Prior to the conduction of this study, no previously published research had characterized adherence or persistence in children using an approach that allowed for direct comparisons between conditions. Results from this study suggest that medication adherence and persistence vary considerably between cohorts of children treated for ADHD, asthma, depression, T2DM, and epilepsy. Based on information available in claims data for commercially-insured children, adherence and persistence in the first 180 days following initiation was high in patients with epilepsy with a median PDC of 0.91, over 60% of patients with a PDC ≥ 0.80, and nearly three in four patients remained on therapy after 180 days. For other conditions, adherence and persistence were considerably lower. Measures were relatively similar for ADHD and depression, while MAP for T2DM treatment with metformin was lower. Adherence and persistence measures for asthma treatment were particularly low, with only 26.76% having a PDC ≥ 0.50 during the first 180 days of therapy.
Cohorts and subgroups with high adherence tended to have high persistence as well. However, the associations between most patient-, parent-, and family-level characteristics considered in this study were inconsistent across the studied childhood conditions. The
exceptions to this observation were child age group at the time of treatment initiation, a parent (either father or mother) covered in the insured family (relative to the absence of a parent), and prior patient, parent, and family adherence to chronic disease medications during the 180 days prior to treatment initiation.
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Consistent with other studies, increased rates of adherence and persistence were seen in younger age groups than older age groups [10, 11, 32, 33, 37]. This difference was most pronounced in ADHD, asthma, and epilepsy, where dependents 13–18 years old were 7–34% and 19–25 years old were 18–38% less likely to be persistent than children 5–12 years old, respectively. Similar trends were observed in adherence as well. However, the effect estimates were less pronounced for adherence. Prior research has suggested that these observed differences in MAP between age groups may be linked to transitional periods of growth and changes in caretaker patient roles as the child takes on more responsibility for their own chronic medical care [12].
Prior MAP has been identified as a significant risk factor for future MAP in adults [10, 44]. To the author’s knowledge, this study is the first to demonstrate this finding in children as well. In the conditions studied, prior chronic medication use was measurable in 10% to 36% of patients depending on the condition under evaluation. For children in this study, prior chronic medication use remained a statistically significant predictor of adherence and persistence after adjusting for other factors for all conditions except epilepsy. The observed effects of a patient’s prior medication adherence were strongest in depression and T2DM and associations were similar for both persistence and adherence measures within each disease. There are a few likely explanations for this. These conditions could differ by patient and/or parent perceptions of the illness and the extent to which chronic treatment is viewed as necessary. For instance, T2DM would widely be viewed as a chronic condition warranting chronic treatment among healthcare providers, parents, and patients. Research has found that perceptions of ADHD are very different with patients and parents making active decisions about when treatments are taken [43]. Patients and parents may perceive asthma as a more acute illness, where chronic medications may be
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viewed as less necessary because rescue medication may be readily available to the patient [54]. The relationship between prior chronic medication adherence and adherence to epilepsy
treatment is different, and possibly due to a ceiling effect where epilepsy treatment adherence is already high. Another possible explanation could be that the classification of medications as chronic medications was relatively crude, leading to imprecision in estimation. While all of the cohorts had the same list of candidate chronic medications, the extent to which prior adherence measures are biased by misclassification of chronic vs. acute medication could be differential between cohorts due to inherent differences in comorbid/co-treatment patterns. Consequently, effects of prior adherence could be attenuated when acute medications classified as chronic medications are more often included in prior adherence calculations.
Considering parents as a source of prior adherent behavior increases the amount of information that is available to explain children’s MAP. A history of chronic medication use for the parent in the year prior to the child’s treatment initiation was common and ranged from 55% (asthma) to 75% (T2DM). After adjustment for patient-level characteristics, including prior adherence and parent-level characteristics, modest but statistically significant effects were observed for the association between parent chronic medication-use and child medication adherence and persistence that ranged from 1.03 to 1.07.
The relationships between prior medication adherence and future medication adherence and persistence are consistent with healthcare resource utilization theory. In Andersen’s model, pathways link contextual characteristics and individual characteristics to health behaviors and outcomes. Increased MAP represents repeated success overcoming individual predispositions and navigating relatively complex systems that can enable or hinder treatment. Furthermore, Andersen’s framework incorporates a feedback loop where the outcomes experienced by patients
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can influence the patient’s future healthcare seeking behaviors. For example, realizing positive treatment effects from treatment could increase a patient’s inclination for seeking more therapy in order to manage their chronic condition (increased MAP), and this positive experience could influence the patient’s desire to take medications in the future.
A number of other variables were considered in this study based on Andersen’s
framework and a review of prior research. In many instances, associations existed between these other factors and child MAP. However, these relationships tended to diverge across diseases, suggesting that general indicators may have condition-specific relationships with child MAP. A good example of this was observed in differences in healthcare resource utilization measures in epilepsy compared to ADHD, depression, and T2DM. For example, an emergency department visit during the year prior to treatment initiation had modest positive effects for MAP for epilepsy and modest negative effects on MAP for other conditions. It is important to consider that the reason for prior emergency department (or any other healthcare research utilization measure in this study) was not cause-specific. One could imagine that the first signs and symptoms of epilepsy would prompt emergency or urgent care that leads to treatment and reinforces future treatment utilization. For the other chronic conditions studied, a prior visit to the emergency department is less likely to be the trigger for patient diagnosis and treatment and play a reduced factor in future MAP behaviors. In this study, nearly 60% of the epilepsy cohort had an emergency department or urgent care visit in the twelve months prior to treatment initiation compared to 21% to 31% for other conditions, lending some support to the notion that emergency department utilization may be more closely related to medical care related to epilepsy than to other chronic conditions. The opposite relationship was observed for outpatient visit utilization prior to initiation. In a related way, increased healthcare utilization prior to the
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treatment of ADHD, asthma, depression, and T2DM may be indicative of a patient’s engagement with healthcare processes involved in differential diagnosis, attempt at non-pharmaceutical treatments, and other medications not included in this study (e.g., acute treatments for asthma and insulin for T2DM). In response to this, researchers can consider developing disease-specific models for child MAP, which would be an appropriate approach to further identify and explore disease-specific risk factors of interest.
This study did not attempt to build causative models for MAP in children. Instead the aims of this study were to explore improvements in patient prediction by leveraging parent characteristics and family characteristics identified in claims data via family linkages. Six models for child MAP were tested for predictive performance in five conditions. Three separate measures were considered when assessing predictive performance of the models: the c-statistic, NRI, and AIC. For child MAP, the addition of parent-level and family-level parameters to patient-level models increased the c-statistic and NRI. The only exception was asthma MAP, where Models 5 and 6 that included family-level predictors did not improve discrimination between cases and non-cases compared to Model 1 based on the c-statistic and NRI. Still, predictive performance based on the c-statistic was around 60–65% across all conditions for child MAP.
Prediction generally increases with the number of variables. In this study, AIC was used to assess model fit at the expense of incorporating additional parameters into the model. Based on AIC, MAP prediction was enhanced across all diseases with the incorporation of parent variables, including prior parental adherence. For predicting persistence in T2DM treatment, the model containing family-level predictors with patient prior adherence and parent prior adherence was the best performing model based on AIC. The model containing family-level predictors and
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family prior adherence was the best performing model based on AIC for adherence to epilepsy treatment. It is worth noting that incremental benefits were consistently observed when parents’ prior adherence was added to models already containing patient characteristics, patient’s prior adherence, and parent characteristics. Considering adjusted effects in multivariate modeling and model performance measures, parents’ prior adherence appears to be an important consideration for a child’s adherence and persistence to medications across multiple chronic conditions.
From a qualitative standpoint, this level of predictive performance based on the c-statistic is still considered modest. Comparisons across models on different data sets are quite
challenging and should be done with some reservation. Based on published results, the observed c-statistics for the depression persistence model containing patient predictors (c-statistic = 0.617) was similar to the depression persistence model published by Bushnell et al. (2016; c-statistic = 0.582) [10]. Bushnell et al. (2018) also published a prior adherence model in anxiety that included parental prior adherence to SSRI, statin, and antihypertensive treatments, in which the highest observed c-statistic among the analyses conducted was 0.654 [11]. This is within the range of the c-statistics observed across adherence models in this study that contained patient and parent predictors, including prior adherence (0.635–0.655). As opposed to employing a more general approach for multiple diseases like the one employed in this study, both of Bushnell et al.’s models were more specific with regard to treatments analyzed and the incorporation of disease- and treatment-specific risk factors thought to influence MAP (e.g., settings of
psychiatric care and counseling, side effects of drugs, dosing, and heterogeneity within disease). It is important to consider the results in light of the many limitations stemming from data sources, methods, and measures that can ultimately bias results in observational research.
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interest may not be captured reliably or at all in insurance claims databases. The reliability of measuring many variables may be unknown. Examples of this include the measurement of comorbidities, which may be included on claims as rule-out diagnoses and short-comings in the data captured. Specific to this study, misclassification bias may occur due to errors for the recorded observation (e.g., incorrect days supply) or imprecision in the study variable or proxy. Additionally, MAP estimates exclude other sources for prescription medications, such as
physician samples and only include prescription medications for which a claim was filled by the pharmacy.
Errors related to data quality also extend to the outcome in this instance. MAP was estimated by measuring days supply from dispensed prescriptions. The medication adherence measure utilized in this study is referred to as secondary adherence, because patients were required to collect their first dispensing of their prescribed drug in order to be included in the analysis. From other studies, we know that some patients that receive a prescription for
medication never go to the pharmacy to receive their drug [19]. Additionally, in the calculation of PDC an inherent assumption is made that patients take the drug dispensed as prescribed for the length of time indicated in days supply. This assumption is typical of MAP studies based on secondary insurance claims data. Still, measurement error would typically lead to an
overestimate of secondary adherence and persistence. In addition, this study also assumed that (1) the drugs prescribed are intended to be used as a chronic treatment, (2) changes in dispensed drugs represented medication switches where a patient could be on either drug compared to prescribed combination therapy where the expectation was that the patient had to be taking multiple drugs to remain adherent/persistent to therapy, and (3) specific to persistence, an acceptable lag between prescriptions for continuous medication could be as long as 60 days.
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Commonly accepted practices have been developed to measure adherence and persistence. Similar normative approaches have not been developed for measuring prior adherence as a risk factor in research. While treatments informing study outcomes (patient adherence and persistence) were selected based on clinical input and treatment guidelines, chronic medications included in the calculation of prior drug adherence were based on information available in the Thompson Reuters Red Book, which classifies drugs based on typical use. Consequently, misclassification could occur related to the classification of the drug (chronic versus not chronic). This is in addition to potential misclassification related to the measurement and classification of PDC. It is also similar to the approach employed by Bushnell et al. [11] with one notable difference. Bushnell et al. required that parents being treated with SSRIs, statins, or antihypertensives also have at least one dispensing during the 6 months prior to the child’s initiation date. Both definitions used for prior adherence based on recent drug
dispensing data for previously dispensed chronic medications are reasonable from a standpoint of face validity. However, the performance of neither measure has been validated or corroborated by results from other adherence approaches, like pill counts, electronic monitoring, or patient diaries.
Deciding on the “best model” for a given outcome is not a straightforward choice. A number of model performance measures have been developed over time. This study focused on three measures: the c-statistic, NRI, and AIC. All of these measures have well-documented, valid criticisms.
The c-statistic has been used for decades to characterize the ability of a model to
discriminate between cases and non-cases. The c-statistic represent the area under the curve of a Receiver-Operator curve where sensitivity is plotted against the false positive rate. The c-statistic
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can be interpreted as the proportion of all pairs of patients where (1) one patient experienced the event while the other patient did not and (2) the patient with the lowest risk score was the patient that did not experience the event [49]. While c-statistics around 0.60 and 0.95 are generally considered low discrimination and high discrimination, respectively, no statistical rationale exists for the cutoffs [49]. Likewise, no statistical basis exists for how large a c-statistic should be for a variable to be included or removed from a predictive model. Nguyen et al. have previously proposed an incremental change of at least 0.005 in the c-statistic for inclusion of a variable in a model [49]. This study compared pre-specified models, so the incremental benefits of adding one variable could only be assessed between Model 1 versus 2 and Model 3 versus 4. This level of incremental change was observed for depression Models 1 and 2 and T2DM Models 3 and 4. Based on the results of this study as analyzed, it is not possible to assess whether parent prior adherence alone yields a change in c-statistic > 0.005 compared to patient-level models.
Furthermore, a change in the c-statistic does not necessarily represent a clinically informative or important change in patient status. For those patients with an already high (or low) risk of event, movement to an increasingly higher (or lower) risk group would not improve clinical
classification of at-risk individuals [50, 55].
NRI is often criticized because it is difficult to interpret. In addition, the continuous NRI calculated in this study suffers from two shared weaknesses of ROC: (1) no statistically-based cutoffs exist for NRI with regard to variable selection and (2) increased performance represent an improvement in clinical classification of at-risk individuals [50]. While NRI was used to identify a best model in this study, this largely ignores that public health professionals designing an intervention may prioritize a broader or narrower intervention depending on a prioritization of
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benefits and risks based on errors in classification. In this study, all three NRI measures were reported for the models that were constructed.
Both the c-statistic and NRI are susceptible to an increase in performance at the risk of overfitting a predictive model. The third measure considered, AIC, is a negative log-likelihood measure that includes a penalty term for the number of parameters or variables included in the model.
𝐴𝐼𝐶 = −2(𝑙𝑜𝑔 − 𝑙𝑖𝑘𝑒𝑙𝑖ℎ𝑜𝑜𝑑) + 2𝐾
AIC is not able to measure the likelihood that one model identifies cases and non-cases better than another model (discrimination). Instead AIC measures a model’s “goodness of fit” to the underlying data (-2(log-likelihood)). In addition, AIC incorporates a penalty term (2K), where K is the number of parameters included in the model. AIC is best used as a tool for model selection between nested models built from the same underlying data.
Given the consistent findings across these disparate measures, results from this study suggest that leveraging parent-level characteristics, including parent’s prior adherence can
inform research in MAP in children. Given the unique situation of children as patients, it is likely worthwhile to consider these factors in future child healthcare resource utilization research.
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CHAPTER 6: PLAN FOR CHANGE