- Email: [email protected]
Examining factors associated with adherence to hormonal therapy in breast cancer patients
Research in Social and Administrative Pharmacy xxx (xxxx) xxx–xxx
Contents lists available at ScienceDirect
Research in Social and Administrative Pharmacy journal homepage: www.elsevier.com/locate/rsap
Examining factors associated with adherence to hormonal therapy in breast cancer patients Chintal H. Shaha,∗,1, Rajesh Balkrishnanb, Vakaramoko Diabya, Hong Xiaoa a b
Pharmaceutical Outcomes and Policy, College of Pharmacy, University of Florida, Gainesville, FL, USA Department of Public Health Sciences, School of Medicine, University of Virginia, Charlottesville, VA, USA
ARTICLE INFO
ABSTRACT
Keywords: Breast cancer Adherence Aromatase inhibitors Tamoxifen MEPS Disparity
Backgroud: Breast cancer is a rampant disease and is highly prevalent among women in the United States. Two out of three breast cancers are hormone receptor positive and hormonal therapies (Tamoxifen and Aromatase Inhibitors) are used to treat this type of breast cancer. However, adherence to these efficacious therapies is relatively low. Purpose: The aim of this study was to identify factors that are associated with adherence to hormonal therapy among breast cancer patients, and the extent to which they influence adherence, by looking at data from a nationally representative database. Methods: A retrospective cross-sectional study was conducted using Medical Expenditure Panel Survey (MEPS) for 2011–2015. Individuals ≥18 years diagnosed with breast cancer utilizing Tamoxifen and Aromatase inhibitors were identified. The Proportion of Days Covered (PDC) adherence measure was used to classify individuals as adherent (PDC≥80%) or non-adherent (PDC < 80%). Multivariable logistic regression was used to determine factors associated with adherence to hormonal therapy. Results: Out of the 354 breast cancer respondents utilizing hormonal therapy, 194 (54.8%) were adherent and 160 (45.20%) were non-adherent. From 2011 through 2015, an increase in the usage of hormonal therapy was observed. Individuals having at least a high school diploma or General Equivalency Diploma (GED) had 2.795 (1.081, 6.941) times the odds of being adherent when compared to those who did not have a high school diploma or GED. Race, insurance status, marital status, poverty level, class of drug (aromatase inhibitor/tamoxifen), age, comorbidities, out-of-pocket costs and region were not significantly associated with adherence to hormonal therapy among breast cancer patients. Conclusions: This study found an association between an individual's level of education and adherence to hormonal therapy among breast cancer patients. These results can be used to help optimize allocation of resources to promote knowledge designed to increase the adherence of breast cancer patients to hormonal therapy.
Introduction Breast cancer is a rampant disease and is highly prevalent among women in the United States. When compared to other cancers, female breast cancer represents 15% of all new cancer cases in the US.1 However, the breast cancer death rate has been decreasing since 1989, which could be attributed to advances in medical treatments, increased awareness, early detection through screening and a greater understanding of tumor biology.2,3 The five-year relative survival rate (age adjusted) for breast cancer patients has increased from 60% in 1954 to 92.2% in 2013.1 There is a plethora of options available to treat breast cancer, with the most appropriate being selected based on the type of breast cancer as well as the stage at diagnosis. Two out of three breast
cancers are hormone receptor positive.4 This means that these cells have receptors for hormones to bind. Hormone therapies act by reducing this attachment of hormones to receptors on cancerous cells. They may achieve this by either lowering levels of hormones in the body or blocking their attachment to these receptors.5 This study focused on the treatment of breast cancer using hormonal therapy. There are two classes of hormonal therapy. One is Selective Estrogen Receptor Modulator (SERM) which consists of the drug Tamoxifen. The other class is Aromatase Inhibitors (AI) and includes the drugs Letrozole, Anastrozole and Exemestane.5 Breast cancers that are hormone receptor (HR) positive should respond well to treatment by hormonal therapies.4 In recent times, though the breast cancer related mortality has decreased by at least 1% per year, the rate of incidence of breast cancer has
Corresponding author. Dept. of Pharmaceutical Health Services Research, 220 Arch Street, 12th floor, Baltimore, MD 21201, USA. E-mail address: [email protected] (C.H. Shah). 1 Department of Pharmaceutical Health Services Research, University of Maryland Baltimore, School of Pharmacy, Baltimore, MD, USA. ∗
https://doi.org/10.1016/j.sapharm.2019.08.005 Received 11 March 2019; Received in revised form 30 July 2019; Accepted 1 August 2019 1551-7411/ © 2019 Elsevier Inc. All rights reserved.
Please cite this article as: Chintal H. Shah, et al., Research in Social and Administrative Pharmacy, https://doi.org/10.1016/j.sapharm.2019.08.005
Research in Social and Administrative Pharmacy xxx (xxxx) xxx–xxx
C.H. Shah, et al.
Fig. 1. Flow diagram illustrating the cohort selection process.
remained constant, across all racial groups.6 One of the driving forces behind this decrease has been advent of newer treatment options, especially hormonal therapy among breast cancer patients.4 Furthermore, a previous study found that use of hormonal therapy has shown to reduce the annual cancer death rate by 31%, among those with endocrine receptor positive breast cancer, regardless of age or tumor characteristics.7 It also found that the use of hormonal therapy resulted in a 40% reduction in the risk of breast cancer recurrence.7 Thus, hormonal therapy, due to its demonstrated efficacy and superior safety profile represents a vital treatment option among HR positive breast cancer populations. Efficacy can be defined as how well a drug performs under ideal and controlled conditions, while effectiveness looks at the performance of a drug in ‘real-world’ conditions.8 Non-adherence is a ‘real-world’ problem. Generally a drug, irrespective of its efficaciousness, cannot be effective unless it is consumed appropriately by the patient.9 Non-adherence is a major problem in healthcare settings across the world. It also often leads to increased costs due to the added expense of hospitalization or restarting treatment. Thus, healthcare providers, as well as payers, focus heavily on ensuring adherence, especially for chronic
conditions, as medications may take time to induce their effects. Hormonal drugs used by breast cancer patients, have been shown to be efficacious in significantly reducing breast cancer mortality as well as its recurrence.4,7 Banning et al10 in a review of thirteen studies, found that among post-menopausal women prescribed Tamoxifen, between 15% and 55% were adherent and among those prescribed Anastrozole, Letrozole or Exemestane, between 31% and 73% of women were adherent. These numbers are very low and highlight the need to implement measures that may enhance adherence to these efficacious drugs. Based on the findings of our literature search (appendix A), it became evident that though previous studies have examined disparities in use of hormonal therapies, they were restricted to either Medicare populations, commercial insurance claims populations, hospital and registry collected data.11–17 The current study aimed to address this gap by using data from the Medical Expenditure Panel Survey (MEPS), which is nationally representative, to examine factors that are associated with adherence to hormonal therapy among breast cancer patients. Identifying such factors can help guide national policy and allow targeting specific populations on which strategies to increase adherence 2
Research in Social and Administrative Pharmacy xxx (xxxx) xxx–xxx
C.H. Shah, et al.
could have the biggest impact. The uniqueness of this study is due to the fact that the MEPS dataset used is nationally representative and this allows us to look at the factors that are associated with adherence to hormonal therapy on a national level and also across different insurers, as it is not limited to a particular insurance type.
days of supply for each prescription. The PDC was calculated using a method similar to the one used by Hsien-Chang Lin et al.32 in their study where they looked at medication adherence using MEPS data. In order to determine the number of days of participation in the study period, the patients were divided into 2 groups-those who were previous users of the drugs and those who initiated therapy in the year from which their demographic and prescription data was collected (new users). Previous users were identified based on whether they had a prescription for the hormonal drug in the preceding year (if they participated) or if they started the drug in a year prior to the current year (as determined using the MEPS variable RXBEGYRX where patients were asked the year in which they first took the drug). Previous users had a constant denominator of 365 days (or 366 days in the case of a leap year) based on regime recommendations from the American Society of Clinical Oncology (ASCO).33 New users were those who started the medication in the year in which prescription data was collected. For new users, their days of participation was determined based on the month in which they received their first prescription (as identified using the MEPS variable RXBEGMM where patients were asked the month in which they first took the drug) and calculating the number of days that were left in the year. To check whether there were systematic differences between these 2 groups, we carried out group difference tests between new users and previous users on all the independent variables. The days of supply for all the hormonal therapy medications were summed for each individual in order to get the numerator. The PDC values were capped at 100% coverage. Adherence was defined as having a PDC equal to or greater than 80% and nonadherence was defined as having a PDC less than 80%, as is the convention and based on previous studies looking at adherence to hormonal therapy among breast cancer patients.11–14,17
Methods Data source Data was obtained from the Medical Expenditure Panel Survey (MEPS) dataset for the years 2011–2015 (five-year period). MEPS is a data source provided by the Agency for Healthcare Research and Quality (AHRQ) and is publicly available.18 It is a large set of survey data collected annually since 1996, from families and individuals, their medical providers and employers across the United States. The key characteristic of this dataset is that it is nationally representative of civilian non-institutionalized Americans. A panel of people is followed for a duration of 2 years, which consists of 5 interview rounds. The MEPS dataset has 2 major components, the household component and the insurance component. The household component collects data from individuals, families and their medical providers whereas the insurance component collects data from employers. This study used the household component, more specifically the full year consolidated data file, the prescribed medicines file and the medical conditions file. Study sample The sample was selected from five years of MEPS data (2011–2015). Women who had been diagnosed with breast cancer were included based on their response to the question that determined whether they had been diagnosed with breast cancer. The prescription drug records were joined to the patient demographic data for patients with breast cancer. Among these patients, only those who had at least one prescription for hormonal therapy drugs were retained. The use of hormonal therapy was determined by using National Drug Codes (NDC) (Appendix B). Patients equal to or greater than 18 years of age, who had non-missing survey weight estimates were included in the analysis. The final cohort included 354 (weighted value = 4,169,282) patient years. The sample selection process has been depicted in Fig. 1.
Proportion of Days Covered Number of days supply of the medication = x 100 Number of days of participation in the study period Statistical analysis All statistical analyses were carried out using the SAS software (version 9.4 SAS Institute Inc., Cary, NC, US). Descriptive statistics were used to summarize the study cohort, their demographic characteristics and study variables. Rao Scott chi square tests were used to see whether differences in the baseline characteristics existed between the two groups (those who were adherent to medication and those who were not adherent to the medication). Since the outcome (adherence) is binary, the method used to model this outcome was logistic regression.34 The logistic model consisted of the independent variables described earlier (based on the findings of the literature search) as predictor variables and medication adherence as the dependent variable. Statistical significance was set at a p-value of 0.05. All of the analyses utilized the survey weights that MEPS provides and employed the ‘SURVEYFREQ’, ‘SURVEYMEANS’, ‘SURVEYLOGISTIC’ and ‘SURVEYFREQ’ procedures in SAS. The logistic model was tested for multicollinearity and goodness of fit and neither of them was an issue.35
Study variables: independent variables Based on the literature on adherence studies in breast cancer patients receiving hormonal therapy, as well as Andersen's Behavioral Model of Health Services Use, independent variables were included to examine their associations with adherence to hormonal therapy.19,20 As per this model, health care utilization was considered to be associated with predisposing factors, enabling factors and need factors.19–23 Predisposing factors describe the individual's propensity to use health care services and the factors included were race, education level, marital status and age.12,13,21,24,25 Enabling factors reflect the means or resources that an individual has available to utilize healthcare services and the ones included were insurance status, socioeconomic status (as determined by poverty level), out of pocket costs for 30-day supply of medication (adjusted for inflation to 2015 values using Consumer Price Index) and geographical region.11,13,21,26 Need factors are those that bring about health services use.21 The Charlson Comorbidity Index was used to control for the overall health of the study sample.27–31 The effect of a class of medication used (Aromatase Inhibitor or Tamoxifen) was also examined. These variables have been listed in Appendix C.
Results Baseline characteristics among people who had breast cancer and at least one prescription of hormonal therapy have been depicted in Table 1. The patients were predominantly white (86.95%) and there was an increase in the usage of hormonal therapy in each subsequent year observed (621,962 users in 2011 rose to 1,012,137 users in 2015). The most prominent insurance type was ‘private insurance only’ (42.18%) and the least frequent type was ‘Medicare and Medicaid’ (5.10%). The largest proportion of people belonged to the age group 50–70 (59.87%), while marital status was relatively evenly distributed. An overwhelming majority of people had at least a high school diploma or General
Study variables: dependent variable – adherence In this study, adherence was assessed using the Proportion of Days Covered (PDC) measure. The prescription medication file contains information on all prescriptions for each patient for a given year as well as 3
Race White Black Other Year 2011 2012 2013 2014 2015 Insurance coverage Private insurance only Medicare only Medicaid only Medicare and Medicaid Medicare and Private insurance Age < 50 50–70 Older than 70 Marital Status Married Not currently married Education level no HS diploma/GED at least HS diploma/GED Charlson Comorbidity score 0 1 2 2+ Poverty level Poor negative/Near Poor Low Income Middle Income High Income Class of drug Tamoxifen AI Out of pocket cost for a 30 day supply of medication Q1 Q2 Q3 Q4 Region Northeast Midwest South West Total
Variable
7.3321
56 67 70 79 82 127.2348
80.2147
3.5364 260.0784 373.4517
144.5625
22.4213 1.1874
25.5040
135 (1,758,490, 42.18%) 49 (549,998, 13.19%) 38 (362,576, 8.70%) 31 (212,637, 5.10%) 101 (1,285,581, 30.83%) 52 (554,708, 13.30%) 210 (2,496,205, 59.87%) 92 (1,118,369, 26.82%) 176 (2,344,276, 56.23%) 178 (1,825,006, 43.77%) 49 (360,500, 8.65%) 305 (3,808,782, 91.35%) 236 (2,189,747, 52.52%) 56 (372,468, 8.93%) 26 (1,084,641, 26.02%) 36 (522,425, 12.53%) 66 (626,782, 15.03%) 56 (549,164, 13.17%) 87 (960,498, 23.04%) 145 (2,032,836, 48.76%) 113 (1,412,621, 33.88%) 241 (2,756,660, 66.12%) (1,005,812, 24.12%) (948,780, 22.76%) (1,060,959, 25.45%) (1,153,731, 27.67%)
88 90 88 88 65 (804,567, 19.30%) 69 (949,283, 22.77%) 140 (1,632,519, 39.16%) 80 (782,913, 18.78%) 354 (4,169,282, 100%)
(621,962, 14.92%) (762,140, 18.28%) (775,687, 18.60%) (997,356, 23.92%) (1,012,137, 24.28%)
607.6019
Rao Scott Chi square value
261 (3,625,386, 86.95%) 64 (321,390, 7.71%) 29 (222,505, 5.34%)
Frequency (Unweighted (weighted, weighted percentage))
Table 1 Baseline characteristics of patients with breast cancer who received hormonal therapy.
4
3
3
1
4
3
1
1
2
4
4
2
df
< .0001
0.7560
< .0001
< .0001
< .0001
< .0001
0.0600
< .0001
< .0001
0.1193
< .0001
P value
C.H. Shah, et al.
Research in Social and Administrative Pharmacy xxx (xxxx) xxx–xxx
(1,076,951, 61.24%) (320,637, 58.30%) (251,738, 69.43%) (114,441, 53.82%)
5 102 (1,259,372, 41.11%) 25 (132,118,33.85%) 16 (163,636,63.18%) 17 (139,081,49./2%) 31 24 37 68
134 (1,804,120, 58.89%) 31 (375,509, 66.15%) 10 (95,350, 36.82%) 19 (140,096, 50.18%)
35 32 50 77
68 (803575, 56.89%) 126 (1611500, 58.46%)
Class of drug Tamoxifen AI
Out of pocket costs for 30-day supply of medication Q1 49 (564099, Q2 54 (610126, Q3 54 (711736, Q4 37 (529113, Region Northeast 47 (537437, Midwest 42 (615092, South 66 (862863, West 39 (399682,
39 36 34 51 18 27 74 41
56.08%) 64.31%) 67.08%) 45.86%) 66.80%) 64.80%) 52.85%) 51.05%)
(267130, (334191, (769655, (383232,
(441712, (338654, (349224, (624617,
33.20%) 35.20%) 47.15%) 48.95%)
43.92%) 35.69%) 32.92%) 54.14%)
45 (609047, 43.11%) 115 (1145161, 41.54%)
41.50%) 42.44%) 41.73%) 42.31%)
24 (216,766, 60.13%) 136 (1,537,441, 40.37%)
25 (143,733, 39.87%) 169 (2,271,341, 59.63%)
(260134, (233069, (400818, (860187,
77 (940,785, 40.13%) 83 (813,423, 44.57%)
99 (1,403,491, 59.87%) 95 (1,011,583, 55.43%)
(366649, 58.50%) (316096, 57.56%) (559680, 58.27%) (1172650, 57.69%)
15 (118,051, 21.28%) 102 (1,137,590, 45.57%) 43 (498,567, 44.58%)
49.34%)
38.76%) 41.70%) 30.57%)
37 (436,657, 78.72%) 108 (1,358,615, 54.43%) 49 (619,802, 55.42%)
52 (651,307, 50.66%)
62 (681,539, 24 (229,361, 12 (110,838, 13 (98,196, 46.18%) 49 (634,274,
36.31%) 54.50%) 44.16%) 27.96%) 48.56%)
73 25 26 18
(225,862, (415,388, (342,581, (278,848, (491,529,
25 37 31 28 39
(396,100, (346,752, (433,106, (718,508, (520,608,
31 30 39 51 43
63.69%) 45.50%) 55.84%) 72.04%) 51.44%)
119 (1,508,253, 41.60%) 30 (166,423, 51.78%) 11 (79,532, 35.74%)
Non-adherent user's frequency (Unweighted (weighted, weighted percentage))
142 (2,117,134, 58.40%) 34 (154,967, 48.22%) 18 (142,973, 64.26%)
Adherent user's frequency (Unweighted (weighted, weighted percentage))
Medicare and Private insurance Age < 50 50–70 > 70 Marital status Married Not currently married Education level no HS diploma/GED at least HS diploma/GED CCI 0 1 2 2 Poverty level Poor negative/Near Poor Low Income Middle Income High Income
Year 2011 2012 2013 2014 2015 Insurance coverage Private insurance only Medicare only Medicaid only Medicare and Medicaid
Race White Black Other
Variable
Table 2 Bivariate analysis with dependent variable (adherence to therapy).
4.3320
6.8882
0.0497
0.0123
5.2711
5.3572
0.4633
7.4656
3.6359
9.0143
1.9173
Rao Scott Chi square
3
3
1
3
3
1
1
2
4
4
2
df
0.2278
0.0755
0.8236
0.9996
0.1530
0.0206
0.4961
0.0239
0.4575
0.0607
0.3834
P value
C.H. Shah, et al.
Research in Social and Administrative Pharmacy xxx (xxxx) xxx–xxx
Research in Social and Administrative Pharmacy xxx (xxxx) xxx–xxx
C.H. Shah, et al.
carried out by Xi Tan and colleagues.14,15 Although the effect of age was significant in the crude, unadjusted bivariate analysis, after carrying out multivariable logistic regression, age was not associated with adherence to hormonal therapy among breast cancer patients. This too was similar to the findings of Xi Tan and colleagues.14 It was found that class of medication was not significantly correlated with adherence to hormonal therapy. Education level was significantly associated with adherence to hormonal therapy among breast cancer patients. Those who have at least a high school diploma or GED had higher odds of being adherent when compared to those who did not. This is useful as it can help target specific populations when implementing an intervention to increase adherence to hormonal therapy among breast cancer patients on, as is often the scenario, a limited budget. Geographical region was not associated with adherence to hormonal therapy among breast cancer patients. However, these results should be interpreted with caution as the confidence intervals were wide. Out of pocket costs could have an impact on adherence to hormonal medication among breast cancer patients. Farias and Du11 reported that out of pocket costs largely explain racial disparities in women's adherence to adjuvant therapy. Out of pocket cost was also found to be a significant predictor of adherence in the study by Hershman et al13 This study found that out of pocket costs was not a statistically significant factor associated with adherence to hormonal therapy in breast cancer patients. Unlike the previous studies, the population in our study was not limited to a specific insurance type. Each insurance type has a different type of risk sharing arrangement (coinsurance or copayment). This study includes all these different arrangements, where other studies have been specific to a particular insurance type, and this could possibly explain the discrepancies in the findings. The fact that the database used to carry out this study was MEPS, which is nationally representative, makes conclusions drawn from this study, by extension, nationally representative to the United States. A study by Junling Wang et al.37 found that MEPS is a perfect database to study racial and ethnic disparities in prescription drugs due to the fact that it is oversampled by Blacks, Asians and Hispanics, giving it the statistical power to pick up disparities that may exist between these populations. Furthermore, the fact that the population was not restricted by the type of insurance or location of residence, the conclusions can be made for a broader population. As a result, this study was unique as it allowed us to make conclusions among groups of patients with public as well as commercial insurance and also compare adherence rates to hormonal therapy among these different groups of insurance holders. Most retrospective studies have some limitations. This study was not immune to these limitations. One of the limitations was that severity of the breast cancer and its stage information was not available and could not be adjusted for. Another limitation was intrinsic to the data from MEPS. The design of data collection is such that one member of the household answers the survey for all the other members of the household. This may lead to some errors. It is possible that the person providing information is not aware of all the conditions of the other members. It is also possible that the person providing the information may mix up some conditions or other details between family members. Since the MEPS questionnaire is extensive, this is likely to happen. Being a survey, it is also susceptible to survey bias. Also, due to incomplete knowledge on every single variable that affects adherence to hormonal therapy as well as limitations of information available in the data, the study was susceptible to omitted variable bias. Other possible limitations could be the relatively low sample size which may lead to issues with power and the inability to adjust for the duration on therapy as well as adherence to therapy in prior years. Noncompliance due to development of adverse effects could not be accounted for due lack of such information. Given that the follow up period was one year, this study was not able to distinguish between non-adherence to therapy and discontinuation of therapy. As this study used cross sectional data, causation is not implied and caution should be exercised in interpreting these results. The study identifies an area that could be targeted when employing
Equivalency Diploma (GED) (91.35%). Almost three quarters of the study sample had a Charlson Comorbidity Index Score (CCI) of 0. Furthermore, as much as 48% of the sample was identified as high income and twothirds were users of Aromatase Inhibitors. Most of the patients belonged to the Southern region (39%) of the United States. There was a significant difference in the distribution of variables such as race, insurance coverage, age, CCI, family income as a percentage of the poverty line, class of drug and region. Group differences did not exist between the new users and previous users based on the independent variables. This study sample was then subjected to bivariate analysis. In this crude analysis (where covariates were not adjusted for), only age and education level showed statistical significance. The results of these analyses are depicted in Table 2. All the covariates were included in the logistic regression model. For the comparisons, ‘white’ race, ‘private insurance only’, the year ‘2015’, ‘high income’ (Socioeconomic status), ‘quarter four’ (out of pocket costs for a 30-day supply of medication) and ‘west’ (region) were set as the reference variables when there were more than two levels. Upon adjusting for the other covariates, the only covariate that was statistically significant was education level. It showed that someone who had at least a high school diploma or GED had 2.795 times (95% confidence interval 1.081–6.941, p value 0.038) the odds of being adherent than someone who did not have a high school diploma or GED. The results of logistic regression have been depicted in Table 3. Discussion This study found that race/ethnicity is not associated with adherence to hormonal therapy in breast cancer patients. Camacho et al.12 found that race did indeed influence adherence to endocrine therapy. Hershman et al.13 on the other hand reported that economic factors explain racial disparities in adherence to hormonal therapy. This study included a variable that classifies the family income as a percentage of the poverty line (poverty level). As a result, it may be adjusting for the economic factors, which could possibly explain the non-association of race/ethnicity with adherence to hormonal therapy. Also, the distribution of the different races (Table 1) was similar to that observed in previous, similar studies.11–13,36 Previous studies were restricted to people who were Medicare patients, or had commercial insurance. Consequently, they were not able to fully investigate the impact that insurance status has on adherence to hormonal therapy among breast cancer patients.11–15,24,28 MEPS data does include people with different types of insurance coverage and this study found that insurance status did not have a significant association with adherence to hormonal therapy. Marital status is a variable that is adjusted for in previous studies and the rationale behind this was that having a spouse may affect a person's behavior in terms of adherence to hormonal therapy.14 Our study found that marital status was not associated with adherence to hormonal therapy among breast cancer patients. This is in concordance with the findings of Xi Tan and colleagues.14 Hershman et al.13 found that household net worth was positively associated with adherence to endocrine therapy and may even partially explain racial differences. Farias and Du11 reported that socioeconomic status (defined as the percentage of residents living below the federal poverty level and metropolitan status) partially explained racial differences in adherence to endocrine therapy. This study does not find any such association. There are several possible explanations for this discrepancy. One possible reason is the difference in the sample characteristics. The study by Hershman et al.13 is restricted to people with commercial insurance and subsequently higher income, while the study by Farias and Du11 is restricted to Medicare enrollees. Another possible explanation could be that the categorization of socioeconomic status varied between studies. Comorbidities were accounted for by using the Charlson Comorbidity Index (CCI) scores. No significant association was found between the CCI scores and adherence to hormonal therapy among breast cancer patients. These results were similar to the results obtained by previous studies 6
Race Black Other Year 2011 2012 2013 2014 Insurance coverage Medicare only Medicaid only Medicare and Medicaid Medicare and Private insurance Marital status Married Poverty level Poor negative/Near poor Low Income Middle Income Class of drug Tamoxifen Age age 50-70 age < 50 Education level at least HS diploma/GED Charlson Comorbidity score 0 1 2 Out of pocket cost for a 30 day supply of medication Q1 Q2 Q3 Region Northeast Midwest South
Variable
0.563 0.835 2.105 1.026 1.311 2.447 0.944 1.952 0.979 0.570 0.871 0.961 1.131 1.039 0.691 0.662 1.662 2.795 1.406 2.123 0.623 1.411 1.916 2.734 1.816 1.809 1.386
2015
Private insurance only
Not currently married High Income
AI Older than 75 no HS diploma/GED 2+
Q4
West
Odds Ratio Point estimate
White
Reference
Table 3 Results of multivariate logistic regression of adherence to hormonal therapy
7
0.683 0.680 0.589
0.549 0.859 1.230
0.554 0.675 0.176
1.081
0.302 0.466
0.341
0.283 0.455 0.502
0.447
0.331 0.491 0.188 0.244
0.895 0.408 0.547 1.067
0.248 0.307
95%Confidence limits
4.826 4.811 3.262
3.626 4.276 6.076
3.568 6.682 2.205
6.941
1.452 6.919
1.404
3.625 2.813 2.150
1.703
2.695 7.754 5.095 1.333
4.951 2.578 3.140 5.611
1.278 2.272
0.5888
0.0880
0.2489
0.0338
0.1146
0.3063
0.9905
0.6865
0.4201
0.1558
0.3877
P value
C.H. Shah, et al.
Research in Social and Administrative Pharmacy xxx (xxxx) xxx–xxx
Research in Social and Administrative Pharmacy xxx (xxxx) xxx–xxx
C.H. Shah, et al.
measures to enhance adherence to hormonal therapy among breast cancer patients. Patients who do not have at least a high school diploma or GED can be given education or special care for instance, Medication Therapy Management (MTM) to ensure that they are adherent to the therapy. It is also useful if some factors incorporated in this study (such as race/ethnicity), do not show a significant association with adherence at a national level but if they show a significant association at a regional level, then disparity in care may exists. By targeting where to implement these interventions to increase adherence, optimization of adherence could be obtained, which should consequently lead to cost savings.
may aim to target those who have lower levels of education, thereby optimizing the effectiveness of the intervention. Data availability The data used for this study was from the Medical Expenditure Panel Survey (MEPS), which is a freely available public data source, from the Agency for Healthcare Research and Quality (AHRQ). It is available at: https://meps.ahrq.gov/mepsweb/data_stats/download_data_files.jsp. Funding
Conclusion
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
The findings of this study suggest that there was an increase in the usage of hormonal therapy over the years and that the level of education has a statistically significant association with adherence to hormonal therapy among breast cancer patients, as was found by analyzing MEPS data from 2011 to 2015. Breast cancer patients who had a higher level of education (defined as having at least a high school diploma or GED) tended to have higher odds of being adherent to hormonal therapy in comparison to those who did not have a high school diploma or GED. Race, insurance status, marital status, poverty level, class of drug, age, comorbidities, out of pocket costs and region did not significantly affect adherence to hormonal therapy among breast cancer patients. Therefore, interventions carried out, especially on a national level, to increase adherence to hormonal therapy
Conflict of interest Dr. Balkrishnan declares holding a consultant/advisory role for Merck and Company. All other authors declare that they have no conflicts of interest. Ethical approval This article does not contain any studies with human participants performed by any of the authors.
APPENDIX A Overview of the literature search Author
Title
Albert J. Farias and Association Between Out-Of-Pocket Costs, Race/ Xianglin L. Du Ethnicity, and Adjuvant Endocrine Therapy Adherence Among Medicare Patients With Breast Cancer11 Fabian T. Camacho, Impact of patient race and geographical factors on Xi Tan, Rajesh initiation and adherence to adjuvant endocrine Balkrishnan et- therapy in Medicare breast cancer survivors12 al.
Dawn L. Hershman, Jennifer Tsui, Alfred I. Neugut et al. Author Xi Tan, Vincent D. Marshall, Rajesh Balkrishnan
Household Net Worth, Racial Disparities, and Hormonal Therapy Adherence Among Women With EarlyStage Breast Cancer13 Title Adjuvant therapy use among Appalachian breast cancer survivors15
Xi Tan, Fabian Camacho, Rajesh Balkrishnan
Geographic disparities in adherence to adjuvant endocrine therapy in Appalachian women with breast cancer14
Carling J. Ursem, Hayden B. Bosworth, Gretchen G. Kimmick Qijia Xuan,Kun Gao, Jingxuan Wang
Adherence to Adjuvant Endocrine Therapy for Breast Cancer:Importance in Women with Low Income16
Data source
Conclusions
SEER-Medicare linked database to identify patients ≥ 65 years of age with hormone receptor–positive breast cancer who were enrolled in Medicare Part D from 2007 to 2009 SEER-Medicare data for the years 2007–2011 to identify patients continuously enrolled in the Fee for Service coverage in Medicare part A, B, and part D one year pre and post diagnosis
Racial/ethnic disparities in AET adherence were largely explained by women's differences in socioeconomic status and out-of-pocket medication costs. Significant differences exist in the initiation and adherence rates based on the geographical location of the patients. Blacks had lower initiation rates than whites, with other races having the highest initiation rates. In terms of adherence, other races were more adherent to the medication than blacks and whites. People not having Medicaid enrollment had lower MPR rates OptumInsight insurance claims database to identify Household net worth partially explains racial diswomen older than age 50 years diagnosed with early parities in hormonal therapy adherence breast cancer, from January 1, 2007, to December 31, 2011, who were using hormonal therapy. Data source Conclusions Medicare claims which was linked to cancer regisTamoxifen, in comparison to AIs was associated tries from four Appalachian states (Pennsylvania, with greater adherence and more persistence. In Ohio, Kentucky and North Carolina) from 2006 to AIs, out of pocket costs, dual eligibility and 2008. coverage gaps significantly influenced adherence and persistence to the drug therapy. Medicare claims which was linked to cancer regisSignificant geographic disparities in adherence to tries from four Appalachian states (Pennsylvania, adjuvant endocrine therapy in the Appalachian Ohio, Kentucky and North Carolina) from 2006 to counties in PA, OH, KY, and NC. 2008. Results of various other studies Improving adherence to endocrine therapy among breast cancer patients in lower socio-economic groups will improve their outcomes
Adherence to Needed Adjuvant Therapy Could Newly diagnosed stage I to stage III breast cancer Adherence to adjuvant therapy can help overcome Decrease Recurrence Rates for Rural Patients With patients from 2000 to 2009 at the Tumor Hospital of the difference between recurrence rates in urban 17 Early Breast Cancer Harbin Medical University and rural populations
8
Research in Social and Administrative Pharmacy xxx (xxxx) xxx–xxx
C.H. Shah, et al.
APPENDIX B National Drug Codes for hormonal therapy drugs Drug
NDC code
AI- Anastrozole
‘007815356′, ‘167290035′, ‘605052985′, ‘001790068′, ‘691890035′, ‘422910374′, ‘001790169′, ‘000540269′ ‘477810108′, ‘000930782′, ‘500902533′, ‘548683004′,
AI- Letrozole AI- Exemestane Tamoxifen
‘430630383′, ‘420430180′, ‘607630376′, ‘216950990′, ‘713350023′, ‘633230772′, ‘245350801′,
‘510790323′, ‘500901193′, ‘636720015′, ‘422540161′, ‘000540164′, ‘000937620′, ‘422540243′,
‘631870080′, ‘500901918′, ‘663360533′, ‘548686130′, ‘604290286′ ‘003782071′, ‘548686252′,
‘633230129′, ‘500902005′, ‘664350415′, ‘551110647′,
‘597622858′, ‘000930784′, ‘518620446′, ‘548684287′,
‘003785001′, ‘003780144′, ‘518620447′, ‘636294413′,
‘008320595′, ‘003780274′, ‘518620449′, ‘680840924′,
‘606870132′, ‘005912232′, ‘518620450′, ‘680840935′,
‘000937536′, ‘500902118′, ‘678770171′, ‘606870112′,
‘001151261′, ‘500902453′, ‘680010155′, ‘621750710′,
‘003786034′, ‘516550638′, ‘680711682′, ‘627560250′,
‘009046195′, ‘519910620′, ‘683820209′, ‘636295269′,
‘009046229′, ‘545696198′, ‘687886774′, ‘658410743′,
‘165710421′, ‘602580866′, ‘697610222′, ‘680840448′,
‘005271712′, ‘006034180′, ‘167290034′, ‘247240030′, ‘519910759′, ‘605053255′, ‘683820363′, ‘551110646′, ‘578842021′, ‘621750888′, ‘627560511′, ‘658410744′, ‘680840803′, ‘691897620′, ‘000540080′, ‘005912233′, ‘545693765′, ‘604290909′,
‘000097663′, ‘005912472′, ‘545695716′, ‘604290910′,
‘548685261′ ‘005912473′, ‘500900485′, ‘500900942′, ‘500901998′, ‘605053035′, ‘605053036′, ‘001790224′, ‘001791952′, ‘637390269′
Appendix C. Supplementary data Supplementary data to this article can be found online at https://doi.org/10.1016/j.sapharm.2019.08.005.
References 21.
1. Howlader N, Noone AM, Krapcho M, et al. Cancer Statistics Review, 1975-2014 SEER Statistics. https://seer.cancer.gov/archive/csr/1975_2014/, Accessed date: 29 April 2018. 2. U.S. Breast Cancer Statistics. Breastcancer.org. http://www.breastcancer.org/ symptoms/understand_bc/statistics Accessed April 29, 2018. 3. Baldassarre G, Belletti B. Molecular biology of breast tumors and prognosis. F1000Research. 2016;5. https://doi.org/10.12688/f1000research.8158.1. 4. American Cancer Society. Breast cancer. https://www.cancer.org/cancer/breastcancer.html, Accessed date: 29 April 2018. 5. Hormone Therapy for Breast Cancer. National Cancer Institute. https://www.cancer. gov/types/breast/breast-hormone-therapy-fact-sheet. Accessed April 29, 2018. 6. Ryerson AB, Eheman CR, Altekruse SF, et al. Annual Report to the Nation on the Status of Cancer, 1975-2012, featuring the increasing incidence of liver cancer. Cancer. 2016;122(9):1312–1337. https://doi.org/10.1002/cncr.29936. 7. Effects of chemotherapy and hormonal therapy for early breast cancer on recurrence and 15-year survival: an overview of the randomised trials. The Lancet. 2005;365(9472):1687–1717. https://doi.org/10.1016/S0140-6736(05)66544-0. 8. Revicki DA, Frank L. Pharmacoeconomic evaluation in the real world. Effectiveness versus efficacy studies. PharmacoEconomics. 1999;15(5):423–434. https://doi.org/ 10.2165/00019053-199915050-00001. 9. Kleinsinger F. The unmet challenge of medication nonadherence. Perm J. 2018;22. https://doi.org/10.7812/TPP/18-033. 10. Banning M. Adherence to adjuvant therapy in post-menopausal breast cancer patients: a review. Eur J Cancer Care. 2012;21(1):10–19. 11. Farias AJ, Du XL. Association between out-of-pocket costs, race/ethnicity, and adjuvant endocrine therapy adherence among medicare patients with breast cancer. J Clin Oncol. 2016;35(1):86–95. https://doi.org/10.1200/JCO.2016.68.2807. 12. Camacho FT, Tan X, Alcalá HE, Shah S, Anderson RT, Balkrishnan R. Impact of patient race and geographical factors on initiation and adherence to adjuvant endocrine therapy in medicare breast cancer survivors. Medicine (Baltim). 2017;96(24):e7147https://doi.org/10.1097/MD.0000000000007147. 13. Hershman DL, Tsui J, Wright JD, Coromilas EJ, Tsai WY, Neugut AI. Household net worth, racial disparities, and hormonal therapy adherence among women with earlystage breast cancer. J Clin Oncol. 2015;33(9):1053–1059. https://doi.org/10.1200/ JCO.2014.58.3062. 14. Tan X, Camacho F, Marshall VD, Donohoe J, Anderson RT, Balkrishnan R. Geographic disparities in adherence to adjuvant endocrine therapy in Appalachian women with breast cancer. Res Soc Adm Pharm RSAP. 2017;13(4):796–810. https:// doi.org/10.1016/j.sapharm.2016.08.004. 15. Tan X, Marshall VD, Anderson RT, Donohoe J, Camacho F, Balkrishnan R. Adjuvant therapy use among Appalachian breast cancer survivors. Medicine (Baltim). 2015;94(26):e1071https://doi.org/10.1097/MD.0000000000001071. 16. Ursem CJ, Bosworth HB, Shelby RA, Hwang W, Anderson RT, Kimmick GG. Adherence to adjuvant endocrine therapy for breast cancer: importance in women with low income. J Women's Health. 2002;24(5):403–408. https://doi.org/10.1089/ jwh.2014.4982 2015. 17. Xuan Q, Gao K, Song Y, et al. Adherence to needed adjuvant therapy could decrease recurrence rates for rural patients with early breast cancer. Clin Breast Canc. 2016;16(6):e165–e173. https://doi.org/10.1016/j.clbc.2016.07.006. 18. Medical expenditure panel survey home. https://meps.ahrq.gov/mepsweb/, Accessed date: 2 October 2018. 19. Andersen RM. National health surveys and the behavioral model of health services use. Med Care. 2008;46(7):647–653. https://doi.org/10.1097/MLR.0b013e31817a835d. 20. Andersen RM, Davidson PL. Improving access to care in America: individual and
22. 23. 24. 25.
26. 27. 28.
29. 30.
31. 32. 33.
34. 35. 36.
37.
9
contextual indicators. Changing the U.S. Health Care System: Key Issues in Health Services Policy and Management. third ed. San Francisco, CA, US: Jossey-Bass; 2007:3–31. Andersen RM. Revisiting the behavioral model and access to medical care: does it matter? J Health Soc Behav. 1995;36(1):1. https://doi.org/10.2307/2137284. Babitsch B, Gohl D, von Lengerke T. Re-revisiting Andersen's behavioral model of health services use: a systematic review of studies from 1998–2011. GMS Psycho-SocMed. 2012;9. https://doi.org/10.3205/psm000089. Andersen R. A behavioral model of families' use of health services. Behav Model Fam Use Health Serv. 1968;25https://www.cabdirect.org/cabdirect/abstract/ 19702701913, Accessed date: 28 January 2019. Wheeler SB, Kohler RE, Reeder-Hayes KE, et al. Endocrine therapy initiation among Medicaid-insured breast cancer survivors with hormone receptor-positive tumors. J Cancer Surv Res Pract. 2014;8(4):603–610. https://doi.org/10.1007/s11764-014-0365-3. Felder TM, Do DP, Lu ZK, Lal LS, Heiney SP, Bennett CL. Racial differences in receipt of adjuvant hormonal therapy among Medicaid enrollees in South Carolina diagnosed with breast cancer. Breast Canc Res Treat. 2016;157(1):193–200. https://doi. org/10.1007/s10549-016-3803-2. U.S. Bureau of labor statistics. https://www.bls.gov/cpi/, Accessed date: 29 April 2018. Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40(5):373–383. Weaver KE, Camacho F, Hwang W, Anderson R, Kimmick G. Adherence to adjuvant hormonal therapy and its relationship to breast cancer recurrence and survival among low-income women. Am J Clin Oncol. 2013;36(2):181–187. https://doi.org/ 10.1097/COC.0b013e3182436ec1. Desai R, Nayak R. Effects of medication nonadherence and comorbidity on health resource utilization in schizophrenia. J Manag Care Spec Pharm. 2018;25(1):37–46. https://doi.org/10.18553/jmcp.2019.25.1.037. Davis-Ajami ML, Wu J, Fink JC. Differences in health services utilization and costs between antihypertensive medication users versus nonusers in adults with diabetes and concomitant hypertension from medical expenditure panel survey pooled years 2006 to 2009. Value Health. 2014;17(1):51–61. https://doi.org/10.1016/j.jval.2013.11.008. Lin H-C, Lucas PT, Feldman SR, Balkrishnan R. Medication use and associated health care outcomes and costs for patients with psoriasis in the United States. J Dermatol Treat. 2012;23(3):196–202. https://doi.org/10.3109/09546634.2010.544708. Lin H-C, Erickson SR, Balkrishnan R. Antidepressant utilization, adherence, and health care spending in the United States: the case of MDD patients 2000-2007. Health Outcomes Res Med. 2011;2(2):e79–e89. https://doi.org/10.1016/j.ehrm.2011.06.003. Burstein HJ, Temin S, Anderson H, et al. Adjuvant endocrine therapy for women with hormone receptor–positive breast cancer: American society of clinical Oncology clinical practice guideline focused update. J Clin Oncol. 2014;32(21):2255–2269. https://doi.org/10.1200/JCO.2013.54.2258. Sperandei S. Understanding logistic regression analysis. Biochem Med. 2014;24(1):12–18. https://doi.org/10.11613/BM.2014.003. Hosmer DW, Hosmer T, Cessie SL, Lemeshow S. A comparison of goodness-of-fit tests for the logistic regression model. Stat Med. 1997;16(9):965–980. https://doi.org/10. 1002/(SICI)1097-0258(19970515)16:9<965::AID-SIM509>3.0.CO;2-O. Farias AJ, Du XL. Racial differences in adjuvant endocrine therapy use and discontinuation in association with mortality among Medicare breast cancer patients by receptor status. Cancer Epidemiol Biomark Prev Publ Am Assoc Cancer Res Cosponsored Am Soc Prev Oncol. 2017;26(8):1266–1275. https://doi.org/10.1158/1055-9965.EPI-17-0280. Wang J, Mullins CD, Zuckerman IH, et al. Medical Expenditure Panel Survey: a valuable database for studying racial and ethnic disparities in prescription drug use. Res Soc Adm Pharm RSAP. 2008;4(3):206–217. https://doi.org/10.1016/j.sapharm.2007. 06.018.
Contents lists available at ScienceDirect
Research in Social and Administrative Pharmacy journal homepage: www.elsevier.com/locate/rsap
Examining factors associated with adherence to hormonal therapy in breast cancer patients Chintal H. Shaha,∗,1, Rajesh Balkrishnanb, Vakaramoko Diabya, Hong Xiaoa a b
Pharmaceutical Outcomes and Policy, College of Pharmacy, University of Florida, Gainesville, FL, USA Department of Public Health Sciences, School of Medicine, University of Virginia, Charlottesville, VA, USA
ARTICLE INFO
ABSTRACT
Keywords: Breast cancer Adherence Aromatase inhibitors Tamoxifen MEPS Disparity
Backgroud: Breast cancer is a rampant disease and is highly prevalent among women in the United States. Two out of three breast cancers are hormone receptor positive and hormonal therapies (Tamoxifen and Aromatase Inhibitors) are used to treat this type of breast cancer. However, adherence to these efficacious therapies is relatively low. Purpose: The aim of this study was to identify factors that are associated with adherence to hormonal therapy among breast cancer patients, and the extent to which they influence adherence, by looking at data from a nationally representative database. Methods: A retrospective cross-sectional study was conducted using Medical Expenditure Panel Survey (MEPS) for 2011–2015. Individuals ≥18 years diagnosed with breast cancer utilizing Tamoxifen and Aromatase inhibitors were identified. The Proportion of Days Covered (PDC) adherence measure was used to classify individuals as adherent (PDC≥80%) or non-adherent (PDC < 80%). Multivariable logistic regression was used to determine factors associated with adherence to hormonal therapy. Results: Out of the 354 breast cancer respondents utilizing hormonal therapy, 194 (54.8%) were adherent and 160 (45.20%) were non-adherent. From 2011 through 2015, an increase in the usage of hormonal therapy was observed. Individuals having at least a high school diploma or General Equivalency Diploma (GED) had 2.795 (1.081, 6.941) times the odds of being adherent when compared to those who did not have a high school diploma or GED. Race, insurance status, marital status, poverty level, class of drug (aromatase inhibitor/tamoxifen), age, comorbidities, out-of-pocket costs and region were not significantly associated with adherence to hormonal therapy among breast cancer patients. Conclusions: This study found an association between an individual's level of education and adherence to hormonal therapy among breast cancer patients. These results can be used to help optimize allocation of resources to promote knowledge designed to increase the adherence of breast cancer patients to hormonal therapy.
Introduction Breast cancer is a rampant disease and is highly prevalent among women in the United States. When compared to other cancers, female breast cancer represents 15% of all new cancer cases in the US.1 However, the breast cancer death rate has been decreasing since 1989, which could be attributed to advances in medical treatments, increased awareness, early detection through screening and a greater understanding of tumor biology.2,3 The five-year relative survival rate (age adjusted) for breast cancer patients has increased from 60% in 1954 to 92.2% in 2013.1 There is a plethora of options available to treat breast cancer, with the most appropriate being selected based on the type of breast cancer as well as the stage at diagnosis. Two out of three breast
cancers are hormone receptor positive.4 This means that these cells have receptors for hormones to bind. Hormone therapies act by reducing this attachment of hormones to receptors on cancerous cells. They may achieve this by either lowering levels of hormones in the body or blocking their attachment to these receptors.5 This study focused on the treatment of breast cancer using hormonal therapy. There are two classes of hormonal therapy. One is Selective Estrogen Receptor Modulator (SERM) which consists of the drug Tamoxifen. The other class is Aromatase Inhibitors (AI) and includes the drugs Letrozole, Anastrozole and Exemestane.5 Breast cancers that are hormone receptor (HR) positive should respond well to treatment by hormonal therapies.4 In recent times, though the breast cancer related mortality has decreased by at least 1% per year, the rate of incidence of breast cancer has
Corresponding author. Dept. of Pharmaceutical Health Services Research, 220 Arch Street, 12th floor, Baltimore, MD 21201, USA. E-mail address: [email protected] (C.H. Shah). 1 Department of Pharmaceutical Health Services Research, University of Maryland Baltimore, School of Pharmacy, Baltimore, MD, USA. ∗
https://doi.org/10.1016/j.sapharm.2019.08.005 Received 11 March 2019; Received in revised form 30 July 2019; Accepted 1 August 2019 1551-7411/ © 2019 Elsevier Inc. All rights reserved.
Please cite this article as: Chintal H. Shah, et al., Research in Social and Administrative Pharmacy, https://doi.org/10.1016/j.sapharm.2019.08.005
Research in Social and Administrative Pharmacy xxx (xxxx) xxx–xxx
C.H. Shah, et al.
Fig. 1. Flow diagram illustrating the cohort selection process.
remained constant, across all racial groups.6 One of the driving forces behind this decrease has been advent of newer treatment options, especially hormonal therapy among breast cancer patients.4 Furthermore, a previous study found that use of hormonal therapy has shown to reduce the annual cancer death rate by 31%, among those with endocrine receptor positive breast cancer, regardless of age or tumor characteristics.7 It also found that the use of hormonal therapy resulted in a 40% reduction in the risk of breast cancer recurrence.7 Thus, hormonal therapy, due to its demonstrated efficacy and superior safety profile represents a vital treatment option among HR positive breast cancer populations. Efficacy can be defined as how well a drug performs under ideal and controlled conditions, while effectiveness looks at the performance of a drug in ‘real-world’ conditions.8 Non-adherence is a ‘real-world’ problem. Generally a drug, irrespective of its efficaciousness, cannot be effective unless it is consumed appropriately by the patient.9 Non-adherence is a major problem in healthcare settings across the world. It also often leads to increased costs due to the added expense of hospitalization or restarting treatment. Thus, healthcare providers, as well as payers, focus heavily on ensuring adherence, especially for chronic
conditions, as medications may take time to induce their effects. Hormonal drugs used by breast cancer patients, have been shown to be efficacious in significantly reducing breast cancer mortality as well as its recurrence.4,7 Banning et al10 in a review of thirteen studies, found that among post-menopausal women prescribed Tamoxifen, between 15% and 55% were adherent and among those prescribed Anastrozole, Letrozole or Exemestane, between 31% and 73% of women were adherent. These numbers are very low and highlight the need to implement measures that may enhance adherence to these efficacious drugs. Based on the findings of our literature search (appendix A), it became evident that though previous studies have examined disparities in use of hormonal therapies, they were restricted to either Medicare populations, commercial insurance claims populations, hospital and registry collected data.11–17 The current study aimed to address this gap by using data from the Medical Expenditure Panel Survey (MEPS), which is nationally representative, to examine factors that are associated with adherence to hormonal therapy among breast cancer patients. Identifying such factors can help guide national policy and allow targeting specific populations on which strategies to increase adherence 2
Research in Social and Administrative Pharmacy xxx (xxxx) xxx–xxx
C.H. Shah, et al.
could have the biggest impact. The uniqueness of this study is due to the fact that the MEPS dataset used is nationally representative and this allows us to look at the factors that are associated with adherence to hormonal therapy on a national level and also across different insurers, as it is not limited to a particular insurance type.
days of supply for each prescription. The PDC was calculated using a method similar to the one used by Hsien-Chang Lin et al.32 in their study where they looked at medication adherence using MEPS data. In order to determine the number of days of participation in the study period, the patients were divided into 2 groups-those who were previous users of the drugs and those who initiated therapy in the year from which their demographic and prescription data was collected (new users). Previous users were identified based on whether they had a prescription for the hormonal drug in the preceding year (if they participated) or if they started the drug in a year prior to the current year (as determined using the MEPS variable RXBEGYRX where patients were asked the year in which they first took the drug). Previous users had a constant denominator of 365 days (or 366 days in the case of a leap year) based on regime recommendations from the American Society of Clinical Oncology (ASCO).33 New users were those who started the medication in the year in which prescription data was collected. For new users, their days of participation was determined based on the month in which they received their first prescription (as identified using the MEPS variable RXBEGMM where patients were asked the month in which they first took the drug) and calculating the number of days that were left in the year. To check whether there were systematic differences between these 2 groups, we carried out group difference tests between new users and previous users on all the independent variables. The days of supply for all the hormonal therapy medications were summed for each individual in order to get the numerator. The PDC values were capped at 100% coverage. Adherence was defined as having a PDC equal to or greater than 80% and nonadherence was defined as having a PDC less than 80%, as is the convention and based on previous studies looking at adherence to hormonal therapy among breast cancer patients.11–14,17
Methods Data source Data was obtained from the Medical Expenditure Panel Survey (MEPS) dataset for the years 2011–2015 (five-year period). MEPS is a data source provided by the Agency for Healthcare Research and Quality (AHRQ) and is publicly available.18 It is a large set of survey data collected annually since 1996, from families and individuals, their medical providers and employers across the United States. The key characteristic of this dataset is that it is nationally representative of civilian non-institutionalized Americans. A panel of people is followed for a duration of 2 years, which consists of 5 interview rounds. The MEPS dataset has 2 major components, the household component and the insurance component. The household component collects data from individuals, families and their medical providers whereas the insurance component collects data from employers. This study used the household component, more specifically the full year consolidated data file, the prescribed medicines file and the medical conditions file. Study sample The sample was selected from five years of MEPS data (2011–2015). Women who had been diagnosed with breast cancer were included based on their response to the question that determined whether they had been diagnosed with breast cancer. The prescription drug records were joined to the patient demographic data for patients with breast cancer. Among these patients, only those who had at least one prescription for hormonal therapy drugs were retained. The use of hormonal therapy was determined by using National Drug Codes (NDC) (Appendix B). Patients equal to or greater than 18 years of age, who had non-missing survey weight estimates were included in the analysis. The final cohort included 354 (weighted value = 4,169,282) patient years. The sample selection process has been depicted in Fig. 1.
Proportion of Days Covered Number of days supply of the medication = x 100 Number of days of participation in the study period Statistical analysis All statistical analyses were carried out using the SAS software (version 9.4 SAS Institute Inc., Cary, NC, US). Descriptive statistics were used to summarize the study cohort, their demographic characteristics and study variables. Rao Scott chi square tests were used to see whether differences in the baseline characteristics existed between the two groups (those who were adherent to medication and those who were not adherent to the medication). Since the outcome (adherence) is binary, the method used to model this outcome was logistic regression.34 The logistic model consisted of the independent variables described earlier (based on the findings of the literature search) as predictor variables and medication adherence as the dependent variable. Statistical significance was set at a p-value of 0.05. All of the analyses utilized the survey weights that MEPS provides and employed the ‘SURVEYFREQ’, ‘SURVEYMEANS’, ‘SURVEYLOGISTIC’ and ‘SURVEYFREQ’ procedures in SAS. The logistic model was tested for multicollinearity and goodness of fit and neither of them was an issue.35
Study variables: independent variables Based on the literature on adherence studies in breast cancer patients receiving hormonal therapy, as well as Andersen's Behavioral Model of Health Services Use, independent variables were included to examine their associations with adherence to hormonal therapy.19,20 As per this model, health care utilization was considered to be associated with predisposing factors, enabling factors and need factors.19–23 Predisposing factors describe the individual's propensity to use health care services and the factors included were race, education level, marital status and age.12,13,21,24,25 Enabling factors reflect the means or resources that an individual has available to utilize healthcare services and the ones included were insurance status, socioeconomic status (as determined by poverty level), out of pocket costs for 30-day supply of medication (adjusted for inflation to 2015 values using Consumer Price Index) and geographical region.11,13,21,26 Need factors are those that bring about health services use.21 The Charlson Comorbidity Index was used to control for the overall health of the study sample.27–31 The effect of a class of medication used (Aromatase Inhibitor or Tamoxifen) was also examined. These variables have been listed in Appendix C.
Results Baseline characteristics among people who had breast cancer and at least one prescription of hormonal therapy have been depicted in Table 1. The patients were predominantly white (86.95%) and there was an increase in the usage of hormonal therapy in each subsequent year observed (621,962 users in 2011 rose to 1,012,137 users in 2015). The most prominent insurance type was ‘private insurance only’ (42.18%) and the least frequent type was ‘Medicare and Medicaid’ (5.10%). The largest proportion of people belonged to the age group 50–70 (59.87%), while marital status was relatively evenly distributed. An overwhelming majority of people had at least a high school diploma or General
Study variables: dependent variable – adherence In this study, adherence was assessed using the Proportion of Days Covered (PDC) measure. The prescription medication file contains information on all prescriptions for each patient for a given year as well as 3
Race White Black Other Year 2011 2012 2013 2014 2015 Insurance coverage Private insurance only Medicare only Medicaid only Medicare and Medicaid Medicare and Private insurance Age < 50 50–70 Older than 70 Marital Status Married Not currently married Education level no HS diploma/GED at least HS diploma/GED Charlson Comorbidity score 0 1 2 2+ Poverty level Poor negative/Near Poor Low Income Middle Income High Income Class of drug Tamoxifen AI Out of pocket cost for a 30 day supply of medication Q1 Q2 Q3 Q4 Region Northeast Midwest South West Total
Variable
7.3321
56 67 70 79 82 127.2348
80.2147
3.5364 260.0784 373.4517
144.5625
22.4213 1.1874
25.5040
135 (1,758,490, 42.18%) 49 (549,998, 13.19%) 38 (362,576, 8.70%) 31 (212,637, 5.10%) 101 (1,285,581, 30.83%) 52 (554,708, 13.30%) 210 (2,496,205, 59.87%) 92 (1,118,369, 26.82%) 176 (2,344,276, 56.23%) 178 (1,825,006, 43.77%) 49 (360,500, 8.65%) 305 (3,808,782, 91.35%) 236 (2,189,747, 52.52%) 56 (372,468, 8.93%) 26 (1,084,641, 26.02%) 36 (522,425, 12.53%) 66 (626,782, 15.03%) 56 (549,164, 13.17%) 87 (960,498, 23.04%) 145 (2,032,836, 48.76%) 113 (1,412,621, 33.88%) 241 (2,756,660, 66.12%) (1,005,812, 24.12%) (948,780, 22.76%) (1,060,959, 25.45%) (1,153,731, 27.67%)
88 90 88 88 65 (804,567, 19.30%) 69 (949,283, 22.77%) 140 (1,632,519, 39.16%) 80 (782,913, 18.78%) 354 (4,169,282, 100%)
(621,962, 14.92%) (762,140, 18.28%) (775,687, 18.60%) (997,356, 23.92%) (1,012,137, 24.28%)
607.6019
Rao Scott Chi square value
261 (3,625,386, 86.95%) 64 (321,390, 7.71%) 29 (222,505, 5.34%)
Frequency (Unweighted (weighted, weighted percentage))
Table 1 Baseline characteristics of patients with breast cancer who received hormonal therapy.
4
3
3
1
4
3
1
1
2
4
4
2
df
< .0001
0.7560
< .0001
< .0001
< .0001
< .0001
0.0600
< .0001
< .0001
0.1193
< .0001
P value
C.H. Shah, et al.
Research in Social and Administrative Pharmacy xxx (xxxx) xxx–xxx
(1,076,951, 61.24%) (320,637, 58.30%) (251,738, 69.43%) (114,441, 53.82%)
5 102 (1,259,372, 41.11%) 25 (132,118,33.85%) 16 (163,636,63.18%) 17 (139,081,49./2%) 31 24 37 68
134 (1,804,120, 58.89%) 31 (375,509, 66.15%) 10 (95,350, 36.82%) 19 (140,096, 50.18%)
35 32 50 77
68 (803575, 56.89%) 126 (1611500, 58.46%)
Class of drug Tamoxifen AI
Out of pocket costs for 30-day supply of medication Q1 49 (564099, Q2 54 (610126, Q3 54 (711736, Q4 37 (529113, Region Northeast 47 (537437, Midwest 42 (615092, South 66 (862863, West 39 (399682,
39 36 34 51 18 27 74 41
56.08%) 64.31%) 67.08%) 45.86%) 66.80%) 64.80%) 52.85%) 51.05%)
(267130, (334191, (769655, (383232,
(441712, (338654, (349224, (624617,
33.20%) 35.20%) 47.15%) 48.95%)
43.92%) 35.69%) 32.92%) 54.14%)
45 (609047, 43.11%) 115 (1145161, 41.54%)
41.50%) 42.44%) 41.73%) 42.31%)
24 (216,766, 60.13%) 136 (1,537,441, 40.37%)
25 (143,733, 39.87%) 169 (2,271,341, 59.63%)
(260134, (233069, (400818, (860187,
77 (940,785, 40.13%) 83 (813,423, 44.57%)
99 (1,403,491, 59.87%) 95 (1,011,583, 55.43%)
(366649, 58.50%) (316096, 57.56%) (559680, 58.27%) (1172650, 57.69%)
15 (118,051, 21.28%) 102 (1,137,590, 45.57%) 43 (498,567, 44.58%)
49.34%)
38.76%) 41.70%) 30.57%)
37 (436,657, 78.72%) 108 (1,358,615, 54.43%) 49 (619,802, 55.42%)
52 (651,307, 50.66%)
62 (681,539, 24 (229,361, 12 (110,838, 13 (98,196, 46.18%) 49 (634,274,
36.31%) 54.50%) 44.16%) 27.96%) 48.56%)
73 25 26 18
(225,862, (415,388, (342,581, (278,848, (491,529,
25 37 31 28 39
(396,100, (346,752, (433,106, (718,508, (520,608,
31 30 39 51 43
63.69%) 45.50%) 55.84%) 72.04%) 51.44%)
119 (1,508,253, 41.60%) 30 (166,423, 51.78%) 11 (79,532, 35.74%)
Non-adherent user's frequency (Unweighted (weighted, weighted percentage))
142 (2,117,134, 58.40%) 34 (154,967, 48.22%) 18 (142,973, 64.26%)
Adherent user's frequency (Unweighted (weighted, weighted percentage))
Medicare and Private insurance Age < 50 50–70 > 70 Marital status Married Not currently married Education level no HS diploma/GED at least HS diploma/GED CCI 0 1 2 2 Poverty level Poor negative/Near Poor Low Income Middle Income High Income
Year 2011 2012 2013 2014 2015 Insurance coverage Private insurance only Medicare only Medicaid only Medicare and Medicaid
Race White Black Other
Variable
Table 2 Bivariate analysis with dependent variable (adherence to therapy).
4.3320
6.8882
0.0497
0.0123
5.2711
5.3572
0.4633
7.4656
3.6359
9.0143
1.9173
Rao Scott Chi square
3
3
1
3
3
1
1
2
4
4
2
df
0.2278
0.0755
0.8236
0.9996
0.1530
0.0206
0.4961
0.0239
0.4575
0.0607
0.3834
P value
C.H. Shah, et al.
Research in Social and Administrative Pharmacy xxx (xxxx) xxx–xxx
Research in Social and Administrative Pharmacy xxx (xxxx) xxx–xxx
C.H. Shah, et al.
carried out by Xi Tan and colleagues.14,15 Although the effect of age was significant in the crude, unadjusted bivariate analysis, after carrying out multivariable logistic regression, age was not associated with adherence to hormonal therapy among breast cancer patients. This too was similar to the findings of Xi Tan and colleagues.14 It was found that class of medication was not significantly correlated with adherence to hormonal therapy. Education level was significantly associated with adherence to hormonal therapy among breast cancer patients. Those who have at least a high school diploma or GED had higher odds of being adherent when compared to those who did not. This is useful as it can help target specific populations when implementing an intervention to increase adherence to hormonal therapy among breast cancer patients on, as is often the scenario, a limited budget. Geographical region was not associated with adherence to hormonal therapy among breast cancer patients. However, these results should be interpreted with caution as the confidence intervals were wide. Out of pocket costs could have an impact on adherence to hormonal medication among breast cancer patients. Farias and Du11 reported that out of pocket costs largely explain racial disparities in women's adherence to adjuvant therapy. Out of pocket cost was also found to be a significant predictor of adherence in the study by Hershman et al13 This study found that out of pocket costs was not a statistically significant factor associated with adherence to hormonal therapy in breast cancer patients. Unlike the previous studies, the population in our study was not limited to a specific insurance type. Each insurance type has a different type of risk sharing arrangement (coinsurance or copayment). This study includes all these different arrangements, where other studies have been specific to a particular insurance type, and this could possibly explain the discrepancies in the findings. The fact that the database used to carry out this study was MEPS, which is nationally representative, makes conclusions drawn from this study, by extension, nationally representative to the United States. A study by Junling Wang et al.37 found that MEPS is a perfect database to study racial and ethnic disparities in prescription drugs due to the fact that it is oversampled by Blacks, Asians and Hispanics, giving it the statistical power to pick up disparities that may exist between these populations. Furthermore, the fact that the population was not restricted by the type of insurance or location of residence, the conclusions can be made for a broader population. As a result, this study was unique as it allowed us to make conclusions among groups of patients with public as well as commercial insurance and also compare adherence rates to hormonal therapy among these different groups of insurance holders. Most retrospective studies have some limitations. This study was not immune to these limitations. One of the limitations was that severity of the breast cancer and its stage information was not available and could not be adjusted for. Another limitation was intrinsic to the data from MEPS. The design of data collection is such that one member of the household answers the survey for all the other members of the household. This may lead to some errors. It is possible that the person providing information is not aware of all the conditions of the other members. It is also possible that the person providing the information may mix up some conditions or other details between family members. Since the MEPS questionnaire is extensive, this is likely to happen. Being a survey, it is also susceptible to survey bias. Also, due to incomplete knowledge on every single variable that affects adherence to hormonal therapy as well as limitations of information available in the data, the study was susceptible to omitted variable bias. Other possible limitations could be the relatively low sample size which may lead to issues with power and the inability to adjust for the duration on therapy as well as adherence to therapy in prior years. Noncompliance due to development of adverse effects could not be accounted for due lack of such information. Given that the follow up period was one year, this study was not able to distinguish between non-adherence to therapy and discontinuation of therapy. As this study used cross sectional data, causation is not implied and caution should be exercised in interpreting these results. The study identifies an area that could be targeted when employing
Equivalency Diploma (GED) (91.35%). Almost three quarters of the study sample had a Charlson Comorbidity Index Score (CCI) of 0. Furthermore, as much as 48% of the sample was identified as high income and twothirds were users of Aromatase Inhibitors. Most of the patients belonged to the Southern region (39%) of the United States. There was a significant difference in the distribution of variables such as race, insurance coverage, age, CCI, family income as a percentage of the poverty line, class of drug and region. Group differences did not exist between the new users and previous users based on the independent variables. This study sample was then subjected to bivariate analysis. In this crude analysis (where covariates were not adjusted for), only age and education level showed statistical significance. The results of these analyses are depicted in Table 2. All the covariates were included in the logistic regression model. For the comparisons, ‘white’ race, ‘private insurance only’, the year ‘2015’, ‘high income’ (Socioeconomic status), ‘quarter four’ (out of pocket costs for a 30-day supply of medication) and ‘west’ (region) were set as the reference variables when there were more than two levels. Upon adjusting for the other covariates, the only covariate that was statistically significant was education level. It showed that someone who had at least a high school diploma or GED had 2.795 times (95% confidence interval 1.081–6.941, p value 0.038) the odds of being adherent than someone who did not have a high school diploma or GED. The results of logistic regression have been depicted in Table 3. Discussion This study found that race/ethnicity is not associated with adherence to hormonal therapy in breast cancer patients. Camacho et al.12 found that race did indeed influence adherence to endocrine therapy. Hershman et al.13 on the other hand reported that economic factors explain racial disparities in adherence to hormonal therapy. This study included a variable that classifies the family income as a percentage of the poverty line (poverty level). As a result, it may be adjusting for the economic factors, which could possibly explain the non-association of race/ethnicity with adherence to hormonal therapy. Also, the distribution of the different races (Table 1) was similar to that observed in previous, similar studies.11–13,36 Previous studies were restricted to people who were Medicare patients, or had commercial insurance. Consequently, they were not able to fully investigate the impact that insurance status has on adherence to hormonal therapy among breast cancer patients.11–15,24,28 MEPS data does include people with different types of insurance coverage and this study found that insurance status did not have a significant association with adherence to hormonal therapy. Marital status is a variable that is adjusted for in previous studies and the rationale behind this was that having a spouse may affect a person's behavior in terms of adherence to hormonal therapy.14 Our study found that marital status was not associated with adherence to hormonal therapy among breast cancer patients. This is in concordance with the findings of Xi Tan and colleagues.14 Hershman et al.13 found that household net worth was positively associated with adherence to endocrine therapy and may even partially explain racial differences. Farias and Du11 reported that socioeconomic status (defined as the percentage of residents living below the federal poverty level and metropolitan status) partially explained racial differences in adherence to endocrine therapy. This study does not find any such association. There are several possible explanations for this discrepancy. One possible reason is the difference in the sample characteristics. The study by Hershman et al.13 is restricted to people with commercial insurance and subsequently higher income, while the study by Farias and Du11 is restricted to Medicare enrollees. Another possible explanation could be that the categorization of socioeconomic status varied between studies. Comorbidities were accounted for by using the Charlson Comorbidity Index (CCI) scores. No significant association was found between the CCI scores and adherence to hormonal therapy among breast cancer patients. These results were similar to the results obtained by previous studies 6
Race Black Other Year 2011 2012 2013 2014 Insurance coverage Medicare only Medicaid only Medicare and Medicaid Medicare and Private insurance Marital status Married Poverty level Poor negative/Near poor Low Income Middle Income Class of drug Tamoxifen Age age 50-70 age < 50 Education level at least HS diploma/GED Charlson Comorbidity score 0 1 2 Out of pocket cost for a 30 day supply of medication Q1 Q2 Q3 Region Northeast Midwest South
Variable
0.563 0.835 2.105 1.026 1.311 2.447 0.944 1.952 0.979 0.570 0.871 0.961 1.131 1.039 0.691 0.662 1.662 2.795 1.406 2.123 0.623 1.411 1.916 2.734 1.816 1.809 1.386
2015
Private insurance only
Not currently married High Income
AI Older than 75 no HS diploma/GED 2+
Q4
West
Odds Ratio Point estimate
White
Reference
Table 3 Results of multivariate logistic regression of adherence to hormonal therapy
7
0.683 0.680 0.589
0.549 0.859 1.230
0.554 0.675 0.176
1.081
0.302 0.466
0.341
0.283 0.455 0.502
0.447
0.331 0.491 0.188 0.244
0.895 0.408 0.547 1.067
0.248 0.307
95%Confidence limits
4.826 4.811 3.262
3.626 4.276 6.076
3.568 6.682 2.205
6.941
1.452 6.919
1.404
3.625 2.813 2.150
1.703
2.695 7.754 5.095 1.333
4.951 2.578 3.140 5.611
1.278 2.272
0.5888
0.0880
0.2489
0.0338
0.1146
0.3063
0.9905
0.6865
0.4201
0.1558
0.3877
P value
C.H. Shah, et al.
Research in Social and Administrative Pharmacy xxx (xxxx) xxx–xxx
Research in Social and Administrative Pharmacy xxx (xxxx) xxx–xxx
C.H. Shah, et al.
measures to enhance adherence to hormonal therapy among breast cancer patients. Patients who do not have at least a high school diploma or GED can be given education or special care for instance, Medication Therapy Management (MTM) to ensure that they are adherent to the therapy. It is also useful if some factors incorporated in this study (such as race/ethnicity), do not show a significant association with adherence at a national level but if they show a significant association at a regional level, then disparity in care may exists. By targeting where to implement these interventions to increase adherence, optimization of adherence could be obtained, which should consequently lead to cost savings.
may aim to target those who have lower levels of education, thereby optimizing the effectiveness of the intervention. Data availability The data used for this study was from the Medical Expenditure Panel Survey (MEPS), which is a freely available public data source, from the Agency for Healthcare Research and Quality (AHRQ). It is available at: https://meps.ahrq.gov/mepsweb/data_stats/download_data_files.jsp. Funding
Conclusion
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
The findings of this study suggest that there was an increase in the usage of hormonal therapy over the years and that the level of education has a statistically significant association with adherence to hormonal therapy among breast cancer patients, as was found by analyzing MEPS data from 2011 to 2015. Breast cancer patients who had a higher level of education (defined as having at least a high school diploma or GED) tended to have higher odds of being adherent to hormonal therapy in comparison to those who did not have a high school diploma or GED. Race, insurance status, marital status, poverty level, class of drug, age, comorbidities, out of pocket costs and region did not significantly affect adherence to hormonal therapy among breast cancer patients. Therefore, interventions carried out, especially on a national level, to increase adherence to hormonal therapy
Conflict of interest Dr. Balkrishnan declares holding a consultant/advisory role for Merck and Company. All other authors declare that they have no conflicts of interest. Ethical approval This article does not contain any studies with human participants performed by any of the authors.
APPENDIX A Overview of the literature search Author
Title
Albert J. Farias and Association Between Out-Of-Pocket Costs, Race/ Xianglin L. Du Ethnicity, and Adjuvant Endocrine Therapy Adherence Among Medicare Patients With Breast Cancer11 Fabian T. Camacho, Impact of patient race and geographical factors on Xi Tan, Rajesh initiation and adherence to adjuvant endocrine Balkrishnan et- therapy in Medicare breast cancer survivors12 al.
Dawn L. Hershman, Jennifer Tsui, Alfred I. Neugut et al. Author Xi Tan, Vincent D. Marshall, Rajesh Balkrishnan
Household Net Worth, Racial Disparities, and Hormonal Therapy Adherence Among Women With EarlyStage Breast Cancer13 Title Adjuvant therapy use among Appalachian breast cancer survivors15
Xi Tan, Fabian Camacho, Rajesh Balkrishnan
Geographic disparities in adherence to adjuvant endocrine therapy in Appalachian women with breast cancer14
Carling J. Ursem, Hayden B. Bosworth, Gretchen G. Kimmick Qijia Xuan,Kun Gao, Jingxuan Wang
Adherence to Adjuvant Endocrine Therapy for Breast Cancer:Importance in Women with Low Income16
Data source
Conclusions
SEER-Medicare linked database to identify patients ≥ 65 years of age with hormone receptor–positive breast cancer who were enrolled in Medicare Part D from 2007 to 2009 SEER-Medicare data for the years 2007–2011 to identify patients continuously enrolled in the Fee for Service coverage in Medicare part A, B, and part D one year pre and post diagnosis
Racial/ethnic disparities in AET adherence were largely explained by women's differences in socioeconomic status and out-of-pocket medication costs. Significant differences exist in the initiation and adherence rates based on the geographical location of the patients. Blacks had lower initiation rates than whites, with other races having the highest initiation rates. In terms of adherence, other races were more adherent to the medication than blacks and whites. People not having Medicaid enrollment had lower MPR rates OptumInsight insurance claims database to identify Household net worth partially explains racial diswomen older than age 50 years diagnosed with early parities in hormonal therapy adherence breast cancer, from January 1, 2007, to December 31, 2011, who were using hormonal therapy. Data source Conclusions Medicare claims which was linked to cancer regisTamoxifen, in comparison to AIs was associated tries from four Appalachian states (Pennsylvania, with greater adherence and more persistence. In Ohio, Kentucky and North Carolina) from 2006 to AIs, out of pocket costs, dual eligibility and 2008. coverage gaps significantly influenced adherence and persistence to the drug therapy. Medicare claims which was linked to cancer regisSignificant geographic disparities in adherence to tries from four Appalachian states (Pennsylvania, adjuvant endocrine therapy in the Appalachian Ohio, Kentucky and North Carolina) from 2006 to counties in PA, OH, KY, and NC. 2008. Results of various other studies Improving adherence to endocrine therapy among breast cancer patients in lower socio-economic groups will improve their outcomes
Adherence to Needed Adjuvant Therapy Could Newly diagnosed stage I to stage III breast cancer Adherence to adjuvant therapy can help overcome Decrease Recurrence Rates for Rural Patients With patients from 2000 to 2009 at the Tumor Hospital of the difference between recurrence rates in urban 17 Early Breast Cancer Harbin Medical University and rural populations
8
Research in Social and Administrative Pharmacy xxx (xxxx) xxx–xxx
C.H. Shah, et al.
APPENDIX B National Drug Codes for hormonal therapy drugs Drug
NDC code
AI- Anastrozole
‘007815356′, ‘167290035′, ‘605052985′, ‘001790068′, ‘691890035′, ‘422910374′, ‘001790169′, ‘000540269′ ‘477810108′, ‘000930782′, ‘500902533′, ‘548683004′,
AI- Letrozole AI- Exemestane Tamoxifen
‘430630383′, ‘420430180′, ‘607630376′, ‘216950990′, ‘713350023′, ‘633230772′, ‘245350801′,
‘510790323′, ‘500901193′, ‘636720015′, ‘422540161′, ‘000540164′, ‘000937620′, ‘422540243′,
‘631870080′, ‘500901918′, ‘663360533′, ‘548686130′, ‘604290286′ ‘003782071′, ‘548686252′,
‘633230129′, ‘500902005′, ‘664350415′, ‘551110647′,
‘597622858′, ‘000930784′, ‘518620446′, ‘548684287′,
‘003785001′, ‘003780144′, ‘518620447′, ‘636294413′,
‘008320595′, ‘003780274′, ‘518620449′, ‘680840924′,
‘606870132′, ‘005912232′, ‘518620450′, ‘680840935′,
‘000937536′, ‘500902118′, ‘678770171′, ‘606870112′,
‘001151261′, ‘500902453′, ‘680010155′, ‘621750710′,
‘003786034′, ‘516550638′, ‘680711682′, ‘627560250′,
‘009046195′, ‘519910620′, ‘683820209′, ‘636295269′,
‘009046229′, ‘545696198′, ‘687886774′, ‘658410743′,
‘165710421′, ‘602580866′, ‘697610222′, ‘680840448′,
‘005271712′, ‘006034180′, ‘167290034′, ‘247240030′, ‘519910759′, ‘605053255′, ‘683820363′, ‘551110646′, ‘578842021′, ‘621750888′, ‘627560511′, ‘658410744′, ‘680840803′, ‘691897620′, ‘000540080′, ‘005912233′, ‘545693765′, ‘604290909′,
‘000097663′, ‘005912472′, ‘545695716′, ‘604290910′,
‘548685261′ ‘005912473′, ‘500900485′, ‘500900942′, ‘500901998′, ‘605053035′, ‘605053036′, ‘001790224′, ‘001791952′, ‘637390269′
Appendix C. Supplementary data Supplementary data to this article can be found online at https://doi.org/10.1016/j.sapharm.2019.08.005.
References 21.
1. Howlader N, Noone AM, Krapcho M, et al. Cancer Statistics Review, 1975-2014 SEER Statistics. https://seer.cancer.gov/archive/csr/1975_2014/, Accessed date: 29 April 2018. 2. U.S. Breast Cancer Statistics. Breastcancer.org. http://www.breastcancer.org/ symptoms/understand_bc/statistics Accessed April 29, 2018. 3. Baldassarre G, Belletti B. Molecular biology of breast tumors and prognosis. F1000Research. 2016;5. https://doi.org/10.12688/f1000research.8158.1. 4. American Cancer Society. Breast cancer. https://www.cancer.org/cancer/breastcancer.html, Accessed date: 29 April 2018. 5. Hormone Therapy for Breast Cancer. National Cancer Institute. https://www.cancer. gov/types/breast/breast-hormone-therapy-fact-sheet. Accessed April 29, 2018. 6. Ryerson AB, Eheman CR, Altekruse SF, et al. Annual Report to the Nation on the Status of Cancer, 1975-2012, featuring the increasing incidence of liver cancer. Cancer. 2016;122(9):1312–1337. https://doi.org/10.1002/cncr.29936. 7. Effects of chemotherapy and hormonal therapy for early breast cancer on recurrence and 15-year survival: an overview of the randomised trials. The Lancet. 2005;365(9472):1687–1717. https://doi.org/10.1016/S0140-6736(05)66544-0. 8. Revicki DA, Frank L. Pharmacoeconomic evaluation in the real world. Effectiveness versus efficacy studies. PharmacoEconomics. 1999;15(5):423–434. https://doi.org/ 10.2165/00019053-199915050-00001. 9. Kleinsinger F. The unmet challenge of medication nonadherence. Perm J. 2018;22. https://doi.org/10.7812/TPP/18-033. 10. Banning M. Adherence to adjuvant therapy in post-menopausal breast cancer patients: a review. Eur J Cancer Care. 2012;21(1):10–19. 11. Farias AJ, Du XL. Association between out-of-pocket costs, race/ethnicity, and adjuvant endocrine therapy adherence among medicare patients with breast cancer. J Clin Oncol. 2016;35(1):86–95. https://doi.org/10.1200/JCO.2016.68.2807. 12. Camacho FT, Tan X, Alcalá HE, Shah S, Anderson RT, Balkrishnan R. Impact of patient race and geographical factors on initiation and adherence to adjuvant endocrine therapy in medicare breast cancer survivors. Medicine (Baltim). 2017;96(24):e7147https://doi.org/10.1097/MD.0000000000007147. 13. Hershman DL, Tsui J, Wright JD, Coromilas EJ, Tsai WY, Neugut AI. Household net worth, racial disparities, and hormonal therapy adherence among women with earlystage breast cancer. J Clin Oncol. 2015;33(9):1053–1059. https://doi.org/10.1200/ JCO.2014.58.3062. 14. Tan X, Camacho F, Marshall VD, Donohoe J, Anderson RT, Balkrishnan R. Geographic disparities in adherence to adjuvant endocrine therapy in Appalachian women with breast cancer. Res Soc Adm Pharm RSAP. 2017;13(4):796–810. https:// doi.org/10.1016/j.sapharm.2016.08.004. 15. Tan X, Marshall VD, Anderson RT, Donohoe J, Camacho F, Balkrishnan R. Adjuvant therapy use among Appalachian breast cancer survivors. Medicine (Baltim). 2015;94(26):e1071https://doi.org/10.1097/MD.0000000000001071. 16. Ursem CJ, Bosworth HB, Shelby RA, Hwang W, Anderson RT, Kimmick GG. Adherence to adjuvant endocrine therapy for breast cancer: importance in women with low income. J Women's Health. 2002;24(5):403–408. https://doi.org/10.1089/ jwh.2014.4982 2015. 17. Xuan Q, Gao K, Song Y, et al. Adherence to needed adjuvant therapy could decrease recurrence rates for rural patients with early breast cancer. Clin Breast Canc. 2016;16(6):e165–e173. https://doi.org/10.1016/j.clbc.2016.07.006. 18. Medical expenditure panel survey home. https://meps.ahrq.gov/mepsweb/, Accessed date: 2 October 2018. 19. Andersen RM. National health surveys and the behavioral model of health services use. Med Care. 2008;46(7):647–653. https://doi.org/10.1097/MLR.0b013e31817a835d. 20. Andersen RM, Davidson PL. Improving access to care in America: individual and
22. 23. 24. 25.
26. 27. 28.
29. 30.
31. 32. 33.
34. 35. 36.
37.
9
contextual indicators. Changing the U.S. Health Care System: Key Issues in Health Services Policy and Management. third ed. San Francisco, CA, US: Jossey-Bass; 2007:3–31. Andersen RM. Revisiting the behavioral model and access to medical care: does it matter? J Health Soc Behav. 1995;36(1):1. https://doi.org/10.2307/2137284. Babitsch B, Gohl D, von Lengerke T. Re-revisiting Andersen's behavioral model of health services use: a systematic review of studies from 1998–2011. GMS Psycho-SocMed. 2012;9. https://doi.org/10.3205/psm000089. Andersen R. A behavioral model of families' use of health services. Behav Model Fam Use Health Serv. 1968;25https://www.cabdirect.org/cabdirect/abstract/ 19702701913, Accessed date: 28 January 2019. Wheeler SB, Kohler RE, Reeder-Hayes KE, et al. Endocrine therapy initiation among Medicaid-insured breast cancer survivors with hormone receptor-positive tumors. J Cancer Surv Res Pract. 2014;8(4):603–610. https://doi.org/10.1007/s11764-014-0365-3. Felder TM, Do DP, Lu ZK, Lal LS, Heiney SP, Bennett CL. Racial differences in receipt of adjuvant hormonal therapy among Medicaid enrollees in South Carolina diagnosed with breast cancer. Breast Canc Res Treat. 2016;157(1):193–200. https://doi. org/10.1007/s10549-016-3803-2. U.S. Bureau of labor statistics. https://www.bls.gov/cpi/, Accessed date: 29 April 2018. Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40(5):373–383. Weaver KE, Camacho F, Hwang W, Anderson R, Kimmick G. Adherence to adjuvant hormonal therapy and its relationship to breast cancer recurrence and survival among low-income women. Am J Clin Oncol. 2013;36(2):181–187. https://doi.org/ 10.1097/COC.0b013e3182436ec1. Desai R, Nayak R. Effects of medication nonadherence and comorbidity on health resource utilization in schizophrenia. J Manag Care Spec Pharm. 2018;25(1):37–46. https://doi.org/10.18553/jmcp.2019.25.1.037. Davis-Ajami ML, Wu J, Fink JC. Differences in health services utilization and costs between antihypertensive medication users versus nonusers in adults with diabetes and concomitant hypertension from medical expenditure panel survey pooled years 2006 to 2009. Value Health. 2014;17(1):51–61. https://doi.org/10.1016/j.jval.2013.11.008. Lin H-C, Lucas PT, Feldman SR, Balkrishnan R. Medication use and associated health care outcomes and costs for patients with psoriasis in the United States. J Dermatol Treat. 2012;23(3):196–202. https://doi.org/10.3109/09546634.2010.544708. Lin H-C, Erickson SR, Balkrishnan R. Antidepressant utilization, adherence, and health care spending in the United States: the case of MDD patients 2000-2007. Health Outcomes Res Med. 2011;2(2):e79–e89. https://doi.org/10.1016/j.ehrm.2011.06.003. Burstein HJ, Temin S, Anderson H, et al. Adjuvant endocrine therapy for women with hormone receptor–positive breast cancer: American society of clinical Oncology clinical practice guideline focused update. J Clin Oncol. 2014;32(21):2255–2269. https://doi.org/10.1200/JCO.2013.54.2258. Sperandei S. Understanding logistic regression analysis. Biochem Med. 2014;24(1):12–18. https://doi.org/10.11613/BM.2014.003. Hosmer DW, Hosmer T, Cessie SL, Lemeshow S. A comparison of goodness-of-fit tests for the logistic regression model. Stat Med. 1997;16(9):965–980. https://doi.org/10. 1002/(SICI)1097-0258(19970515)16:9<965::AID-SIM509>3.0.CO;2-O. Farias AJ, Du XL. Racial differences in adjuvant endocrine therapy use and discontinuation in association with mortality among Medicare breast cancer patients by receptor status. Cancer Epidemiol Biomark Prev Publ Am Assoc Cancer Res Cosponsored Am Soc Prev Oncol. 2017;26(8):1266–1275. https://doi.org/10.1158/1055-9965.EPI-17-0280. Wang J, Mullins CD, Zuckerman IH, et al. Medical Expenditure Panel Survey: a valuable database for studying racial and ethnic disparities in prescription drug use. Res Soc Adm Pharm RSAP. 2008;4(3):206–217. https://doi.org/10.1016/j.sapharm.2007. 06.018.