski, M, Keller, SD A 12-item short-form health survey: construction of scales and preliminary tests of reliability and validity.Med Care1996;34,220-233. [PubMed]
 
Juniper, EF, Howland, WC, Roberts, NB, et al Measuring quality of life in children with rhinoconjunctivitis.J Allergy Clin Immunol1998;101,163-170. [PubMed]
 
Spence, SH Structure of anxiety symptoms among children: a confirmatory factor-analytic study.J Abnorm Psychol1997;106,280-297. [PubMed]
 
Juniper, EF, Guyatt, GH, Feeny, DH, et al Measuring quality of life in children with asthma.Qual Life Res1996;5,35-46. [PubMed]
 
Juniper, EF, Guyatt, GH, Streiner, DL, et al Clinical impact versus factor analysis for quality of life questionnaire construction.J Clin Epidemiol1997;50,233-238. [PubMed]
 
McMillan, CV, Honeyford, RJ, Datta, J, et al The development of a new measure of quality of life for young people with diabetes mellitus: the ADDQoL-Teen.Health Qual Life Outcomes2004;2,61. [PubMed]
 
Landgraf, JM, Abetz, l Measuring health outcomes in pediatric populations: issues in psychometrics and application. Spilker, B eds.Quality of life and pharmacoeconomics in clinical trials2004,793-802 Lippincott-Raven Publishers. Philadelphia, PA:
 
Marchant, JM, Masters, IB, Taylor, SM, et al Evaluation and outcome of young children with chronic cough.Chest2006;129,1132-1141. [PubMed]
 
Chang, AB, Newman, RG, Carlin, J, et al Subjective scoring of cough in children: parent-completed vs child-completed diary cards vs an objective method.Eur Respir J1998;11,462-466. [PubMed]
 
Varni, JW, Burwinkle, TM, Seid, M, et al The PedsQL 4.0 as a pediatric population health measure: feasibility, reliability, and validity.Ambul Pediatr2003;3,329-341. [PubMed]
 
Chang, AB, Landau, LI, van Asperen, PP, et al Cough in children: definitions and clinical evaluation.Med J Aust2006;184,398-403. [PubMed]
 
Chang, AB, Phelan, PD, Robertson, CF, et al Relationship between measurements of cough severity.Arch Dis Child2003;88,57-60. [PubMed]
 
Varni, JW, Burwinkle, TM, Szer, IS The PedsQL Multidimensional Fatigue Scale in pediatric rheumatology: reliability and validity.J Rheumatol2004;31,2494-2500. [PubMed]
 
Gorsuch, RL. Factor analysis. 1983; Lawrence Erlbaum Associates. New York, NY:.
 
Smith, GT, McCarthy, DM Methodological considerations in the refinement of clinical assessment instruments.Psychol Assess1995;7,300-308
 
Chang, AB, Phelan, PD, Roberts, RGD, et al Capsaicin cough receptor sensitivity test in children.Eur Respir J1996;9,2220-2223. [PubMed]
 
Abbott, J, Gee, L Quality of life in children and adolescents with cystic fibrosis: implications for optimizing treatments and clinical trial design.Paediatr Drugs2003;5,41-56. [PubMed]
 

Figures

Figure Jump LinkFigure 1. Preintervention and postintervention median subscale scores (psychometric method) for the PC-QOL as reported by parents.Grahic Jump Location

Tables

Table Graphic Jump Location
Table 1. Impact Rank Order of the PC-QOL Items
Table Graphic Jump Location
Table 2. Factor Loadings, Eigenvalues, Percentages of Variance, and Internal Consistency Measures for the Final 26-Item PC-QOL Inventory Along With Interfactor Correlations and Correlations With Clinical Impact Total Scores*
* 

FF = frequency factor; WF = worry factor.

Table Graphic Jump Location
Table 3. Items From the Clinical Impact Method That Were Not Included Using the Psychometric Method
Table Graphic Jump Location
Table 4. Items From the Psychometric Method That Were Not Included in the Impact Method
Table Graphic Jump Location
Table 5. Spearman Correlations Between the Subscale and Total Scores of the PC-QOL (Psychometric and Clinical Impact Analyses) and the Dimensions of the PedsQL4.0 (2- to 4-Year-Old Age Group) and the SF-12v2*
* 

See Table 2 for abbreviations not used in the text.

 

p < 0.05.

 

p < 0.01.

§ 

p < 0.001.

Table Graphic Jump Location
Table 6. Preintervention and Postintervention Scores for QOL Factors and Clinical Impact Scores*
* 

Values are given as median (IQR), unless otherwise indicated. See Table 2 for abbreviations not used in the text.

 

p < 0.001 (Wilcoxon signed rank test).

Table Graphic Jump Location
Table 7. Scores for QOL Factors and Clinical Impact Scores for Those Children Who Did and Did Not Cease Coughing at Postintervention*
* 

Values are given as the median (IQR), unless otherwise indicated.

 

Mann-Whitney U test.

 

p < 0.001.

§ 

p < 0.01.

Table Graphic Jump Location
Table 8. 
Table Graphic Jump Location
Table 8A. Appendix—Continued
Table Graphic Jump Location
Table 8B. Appendix—Continued

References

Irwin, RS (2006) Introduction to the diagnosis and management of cough: ACCP Evidence-Based Clinical Practice Guidelines.Chest129(suppl),25S-S27
 
Cornford, CS Why patients consult when they cough: a comparison of consulting and non-consulting patients.Br J Gen Pract1998;48,1751-1754. [PubMed]
 
Faniran, AO, Peat, JK, Woolcock, AJ Persistent cough: is it asthma?Arch Dis Child1998;79,411-414. [PubMed] [CrossRef]
 
Birring, SS, Prudon, B, Carr, AJ, et al Development of a symptom specific health status measure for patients with chronic cough: Leicester Cough Questionnaire (LCQ).Thorax2003;58,339-343. [PubMed]
 
French, CT, Irwin, RS, Fletcher, KE, et al Evaluation of a cough-specific quality-of-life questionnaire.Chest2002;121,1123-1131. [PubMed]
 
Thomson, F, Masters, IB, Chang, AB Persistent cough in children: overuse of medications.J Paediatr Child Health2002;38,578-581. [PubMed]
 
Russell, G Very high dose inhaled corticosteroids: panacea or poison?Arch Dis Child2006;91,802-804. [PubMed]
 
Juniper, EF Interpreting quality of life data: should we listen to the patient or the clinician?Ann Allergy Asthma Immunol2003;91,115-116. [PubMed]
 
Juniper, EF, Guyatt, GH, Feeny, DH, et al Measuring quality of life in the parents of children with asthma.Qual Life Res1996;5,27-34. [PubMed]
 
Wiebe, S, Guyatt, G, Weaver, B, et al Comparative responsiveness of generic and specific quality-of-life instruments.J Clin Epidemiol2003;56,52-60. [PubMed]
 
Kalpaklioglu, AF, Kara, T, Kurtipek, E, et al Evaluation and impact of chronic cough: comparison of specific vs generic quality-of-life questionnaires.Ann Allergy Asthma Immunol2005;94,581-585. [PubMed]
 
Ware, J, Jr, Kosinski, M, Keller, SD A 12-item short-form health survey: construction of scales and preliminary tests of reliability and validity.Med Care1996;34,220-233. [PubMed]
 
Juniper, EF, Howland, WC, Roberts, NB, et al Measuring quality of life in children with rhinoconjunctivitis.J Allergy Clin Immunol1998;101,163-170. [PubMed]
 
Spence, SH Structure of anxiety symptoms among children: a confirmatory factor-analytic study.J Abnorm Psychol1997;106,280-297. [PubMed]
 
Juniper, EF, Guyatt, GH, Feeny, DH, et al Measuring quality of life in children with asthma.Qual Life Res1996;5,35-46. [PubMed]
 
Juniper, EF, Guyatt, GH, Streiner, DL, et al Clinical impact versus factor analysis for quality of life questionnaire construction.J Clin Epidemiol1997;50,233-238. [PubMed]
 
McMillan, CV, Honeyford, RJ, Datta, J, et al The development of a new measure of quality of life for young people with diabetes mellitus: the ADDQoL-Teen.Health Qual Life Outcomes2004;2,61. [PubMed]
 
Landgraf, JM, Abetz, l Measuring health outcomes in pediatric populations: issues in psychometrics and application. Spilker, B eds.Quality of life and pharmacoeconomics in clinical trials2004,793-802 Lippincott-Raven Publishers. Philadelphia, PA:
 
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