Asthma is one of the most prevalent chronic conditions in children, affecting 8.4% of American children and adolescents (approximately 6.2 million) in 2015.1 Although previous studies have found that poor asthma control is associated with impaired physical, psychological and social aspects of health-related quality of life (HRQoL),2 some studies have suggested that impaired psychological aspects of HRQoL (i.e., anxiety and depressive symptoms) causes poor asthma control.3 The effects of impaired HRQoL domains (e.g., fatigue, pain, mobility, and peer relationships) in addition to anxiety and depressive symptoms on subsequent asthma outcomes in pediatric populations has been inadequately studied. This study aimed to test the usefulness of HRQoL assessed by the Patient-Reported Outcomes Measurement Information System® (PROMIS®) Pediatric measures4,5 collected at baseline and three subsequent time points, with a meaningful threshold (e.g., good or poor HRQoL), to indicate the asthma control status over time among children with asthma from low-income families.
This study is a secondary analysis utilizing data collected from the longitudinal PROMIS Pediatric Asthma Study (PAS) designed to evaluate clinical validity for the PROMIS Pediatric measures based on the course of two 13-week windows across a two-year period.4 Enrollment criteria for children/adolescents were ages 8-17.9 years; an asthma diagnosis (ICD-9-CM 493.1, 493.2, or 493.x); ≥ two medical encounters due to asthma in the last 12 months; and continuous enrollment in the Florida Medicaid for the previous six months. Asthma control and HRQoL were evaluated at four time points: baseline in the first year, a follow-up in the first year, baseline in the second year, and a follow-up in the second year.
The Asthma Control and Communication Instrument (ACCI)6 was used to assess asthma control status reported by parents through our research website. Based on the National Asthma Education Prevention Program Expert Panel Report-3 (NAEPP EPR-3), the five items from the asthma control domain of the ACCI were used to categorize asthma control status. If all five items indicated well-controlled asthma, a child/adolescent was classified as having good control; otherwise, he/she was classified as poor control.6 PROMIS Pediatric Short-Forms4,5 were administered to assess HRQoL, including asthma impact, pain interference, fatigue, depressive symptoms, anxiety, mobility, and peer relationships domains. Domain scores were calculated and converted to a T-metric (mean=50/SD=10).7 Higher scores on asthma impact, pain interference, fatigue, depressive symptoms, and anxiety domains represented worse HRQoL; lower scores on mobility and peer relationships domains represented worse HRQoL.
The status of asthma control over four time points served as the main outcome variable. Children/adolescents with good asthma control for three or more time points were classified as “consistently good control;” otherwise, they were classified as “consistently poor control.” The status of HRQoL over four time points on each PROMIS domain served as the main independent variables. At each time point, a child/adolescent's HRQoL domain score that differed by five points from 50 (i.e., 0.5 SD from the PROMIS Pediatric calibration sample) was considered as a minimally important difference.8 Specifically, a child/adolescent with scores >45 on asthma impact, pain interference, fatigue, depressive symptoms, and anxiety domains was classified as “fair/poor HRQoL” and ≤45 as “good HRQoL,” whereas scores <55 on mobility and peer relationships domains was classified as “fair/poor HRQoL” and ≥55 as “good HRQoL.” Children/adolescents with good HRQoL at three or more time points were classified as having “consistently good HRQoL;” otherwise, they were classified as “consistently fair/poor HRQoL.”
Multivariable logistic regression was performed to test associations between the status of HRQoL over time in each domain and the status of asthma control over time, adjusting for covariates (the child's age and overweight status, and the parental age and marital status). Additional multivariable logistic analyses were conducted to test associations of HRQoL status over time with asthma control status over time utilizing fewer time points (i.e., two and three time points).
There were 148 participant dyads that had complete HRQoL and asthma control data across all four time points eligible for this analysis. Among children/adolescents, the mean age was 12.0 (SD 2.4) years old; 43.2% were female; most were non-white (64.2%); and almost half were overweight (46.2%). Among parents, the mean age was 41.7 (SD 9.0) years old; the majority were female (89.9%) and non-white (58.8%). Multivariable associations between HRQoL status of each domain over time and asthma control status over time are presented in Table 1. For all HRQoL domains, consistently fair/poor HRQoL was significantly associated with an increased odds of consistently poor asthma control (ORs=2.58-3.28, p's ≤0.05), except for mobility (OR=2.05, p >0.05). Additional multivariable analyses (see Online Supplement) suggest that, except for the asthma impact and mobility domains, the use of four time points to classify HRQoL status produced the most significant correlation with asthma control status.
Table 1. Multivariable association between HRQoL status over time and asthma control status over time.
| Consistently Poor Asthma Control, Odds Ratio a | 95% Confidence Interval | p-value | ||
|---|---|---|---|---|
| Asthma Impact | ||||
|
| ||||
| Consistently fair/poor HRQoL b | 3.24 | 1.52-6.90 | < 0.01 | |
| Child Age | 0.93 | 0.78-1.11 | 0.40 | |
| Overweight c | 1.36 | 0.63-2.92 | 0.43 | |
| Parent Age | 0.98 | 0.94-1.02 | 0.33 | |
| Marital Status d | ||||
| Never Married | 2.82 | 0.92-8.63 | 0.07 | |
| Divorced | 1.82 | 0.62-5.40 | 0.06 | |
| Other | 2.63 | 0.95-7.34 | 0.30 | |
|
| ||||
| Pain Interference | ||||
|
| ||||
| Consistently fair/poor HRQoL b | 2.84 | 1.36-5.92 | < 0.01 | |
| Child Age | 0.94 | 0.79-1.12 | 0.51 | |
| Overweight c | 1.54 | 0.73-3.25 | 0.25 | |
| Parent Age | 0.98 | 0.94-1.02 | 0.33 | |
| Marital Status d | ||||
| Never Married | 1.99 | 0.68-5.77 | 0.21 | |
| Divorced | 3.11 | 1.12-8.65 | < 0.05 | |
| Other | 1.86 | 0.65-5.32 | 0.25 | |
|
| ||||
| Fatigue | ||||
|
| ||||
| Consistently fair/poor HRQoL b | 2.80 | 1.33-5.90 | < 0.01 | |
| Child Age | 0.96 | 0.81-1.14 | 0.64 | |
| Overweight c | 1.51 | 0.72-3.16 | 0.27 | |
| Parent Age | 0.97 | 0.93-1.01 | 0.19 | |
| Marital Status d | ||||
| Never Married | 2.34 | 0.81-6.73 | 0.11 | |
| Divorced | 2.90 | 1.05-8.01 | < 0.05 | |
| Other | 1.62 | 0.57-4.60 | 0.36 | |
|
| ||||
| Depressive Symptoms | ||||
|
| ||||
| Consistently fair/poor HRQoL b | 2.78 | 1.28-6.01 | < 0.01 | |
| Child Age | 0.95 | 0.80-1.13 | 0.56 | |
| Overweight c | 1.81 | 0.86-3.80 | 0.12 | |
| Parent Age | 0.98 | 0.94-1.02 | 0.27 | |
| Marital Status d | ||||
| Never Married | 2.82 | 0.97-8.23 | 0.06 | |
| Divorced | 2.77 | 1.00-7.66 | 0.05 | |
| Other | 1.51 | 0.54-4.22 | 0.43 | |
|
| ||||
| Anxiety | ||||
|
| ||||
| Consistently fair/poor HRQoL b | 2.58 | 1.25-5.35 | 0.01 | |
| Child Age | 0.93 | 0.78-1.11 | 0.41 | |
| Overweight c | 1.54 | 0.73-3.22 | 0.25 | |
| Parent Age | 0.98 | 0.94-1.02 | 0.36 | |
| Marital Status d | ||||
| Never Married | 2.26 | 0.79-6.50 | 0.13 | |
| Divorced | 2.60 | 0.92-7.15 | 0.07 | |
| Other | 1.39 | 0.50-3.88 | 0.53 | |
|
| ||||
| Mobility | ||||
|
| ||||
| Consistently fair/poor HRQoL b | 2.05 | 0.77-5.44 | 0.15 | |
| Child Age | 0.96 | 0.81-1.13 | 0.61 | |
| Overweight c | 1.49 | 0.72-3.08 | 0.29 | |
| Parent Age | 0.98 | 0.94-1.02 | 0.23 | |
| Marital Status d | ||||
| Never Married | 2.19 | 0.77-6.21 | 0.14 | |
| Divorced | 2.85 | 1.05-7.74 | < 0.05 | |
| Other | 1.65 | 0.58-4.70 | 0.35 | |
|
| ||||
| Peer Relationships | ||||
|
| ||||
| Consistently fair/poor HRQoL b | 3.28 | 1.25-8.61 | 0.01 | |
| Child Age | 1.00 | 0.84-1.19 | 0.98 | |
| Overweight c | 1.63 | 0.78-3.41 | 0.20 | |
| Parent Age | 0.98 | 0.93-1.02 | 0.22 | |
| Marital Status d | ||||
| Never Married | 2.41 | 0.83-6.94 | 0.10 | |
| Divorced | 2.53 | 0.92-7.01 | 0.07 | |
| Other | 1.38 | 0.49-3.88 | 0.54 | |
Consistently good asthma control status is the reference group
Mean scores < 55 for mobility or peer relationships domain and mean scores > 45 for other domains
Not overweight is the reference group
Married is the reference group
These findings demonstrate that it is clinically important to monitor HRQoL over time in pediatric asthma as consistently poor HRQoL across 4 time points is associated with poorly controlled asthma. This knowledge can facilitate clinical decision-making to minimize risk and guide medication adjustments in pediatric asthma as physicians can identify subgroups of patients at risk of adverse asthmatic outcomes (e.g., consistently poor asthma control). The use of electronic systems and mobile technology, e.g., cell phones and tablet apps or well-child check planners,9 may offer a low-cost approach to continuously collect HRQoL data. This paradigm shift, from interval disease-/symptom-based management during office visits to one where patients and families routinely report information, will give providers an opportunity to determine risk of exacerbation and consistently poor asthma control in the absence of more frequent clinic visits. Further, utilizing remote reporting of HRQoL in conjunction with asthma- specific home visits implemented by community health workers for vulnerable, high-risk children may help physicians identify when more intensive interventions are needed. Additionally, identifying an appropriate HRQoL reporting period10 and incentivizing individuals to report HRQoL over time becomes an emerging issue to address in future studies.
Supplementary Material
Online Supplement: Multivariable association between HRQoL status and asthma control status by the use of different time points
Acknowledgments
The authors would like to thank Elizabeth Shenkman, PhD and Caprice Knapp, PhD, who assisted with study design; and Pey-Shen Wen, PhD and Nammi Ketheeswaran, MPH, who assisted with data collection and project coordination for the PROMIS® Pediatric Asthma Study.
Financial Support: This work was supported by the National Institutes of Health (grants U01 AR052181 and K23 HD057146) and American Lebanese Syrian Associated Charities.
PROMIS® was funded with cooperative agreements from the National Institutes of Health (NIH) Common Fund Initiative (Northwestern University, PI: David Cella, PhD, U54AR057951, U01AR052177; Northwestern University, PI: Richard C. Gershon, PhD, U54AR057943; American Institutes for Research, PI: Susan (San) D. Keller, PhD, U54AR057926; State University of New York, Stony Brook, PIs: Joan E. Broderick, PhD and Arthur A. Stone, PhD, U01AR057948, U01AR052170; University of Washington, Seattle, PIs: Heidi M. Crane, MD, MPH, Paul K. Crane, MD, MPH, and Donald L. Patrick, PhD, U01AR057954; University of Washington, Seattle, PI: Dagmar Amtmann, PhD, U01AR052171; University of North Carolina, Chapel Hill, PI: Harry A. Guess, MD, PhD (deceased), Darren A. DeWalt, MD, MPH, U01AR052181; Children's Hospital of Philadelphia, PI: Christopher B. Forrest, MD, PhD, U01AR057956; Stanford University, PI: James F. Fries, MD, U01AR052158; Boston University, PIs: Alan Jette, PT, PhD, Stephen M. Haley, PhD (deceased), and David Scott Tulsky, PhD (University of Michigan, Ann Arbor), U01AR057929; University of California, Los Angeles, PIs: Dinesh Khanna, MD (University of Michigan, Ann Arbor) and Brennan Spiegel, MD, MSHS, U01AR057936; University of Pittsburgh, PI: Paul A. Pilkonis, PhD, U01AR052155; Georgetown University, PIs: Carol. M. Moinpour, PhD (Fred Hutchinson Cancer Research Center, Seattle) and Arnold L. Potosky, PhD, U01AR057971; Children's Hospital Medical Center, Cincinnati, PI: Esi M. Morgan DeWitt, MD, MSCE, U01AR057940; University of Maryland, Baltimore, PI: Lisa M. Shulman, MD, U01AR057967; and Duke University, PI: Kevin P. Weinfurt, PhD, U01AR052186). NIH Science Officers on this project have included Deborah Ader, PhD, Vanessa Ameen, MD (deceased), Susan Czajkowski, PhD, Basil Eldadah, MD, PhD, Lawrence Fine, MD, DrPH, Lawrence Fox, MD, PhD, Lynne Haverkos, MD, MPH, Thomas Hilton, PhD, Laura Lee Johnson, PhD, Michael Kozak, PhD, Peter Lyster, PhD, Donald Mattison, MD, Claudia Moy, PhD, Louis Quatrano, PhD, Bryce Reeve, PhD, William Riley, PhD, Peter Scheidt, MD, Ashley Wilder Smith, PhD, MPH, Susana Serrate-Sztein, MD, William Phillip Tonkins, DrPH, Ellen Werner, PhD, Tisha Wiley, PhD, and James Witter, MD, PhD. The contents of this article used data developed under PROMIS. These contents do not necessarily represent an endorsement by the U.S. Federal Government or PROMIS. See www.nihpromis.org for additional information on the PROMIS® initiative.
The funding source had no involvement in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; and the decision to submit the article for publication.
Footnotes
Conflict of Interest: No conflict of interests for all co-authors.
Role of authors: CR Howell – conception and design of the study, analysis and interpretation of the data, preparation and critical revision of the manuscript
LA Thompson – conception and design of the study, preparation and critical revision of the manuscript
HE Gross – conception and design of the study, generation of the data, preparation and critical revision of the manuscript
BB Reeve – preparation and critical revision of the manuscript
SW Huang – preparation and critical revision of the manuscript
DA DeWalt – conception and design of the study, preparation and critical revision of the manuscript
IC Huang – conception and design of the study, generation of the data, analysis and interpretation of the data, preparation and critical revision of the manuscript
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Supplementary Materials
Online Supplement: Multivariable association between HRQoL status and asthma control status by the use of different time points