The rate of lung transplantation has increased markedly in both the United States and worldwide within the last decade 1–3. This increase has coincided with improvements in posttransplant survival 1, 2. As for other types of organ recipients, lung recipients' adherence to the posttransplant medical regimen—the degree to which patients' behavior coincides with posttransplant medical recommendations 4, 5—is important for maintaining health 6–10. Indeed, adherence to the regimen is of heightened concern in lung transplantation relative to other types of transplantation because of the greater sensitivity of lung grafts to infection and both acute and chronic rejection 6, 9. Thus, careful adherence in areas such as taking immunosuppressants, attending clinic appointments, and home monitoring (e.g., spirometry) are widely agreed to be critical to minimize morbidity and mortality after lung transplantation 6, 9, 11.
It is striking, therefore, that only a handful of investigations have examined nonadherence rates in lung recipients. In a meta-analysis of the adherence literature in adult transplantation 12, we found that of the 147 studies published through 2005, merely 3% (n=5) reported on lung recipients 10, 13–16. Aside from qualitative work 17, we have identified only one additional empirical study of adherence in lung recipients published since our meta-analysis 18. These few reports are all cross-sectional surveys, and most consider adherence to only a single component of the posttransplant regimen (e.g., substance use, or taking medications) 10, 13–16, 18. While our meta-analysis identified important differences in adherence rates across different types of transplant recipients (kidney vs. liver vs. heart), we could not include lung recipient reports in these comparisons due to the very small number of such studies. No study of adherence in lung recipients has itself included comparison groups of other transplant recipients.
In short, little is known about rates of nonadherence to the full range of components of the medical regimen after lung transplantation or whether these rates differ from those observed in other types of transplant recipients. Moreover, risk factors for nonadherence have yet to be determined prospectively in lung recipients 12, 19, 20. Absent risk factor identification, it is difficult to develop interventions to prevent or limit nonadherence.
The purpose of the present report is to determine both the rates and risk factors for nonadherence in lung recipients. We included a comparison group of heart transplant recipients. We judged that this would constitute the most appropriate comparison group since heart recipients are the only other major group of thoracic transplant patients, both groups generally receive care from the same or overlapping teams posttransplant, and they are required to adhere to similarly multifaceted medical regimens 19, 20.
Our study, therefore, prospectively evaluated a) whether lung recipients differed from heart recipients in the nature, rate, and pattern of nonadherence to each component of the medical regimen during the first 2 years posttransplant; and b) the extent to which persistent nonadherence to each component was predicted by a set of potential risk factors. We adopted the World Health Organization (WHO) conceptualization of risk factors 5, which posits that factors from multiple medical and psychosocial domains should predict nonadherence outcomes.
All adults (aged ≥18) receiving their first lung or heart transplant were contacted at 2 months posttransplant (n=327). They were transplanted between November 1999 and August 2004 in the Cardiothoracic Transplantation (CT) Program, University of Pittsburgh Medical Center. (In the CT Program, a single nurse coordinator supervisor works with all CT physicians to monitor all of the clinical follow-up activities of individual nurse coordinators for both lung and heart recipients.) The sample refusal rate was 7.0% (n=23). The 304 individuals enrolled (178 lung recipients; 126 heart recipients) did not differ from those who refused on type of transplant or demographic characteristics.
Across the study follow-up period, 30 of the 304 patients (9.9%, 19 lung recipients, 11 heart recipients) refused to continue their participation and 35 died (11.5%, 32 lung recipients, 3 heart recipients). Lung recipients were no more likely to drop out than heart recipients (χ2(df=1)=0.31, P=0.576), but they were more likely to be lost due to death (χ2(continuity corrected; df=1)=16.12, P<.001). This difference is addressed in analyses below. There were no large or statistically significant differences between reason for loss to follow-up and any other variable.
Table 1 presents demographic and transplant-related characteristics for the sample. Patients were similar to the respective U.S. populations of lung and heart recipients transplanted during the same time period on distributions of gender, age, ethnicity, and indication for transplant 21. The significant between-group differences in Table 1 for several of these variables are also consistent with national data and led us to consider these variables as possible contributors to between-group differences in adherence. Although lung patients were hospitalized longer posttransplant than heart recipients, the groups were similar on functional status (Karnofsky Index) 22, 23 and proportions experiencing treated rejection episodes in the early posttransplant period.
The protocol had Institutional Review Board approval. All respondents gave written informed consent. Data on patient adherence and psychosocial status were collected in individual 90- to 120-minute structured interviews conducted on three occasions during the first year posttransplant (2, 7, and 12 months) and two occasions thereafter (18 and 24 months). Interviews were conducted by trained interviewers with master's degrees in mental health or behavioral disciplines.
When possible, interviews were scheduled to coincide with routine outpatient CT Program evaluations. Otherwise, interviews were conducted in patients' homes or by telephone. There were no significant differences due to interview mode for any variable. Each recipient's primary family caregiver (the individual identified by the patient as providing the most daily care and assistance; the spouse for 66% of the sample) was also interviewed at each time point about the patient's adherence. Information on transplant-related characteristics (e.g., indication for transplant) was obtained from medical records.
Nonadherence to the Posttransplant Medical Regimen
Meta-analyses 12, 24, 25 show that self-report (alone or combined with other assessment strategies, such as self-report+informant report) is more likely to yield evidence of nonadherence than any other approach (e.g., reliance on indirect methods such as medication monitoring or biologic measures). Other commentaries also recommend combined strategies 26. Thus, we assessed patient adherence through a combination of patient and family caregiver report. We used the Health Habits Survey (patient and caregiver versions) that we developed and administered previously 27, 28. Its items derive from those originally administered to kidney recipients 29; the items were adapted to requirements specific to cardiothoracic recipients. The survey was designed using Feinstein's “clinimetrics” approach 30, 31 to maximize accurate reporting of adherence (i.e., attention to item wording and responses scales to encourage truthful answers; use of trained interviewers who took care to establish rapport with respondents). Regarding psychometric properties, interviewer interrater reliability exceeded an intraclass correlation of 0.97 on each item for the present sample. We have previously demonstrated that the items had predictive validity for health outcomes in heart recipients 28.
The survey assessed posttransplant nonadherence in nine areas: a) taking the primary immunosuppressant (cyclosporine or tacrolimus); b) clinic appointment attendance; c) completing blood work; d) home blood pressure monitoring; e) following a prescribed diet; f) following a prescribed exercise plan; g) abstaining from tobacco use; h) limiting alcohol consumption; and i) performing home spirometry (lung recipients only). Questions inquired about each area either since transplant (at the 2-month assessment) or since the previous assessment (at subsequent interviews). Although questions used an ordinal response format (e.g., respondents indicated how often patients missed taking medications: daily, several times a week, several times a month, etc.), responses on each item were dichotomized to indicate whether recipients adhered to the minimum level acceptable by the CT Program. These levels are specified in Table 2.
Recipients and caregivers showed high concordance in their reports of recipients' adherence in each area (85–97% at each assessment). To arrive at a single measure of nonadherence for each area, we judged that respondents were more likely to underreport nonadherence than to state that a recipient was nonadherent when he/she was in fact adherent. Thus, we took any report of nonadherence, whether from recipient or caregiver, as evidence of nonadherence in a given area.
Potential Risk Factors
These variables represented the five classes of factors in the WHO conceptualization of potential determinants of nonadherence 5.
We obtained information on patients' gender, age, ethnicity, education, and income at the initial interview.
Healthcare System Access Factors
Distance of the patient's home address from the transplant center was determined. Insurance status at transplant was obtained from medical records and coded according to whether patients relied exclusively on public insurance (e.g., Medicaid).
Transplant-Related Health Characteristics
Patients' health status during the first 2 months of recovery was considered. Duration of posttransplant hospitalization and occurrence of treated acute graft rejection in the first 2 months were extracted from medical records. Physical functional status was assessed by the interviewers using the Karnofsky Index at 2 months posttransplant. Cognitive status was evaluated at the 2-month interview with the Mini-Mental State Exam 32. We also obtained pretransplant lifetime history of mood or anxiety disorders (major depression, generalized anxiety disorder, panic disorder) via the Structured Clinical Interview for DSM-IV 33 at the initial assessment.
Posttransplant Treatment-Related Factors
At the initial interview, patients answered a series of questions about effects of treatment. This included one item about whether or not they felt unhappy with any medications' effects on their physical appearance 29, and additional items about whether or not they felt that each component of their medical regimen (e.g., taking immunosuppressants, attending clinic appointments) interfered with daily life. These latter items were adapted from an instrument measuring opinions about medical treatment 34. Their primary maintenance immunosuppressant (cyclosporine or tacrolimus) was obtained from medical records.
Patient-Related Psychosocial Characteristics
Two types of characteristics, intrapersonal resources and interpersonal resources, were considered 19. Scales assessing each were administered at the initial interview. All have well-established psychometric properties. Internal consistency reliability coefficients reported below pertain to the present sample.
Seven measures of intrapersonal resources were administered. Feelings of emotional well-being were assessed with the depression, anxiety, and anger-hostility subscales of the Symptom Checklist 90 35 (Cronbach's alpha=0.88, 0.84, 0.84, respectively). To identify clinically significant distress on each subscale, individuals were categorized on each according to whether their scores were more than 1 SD above the gender-specific normative mean (i.e., higher than 84% of the normative sample) 35.
Optimism was measured with the Life Orientation Test 36, which assesses expectations about the future (alpha=0.75). The Multidimensional Health Locus of Control Scale 37 measured the extent to which respondents believed that a) they could influence their health outcomes (internal locus of control, alpha = 0.65); b) their health outcomes were due to healthcare professionals (care provider locus of control, alpha = 0.74); and c) their health outcomes occurred by chance (chance locus of control, alpha=0.71).
Two areas of interpersonal resources were assessed. The supportiveness (both emotionally and practically) of the recipient's relationship with their primary family caregiver was assessed with a measure adapted from Spanier 38 and Pearlin and Schooler 39 and used in other studies 40, 41 (alpha=0.92). Friend support was assessed with a scale concerning the degree to which respondents believed they could rely on friends for emotional and practical support 42 (alpha=0.89). These two measures had skewed distributions; scores were dichotomized to identify respondents with the poorest support (lower third of the distribution) relative to remaining respondents.
Descriptive data were examined regarding proportions of patients who were nonadherent at each assessment. Survival analysis, using a life-table approach 43, was used to determine the cumulative incidence of persistent nonadherence (defined as nonadherence at ≥2 consecutive assessments). We used life-table rather than Kaplan-Meier methods because, by the design of this clinical epidemiologic study, individuals reported on nonadherence occurring during the period since last assessment. The survival analyses included all persons until the point of censoring.
The associations of potential risk factors with whether or not patients experienced persistent nonadherence during the 2-year study period were examined via bivariate analyses, followed by logistic regression analysis. We considered 24 potential risk factors. Given our total sample size, we maintained an appropriate respondent-to-variable ratio within the recommended range of 10:1 to 15:1 44. Before multivariate analyses, risk factors were examined and found to adequately meet analytic assumptions 44.
Cross-Sectional Rates of Nonadherence
Table 2 shows the proportions of lung and heart recipients who were nonadherent in each area at each time point, based on data from all recipients available at the time point. Lung recipients showed significantly lower nonadherence rates than heart recipients in the areas of diet and tobacco use at most or all assessments. Lung recipients were significantly more nonadherent than heart recipients in monitoring blood pressure early posttransplant, but the groups did not differ by the final assessment.
Table 2 also shows that a) at each timepoint, nonadherence rates varied widely across the areas assessed, and b) the proportion of nonadherent patients increased from earlier to later assessments in all areas except diet (see tests for differences across assessments, last column of table). Despite uniform increases, the areas of nonadherence themselves were not highly intercorrelated among either lung or heart recipients. For example, at the 2-month assessment, the median intercorrelation among the nine nonadherence areas was 0.06 with an interquartile range of 0.02–0.09 and (excluding the one outlier noted below) an absolute range of 0.00–0.15. Intercorrelations at other assessments were similarly small. The single exception was that nonadherence to spirometry was moderately correlated with nonadherence to blood pressure monitoring at each of the five assessments (median r=0.36).
Longitudinal Pattern of Onset of Persistent Nonadherence
The data in Table 2 are cross-sectional “snapshots” of nonadherence at individual assessments. Persistent nonadherence across multiple assessments, and its timing of onset, may be of greater clinical significance than behavior at any single evaluation. Thus, for each area of the regimen, we identified individuals showing nonadherence at ≥2 consecutive assessments. Given our assessment schedule, this corresponds to nonadherence enduring for 6 months or more. Survival analyses comparing the timing of onset of persistent nonadherence (i.e., cumulative incidence rates) in lung versus heart recipients are shown in Figure 1. For example, Figure 1A shows that lung recipients had a significantly lower cumulative incidence of persistent nonadherence to the primary immunosuppressant by 2 years posttransplant (13%), compared to heart recipients (21%; Wilcoxon χ2=4.46, P=0.035). Lung recipients were also significantly less likely to become persistently nonadherent in the areas of diet (Fig. 1F) and tobacco use (Fig. 1G). However, lung recipients showed significantly greater incidence rates of persistent nonadherence to completing blood work (Fig. 1C) and monitoring blood pressure (Fig. 1D). They had a high rate of persistent nonadherence to home spirometry (cumulative incidence, 62%; Fig. 1I).
To address the possibility that the differential death rates between lung and heart recipient were responsible for these findings, we repeated the survival analyses including only the recipients who remained alive throughout the study period (and among whom loss to follow-up was not associated with type of transplant). Results were identical to those described above (i.e., the shape of the incidence curves and the final rates were unchanged).
Risk Factors for Persistent Nonadherence
To limit the likelihood of type I error, we took several steps to control the number of tests examining risk factor–nonadherence associations. First, we grouped the nonadherence outcomes into four domains, corresponding to major components of the regimen: a) taking immunosuppressant medications; b) clinical monitoring (persistent nonadherence to either clinic appointments or completing blood work); c) home self-care (persistent nonadherence to either blood pressure monitoring, diet or exercise); and d) spirometry. (Spirometry was considered separately from other home self-care activities because it was assessed only in lung recipients. We did not include a domain for substance use nonadherence because these behaviors were too rare.) Second, before the multivariate analyses, we examined bivariate associations of each risk factor with persistent nonadherence in each domain. Potential risk factors showing small associations (r<0.15) with all four nonadherence domains were eliminated from further consideration (cf. Table 3, footnote b). Remaining risk factors were entered simultaneously into logistic regression analysis, with persistent nonadherence in a given domain (i.e., persistent nonadherence to any of the areas encompassed by the domain) as the outcome. Results are shown in Table 3. The table presents odds ratios (ORs), generated from the regression coefficients, and the 95% confidence interval for each OR. Each OR indicates the degree of increased risk of persistent nonadherence if a recipient possessed the risk factor.
Recipients relying on public health insurance were at heightened risk for nonadherence in all domains (ORs of 2.26 to 4.00). Poor caregiver support also predicted increased likelihood of persistent nonadherence in all domains (ORs of 1.81 to 2.59). Lower internal locus of control predicted increased risk of persistent nonadherence to home self-care in all recipients, and spirometry in lung recipients. Male gender was protective against home self-care nonadherence.
Finally, interaction terms between type of transplant and each factor were added to the regressions to determine whether any risk factor effects varied across lung versus heart recipients. Results indicated unique effects for two factors. First, feeling unhappy with medications' effects on one's physical appearance increased the risk for persistent immunosuppressant nonadherence in heart but not lung recipients (interaction effect, beta=2.0, SE=0.81, P=0.012): heart recipients unhappy with such effects were more than 4 times more likely to be persistently nonadherent than remaining heart recipients (OR=4.44, P=0.002), while risk for persistent nonadherence was not related to this variable in lung recipients (OR=0.70, P=0.498). Second, poor support from friends predicted greater risk for persistent nonadherence to home self-care in lung but not heart recipients (interaction effect, beta=1.9, SE=0.74, P=0.008): lung recipients with low friend support were almost three times more likely to be persistently nonadherent than lung recipients with higher support (OR=2.94, P=0.010). The risk of persistent nonadherence was not associated with friend support in heart recipients (OR=0.58, P=0.213).
Our study is the first to prospectively examine rates and risk factors for nonadherence to a full range of components of the medical regimen in lung transplant recipients. Moreover, we put our findings for lung recipients into a larger context by directly comparing them to heart recipients recruited from the same transplant program during the same time period. As such, the present report begins to address some of the key gaps in the literature on posttransplant adherence outcomes 12, 19, 20, 45.
The nonadherence rates in our lung recipient cohort show many similarities with those reported across the larger (nonlung) transplant literature 12. First, most recipients in our sample were adherent to each component of the medical regimen. The exceptions to this pattern were blood pressure monitoring and spirometry, as discussed further below. The second point of similarity is the marked variability in our sample's nonadherence rates across areas of the medical regimen, with nonadherence to tobacco and alcohol use being relatively rare while nonadherence to home monitoring activities, diet, and exercise was much more common 12. Third, our finding that nonadherence in most areas increased with time is consistent with the typical pattern reported in chronic disease and transplant populations 4, 19, 27, 45–48. Fourth, as observed in other transplant cohorts 27, 45, 49, 50, the areas of nonadherence in our sample were generally not highly interrelated. Thus, we found little evidence of an overall “profile” of typical nonadherence among respondents, with the exception that there was a moderate correlation between lung recipients' nonadherence to blood pressure monitoring and spirometry; a similar association was reported previously 16. This association, plus the high nonadherence rates in these two areas, suggests that a comprehensive plan to improve home monitoring activities could be beneficial for lung recipients.
In addition to general comparisons with the larger literature, our direct examination of differences between lung and heart recipients indicates that lung recipients were neither uniformly better nor worse than these other patients in adhering to their regimen. Given the importance of immunosuppressants for preventing graft rejection, it is noteworthy that lung recipients' cumulative incidence of persistent nonadherence in this area was significantly lower than that in heart recipients. Tobacco use, which has been linked to major morbidities and mortality after all types of organ transplant 51–53, was also significantly lower among the lung recipients. However, lung recipients were less adherent to blood work requirements and blood pressure monitoring. Moreover, we have already noted that, by 2 years posttransplant, a majority of lung recipients no longer performed spirometry several times weekly, as required. Indeed, among this nonadherent group, most performed spirometry less than monthly or not at all. This is of major concern given the importance of spirometry for providing early indications of possible infection and graft rejection 6, 9, 11.
Several small studies of interventions to promote home spirometry show that lung recipients will perform frequent spirometry according to protocol for extended time periods 54–56. But our findings suggest that maintenance of this behavior outside of intervention research is more difficult. Some of our respondents spontaneously commented that they stopped spirometry because, beyond the first few months posttransplant, CT Program staff did not routinely review recipients' diaries of their spirometry readings. Similar comments were offered regarding blood pressure readings. Such remarks suggest that these recipients did not understand that these activities should be done entirely for recipients' own benefit. This point ties in with our risk factor findings: patients perceiving themselves to have little influence over their health were more than 1.5 times more likely to become persistently nonadherent to spirometry and other home self-care activities. The importance of this risk factor for lung recipient health outcomes has been noted elsewhere 57. In short, whether it is failure to fully understand that certain behaviors are important for health, or failure to believe that one can affect one's health, the implication may be that tailoring posttransplant education to “empower” (rather than only to instruct) patients is essential for maintaining adherence.
We examined a wide array of risk factors for persistent nonadherence. We found little to no impact for most demographic and transplant-related health history characteristics. This is not surprising; such variables have shown limited, contradictory effects on adherence in many transplant populations 12, 45. We also found no evidence that treatment-related characteristics were related to nonadherence, at least among lung recipients.
In contrast, variables reflecting healthcare system access and patient psychosocial characteristics were important risk factors for nonadherence in multiple areas. Of particular note, patients with only public health insurance were up to four times more likely to become persistently nonadherent to components of the regimen. Reliance on public insurance has repeatedly been found to increase the risk for poor health outcomes posttransplant 58–61, even controlling for income and other socioeconomic variables 59, 61. The mechanism for this effect may be due in part to reduced adherence, although the link between insurance status and adherence has seldom been examined 58, 60. Our findings suggest the importance of identifying strategies to strengthen financial resources for patient healthcare requirements. This includes increasing the availability of low- and no-cost medication programs, increasing transplant teams' familiarity with such programs, and reducing patient perceptions of stigma associated with program participation 62. However, cost coverage options in areas beyond medications remain bleak. Third-party reimbursement for activities such as exercise, dietary counseling, and training in other home self-care activities is rare, particularly through publicly funded insurance 63.
Finally, we found that two types of patient psychosocial factors affected nonadherence risk. We commented earlier on the impact of patients' perceptions of control over their health. The second important type of psychosocial factor was patients' perceived social support. Recipients without a supportive relationship with their primary family caregiver were at increased risk of nonadherence in all outcome areas. In addition, at least for lung recipients, lack of support from friends increased their likelihood of nonadherence to home self-care activities. We and others have repeatedly observed patients' supports from family and friends to be critical predictors of a range of adult posttransplant emotional and behavioral outcomes 19, 20, 27, 45, 64, 65; the present study extends this finding to lung recipients.
There are limitations to our research. First, we studied recipients at a single site and this could affect our findings' generalizability. Second, there are other potential risk factors in each of the five WHO-based domains that we did not examine but which could influence posttransplant adherence. For example, other healthcare system access factors have been identified as important in other chronic disease populations 5, including the duration of face-to-face interactions with healthcare professionals and the extent of insurance reimbursements for specific components of the medical regimen. Other treatment-related variables—such as distress over a full range of side effects 18 and patient-related psychosocial characteristics such as beliefs about one's medical condition 66—may affect posttransplant adherence. A sample size even larger than ours is required to examine the many additional potential predictors, as well as sensitively evaluate whether their effects vary by type of transplant. Third, concerning the measurement of nonadherence itself, we relied on patient and family caregiver reports and we did not collect information from clinician evaluations or indirect assessments (e.g., electronic medication monitoring). Our assessment strategy was based on meta-analytic findings, our own empirical comparisons of methods 27, 67, and other commentaries 26. Moreover, patient report, using techniques to maximize complete disclosure such as those we employed, has greater clinical relevance than many other research-based methods; it is less expensive and easier to integrate into routine clinical practice 12, 68.
For future work, our findings suggest that intervention development should be a priority for those areas where lung recipients' nonadherence rates are high and the consequences of nonadherence are significant. This should include, for example, home spirometry. Interventions to increase patients' feelings of competence and control at managing their health have proven useful in transplant and other chronic disease groups 69, 70. Involving family or even close friends in these efforts may enable patients to better draw on interpersonal relationships to promote adherence and optimal health outcomes. While risk factors such as type of health insurance may not themselves be easily modified through patient- focused interventions, assisting patients to seek alternative resources for cost coverage may reduce the likelihood of nonadherence for financial reasons 60, 71. Moreover, the impact of health insurance on our outcomes implies that not only patient-focused interventions but strategies to address healthcare system access factors are needed.
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