ABSTRACT
Background
Medication adherence is essential in managing cystic fibrosis (CF). The role of socioeconomic factors for medication adherence in people with CF is poorly understood, and their differential impact across the life course is underexplored. This study investigates associations between measures of socioeconomic status (SES)—educational attainment, household income, and health insurance type—and adherence to CF medications across age groups.
Methods
We conducted a cross‐sectional analysis of data collected during the validation of the Daily Care Check‐In, a measure of adherence barriers in people with CF. Adherence was measured as a composite medication possession ratio (cMPR) averaged across five CF‐specific medications, with data collected from pharmacy records. Sociodemographic and clinical data were collected through self‐report and medical record review.
Results
A total of 405 participants completed the study, with an overall cMPR of 45.6%, lowest (38.5%) among young adults (aged 18−26 years) and highest (53.0%) among adolescents (aged 13−17 years). Lower household income and lack of college degree were associated with lower cMPR, more interference from adherence barriers, and decreased self‐efficacy, as well as with increased depressive and anxiety symptoms. Similar associations, but less consistent, were observed for public health insurance. When stratified by age, associations between SES measures and adherence were most evident in adolescents, followed by adults, but absent in young adults, bringing into focus challenges with measuring SES in the 18−25 years age group.
Conclusion
Lower SES is associated with worse medication adherence, more interference from adherence barriers, and lower self‐efficacy. Associations vary by SES measure and age group, calling for a nuanced approach to adherence interventions in this population.
Keywords: adherence, cystic fibrosis, socioeconomic status
1. Introduction
Successful management of cystic fibrosis (CF) necessitates complex and time‐consuming daily treatments to counter the cumulative effects of a progressive disease that affects multiple organ systems [1]. Poor adherence to CF therapies has been associated with lower lung function, increased number of pulmonary exacerbations requiring intravenous antibiotics, longer hospital stays, and increased morbidity and mortality [2, 3, 4, 5, 6, 7]. Despite its importance, treatment adherence in CF is suboptimal, reported to range between 40% and 70% depending on the specific treatment component [8, 9, 10, 11, 12, 13], with a marked decrease during young adulthood that improves slightly in later adulthood [11, 12, 14, 15].
Treatment adherence is influenced by multiple factors: individual (self‐efficacy, perceptions of treatment burden), interpersonal (family functioning, social support), institutional (healthcare system), community (access to services), and societal (health policies and laws) [16]. Often, adherence to medical recommendations is viewed as a personal choice, without fully considering its complex nature [12, 13]. As with other health‐related behaviors, treatment adherence is impacted by the social context, which influences what behavioral choices are available to an individual and acceptable in their social environment [17, 18]. One's socioeconomic circumstances exert a particularly powerful influence on health behaviors. For example, cost burden and lack of health insurance are widely recognized barriers to treatment adherence [19, 20, 21, 22, 23, 24, 25, 26], with the number of additional social risk factors having a compound effect on an individual's ability to follow medical recommendations [27].
Socioeconomic status (SES) refers to one's access to material resources, social and professional networks, and various life opportunities (e.g., educational attainment and career, housing, and healthcare options) [28]. While SES is a known correlate of adherence, no single indicator captures its multidimensionality, and different SES indicators (education, income, health insurance) have been linked to different proximate drivers of health‐related behaviors [29, 30]. Further, SES measures may perform differently at various life stages [31]. Additionally, income at similar educational levels varies significantly across racial/ethnic, gender, and age groups [32].
Across ages, lower SES has been associated with worse CF outcomes, but its impact on CF‐related health behaviors is poorly understood. Maternal education, household income, and household composition have been associated with adherence to airway clearance therapy in children with CF [9]. Educational attainment has also been associated with adherence to airway clearance therapy in adults with CF [33]. However, few studies have examined associations between specific markers of SES and adherence to the CF medical regimen using a single cohort spanning age groups. Furthermore, perceptions, experiences, and attitudes that are important for medication adherence, such as perceptions of treatment burden [34, 35, 36], also may differ by SES. Similarly, self‐efficacy, an individual's confidence in their capacity to undertake tasks or achieve goals, is central to disease self‐management [37] but may be undermined by financial stress [38]. Yet, the role of SES for adherence‐related beliefs among people with CF of varying ages remains understudied.
Given the importance of treatment adherence in CF, it is essential to understand its multifactorial drivers and antecedents, including the complex role of SES [39, 40]. Such understanding is critical for personalized interventions to help individuals with CF sustain complex daily care. In this study, we evaluated the association of SES indicators (education, income, and health insurance type) with medication adherence and adherence‐related beliefs (perceptions of barriers, treatment burden, and self‐efficacy), overall and within clinically meaningful age groups (adolescence, early adulthood, and adulthood).
2. Methods
2.1. Participants and Procedures
We performed a secondary analysis of data from a cross‐sectional observational study that validated a new measure of adherence barriers, the Daily Care Check‐In (DCC) [41]. The study was conducted between March 2017 and 2018 at 13 pediatric and nine adult US CF care programs participating in the CF Foundation Success with Therapies Research Consortium (STRC) [42]. Individuals with a CF diagnosis aged 13 years or older were eligible to participate if they were comfortable reading or speaking English and had been prescribed at least one of the following pulmonary medications for a minimum of 12 months: azithromycin, dornase alfa, hypertonic saline, inhaled antibiotics (tobramycin or aztreonam), or a cystic fibrosis transmembrane conductance regulator (CFTR) modulator (ivacaftor or lumacaftor/ivacaftor). Research coordinators administered the DCC survey along with depressive and anxiety symptoms and a demographic questionnaire during clinic visits after obtaining informed consent. Health data were abstracted from medical records. Adherence data (pharmacy refill records from the previous 12 months) were obtained from all pharmacies on record. Participants were compensated for completing the surveys. The study was approved by the Institutional Review Board at Boston Children's Hospital for all participating sites (protocol STRC‐106‐16‐01).
2.2. Measures
2.2.1. SES
SES was assessed with three common self‐reported measures—educational attainment, annual household income, and health insurance type—which are the most often used measures of SES in CF studies [43, 44, 45, 46]. Educational attainment was queried in seven categories (some high school, high school diploma or GED, vocational or trade school, associate's degree, some college, Bachelor's degree, graduate or professional degree) and dichotomized as having a college degree (yes/no), which is common in the general [47] and CF‐specific literature [43, 44, 45, 46] and supported by evidence that individuals with some college are more similar to those with a high‐school education than to counterparts with a college degree [48]. For adolescents < 18 years old, the educational attainment of the primary caregiver was obtained. Annual household income was queried in 11 categories and dichotomized at $80,000/year based on the sample distribution. Health insurance was queried in seven categories and dichotomized as any private or military versus only public, self‐pay, or no insurance, as done previously [41, 49]. All three SES variables were dichotomized due to the limited sample size, and cutoffs were based on sample distribution.
2.2.2. Medication Adherence
Medication adherence was derived from dispensing records for the previously listed CF medications obtained from the pharmacies, self‐reported by participants. A medication possession ratio (MPR) (i.e., the number of days of dispensed drug divided by the number of days for which the drug was prescribed) was calculated for each prescribed medication during the 12‐month period. Values were truncated to 100%. The MPR for individual medications were averaged to create a composite MPR (cMPR) as done previously [49].
2.2.3. Adherence‐Related Beliefs
Adherence barriers were measured by the DCC. This validated measure includes an Occurrence scale that queries specific barriers such as physical discomfort, insurance issues, and financial concerns experienced in the past 6 months (using a yes/no response) and sums the “yes” responses (score range 0−18), and an Interference scale, which measures the extent to which an endorsed barrier interferes with completing therapies (5‐point Likert scale, “never” to “always,” score range 0−90) [41]. Perceived treatment burden was assessed with the Treatment Burden scale from the teen/adult version of the CF Questionnaire‐Revised (CFQ‐R) [50], a widely used and validated health‐related quality of life instrument for CF, each measured on a 0−100 scale, with higher scores indicating greater perceived treatment burden. Self‐efficacy was assessed using the Self‐Efficacy subscale (19 items) of the CF Medication Beliefs Questionnaire (CF‐MBQ), a measure with established psychometrics [51]. The subscale assesses individuals' confidence in their ability to complete their CF treatment regimen in various conditions (“when you are tired or sleepy,” or “when nobody is around to notice”).
2.2.4. Health Characteristics
Lung function was operationalized as percent predicted forced expiratory volume in 1 s (ppFEV1) calculated with the 2012 Global Lung Function Initiative (GLI) equations [52], which were used by CF care programs at the time of our data collection. Body mass index (BMI) was obtained as percentiles (BMIp) using CDC charts for adolescents and BMI (kg/m2) for adults, with underweight status defined as BMIp < 10%ile for adolescents and BMI < 18.5 kg/m2 for adults, and overweight or obese status defined as BMIp ≥ 85%ile for adolescents and ≥ 25 kg/m2 for adults [53]. ppFEV1 and BMI values were recorded from the clinic visit closest to enrollment. Intravenous (IV) antibiotics use in the past 12 months (yes/no) was obtained from medical record review. Depressive symptoms were measured by the Patient Health Questionnaire‐8 (PHQ‐8) scale [54]. Anxiety symptoms were measured by the General Anxiety Disorder‐7 (GAD‐7) scale [55]. PHQ‐8 and GAD‐7 scales were used both continuously and dichotomously, with a cut point of ≥ 5 to capture a threshold for minimal mood symptoms.
2.2.5. Demographic Characteristics
Age was grouped into three categories (13−17 years, adolescents; 18−26 years, young adults; and > 26 years, adults) to capture the transitional period to financial independence and independent health insurance coverage as previously described [56]. Biological sex (male vs. female), ethnicity (Hispanic vs. non‐Hispanic), and race (White, African American, Asian or Pacific Islander, and Other or declined to answer) were self‐reported.
2.3. Statistical Analysis
Participant characteristics were summarized using frequency counts, means, and standard deviations for the full sample and the three age groups. Differences in medication adherence, adherence‐related beliefs, and health outcomes were assessed across SES indicators (educational attainment, annual household income, and health insurance type). Bivariate analyses were conducted using Chi‐Square, one‐way ANOVAs, and Mann−Whitney U tests. Less than 20% of all data were missing, and missing observations were omitted from the analyses. Statistical significance was tested at α = 0.05. Given the exploratory nature of this study, no corrections for multiple comparisons were made. Analyses were conducted using Statistical Analysis System (SAS) version 9.4, SAS Institute Inc., Cary, NC.
3. Results
A total of 405 participants completed the study. Participant characteristics are reported in Table 1. The mean age was 24.8 years (SD = 11.2), with 33.6% (n = 136) aged 13−17 years (adolescents), 31.1% (n = 126) aged 18−26 years (young adults), and 35.3% (n = 143) aged > 26 years (adults). Approximately half of the participants were female (n = 204, 50.4%), and the majority were non‐Hispanic White (n = 373, 92.1%). Less than half of the sample had a college degree (n = 156, 38.7%) and income > $80,000 (n = 159, 41.1%) but more than two‐thirds had any private insurance (n = 271, 67.8%). More than one‐third of the sample had at least mild symptoms of depression (PHQ‐8 ≥ 5, n = 131, 38.8%) and/or anxiety (GAD‐7 ≥ 5, n = 128, 37.1%).
Table 1.
Participant characteristics: overall and by age group (N = 405).
| Demographic characteristics | Overall (N = 405) | 13−17 years old (n = 136, 33.6%) | 18−26 years old (n = 126, 31.1%) | > 26 years old (n = 143, 35.3%) | p |
|---|---|---|---|---|---|
| Age (years), mean (SD) | 24.8 (11.2) | 15.5 (1.4) | 21.1 (2.2) | 37.0 (10.3) | < 0.001 |
| Female sex, n (%) | 204 (50.4) | 74 (54.4) | 58 (46.0) | 72 (50.4) | 0.399 |
| Hispanic ethnicity, n (%) | 33 (8.2) | 10 (7.5) | 6 (4.8) | 17 (11.9) | 0.101 |
| Race, n (%) | |||||
| White | 373 (92.1) | 128 (94.1) | 113 (89.7) | 132 (92.3) | 0.392 |
| African American | 15 (3.7) | 2 (1.5) | 6 (4.8) | 7 (4.9) | |
| Asian or Pacific Islander | 3 (0.7) | 2 (1.5) | 1 (0.8) | 0 (0) | |
| Other or declined to answer | 14 (3.5) | 4 (2.9) | 6 (4.8) | 4 (2.8) | |
| Educational attainment, n (%)a | |||||
| No college degree | 247 (61.3) | 60 (44.8) | 114 (90.5) | 73 (51.1) | < 0.001 |
| College degree | 156 (38.7) | 74 (55.2) | 12 (9.5) | 70 (49.0) | |
| Annual household income, n (%) | |||||
| < $80,000 | 228 (58.9) | 58 (45.0) | 79 (64.8) | 91 (66.9) | < 0.001 |
| ≥ $80,000 | 159 (41.1) | 71 (55.0) | 43 (35.3) | 45 (33.1) | |
| Health insurance, n (%) | |||||
| Any private or military | 271 (67.8) | 92 (67.7) | 85 (69.1) | 94 (66.7) | 0.914 |
| Only public, self‐pay, or none | 129 (32.3) | 44 (32.4) | 38 (30.9) | 47 (33.3) | |
| Health characteristics | |||||
| Nutritional status,b n (%) | |||||
| Underweight | 33 (8.3) | 9 (6.6) | 17 (13.8) | 7 (5.0) | < 0.001 |
| Normal weight | 277 (69.3) | 113 (83.1) | 83 (67.5) | 81 (57.5) | |
| Overweight | 90 (22.5) | 14 (10.3) | 23 (18.7) | 53 (37.6) | |
| FEV1% predicted, mean (SD) | 74.5 (23.9) | 88.9 (17.7) | 75 (22.8) | 60.3 (21.5) | < 0.001 |
| FEV1% predicted, n (%) | |||||
| < 40% | 37 (9.1) | 1 (0.7) | 9 (7.1) | 27 (18.9) | < 0.001 |
| −69% | 133 (32.8) | 18 (13.2) | 48 (38.1) | 67 (46.9) | |
| ≥ 70% | 235 (58.0) | 117 (86.0) | 69 (54.8) | 49 (34.3) | |
| BMI (age ≥ 19, n = 188), mean (SD) | 23.5 (3.9) | — | 22.3 (3.5) | 24.1 (3.9) | 0.002 |
| BMI percentile (age < 19, n = 217), mean (SD) | 51.2 (27.0) | 55 (25.4) | — | — | — |
| Pancreatic insufficiency, n (%) | 375 (92.6) | 129 (94.9) | 118 (93.7) | 128 (89.5) | 0.202 |
| IV antibiotics course in the past year, n (%) | 209 (51.7) | 53 (39.0) | 69 (54.8) | 87 (61.3) | < 0.001 |
| PHQ‐8 (Depressive symptoms), mean (SD) | 4.5 (4.6) | 4.4 (5.2) | 4.6 (4.5) | 4.5 (4.2) | 0.925 |
| PHQ‐8 ≥ 5, n (%) | 131 (38.8) | 44 (38.3) | 39 (39.4) | 48 (38.7) | 0.986 |
| GAD‐7 (Anxiety symptoms), mean (SD) | 4.4 (4.9) | 4.6 (5.1) | 4.8 (5.2) | 4.0 (4.4) | 0.407 |
| GAD‐7 ≥ 5, n (%) | 128 (37.1) | 45 (38.8) | 42 (42.0) | 41 (31.8) | 0.255 |
| Adherence‐related characteristics | |||||
| Composite MPR (%), mean (SD) | 45.6 (24.7) | 53.0 (24.2) | 38.5 (24.6) | 44.6 (23.5) | < 0.001 |
| < 50%, n (%) | 223 (61.1) | 62 (49.6) | 78 (69.6) | 83 (64.8) | 0.004 |
| 50%−79%, n (%) | 100 (27.4) | 39 (31.2) | 27 (24.1) | 34 (26.6) | |
| ≥ 80%, n (%) | 42 (11.5) | 24 (19.2) | 7 (6.3) | 11 (8.6) | |
| Barriers occurrence, mean (SD) | 6.8 (4.2) | 5.7 (3.9) | 7.8 (4.4) | 7.1 (4.0) | < 0.001 |
| Barriers interference, mean (SD) | 18.5 (14.0) | 14.9 (12.8) | 22.2 (14.7) | 18.6 (13.6) | < 0.001 |
| Treatment burden, mean (SD) | 67.8 (11.7) | 69.0 (12.3) | 67.5 (11.6) | 67.0 (11.3) | 0.377 |
| Self‐efficacy, mean (SD) | 7.1 (2.1) | 7.1 (2.3) | 6.9 (2.0) | 7.4 (2.0) | 0.154 |
Note: p values calculated using chi‐square and Fisher's exact tests for categorical variables and one‐way ANOVAs for numerical variables. Bolded values indicate significance at α = 0.05. Percentages may not add to 100% due to rounding.
Missing data: Ethnicity ( < 18 n = 2, 1.5%; 18−26 n = 1, 0.8%), education ( < 18 n = 2, 1.5%), income ( < 18 n = 7, 5.2%; 18−26 n = 4, 3.2%; > 26 n = 7, 4.9%), health insurance (18−26 n = 3, 2.4%; > 26 n = 2, 1.4%), nutritional status (18−26 n = 3, 2.4%; > 26 n = 2, 1.4%), CFRD ( < 18 n = 3, 2.2%; 18−26 n = 2, 1.6%; > 26 n = 2, 1.4%), IV antibiotics in the past year ( > 26 n = 1, 0.7%), composite MPR ( < 18 n = 11, 8.1%; 18−26 n = 14, 11.1%; > 26 n = 15, 10.5%), depressive symptoms ( < 18 n = 21, 15.4%; 18−26 n = 27, 21.4%; > 26 n = 19, 13.3%), anxiety symptoms ( < 18 n = 20, 14.7%; 18−26 n = 26, 20.6%; > 26 n = 14, 9.8%), self‐efficacy and treatment burden ( < 18 n = 20, 14.7%; 18−26 n = 26, 20.6%; > 26 n = 16, 11.2%).
Abbreviations: FEV1 = forced expiratory volume in one second, GAD = generalized anxiety disorder, MPR = medication possession ratio, PHQ = patient health questionnaire.
For participants age < 18 years, caregiver educational attainment was used.
Underweight (BMI percentile < 10%, BMI < 18.5 kg/m2), normal weight (BMI percentile 10% to < 85%, BMI 18.5 to < 25 kg/m2), overweight (BMI percentile ≥ 85%, BMI ≥ 25 kg/m2).
The overall medication adherence was 45.6%, and only 38.9% (n = 142) had a cMPR ≥ 50% (Table 1). Medication adherence varied by age: young adults had the lowest average adherence (38.5%, SD = 24.6) compared to adults (44.6%, SD = 23.5) and adolescents, who had the highest adherence (53.0%, SD = 24.2). The same pattern was observed for reported barriers to adherence and the extent of their interference: young adults reported the highest number of barriers (7.8, SD = 4.39) and most interference (22.2, SD = 14.67) compared to adults (barriers 7.1, SD = 3.97; interference 18.6, SD = 13.58) and adolescents, who reported the fewest number of barriers (5.7, SD = 3.89) and lowest interference (14.9, SD = 12.79). Perceived self‐efficacy and treatment burden did not differ by age.
3.1. Associations With SES: Full Sample
Table 2 presents associations between SES measures (educational attainment, annual household income, and health insurance) and medication adherence, adherence beliefs, and health outcomes. Lack of a college degree was associated with lower medication adherence, greater interference from barriers, and lower self‐efficacy, as well as with worse health outcomes, including suboptimal nutritional status (either under‐ or overweight) and increased prevalence of depressive and anxiety symptoms. Participants with annual household income < $80,000 had lower medication adherence and reported more barriers to adherence, greater interference from barriers, and lower self‐efficacy compared to those with a household income ≥ $80,000, yet they reported lower treatment burden. They also had worse disease outcomes—lower ppFEV1%, higher prevalence of IV antibiotics in the past year, and suboptimal nutritional status (either under‐ or overweight)—and more depression and anxiety symptoms compared to counterparts with a household income ≥ $80,000. Participants with only public, self‐pay, or no health insurance reported significantly more interference from barriers and lower self‐efficacy than counterparts with any private or military health insurance. They also had lower ppFEV1%, higher use of IV antibiotics, worse nutritional status, and increased prevalence of depressive and anxiety symptoms compared to those with any private or military insurance.
Table 2.
Association of socioeconomic status (SES) measures with medication adherence, adherence‐related beliefs, CF health outcomes, and mental health.
| Measure | Educational attainmenta (n = 403) | Annual household income (n = 387) | Health insurance type (n = 400) | ||||||
|---|---|---|---|---|---|---|---|---|---|
| No college degree (n = 247) | College degree (n = 156) | p | < $80k (n = 228) | ≥ $80k (n = 159) | p | Only public, self‐pay, or none (n = 129) | Any private/military (n = 271) | p | |
| Medication adherence | |||||||||
| Composite MPR | 41.8 (23.9) | 52.1 (24.7) | < 0.001 | 40.9 (23.6) | 51.9 (25.2) | < 0.001 | 43.4 (21.7) | 46.6 (26.0) | 0.320 |
| Adherence‐related beliefs | |||||||||
| Barriers occurrence | 7.1 (4.3) | 6.3 (3.9) | 0.052 | 7.5 (4.2) | 6.0 (4.0) | < 0.001 | 7.4 (4.2) | 6.6 (4.2) | 0.054 |
| Barriers interference | 20.4 (14.9) | 15.4 (11.8) | 0.002 | 20.9 (14.2) | 15.5 (13.1) | < 0.001 | 21.2 (14.6) | 17.4 (13.5) | 0.007 |
| Treatment burden | 67.4 (11.3) | 68.3 (12.3) | 0.695 | 66.2 (11.2) | 69.8 (12.1) | 0.011 | 66.4 (11.2) | 68.4 (12.0) | 0.216 |
| Self‐efficacy | 6.8 (2.1) | 7.7 (2.0) | < 0.001 | 6.9 (2.2) | 7.5 (2.1) | 0.010 | 6.7 (2.2) | 7.4 (2.1) | 0.003 |
| Health outcomes | |||||||||
| FEV1% predicted | 73.7 (24.3) | 75.5 (23.2) | 0.395 | 71.1 (23.4) | 79.2 (24.1) | < 0.001 | 70.2 (24.9) | 76.3 (23.2) | 0.016 |
| IV antibiotics in the past year, n (%) | 134 (54.3) | 74 (47.7) | 0.204 | 131 (57.7) | 66 (41.5) | 0.002 | 78 (60.9) | 128 (47.2) | 0.011 |
| Nutritional status, n (%) | 0.039 | 0.002 | 0.021 | ||||||
| Underweight | 25 (10.3) | 7 (4.6) | 24 (10.7) | 8 (5.1) | 12)9.5) | ||||
| Normal weight | 159 (65.2) | 117 (76.0) | 140 (62.5) | 125 (79.1) | 77 (60.6) | 198 (73.9) | |||
| Overweight | 60 (24.6) | 30 (7.4) | 60 (26.8) | 25 (15.8) | 38 (29.9) | 49 (18.3) | |||
| PHQ‐8 (Depressive symptoms) | 5.1 (4.9) | 3.5 (3.9) | 0.003 | 4.8 (4.5) | 3.8 (4.6) | 0.003 | 5.4 (4.9) | 4.0 (4.4) | 0.004 |
| PHQ‐8 ≥ 5, n (%) | 89 (44.1) | 40 (29.9) | 0.009 | 85 (43.6) | 39 (30.0) | 0.014 | 54 (48.2) | 76 (34.2) | 0.013 |
| GAD‐7 (Anxiety symptoms) | 4.9 (5.0) | 3.6 (4.3) | 0.028 | 4.9 (5.0) | 3.5 (4.7) | < 0.001 | 5.1 (5.1) | 4.1 (4.8) | 0.057 |
| GAD‐7 ≥ 5, n (%) | 90 (43.7) | 37 (27.0) | 0.002 | 84 (42.4) | 36 (26.9) | 0.004 | 55 (47.8) | 72 (31.9) | 0.004 |
Note: Results are presented as mean (SD) unless otherwise indicated.
p values calculated using chi‐square and Fisher's exact tests for categorical outcomes and Wilcoxon rank sum tests for continuous outcomes. Bolded values indicate significance at α = 0.05. Percentages may not add to 100% due to rounding.
Abbreviations: FEV1 = forced expiratory volume in one second, GAD = generalized anxiety disorder, MPR = medication possession ratio, PHQ = patient health questionnaire.
For participants aged < 18 years, caregiver educational attainment was used.
3.2. Associations With SES: Adolescents (Age 13−17)
Table 3 shows the associations between SES and medication adherence, adherence beliefs, and health outcomes among participants under the age of 18. Adolescents whose caregivers did not have a college degree had lower adherence and reported more interference from barriers and lower self‐efficacy than counterparts with college‐educated caregivers. Additionally, those without college‐educated caregivers had a higher prevalence of suboptimal nutritional status and mild or greater depression and anxiety. Adolescents in families with an annual household income < $80,000 had lower adherence and reported more barriers and more interference from barriers than counterparts in higher‐income households. They also had a higher prevalence of IV antibiotics, worse nutritional status, more anxiety symptoms, and higher prevalence of mild or greater anxiety compared to those in higher‐income households. Having only public or no insurance was associated with a greater number of adherence barriers, more interference from barriers, and lower self‐efficacy, as well as with worse nutritional status and increased prevalence of mild or greater anxiety than having any private or military health insurance.
Table 3.
Association of socioeconomic status (SES) measures with adolescents' (13−17 years old) medication adherence, adherence‐related beliefs, CF health outcomes, and mental health.
| Measure | Caregiver educational attainment (n = 134) | Annual household income (n = 129) | Health insurance type (n = 136) | ||||||
|---|---|---|---|---|---|---|---|---|---|
| No college degree (n = 60) | College degree (n = 74) | p | < $80k (n = 58) | ≥ $80k (n = 71) | p | Only public, self‐pay, or none (n = 44) | Any private/military (n = 92) | p | |
| Medication adherence | |||||||||
| Composite MPR | 47.0 (23.5) | 58.5 (23.2) | 0.008 | 47.0 (21.9) | 58.2 (25.0) | 0.011 | 47.9 (21.8) | 55.5 (25.1) | 0.079 |
| Adherence‐related beliefs | |||||||||
| Barriers occurrence | 6.3 (4.0) | 5.1 (3.8) | 0.086 | 6.6 (4.2) | 5.0 (3.5) | 0.028 | 6.8 (4.3) | 5.2 (3.6) | 0.030 |
| Barriers interference | 17.7 (13.8) | 12.3 (11.3) | 0.020 | 17.9 (13.7) | 12.4 (11.4) | 0.020 | 19.5 (14.5) | 12.8 (11.3) | 0.010 |
| Treatment burden | 67.6 (11.2) | 69.8 (13.2) | 0.416 | 66.1 (12.5) | 70.8 (11.9) | 0.050 | 68.3 (11.5) | 69.3 (12.7) | 0.732 |
| Self‐efficacy | 6.5 (2.2) | 7.6 (2.2) | 0.003 | 6.6 (2.3) | 7.4 (2.3) | 0.072 | 6.3 (2.4) | 7.4 (2.2) | 0.018 |
| Health outcomes | |||||||||
| FEV1% predicted | 89.7 (19.9) | 88.5 (15.9) | 0.621 | 87.0 (19.1) | 90.7 (17.1) | 0.286 | 89.8 (20.2) | 88.4 (16.5) | 0.499 |
| IV antibiotics in the past year, n (%) | 25 (41.7) | 27 (36.5) | 0.541 | 28 (48.3) | 20 (28.2) | 0.019 | 18 (40.9) | 35 (38.0) | 0.749 |
| Nutritional status, n (%) | 0.014 | 0.002 | 0.026 | ||||||
| Underweight | 6 (10.0) | 2 (2.7) | 7 (12.1) | 2 (2.8) | 3 (6.8) | 6 (6.5) | |||
| Normal weight | 44 (73.3) | 68 (91.9) | 40 (69.0) | 66 (93.0) | 32 (72.7) | 81 (88.0) | |||
| Overweight | 10 (16.7) | 4 (5.4) | 11 (19.0) | 3 (4.2) | 9 (20.5) | 5 (5.4) | |||
| PHQ‐8 (Depressive symptoms) | 5.2 (5.5) | 3.4 (4.4) | 0.073 | 5.0 (5.4) | 3.9 (5.2) | 0.095 | 5.4 (5.3) | 3.9 (5.2) | 0.067 |
| PHQ‐8 ≥ 5, n (%) | 24 (51.1) | 19 (28.4) | 0.018 | 24 (49.0) | 19 (31.2) | 0.057 | 19 (50.0) | 25 (32.5) | 0.069 |
| GAD‐7 (Anxiety symptoms) | 5.3 (5.0) | 3.9 (4.8) | 0.062 | 5.7 (5.5) | 3.8 (4.9) | 0.021 | 5.8 (5.1) | 4.0 (5.1) | 0.051 |
| GAD‐7 ≥ 5, n (%) | 24 (51.1) | 21 (30.9) | 0.040 | 24 (48.0) | 18 (29.5) | 0.046 | 21 (55.3) | 24 (30.8) | 0.011 |
Note: Results are presented as mean (SD) unless otherwise indicated.
p values calculated using chi‐square and Fisher's exact tests for categorical outcomes and Wilcoxon rank sum tests for continuous outcomes. Bolded values indicate significance at α = 0.05. Percentages may not add to 100% due to rounding.
Abbreviations: FEV1 = forced expiratory volume in one second, GAD = generalized anxiety disorder, MPR = medication possession ratio, PHQ = patient health questionnaire.
3.3. Associations With SES: Young Adults (Age 18−26)
As shown in Table 4, college education was not significantly associated with medication adherence, adherence beliefs, or health outcomes in this age group. Young adults with an annual household income < $80,000 had lower self‐efficacy than higher‐income counterparts. They also had worse nutritional status compared to those with an annual household income of ≥ $80,000. Young adults with only public, self‐pay, or no insurance had lower ppFEV1% than young adults with any private or military health insurance.
Table 4.
Association of socioeconomic status (SES) measures with young adults' (18−26 years old) medication adherence, adherence‐related beliefs, CF health outcomes, and mental health.
| Measure | Educational attainment (n = 126) | Annual household income (n = 122) | Health insurance type (n = 123) | ||||||
|---|---|---|---|---|---|---|---|---|---|
| No college degree (n = 114) | College degree ( n = 12) | p | < $80k (n = 79) | ≥ $80k (n = 43) | p | Only public, self‐pay, or none (n = 38) | Any private/military (n = 85) | p | |
| Medication adherence | |||||||||
| Composite MPR | 38.2 (23.9) | 41.6 (33.5) | 0.979 | 37.1 (23.6) | 41.1 (27.2) | 0.571 | 37.7 (21.2) | 38.9 (26.4) | 0.870 |
| Adherence‐related beliefs | |||||||||
| Barriers occurrence | 7.6 (4.5) | 9.4 (3.2) | 0.227 | 8.2 (4.4) | 7.1 (4.4) | 0.137 | 7.8 (4.7) | 7.9 (4.3) | 0.887 |
| Barriers interference | 21.9 (15.0) | 24.8 (10.9) | 0.430 | 23.4 (14.5) | 19.8 (15.3) | 0.103 | 23.8 (16.0) | 22.0 (14.0) | 0.453 |
| Treatment burden | 67.4 (12.1) | 68.2 (7.3) | 0.626 | 66.2 (11.3) | 69.1 (11.2) | 0.130 | 66.3 (12.1) | 68.0 (11.3) | 0.606 |
| Self‐efficacy | 6.9 (2.0) | 6.9 (2.2) | 0.813 | 6.6 (2.2) | 7.5 (1.6) | 0.038 | 6.5 (2.0) | 7.1 (2.0) | 0.178 |
| Health outcomes | |||||||||
| FEV1% predicted | 75.3 (23.0) | 71.6 (21.9) | 0.675 | 72.6 (22.5) | 79.8 (22.9) | 0.069 | 66.5 (20.9) | 78.0 (22.9) | 0.008 |
| IV antibiotics in the past year, n (%) | 63 (55.3) | 6 (50.0) | 0.728 | 46 (58.2) | 21 (48.8) | 0.319 | 24 (63.2) | 44 (51.8) | 0.240 |
| Nutritional status, n (%) | 0.339 | 0.006 | 0.106 | ||||||
| Underweight | 17 (15.3) | 0 (0) | 14 (18.2) | 3 (7.1) | 7 (18.9) | 10 (12.1) | |||
| Normal weight | 74 (66.7) | 9 (75.0) | 44 (57.1) | 36 (85.7) | 20 (54.1) | 61 (73.5) | |||
| Overweight | 20 (18.0) | 3 (25.0) | 19 (24.7) | 3 (7.1) | 10 (27.0) | 12 (14.5) | |||
| PHQ‐8 (Depressive symptoms) | 4.7 (4.5) | 3.8 (4.1) | 0.591 | 4.6 (4.4) | 4.3 (4.2) | 0.867 | 5.0 (4.7) | 4.5 (4.4) | 0.618 |
| PHQ‐8 ≥ 5, n (%) | 36 (40.9) | 3 (27.3) | 0.519 | 26 (40.6) | 11 (35.5) | 0.630 | 15 (44.1) | 24 (38.1) | 0.564 |
| GAD‐7 (Anxiety symptoms) | 4.8 (5.3) | 4.6 (4.3) | 0.846 | 5.1 (5.1) | 4.0 (5.4) | 0.137 | 4.4 (4.6) | 5.1 (5.5) | 0.720 |
| GAD‐7 ≥ 5, n (%) | 37 (41.6) | 5 (45.5) | 1.000 | 30 (46.9) | 10 (31.3) | 0.143 | 16 (47.1) | 26 (40.6) | 0.540 |
Note: Results are presented as mean (SD) unless otherwise indicated.
p values calculated using chi‐square and Fisher's exact tests for categorical outcomes and Wilcoxon rank sum tests for continuous outcomes. Bolded values indicate significance at α = 0.05. Percentages may not add to 100% due to rounding.
Abbreviations: FEV1 = forced expiratory volume in one second, GAD = generalized anxiety disorder, MPR = medication possession ratio, PHQ = patient health questionnaire.
3.4. Associations With SES: Adults (Age ≫ 26 Years)
Table 5 presents associations between SES and medication adherence, adherence‐related beliefs, and health outcomes among the adults in our sample. Adults without a college degree had lower self‐efficacy, more depressive symptoms, and higher prevalence of mild or greater anxiety compared to adults with a college degree. Lower annual income (< $80,000) was associated with lower medication adherence, as well as with more depressive and anxiety symptoms, and a higher prevalence of mild or greater depressive symptoms. Adults with only public, self‐pay, or no health insurance had a higher prevalence of IV antibiotics and more depressive symptoms than those with any private or military insurance.
Table 5.
Association of socioeconomic status (SES) measures with adults' (> 26 years old) medication adherence, adherence‐related beliefs, CF health outcomes, and mental health.
| Measure | Educational attainment (n = 143) | Annual household income (n = 136) | Health insurance (n = 141) | ||||||
|---|---|---|---|---|---|---|---|---|---|
| No college degree (n = 73) | College degree (n = 70) | p | < $80k (n = 91) | ≥ $80k (n = 45) | p | Only Public, self‐pay, or none (n = 47) | Any private/military (n = 94) | p | |
| Medication adherence | |||||||||
| Composite MPR | 42.9 (23.7) | 46.4 (23.4) | 0.396 | 40.1 (24.2) | 52.0 (20.0) | 0.014 | 43.9 (21.5) | 44.3 (24.2) | 0.979 |
| Adherence‐related beliefs | |||||||||
| Barriers occurrence | 7.2 (4.2) | 7.0 (3.8) | 0.840 | 7.6 (4.0) | 6.6 (4.0) | 0.141 | 7.6 (3.6) | 6.9 (4.2) | 0.309 |
| Barriers interference | 20.2 (15.3) | 17.0 (11.4) | 0.386 | 20.7 (14.1) | 16.2 (12.3) | 0.070 | 20.8 (13.3) | 17.7 (13.7) | 0.114 |
| Treatment burden | 67.2 (10.5) | 66.7 (12.1) | 0.556 | 66.4 (10.4) | 68.8 (13.2) | 0.541 | 64.9 (10.0) | 67.9 (11.8) | 0.196 |
| Self‐efficacy | 7.1 (2.2) | 7.8 (1.8) | 0.046 | 7.3 (2.0) | 7.6 (2.2) | 0.312 | 7.1 (2.0) | 7.6 (2.1) | 0.115 |
| Health outcomes | |||||||||
| FEV1% predicted | 58.2 (20.2) | 62.5 (22.7) | 0.269 | 59.8 (20.5) | 60.4 (23.2) | 0.915 | 54.8 (19.3) | 62.9 (22.3) | 0.057 |
| IV antibiotics in the past year, n (%) | 46 (63.0) | 41 (59.4) | 0.660 | 57 (63.3) | 25 (55.6) | 0.383 | 36 (78.3) | 49 (52.1) | 0.003 |
| Nutritional status, n (%) | 0.360 | 0.342 | 0.751 | ||||||
| Underweight | 2 (2.7) | 5 (7.4) | 3 (3.4) | 3 (6.7) | 2 (4.4) | 5 (5.4) | |||
| Normal weight | 41 (56.2) | 40 (58.8) | 56 (62.9) | 23 (51.1) | 25 (54.4) | 56 (60.2) | |||
| Overweight | 30 (41.1) | 23 (33.8) | 30 (33.7) | 19 (42.2) | 19 (41.3) | 32 (34.4) | |||
| PHQ‐8 (Depressive symptoms) | 5.5 (4.8) | 3.6 (3.3) | 0.026 | 4.9 (4.2) | 3.2 (3.8) | 0.008 | 5.8 (4.7) | 3.8 (3.7) | 0.011 |
| PHQ‐8 ≥ 5, n (%) | 28 (44.4) | 20 (32.8) | 0.183 | 35 (42.7) | 9 (23.7) | 0.045 | 20 (50.0) | 27 (32.9) | 0.069 |
| GAD‐7 (Anxiety symptoms) | 4.6 (4.7) | 3.3 (3.9) | 0.110 | 4.4 (4.5) | 2.9 (3.7) | 0.019 | 5.0 (5.4) | 3.5 (3.7) | 0.136 |
| GAD‐7 ≥ 5, n (%) | 28 (42.4) | 13 (20.6) | 0.008 | 30 (35.7) | 8 (19.5) | 0.065 | 18 (41.9) | 22 (26.2) | 0.072 |
Note: Results are presented as mean (SD) unless otherwise indicated.
p values calculated using chi‐square and Fisher's exact tests for categorical outcomes and Wilcoxon rank sum tests for continuous outcomes. Bolded values indicate significance at α = 0.05. Percentages may not add to 100% due to rounding.
Abbreviations: FEV1 = forced expiratory volume in one second, GAD = generalized anxiety disorder, MPR = medication possession ratio, PHQ = patient health questionnaire.
4. Discussion
This study of the association between SES and medication adherence and its barriers in people with CF showed that, in general, lower SES is associated with worse medication adherence, greater experience of or interference from adherence barriers, and lower self‐efficacy, as well as worse CF health and mental health outcomes. These findings were most consistent when measuring SES by household income and least consistent when using health insurance as a measure. Additionally, SES measures had different associations with adherence and adherence‐related beliefs depending on age, demonstrating that a nuanced approach to SES measurement and a consideration of age are essential.
When stratified by age group, medication adherence in adolescents was associated both with caregiver education and household income, with a trend toward association with health insurance type. Adherence‐related beliefs were associated with all measures of SES in this age group, with the exception of treatment burden, which was not associated with any SES indicator. Additionally, there was no association between self‐efficacy and household income in the adolescent group. Among adults, adherence and self‐efficacy were worse in the lower‐income and lower‐education groups, respectively. Among young adults, lower household income was associated with lower self‐efficacy. In contrast to the adolescent and adult groups, there were no other significant associations between SES measures and adherence or adherence‐related beliefs in the young adults group. It is possible that heterogeneity in the degree of independence during this life stage affected the results. For example, a 20‐year‐old may be a college student dependent on their parents for housing and health insurance or may be fully employed and living independently. It is also likely that educational attainment has not yet yielded the benefits associated with it in later life stages. Similarly, annual earnings of young adults are inconsistent and likely limited, particularly among those enrolled in college either full‐ or part‐time, with possible implications for the lack of observed significance.
There was a consistent pattern of adherence and adherence beliefs across age groups: adolescents had the highest adherence and lowest perceived barriers and barriers interference to adherence; young adults had the lowest adherence and highest perceived barriers and interference; and adults were in the middle. These results likely reflect the various types of disease management support that adolescents receive from their caregivers and families, and the challenges of decreasing parental support and increasing independence during young adulthood. Our findings also suggest that low SES may have a stronger detrimental effect on caregivers' ability to support adherence in their adolescents than on adults' own disease self‐management. Lower‐income adults with CF had lower adherence, but adherence barriers did not differ by SES. On the other hand, the most consistent associations between SES and mental health symptoms were among adults with CF, suggesting that the role of mental health in treatment adherence deserves further investigation.
SES is a multidimensional construct comprising various factors (e.g., economic resources, human capital, social capital) that may operate differently at specific stages of the life course [57]. The measurement of SES in health research, by necessity, simplifies its scope. We used common indicators that represent distinct SES aspects and contribute differently for health: education is thought to be related most closely to health behaviors [58, 59, 60], while income is associated with health outcomes impacted by material resources [58, 60, 61, 62]. Insurance type, an imperfect measure of SES due to variations in public insurance eligibility by state, age, and disease severity [63], has nevertheless been the most used SES indicator in CF research due to its availability in the CFF Patient Registry and medical records. In our study, all three SES indicators showed associations with adherence and adherence‐related factors, but these associations differed by age group and were strongest among adolescents.
There is a well‐documented socioeconomic gradient in CF health outcomes such as lung function, weight status, pulmonary exacerbations, and mortality [64, 65, 66, 67, 68]. Our study confirmed this association, with worse lung function and nutritional status and increased IV antibiotic treatments in the lower‐SES group, as well as increased symptoms of anxiety and depression. These associations were noted for educational attainment, household income, and health insurance status, especially among adolescents. While socioeconomic disparities in CF health outcomes can be attributed to material deprivation [69] manifested in social risk factors such as food insecurity [53] and adverse environmental exposures [70], our study suggests the importance of material resources for mitigating barriers to adherence, supporting self‐efficacy, and reducing chronic stress, which can lead to anxiety and depression. Given the well‐established association of low SES and depression with adverse CF outcomes [2, 3, 64, 67, 68, 69, 71, 72], there are likely important and possibly causative interactions between these risk factors that merit further exploration.
This multicenter study is the first evaluation of the role of SES in the adherence behaviors and beliefs of people with CF across age groups. Although the study sample was relatively large for this population, stratifying the analyses by age group limited the statistical power to establish gradient associations between SES indicators and the outcomes of interest. We also acknowledge that the study sample may not be representative of all people with CF: CF care programs that recruited participants had an interest in adherence as indicated by their membership in the CFF STRC, and participants were recruited as a convenience sample. Recruiting only English‐speaking participants may have further limited the sociodemographic diversity of the sample. Another weakness was the reliance on MPR as a measure of adherence, as data on prescription refills are not always accurate, and filled prescriptions may not necessarily be taken as directed [73]. Adherence was assessed as a composite rather than by type of medication (e.g., oral vs inhaled) due to the limited sample, and the study was conducted before the availability of elexacaftor/tezacaftor/ivacaftor, which may have affected the use of other medications. Finally, any inference of causality or causal direction in this cross‐sectional study is speculative.
Despite these limitations, our findings contribute insight into the behavioral and psychosocial antecedents of adverse health outcomes in people with CF from socioeconomically disadvantaged backgrounds. We showed that lower SES is associated with decreased medication adherence, more interference from adherence barriers, and worse self‐efficacy. Thus, suboptimal adherence may be a contributor to documented socioeconomic disparities in CF health outcomes. Our findings suggest a stronger role for income and education over health insurance in this association. Future studies should explore the mechanism of these relationships further, as well as the relative contribution of other factors influencing disease self‐management in CF, to provide insights into how they may be addressed.
Author Contributions
Gabriela R. Oates: conceptualization, methodology, writing–original draft. Kristin A. Riekert: writing–review and editing, data curation, funding acquisition, methodology, investigation. Christine Ford: formal analysis, writing–review and editing, visualization, methodology, data curation. Kimberly M. Dickinson: writing–review and editing, methodology. Jennifer L. Butcher: writing–review and editing, methodology. Diana Naranjo: writing–review and editing, methodology. Hanna Phan: writing–review and editing, methodology. Cori Daines: writing–review and editing, methodology. Hannah Grabowski: project administration, writing–review and editing. Michael S. Schechter: conceptualization, methodology, writing–original draft.
Conflicts of Interest
The authors declare no conflicts of interest.
Acknowledgments
This work was supported by the Cystic Fibrosis Foundation (QUITTIN14Q10, SAWICK14PE1, RIEKER15PE0, and BARRIERS16PE0).
Data Availability Statement
The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.