A Systematic Review and Meta-analysis of the Factors Associated With Nonadherence and Discontinuation of Statins Among People Aged ≥65 Years

A Systematic Review and Meta-analysis of the Factors Associated With Nonadherence and... Abstract Background Older individuals (aged ≥65 years) are commonly prescribed statins but may experience a range of barriers in adhering to therapy. The factors associated with poor statin adherence and/or discontinuation among this population have not been comprehensively reviewed. Methods We conducted a systematic review to identify English articles published through December 12, 2016 that reported factors associated with nonadherence and/or discontinuation of statins among older persons. Data were pooled via random-effects meta-analysis techniques. Results Forty-five articles reporting data from more than 1.8 million older statin users from 13 countries were included. The factors associated with increased statin nonadherence were black/non-white race (odds ratio [OR] 1.66, 95% confidence interval [CI] 1.39–1.98), female gender (OR 1.08, 95% CI 1.03–1.13), current smoker (OR 1.12, 95% CI 1.03–1.21), higher copayments (OR 1.38, 95% CI 1.25–1.52), new user (OR 1.58, 95% CI 1.21–2.07), lower number of concurrent cardiovascular medications (OR 1.08, 95% CI 1.06–1.09), primary prevention (OR 1.49, 95% CI 1.40–1.59), having respiratory disorders (OR 1.17, 95% CI 1.12–1.23) or depression (OR 1.11, 95% CI 1.06–1.16), and not having renal disease (OR 1.09, 95% CI 1.04–1.14). The factors associated with increased statin discontinuation were lower income status (OR 1.20, 95% CI 1.06–1.36), current smoker (OR 1.14, 95% CI 1.06–1.23), higher copayment (OR 1.61, 95% CI 1.53–1.70), higher number of medications (OR 1.04, 95% CI 1.01–1.06), presence of dementia (OR 1.18, 95% CI 1.02–1.36), cancer (OR 1.22, 95% CI 1.11–1.33) or respiratory disorders (OR 1.19, 95% CI 1.05–1.34), primary prevention (OR 1.66, 95% CI 1.24–2.22), and not having hypertension (OR 1.13, 95% CI 1.07–1.20) or diabetes (OR 1.09, 95% CI 1.04–1.15). Conclusion Interventions that target potentially modifiable factors including financial and social barriers, patients’ perceptions about disease risk as well as polypharmacy may improve statin use in the older population. HMG-CoA reductase inhibitors, Adherence, Persistence, Risk indicators Background Cardiovascular disease (CVD) is a leading cause of global morbidity and mortality, accounting for 17.9 million deaths in 2015 (1). The impact of CVD on health systems and national economies is significant. In the United States, the total cost attributed to CVD exceeded USD$300 billion in 2011/2012 (2), while in England, CVD cost the National Health Service (NHS) nearly £7 billion in 2012/2013 (3). Thus, the prevention and management of CVD is of major importance to clinicians, policy makers and other interest groups (eg, payers). Statins have been demonstrated in numerous clinical studies to be highly efficacious for the prevention of CVD (4). To achieve the desired clinical effects of statin treatment, patients ought to follow the treatment plan. However, adherence (the extent to which patients follow the dosing regimen) (5) and persistence (the extent to which patients continue treatment for the prescribed duration) (5) among statin users have been documented to be poor (6–8). Older individuals experience significant barriers toward adherence (9), with about half nonadherent and a quarter discontinuing statins within the first treatment year (10). Studies have suggested that patient, medical history, and health system-related factors may individually or by interaction influence adherence and persistence among statin users (6,11). However, no systematic reviews on the predictors of nonadherence and/or discontinuation among older statin users have been published. Since older patients have distinctive characteristics, a greater understanding of the factors that are associated with suboptimal statin use in this population is essential to facilitate the development of targeted interventions. Accordingly, we reviewed the literature in order to identify factors associated with nonadherence and discontinuation among older statin users (aged ≥65 years). The current study was part of a larger review on the patterns and barriers to statin use in older people (12). Methods Search Strategy The systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and the detailed protocol has been published elsewhere (12). To identify relevant studies, we searched Medline, Embase, CINAHL, PsycINFO, National Health Service Economic and Evaluation Database (NHSEED), Database of Abstracts of Reviews of Effects (DARE) and the Cochrane Central Register of Controlled Trials from inception till December 12, 2016. The search terms adopted included those related to the intervention (“statins”, “hydroxymethylglutaryl-coA reductase inhibitors” or individual generic and proprietary names) and outcomes (“patient compliance”, “medication adherence/nonadherence”, “persistence”, “discontinuation”, “drop out”, etc.); the full search strategy is provided in Supplementary Table S1. Searches were restricted to English language and authors were contacted for unpublished data when necessary. Study Selection and Evaluation Articles were considered for inclusion if predictors of nonadherence and/or discontinuation among older statin users were reported. Studies adopting objective adherence measurements (such as pill counts and refill data) were eligible for inclusion. For studies that utilized self-reports, only those adopting validated scales were selected. For studies that measured adherence via the medication possession ratio, proportion of days covered (PDC), or proportion of doses taken, only those employing an 80% cutoff to dichotomize adherence were considered. The 80% threshold has been reported to represent the minimum adherence level needed to achieve satisfactory clinical effect of statin therapy (13,14). We did not discriminate studies on the basis of methods used to assess discontinuation. Studies that adopted the permissible gap method (ie, specified the maximum break in refill beyond which a user is considered to have discontinued treatment) (5) or relied on other approaches including patient self-reports were considered. We assessed the methodological quality of observational studies by using a set of questions formulated with reference to the National Institute of Health (NIH) Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies (15). The quality of randomized clinical trials (RCTs) was assessed through the use of the Joanna Briggs Institute’s (JBI) critical appraisal checklist for RCTs (16). Studies that scored ≥70% of the applied assessment criteria were graded as high quality. Data Extraction and Analysis A standardized data extraction template was used to collect descriptive information (eg, study reference, country, adherence definition, etc.) and predictors’ data. Two reviewers (R.O., A.J.) independently extracted and cross-checked the data. To ensure consistency in reporting, the direction of effect of studies reporting predictors of adherence or persistence was reversed. Each predictor’s overall effect was determined via random effects meta-analysis with inverse variance weighting. The effect measure of interest was odds ratios (OR) (12). If a study utilized another effect measure (eg, standard mean difference), it was converted to an OR (17). When available, adjusted effect size estimates were preferred. If predictors were reported per subgroupings (eg, males and females), these were included as separate terms in the meta-analysis. The I2 statistic was used to quantify statistical heterogeneity across studies. The robustness of pooled estimates was tested through leave-one-out sensitivity analyses (17). Subgroup analyses based on region of study (North America vs rest of the world), sample size (<10,000 vs ≥10,000), and year of study (<2012 vs ≥2012) were performed to explore possible sources of heterogeneity between studies. Analyses were conducted with MetaXL (18)—an add-in meta-analysis tool in Microsoft Excel. Results Following electronic searches and removal of duplicates, the titles and abstracts of 7,705 articles were screened. The full text was evaluated for 398 articles and 45 articles (43 unique studies) were included in the final review (Figure 1) (19–63). The included articles were published in the period 2002–2016, with the majority (73%) published in the last 5 years (2012–2016). The included studies involved a total population of 1,842,054 sampled from 13 countries. Thirty three percent of studies were from North America, 53% from Europe and 14% from the rest of the world (Supplementary Table S2). The majority (80%) of studies from which nonadherence predictors were retrieved utilized the PDC methodology. Similarly, 81% of studies reporting discontinuation predictors adopted the permissible gap method (ie, they defined a maximum break in statin use beyond which patients were considered to have discontinued treatment) (5). Figure 1. View largeDownload slide Flow diagram of study selection. Figure 1. View largeDownload slide Flow diagram of study selection. Factors Associated With Nonadherence Across the studies retrieved, a total of 107 factors were assessed for their association with statin nonadherence. However, data were only able to be summarized for 31 variables, the majority (n = 21) of which showed no significant association with nonadherence upon pooling. Demographic, lifestyle, and health system-related factors The association between age and statin nonadherence was not reported in a consistent manner across studies. Increasing age (per additional year or decade) was associated with higher nonadherence in three studies (31,43,52); two studies reported an inverse association between age and nonadherence (28,49), and five studies found no association between age and nonadherence (29,33,41,48,58). Although, some studies that stratified age into categories (eg, 65–69, ≥75, etc.) reported higher nonadherence among higher age-groups (24,32,37,55), others found lower, insignificant associations or inconsistent trends in nonadherence among higher age-groups (26,35,36,51,62,63). Thus, among this older population, the association between age and nonadherence to statin therapy is equivocal. Compared with men, women were more likely to be nonadherent to statin therapy (OR 1.08, 95% confidence interval [CI] 1.03–1.13). There were also racial differences in statin adherence, with black or non-white race having a 66% higher likelihood of nonadherence than white populations (OR 1.66, 95% CI 1.39–1.98). Being a current smoker was associated with a higher likelihood of nonadherence (OR 1.12, 95% CI 1.03–1.21). In comparison to prevalent users, new users were more likely to be nonadherent (OR 1.58, 95% CI 1.21–2.07). Higher copayment/cost also increased the likelihood of nonadherence (OR 1.38, 95% CI 1.25–1.52). Comorbidities and treatment-related factors A history of CVD (myocardial infarction, or stroke) was associated with higher adherence; patients receiving statins for primary prevention had a 49% higher likelihood of nonadherence (OR 1.49, 95% CI 1.40–1.59) (Table 1). Similarly, renal disease and taking other cardiovascular medications were also associated with higher adherence; those without renal disease had a 9% greater likelihood of nonadherence (OR 1.09; 95% CI 1.04–1.14) and those on a lower number of other cardiovascular medications had an 8% greater likelihood of nonadherence (OR 1.08, 95% CI 1.06–1.09). There was an 11% and 17% higher likelihood of nonadherence among statin users who had depression (OR 1.11, 95% CI 1.06–1.16) or respiratory disorder (chronic obstructive pulmonary disease or asthma) (OR 1.17, 95% CI 1.12–1.23), respectively. Table 1. Factors Associated With Nonadherence Among Older Statin Users Factors  Number of Studies Pooled  Sample Size  Odds Ratio (95% CI)  I2 Statistic  p Value  Demographic/Lifestyle  Female gender  25  462,344  1.08 (1.03–1.13)  0.86  <.001  Black or non-white race  6  105,368  1.66 (1.39–1.98)  0.94  <.001  Unmarried vs Married  3  37,102  0.96 (0.76–1.16)  0.54  .110  Lower income status  10  252,182  1.12 (0.96–1.30)  0.98  <.001  Low education  2  29,199  1.01 (0.98–1.03)  0.00  .700  Alcohol/substance abuse  2  34,523  1.05 (0.96–1.14)  0.64  .100  Current smoker  3  15,185  1.12 (1.03–1.21)  0.00  .500  Health system  Higher copayment  4  136,022  1.38 (1.25–1.52)  0.65  .040  Treated by nonspecialist*  4  110,552  1.07 (0.93–1.23)  0.00  .390  Receiving treatment outside hospital  3  103,550  1.05 (0.96–1.14)  0.00  .490  Treatment related  New user  8  104,136  1.58 (1.21–2.07)  0.99  <.001  Moderate vs low intensity therapy  5  106,648  0.91 (0.58–1.44)  0.98  <.001  High vs low intensity therapy  5  106,648  1.08 (0.88–1.99)  0.87  <.001  Atorvastatin vs Simvastatin**  5  45,316  0.95 (0.82–1.09)  0.74  <.001  Rosuvastatin vs simvastatin**  4  43,678  1.13 (0.76–1.69)  0.85  <.001  Fluvastatin vs Simvastatin**  3  40,983  1.15 (0.83–1.59)  0.84  <.001  Pravastatin vs simvastatin**  3  40,983  1.01 (0.91–1.11)  0.00  .420  Lovastatin vs. simvastatin**  2  37,440  1.16 (0.99–1.34)  0.00  .530  Concurrent CV medications (per addition)  2  124,380  0.93 (0.92–0.94)  0.00  .590  No. of other medications  4  139,186  1.01 (0.99–1.02)  0.85  <.001  Cardiac comorbidities  No CVD (1oprevention)  6  197,897  1.49 (1.40–1.59)  0.79  <.001  No hypertension  11  258,901  0.96 (0.85–1.08)  0.93  <.001  No dyslipidaemia  5  179,324  1.14 (0.94–1.38)  0.90  <.001  No diabetes  16  240,194  0.98 (0.93–1.03)  0.77  <.001  Noncardiac comorbidities  Dementia  5  227,230  1.03 (0.78–1.37)  0.93  <.001  Depression  6  165,889  1.11 (1.06–1.16)  0.34  .180  Respiratory disorders‡  5  107,505  1.17 (1.12–1.23)  0.00  .600  Obesity  2  34,200  1.38 (0.95–2.00)  0.00  .960  Cancer  7  89,470  1.00 (0.91–1.10)  0.43  .100  No renal disease  7  112,286  1.09 (1.04–1.14)  0.00  .680  Rheumatoid arthritis  3  37,440  1.07 (0.92–1.24)  0.09  .290  Factors  Number of Studies Pooled  Sample Size  Odds Ratio (95% CI)  I2 Statistic  p Value  Demographic/Lifestyle  Female gender  25  462,344  1.08 (1.03–1.13)  0.86  <.001  Black or non-white race  6  105,368  1.66 (1.39–1.98)  0.94  <.001  Unmarried vs Married  3  37,102  0.96 (0.76–1.16)  0.54  .110  Lower income status  10  252,182  1.12 (0.96–1.30)  0.98  <.001  Low education  2  29,199  1.01 (0.98–1.03)  0.00  .700  Alcohol/substance abuse  2  34,523  1.05 (0.96–1.14)  0.64  .100  Current smoker  3  15,185  1.12 (1.03–1.21)  0.00  .500  Health system  Higher copayment  4  136,022  1.38 (1.25–1.52)  0.65  .040  Treated by nonspecialist*  4  110,552  1.07 (0.93–1.23)  0.00  .390  Receiving treatment outside hospital  3  103,550  1.05 (0.96–1.14)  0.00  .490  Treatment related  New user  8  104,136  1.58 (1.21–2.07)  0.99  <.001  Moderate vs low intensity therapy  5  106,648  0.91 (0.58–1.44)  0.98  <.001  High vs low intensity therapy  5  106,648  1.08 (0.88–1.99)  0.87  <.001  Atorvastatin vs Simvastatin**  5  45,316  0.95 (0.82–1.09)  0.74  <.001  Rosuvastatin vs simvastatin**  4  43,678  1.13 (0.76–1.69)  0.85  <.001  Fluvastatin vs Simvastatin**  3  40,983  1.15 (0.83–1.59)  0.84  <.001  Pravastatin vs simvastatin**  3  40,983  1.01 (0.91–1.11)  0.00  .420  Lovastatin vs. simvastatin**  2  37,440  1.16 (0.99–1.34)  0.00  .530  Concurrent CV medications (per addition)  2  124,380  0.93 (0.92–0.94)  0.00  .590  No. of other medications  4  139,186  1.01 (0.99–1.02)  0.85  <.001  Cardiac comorbidities  No CVD (1oprevention)  6  197,897  1.49 (1.40–1.59)  0.79  <.001  No hypertension  11  258,901  0.96 (0.85–1.08)  0.93  <.001  No dyslipidaemia  5  179,324  1.14 (0.94–1.38)  0.90  <.001  No diabetes  16  240,194  0.98 (0.93–1.03)  0.77  <.001  Noncardiac comorbidities  Dementia  5  227,230  1.03 (0.78–1.37)  0.93  <.001  Depression  6  165,889  1.11 (1.06–1.16)  0.34  .180  Respiratory disorders‡  5  107,505  1.17 (1.12–1.23)  0.00  .600  Obesity  2  34,200  1.38 (0.95–2.00)  0.00  .960  Cancer  7  89,470  1.00 (0.91–1.10)  0.43  .100  No renal disease  7  112,286  1.09 (1.04–1.14)  0.00  .680  Rheumatoid arthritis  3  37,440  1.07 (0.92–1.24)  0.09  .290  Note: CI = Confidence interval. *Specialist refers to cardiologist; 1o primary; CVD = cardiovascular disease (myocardial infarction or stroke); ‡respiratory disorders refers to asthma or chronic obstructive pulmonary disease; **Indicates statin at index prescription; CV = cardiovascular. View Large Table 1. Factors Associated With Nonadherence Among Older Statin Users Factors  Number of Studies Pooled  Sample Size  Odds Ratio (95% CI)  I2 Statistic  p Value  Demographic/Lifestyle  Female gender  25  462,344  1.08 (1.03–1.13)  0.86  <.001  Black or non-white race  6  105,368  1.66 (1.39–1.98)  0.94  <.001  Unmarried vs Married  3  37,102  0.96 (0.76–1.16)  0.54  .110  Lower income status  10  252,182  1.12 (0.96–1.30)  0.98  <.001  Low education  2  29,199  1.01 (0.98–1.03)  0.00  .700  Alcohol/substance abuse  2  34,523  1.05 (0.96–1.14)  0.64  .100  Current smoker  3  15,185  1.12 (1.03–1.21)  0.00  .500  Health system  Higher copayment  4  136,022  1.38 (1.25–1.52)  0.65  .040  Treated by nonspecialist*  4  110,552  1.07 (0.93–1.23)  0.00  .390  Receiving treatment outside hospital  3  103,550  1.05 (0.96–1.14)  0.00  .490  Treatment related  New user  8  104,136  1.58 (1.21–2.07)  0.99  <.001  Moderate vs low intensity therapy  5  106,648  0.91 (0.58–1.44)  0.98  <.001  High vs low intensity therapy  5  106,648  1.08 (0.88–1.99)  0.87  <.001  Atorvastatin vs Simvastatin**  5  45,316  0.95 (0.82–1.09)  0.74  <.001  Rosuvastatin vs simvastatin**  4  43,678  1.13 (0.76–1.69)  0.85  <.001  Fluvastatin vs Simvastatin**  3  40,983  1.15 (0.83–1.59)  0.84  <.001  Pravastatin vs simvastatin**  3  40,983  1.01 (0.91–1.11)  0.00  .420  Lovastatin vs. simvastatin**  2  37,440  1.16 (0.99–1.34)  0.00  .530  Concurrent CV medications (per addition)  2  124,380  0.93 (0.92–0.94)  0.00  .590  No. of other medications  4  139,186  1.01 (0.99–1.02)  0.85  <.001  Cardiac comorbidities  No CVD (1oprevention)  6  197,897  1.49 (1.40–1.59)  0.79  <.001  No hypertension  11  258,901  0.96 (0.85–1.08)  0.93  <.001  No dyslipidaemia  5  179,324  1.14 (0.94–1.38)  0.90  <.001  No diabetes  16  240,194  0.98 (0.93–1.03)  0.77  <.001  Noncardiac comorbidities  Dementia  5  227,230  1.03 (0.78–1.37)  0.93  <.001  Depression  6  165,889  1.11 (1.06–1.16)  0.34  .180  Respiratory disorders‡  5  107,505  1.17 (1.12–1.23)  0.00  .600  Obesity  2  34,200  1.38 (0.95–2.00)  0.00  .960  Cancer  7  89,470  1.00 (0.91–1.10)  0.43  .100  No renal disease  7  112,286  1.09 (1.04–1.14)  0.00  .680  Rheumatoid arthritis  3  37,440  1.07 (0.92–1.24)  0.09  .290  Factors  Number of Studies Pooled  Sample Size  Odds Ratio (95% CI)  I2 Statistic  p Value  Demographic/Lifestyle  Female gender  25  462,344  1.08 (1.03–1.13)  0.86  <.001  Black or non-white race  6  105,368  1.66 (1.39–1.98)  0.94  <.001  Unmarried vs Married  3  37,102  0.96 (0.76–1.16)  0.54  .110  Lower income status  10  252,182  1.12 (0.96–1.30)  0.98  <.001  Low education  2  29,199  1.01 (0.98–1.03)  0.00  .700  Alcohol/substance abuse  2  34,523  1.05 (0.96–1.14)  0.64  .100  Current smoker  3  15,185  1.12 (1.03–1.21)  0.00  .500  Health system  Higher copayment  4  136,022  1.38 (1.25–1.52)  0.65  .040  Treated by nonspecialist*  4  110,552  1.07 (0.93–1.23)  0.00  .390  Receiving treatment outside hospital  3  103,550  1.05 (0.96–1.14)  0.00  .490  Treatment related  New user  8  104,136  1.58 (1.21–2.07)  0.99  <.001  Moderate vs low intensity therapy  5  106,648  0.91 (0.58–1.44)  0.98  <.001  High vs low intensity therapy  5  106,648  1.08 (0.88–1.99)  0.87  <.001  Atorvastatin vs Simvastatin**  5  45,316  0.95 (0.82–1.09)  0.74  <.001  Rosuvastatin vs simvastatin**  4  43,678  1.13 (0.76–1.69)  0.85  <.001  Fluvastatin vs Simvastatin**  3  40,983  1.15 (0.83–1.59)  0.84  <.001  Pravastatin vs simvastatin**  3  40,983  1.01 (0.91–1.11)  0.00  .420  Lovastatin vs. simvastatin**  2  37,440  1.16 (0.99–1.34)  0.00  .530  Concurrent CV medications (per addition)  2  124,380  0.93 (0.92–0.94)  0.00  .590  No. of other medications  4  139,186  1.01 (0.99–1.02)  0.85  <.001  Cardiac comorbidities  No CVD (1oprevention)  6  197,897  1.49 (1.40–1.59)  0.79  <.001  No hypertension  11  258,901  0.96 (0.85–1.08)  0.93  <.001  No dyslipidaemia  5  179,324  1.14 (0.94–1.38)  0.90  <.001  No diabetes  16  240,194  0.98 (0.93–1.03)  0.77  <.001  Noncardiac comorbidities  Dementia  5  227,230  1.03 (0.78–1.37)  0.93  <.001  Depression  6  165,889  1.11 (1.06–1.16)  0.34  .180  Respiratory disorders‡  5  107,505  1.17 (1.12–1.23)  0.00  .600  Obesity  2  34,200  1.38 (0.95–2.00)  0.00  .960  Cancer  7  89,470  1.00 (0.91–1.10)  0.43  .100  No renal disease  7  112,286  1.09 (1.04–1.14)  0.00  .680  Rheumatoid arthritis  3  37,440  1.07 (0.92–1.24)  0.09  .290  Note: CI = Confidence interval. *Specialist refers to cardiologist; 1o primary; CVD = cardiovascular disease (myocardial infarction or stroke); ‡respiratory disorders refers to asthma or chronic obstructive pulmonary disease; **Indicates statin at index prescription; CV = cardiovascular. View Large Factors Associated With Statin Discontinuation Across the studies retrieved, a total of 81 factors were assessed for their association with statin discontinuation. However, data were only able to be summarized for 27 variables, the majority (n = 17) of which showed no significant association with statin discontinuation upon pooling. Demographic, lifestyle, and health system-related factors Similar to nonadherence, the association between age and statin discontinuation was not reported in a consistent manner. Three studies reported increasing age (per additional year or decade) to be associated with higher discontinuation (21,28,45); in contrast, two found no significant association with increasing age (30,50). While some studies that stratified age into categories reported higher discontinuation among older groupings (25,26), others reported lower or inconsistent trends in discontinuation among higher age groups (22,44,61). Thus, the overall association between older age and statin discontinuation is equivocal. Unlike nonadherence, there was no significant association between gender and statin discontinuation, or race. Being a current smoker was associated with a 14% higher likelihood of discontinuation (OR 1.14, 95% CI 1.06–1.23) while lower income status was associated with a 20% higher likelihood of discontinuation (OR 1.20, 95% CI 1.06–1.36). Copayments also had an adverse impact on statin continuation; higher copayment was associated with a 61% higher likelihood of discontinuation (OR 1.61, 95% CI 1.53–1.70). Comorbidities and treatment-related factors A history of CVD was associated with lower discontinuation; patients taking statins for primary prevention had a 66% higher likelihood of discontinuation (OR 1.66, 95% CI 1.24–2.22). Similarly, having hypertension or diabetes had a positive impact on statin continuation; patients without hypertension had a 13% higher likelihood of discontinuing statin therapy (OR 1.13, 95% CI 1.07–1.20), and patients without diabetes had a 9% increased odds of discontinuation (OR 1.09, 95% CI 1.04–1.15) (Table 2). In contrast, having dementia, cancer or respiratory disorders (chronic obstructive pulmonary disease or asthma) were associated with a higher likelihood of discontinuation. Pooled results from 5 studies showed that the concurrent use of a higher number of other medications was associated with 4% higher likelihood of statin discontinuation (OR 1.04, 95% CI 1.01–1.06). Table 2. Factors Associated With Discontinuation Among Older Statin Users Factors  Number of Studies Pooled  Sample Size  Odds Ratio (95% CI)  I2 Statistic  p Value  Demographic/Lifestyle  Female gender  19  590,356  1.03 (0.98–1.09)  0.91  <.001  Black or non-white race  4  274,849  1.57 (0.92–2.68)  0.91  <.001  Unmarried vs Married  2  7,741  1.12 (0.99–1.26)  0.00  .380  Low income status  7  135,074  1.20 (1.06–1.36)  0.89  <.001  Current smoker  6  280,988  1.14 (1.06–1.23)  0.70  .010  Health system  Higher copayment  2  42,394  1.61 (1.53–1.70)  0.00  .980  Treated by nonspecialist*  2  9,603  1.41 (0.97–2.08)  0.28  .642  Treatment related  New user  2  10,451  1.08 (0.97–1.20)  0.08  .300  Moderate vs low intensity therapy  2  68,389  1.02 (0.97–1.07)  0.78  .030  High vs low intensity therapy  2  68,389  1.07 (0.94–1.21)  0.80  .030  Atorvastatin vs Simvastatin**  4  275,644  1.00 (0.97–1.03)  0.28  .240  Rosuvastatin vs Simvastatin**  4  275,644  1.10 (0.92–1.33)  0.80  <.001  Fluvastatin vs Simvastatin**  3  272,949  1.02 (0.85–1.23)  0.67  .050  Pravastatin vs Simvastatin**  3  272,949  0.99 (0.95–1.03)  0.00  .570  Higher number of other medications  5  422,567  1.04 (1.01–1.06)  0.98  <.001  Use of aspirin  2  269,406  0.93 (0.80–1.09)  0.99  <.001  Cardiac comorbidities  No CVD (1oprevention)  7  544,982  1.66 (1.24–2.22)  0.99  <.001  No hypertension  10  537,759  1.13 (1.07–1.20)  0.96  <.001  No dyslipidaemia  4  29,491  1.31 (0.83–2.06)  0.94  <.001  No diabetes  11  540,454  1.09 (1.04–1.15)  0.93  <.001  Noncardiac comorbidities  Dementia  6  347,451  1.18 (1.02–1.36)  0.79  <.001  Depression  4  7,975  0.92 (0.80–1.06)  0.00  .470  Respiratory disorders‡  5  344,703  1.19 (1.05–1.34)  0.94  <.001  Obesity  3  7,741  1.10 (0.58–2.09)  0.42  .020  Cancer  6  284,039  1.22 (1.11–1.33)  0.84  <.001  Renal disease  5  280,708  1.18 (0.98–1.42)  0.77  .010  Rheumatoid arthritis  3  276,314  1.00 (0.96–1.05)  0.48  <.001  Factors  Number of Studies Pooled  Sample Size  Odds Ratio (95% CI)  I2 Statistic  p Value  Demographic/Lifestyle  Female gender  19  590,356  1.03 (0.98–1.09)  0.91  <.001  Black or non-white race  4  274,849  1.57 (0.92–2.68)  0.91  <.001  Unmarried vs Married  2  7,741  1.12 (0.99–1.26)  0.00  .380  Low income status  7  135,074  1.20 (1.06–1.36)  0.89  <.001  Current smoker  6  280,988  1.14 (1.06–1.23)  0.70  .010  Health system  Higher copayment  2  42,394  1.61 (1.53–1.70)  0.00  .980  Treated by nonspecialist*  2  9,603  1.41 (0.97–2.08)  0.28  .642  Treatment related  New user  2  10,451  1.08 (0.97–1.20)  0.08  .300  Moderate vs low intensity therapy  2  68,389  1.02 (0.97–1.07)  0.78  .030  High vs low intensity therapy  2  68,389  1.07 (0.94–1.21)  0.80  .030  Atorvastatin vs Simvastatin**  4  275,644  1.00 (0.97–1.03)  0.28  .240  Rosuvastatin vs Simvastatin**  4  275,644  1.10 (0.92–1.33)  0.80  <.001  Fluvastatin vs Simvastatin**  3  272,949  1.02 (0.85–1.23)  0.67  .050  Pravastatin vs Simvastatin**  3  272,949  0.99 (0.95–1.03)  0.00  .570  Higher number of other medications  5  422,567  1.04 (1.01–1.06)  0.98  <.001  Use of aspirin  2  269,406  0.93 (0.80–1.09)  0.99  <.001  Cardiac comorbidities  No CVD (1oprevention)  7  544,982  1.66 (1.24–2.22)  0.99  <.001  No hypertension  10  537,759  1.13 (1.07–1.20)  0.96  <.001  No dyslipidaemia  4  29,491  1.31 (0.83–2.06)  0.94  <.001  No diabetes  11  540,454  1.09 (1.04–1.15)  0.93  <.001  Noncardiac comorbidities  Dementia  6  347,451  1.18 (1.02–1.36)  0.79  <.001  Depression  4  7,975  0.92 (0.80–1.06)  0.00  .470  Respiratory disorders‡  5  344,703  1.19 (1.05–1.34)  0.94  <.001  Obesity  3  7,741  1.10 (0.58–2.09)  0.42  .020  Cancer  6  284,039  1.22 (1.11–1.33)  0.84  <.001  Renal disease  5  280,708  1.18 (0.98–1.42)  0.77  .010  Rheumatoid arthritis  3  276,314  1.00 (0.96–1.05)  0.48  <.001  Note: CI = Confidence interval. *Specialist refers to cardiologist; 1o primary; CVD =Cardiovascular disease (myocardial infarction or stroke); ‡respiratory disorders refers to asthma or chronic obstructive pulmonary disease;**Indicates statin at index prescription. View Large Table 2. Factors Associated With Discontinuation Among Older Statin Users Factors  Number of Studies Pooled  Sample Size  Odds Ratio (95% CI)  I2 Statistic  p Value  Demographic/Lifestyle  Female gender  19  590,356  1.03 (0.98–1.09)  0.91  <.001  Black or non-white race  4  274,849  1.57 (0.92–2.68)  0.91  <.001  Unmarried vs Married  2  7,741  1.12 (0.99–1.26)  0.00  .380  Low income status  7  135,074  1.20 (1.06–1.36)  0.89  <.001  Current smoker  6  280,988  1.14 (1.06–1.23)  0.70  .010  Health system  Higher copayment  2  42,394  1.61 (1.53–1.70)  0.00  .980  Treated by nonspecialist*  2  9,603  1.41 (0.97–2.08)  0.28  .642  Treatment related  New user  2  10,451  1.08 (0.97–1.20)  0.08  .300  Moderate vs low intensity therapy  2  68,389  1.02 (0.97–1.07)  0.78  .030  High vs low intensity therapy  2  68,389  1.07 (0.94–1.21)  0.80  .030  Atorvastatin vs Simvastatin**  4  275,644  1.00 (0.97–1.03)  0.28  .240  Rosuvastatin vs Simvastatin**  4  275,644  1.10 (0.92–1.33)  0.80  <.001  Fluvastatin vs Simvastatin**  3  272,949  1.02 (0.85–1.23)  0.67  .050  Pravastatin vs Simvastatin**  3  272,949  0.99 (0.95–1.03)  0.00  .570  Higher number of other medications  5  422,567  1.04 (1.01–1.06)  0.98  <.001  Use of aspirin  2  269,406  0.93 (0.80–1.09)  0.99  <.001  Cardiac comorbidities  No CVD (1oprevention)  7  544,982  1.66 (1.24–2.22)  0.99  <.001  No hypertension  10  537,759  1.13 (1.07–1.20)  0.96  <.001  No dyslipidaemia  4  29,491  1.31 (0.83–2.06)  0.94  <.001  No diabetes  11  540,454  1.09 (1.04–1.15)  0.93  <.001  Noncardiac comorbidities  Dementia  6  347,451  1.18 (1.02–1.36)  0.79  <.001  Depression  4  7,975  0.92 (0.80–1.06)  0.00  .470  Respiratory disorders‡  5  344,703  1.19 (1.05–1.34)  0.94  <.001  Obesity  3  7,741  1.10 (0.58–2.09)  0.42  .020  Cancer  6  284,039  1.22 (1.11–1.33)  0.84  <.001  Renal disease  5  280,708  1.18 (0.98–1.42)  0.77  .010  Rheumatoid arthritis  3  276,314  1.00 (0.96–1.05)  0.48  <.001  Factors  Number of Studies Pooled  Sample Size  Odds Ratio (95% CI)  I2 Statistic  p Value  Demographic/Lifestyle  Female gender  19  590,356  1.03 (0.98–1.09)  0.91  <.001  Black or non-white race  4  274,849  1.57 (0.92–2.68)  0.91  <.001  Unmarried vs Married  2  7,741  1.12 (0.99–1.26)  0.00  .380  Low income status  7  135,074  1.20 (1.06–1.36)  0.89  <.001  Current smoker  6  280,988  1.14 (1.06–1.23)  0.70  .010  Health system  Higher copayment  2  42,394  1.61 (1.53–1.70)  0.00  .980  Treated by nonspecialist*  2  9,603  1.41 (0.97–2.08)  0.28  .642  Treatment related  New user  2  10,451  1.08 (0.97–1.20)  0.08  .300  Moderate vs low intensity therapy  2  68,389  1.02 (0.97–1.07)  0.78  .030  High vs low intensity therapy  2  68,389  1.07 (0.94–1.21)  0.80  .030  Atorvastatin vs Simvastatin**  4  275,644  1.00 (0.97–1.03)  0.28  .240  Rosuvastatin vs Simvastatin**  4  275,644  1.10 (0.92–1.33)  0.80  <.001  Fluvastatin vs Simvastatin**  3  272,949  1.02 (0.85–1.23)  0.67  .050  Pravastatin vs Simvastatin**  3  272,949  0.99 (0.95–1.03)  0.00  .570  Higher number of other medications  5  422,567  1.04 (1.01–1.06)  0.98  <.001  Use of aspirin  2  269,406  0.93 (0.80–1.09)  0.99  <.001  Cardiac comorbidities  No CVD (1oprevention)  7  544,982  1.66 (1.24–2.22)  0.99  <.001  No hypertension  10  537,759  1.13 (1.07–1.20)  0.96  <.001  No dyslipidaemia  4  29,491  1.31 (0.83–2.06)  0.94  <.001  No diabetes  11  540,454  1.09 (1.04–1.15)  0.93  <.001  Noncardiac comorbidities  Dementia  6  347,451  1.18 (1.02–1.36)  0.79  <.001  Depression  4  7,975  0.92 (0.80–1.06)  0.00  .470  Respiratory disorders‡  5  344,703  1.19 (1.05–1.34)  0.94  <.001  Obesity  3  7,741  1.10 (0.58–2.09)  0.42  .020  Cancer  6  284,039  1.22 (1.11–1.33)  0.84  <.001  Renal disease  5  280,708  1.18 (0.98–1.42)  0.77  .010  Rheumatoid arthritis  3  276,314  1.00 (0.96–1.05)  0.48  <.001  Note: CI = Confidence interval. *Specialist refers to cardiologist; 1o primary; CVD =Cardiovascular disease (myocardial infarction or stroke); ‡respiratory disorders refers to asthma or chronic obstructive pulmonary disease;**Indicates statin at index prescription. View Large Heterogeneity Assessment Subgroup analyses based on region of participants, sample size or study year did not reveal any consistent pattern in the source of heterogeneity as evidenced by the change in the I2 statistic. For example, in the case of new user as a nonadherence predictor, the I2 statistic remained unchanged across all subgroupings (Supplementary Table S3). However, reduction in heterogeneity were observed for dementia as a discontinuation predictor in studies with sample size more than 10,000 (I2 = 0.00) as well as for depression as a nonadherence predictor in North American studies (I2 = 0.00). In all such cases, however, the heterogeneity levels were not statistically significant (p > .05). Discussion This comprehensive review of statin use among older persons found multiple sociodemographic, health system, comorbidities, and treatment-related factors to be associated with nonadherence and discontinuation. Female gender was associated with higher nonadherence as has been observed for many other drug therapeutic classes (64). This may be due to the higher number of comorbidities in women (and requirement for more medications) or that along with their health providers (65,66), women tend to perceive themselves to be at low cardiovascular risk (67), and may question the relevance of statin treatment or exhibit more concerns towards side effects (11). However, as women tend to show greater concern for their health and engage more frequently with the health system than men (68); there is greater opportunity for clinicians to intervene and improve adherence. Racial disparities in CVD have been well documented, with increasing calls for measures to reduce the gap (69). Targeted interventions to address the social and behavioral issues that result in poor adherence among minority groups will be necessary to reduce the poor cardiovascular outcomes observed among such populations. The implementation of health system cost-sharing measures such as copayments will need to be carefully considered especially with respect to low-income individuals as well as those with higher need for medical care. Our findings indicate that these are significant barriers to adhering to and continuing necessary preventive therapies (70). This is particularly important for the older population, the majority of whom are not in paid employment, and therefore may have fewer avenues for continued income. Indeed, some studies have suggested that offering full medication reimbursement (for statins and other cardiovascular medications) to high-risk groups such as postmyocardial infarction patients, may be cost saving for the health system in both the short-term (71) and long-term (72). The perception of risks or benefits of pharmacological intervention as described by psychological theories such as the health belief model are known to positively correlate with patients’ behavior (73). For example, statin users who have not experienced a cardiovascular event (ie, primary prevention) or have no other cardiovascular related conditions (eg, hypertension) may perceive themselves to be at lower risk and therefore be less likely to adhere to treatment (6,73). In a U.S. study, more than one-third of older adults who were nonadherent to statins became adherent following hospitalization for acute myocardial infarction (74). Patients’ perception may however differ significantly from actual cardiovascular risk with one study reporting that more than 90% of high-risk individuals underestimated their cardiovascular risk (75). Interventions to improve patient-provider interactions/relationships toward ensuring efficient communication of risk-related information are therefore important to improve adherence and reduce discontinuation of statin therapy. The evidence supporting the use of statins in the elderly particularly for primary prevention is less clear and a matter of active debate (76–78). Studies have suggested that the uncertainty around treatment benefit and risks may have had an adverse impact on statin adherence and continuation rates (79–81). Thus, it is important for clinicians to regularly inform their patients about the available evidence regarding the risks and benefits of statin therapy, and objectively discuss with them how this evidence relates to their own clinical circumstances and why their use may (or may not) be necessary. Patient buy-in following such engagements is likely to yield better adherence and treatment continuation (82). Our review also found that the presence of other noncardiac comorbidities has an adverse impact on statin adherence and continuation. Depression reduced the likelihood of statin adherence, as previously seen in an earlier meta-analysis where depression was associated with a 76% higher likelihood of nonadherence among multiple medications (83). The presence of respiratory disorders (chronic obstructive pulmonary disease or asthma) also increased the likelihood of nonadherence or discontinuation. Although the exact interactions between these noncardiac conditions and statin adherence and persistence are unclear, measures to manage these diseases among the older population may have a positive impact on statin use. In the case of terminal diseases such as dementia and cancer, higher statin discontinuation may be due to the desire to reduce pill burden and the fact that there is little evidence to support statin continuation in persons who have a shortened life expectancy (57). A recent RCT suggested that discontinuing statins in patients in palliative settings was not only safe, but improved quality of life and reduced healthcare costs (84). Overall, it appears also that measures to reduce polypharmacy will be essential to improving adherence and treatment continuation in the older population, especially as this population has increased susceptibility to adverse effects. Nonetheless, the point is not for clinicians to simply impose a set number of medications and try to stay below it (85), but to carefully evaluate each patients’ treatment package on a case-by-case basis. Clearly, no single intervention can eliminate all of the factors associated with statin nonadherence or discontinuation; multi-faceted approaches will be needed to provide the kind of improvement required. Interventions are best applied as soon as patients commence treatment as the risk of nonadherence is higher among new users. However, if improvements are to be sustained over the long-term, interventions may need to be integrated within patient management plans and provided on an ongoing basis (82). This review has several strengths. First, we included a large number of studies from several countries. Secondly, only studies that utilized validated techniques for measuring adherence were included thereby ensuring consistency. In contrast to a previous review that combined predictors of nonadherence and discontinuation (6), we adopted a clear distinction between these essentially different parameters (5). The reported pooled estimates were unchanged by leave-one-out sensitivity analyses indicating that they were robust. Finally, considering that more than 70% of the included studies were recently published (2012–2016); the reported associations are likely to represent current trends. Our study also has some limitations. First, significant heterogeneity across studies was evident; studies with different sample size and measurement techniques were combined. Second, where adjusted estimates were utilized, studies employed different levels of adjustment for confounders. Third, the predictors were pooled from studies spanning nearly 15 years and it is possible that statin use may have changed over this period. Fourth, restricting our study to English language may limit the generalizability of our findings. Finally, none of the studies included in the review looked at patient-reported reasons for nonadherence or discontinuation which may be additionally important to inform the design of interventions (86). Conclusions A number of sociodemographic, health system, comorbidities, and treatment-related factors are associated with nonadherence to and discontinuation of statin therapy. Some of these factors are potentially modifiable and could be the focus of interventions aimed at improving statin use in older populations. Measures to address financial and social barriers, patients’ risk misconceptions as well as to reduce polypharmacy may be particularly important to improving statin adherence and reducing discontinuation. Supplementary Material Supplementary data is available at The Journals of Gerontology, Series A: Biological Sciences and Medical Sciences online. Funding R.O. is supported by a Monash Graduate Scholarship and Monash International Postgraduate Research Scholarship for his doctoral studies. S.Z. is funded by a National Health and Medical Research Council Senior Research Fellowship. No other funding has been received to undertake this work. Conflict of Interest S.Z. reports past participation in advisory boards and/or receiving honoraria from: Amgen Australia; AstraZeneca/Bristol-Myers Squibb Australia; Janssen-Cilag; Merck, Sharp, and Dohme (Australia); Novartis Australia; Novo Nordisk; Sanofi; Servier Laboratories; Takeda Australia; and Monash University (undertaking contract work for AstraZeneca Pty Limited/Bristol- Myers Squibb Australia Pty Limited) for work unrelated to this study. Acknowledgments We are grateful to authors who generously provided us with unpublished data. References 1. Roth GA, Johnson C, Abajobir Aet al.   Global, regional, and national burden of cardiovascular diseases for 10 causes, 1990 to 2015. J Am Coll Cardiol . 2017; 70: 1– 25. doi: 10.1016/j.jacc.2017.04.052 Google Scholar CrossRef Search ADS PubMed  2. Writing Group M, Mozaffarian D, Benjamin EJ, Go ASet al.   Heart disease and stroke statistics-2016 update: A report from the American Heart Association. 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Abstract

Abstract Background Older individuals (aged ≥65 years) are commonly prescribed statins but may experience a range of barriers in adhering to therapy. The factors associated with poor statin adherence and/or discontinuation among this population have not been comprehensively reviewed. Methods We conducted a systematic review to identify English articles published through December 12, 2016 that reported factors associated with nonadherence and/or discontinuation of statins among older persons. Data were pooled via random-effects meta-analysis techniques. Results Forty-five articles reporting data from more than 1.8 million older statin users from 13 countries were included. The factors associated with increased statin nonadherence were black/non-white race (odds ratio [OR] 1.66, 95% confidence interval [CI] 1.39–1.98), female gender (OR 1.08, 95% CI 1.03–1.13), current smoker (OR 1.12, 95% CI 1.03–1.21), higher copayments (OR 1.38, 95% CI 1.25–1.52), new user (OR 1.58, 95% CI 1.21–2.07), lower number of concurrent cardiovascular medications (OR 1.08, 95% CI 1.06–1.09), primary prevention (OR 1.49, 95% CI 1.40–1.59), having respiratory disorders (OR 1.17, 95% CI 1.12–1.23) or depression (OR 1.11, 95% CI 1.06–1.16), and not having renal disease (OR 1.09, 95% CI 1.04–1.14). The factors associated with increased statin discontinuation were lower income status (OR 1.20, 95% CI 1.06–1.36), current smoker (OR 1.14, 95% CI 1.06–1.23), higher copayment (OR 1.61, 95% CI 1.53–1.70), higher number of medications (OR 1.04, 95% CI 1.01–1.06), presence of dementia (OR 1.18, 95% CI 1.02–1.36), cancer (OR 1.22, 95% CI 1.11–1.33) or respiratory disorders (OR 1.19, 95% CI 1.05–1.34), primary prevention (OR 1.66, 95% CI 1.24–2.22), and not having hypertension (OR 1.13, 95% CI 1.07–1.20) or diabetes (OR 1.09, 95% CI 1.04–1.15). Conclusion Interventions that target potentially modifiable factors including financial and social barriers, patients’ perceptions about disease risk as well as polypharmacy may improve statin use in the older population. HMG-CoA reductase inhibitors, Adherence, Persistence, Risk indicators Background Cardiovascular disease (CVD) is a leading cause of global morbidity and mortality, accounting for 17.9 million deaths in 2015 (1). The impact of CVD on health systems and national economies is significant. In the United States, the total cost attributed to CVD exceeded USD$300 billion in 2011/2012 (2), while in England, CVD cost the National Health Service (NHS) nearly £7 billion in 2012/2013 (3). Thus, the prevention and management of CVD is of major importance to clinicians, policy makers and other interest groups (eg, payers). Statins have been demonstrated in numerous clinical studies to be highly efficacious for the prevention of CVD (4). To achieve the desired clinical effects of statin treatment, patients ought to follow the treatment plan. However, adherence (the extent to which patients follow the dosing regimen) (5) and persistence (the extent to which patients continue treatment for the prescribed duration) (5) among statin users have been documented to be poor (6–8). Older individuals experience significant barriers toward adherence (9), with about half nonadherent and a quarter discontinuing statins within the first treatment year (10). Studies have suggested that patient, medical history, and health system-related factors may individually or by interaction influence adherence and persistence among statin users (6,11). However, no systematic reviews on the predictors of nonadherence and/or discontinuation among older statin users have been published. Since older patients have distinctive characteristics, a greater understanding of the factors that are associated with suboptimal statin use in this population is essential to facilitate the development of targeted interventions. Accordingly, we reviewed the literature in order to identify factors associated with nonadherence and discontinuation among older statin users (aged ≥65 years). The current study was part of a larger review on the patterns and barriers to statin use in older people (12). Methods Search Strategy The systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and the detailed protocol has been published elsewhere (12). To identify relevant studies, we searched Medline, Embase, CINAHL, PsycINFO, National Health Service Economic and Evaluation Database (NHSEED), Database of Abstracts of Reviews of Effects (DARE) and the Cochrane Central Register of Controlled Trials from inception till December 12, 2016. The search terms adopted included those related to the intervention (“statins”, “hydroxymethylglutaryl-coA reductase inhibitors” or individual generic and proprietary names) and outcomes (“patient compliance”, “medication adherence/nonadherence”, “persistence”, “discontinuation”, “drop out”, etc.); the full search strategy is provided in Supplementary Table S1. Searches were restricted to English language and authors were contacted for unpublished data when necessary. Study Selection and Evaluation Articles were considered for inclusion if predictors of nonadherence and/or discontinuation among older statin users were reported. Studies adopting objective adherence measurements (such as pill counts and refill data) were eligible for inclusion. For studies that utilized self-reports, only those adopting validated scales were selected. For studies that measured adherence via the medication possession ratio, proportion of days covered (PDC), or proportion of doses taken, only those employing an 80% cutoff to dichotomize adherence were considered. The 80% threshold has been reported to represent the minimum adherence level needed to achieve satisfactory clinical effect of statin therapy (13,14). We did not discriminate studies on the basis of methods used to assess discontinuation. Studies that adopted the permissible gap method (ie, specified the maximum break in refill beyond which a user is considered to have discontinued treatment) (5) or relied on other approaches including patient self-reports were considered. We assessed the methodological quality of observational studies by using a set of questions formulated with reference to the National Institute of Health (NIH) Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies (15). The quality of randomized clinical trials (RCTs) was assessed through the use of the Joanna Briggs Institute’s (JBI) critical appraisal checklist for RCTs (16). Studies that scored ≥70% of the applied assessment criteria were graded as high quality. Data Extraction and Analysis A standardized data extraction template was used to collect descriptive information (eg, study reference, country, adherence definition, etc.) and predictors’ data. Two reviewers (R.O., A.J.) independently extracted and cross-checked the data. To ensure consistency in reporting, the direction of effect of studies reporting predictors of adherence or persistence was reversed. Each predictor’s overall effect was determined via random effects meta-analysis with inverse variance weighting. The effect measure of interest was odds ratios (OR) (12). If a study utilized another effect measure (eg, standard mean difference), it was converted to an OR (17). When available, adjusted effect size estimates were preferred. If predictors were reported per subgroupings (eg, males and females), these were included as separate terms in the meta-analysis. The I2 statistic was used to quantify statistical heterogeneity across studies. The robustness of pooled estimates was tested through leave-one-out sensitivity analyses (17). Subgroup analyses based on region of study (North America vs rest of the world), sample size (<10,000 vs ≥10,000), and year of study (<2012 vs ≥2012) were performed to explore possible sources of heterogeneity between studies. Analyses were conducted with MetaXL (18)—an add-in meta-analysis tool in Microsoft Excel. Results Following electronic searches and removal of duplicates, the titles and abstracts of 7,705 articles were screened. The full text was evaluated for 398 articles and 45 articles (43 unique studies) were included in the final review (Figure 1) (19–63). The included articles were published in the period 2002–2016, with the majority (73%) published in the last 5 years (2012–2016). The included studies involved a total population of 1,842,054 sampled from 13 countries. Thirty three percent of studies were from North America, 53% from Europe and 14% from the rest of the world (Supplementary Table S2). The majority (80%) of studies from which nonadherence predictors were retrieved utilized the PDC methodology. Similarly, 81% of studies reporting discontinuation predictors adopted the permissible gap method (ie, they defined a maximum break in statin use beyond which patients were considered to have discontinued treatment) (5). Figure 1. View largeDownload slide Flow diagram of study selection. Figure 1. View largeDownload slide Flow diagram of study selection. Factors Associated With Nonadherence Across the studies retrieved, a total of 107 factors were assessed for their association with statin nonadherence. However, data were only able to be summarized for 31 variables, the majority (n = 21) of which showed no significant association with nonadherence upon pooling. Demographic, lifestyle, and health system-related factors The association between age and statin nonadherence was not reported in a consistent manner across studies. Increasing age (per additional year or decade) was associated with higher nonadherence in three studies (31,43,52); two studies reported an inverse association between age and nonadherence (28,49), and five studies found no association between age and nonadherence (29,33,41,48,58). Although, some studies that stratified age into categories (eg, 65–69, ≥75, etc.) reported higher nonadherence among higher age-groups (24,32,37,55), others found lower, insignificant associations or inconsistent trends in nonadherence among higher age-groups (26,35,36,51,62,63). Thus, among this older population, the association between age and nonadherence to statin therapy is equivocal. Compared with men, women were more likely to be nonadherent to statin therapy (OR 1.08, 95% confidence interval [CI] 1.03–1.13). There were also racial differences in statin adherence, with black or non-white race having a 66% higher likelihood of nonadherence than white populations (OR 1.66, 95% CI 1.39–1.98). Being a current smoker was associated with a higher likelihood of nonadherence (OR 1.12, 95% CI 1.03–1.21). In comparison to prevalent users, new users were more likely to be nonadherent (OR 1.58, 95% CI 1.21–2.07). Higher copayment/cost also increased the likelihood of nonadherence (OR 1.38, 95% CI 1.25–1.52). Comorbidities and treatment-related factors A history of CVD (myocardial infarction, or stroke) was associated with higher adherence; patients receiving statins for primary prevention had a 49% higher likelihood of nonadherence (OR 1.49, 95% CI 1.40–1.59) (Table 1). Similarly, renal disease and taking other cardiovascular medications were also associated with higher adherence; those without renal disease had a 9% greater likelihood of nonadherence (OR 1.09; 95% CI 1.04–1.14) and those on a lower number of other cardiovascular medications had an 8% greater likelihood of nonadherence (OR 1.08, 95% CI 1.06–1.09). There was an 11% and 17% higher likelihood of nonadherence among statin users who had depression (OR 1.11, 95% CI 1.06–1.16) or respiratory disorder (chronic obstructive pulmonary disease or asthma) (OR 1.17, 95% CI 1.12–1.23), respectively. Table 1. Factors Associated With Nonadherence Among Older Statin Users Factors  Number of Studies Pooled  Sample Size  Odds Ratio (95% CI)  I2 Statistic  p Value  Demographic/Lifestyle  Female gender  25  462,344  1.08 (1.03–1.13)  0.86  <.001  Black or non-white race  6  105,368  1.66 (1.39–1.98)  0.94  <.001  Unmarried vs Married  3  37,102  0.96 (0.76–1.16)  0.54  .110  Lower income status  10  252,182  1.12 (0.96–1.30)  0.98  <.001  Low education  2  29,199  1.01 (0.98–1.03)  0.00  .700  Alcohol/substance abuse  2  34,523  1.05 (0.96–1.14)  0.64  .100  Current smoker  3  15,185  1.12 (1.03–1.21)  0.00  .500  Health system  Higher copayment  4  136,022  1.38 (1.25–1.52)  0.65  .040  Treated by nonspecialist*  4  110,552  1.07 (0.93–1.23)  0.00  .390  Receiving treatment outside hospital  3  103,550  1.05 (0.96–1.14)  0.00  .490  Treatment related  New user  8  104,136  1.58 (1.21–2.07)  0.99  <.001  Moderate vs low intensity therapy  5  106,648  0.91 (0.58–1.44)  0.98  <.001  High vs low intensity therapy  5  106,648  1.08 (0.88–1.99)  0.87  <.001  Atorvastatin vs Simvastatin**  5  45,316  0.95 (0.82–1.09)  0.74  <.001  Rosuvastatin vs simvastatin**  4  43,678  1.13 (0.76–1.69)  0.85  <.001  Fluvastatin vs Simvastatin**  3  40,983  1.15 (0.83–1.59)  0.84  <.001  Pravastatin vs simvastatin**  3  40,983  1.01 (0.91–1.11)  0.00  .420  Lovastatin vs. simvastatin**  2  37,440  1.16 (0.99–1.34)  0.00  .530  Concurrent CV medications (per addition)  2  124,380  0.93 (0.92–0.94)  0.00  .590  No. of other medications  4  139,186  1.01 (0.99–1.02)  0.85  <.001  Cardiac comorbidities  No CVD (1oprevention)  6  197,897  1.49 (1.40–1.59)  0.79  <.001  No hypertension  11  258,901  0.96 (0.85–1.08)  0.93  <.001  No dyslipidaemia  5  179,324  1.14 (0.94–1.38)  0.90  <.001  No diabetes  16  240,194  0.98 (0.93–1.03)  0.77  <.001  Noncardiac comorbidities  Dementia  5  227,230  1.03 (0.78–1.37)  0.93  <.001  Depression  6  165,889  1.11 (1.06–1.16)  0.34  .180  Respiratory disorders‡  5  107,505  1.17 (1.12–1.23)  0.00  .600  Obesity  2  34,200  1.38 (0.95–2.00)  0.00  .960  Cancer  7  89,470  1.00 (0.91–1.10)  0.43  .100  No renal disease  7  112,286  1.09 (1.04–1.14)  0.00  .680  Rheumatoid arthritis  3  37,440  1.07 (0.92–1.24)  0.09  .290  Factors  Number of Studies Pooled  Sample Size  Odds Ratio (95% CI)  I2 Statistic  p Value  Demographic/Lifestyle  Female gender  25  462,344  1.08 (1.03–1.13)  0.86  <.001  Black or non-white race  6  105,368  1.66 (1.39–1.98)  0.94  <.001  Unmarried vs Married  3  37,102  0.96 (0.76–1.16)  0.54  .110  Lower income status  10  252,182  1.12 (0.96–1.30)  0.98  <.001  Low education  2  29,199  1.01 (0.98–1.03)  0.00  .700  Alcohol/substance abuse  2  34,523  1.05 (0.96–1.14)  0.64  .100  Current smoker  3  15,185  1.12 (1.03–1.21)  0.00  .500  Health system  Higher copayment  4  136,022  1.38 (1.25–1.52)  0.65  .040  Treated by nonspecialist*  4  110,552  1.07 (0.93–1.23)  0.00  .390  Receiving treatment outside hospital  3  103,550  1.05 (0.96–1.14)  0.00  .490  Treatment related  New user  8  104,136  1.58 (1.21–2.07)  0.99  <.001  Moderate vs low intensity therapy  5  106,648  0.91 (0.58–1.44)  0.98  <.001  High vs low intensity therapy  5  106,648  1.08 (0.88–1.99)  0.87  <.001  Atorvastatin vs Simvastatin**  5  45,316  0.95 (0.82–1.09)  0.74  <.001  Rosuvastatin vs simvastatin**  4  43,678  1.13 (0.76–1.69)  0.85  <.001  Fluvastatin vs Simvastatin**  3  40,983  1.15 (0.83–1.59)  0.84  <.001  Pravastatin vs simvastatin**  3  40,983  1.01 (0.91–1.11)  0.00  .420  Lovastatin vs. simvastatin**  2  37,440  1.16 (0.99–1.34)  0.00  .530  Concurrent CV medications (per addition)  2  124,380  0.93 (0.92–0.94)  0.00  .590  No. of other medications  4  139,186  1.01 (0.99–1.02)  0.85  <.001  Cardiac comorbidities  No CVD (1oprevention)  6  197,897  1.49 (1.40–1.59)  0.79  <.001  No hypertension  11  258,901  0.96 (0.85–1.08)  0.93  <.001  No dyslipidaemia  5  179,324  1.14 (0.94–1.38)  0.90  <.001  No diabetes  16  240,194  0.98 (0.93–1.03)  0.77  <.001  Noncardiac comorbidities  Dementia  5  227,230  1.03 (0.78–1.37)  0.93  <.001  Depression  6  165,889  1.11 (1.06–1.16)  0.34  .180  Respiratory disorders‡  5  107,505  1.17 (1.12–1.23)  0.00  .600  Obesity  2  34,200  1.38 (0.95–2.00)  0.00  .960  Cancer  7  89,470  1.00 (0.91–1.10)  0.43  .100  No renal disease  7  112,286  1.09 (1.04–1.14)  0.00  .680  Rheumatoid arthritis  3  37,440  1.07 (0.92–1.24)  0.09  .290  Note: CI = Confidence interval. *Specialist refers to cardiologist; 1o primary; CVD = cardiovascular disease (myocardial infarction or stroke); ‡respiratory disorders refers to asthma or chronic obstructive pulmonary disease; **Indicates statin at index prescription; CV = cardiovascular. View Large Table 1. Factors Associated With Nonadherence Among Older Statin Users Factors  Number of Studies Pooled  Sample Size  Odds Ratio (95% CI)  I2 Statistic  p Value  Demographic/Lifestyle  Female gender  25  462,344  1.08 (1.03–1.13)  0.86  <.001  Black or non-white race  6  105,368  1.66 (1.39–1.98)  0.94  <.001  Unmarried vs Married  3  37,102  0.96 (0.76–1.16)  0.54  .110  Lower income status  10  252,182  1.12 (0.96–1.30)  0.98  <.001  Low education  2  29,199  1.01 (0.98–1.03)  0.00  .700  Alcohol/substance abuse  2  34,523  1.05 (0.96–1.14)  0.64  .100  Current smoker  3  15,185  1.12 (1.03–1.21)  0.00  .500  Health system  Higher copayment  4  136,022  1.38 (1.25–1.52)  0.65  .040  Treated by nonspecialist*  4  110,552  1.07 (0.93–1.23)  0.00  .390  Receiving treatment outside hospital  3  103,550  1.05 (0.96–1.14)  0.00  .490  Treatment related  New user  8  104,136  1.58 (1.21–2.07)  0.99  <.001  Moderate vs low intensity therapy  5  106,648  0.91 (0.58–1.44)  0.98  <.001  High vs low intensity therapy  5  106,648  1.08 (0.88–1.99)  0.87  <.001  Atorvastatin vs Simvastatin**  5  45,316  0.95 (0.82–1.09)  0.74  <.001  Rosuvastatin vs simvastatin**  4  43,678  1.13 (0.76–1.69)  0.85  <.001  Fluvastatin vs Simvastatin**  3  40,983  1.15 (0.83–1.59)  0.84  <.001  Pravastatin vs simvastatin**  3  40,983  1.01 (0.91–1.11)  0.00  .420  Lovastatin vs. simvastatin**  2  37,440  1.16 (0.99–1.34)  0.00  .530  Concurrent CV medications (per addition)  2  124,380  0.93 (0.92–0.94)  0.00  .590  No. of other medications  4  139,186  1.01 (0.99–1.02)  0.85  <.001  Cardiac comorbidities  No CVD (1oprevention)  6  197,897  1.49 (1.40–1.59)  0.79  <.001  No hypertension  11  258,901  0.96 (0.85–1.08)  0.93  <.001  No dyslipidaemia  5  179,324  1.14 (0.94–1.38)  0.90  <.001  No diabetes  16  240,194  0.98 (0.93–1.03)  0.77  <.001  Noncardiac comorbidities  Dementia  5  227,230  1.03 (0.78–1.37)  0.93  <.001  Depression  6  165,889  1.11 (1.06–1.16)  0.34  .180  Respiratory disorders‡  5  107,505  1.17 (1.12–1.23)  0.00  .600  Obesity  2  34,200  1.38 (0.95–2.00)  0.00  .960  Cancer  7  89,470  1.00 (0.91–1.10)  0.43  .100  No renal disease  7  112,286  1.09 (1.04–1.14)  0.00  .680  Rheumatoid arthritis  3  37,440  1.07 (0.92–1.24)  0.09  .290  Factors  Number of Studies Pooled  Sample Size  Odds Ratio (95% CI)  I2 Statistic  p Value  Demographic/Lifestyle  Female gender  25  462,344  1.08 (1.03–1.13)  0.86  <.001  Black or non-white race  6  105,368  1.66 (1.39–1.98)  0.94  <.001  Unmarried vs Married  3  37,102  0.96 (0.76–1.16)  0.54  .110  Lower income status  10  252,182  1.12 (0.96–1.30)  0.98  <.001  Low education  2  29,199  1.01 (0.98–1.03)  0.00  .700  Alcohol/substance abuse  2  34,523  1.05 (0.96–1.14)  0.64  .100  Current smoker  3  15,185  1.12 (1.03–1.21)  0.00  .500  Health system  Higher copayment  4  136,022  1.38 (1.25–1.52)  0.65  .040  Treated by nonspecialist*  4  110,552  1.07 (0.93–1.23)  0.00  .390  Receiving treatment outside hospital  3  103,550  1.05 (0.96–1.14)  0.00  .490  Treatment related  New user  8  104,136  1.58 (1.21–2.07)  0.99  <.001  Moderate vs low intensity therapy  5  106,648  0.91 (0.58–1.44)  0.98  <.001  High vs low intensity therapy  5  106,648  1.08 (0.88–1.99)  0.87  <.001  Atorvastatin vs Simvastatin**  5  45,316  0.95 (0.82–1.09)  0.74  <.001  Rosuvastatin vs simvastatin**  4  43,678  1.13 (0.76–1.69)  0.85  <.001  Fluvastatin vs Simvastatin**  3  40,983  1.15 (0.83–1.59)  0.84  <.001  Pravastatin vs simvastatin**  3  40,983  1.01 (0.91–1.11)  0.00  .420  Lovastatin vs. simvastatin**  2  37,440  1.16 (0.99–1.34)  0.00  .530  Concurrent CV medications (per addition)  2  124,380  0.93 (0.92–0.94)  0.00  .590  No. of other medications  4  139,186  1.01 (0.99–1.02)  0.85  <.001  Cardiac comorbidities  No CVD (1oprevention)  6  197,897  1.49 (1.40–1.59)  0.79  <.001  No hypertension  11  258,901  0.96 (0.85–1.08)  0.93  <.001  No dyslipidaemia  5  179,324  1.14 (0.94–1.38)  0.90  <.001  No diabetes  16  240,194  0.98 (0.93–1.03)  0.77  <.001  Noncardiac comorbidities  Dementia  5  227,230  1.03 (0.78–1.37)  0.93  <.001  Depression  6  165,889  1.11 (1.06–1.16)  0.34  .180  Respiratory disorders‡  5  107,505  1.17 (1.12–1.23)  0.00  .600  Obesity  2  34,200  1.38 (0.95–2.00)  0.00  .960  Cancer  7  89,470  1.00 (0.91–1.10)  0.43  .100  No renal disease  7  112,286  1.09 (1.04–1.14)  0.00  .680  Rheumatoid arthritis  3  37,440  1.07 (0.92–1.24)  0.09  .290  Note: CI = Confidence interval. *Specialist refers to cardiologist; 1o primary; CVD = cardiovascular disease (myocardial infarction or stroke); ‡respiratory disorders refers to asthma or chronic obstructive pulmonary disease; **Indicates statin at index prescription; CV = cardiovascular. View Large Factors Associated With Statin Discontinuation Across the studies retrieved, a total of 81 factors were assessed for their association with statin discontinuation. However, data were only able to be summarized for 27 variables, the majority (n = 17) of which showed no significant association with statin discontinuation upon pooling. Demographic, lifestyle, and health system-related factors Similar to nonadherence, the association between age and statin discontinuation was not reported in a consistent manner. Three studies reported increasing age (per additional year or decade) to be associated with higher discontinuation (21,28,45); in contrast, two found no significant association with increasing age (30,50). While some studies that stratified age into categories reported higher discontinuation among older groupings (25,26), others reported lower or inconsistent trends in discontinuation among higher age groups (22,44,61). Thus, the overall association between older age and statin discontinuation is equivocal. Unlike nonadherence, there was no significant association between gender and statin discontinuation, or race. Being a current smoker was associated with a 14% higher likelihood of discontinuation (OR 1.14, 95% CI 1.06–1.23) while lower income status was associated with a 20% higher likelihood of discontinuation (OR 1.20, 95% CI 1.06–1.36). Copayments also had an adverse impact on statin continuation; higher copayment was associated with a 61% higher likelihood of discontinuation (OR 1.61, 95% CI 1.53–1.70). Comorbidities and treatment-related factors A history of CVD was associated with lower discontinuation; patients taking statins for primary prevention had a 66% higher likelihood of discontinuation (OR 1.66, 95% CI 1.24–2.22). Similarly, having hypertension or diabetes had a positive impact on statin continuation; patients without hypertension had a 13% higher likelihood of discontinuing statin therapy (OR 1.13, 95% CI 1.07–1.20), and patients without diabetes had a 9% increased odds of discontinuation (OR 1.09, 95% CI 1.04–1.15) (Table 2). In contrast, having dementia, cancer or respiratory disorders (chronic obstructive pulmonary disease or asthma) were associated with a higher likelihood of discontinuation. Pooled results from 5 studies showed that the concurrent use of a higher number of other medications was associated with 4% higher likelihood of statin discontinuation (OR 1.04, 95% CI 1.01–1.06). Table 2. Factors Associated With Discontinuation Among Older Statin Users Factors  Number of Studies Pooled  Sample Size  Odds Ratio (95% CI)  I2 Statistic  p Value  Demographic/Lifestyle  Female gender  19  590,356  1.03 (0.98–1.09)  0.91  <.001  Black or non-white race  4  274,849  1.57 (0.92–2.68)  0.91  <.001  Unmarried vs Married  2  7,741  1.12 (0.99–1.26)  0.00  .380  Low income status  7  135,074  1.20 (1.06–1.36)  0.89  <.001  Current smoker  6  280,988  1.14 (1.06–1.23)  0.70  .010  Health system  Higher copayment  2  42,394  1.61 (1.53–1.70)  0.00  .980  Treated by nonspecialist*  2  9,603  1.41 (0.97–2.08)  0.28  .642  Treatment related  New user  2  10,451  1.08 (0.97–1.20)  0.08  .300  Moderate vs low intensity therapy  2  68,389  1.02 (0.97–1.07)  0.78  .030  High vs low intensity therapy  2  68,389  1.07 (0.94–1.21)  0.80  .030  Atorvastatin vs Simvastatin**  4  275,644  1.00 (0.97–1.03)  0.28  .240  Rosuvastatin vs Simvastatin**  4  275,644  1.10 (0.92–1.33)  0.80  <.001  Fluvastatin vs Simvastatin**  3  272,949  1.02 (0.85–1.23)  0.67  .050  Pravastatin vs Simvastatin**  3  272,949  0.99 (0.95–1.03)  0.00  .570  Higher number of other medications  5  422,567  1.04 (1.01–1.06)  0.98  <.001  Use of aspirin  2  269,406  0.93 (0.80–1.09)  0.99  <.001  Cardiac comorbidities  No CVD (1oprevention)  7  544,982  1.66 (1.24–2.22)  0.99  <.001  No hypertension  10  537,759  1.13 (1.07–1.20)  0.96  <.001  No dyslipidaemia  4  29,491  1.31 (0.83–2.06)  0.94  <.001  No diabetes  11  540,454  1.09 (1.04–1.15)  0.93  <.001  Noncardiac comorbidities  Dementia  6  347,451  1.18 (1.02–1.36)  0.79  <.001  Depression  4  7,975  0.92 (0.80–1.06)  0.00  .470  Respiratory disorders‡  5  344,703  1.19 (1.05–1.34)  0.94  <.001  Obesity  3  7,741  1.10 (0.58–2.09)  0.42  .020  Cancer  6  284,039  1.22 (1.11–1.33)  0.84  <.001  Renal disease  5  280,708  1.18 (0.98–1.42)  0.77  .010  Rheumatoid arthritis  3  276,314  1.00 (0.96–1.05)  0.48  <.001  Factors  Number of Studies Pooled  Sample Size  Odds Ratio (95% CI)  I2 Statistic  p Value  Demographic/Lifestyle  Female gender  19  590,356  1.03 (0.98–1.09)  0.91  <.001  Black or non-white race  4  274,849  1.57 (0.92–2.68)  0.91  <.001  Unmarried vs Married  2  7,741  1.12 (0.99–1.26)  0.00  .380  Low income status  7  135,074  1.20 (1.06–1.36)  0.89  <.001  Current smoker  6  280,988  1.14 (1.06–1.23)  0.70  .010  Health system  Higher copayment  2  42,394  1.61 (1.53–1.70)  0.00  .980  Treated by nonspecialist*  2  9,603  1.41 (0.97–2.08)  0.28  .642  Treatment related  New user  2  10,451  1.08 (0.97–1.20)  0.08  .300  Moderate vs low intensity therapy  2  68,389  1.02 (0.97–1.07)  0.78  .030  High vs low intensity therapy  2  68,389  1.07 (0.94–1.21)  0.80  .030  Atorvastatin vs Simvastatin**  4  275,644  1.00 (0.97–1.03)  0.28  .240  Rosuvastatin vs Simvastatin**  4  275,644  1.10 (0.92–1.33)  0.80  <.001  Fluvastatin vs Simvastatin**  3  272,949  1.02 (0.85–1.23)  0.67  .050  Pravastatin vs Simvastatin**  3  272,949  0.99 (0.95–1.03)  0.00  .570  Higher number of other medications  5  422,567  1.04 (1.01–1.06)  0.98  <.001  Use of aspirin  2  269,406  0.93 (0.80–1.09)  0.99  <.001  Cardiac comorbidities  No CVD (1oprevention)  7  544,982  1.66 (1.24–2.22)  0.99  <.001  No hypertension  10  537,759  1.13 (1.07–1.20)  0.96  <.001  No dyslipidaemia  4  29,491  1.31 (0.83–2.06)  0.94  <.001  No diabetes  11  540,454  1.09 (1.04–1.15)  0.93  <.001  Noncardiac comorbidities  Dementia  6  347,451  1.18 (1.02–1.36)  0.79  <.001  Depression  4  7,975  0.92 (0.80–1.06)  0.00  .470  Respiratory disorders‡  5  344,703  1.19 (1.05–1.34)  0.94  <.001  Obesity  3  7,741  1.10 (0.58–2.09)  0.42  .020  Cancer  6  284,039  1.22 (1.11–1.33)  0.84  <.001  Renal disease  5  280,708  1.18 (0.98–1.42)  0.77  .010  Rheumatoid arthritis  3  276,314  1.00 (0.96–1.05)  0.48  <.001  Note: CI = Confidence interval. *Specialist refers to cardiologist; 1o primary; CVD =Cardiovascular disease (myocardial infarction or stroke); ‡respiratory disorders refers to asthma or chronic obstructive pulmonary disease;**Indicates statin at index prescription. View Large Table 2. Factors Associated With Discontinuation Among Older Statin Users Factors  Number of Studies Pooled  Sample Size  Odds Ratio (95% CI)  I2 Statistic  p Value  Demographic/Lifestyle  Female gender  19  590,356  1.03 (0.98–1.09)  0.91  <.001  Black or non-white race  4  274,849  1.57 (0.92–2.68)  0.91  <.001  Unmarried vs Married  2  7,741  1.12 (0.99–1.26)  0.00  .380  Low income status  7  135,074  1.20 (1.06–1.36)  0.89  <.001  Current smoker  6  280,988  1.14 (1.06–1.23)  0.70  .010  Health system  Higher copayment  2  42,394  1.61 (1.53–1.70)  0.00  .980  Treated by nonspecialist*  2  9,603  1.41 (0.97–2.08)  0.28  .642  Treatment related  New user  2  10,451  1.08 (0.97–1.20)  0.08  .300  Moderate vs low intensity therapy  2  68,389  1.02 (0.97–1.07)  0.78  .030  High vs low intensity therapy  2  68,389  1.07 (0.94–1.21)  0.80  .030  Atorvastatin vs Simvastatin**  4  275,644  1.00 (0.97–1.03)  0.28  .240  Rosuvastatin vs Simvastatin**  4  275,644  1.10 (0.92–1.33)  0.80  <.001  Fluvastatin vs Simvastatin**  3  272,949  1.02 (0.85–1.23)  0.67  .050  Pravastatin vs Simvastatin**  3  272,949  0.99 (0.95–1.03)  0.00  .570  Higher number of other medications  5  422,567  1.04 (1.01–1.06)  0.98  <.001  Use of aspirin  2  269,406  0.93 (0.80–1.09)  0.99  <.001  Cardiac comorbidities  No CVD (1oprevention)  7  544,982  1.66 (1.24–2.22)  0.99  <.001  No hypertension  10  537,759  1.13 (1.07–1.20)  0.96  <.001  No dyslipidaemia  4  29,491  1.31 (0.83–2.06)  0.94  <.001  No diabetes  11  540,454  1.09 (1.04–1.15)  0.93  <.001  Noncardiac comorbidities  Dementia  6  347,451  1.18 (1.02–1.36)  0.79  <.001  Depression  4  7,975  0.92 (0.80–1.06)  0.00  .470  Respiratory disorders‡  5  344,703  1.19 (1.05–1.34)  0.94  <.001  Obesity  3  7,741  1.10 (0.58–2.09)  0.42  .020  Cancer  6  284,039  1.22 (1.11–1.33)  0.84  <.001  Renal disease  5  280,708  1.18 (0.98–1.42)  0.77  .010  Rheumatoid arthritis  3  276,314  1.00 (0.96–1.05)  0.48  <.001  Factors  Number of Studies Pooled  Sample Size  Odds Ratio (95% CI)  I2 Statistic  p Value  Demographic/Lifestyle  Female gender  19  590,356  1.03 (0.98–1.09)  0.91  <.001  Black or non-white race  4  274,849  1.57 (0.92–2.68)  0.91  <.001  Unmarried vs Married  2  7,741  1.12 (0.99–1.26)  0.00  .380  Low income status  7  135,074  1.20 (1.06–1.36)  0.89  <.001  Current smoker  6  280,988  1.14 (1.06–1.23)  0.70  .010  Health system  Higher copayment  2  42,394  1.61 (1.53–1.70)  0.00  .980  Treated by nonspecialist*  2  9,603  1.41 (0.97–2.08)  0.28  .642  Treatment related  New user  2  10,451  1.08 (0.97–1.20)  0.08  .300  Moderate vs low intensity therapy  2  68,389  1.02 (0.97–1.07)  0.78  .030  High vs low intensity therapy  2  68,389  1.07 (0.94–1.21)  0.80  .030  Atorvastatin vs Simvastatin**  4  275,644  1.00 (0.97–1.03)  0.28  .240  Rosuvastatin vs Simvastatin**  4  275,644  1.10 (0.92–1.33)  0.80  <.001  Fluvastatin vs Simvastatin**  3  272,949  1.02 (0.85–1.23)  0.67  .050  Pravastatin vs Simvastatin**  3  272,949  0.99 (0.95–1.03)  0.00  .570  Higher number of other medications  5  422,567  1.04 (1.01–1.06)  0.98  <.001  Use of aspirin  2  269,406  0.93 (0.80–1.09)  0.99  <.001  Cardiac comorbidities  No CVD (1oprevention)  7  544,982  1.66 (1.24–2.22)  0.99  <.001  No hypertension  10  537,759  1.13 (1.07–1.20)  0.96  <.001  No dyslipidaemia  4  29,491  1.31 (0.83–2.06)  0.94  <.001  No diabetes  11  540,454  1.09 (1.04–1.15)  0.93  <.001  Noncardiac comorbidities  Dementia  6  347,451  1.18 (1.02–1.36)  0.79  <.001  Depression  4  7,975  0.92 (0.80–1.06)  0.00  .470  Respiratory disorders‡  5  344,703  1.19 (1.05–1.34)  0.94  <.001  Obesity  3  7,741  1.10 (0.58–2.09)  0.42  .020  Cancer  6  284,039  1.22 (1.11–1.33)  0.84  <.001  Renal disease  5  280,708  1.18 (0.98–1.42)  0.77  .010  Rheumatoid arthritis  3  276,314  1.00 (0.96–1.05)  0.48  <.001  Note: CI = Confidence interval. *Specialist refers to cardiologist; 1o primary; CVD =Cardiovascular disease (myocardial infarction or stroke); ‡respiratory disorders refers to asthma or chronic obstructive pulmonary disease;**Indicates statin at index prescription. View Large Heterogeneity Assessment Subgroup analyses based on region of participants, sample size or study year did not reveal any consistent pattern in the source of heterogeneity as evidenced by the change in the I2 statistic. For example, in the case of new user as a nonadherence predictor, the I2 statistic remained unchanged across all subgroupings (Supplementary Table S3). However, reduction in heterogeneity were observed for dementia as a discontinuation predictor in studies with sample size more than 10,000 (I2 = 0.00) as well as for depression as a nonadherence predictor in North American studies (I2 = 0.00). In all such cases, however, the heterogeneity levels were not statistically significant (p > .05). Discussion This comprehensive review of statin use among older persons found multiple sociodemographic, health system, comorbidities, and treatment-related factors to be associated with nonadherence and discontinuation. Female gender was associated with higher nonadherence as has been observed for many other drug therapeutic classes (64). This may be due to the higher number of comorbidities in women (and requirement for more medications) or that along with their health providers (65,66), women tend to perceive themselves to be at low cardiovascular risk (67), and may question the relevance of statin treatment or exhibit more concerns towards side effects (11). However, as women tend to show greater concern for their health and engage more frequently with the health system than men (68); there is greater opportunity for clinicians to intervene and improve adherence. Racial disparities in CVD have been well documented, with increasing calls for measures to reduce the gap (69). Targeted interventions to address the social and behavioral issues that result in poor adherence among minority groups will be necessary to reduce the poor cardiovascular outcomes observed among such populations. The implementation of health system cost-sharing measures such as copayments will need to be carefully considered especially with respect to low-income individuals as well as those with higher need for medical care. Our findings indicate that these are significant barriers to adhering to and continuing necessary preventive therapies (70). This is particularly important for the older population, the majority of whom are not in paid employment, and therefore may have fewer avenues for continued income. Indeed, some studies have suggested that offering full medication reimbursement (for statins and other cardiovascular medications) to high-risk groups such as postmyocardial infarction patients, may be cost saving for the health system in both the short-term (71) and long-term (72). The perception of risks or benefits of pharmacological intervention as described by psychological theories such as the health belief model are known to positively correlate with patients’ behavior (73). For example, statin users who have not experienced a cardiovascular event (ie, primary prevention) or have no other cardiovascular related conditions (eg, hypertension) may perceive themselves to be at lower risk and therefore be less likely to adhere to treatment (6,73). In a U.S. study, more than one-third of older adults who were nonadherent to statins became adherent following hospitalization for acute myocardial infarction (74). Patients’ perception may however differ significantly from actual cardiovascular risk with one study reporting that more than 90% of high-risk individuals underestimated their cardiovascular risk (75). Interventions to improve patient-provider interactions/relationships toward ensuring efficient communication of risk-related information are therefore important to improve adherence and reduce discontinuation of statin therapy. The evidence supporting the use of statins in the elderly particularly for primary prevention is less clear and a matter of active debate (76–78). Studies have suggested that the uncertainty around treatment benefit and risks may have had an adverse impact on statin adherence and continuation rates (79–81). Thus, it is important for clinicians to regularly inform their patients about the available evidence regarding the risks and benefits of statin therapy, and objectively discuss with them how this evidence relates to their own clinical circumstances and why their use may (or may not) be necessary. Patient buy-in following such engagements is likely to yield better adherence and treatment continuation (82). Our review also found that the presence of other noncardiac comorbidities has an adverse impact on statin adherence and continuation. Depression reduced the likelihood of statin adherence, as previously seen in an earlier meta-analysis where depression was associated with a 76% higher likelihood of nonadherence among multiple medications (83). The presence of respiratory disorders (chronic obstructive pulmonary disease or asthma) also increased the likelihood of nonadherence or discontinuation. Although the exact interactions between these noncardiac conditions and statin adherence and persistence are unclear, measures to manage these diseases among the older population may have a positive impact on statin use. In the case of terminal diseases such as dementia and cancer, higher statin discontinuation may be due to the desire to reduce pill burden and the fact that there is little evidence to support statin continuation in persons who have a shortened life expectancy (57). A recent RCT suggested that discontinuing statins in patients in palliative settings was not only safe, but improved quality of life and reduced healthcare costs (84). Overall, it appears also that measures to reduce polypharmacy will be essential to improving adherence and treatment continuation in the older population, especially as this population has increased susceptibility to adverse effects. Nonetheless, the point is not for clinicians to simply impose a set number of medications and try to stay below it (85), but to carefully evaluate each patients’ treatment package on a case-by-case basis. Clearly, no single intervention can eliminate all of the factors associated with statin nonadherence or discontinuation; multi-faceted approaches will be needed to provide the kind of improvement required. Interventions are best applied as soon as patients commence treatment as the risk of nonadherence is higher among new users. However, if improvements are to be sustained over the long-term, interventions may need to be integrated within patient management plans and provided on an ongoing basis (82). This review has several strengths. First, we included a large number of studies from several countries. Secondly, only studies that utilized validated techniques for measuring adherence were included thereby ensuring consistency. In contrast to a previous review that combined predictors of nonadherence and discontinuation (6), we adopted a clear distinction between these essentially different parameters (5). The reported pooled estimates were unchanged by leave-one-out sensitivity analyses indicating that they were robust. Finally, considering that more than 70% of the included studies were recently published (2012–2016); the reported associations are likely to represent current trends. Our study also has some limitations. First, significant heterogeneity across studies was evident; studies with different sample size and measurement techniques were combined. Second, where adjusted estimates were utilized, studies employed different levels of adjustment for confounders. Third, the predictors were pooled from studies spanning nearly 15 years and it is possible that statin use may have changed over this period. Fourth, restricting our study to English language may limit the generalizability of our findings. Finally, none of the studies included in the review looked at patient-reported reasons for nonadherence or discontinuation which may be additionally important to inform the design of interventions (86). Conclusions A number of sociodemographic, health system, comorbidities, and treatment-related factors are associated with nonadherence to and discontinuation of statin therapy. Some of these factors are potentially modifiable and could be the focus of interventions aimed at improving statin use in older populations. Measures to address financial and social barriers, patients’ risk misconceptions as well as to reduce polypharmacy may be particularly important to improving statin adherence and reducing discontinuation. Supplementary Material Supplementary data is available at The Journals of Gerontology, Series A: Biological Sciences and Medical Sciences online. Funding R.O. is supported by a Monash Graduate Scholarship and Monash International Postgraduate Research Scholarship for his doctoral studies. S.Z. is funded by a National Health and Medical Research Council Senior Research Fellowship. No other funding has been received to undertake this work. Conflict of Interest S.Z. reports past participation in advisory boards and/or receiving honoraria from: Amgen Australia; AstraZeneca/Bristol-Myers Squibb Australia; Janssen-Cilag; Merck, Sharp, and Dohme (Australia); Novartis Australia; Novo Nordisk; Sanofi; Servier Laboratories; Takeda Australia; and Monash University (undertaking contract work for AstraZeneca Pty Limited/Bristol- Myers Squibb Australia Pty Limited) for work unrelated to this study. Acknowledgments We are grateful to authors who generously provided us with unpublished data. References 1. Roth GA, Johnson C, Abajobir Aet al.   Global, regional, and national burden of cardiovascular diseases for 10 causes, 1990 to 2015. J Am Coll Cardiol . 2017; 70: 1– 25. doi: 10.1016/j.jacc.2017.04.052 Google Scholar CrossRef Search ADS PubMed  2. Writing Group M, Mozaffarian D, Benjamin EJ, Go ASet al.   Heart disease and stroke statistics-2016 update: A report from the American Heart Association. 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The Journals of Gerontology Series A: Biomedical Sciences and Medical SciencesOxford University Press

Published: Jan 19, 2018

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