Older adults with coexisting asthma and chronic obstructive pulmonary disease (COPD), known as asthma-COPD overlap, who take fixed-dose combinations of inhaled corticosteroids and long-acting β agonists may be less likely to have persistent low adherence to initial maintenance therapy.
Objectives: To examine the impact of initial maintenance therapy (IMT) type (inhaled corticosteroid [ICS] vs fixed-dose combination of ICS and long-acting β agonist [ICS/LABA]) on trajectories of adherence among older adults (≥ 65 years) with coexisting asthma and chronic obstructive pulmonary disease (COPD), known as asthma-COPD overlap (ACO).
Study Design: We used a longitudinal, retrospective cohort design.
Methods: This study used a cohort of older adults with ACO using longitudinal data from a 10% sample of Optum’s Deidentified Clinformatics Data Mart. We adopted group-based trajectory modeling to identify medication adherence trajectories over 12 months. Multinomial logistic regressions were used to evaluate the unadjusted and adjusted associations of IMT medication and adherence trajectory categories. All analyses accounted for treatment option selection bias with inverse probability treatment weighting.
Results: Of 1555 individuals, 73% of the sample used ICS/LABA for IMT. Four medication adherence trajectories were observed regardless of regimen: (1) persistent high adherence (12.0%), (2) progression to high adherence (20.8%), (3) progression to low adherence (10.5%), and (4) persistent low adherence (56.7%). Those who were initiated on ICS/LABA were less likely to have persistent low adherence (unadjusted odds ratio [OR], 0.44; 95% CI, 0.29-0.67) compared with those initiated on ICS monotherapy when “persistent high adherence” was used as the reference group. The relationship remained significant in adjusted regressions (adjusted OR, 0.38; 95% CI, 0.24-0.59).
Conclusions: Real-world evidence suggests that using ICS/LABA for IMT may decrease the likelihood of persistent low adherence over time among older adults with ACO compared with ICS monotherapy.
Am J Manag Care. 2021;27(11):463-470. https://doi.org/10.37765/ajmc.2021.88773
A total of 57% of older Medicare beneficiaries with coexisting asthma and chronic obstructive pulmonary disease (COPD), known as asthma-COPD overlap (ACO), had persistent low adherence to initial maintenance therapy (IMT). The adjusted odds of persistent low adherence to IMT were 62% less among those taking fixed-dose combinations of inhaled corticosteroids and long-acting β agonists (ICS/LABAs) compared with inhaled corticosteroid (ICS) monotherapy.
Many older adults have coexisting asthma and chronic obstructive pulmonary disease (COPD), known as asthma-COPD overlap (ACO).1 Treatment for ACO requires treating both the underlying asthma and COPD by using maintenance therapy with inhaled medications.2 In 2017, the joint guidelines of the Global Initiative for Asthma (GINA) and Global Initiative for Chronic Obstructive Lung Disease recommended an inhaled corticosteroid (ICS) with or without a long-acting β agonist (LABA) for initial maintenance therapy (IMT) in individuals with ACO.2 Another important supporting reason for considering ICS/LABA as the preferable IMT option for ACO relates to the recent changes in the GINA guideline for asthma. Since 2019, a fixed-dose combination of ICS and formoterol, a LABA compound, is recommended as the first-line treatment for asthma.3 By considering the similarity of treatment approaches of asthma and ACO, it is possible to extend this recommendation for ACO as well.
Compared with ICS monotherapy, the use of ICS/LABA for IMT of ACO has some advantages. First, ICS/LABA can reduce both the inflammatory nature of asthma and the obstructive nature of COPD.4 Second, findings of prior studies indicate that using ICS/LABA may improve medication adherence compared with using ICS. Although the association of using ICS/LABA and adherence to IMT has not been directly studied among patients with ACO, these findings have been noted among patients with asthma.5-9 One possible reason is that the use of LABA in fixed-dose combination with ICS has demonstrated faster onset of improvement compared with ICS monotherapy, which may increase the medication value for the patient.10,11 Finally, a review article suggested that ICS/LABA has the potential to reduce the risk of myocardial infarction, hospitalization, and death among patients with ACO compared with ICS monotherapy.12
To date, no study has evaluated adherence to IMT or the effect of IMT type on medication adherence among older adults with ACO. However, this is a crucial topic to better understand because older adults are the most vulnerable group of individuals to experience poor adherence and subsequent negative outcomes. Older adults (> 70 years) often have a higher prevalence of poor medication adherence compared with younger adults (≤ 50 years) due to regimen complexity, polypharmacy, prescription costs, and multiple comorbidities.13,14 For example, results of prior studies have suggested that 70% of patients with COPD15 and nearly 50% of adults with asthma16 have poor adherence to IMT, suggesting that such patterns may also be present in older adults with ACO. Poor medication adherence can amplify the negative health effects of ACO in older adults. Among patients with asthma or COPD, poor adherence to maintenance therapy is associated with increased risk of adverse events such as hospitalization, disease burden, and mortality.17-19 Therefore, older individuals with ACO may also experience these negative consequences, perhaps to an even greater degree.
Therefore, the objectives of this study were to identify adherence trajectories of IMT and to assess the impact of IMT type on adherence trajectories among older adults with ACO by using group-based trajectory modeling (GBTM). The GBTM method has been previously used for studying trajectories of medication adherence to IMT among patients with asthma20; this study is the first application of the GBTM approach for examining individuals with ACO.
This study used health insurance claims from a 10% sample of Optum’s Clinformatics Data Mart from January 1, 2007, through June 30, 2017. This database contains information on prescription claims (National Drug Codes, generic drug names, prescription fill dates, days’ supply, and costs of medications), inpatient and outpatient medical claims (International Classification of Diseases, Ninth Revision [ICD-9] and Tenth Revision [ICD-10] diagnoses, dates of services, and costs of services), and eligibility information (sex, date of birth, plan type, and insurance type).
We used a longitudinal retrospective cohort design with a 24-month observation period (12-month baseline and 12-month follow-up periods). We anchored the baseline period and the follow-up period to the index date (ie, first observed ICS or ICS/LABA fill date between October 1, 2008, and September 30, 2016). Baseline consisted of 12 months before the index date. The baseline period was used to identify ACO, clinical characteristics, and exclusion of those with any maintenance therapy history. In the 12-month follow-up period, we measured medication adherence every month.
Study Cohort: Older Adults With ACO
Only individuals with ACO diagnoses were included in this study. The first step involved the identification of patients with either asthma or COPD during the baseline period. We identified asthma or COPD by using ICD-9, Clinical Modification or ICD-10, Clinical Modification codes (eAppendix A [eAppendices available at ajmc.com]). These diagnosis codes were derived from published research.21,22 Individuals were required to have at least 1 inpatient visit or 2 outpatient visits (at least 14 days apart) with either asthma or COPD codes. Individuals with both asthma and COPD diagnoses during the baseline were identified as individuals with ACO.
The study cohort included only older (≥ 65 years) Medicare Advantage beneficiaries who initiated maintenance therapy with ICS monotherapy or ICS/LABA therapy from October 1, 2008, to September 30, 2016. These patients were required to have continuous enrollment in both the baseline and follow-up periods. We excluded individuals who used any maintenance therapy (ie, ICS, LABA, long-acting muscarinic antagonist [LAMA], and their combination forms) during the 12-month preindex period (eAppendix B).
Dependent variables: medication adherence and medication adherence trajectory categories derived from GBTM. Based on the days supplied, we measured adherence to ICS or ICS/LABA for every 30 days during the follow-up period. We created binary variables (yes/no) for each month after the index date by using the days supplied variable.20 For example, a person would have a “yes” value for adherence in the months of X and X + 1 if the number of days supplied was 60 days during month X. These 12 dichotomized monthly indicators were used in GBTM to define trajectories of adherence. These trajectories were used as the dependent variable to analyze the association of IMT type to medication adherence.
Key independent variable: type of IMT (ICS and ICS/LABA). Two usual treatment options for IMT were ICS monotherapy and fixed-dose combination of ICS/LABA. National Drug Codes were used to identify these medications. To ensure that individuals used ICS or ICS/LABA as IMT, we defined a look-back period of 365 days, and only individuals who did not have any ICS, ICS/LABA, LABA, or LAMA use were considered as initiating maintenance therapy.
Other independent variables. Selection of covariates was guided by the Anderson health behavior model.23 Per the framework, adherence to IMT may be affected by (1) predisposing variables (sex [male or female]; age); (2) enabling factors (insurance plan [health maintenance organization (HMO) or non-HMO]; annual out-of-pocket prescription costs); (3) need factors (dementia [yes/no]; depression [yes/no]; anxiety [yes/no]; diabetes [yes/no]; heart diseases [the presence of at least 1 of the following: chronic heart failure, coronary artery disease, or arrhythmia] [yes/no]; hypertension [yes/no]; the number of other chronic conditions [cancer, arthritis, hyperlipidemia, hepatitis, HIV, osteoporosis, chronic kidney disease, stroke]; the number of short-acting β agonist [SABA] canisters dispensed during the baseline; the number of courses [the number of separate prescriptions issued] of oral corticosteroids during the baseline; the number of courses of antibiotics during the baseline; polypharmacy [concomitant use of 5 or more classes of medications within a 90-day period before the index date]); (4) personal health practices (obesity diagnosis [yes/no]; tobacco use diagnosis [yes/no]); and (5) external factors (region [Northeast, Midwest, South, or West]).
Accounting for selection bias of treatment. To control for observed selection bias, we derived inverse probability of treatment weights (IPTWs). IPTWs were estimated from a logistic regression in which IMT type (ICS vs ICS/LABA) was the dependent variable. The covariates included sex; age; being enrolled in an HMO plan; out-of-pocket prescription costs; presence of diabetes, anxiety, depression, or heart diseases; number of SABA canisters; tobacco use diagnosis; the number of courses of antibiotics; and region of residency. IPTWs were then used in the GBTM of medication adherence. Once the medication adherence patterns were identified, we compared the unadjusted and adjusted associations of IMT type with the trajectory groups using the IPTW-adjusted multinomial logistic regressions.
Identification of medication adherence trajectory groups. We identified clusters of patients with similar adherence patterns using the GBTM method. GBTM uses the maximum likelihood estimation to identify changes in medication adherence patterns over time.24,25 GBTM uses the data to empirically group individuals. Under this method, patterns of adherence are identified based on linear and nonlinear specifications and the probabilities of each individual belonging to these trajectories are calculated. Individuals are assigned to the group for which they have the highest probability. We used different specifications to identify these groups by using either cubic, quadratic, or quartic terms.20,24
Evaluating medication adherence by using generalized estimating equations.In addition to GBTM, we also used unadjusted and adjusted generalized estimating equations (GEEs) to evaluate the association of IMT type to medication adherence over time. We used population-averaged GEEs for repeated measures. Results from the GEE analyses are summarized in eAppendix C. All data management and analyses were conducted with SAS 9.4 (SAS Institute Inc), and Stata 14 (StataCorp LLC) was used for GBTM and GEE modeling. This study was approved for exemption by the West Virginia University Institutional Review Board.
Sample Baseline Characteristics
In our study cohort of older adults with ACO who were initiated on ICS or ICS/LABA as IMT (N = 1554), the majority were women (66.6%); the mean (SD) age was 75.2 (6.1) years. The majority of individuals were enrolled in an HMO plan (64.9%). The baseline characteristics of our study cohort are provided in eAppendix D. A majority (73.2%) used ICS/LABA and 26.8% used ICS for IMT.
IPTW-Adjusted Baseline Characteristics by IMT Type
The unweighted numbers and IPTW-adjusted weighted percentages of baseline characteristics by IMT type are displayed in Table 1, in which it can be seen that no statistically significant differences in baseline characteristics emerged between the 2 groups after IPTW adjustments.
Identification of Medication Adherence Trajectories With GBTM
We initially specified 8 groups with quadratic and higher-order polynomial specifications. After a preliminary examination of the results, we finalized trajectories to 4 groups based on the following criteria: (1) the Bayesian information criterion; (2) significant P values (< .05) for the highest-order polynomial parameter; (3) at least 5% of the population assigned to each trajectory group; (4) no polynomial overfitting; (5) narrow CIs; and (6) clinically meaningful grouping.26-29 The final trajectory model passed all Nagin’s diagnostic criteria of the “average of posterior probabilities greater than 0.7” and the “odds of correct classification greater than 0.5” for all groups.27,29
As shown in the Figure, we identified 4 different adherence trajectories. These trajectories can be described as: (1) persistent low adherence (56.7%), (2) persistent high adherence (12.0%), (3) progression to low adherence (10.5%), and (4) progression to high adherence (20.8%).
Unadjusted and Adjusted Associations of IMT Type With Adherence Trajectory Groups
The 4 adherence trajectories were significantly different in terms of IMT type (Table 2), with a higher percentage of individuals who used ICS/LABA having persistent high adherence compared with those prescribed ICS monotherapy (82.9% vs 68.1%). Unadjusted odds ratios (UORs), adjusted odds ratios (AORs), and 95% CIs from the multinomial logistic regressions on medication adherence categories are summarized in Table 3. In the unadjusted multinomial logistic regression models, those who were initiated on ICS/LABA were 56% less likely to have persistent low adherence (UOR, 0.44; 95% CI, 0.29-0.67) compared with those with ICS monotherapy when “persistent high adherence” was used as the reference group for the dependent variable. The relationship remained significant in adjusted regressions that controlled for only predisposing factors (AOR, 0.44; 95% CI, 0.29-0.66) and remained significant in all subsequent models. In the fully adjusted model, those with ICS/LABA as IMT were less likely to have persistent low adherence (AOR, 0.37; 95% CI, 0.24-0.59).
This study is the first to use a GBTM approach to report longitudinal patterns of adherence to IMT among older adults with ACO who sought care in real-world practices and who were initiated on ICS or ICS/LABA for maintenance therapy. In our study cohort of older adults with ACO, ICS/LABA use was very common, with 3 in 4 older adults using combination therapy. In a study conducted on adults (≥ 18 years) using 2008 and 2011 Truven Health Analytics MarketScan Commercial and Medicare Supplemental databases, approximately 75% of adults with ACO and on maintenance therapy were prescribed ICS/LABA.22 The differences in the prevalence rate of ICS/LABA can be due to differences in study population, time period, and insurance coverage.
We identified 4 distinct patterns of medication adherence: (1) persistent low adherence, (2) persistent high adherence, (3) progression to low adherence, and (4) progression to high adherence. Overall, the majority of older adults (57%) had persistent poor adherence and only 11% had persistent high adherence. As there are no published studies on medication adherence trajectories over time among older adults with ACO, we compared our results with those of studies that focused on adults with asthma or COPD. A recent study conducted in Australia reported that the “persistent low adherence” trajectory to IMT was observed among 58% of individuals aged between 12 and 45 years.20 Studies that use proportion of days covered (PDC) for measuring medication adherence also reported the high prevalence of medication nonadherence (< 80% PDC) among patients with asthma or COPD. For example, a systematic review of 39 clinical trials reported that only 47% to 57% of individuals with asthma were adherent to maintenance therapy.16 Moreover, in a retrospective cohort study on Medicare beneficiaries with COPD, the average monthly adherence to IMT plateaued at 35% by the seventh month.30 Our study’s finding demonstrating that the majority of older adults with ACO had persistent low adherence is concerning. This finding suggests that there is significant room for improvement in adherence to achieve better disease management, improved patient safety, and reduced exposure to unnecessary treatment intensification.
The results of this study confirmed that the adherence to IMT varies over time. For example, one-third of patients changed their adherence behavior after 6 months of starting IMT. In this study, we are unable to find reasons for adherence changes after 6 months. Some possible reasons for this change might be step-down treatment, improvement of symptoms, and augmentation of medications. However, this pattern itself may indicate a heightened surveillance of adults who were initiated on IMT, and there may be a limited window of opportunity to deliver evidence-based interventions that improve adherence. Similar to asthma and COPD, some possible interventions to improve medication adherence among adults with ACO are motivational interviewing,31 coaching and patient education,32 shared decision-making,33 and simplification of regimen.34 Our study findings suggest that these interventions may need to be delivered at the right time to improve medication adherence.
Our study findings also suggest that the type of IMT may be an important modifiable factor for reducing the risk of consistent low adherence. In our study, older adults who were initiated on ICS/LABA were less likely to have persistent low adherence. Results from previous studies among patients with asthma also support this finding. A clinical trial conducted by Perrin et al reported that adherence to ICS/LABA was marginally higher compared with ICS monotherapy.35 Similarly, 4 observational studies among patients with asthma concluded that use of ICS/LABA is likely to improve adherence to IMT or maintenance therapy compared with ICS.5-9 As stated in the introduction, ICS/LABA confers many advantages. Although many patients perceive that inhalers are effective in relieving symptoms, this perceived efficacy of ICS/LABA may be higher. This higher perceived efficacy can be due to a faster improvement of symptoms by using ICS/LABA. A previous clinical trial among patients with asthma has reported that ICS/LABA resulted in a faster improvement in asthma symptoms compared with ICS monotherapy for IMT.36 Simplifying a medication regimen is optimal for improved adherence, but prescribers do not need to order 2 inhalers to achieve dual therapy; ICS/LABAs are available in combination as 1 inhaler. Future randomized clinical trials are needed to investigate the differences in improvement of symptoms between ICS/LABA and ICS for IMT of ACO. Our study findings also suggest that a therapy-related factor (type of IMT) may be an important modifiable factor in reducing the risk of consistent low adherence.
This study has some limitations. One limitation is the use of medication fills data for assessing adherence, which assumes that filled medications are taken and used properly by patients. This assumption can result in overestimating adherence. Because this study used claims data, it has the inherent limitations of this source of data, such as possible under- or overcoding of diagnoses and lack of lung function results. The latter may also affect ACO diagnosis based on asthma and COPD diagnosis codes. Moreover, claims data were used for identification of ACO cases, which may cause misclassification bias. Claims data may also lack clinical markers of disease severity and information on behavioral variables. Other factors that may affect adherence to IMT (eg, patient-physician communication, severity of symptoms, fear of adverse effects, family support) were not adjusted for, but these may change the impact of IMT type on adherence behavior. Lastly, initial prescription of ICS/LABA may be related to more severe airflow obstruction, and this group of patients may have higher severity in the baseline period compared with patients who are prescribed ICS monotherapy.
Among older adults with ACO seeking care in real-world settings, we identified a potential concern of high rates of nonadherence based on fill rates. Our study findings suggest that using ICS/LABA for IMT may reduce the risk of persistent low adherence over time among older adults with ACO. Future studies need to target older patients with consistently low adherence to maintenance therapy to explore drug-related and non–drug-related factors that are barriers to or facilitators of adherence over time.
Author Affiliations: Department of Pharmaceutical Systems and Policy (MN, TJL, ND, US) and Department of Clinical Pharmacy (MA), West Virginia University School of Pharmacy, Morgantown, WV; System College of Pharmacy, University of North Texas (SSM), Fort Worth, TX.
Source of Funding: None.
Author Disclosures: Dr Sambamoorthi’s work was partially supported by grant NIMHD-5U54MD006882-10. The remaining authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.
Authorship Information: Concept and design (MN, TJL, ND, SSM, US); acquisition of data (US); analysis and interpretation of data (MN, MA, US); drafting of the manuscript (MN, TJL, MA, ND, US); critical revision of the manuscript for important intellectual content (MN, TJL, MA, ND, SSM, US); statistical analysis (MN, US); provision of patients or study materials (MN); and supervision (ND, SSM, US).
Address Correspondence to: Mona Nili, PhD, PharmD, MBA, Department of Pharmaceutical Systems and Policy, West Virginia University School of Pharmacy, Robert C. Byrd Health Sciences Center North, PO Box 9510, Morgantown, WV 26506-9510. Email: firstname.lastname@example.org.
1. Kumbhare S, Pleasants R, Ohar JA, Strange C. Characteristics and prevalence of asthma/chronic obstructive pulmonary disease overlap in the United States. Ann Am Thorac Soc. 2016;13(6):803-810. doi:10.1513/AnnalsATS.201508-554OC
2. Diagnosis and initial treatment of asthma, COPD, and asthma-COPD overlap. Global Initiative for Asthma. April 2017. Accessed October 4, 2020. https://ginasthma.org/wp-content/uploads/2019/11/GINA-GOLD-2017-overlap-pocket-guide-wms-2017-ACO.pdf
3. Reddel HK, FitzGerald JM, Bateman ED, et al. GINA 2019: a fundamental change in asthma management: treatment of asthma with short-acting bronchodilators alone is no longer recommended for adults and adolescents. Eur Respir J. 2019;53(6):1901046. doi:10.1183/13993003.01046-2019
4. Ishiura Y, Fujimura M, Shiba Y, Ohkura N, Hara J, Kasahara K. A comparison of the efficacy of once-daily fluticasone furoate/vilanterole with twice-daily fluticasone propionate/salmeterol in asthma-COPD overlap syndrome. Pulm Pharmacol Ther. 2015;35:28-33. doi:10.1016/J.PUPT.2015.10.005
5. Axelsson M, Emilsson M, Brink E, Lundgren J, Torén K, Lötvall J. Personality, adherence, asthma control and health-related quality of life in young adult asthmatics. Respir Med. 2009;103(7):1033-1040. doi:10.1016/j.rmed.2009.01.013
6. Wu AC, Butler MG, Li L, et al. Primary adherence to controller medications for asthma is poor. Ann Am Thorac Soc. 2015;12(2):161-166. doi:10.1513/AnnalsATS.201410-459OC
7. Marceau C, Lemière C, Berbiche D, Perreault S, Blais L. Persistence, adherence, and effectiveness of combination therapy among adult patients with asthma. J Allergy Clin Immunol. 2006;118(3):574-581. doi:10.1016/j.jaci.2006.06.034
8. Stoloff SW, Stempel DA, Meyer J, Stanford RH, Carranza Rosenzweig JR. Improved refill persistence with fluticasone propionate and salmeterol in a single inhaler compared with other controller therapies. J Allergy Clin Immunol. 2004;113(2):245-251. doi:10.1016/j.jaci.2003.10.011
9. Stempel DA, Stoloff SW, Carranza Rosenzweig JR, Stanford RH, Ryskina KL, Legorreta AP. Adherence to asthma controller medication regimens. Respir Med. 2005;99(10):1263-1267. doi:10.1016/j.rmed.2005.03.002
10. Ringdal N, Eliraz A, Pruzinec P, et al; International Study Group. The salmeterol/fluticasone combination is more effective than fluticasone plus oral montelukast in asthma. Respir Med. 2003;97(3):234-241. doi:10.1053/rmed.2003.1436
11. O’Byrne PM, Barnes PJ, Rodriguez-Roisin R, et al. Low dose inhaled budesonide and formoterol in mild persistent asthma: the OPTIMA randomized trial. Am J Respir Crit Care Med. 2001;164(8, pt 1):1392-1397. doi:10.1164/ajrccm.164.8.2104102
12. Amegadzie JE, Gorgui J, Acheampong L, Gamble JM, Farrell J, Gao Z. Comparative safety and effectiveness of inhaled bronchodilators and corticosteroids for treating asthma–COPD overlap: a systematic review and meta-analysis. J Asthma. 2021;58(3):344-359. doi:10.1080/02770903.2019.1687716
13. Corsonello A, Pedone C, Lattanzio F, et al. Regimen complexity and medication nonadherence in elderly patients. Ther Clin Risk Manag. 2009;5(1):209-216. doi:10.2147/tcrm.s4870
14. Walsh CA, Cahir C, Tecklenborg S, Byrne C, Culbertson MA, Bennett KE. The association between medication non-adherence and adverse health outcomes in ageing populations: a systematic review and meta-analysis. Br J Clin Pharmacol. 2019;85(11):2464-2478. doi:10.1111/bcp.14075
15. Jung E, Pickard AS, Salmon JW, Bartle B, Lee TA. Medication adherence and persistence in the last year of life in COPD patients. Respir Med. 2009;103(4):525-534. doi:10.1016/j.rmed.2008.11.004
16. Normansell R, Kew KM, Stovold E. Interventions to improve adherence to inhaled steroids for asthma. Cochrane Database Syst Rev. 2017;4(4):CD012226. doi:10.1002/14651858.CD012226.pub2
17. van Boven JFM, Chavannes NH, van der Molen T, Rutten-van Mölken MPMH, Postma MJ, Vegter S. Clinical and economic impact of non-adherence in COPD: a systematic review. Respir Med. 2014;108(1):103-113. doi:10.1016/j.rmed.2013.08.044
18. Williams LK, Pladevall M, Xi H, et al. Relationship between adherence to inhaled corticosteroids and poor outcomes among adults with asthma. J Allergy Clin Immunol. 2004;114(6):1288-1293. doi:10.1016/j.jaci.2004.09.028
19. Suissa S, Ernst P, Benayoun S, Baltzan M, Cai B. Low-dose inhaled corticosteroids and the prevention of death from asthma. N Engl J Med. 2000;343(5):332-336. doi:10.1056/NEJM200008033430504
20. van Boven JFM, Koponen M, Lalic S, et al. Trajectory analyses of adherence patterns in a real-life moderate to severe asthma population. J Allergy Clin Immunol Pract. 2020;8(6):1961-1969.e6. doi:10.1016/j.jaip.2019.12.002
21. Van Dyke MK, Hinds D, Dickinson H, Sansbury L. Evaluation of ICD-9 to ICD-10 conversion on estimates of asthma and COPD in a US commercial claims database. In: American Thoracic Society International Conference Abstracts. American Thoracic Society; 2018:A4868. https://www.atsjournals.org/doi/abs/10.1164/ajrccm-conference.2018.197.1_MeetingAbstracts.A4868
22. Wurst KE, St Laurent S, Hinds D, Davis KJ. Disease burden of patients with asthma/COPD overlap in a US claims database: impact of ICD-9 coding-based definitions. COPD J Chronic Obstr Pulm Dis. 2017;14(2):200-209. doi:10.1080/15412555.2016.1257598
23. Andersen RM. Revisiting the behavioral model and access to medical care: does it matter? J Health Soc Behav. 1995;36(1):1-10. doi:10.2307/2137284
24. Hickson RP, Annis IE, Killeya-Jones LA, Fang G. Opening the black box of the group-based trajectory modeling process to analyze medication adherence patterns: an example using real-world statin adherence data. Pharmacoepidemiol Drug Saf. 2020;29(3):357-362. doi:10.1002/pds.4917
25. Hoch H, Pickett K, Brinton J, Szefler SJ. Evaluating adherence data in the clinical setting: what tools should we use to describe behavior? In: American Thoracic Society International Conference Abstracts. American Thoracic Society; 2019:A6735. doi:10.1164/ajrccm-conference.2019.199.1_meetingabstracts.a6735
26. Nagin DS. Group-Based Modeling of Development. Harvard University Press; 2005.
27. Nagin DS. Analyzing developmental trajectories: a semiparametric, group-based approach. Psychol Methods. 1999;4(2):139-157. doi:10.1037/1082-989X.4.2.139
28. Jones BL, Nagin DS. Proc TRAJ: a SAS procedure for group-based modeling of longitudinal data. Paper presented at: 135th American Public Health Association Annual Meeting and Exposition; November 3-7, 2007; Washington, DC. https://www.researchgate.net/publication/266822262_Proc_TRAJ_A_SAS_Procedure_for_Group-Based_Modeling_of_Longitudinal_Data
29. Nagin DS, Odgers CL. Group-based trajectory modeling in clinical research. Annu Rev Clin Psychol. 2010;6(1):109-138. doi:10.1146/annurev.clinpsy.121208.131413
30. Albrecht JS, Park Y, Hur P, et al. Adherence to maintenance medications among older adults with chronic obstructive pulmonary disease. the role of depression. Ann Am Thorac Soc. 2016;13(9):1497-1504. doi:10.1513/AnnalsATS.201602-136OC
31. Minassian A, Doran NM. Integrating motivational interviewing into pulmonary healthcare. In: Enhancing Patient Engagement in Pulmonary Healthcare. Springer; 2020:79-103.
32. Benzo R, Vickers K, Novotny PJ, et al. Health coaching and chronic obstructive pulmonary disease rehospitalization. a randomized study. Am J Respir Crit Care Med. 2016;194(6):672-680. doi:10.1164/rccm.201512-2503OC
33. Granados-Santiago M, Valenza MC, López-López L, Prados-Román E, Rodríguez-Torres J, Cabrera-Martos I. Shared decision-making and patient engagement program during acute exacerbation of COPD hospitalization: a randomized control trial. Patient Educ Couns. 2020;103(4):702-708. doi:10.1016/j.pec.2019.12.004
34. Hassan M, Davies SE, Trethewey SP, Mansur AH. Prevalence and predictors of adherence to controller therapy in adult patients with severe/difficult-to-treat asthma: a systematic review and meta-analysis. J Asthma. 2020;57(12):1379-1388. doi:10.1080/02770903.2019.1645169
35. Perrin K, Williams M, Wijesinghe M, James K, Weatherall M, Beasley R. Randomized controlled trial of adherence with single or combination inhaled corticosteroid/long-acting β-agonist inhaler therapy in asthma. J Allergy Clin Immunol. 2010;126(3):505-510. doi:10.1016/j.jaci.2010.06.033
36. Matsunaga K, Kawabata H, Hirano T, Sugiura H, Minakata Y, Ichinose M. Difference in time-course of improvement in asthma control measures between budesonide and budesonide/formoterol. Pulm Pharmacol Ther. 2013;26(2):189-194. doi:10.1016/j.pupt.2012.10.006