Pediatric oncology

C H A P T E R

7 Pediatric oncology Ahna L.H. Pai1, 2, 3, Meghan E. McGrady1, 3, Lauren Szulczewski1, 3 1

Patient and Family Wellness Center, Cancer and Blood Diseases Institute, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, United States; 2 Center for Adherence and Self-Management, Behavioral Medicine and Clinical Psychology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, United States; 3 Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, United States

Overview of pediatric oncology More than 25 years ago, the World Health Organization cited suboptimal medication adherence as one of the greatest threats to health outcomes and treatment failure among youth with a chronic medical condition (Sebate´, 2003). Consistent and robust evidence now shows that suboptimal medication adherence among children and adolescents with cancer undermines many advances in cancer treatments, threatens the well-being of youth diagnosed with cancer, and likely contributes to mortality (Bhatia et al., 2015; Kennard et al., 2004). The purpose of this chapter is to provide a cursory understanding of pediatric cancer treatment regimens and review the literature regarding adherence in pediatric cancer, including rates of suboptimal adherence, risk factors for suboptimal adherence, and methods for assessing and promoting selfmanagement and adherence in youth with cancer. The chapter will conclude by examining emerging areas of and existing gaps in adherence research in pediatric oncology.

Prevalence Each year, more than 300,000 children and adolescents aged 0e19 years are diagnosed with cancer worldwide (Steliarova-Foucher et al., 2017). Incidence rates of childhood cancer diagnoses are steadily increasing by

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approximately 0.6% annually (Noone et al., 2018), but prevalence rates of specific tumor types differ based on a number of factors (i.e., age, region, and race and ethnicity) (Steliarova-Foucher et al., 2017). For example, the most common cancer types shift across development, with leukemias representing the most common cancer in children aged 0e9 years and lymphomas occurring most commonly in adolescents aged 15e19 years. Potentially reflecting regional differences in genetic and environmental (e.g., pesticide exposure, sun exposure) risk factors, the most common cancer in adolescents aged 15e19 years is leukemia in South America, south and southeast Asia and within Native American and white Hispanic children in the United States (Steliarova-Foucher et al., 2017). In contrast, epithelial cancers and melanoma are most common in the regions of Oceania, East Asia, Central America, and the Caribbean and among white non-Hispanic children in the United States (SteliarovaFoucher et al., 2017). Despite the number of factors that influence cancer prevalence, the most common cancer diagnosis both worldwide and in the United States among children aged 0e14 years is leukemia (i.e., acute lymphoblastic leukemia, acute myelogenous leukemia) and among adolescents aged 15e19 years is lymphoma (i.e., Hodgkin lymphoma, nonHodgkin lymphoma; Steliarova-Foucher et al., 2017).

Etiology Cancer is a group of related diseases in which gene mutations cause the body’s cells to divide uncontrollably and spread into surrounding tissue (National Cancer Institute, 2018). Approximately 10% of pediatric cancers are the result of an inherited genetic mutation (e.g., retinoblastoma; National Cancer Institute, 2018). The causes of the remaining 90% of pediatric cancers are unknown (National Cancer Institute, 2018). While exposure to radiation, pesticides, and pollution are associated with an increased risk of pediatric cancer, definitive causal links for most pediatric cancers have yet to be established.

Treatment Treatment courses for pediatric cancers vary widely depending on the specific diagnosis, how advanced the cancer is at diagnosis, and whether the disease progresses during active treatment. The majority of childhood cancer protocols comprise multiple treatment modalities, the most common of which are listed in Table 7.1. To illustrate a potential treatment course, a regimen for acute lymphoblastic leukemia, the most common type of childhood cancer that includes a 2.5e3.5 year self-managed oral chemotherapy regimen, is included as an exemplar. The acute lymphoblastic leukemia regimen comprises three to four phases depending on

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TABLE 7.1 Types of Cancer Treatment. Treatment (example procedure and cancer treated) Surgery A procedure in which a surgeon removes cancer from the body (e.g., nephrectomy for Wilm’s tumor). Radiation therapy High doses of radiation are used to kill cancer cells and shrink tumors (e.g., proton therapy for medulloblastoma). Chemotherapy A class of medications that kill cancer cells (e.g., 6-mercaptopurine for acute lymphoblastic leukemia). Immunotherapy A treatment that helps the immune system fight cancer (e.g., interferon for acute lymphoblastic leukemia). Hormone therapy A cancer treatment that removes hormones or blocks their action and stops cancer cells from growing (e.g., corticosteroids for thyroid cancer). Gene therapy Genetic engineering of the patient’s own cells to increase targeting of a specific leukemia protein and then accelerate killing of the target (e.g., chimeric antigen receptor T cells for B cell acute lymphoblastic leukemia). Hematopoietic stem cell transplants A procedure that destroys blood-forming stem cells by high doses of chemotherapy or radiation therapy then restores the blood-forming cells from a donor (i.e., allogeneic) or the individual’s own treated stem cell via infusion (i.e., autologous) (e.g., allogeneic transplant for acute myeloid leukemia).

how advanced the disease is at diagnosis and if the disease progresses during treatment: (1) remission induction phase, (2) intensification, and (3) maintenance. Remission induction phase Following diagnosis, the remission induction phase is administered to induce a complete remission (i.e., no signs or symptoms of cancer). This phase typically lasts 4 weeks and includes a combination of three or four medications: vincristine, prednisone or dexamethasone, and L-asparaginase delivered with or without an anthracycline (either doxorubicin or daunorubicin). Youth are typically hospitalized for the duration of

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remission induction and medications can be administered orally, intravenously, intramuscularly, or intrathecally. The majority of newly diagnosed youth achieve complete remission by the end of this first 4-week treatment phase (Mo¨ricke et al., 2010; Pui et al., 2010; Oudot et al., 2008; Salzer et al., 2010; Silverman, Stevenson, O’Brien, Asselin, Barr et al., 2010). Intensification After remission, additional treatments are delivered in the intensification (also called consolidation) phase of treatment. Intensification may include intrathecal chemotherapy (i.e., chemotherapy administered directly into the cerebral spinal fluid of the spine) to prevent the spread of the leukemia cells to the brain and spinal cord. For youth with more advanced disease or who demonstrate suboptimal treatment response, total body irradiation or a hematopoietic stem cell transplant may be administered to kill any remaining cancer cells. Following hematopoietic stem cell transplant, medications are prescribed to prevent infections and graft-versus-host disease (i.e., when the donor cells see the body’s cells as different and attack them). Maintenance Following intensification, youth with acute lymphoblastic leukemia enter the maintenance phase, which typically lasts 2.5e3.5 years. During this phase, self-management responsibilities shift almost completely to the youth with cancer and their families, as they self-manage oral chemotherapy medications at home and attend weekly or monthly outpatient appointments. The most common self-managed chemotherapy medication used to treat acute lymphoblastic leukemia is 6-mercaptopurine (6-MP). The therapeutic effect of 6-MP is a result of the conversion to cytotoxic thioguanine nucleotides (TGNs) that disrupt the DNA structure of cancer cells (Lennard & Lilleyman, 1989). A second metabolic pathway, catalyzed by an enzyme thiopurine methyltransferase, generates another metabolite methylated mercaptopurines (MMPs; Lennard, 1992). Low to undetectable levels of both TGN and MMP in red blood cells of individuals with leukemia may represent suboptimal adherence and are associated with a greater risk for relapse (Traore et al., 2006). Ongoing treatment developments Treatment regimens for pediatric cancers are continually evolving as new medications and formulations are developed and evaluated. Increasingly, chemotherapy medications previously administered intravenously, are now being prescribed in oral formulations as outpatient medications. This shift increases the medication self-management

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responsibilities of youth with cancer and their families (National Cancer Institute, 2018). As the number of youth with cancer receiving outpatient treatment continues to grow, an increasing emphasis will need to be placed on ensuring that youth and their families are equipped to manage large portions of their own treatment.

Outcomes Treatment advances, including the development of new medications, have led to dramatic improvements in 5-year survival rates for many pediatric cancers such as acute lymphoblastic leukemia (from 57% in 1975 to 92% in 2012). Unfortunately, 5-year survival rates for other pediatric cancer diagnoses (e.g., diffuse intrinsic pontine glioma) remain low and/ or relatively unchanged (National Cancer Institute, 2018). Due to this continued burden of disease, cancer remains the leading cause of diseaserelated death among children in the United States (American Cancer Society, 2018; Xu, Murphy, Kochanek, Bastian, & Arias, 2018) and many European countries (Kyu et al., 2018) and a major cause of death worldwide (The Global Burden of Disease Child and Adolescent Health Collaboration, 2017).

Rates of and outcomes associated with suboptimal adherence to oral medications Chemotherapy medication Estimates of the rates of suboptimal chemotherapy adherence range widely (i.e., 4%e60%; see Table 7.2), due, in part, to differences in the methods used to measure adherence (e.g., self-report, electronic monitoring devices, chart reviews) and operational definitions of adherence across studies. The majority of adherence research in pediatric cancer has examined 6-MP adherence. A Children’s Oncology Group study including youth across 94 institutions found that approximately 42% of youth take less than 95% of their doses of 6-MP (Bhatia et al., 2015). To our knowledge, this study is the first to identify a clinically relevant adherence cut-point in pediatric oncology; youth taking fewer than 95% of prescribed 6-MP doses have a 2.7-fold increased risk of relapse compared with more adherent youth (Bhatia et al., 2015). A study of methotrexate, an oral chemotherapy often prescribed in conjunction with 6-MP for acute lymphoblastic leukemia, found that youth miss an average of 19% of methotrexate doses when assessed via medication refill data (Wu, Stenehjem et al., 2018). In a sample of Mexican children

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TABLE 7.2 Adherence Rates Across Studies.

Citation

Estimate of suboptimal adherence

N

Adherence measure

Bhatia et al., 2015

470

Electronic monitoring

42% < 95% of doses taken

Lau, Matsui, Greenberg, & Koren, 1998

24

Electronic monitoring

33% < 90% of doses taken

Rohan et al., 2015

139

Electronic monitoring

44% < 95% of doses taken

de Oliveira et al., 2004

39

Self-reported

33% missing doses; no time frame

de Oliveira et al., 2005

73

Self-reported

27%
2 doses missed in maintenance

Pai et al. 2008

53

Self-reported

20%
1 dose missed in the last week

Khalek et al., 2015

129

Self-reported

56%
2 doses missed; no time frame

Davies, Lennard, & Lilleyman, 1993

35

Serum TGN

27% 1:9 ratio of highest to lowest TGN concentration (pmol/8 108 red cells)

de Oliveira et al., 2004

39

Serum TGN andand MMP

17% simultaneous decrease of TGN and MMP in relation to other samples

Lennard, Welch, & Lilleyman, 1995

327

Serum TGN andand MMP

10% lower quartile TMP andand MMP

Pai et al. 2008

51

Serum TGN andand MMP

53% low TGN/ MMP at least at one of three time points (1 week, day 56, day 112).

6-Mercaptopurine

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Rates of and outcomes associated with suboptimal adherence

TABLE 7.2 Adherence Rates Across Studies.dcont’d Estimate of suboptimal adherence

Citation

N

Adherence measure

Khalek et al., 2015

129

Serum TMP andand MMP

50% low serum metabolite levels

Macdougall, McEliigott, Ross, Greef, & Poole, 1992

39

Urinary 6-MP

19% no detectible 6-MP

de Oliveira et al., 2004

39

Chart review

31% documented poor adherence

Wu et al., 2018

900

Medication possession ratio

15% of doses not taken

Jaime-Pe´rez et al., 2009

49

Serum methotrexate level

29% nondetectable methotrexate levels in more than one of three random samples

Wu et al., 2018

900

Medication possession ratio

19% of doses not taken

52

Self-report

23% >1 dose missed in the past week

Smith et al., 1979

52

Urinary 17-ketogenic steroid

39% 17-kg/ creatine ratio <18.7

Lansky et al., 1983

31

Urinary 17-ketogenic steroid

42% 17-kg/ creatine ratio <18.7

Festa et al., 1992

21

Dehydroepiandrosterone sulfate (DHEA-S) levels

52% lack of appropriate DHEA-S suppression

Methotrexate

Imatinib Mancini et al., 2012

Prednisone

Continued

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TABLE 7.2 Adherence Rates Across Studies.dcont’d

Citation

N

Adherence measure

Estimate of suboptimal adherence

Trimethoprim/sulfamethoxazole Kennard et al., 2004

44

Serum metabolite levels

27% had no detectable medication

29

Bioassay with micrococcus luteus

48% lack of detectable urinary penicillin

Christiansen, Taylor, & Duggan, 2008

55

Youth report

4% forgot
1 dose more than 1/month; 27% rarely forgot a dose

Hullman et al., 2015

103

Caregiver and adolescent report

57% youth and 47% caregivers reported less than perfect adherence in the past 7 days

Jaime-Pe´rez et al., 2009

49

Youth report

10%
2 doses missed

MacDougall et al.,1989

45

Caregiver report

30% forgot to give
1 dose

Tebbi et al., 1986

46

a

18.8% at 2 weeks, 39.5% at 20 weeks 35% at 50 weeks postdiagnosis missed
1 dose

Tebbi et al., 1988

16

a

47% at 20 weeks postdiagnosis missed
1 dose

Tebbi et al., 1988

20

a

60%
1 dose missed in the past month

Jaime-Perez et al., 2017

57

Caregiver and youth report and review of clinical files

21%
2 doses missed

Penicillin Festa et al., 1992

Multiple Medications

a

Caregiver and youth report

Caregiver and youth report

Caregiver and youth report

Caregiver and youth data are not reported separately.

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below 15 years of age diagnosed with B cell acute lymphoblastic leukemia, serum methotrexate levels indicative of suboptimal adherence were present in 29% of youth on at least one occasion (Jaime-Pe´rez, Go´mez-Almaguer, Sandoval-Gonza´lez, Chapa-Rodrı´guez, & Gonza´lezLlano, 2009).

Prophylactic medications In addition to chemotherapy, prophylactic medications are prescribed to decrease infection risk (Kennard et al., 2004). Rates of suboptimal adherence to prophylactic medications, including penicillin (Festa, Tamaroff, Chasalow, & Lanzkowsky, 1992), prednisone (Festa et al., 1992), and trimethoprim/sulfamethoxazole (Bactrim) range from 27% to 52% (Kennard et al., 2004; Pizzo et al., 1983). Suboptimal adherence to trimethoprim/sulfamethoxazole places youth at an increased risk of fever or infection (Pizzo et al., 1983) and death (Kennard et al., 2004). Prophylactic medications are also prescribed to hematopoietic stem cell transplant recipients following transplant to prevent life-threatening infections and graft-versus-host disease (for allogeneic transplants). A small pilot recently showed that six adolescent hematopoietic stem cell transplant recipients missed an average of 27% of the prescribed outpatient doses and had perfect adherence on only 56% of the monitored days (McGrady, Williams, Davies, & Pai, 2014). Similar patterns of adherence were observed among 50 younger (0e16 years of age) hematopoietic stem cell transplant recipients as well. In this study, immunosuppressant adherence averaged only 63% of prescribed doses taken at 1 month postdischarge and fell to 57% at 6 months postdischarge (Pai et al., 2018). This study also showed that, for youth who received an allogeneic transplant, lower rates of medication adherence were associated with higher infection rates after controlling for age and time since transplant (Pai et al., 2018).

Guiding theories in pediatric oncology self-management and treatment adherence There are only two self-management and treatment adherence theories specific to pediatric oncology, one by Landier et al. (2011) and the other by McGrady, Brown, & Pai (2016). The first was proposed by Landier et al. (2011) who conducted semistructured interviews with 17 childhood leukemia survivors and 21 caregivers to inform the development of a threephase model. The first phase, “recognizing the threat,” describes how families recognize the life-threatening nature of disease and make initial

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treatment decisions. This is followed by the second phase, “taking control,” during which the family recognizes the connection between taking oral chemotherapy as prescribed and curing the leukemia and takes on the responsibility for the treatment regimen. Families who make the connection between medication taking and outcomes demonstrate better adherence than those who do not (Landier et al., 2011). The final phase, “managing for the duration,” distinguishes between families who effectively self-manage the treatment regimen (i.e., ultimately cope with side effects, follow regimen instructions, integrate a positive outlook into life, and feel supported) and those who experience more difficulties (i.e., negative experiences with medication taking, emotions undermining adherence, and unclear allocation of treatment responsibility). The second model, developed based on qualitative interviews with adolescents and young adults with cancer, posits that adherence is influenced by four main domains: (1) the interplay between the medication’s characteristics and the individual’s goals and values (i.e., beliefs about consequences); (2) the individual’s skills to facilitate adherence (i.e., problem-solving); (3) the individual’s knowledge (i.e., purpose of prescribed medications); and (4) environmental and social network factors (i.e., support from caregivers) (McGrady et al., 2016). Both of these models are promising but have yet to be evaluated in the study of self-management and adherence among youth with cancer. A third model, the Pediatric Self-management Model (Modi et al. 2012), is based on the larger pediatric literature and provides a heuristic that can guide future research and the integration of adherence care into evolving pediatric oncology practice. As described in the Introduction chapter of this book, the Pediatric Self-management Model posits that modifiable and nonmodifiable individual, family, community, and healthcare system factors influence self-management of treatment regimens and ultimately adherence and health outcomes. The following sections apply the Pediatric Self-management Model to guide the review of the evidence base on self-management and adherence in pediatric oncology.

Risk factors for suboptimal medication adherence Individual Age is the most studied and consistent nonmodifiable factor influencing self-management and adherence in pediatric oncology. Adolescents and young adults with cancer (i.e., defined by the National Cancer Institute as individuals between 15 and 39 years old) demonstrate lower rates of adherence than their younger or older counterparts (Butow et al., 2010). Asian American, African American, and Hispanic adolescent and

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young adults have been shown to be especially at risk for suboptimal adherence in a number of studies (Bhatia et al., 2012, 2014; Kaul et al., 2017; Kondryn, Edmondson, Hill, & Eden, 2011; Landier et al., 2017; Mancini et al. 2012; Wu, Stenehjem et al., 2018). Longer time since diagnosis is also associated with lower adherence, with adherence rates falling by as much as 40% over a 1-month period of time (Rohan et al., 2015). Finally, more frequent and severe treatment side effects (e.g., weakness, nausea, fatigue, pain; Mancini et al., 2012; Morrison, Martsolf, Wehrkamp, Tehan, & Pai, 2017; Tamaroff, Festa, Adesman, & Walco, 1992) and lower perceptions of one’s overall physical health are associated with decreased adherence (Kleinke & Classen, 2018). These findings highlight the importance of monitoring and promoting adherence over the developmental and illness course. Modifiable factors associated with suboptimal adherence include illness and regimen knowledge (Eagleton, Walker, & Barber, 1993), beliefs about capabilities and consequences, and psychological distress. Studies in pediatric oncology are largely consistent with the pediatric literature, suggesting that knowledge is a necessary but not sufficient for effective self-management and optimal adherence (Tebbi, 1993; Tebbi et al., 1986). However, there is inconsistent evidence regarding the type and level of illness and regimen knowledge necessary for optimal adherence. Specifically, oral medication adherence has been associated with knowledge about the illness but not the treatment itself (Tamaroff et al., 1992). In contrast, other studies found that treatment knowledge is most important for promoting adherence (Tebbi, 1993; Tebbi et al., 1986). These divergent findings are likely a reflection of methodological issues and a small number of studies and highlight the need for more research to inform clinical practice. An individual’s beliefs about their capabilities and the consequences of medication adherence (or nonadherence) may also influence medication-taking behavior among youth with cancer. In a study of youth with a variety of cancer diagnoses, lower levels of self-esteem were associated with lower adherence to trimethoprim/sulfamethoxazole per self-report and serum assay (Kennard et al., 2004). Adolescents and young adults who were nonadherent to oral prophylactic medications had less realistic perceptions of their vulnerability to side effects and symptoms and were more likely to use denial than adherent adolescents and young adults (Tamaroff et al., 1992). In addition, adolescents and young adults with suboptimal adherence report fewer future-oriented goals than adherent adolescents (Hullman, Brumley, & Schwartz, 2015). The final individual-level factor associated with adherence in pediatric cancer is psychological distress. The relationship between psychological

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adjustment and adherence in pediatric oncology reflects the broader pediatric literature (Bender, 2002; Burra et al., 2011: Gonzalez, Tanenbaum, & Commissariat, 2016), with higher levels of depression being associated with a higher likelihood of undetectable serum levels to prescribed antibiotics (Kennard et al., 2004). Among adolescents and young adults undergoing hematopoietic cell transplantation, feelings of isolation are also associated with lower adherence (Morrison et al., 2017).

Family Parents and other family members are often responsible for many aspects of the youth’s treatment regimen due to the child’s developmental level or the child’s inability to manage it themselves because of extreme illness. Therefore, understanding family factors of self-management are critical for promoting adherence to pediatric cancer regimens. Nonmodifiable family factors associated with suboptimal adherence are largely socioeconomic in nature, including low family income and poverty (Mancini et al., 2012; Tebbi et al., 1986), low educational attainment (Khalek, Sherif, Kamal, Gharib, & Shawky, 2015), and a greater number of children in the home (Tebbi et al., 1986). There are also a number of modifiable family factors that influence pediatric cancer self-management and adherence. Higher levels of caregivereyouth incongruence in perceptions of the home environment, including family conflict, cohesion, and expressiveness, are associated with lower adherence to prophylactic antibiotic regimens (Kennard et al., 2004). Youth who report suboptimal adherence also tend to report less family support and overprotectiveness from their secondary caregiver than adolescents who reported perfect adherence (Hullman et al., 2015). Youth in families who establish a clear allocation of responsibility for elements of the medication regimen have better adherence than those families for whom responsibility is unclear for specific regimen tasks (Landier et al., 2011; Tebbi, Richards, Cummings, Zevon, & Mallon, 1988). Youth who heavily rely on their caregivers to implement their treatment regimen are more likely to have better adherence than their peers who either share or manage their medications on their own (Malbasa, Kodish, & Santacroce, 2007; Tebbi et al., 1988).

Community Little is known about how community factors influence self-management and adherence in youth with cancer. However, significant healthcare disparities suggest that community factors are related to adherence and associated health outcomes. The most compelling evidence is from a study by Bona and colleagues which demonstrated that children

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who live in high-poverty areas are more likely to relapse early and have lower survival rates than those from lower-poverty areas (Bona et al., 2016). The authors suggested that decreased adherence may be one mechanism by which living in a high-poverty area results such dramatic health disparities (Bona et al., 2016). For instance, the lack of available supportive care, limited access to medical care, and inadequate transportation to clinics and pharmacies could all be significant barriers to adherence.

Health care system Although understudied, there are a number of modifiable factors in the healthcare system that influence adherence. Oral medication regimens are often prescribed with restrictions on how and when to take the medications to optimize absorption and metabolism, as well as prevent adverse interactions. Unfortunately, these well-intended protocol requirements present significant logistical barriers to adherence. Youth report that restricting medication administration to the evening and restricting food and/or dairy in a 1e2 hour period before or after taking their chemotherapy medication were significant barriers to adherence (Landier et al., 2011). Indeed, those youth who took their 6-MP with dairy and who took their medications at varying times were significantly more likely to miss doses of their medications all together (Landier, Hageman et al., 2017). However, this same study demonstrated that among adherent youth, there was no association between TGN levels and taking 6-MP with or without food or dairy. Therefore, limiting restrictions on when and how medications can be taken decreases barriers and better facilitates adherence for youth with cancer (Landier, Hagemen et al., 2017). Finally, insurance payer is a significant predictor of adherence and, potentially, medication type. Specifically, youth covered by the Children’s Health Insurance Program (CHIP) have significantly lower adherence to 6-MP than youth with commercial or Medicaid insurance, whereas youth with commercial insurance had significantly higher levels of adherence compared with those with Medicaid. Similarly, youth taking methotrexate who have CHIP or Medicaid had significantly lower levels of adherence compared with those with commercial insurance and youth with CHIP had significantly lower adherence than those with Medicaid (Wu, Stenehjem et al., 2018). Consistent with the Pediatric Self-management Model, research in pediatric cancer suggests individual, family, community, and healthcare systems factors are related to adherence and self-management. The majority of these factors are amenable to intervention; however, only a handful of intervention studies have been conducted (Beale, Kato, Marin-Bowling, Guthrie, & Cole, 2007; Kato, Cole, Bradlyn, & Pollock, 2008; Linder et al., 2019; Wu, Linder, et al., 2018). As a result, additional research elucidating

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predictors of adherence using rigorous methodology is needed to inform the development of adherence promotion efforts for this population.

Evidence-based medication adherence assessment Evidence-based adherence assessment, monitoring, and promotion are fundamental and necessary components of comprehensive clinical cancer care and are widely recognized as standards of care for pediatric oncology (Pai & McGrady, 2015; Wiener, Kazak, Noll, Patenaude, & Kupst, 2015). The current standard calls for the implementation of routine and standardized assessment of medication adherence. While there is currently no gold standard for assessing adherence for any self-managed pediatric oncology treatment regimen (Quittner, Modi, Lemanek, Ievers-Landis, & Rapoff, 2007), self-report measures, electronic monitoring devices, and bioassays are among the most frequently used methods in pediatric oncology.

Self-reported adherence The most commonly used method to assess medication adherence is self-report (Scalia et al. 2018). Self-report methods require relatively little training to administer, are free or low cost, and can be integrated within standard clinical interviews. Although asking youth and their families about their medication adherence will likely provide an overestimate of adherence behaviors, doing so is a recommended part of standard care (Pai & McGrady, 2015). By engaging families in discussions about their own adherence, the medical team communicates that adherence is critical to care, adherence can be challenging, and the medical team is available to support their adherence efforts. Self- and parent-reported assessments of medication adherence can be obtained either in-person (through clinical interview) or via questionnaires. Structured interviews such as the Medication Adherence Measure are beneficial because they ensure standardized language to assess each specific medication during a specific period of time (e.g., in the past 7 days, how many times have you [has your child] missed a dose of [INSERT MEDICATION NAME]; Zelikovsky, 2001). If the timing of the dose is important, the assessment may also include questions such as “How many times did you take your medication late?” and “How late did you take your doses?” As noted above, self-reported and caregiver-reported adherence estimates are often inflated, especially among those who have suboptimal adherence (Landier et al., 2017; Pai, Drotar, & Kodish, 2008). For example, Landier et al. (2017) compared self-report to electronic pill bottle data (Medication Event Monitoring System; MEMS) for youth with acute

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lymphoblastic leukemia prescribed an outpatient oral medication regimen. More than three-quarters (84%) of youth and caregivers reported adherence rates that were higher than those obtained from electronic monitors at some point during the 4-month study period. Only 12% of youth were “perfect reporters” and 24% were classified as “overreporters” (i.e., self-reported adherence exceeded electronically monitored adherence by 5 or more days for
50% of study months). “Overreporters” were more likely to have suboptimal adherence (<95% of prescribed doses taken) than youth classified as “perfect reporters” or “underreporters,” highlighting the fact that reliance on self-report may prevent medical teams from identifying youth most likely to benefit from adherence promotion efforts.

Electronic measurement of adherence A more objective and commonly used adherence assessment in pediatric oncology research are electronic pill boxes or bottles (Scalia et al., 2018). Electronic adherence-monitoring devices record a date and time stamp each time the device is opened, serving as a proxy for medicationtaking behavior (McGrady, et al., 2018). Electronically monitored adherence also allows for a more detailed examination of adherence across time. For example, Rohan et al. (2015) identified three groups of youth (ages 7e19 years) with distinct patterns of electronically monitored adherence during a 30-day period: optimal adherence (76%), deteriorating adherence (17%), and chronic nonadherence group (7%). In clinical practice, electronic pill bottles and boxes are largely underutilized. Although several accurate and reliable commercially available electronic pill bottles/boxes exist, a number of barriers prevent them from being implemented in practice (McGrady et al., 2018). First, data from these devices exist outside of the electronic medical record. As a result, to view these data, providers utilize additional software and, in some instances, hardware, introducing multiple logistical challenges that require medical teams to build new independent but unsustainable workflows. In addition, insurance does not currently reimburse the cost of electronic adherence monitors, making it cost-prohibitive for many clinical practices. For clinical programs that want to integrate electronic monitoring into their adherence care plans, several factors need to be considered when choosing the adherence monitor, including the funding available to purchase devices, formulation of the medication (e.g., liquid/ solid), amount and size of the medication(s), patient’s preference for storing their medication (e.g., youth prefers a pillbox to organize their medications), reminder/alert features of the device (e.g., sends reminder texts), and the availability of cell service in the patient’s home for devices that require cellular connectivity (McGrady et al., 2018). Overcoming these challenges

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could be well worth the additional efforts, as the detailed information obtained from electronic monitors can help to identify patterns of adherence that are otherwise unknown. Bioassays: Examining blood levels of medication or medication metabolites for assessing adherence within the pediatric population is a desirable objective measure that ensures medication was ingested. For individuals prescribed 6-MP, TGN and MMP metabolite levels can be used as a proxy for 6-MP exposure and thus adherence behavior (Rohan et al., 2015). Specifically, near-zero TGN and MMP levels can confirm suboptimal adherence. Beyond this, however, the use of serum 6-MP metabolites as a sole indicator of adherence is limited. First, there are no validated 6-MP metabolite thresholds indicative of suboptimal adherence that can be applied to a single lab level. Moreover, changes in dose intensity, prescribed drug interruptions among adherent youth (Bhatia et al., 2015), poor absorption of the medication by the gut, and intraindividual variability in 6-MP metabolism can mimic suboptimal adherence. Finally, it is unclear how the high rate of adherence required to prevent relapse (i.e., 95%) applies to changes in routine blood levels in youth in the context of low yet clinically significant rates suboptimal adherence. In sum, 6-MP metabolite and other medication blood levels are still best used as one piece of information used in combination with other adherence measures.

Other measures of adherence Although used less frequently in pediatric oncology research, provider estimates of adherence and refill data are two additional methods of assessing adherence in pediatric oncology. However, provider estimates of adherence are generally poor indicators of adherence. In a recent French study, physicians correctly identified only 42% of youth with selfreported adherence difficulties and errors tended to overestimate adherence (Mancini et al., 2012). In contrast, the use of pharmacy refill data provides estimates of adherence that are relatively consistent with other adherence assessment methods. Findings from a national claims database of youth, aged 0e21 years, diagnosed with leukemia on maintenance therapy, had an average 6-MP adherence rate of 85%, which was estimated to be comparable or only slightly lower than rates found using electronic monitoring (Wu, Stenehjem et al 2018). Medication refill data do not provide daily patterns of adherence or the ability to account for other critical and common events in pediatric oncology care, such as prescribed gaps in therapy due to neutropenia (i.e., low white blood cell counts), hospitalizations, or other illness-related factors. Additional studies are needed to directly compare refill data with other adherence assessment methods and to understand the research and clinical applications.

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In all, assessing adherence in pediatric oncology populations is a recommended component of clinical care, and teams interested in assessing adherence are encouraged to consider the characteristics of their team and environment (i.e., resources, staffing, clinic flow) and patient population (i.e., types of treatment regimens) in conjunction with the strengths and weaknesses of each of the aforementioned measurement strategies when selecting the most appropriate measure of adherence.

Evidence-based adherence interventions Research examining adherence-promoting interventions in either pediatric or adult oncology or hematopoietic stem cell transplant populations is limited (Hall et al., 2016; Morrison et al., 2017). In the general pediatric literature, meta-analyses show that behavioral and multicomponent adherence interventions are more effective than educational interventions alone (Kahana, Drotar, & Frazier, 2008) and that adherence interventions delivered in context of clinical care have effect sizes comparable with or greater than those delivered by a single behaviorally trained interventionist (Wu & Pai, 2014). Consistent with the broader pediatric adherence promotion literature (Pai & McGrady, 2014), however, effect sizes from the few interventions designed to promote adherence in youth with cancer are small.

Research to date The most well-known adherence-promoting intervention in pediatric oncology is the Re-Mission intervention. Re-Mission is a videogame in which youth with cancer navigate a nanobot to engage in positive selfcare behaviors, including taking oral chemotherapy, antibiotics, and stool softener; practicing good mouth care; using relaxation techniques; and making healthy nutritional choices. Both trimethoprime sulfamethoxazole measured via electronic devices (n ¼ 200) and 6-MP adherence assessed via serum metabolite assays (n ¼ 54) were significantly greater in the intervention than control group (Kato et al., 2008). Knowledge also significantly increased in the intervention versus the control group (Beale et al., 2007). However, the magnitude of the effect was not dependent on whether the youth played the video game or how much time was spent playing the game. In addition, only 28% of participants fully completed the requested 1 hour of game play per week. Thus, the mechanism by which adherence improved in this study is unclear. Overall, the intervention provided initial evidence that oral medication adherence could be improved in adolescents and young adults. Another study examined a preexisting medication adherence mobile application, Dosecast Pro (Wu, Linder, et al., 2018). Investigators provided

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the paid mobile application to adolescents and young adults (aged 15e 29 years) with cancer. Within the application, participants were able to create a personalized reminder schedule and track their adherence. While participants found the application easy to use and viewed the application as useful (Wu et al., 2018), adherence did not significantly improve following the intervention (Linder et al., 2019), suggesting that phone applications and text messaging targeting medication adherence are better viewed as an assistive rather than stand-alone intervention. In addition to technology-focused interventions, researchers have begun to develop and test the efficacy of multicomponent interventions in pediatric oncology. One pilot randomized trial of a multicomponent adherence intervention included caregivers of youth with acute lymphoblastic leukemia in Yogyakarta, Indonesia (Sitaresmi, Mostert, Gundy, Ismail, & Veerman, 2013). All caregivers received educational materials (i.e., verbal explanation, information booklet, information audiocassette, and informational DVD) explaining the disease, its treatment, possible side effects, the importance of adherence, and the right to receive free chemotherapy. In addition, caregivers assigned to the intervention received a medication diary book to track medication adherence. Among youth with caregivers with higher education (senior high school or higher), event-free survival at 3 years postdiagnosis was higher in the intervention than control condition. No group differences were found for caregivers with lower education levels. Unfortunately, conclusions regarding the impact of this trial on adherence behavior and the mechanisms resulting in improved event-free survival were not directly accessed.

Ongoing trials A large randomized clinical trial of an adherence-promoting intervention is currently being conducted by the Children’s Oncology Group for youth with acute lymphoblastic leukemia and their caregivers (Gupta & Bhatia, 2017). The intervention consists of multimedia interactive education, physician-initiated web-based medication scheduling, printed schedules, and a daily text message reminder to prompt directly supervised therapy by a designated caregiver. The results of this study have the potential to shape adherence care standards in pediatric oncology.

Case study Charlotte is a 5-year-old female diagnosed with acute lymphoblastic leukemia. Six weeks after completing therapy, Charlotte relapsed and was readmitted to reinduce remission and receive a hematopoietic stem cell

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transplant. Although her hematopoietic stem cell transplant went as expected, Charlotte had difficulty taking oral medications. Therefore, all medications were being administered intravenously. After returning home from the hospital, however, Charlotte and her family would be prescribed an oral medication regimen. As Charlotte was nearing discharge, the medical team became concerned about the ability of her parents, Mr. and Mrs. Johnson, to reliably administer oral medications at home. Compounding the medical team’s concerns, Charlotte’s parents, who were present throughout her admission, planned to return to fulltime work following discharge. Charlotte’s grandparents were going to be her primary caregivers. To trial oral medication administration, the medical team transitioned one medication from intravenous to oral liquid form. As a result of the aforementioned concerns, the medical team consulted Dr. Smith, a pediatric psychologist approximately 3 weeks following Charlotte’s HCT to evaluate and provide interventions to improve adherence throughout this transition. Dr. Smith introduced psychology services to Charlotte and her parents and conducted an initial evaluation. Dr. Smith determined that the timing for behavioral intervention was not appropriate due to severe mucositis that prevented Charlotte from swallowing anything. After Charlotte’s mucositis and pain were better controlled, Dr. Smith returned to assess medication-taking behaviors. Mrs. Johnson indicated that it was currently taking over an hour to take one pill, resulting in Mrs. Johnson being in tears out of frustration every day. Dr. Smith then assessed for barriers to medication taking. Charlotte noted that she prefers liquid medications but does not like the taste of them. Although putting crushed pills in chocolate pudding had helped in the past, Charlotte now “hates” chocolate pudding and visibly gagged at the idea of crushing her pills and mixing them with anything. In contrast, her mother reported that the primary barrier was inconsistent expectations that she placed on Charlotte which she attributed to guilt for “forcing” her to take medication for so many years. Both Mrs. Johnson and Charlotte reported that Charlotte took her medication better for her father. Throughout her initial treatments for acute lymphoblastic leukemia, Charlotte’s parents had relied heavily on reinforcement and Charlotte indicated that she liked getting toys (e.g., sometimes $10 a toy) for taking her medication. Now that outpatient medications were going to be prescribed, Mr. and Mrs. Johnson were concerned that they could not continue to afford the reinforcement system used during Charlotte’s inpatient stay and were unsure of how to modify their behavioral plan to make it sustainable. They acknowledged that they have not delivered the reinforcement consistently. Mr. and Mrs. Johnson also frequently “negotiated” with Charlotte, allowing her to decide what medication she would and would not take at a given time to earn a reward.

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Following the initial assessment, Dr. Smith returned and guided Charlotte and her parents through problem-solving steps to address Charlotte’s adherence barriers to the currently prescribed liquid oral medication. Charlotte identified that she wanted to drink mango juice immediately after taking her medication to mask the taste. Her parents then reserved the mango drink for medication time. Next, Mr. and Mrs. Johnson were provided a structured reinforcement plan to implement that allowed Charlotte to earn tokens each time she successfully took her medication. Charlotte could later redeem her tokens for prizes. “Success” was defined as Charlotte taking all of her oral medication within 10 minutes of her parents’ request. On her first administration following the initiation of the system, Charlotte did not take her medication within 10 minutes and did not earn her token, which resulted in Charlotte feeling upset. During the second administration later that day, Charlotte took her medication within 10 minutes and, as a result, earned a token. The next day, another medication was switched from intravenous administration to an oral pill form. At Charlotte’s request, Dr. Smith talked with her medical team to determine if the pill could be administered in a liquid form. Unfortunately, it was not available in a liquid form but was able to be crushed. Therefore, Charlotte was again engaged in guided problem-solving to determine how she wanted to take the medication. She was willing to attempt to mix her crushed pill with applesauce, and her parents established that she did best with a flavored applesauce to better mask the taste. In addition, even though Charlotte met her timing goal for one of the two administrations on the first day of the intervention, Mrs. Johnson noted that administering medication to Charlotte was still a significant stressor. Therefore, Mr. and Mrs. Johnson decided that Mr. Johnson would take primary responsibility for administering medications. This assisted in managing Mrs. Johnson’s stress and eliminated reinforcement (i.e., undivided attention from mother) for avoidance behaviors (i.e., “waiting for dad”). On the third day postintervention initiation, no additional medications were changed to oral form to facilitate success with medication taking. Charlotte took both of her medications within 2 minutes with minimal distress for her or her father. The next 4 days each consisted of one additional medication being changed from IV to oral form, resulting in a total of six oral medications that were administered between one and three times per day. Of these medications, one was significantly larger in volume and sometimes led Charlotte to vomit. Along with Charlotte and her parents, it was determined to give this medication approximately 30 minutes prior to the other medications so that if she did vomit, Charlotte would not need to retake all of her medications. Another medication tasted particularly bad to Charlotte, and she decided to take it last, hold her nose, and do it quickly. The fifth medication had been originally administered in IV form after

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Charlotte went to bed. When it was initially changed to oral form, the timing was not adjusted and Charlotte was being woken up to take her medication. Charlotte was not successful with this dose, and Dr. Smith talked with the medical team about changing the timing of the dose, which was feasible. The final medication did not present any new challenges. After 1 week, all but one of Charlotte’s IV medications were transitioned to oral form. Because the last medication was of high importance and had a particularly bad taste in liquid formulation, Charlotte’s mother and father agreed to come to clinic three times per week for IV administration. At this point, the medical team established a tentative discharge date for 1 week later to ensure success was maintained and to allow for discharge teaching completion. During that week, the psychologist followed up and reinforced Charlotte’s and her parents’ ongoing adherence efforts. Charlotte was successfully discharged on six oral medications. Dr. Smith followed up with Charlotte’s family postdischarge during a clinic visit. The strategies that they used in the inpatient setting continued to be successful. However, some additional barriers remained. When Charlottes’ father returned to work, her grandparents began to manage her medications; Charlotte was reluctant to take her medications from them. However, Mr. Johnson emphasized the importance of remaining consistent with structured reinforcement, and they were able to successfully administer Charlotte’s medications.

Emerging areas and conclusions Rates of suboptimal adherence remain high, undermining the effectiveness of pediatric oncology treatment protocols and increasing the risk of morbidity and mortality for youth with cancer. To facilitate the integration of adherence care into pediatric oncology practice, continued research is needed to fill key gaps in our existing knowledge. First, additional research to determine clinically relevant cut-points for adherence to medications (in addition to 6-MP) is needed to guide clinical decision-making at the point of care, facilitate comparisons across adherence studies, and provide researchers with a clinically meaningful endpoint for future adherence promotion trials. Second, while results from available intervention studies are promising, the specific behavior change techniques most likely to improve adherence among youth with cancer remain unknown. Identifying specific mechanisms of behavior change will facilitate the personalization of interventions based on an individual’s barriers and where they are in an often dynamic pediatric oncology treatment course. Finally, targeted efforts to translate research into the delivery of routine interdisciplinary adherence care will be needed to realize the potential of existing and new therapies to improve the quality of life and chances of survival in youth with cancer.

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