The multifaceted medical regimen after organ transplantation developed as a direct result of decades of both clinical trials of treatment strategies, and the common trials and errors of clinical practice. It is therefore of little surprise that nonadherence to components of this regimen—the degree to which patients’ behavior fails to coincide with posttransplant medical recommendations (1, 2)—has repeatedly been found to predict morbidity and mortality (3–20). Given these effects, it is alarming that we continue to have little understanding of either the prevalence or risk factors for posttransplant nonadherence.
Indeed, despite extensive empirical research, the conclusions offered by numerous reviews of this literature (3, 9, 21–32) have been largely invariant. First, it is unclear whether nonadherence to any given component of the regimen is very common or relatively rare. Reviews typically provide a wide range of observed nonadherence rates. For example, crude rates (unadjusted for follow-up duration) of medication nonadherence have been reported to range from 0% to 68% (22, 23, 26, 27, 30). Reviews attempting to estimate average nonadherence rates (3, 24, 28) have been hampered by differences in study methodology and follow-up duration—factors that make comparisons across studies difficult. Thus, although it has been suggested that methodological differences in adherence assessment across studies may explain the rate variations (3, 26, 29, 32–34), there has been no systematic analysis of whether such differences do, in fact, affect the findings.
The second general conclusion offered in major reviews is that the evidence is inconsistent regarding what psychosocial factors increase patients’ risk for nonadherence to any given component of the regimen, or whether factors important for one component (e.g., taking medications) are equally important for others (e.g., keeping clinic appointments) (21, 26, 27, 31). For example, there are contradictory findings on whether patient gender, age, or ethnicity are associated with posttransplant nonadherence (21, 24, 26, 29, 30). Other factors, such as social supports and perceived self-efficacy, are routinely included in conceptual models of determinants of adherence, (35–39) yet the magnitude of their actual impact on transplant recipient behavior remains obscure (21, 24, 26, 29, 30).
Without clearly understanding the clinical epidemiology of posttransplant nonadherence—both its rate of occurrence and its risk factors—we have little hope of mounting adherence-promoting interventions that are targeted to the appropriate patients, are cost effective, and have a reasonable chance of success. Without a more precise cumulation of the evidence than has been possible in even the best nonquantitative, narrative reviews, it seems likely that this literature will continue to be dominated by purely descriptive studies that may add incrementally to the knowledge base, but may not provide any critical new information to hasten the advance to adherence intervention research and clinical application. Finally, in the absence of “best estimates” based on available data regarding nonadherence risk for a given transplant recipient, it is difficult for clinicians to judge whether the likelihood of this problem is great or small, or whether a patient’s nonadherence risk differs substantially across components of the medical regimen (e.g., medication-taking vs. clinic appointment attendance vs. following diet or exercise plans).
We thus used quantitative, meta-analytic methods to achieve two goals. First, we sought to precisely estimate the rate of nonadherence to each component of the medical regimen both across all types of adult organ transplantation and within specific types of transplant. Where there was substantial variation in rates across studies, we examined whether this variation could be explained by differences in study characteristics (e.g., study design). Second, we aimed to determine whether nonadherence was associated with patient psychosocial risk factors.
MATERIALS AND METHODS
The methodology for conducting and reporting the meta-analysis followed state-of-the-art guidelines (40, 41).
Search Strategy and Study Selection
The study retrieval and selection strategy is detailed in Figure 1. From 1,837 citations meeting initial inclusion/exclusion criteria, 555 were retrieved. Their bibliographies were searched, yielding 136 additional papers. Studies were excluded if, for the nonadherence outcomes examined (described below), they did not provide either: a) the number of persons who were nonadherent and the duration of observation time for the sample, or b) the association of nonadherence with at least one of the psychosocial risk factors we considered. (These are described below.) Studies were excluded if they reported on nonadherence exclusively in patients with poor health outcomes (e.g., graft loss, death). The rationale for this was that studies limited to patients with poor outcomes, some of which can result from nonadherence, would not provide rate information generalizable to the broad transplant population. We also excluded studies limited exclusively to patients requiring transplants due to substance abuse/dependence. Patients with substance use histories are at heightened risk of substance use posttransplant, as discussed further below. Thus, studies focused exclusively on these patients would not provide posttransplant substance use rate estimates that would be generalizable to all transplant recipients. In contrast, studies included in our analysis drew their samples from the larger population of transplant recipients at a given site (and thereby included a range of patients, both with and without substance abuse/dependence histories).
Pairs of us (one of whom was M.A.D.) reviewed all studies. There were sufficient numbers of studies (i.e., >5) examining the following outcomes in order to include them in the meta-analysis: immunosuppressive medication nonadherence, tobacco use, alcohol use, illicit drug use, failure to attend clinic appointments, failure to complete blood work and tests, failure to monitor vital signs (e.g., blood pressure), failure to follow dietary requirements, and failure to follow an exercise plan. In addition, some studies reported a nonspecific “global” nonadherence outcome (reflecting nonadherence in multiple, often unspecified, areas).
Following the standards of the Cochrane Collaboration (42, 43) and other meta-analyses of nonadherence in nontransplant populations (44), we extracted information from each study on the occurrence of nonadherence in each outcome area based on the study authors’ definition of clinically significant nonadherence (e.g., taking less than a specified percentage of medication, failing to engage in a behavior at an acceptable level, having a value on a blood test beyond an acceptable range). Authors’ definitions reflected their transplant programs’ requirements. (These definitions necessarily varied depending on studies’ method of nonadherence assessment; we evaluated the impact of method differences on outcome as described below.) For most nonadherence outcomes, authors’ definitions did not vary by type of transplant studied. However, for alcohol use, almost all liver recipient studies defined nonadherence as any alcohol consumption, whereas studies of other organ recipients generally defined nonadherence as higher levels of drinking.
The only exception to our reliance on investigators’ own definitions of nonadherence were for tobacco use and failure to exercise. Until recently, neither behavior was considered to reflect posttransplant nonadherence except among cardiothoracic recipients. Thus, for tobacco use, we simply recorded information on any vs. no tobacco use. For exercise, most studies reported that—even if not required—at least “moderate” exercise (e.g., vigorous activity at least several times weekly) was desirable (45, 46). Thus, for this outcome, we recorded information on nonadherence to an exercise plan when it was required; otherwise, we recorded whether recipients engaged in less than moderate exercise, based on each author’s definition of moderate exercise.
Assessment-Related and Other Study Characteristics
We categorized the method used to assess nonadherence outcomes into five broad groupings (44): a) self-report (e.g., patient interview, paper-and-pencil survey); b) collateral report by family or healthcare professional (e.g., through interview or survey); c) biologic or other “indirect” (47) measure (e.g., blood level, electronic medication monitoring); d) historical data retrieved from medical records; and e) combinations of these methods (e.g., self report plus medical record data). We recorded descriptive information about each investigation (e.g., geographic region of the study site, study design). Finally, following current meta-analysis standards, (40) pairs of us rated each study on five components of methodologic quality using a validated scoring system for each (48). We employed a consensus approach where any disagreements were resolved before assigning a final rating. The five components (each rated as 1=yes, 0=no) were whether: a) the sample was clearly described (e.g., including demographic information, dates of transplant); b) the patients approached for enrollment were representative of the study site’s transplant population; c) the sample enrolled was representative of those approached; d) the source of nonadherence data and the time period covered by the nonadherence measure were clearly described; and e) analyses of nonadherence rates were appropriate (i.e., if patients varied in follow-up duration, rates were calculated via survival analysis techniques). A composite quality score was created for each study which was the count of the number of the five areas rated as yes (total score range, 0–5).
Psychosocial Risk Factors for Nonadherence
We extracted information from each study on the size of the association of each nonadherence outcome with a series of psychosocial characteristics. We aimed to examine as many as possible of the characteristics hypothesized in the adherence literature to serve as risk factors (2, 21, 24–27, 30–32, 35–39, 44). However, for any single nonadherence area, there were only sufficient numbers of studies (i.e., >5) examining the following variables in order for us to consider them: patient gender, age, education, income, nonwhite vs. white ethnicity, social support, perceived health, and pretransplant substance use.
Examination of Nonadherence Rates Across Outcome Areas
Patients in most studies had unequal follow-up time, typically because they entered a given study at different time points (e.g., different transplant dates) or were lost to follow- up at varying time points (e.g., due to death). As in other clinical epidemiologic contexts when the goal is to compare rates in the face of differences in observation time (49, 50), we examined event rates—cases of nonadherence—per 100 persons per year (i.e., per 100 person-years of observation). If total person-years of observation was not reported directly, we calculated it from either reported cumulative probabilities or descriptive information about the distribution of follow- up duration in the sample (49). The person-years metric is routinely used in examining health-related outcome rates in transplant (51) and other populations (52–55).
We calculated the pooled estimate of the nonadherence rate in each outcome area (rate=cases per 100 persons per year [PPY]) across all contributing studies, as well as within each organ transplant type. The pooled estimate is a weighted average that takes within-study variance into account. It was generated under a random effects model, in order to allow generalizability beyond the retrieved studies (56). For each statistically significant pooled estimate, we evaluated the impact of publication bias (i.e., that studies finding nonadherence rates exceeding zero may have been more likely to have been published) by calculating the “fail-safe N.” This is the number of missing studies obtaining null findings that would need to be added to the analysis so that the pooled estimate would no longer be statistically significant (57, 58).
When there was substantial variability across studies in nonadherence rates in a given outcome area (based on the Q test for heterogeneity), we used random effects meta-regression to determine whether the variability could be explained by four study characteristics: geographic location, design, quality, and nonadherence assessment methodology.
Examination of Psychosocial Risk Factors for Nonadherence
The association of nonadherence with each psychosocial variable was examined by extracting or calculating r, the Pearson correlation coefficient. This effect size (ES) indicates the strength and direction of association between pairs of variables. It was chosen for its flexibility and applicability across measurement scenarios (i.e., with continuous, dichotomous, or ranked variables) (59, 60). For each statistically significant average ES, we calculated the fail-safe N to estimate the impact of publication bias. We determined whether there was significant variability in ES across studies with the Q test. When there was significant variability, we examined whether study characteristics accounted for it.
Table 1 describes the 147 studies that met our inclusion criteria. (The appendix lists each study and the nonadherence outcomes examined; a bibliography is available from M.A.D.) The largest proportion of studies (49%) focused on kidney recipients, followed by heart recipients (23%) and liver recipients (20%). Few studies examined other types of organ recipients. Over 29,000 patients were included across all studies, contributing over 88,000 person-years of observation.
What Are the Nonadherence Rates in Each Outcome Area and Do These Rates Differ by Type of Transplantation?
Figure 2 shows the average rate and 95% confidence interval (CI) for each nonadherence outcome. For example, the average rate for immunosuppressant nonadherence was 22.6, or approximately 23 cases per 100 PPY. The average rates for all outcomes differ significantly from zero (i.e., CIs do not include zero). The rates are low and the CIs are narrow for substance use outcomes and attending clinic appointments. The highest nonadherence rates, with wider CIs, are found for taking immunosuppressants, exercise, diet, and monitoring vital signs.
The large fail-safe Ns for all rates in Figure 2 indicate that these average rates are very robust to the discovery of many additional studies reporting null findings (i.e., the observed rates are not artifacts of publication bias favoring reports finding large, significant nonadherence rates).
The Q tests in Figure 2 indicate that, except for illicit drug use, there is significant variability in the rates across studies. Is this variability due to differences between types of transplant? Table 2 shows the nonadherence rates for each outcome separately by transplant type. There are significant differences in only two outcome areas. Kidney recipient studies have a higher rate of immunosuppressant nonadherence (35.6 cases per 100 PPY) than studies of heart recipients (14.5 cases per 100 PPY) or liver recipients (6.7 cases per 100 PPY). Heart recipient studies show the highest rate of exercise nonadherence. (Although pancreas transplantation had a similarly high rate, it was excluded from between-group comparisons because it was based on a single study.)
Do Study-Related Characteristics Account for Variability in Nonadherence Rates?
We focused on the nine nonadherence areas where there was significant variability. Because meaningful analysis is difficult with small numbers of studies, we limited the analyses to nonadherence outcomes with >10 contributing studies. Table 3 displays the results for the six outcomes meeting this criterion. For each outcome, the nonadherence rate is shown according to study characteristic. For example, for taking immunosuppressants, the average nonadherence rate was 33.4 cases per 100 PPY for North American studies and 13.5 for studies from Europe and elsewhere. This difference was significant (Z=2.01, P<0.05). This test statistic was determined by meta-regression, in which the effects of all other study characteristics in Table 3 (study design, quality, nonadherence assessment method), as well as type of transplant, were controlled.
Table 3 shows that there were significant differences depending on study geographic location for two other nonadherence outcomes: European studies showed a lower rate of tobacco use, but a higher rate of exercise nonadherence than North American studies. Prospective studies obtained higher rates of clinic appointment nonadherence than cross-sectional/retrospective studies. Moderate quality studies showed higher rates of immunosuppressant nonadherence, and moderate to high quality studies showed higher rates of exercise nonadherence. For nonadherence assessment method, self-report methodology was associated with the highest rate of immunosuppressant nonadherence. The use of a multiple-method approach was associated with the highest rates of alcohol use and exercise nonadherence. A medical records approach was associated with the highest rate of clinic appointment nonadherence.
Are Nonadherence Rates Associated With Psychosocial Variables?
Because few studies examined psychosocial risk factors for the individual nonadherence outcomes, we collapsed the outcomes into four broad categories to consider in relation to psychosocial variables: immunosuppressant nonadherence; healthcare follow-up nonadherence (studies of clinic appointment attendance, blood work and tests, monitoring vital signs, or global nonadherence; the latter was included here because most studies based global nonadherence predominantly on these behaviors); diet/exercise nonadherence (studies of either outcome); and substance use (studies of tobacco, alcohol, or illicit drug use).
Figure 3 shows the direction and strength of the correlations of psychosocial variables and these four outcome categories. For example, across the 19 reports contributing data on the correlation of gender and immunosuppressant medication nonadherence, the average ES was r=−0.01 (CI: −0.03, 0.02). Psychosocial variables were considered relative to a given nonadherence outcome area (and were included in Figure 3) only if they were examined in >5 studies of that outcome area. Thus, for example, education could only be examined for immunosuppressant nonadherence and healthcare follow-up nonadherence.
Few significant psychosocial variables were found. Nonwhite ethnicity, poorer social support, and poorer perceived health were significantly associated with greater immunosuppressant nonadherence (average ES of 0.06, 0.10, and 0.15, respectively; CIs not overlapping with zero). The fail-safe N of 8 for nonwhite ethnicity is small, but the fail-safe Ns of 35 and 36 for the other two psychosocial variables suggest that these associations are fairly robust to the discovery of additional reports with null effects. Lower income was associated with greater healthcare follow-up nonadherence, but the fail-safe N was only 7. Pretransplant substance use was strongly correlated with posttransplant nonadherence to substance use restrictions, with a very large fail-safe N.
Q was significant for several ESs in Figure 3. However, neither type of transplant nor any study characteristic (e.g., design, quality) significantly accounted for the observed variability in ESs across studies. In addition, the variability was not explained by differences within any broad category of nonadherence considered (e.g., within the category of diet/exercise, there were no differences between studies of diet nonadherence vs. studies of exercise nonadherence).
The average nonadherence rates in the 10 outcome areas indicate that most transplant recipients are adherent. Nevertheless, all of the nonadherence rates reliably exceed zero and some rates are relatively high. The summary of rates in Table 2 provides clinicians with an essential tool to quickly gauge nonadherence risk in their patient populations, according to area of the regimen and transplant type. Risk estimates provide the bedrock for formulating clinical strategies to avoid or reduce risk, as discussed further below. From a research standpoint, Table 2 also clearly shows the areas of the regimen and types of transplant for which we continue to have limited knowledge regarding nonadherence risk.
Immunosuppressant nonadherence has been one of the most frequently studied areas, and we found it to be among those with the highest nonadherence rates: on average, the posttransplant healthcare provider can expect to see 23 nonadherent patients for every 100 individuals during a given year of follow-up. This rate is over six times greater than the nonadherence rates for tobacco or alcohol use, and it is almost four times greater than the rate of clinic appointment nonadherence. Instead, the rate is very similar to the nonadherence rates to prescribed diet and exercise, which are themselves recognized as pervasive problems in patient populations well beyond transplantation (44). The magnitude of the immunosuppressant nonadherence rate is of particular concern given these medications’ role in preventing graft rejection, related morbidities, and mortality (3–5, 7, 8, 11, 12, 20). Moreover, the relatively high rate is surprising because: a) many transplant programs attempt to screen transplant candidates to ensure that they understand and are likely to adhere to posttransplant medical care requirements (61) and b) the lifelong need to take immunosuppressant medications is routinely emphasized in posttransplant clinical follow-up care (62).
Immunosuppressant nonadherence is not, however, equally common across all types of transplant recipients. We found that it is most common in kidney recipients, with a rate of 36 cases per 100 PPY—a rate more than twice that observed in heart recipients and over five times greater than in liver recipients. The rates may be lower in these latter groups be-cause, although there is considerable variability across programs, more stringent psychosocial criteria may be applied in the transplant candidate selection process than are applied for kidney candidates (63, 64) and/or because the consequences of graft loss are more severe: unlike kidney recipients who may return to dialysis, the options for prolonging life for most heart or liver recipients are considerably more limited. Yet the rates of just 14 to 15 nonadherent heart recipients or 7 liver recipients per 100 PPY may be clinically unacceptable as well: all of these rates are at least double the rate of tobacco use, which even at its relatively lower level of 3 to 4 patients per 100 PPY, is still a powerful contributor to posttransplant morbidity and mortality (15, 18, 65, 66).
We also found geographic variations in immunosuppressant nonadherence rates. North American studies (largely from the United States) show higher rates than studies predominantly from Europe. The organization and financing of healthcare services and insurance coverage is very different in the United States than in many European countries, and this may play a key role in patients’ ability to adhere to their medication regimen. Indeed, a major summary of evidence regarding treatment adherence in chronic diseases concluded that such systems-related factors can have greater impact on patient behavior than any patient- specific characteristics (2). Ultimately, whether nonadherence to any element of the regimen varies by factors such as geographic region (or the type of healthcare services available by region) can only be fully answered through primary data collection efforts designed to directly compare patient groups while holding constant other sources of variation.
Beyond these conclusions regarding immunosuppressant nonadherence, it is noteworthy that average nonadherence rates varied considerably across components of the regimen. However, except for exercise nonadherence, they did not vary significantly by transplant type. Across components of the regimen, the nonadherence rates range from lows of approximately 1 to 4 cases per 100 PPY (tobacco, alcohol and illicit drug use), to highs of 19 to 25 cases per 100 PPY (nonadherence to exercise, diet, immunosuppressants, and other required behaviors). This variation has important clinical and research implications: it further supports the view that posttransplant nonadherence cannot be approached as a unified, single entity and that, instead, each specific behavioral area must be individually considered (21, 26, 44).
We also found that the assessment method can affect observed nonadherence rates (44). Self-report assessments yielded the highest immunosuppressant nonadherence rates—a finding similar to that obtained for medication-taking in a meta-analysis of nonadherence in nontransplant chronic disease populations (44). Thus, while indirect measures such as electronic medication monitoring have been described as the best choice for assessing medication nonadherence (3, 9, 33, 34, 67, 68), they may not in fact be optimal. Furthermore, they are expensive, labor intensive and often impractical in clinical settings (22, 47, 69). Our findings suggest that self-report methodologies, particularly those employing strategies to maximize accurate reports (1, 26, 37, 47, 69–71), should have a prominent place in both the clinical and research evaluation of transplant-related medication nonadherence. However, for other areas (e.g., exercise, alcohol use), self-report should be combined, when possible, with other methods, since we found that multiple-method assessments detected the highest nonadherence rates in these areas.
One of our major aims was to determine the degree to which nonadherence was associated with patient psychosocial characteristics. Among the psychosocial variables that we could examine, the most prominent finding was that pretransplant substance use history strongly predicted nonadherence to substance use restrictions posttransplant (r=.62). This suggests the importance of—and need for better— substance use interventions before transplant (and thereafter) in order to prevent posttransplant substance use. However, the other psychosocial variables that we considered were not highly related to any nonadherence area. Even when statistically reliable associations were found, the effect sizes were very small. Should clinicians view these findings as cause for pessimism, insofar as they suggest that patients at risk for nonadherence cannot be readily identified on the basis of personal characteristics? On the contrary, the findings may open the door to a major shift to a focus on provider-related and health systems-level factors that may be more important determinants of transplant recipient medical adherence (2, 27). Thus, although they have not yet received detailed consideration in transplant populations, many such factors have been found to influence adherence behaviors in other patient groups. They include the frequency and duration of clinic appointments and face-to-face interaction with providers; whether there is financial reimbursement for patient counseling and education on medical regimen management; whether and how much financial reimbursement is provided by health insurance plans for medications and costs related to other healthcare-promoting behaviors; whether patients receive care from the same provider over time, or fail to do so due to staff turnover or insurance issues (2). While some of these factors will be difficult for any single transplant clinician to address in efforts to improve patients’ adherence to the posttransplant regimen, others might be readily addressed through use of innovative strategies to facilitate patient education and communication with the transplant team (e.g., through internet-based applications) (72).
There are limitations to the meta-analysis. Although the electronic databases included English-language abstracts of all non-English studies (thereby allowing us to screen studies regardless of language of publication and determine those clearly not relevant), we could not review the full text of 19 non-English reports that were potentially relevant to our analysis. This group is minute relative to the body of reports reviewed. Moreover, the 147 studies included in our analyses came from 28 countries (including 22 where English is not the primary language), indicating significant international representation. We also excluded studies of pediatric transplant recipients; our findings are therefore not generalizable to them. We excluded pediatric samples because adherence issues in children are very different than those for adults (73, 74), and hence could lead to very different nonadherence rates than those for adult samples. Other limitations of our analysis reflect the limitations of the research literature. For example, we could not examine changes in nonadherence rates over time posttransplant because very few studies provided any relevant data and none examined predictors of nonadherence as a function of time posttransplant. We could not examine additional patient-specific variables proposed to be important correlates of nonadherence (e.g., pretransplant psychiatric history, knowledge of the regimen, physical functional limitations, transplant-related financial hardship) (2, 21, 24–27, 30–32, 35–39, 69) because too few studies have included them.
The limitations in the literature, combined with our findings, suggest avenues for future work. We have noted that research should pursue a richer array of potential correlates of nonadherence. This would allow for the identification of those with strong enough effects to warrant interventions that target them and that thereby might improve adherence. But efforts to test adherence-promoting interventions need not await additional correlational studies. Numerous intervention strategies appear promising in other chronic disease populations faced with complex regimens (75, 76). In addition, some transplant-focused organizations have recently developed and disseminated their own adherence promotion materials (77). These materials could benefit from formal, empirical evaluation efforts that, even if not involving randomized trials, could provide preliminary information regarding potential effectiveness. Given our findings of high rates of nonadherence to some components of the posttransplant regimen, continued research and clinical efforts to ameliorate these rates cannot afford to delay a major move to intervention research and evaluation.
1. Haynes RB, Taylor DW, Sackett DL (eds). Compliance in Health Care. Baltimore: Johns Hopkins University Press, 1979.
2. World Health Organization (2003). Adherence to long-term therapies: Evidence for action. Available at: www.who.int/chronic_conditions/adherencereport/en/print.html
. Accessed September 7, 2006.
3. Butler JA, Roderick P, Mullee M, et al. Frequency and impact of nonadherence to immunosuppressants after renal transplantation: A systematic review. Transplantation
2004; 77: 769.
4. De Geest S, Borgermans L, Gemoets H, et al. Incidence, determinants, and consequences of subclinical noncompliance with immunosuppessive therapy in renal transplant recipients. Transplantation
1995; 59: 340.
5. De Geest S, Abraham I, Moons P, et al. Late acute rejection and subclinical noncompliance with cyclosporine therapy in heart recipients. J Heart Lung Transplant
1998; 17: 854.
6. De Geest S, Dobbels F, Martin S, et al. Clinical risk associated with appointment noncompliance in heart transplant recipients. Prog Transplant
2000; 10: 162.
7. Dew MA, Kormos RL, Roth LH, et al. Early post-transplant medical compliance and mental health predict physical morbidity and mortality one to three years after heart transplantation. J Heart Lung Transplant
1999; 18: 549.
8. Gaston RS, Hudson SL, Ward M, et al. Late renal allograft loss: Noncompliance masquerading as chronic rejection. Transplant Proc
1999; 31: 21S.
9. Gaston RS. Noncompliance in solid organ transplantation. Curr Opin Organ Transplant
2001; 6: 164.
10. Gayowski T, Singh N, Keyes L, et al. Late-onset renal failure after liver transplantation: Role of posttransplant alcohol use. Transplantation
2000; 69: 383.
11. Ghods AJ, Nasrollahzadeh D. Noncompliance with immunnosuppressive (sic) medications after renal transplantation. Experimental Clin Transplant
2003; 1: 39.
12. Hilbrands LB, Hoitsma AJ, Koene RAP. Medication compliance after renal transplantation. Transplantation
1995; 60: 914.
13. Haustein SV, McGuire BM, Eckhoff DE, et al. Impact of noncompliance and donor/recipient race matching on chronic liver rejection. Transplant Proc
2002; 34: 1497.
14. Lucey MR, Carr K, Beresford TP, et al. Alcohol use after liver transplantation in alcoholics: A clinical cohort follow-up study. Hepatology
1997; 25: 1223.
15. Nägele H, Kalmár P, Rödiger W, Stubbe HM. Smoking after heart transplantation: An under-estimated hazard? Eur J Cardiothorac Surg
1997; 12: 70.
16. Nevins TE, Kruse L, Skeans MA, Thomas W. The natural history of azathioprine compliance after renal transplantation. Kidney Internat
2001; 60: 1565.
17. Pageaux G-P, Bismuth M, Perney P, et al. Alcohol relapse after liver transplantation for alcoholic liver disease: Does it matter? J Hepatol
2003; 38: 629.
18. Radovancevic B, Poindexter S, Birovljev S, et al. Risk factors for the development of accelerated coronary artery disease in cardiac transplant recipients. Eur J Cardiothorac Surg
1990; 4: 309.
19. Schweizer RT, Rovelli M, Palmeri D, et al. Noncompliance in organ transplant recipients. Transplantation
1990; 49: 374.
20. Vlaminck H, Maes B, Evers G, et al. Prospective study on late consequences of subclinical non-compliance with immunosuppressive therapy in renal transplant patients. Am J Transplant
2004; 4: 1509.
21. Bunzel B, Laederach-Hofmann K. Solid organ transplantation: Are there predictors for posttransplant noncompliance? A literature overview. Transplantation
2000; 70: 711.
22. Chisholm MA. Issues of adherence to immunosuppressant therapy after solid-organ transplantation. Drugs
2002; 62: 567.
23. Colón EA, Popkin MK, Matas AJ, Callies AL. Overview of noncompliance in renal transplantation. Transplant Rev
1991; 5: 175.
24. Denhaerynck K, Dobbels F, Cleemput I, et al. Prevalence, consequences, and determinants of nonadherence in adult renal transplant patients: A literature review. Transplant Internat
2005; 18: 1121.
25. Desmyttere A, Dobbels F, Cleemput I, De Geest S. Noncompliance in organ transplantation: Is it worth worrying about? Acta Gastroenterol Belg
2005; 68: 347.
26. Dew MA, Dunber-Jacob J, Switzer GE, et al. Adherence to the medical regimen in transplantation. In: Rodrigue JR, ed. Biopsychosocial Perspectives on Transplantation. New York: Kluwer Academic/Plenum Publishers, 2001: 93.
27. De Geest S, Dobbels F, Fluri C, et al. Adherence to the therapeutic regimen in heart, lung
, and heart-lung
transplant recipients. J Cardiovasc Nurs
2005; 20: S88.
28. Jindel RM, Joseph JT, Morris MC, et al. Noncompliance after kidney transplantation: A systematic review. Transplant Proc
2003; 35: 2868.
29. Kaul V, Khurana S, Munoz S. Management of medication noncompliance in solid-organ transplant recipients. BioDrugs
2000; 13: 313.
30. Loghman-Adham M. Medication noncompliance in patients with chronic disease: Issues in dialysis and renal transplantation. Am J Managed Care
2003; 9: 155.
31. Laederach-Hofmann K, Bunzel B. Noncompliance in organ transplant recipients: A literature review. Gen Hosp Psychiatry
2000; 22: 412.
32. Wainwright SP, Gould D. Non-adherence with medications in organ transplant patients: A literature review. J Adv Nurs
1997; 26: 968.
33. De Geest S, Abraham I, Dunbar-Jacob J. Measuring transplant patients’ compliance with immunosuppressive therapy. Western J Nurs Res
1996; 18: 595.
34. De Geest S, Vanhaecke J. Methodological issues in transplant compliance research. Transplant Proc 1999; 31: 81S.
35. Bowen DJ, Helmes A, Lease E. Predicting compliance: How are we doing? In: Burke LE, Ockene IS, eds. Compliance in Healthcare and Research. Armonk, NY: Futura, 2001: 25.
36. DiMatteo MR, Martin LR. Health Psychology. Boston: Allyn & Bacon, 2002.
37. Dunbar-Jacob J, Erlen JA, Schlenk EA, et al. Adherence in chronic disease. Annu Rev Nurs Res
2000; 18: 48.
38. Elder JP, Ayala GX, Harris S. Theories and intervention approaches to health-behavior change in primary care. Am J Prev Med
1999; 17: 275.
39. Salovey P, Rothman AJ, Rodin J. Health behavior. In: Gilbert DT, Fiske ST, Gardner L, eds. The Handbook of Social Psychology, Vol 2. 4th ed. Boston: McGraw Hill, 1998.
40. Stroup DF, Berlin JA, Morton SC, et al. Meta-analysis of observational studies in epidemiology (MOOSE): A proposal for reporting. JAMA
2000; 283: 2008.
41. Moher D, Cook DJ, Eastwood S, et al. Improving the quality of reports of meta-analyses of randomised controlled trials: The QUOROM statement. Lancet
1999; 354: 1896.
42. Haynes RB, Yao X, Degani A, et al. Interventions for enhancing medication adherence. Cochrane Consumers and Communication Group, Cochrane Database of Systematic Reviews 2006; 4: Article No. CD000011.
43. McDonald HP, Garg AX, Haynes RB. Interventions to enhance patient adherence to medication prescriptions: Scientific review. JAMA
2002; 288: 2868.
44. DiMatteo MR. Variations in patients’ adherence to medical recommendations: A quantitative review of 50 years of research. Med Care
2004; 42: 200.
45. Painter PL, Hector L, Ray K, et al. A randomized trial of exercise training after renal transplantation. Transplantation
2002; 74: 42.
46. Krasnoff JB, Vintro AQ, Ascher NL, et al. Objective measures of health-related quality of life over 24 months post-liver transplantation. Clin Transplant
2005; 19: 1.
47. Farmer KC. Methods for measuring and monitoring medication regimen adherence in clinical trials and clinical practice. Clin Therapeutics
1999; 21: 1074.
48. Downs SH, Black N. The feasibility of creating a checklist for the assessment of the methodological quality both of randomised and non-randomised studies of health care interventions. J Epidemiol Community Health
1998; 52: 377.
49. Lilienfeld DE, Stolley PD. Foundations of epidemiology, 3rd ed. NY: Oxford University Press, 1994.
50. Kalbfleisch JD, Prentice RL. The statistical analysis of failure time data, 2nd ed. Hoboken, NJ: Wiley, 2002.
51. U.S. Organ Procurement and Transplantation Network and the Scientific Registry of Transplant Recipients. 2005 Annual Report, Transplant Data 1995–2004. Available at: www.optn.org/AR2005/default.htm
. Accessed July 29, 2006.
52. Neuzil KM, Mellen BG, Wright PF, et al. The effect of influenza on hospitalizations, outpatient visits, and courses of antibiotics in children. N Engl J Med
2000; 342: 225.
53. Palella FJ Jr, Delaney KM, Moorman AC, et al. Declining morbidity and mortality among patients with advanced human immunodeficiency virus infection. HIV Outpatient Study Investigators. NEJM
1998; 338: 853.
54. Eaden JA, Abrams KR, Mayberry JF. The risk of colorectal cancer in ulcerative colitis: A meta-analysis. Gut
2001; 48: 526.
55. Law MR, Wald NJ, Rudnicka AR. Quantifying effect of statins on low density lipoprotein cholesterol, ischaemic heart disease, and stroke: Systematic review and meta-analysis. BMJ
2003; 326: 1423.
56. Fleiss JL. The statistical basis of meta-analysis. Stat Methods Med Res
1993; 2: 121.
57. Begg CB. Publication bias. In Cooper H, Hedges LV, eds. Handbook of Research Synthesis. New York: Russell Sage, 1994; 399.
58. Rosenthal R. The “file-drawer problem” and tolerance for null results. Psychol Bull
1979; 86: 638.
59. Rosenthal R. Parametric measures of effect size. In Cooper H, Hedges LV, eds. Handbook of Research Synthesis. New York: Russell Sage, 1994; 231.
60. Rosenthal R, DiMatteo MR. Meta-analysis: Recent developments in quantitative methods for literature reviews. Annu Rev Psychology
2001; 52: 59.
61. Olbrisch ME, Benedict SM, Ashe K, Levenson JL. Psychological assessment and care of organ transplant patients. J Consult Clin Psychol
2002; 70: 771.
62. Dumas-Hicks DH. Immunosuppression. In Cupples SA, Ohler L, eds. Transplantation Nursing Secrets. Philadelphia: Hanley & Belfus, 2003; 67.
63. Levenson JL, Olbrisch ME. Psychosocial evaluation of organ transplant candidates: A comparative survey of process, criteria, and outcomes in heart, liver, and kidney transplantation. Psychosomatics
1993; 34: 314.
64. Levenson JL, Olbrisch ME, Kulstad J. Psychosocial evaluation of organ transplant candidates: Comparative surveys of process criteria and outcomes 1989 and 2000. Psychosomatics
2002; 43: 167.
65. Collins CM, Beresford TP. Smoking and heart transplantation. Curr Opin Organ Transplant
1999; 4: 155.
66. Muñoz SJ. Tobacco use by liver transplant recipients: Grappling with a smoking gun. Liver Transplant
2005; 11: 606.
67. Düsing R, Lottermoser K, Mengden T. Compliance with drug therapy—new answers to an old question. Nephrol Dial Transplant
2001; 16: 1317.
68. Weng FL, Israni AJ, Joffe MM, et al. Race and electronically measured adherence to immunosuppressive medications after deceased donor renal transplantation. J Am Soc Nephrol
2005; 16: 1839.
69. Chapman JR. Compliance: The patient, the doctor, and the medication? Transplantation
2004; 77: 782.
70. DiMartini A, Day N, Dew MA, et al. Alcohol use following liver transplantation: A comparison of follow-up methods. Psychosomatics
2001; 42: 55.
71. Hilbert GA. Accuracy of self-reported measures of compliance. Nurs Res
1985; 34: 319.
72. Dew MA, Goycoolea JM, Harris RC, et al. An internet-based intervention to improve psychosocial outcomes in heart transplant recipients and family caregivers: Development and evaluation. J Heart Lung Transplant
2004; 23: 745.
73. Griffin KJ, Elkin TD. Non-adherence in pediatric transplantation: A review of the existing literature. Pediatric Transplant
2001; 5: 246.
74. Neu AM. Special issues in pediatric kidney transplantation. Adv Chronic Kidney Dis
2006; 13: 62.
75. Roter DL, Hall JA, Merisca R, et al. Effectiveness of interventions to improve patient compliance: A meta-analysis. Med Care
1998; 36: 1138.
76. Haynes RB, Yao X, Degani A, et al. Interventions to enhance medication adherence. Cochrane Database of Systematic Reviews 2005; 4: CD000011.
77. International Transplant Nurses Society. Patient Education Materials. Available at: www.itns.org/education/patient-materials
. Accessed July 29, 2006.